Fury by Tetrad on TESLA v2Fury by Tetrad — TSLA v2 (Free Version)
📊 Fury v2 on TSLA — Financial Snapshot
First trade: August 11, 2010
Last trade: September 5, 2025
Net Profit: $10,549.10 (≈ +10,549%)
Gross Profit: $10,554.36
Gross Loss: $5.26
Commission Paid: $86.95
⚖️ Risk/Return Ratios
Sharpe Ratio: 0.42
Sortino Ratio: 17.63
Profit Factor: 2005.38
🔄 Trade Statistics
Total Trades: 37
Winning Trades: 37
Losing Trades: 0
Win Rate: 100%
Fury is a momentum-reversion hybrid designed for Tesla (TSLA) on higher-liquidity timeframes. It combines Bollinger Bands (signal extremes) with RSI (exhaustion filter) to time mean-reversion pops/drops, then exits via price multipliers or optional time-based stops. A Market Direction toggle (Market Neutral / Long Only / Short Only) lets you align with macro bias or risk constraints. Intrabar simulation is enabled for realistic stop/limit behavior, and labeled entries/exits improve visual auditability.
How it works
Entries:
• Long when price pierces lower band and RSI is below the long threshold.
• Short when price pierces upper band and RSI is above the short threshold.
Exits:
• Profit targets via entry×multiplier (independent for long/short).
• Optional price-based stop factors per side.
• Optional time stop (N days) to cap trade duration.
Controls:
• Market Direction switch (Neutral / Long Only / Short Only).
• Tunable BB length/multiplier, RSI length/thresholds, exit multipliers, stops.
Intended use
Swing or position trading TSLA; can be adapted to other high-beta equities with parameter retuning. Use on liquid timeframes and validate with robust out-of-sample testing.
Disclaimers
Backtests are approximations; past performance ≠ future results. Educational use only. Not financial advice.
Stay connected
Follow on TradingView for updates • Telegram: t.me • Website: tetradprotocol.com
Pesquisar nos scripts por "profit factor"
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
RSI-Adaptive T3 + Squeeze Momentum Strategy✅ Strategy Guide: RSI-Adaptive T3 + Squeeze Momentum Strategy
📌 Overview
The RSI-Adaptive T3 + Squeeze Momentum Strategy is a dynamic trend-following strategy based on an RSI-responsive T3 moving average and Squeeze Momentum detection .
It adapts in real-time to market volatility to enhance entry precision and optimize risk.
⚠️ This strategy is provided for educational and research purposes only.
Past performance does not guarantee future results.
🎯 Strategy Objectives
The main objective of this strategy is to catch the early phase of a trend and generate consistent entry signals.
Designed to be intuitive and accessible for traders from beginner to advanced levels.
✨ Key Features
RSI-Responsive T3: T3 length dynamically adjusts according to RSI values for adaptive trend detection
Squeeze Momentum: Combines Bollinger Bands and Keltner Channels to identify trend buildup phases
Visual Triggers: Entry signals are generated from T3 crossovers and momentum strength after squeeze release
📊 Trading Rules
Long Entry:
When T3 crosses upward, momentum is positive, and the squeeze has just been released.
Short Entry:
When T3 crosses downward, momentum is negative, and the squeeze has just been released.
Exit (Reversal):
When the opposite condition to the entry is triggered, the position is reversed.
💰 Risk Management Parameters
Pair & Timeframe: BTC/USD (30-minute chart)
Capital (simulated): $30,00
Order size: `$100` per trade (realistic, low-risk sizing)
Commission: 0.02%
Slippage: 2 pips
Risk per Trade: 5%
Number of Trades (backtest period): 181
📊 Performance Overview
Symbol: BTC/USD
Timeframe: 30-minute chart
Date Range: January 1, 2024 – July 3, 2025
Win Rate: 47.8%
Profit Factor: 2.01
Net Profit: 173.16 (units not specified)
Max Drawdown: 5.77% or 24.91 (0.79%)
⚙️ Indicator Parameters
Indicator Name: RSI-Adaptive T3 + Squeeze Momentum
RSI Length: 14
T3 Min Length: 5
T3 Max Length: 50
T3 Volume Factor: 0.7
BB Length: 27 (Multiplier: 2.0)
KC Length: 20 (Multiplier: 1.5, TrueRange enabled)
🖼 Visual Support
T3 slope direction, squeeze status, and momentum bars are visually plotted on the chart,
providing high clarity for quick trend analysis and execution.
🔧 Strategy Improvements & Uniqueness
Inspired by the RSI Adaptive T3 by ChartPrime and Squeeze Momentum Indicator by LazyBear ,
this strategy fuses both into a hybrid trend-reversal and momentum breakout detection system .
Compared to traditional trend-following methods, it excels at capturing early trend signals with greater sensitivity .
✅ Summary
The RSI-Adaptive T3 + Squeeze Momentum Strategy combines momentum detection with volatility-responsive risk management.
With a strong balance between visual clarity and practicality, it serves as a powerful tool for traders seeking high repeatability.
⚠️ This strategy is based on historical data and does not guarantee future profits.
Always use appropriate risk management when applying it.
TradingIQ - Counter Strike IQIntroducing "Counter Strike IQ" by TradingIQ
Counter Strike IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade upside/downside breakouts of varying significance. By integrating artificial intelligence and IQ Technology, Counter Strike IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations.
Philosophy of Counter Strike IQ
Counter Strike IQ operates on a single premise: Support and resistance levels cannot hold forever. At some point either side must break for the underlying asset to exhibit trends; otherwise, prices would be confined to an infinitely narrowing range.
Counter Strike IQ is designed to work straight out of the box. In fact, its simplicity requires just four user settings to manage output, making it incredibly straightforward to manage.
Minimum ATR Profit, Minimum ATR Stop, EMA Filter and EMA Filter Length are the only settings that manage the performance of Counter Strike IQ!
Traders don’t have to spend hours adjusting settings and trying to find what works best - Counter Strike IQ handles this on its own.
Key Features of Counter Strike IQ
Self-Learning Breakout Detection
Employs AI and IQ Technology to identify notable breakouts in real-time.
AI-Generated Trading Signals
Provides breakout trading signals derived from self-learning algorithms.
Comprehensive Trading System
Offers clear entry and exit labels.
Performance Tracking
Records and presents trading performance data, easily accessible for user analysis.
Self-Learning Trading Exits
Counter Strike IQ learns where to exit positions.
Long and Short Trading Capabilities
Supports both long and short positions to trade various market conditions.
Strike Channel
The Strike Channel represents what Counter Strike IQ considers a tradable long opportunity or a tradable short opportunity. The Strike Channel is dynamic and adjusts from chart to chart.
IQ Graph Gradient
Introduces the IQ Graph Gradient, designed to classify extreme values in price on a grand scale.
How It Works
Counter Strike IQ operates on a straightforward heuristic: go long during significant upside price moves that break established resistance levels and go short during significant downside price moves that break established support levels.
IQ Technology, TradingIQ's proprietary AI algorithm, defines what constitutes a “significant price move” and what’s considered a tradable breakout. For Counter Strike IQ, this algorithm evaluates all historical support/resistance breaks and any subsequent breakouts. For instance, the price move following up to a breakout is measured and learned from, including the significance of the identified support/resistance level (how long it’s been active, how far price moved away from it, etc). By analyzing these patterns, Counter Strike IQ adapts to identify and trade similar future breakout sequences.
In simple terms, Counter Strike IQ learns from violations of historical support/resistance levels to identify potential entry points at currently established support/resistance levels. Using this knowledge, it determines the optimal, current support/resistance price level where a breakout has a higher chance of occurring.
For long positions, Counter Strike IQ places a stop-market order at the AI-identified resistance point. If price violates this level a market order will be placed and a long position entered. Of course, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. After the position is entered TP1 is placed (identifiable on the price chart). TP1 has a twofold purpose:
Acts as a legitimate profit target to exit 50% of the position.
Once TP1 is closed over, the initial stop loss is converted to a trailing stop, and the long position remains active so long as price continues to uptrend.
For short positions, Counter Strike IQ places a stop-market order at the AI-identified support point. If price violates this level a market order will be placed and a short position entered. Again, this is how the algorithm trades, users can elect to use a stop-limit order amongst other order types for position entry. Upon entry TP1 is placed (identifiable on the price chart). TP1 has a twofold purpose:
Acts as a legitimate profit target to exit 50% of the position.
Once TP1 is closed over, the initial stop loss is converted to a trailing stop, and the short position remains active so long as price continues to downtrend.
As a trading system, Counter Strike IQ exits TP1 using a limit order, with all stop losses exited as stop market orders.
What Classifies As a Tradable Upside Breakout or Tradable Downside Breakout?
For Counter Strike IQ, tradable price breakouts are not manually set but are instead learned by the system. What qualifies as a significant upside or downside breakout in one market might not hold the same significance in another. Counter Strike IQ continuously analyzes historical and current support/resistance levels, how far price has extended from those levels, the raw-dollar price move leading up to a violation of those levels, their longevity, and more, to determine which future levels have a higher chance of breaking out when retested!
The image above illustrates the Strike Channel and explains the corresponding prices and levels
The green upper line represents the Long Breakout Point.
The pink lower line represents the Short Breakout Point.
Any price between the two deviation points is considered “Acceptable”.
The image above shows a long position being entered after the Upside Breakout Point was reached.
Green arrows indicate that the strategy entered a long position at the highlighted price level.
Blue arrows indicate that the strategy exited a position, whether at TP1, the initial stop loss, or at the trailing stop.
Blue lines indicate the TP1 level for the current trade. Red lines indicate the initial stop loss price.
If price closes above TP1, the initial stop loss will be replaced with a trailing stop. A blue line (similar to the blue line shown for TP1) will trail price and correspond to the trailing stop price of the trade.
The image above shows the trailing stop price, represented by a blue line, used for the long position!
You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price.
The image above shows a short position being entered after the Downside Breakout Point was reached.
Red arrows indicate that the strategy entered a short position at the highlighted price level.
Blue arrows indicate that the strategy exited a position, whether at TP1, the initial stop loss, or at the trailing stop.
Blue lines indicate the TP1 level for the current trade. Red lines indicate the initial stop loss price.
If price closes below TP1, the initial stop loss will be replaced with a trailing stop. A blue line (similar to the blue line shown for TP1) will trail price and correspond to the trailing stop price of the trade.
The image above shows the trailing stop price, represented by a blue line, used for the short position!
You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price.
IQ Gradient Graph
The IQ Gradient Graph provides a macro characterization of extreme prices.
The lower macro extremity of the IQ Gradient Graph is colored green, while the upper macro extremity is colored red.
Minimum Profit Target And Stop Loss
The Minimum ATR Profit Target and Minimum ATR Stop Loss setting control the minimum allowed profit target and stop loss distance. On most timeframes users won’t have to alter these settings; however, on very-low timeframes such as the 1-minute chart, users can increase these values so gross profits exceed commission.
After changing either setting, Counter Strike IQ will retrain on historical data - accounting for the newly defined minimum profit target or stop loss.
AI Direction
The AI Direction setting controls the trade direction Counter Strike IQ is allowed to take.
“Trade Longs” allows for long trades.
“Trade Shorts” allows for short trades.
EMA Filter
The EMA Filter setting controls whether the AI should implement an EMA trading filter. Simply, if the EMA Filter is active, long trades can only initiate if price is trading above the user-defined EMA. Conversely, short trades can only initiate if price is trading below the user-defined EMA.
The image above shows the EMA Filter in action!
Verifying Counter Strike IQ’s Effectiveness
Counter Strike IQ automatically tracks its performance and displays the profit factor for the long strategy and the short strategy it uses. This information can be found in the table located in the top-right corner of your chart showing.
This table shows the long strategy profit factor and the short strategy profit factor.
The image above shows the long strategy profit factor and the short strategy profit factor for Counter Strike IQ.
A profit factor greater than 1 indicates a strategy profitably traded historical price data.
A profit factor less than 1 indicates a strategy unprofitably traded historical price data.
A profit factor equal to 1 indicates a strategy did not lose or gain money when trading historical price data.
Using Counter Strike IQ
While Counter Strike IQ is a full-fledged trading system with entries and exits - manual traders can certainly make use of its on chart indications and visualizations.
The hallmark feature of Counter Strike IQ is its ability to signal a breakout near its origin point. Long entries are often signaled near the start of a large upside price move; short entries are often signaled near the start of a large downside price move.
For live analysis, the Strike Channel serves as a valuable tool for identifying breakout points.
The further price moves toward the Upside Breakout Point (green), the stronger the indication that price might breakout to the upside. Conversely, the deeper price reaches toward the Downside Breakout Point (red), the stronger the indication that price might breakout to the downside.
Of course, should buying or selling pressure stall, price may fail to breakout at the identified breakout level. This is a natural consequence of any breakout trading strategy!
With this information at hand, traders can quickly switch between charts and timeframes to identify optimized areas of interest.
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
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What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
AlgoBuilder [Trend-Following] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely on and trade based on historical and backtested data using automation. The main goal is to build profitable trend-following strategies that outperform the underlying asset in terms of returns while minimizing drawdown. For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based trailing stop-loss mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability and sentiment function for traders who want to implement probabilities and market sentiment right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, compound annual growth rate (CAGR), profit factor, average trade, average risk-reward ratio (RR), and more. This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading (1x):
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on trend-following and risk management.
- (1x) This mode ensures no stacking of positions, allowing for only one running position or trade at a time.
◓: Mode | %: Risk percentage per trade
2. Trading (2x):
Similar to the 1x mode but allows for two pyramiding entries.
This approach enables traders to increase their position size as the trade moves in their favor, potentially enhancing profits during strong bullish trends.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes 100% of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 100% of equity to buy the asset)
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>/<) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
MA #1: Fast MA | MA #2: Medium MA | MA #3: Slow MA
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 1.5
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (14) * 1.5
⍺: ADR period | Σ: ADR Multiplier
Application in Strategy:
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detec buyside and sellside liquidity levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
There are two built-in trailing stop-loss (SL) options you can choose from while in a trade:
1. External Trailing Stop-Loss:
- Uses sell-side liquidity to trail your stop-loss, allowing price to consolidate before continuation. This method is less aggressive and provides more room for price fluctuations.
Example - External - Wick below the trailing SL - 12H trailing timeframe
⍺: Exit type | Σ: Trailing stop-loss timeframe
2. Internal Trailing Stop-Loss:
- Uses the most recent swing low with a period of 2 to trail your stop-loss. This method is more aggressive compared to the external trailing stop-loss, as it tightens the stop-loss closer to the current price action.
Example - Internal - Close below the trailing SL - 6H trailing timeframe
⍺: Exit type | Σ: Trailing stop-loss timeframe
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
- You can choose to set a break-even level at which your initial stop-loss moves to the entry price as soon as it hits, and your trailing stop-loss gets activated (if enabled).
- You can select either a percentage (%) or risk-to-reward (RR) based break-even, allowing you to set your break-even level as a percentage amount above the entry price or based on RR.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
The underlying calculations involve determining the price levels at which these actions are triggered. For break-even, it moves the initial stop-loss to the entry price and activate the trailing stop-loss once the break-even level is reached.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 50%
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What's the sentiment Filter? What are the underlying calculations?
Sentiment filter aims to calculate the percentage level of bullish or bearish fluctuations within equally divided price sections, in the latest price range.
Calculations:
This filter calculates the current sentiment by identifying the highest swing high and the lowest swing low, then evenly dividing the distance between them into percentage amounts. If the price is above the 50% mark, it indicates bullishness, whereas if it's below 50%, it suggests bearishness.
Sentiment Bias Identification:
Bullish Bias: The current price is trading above the 50% daily range.
Bearish Bias: The current price is trading below the 50% daily range.
Example - Sentiment Enabled | Bullish degree above 50% | Bullish sentimental bias
>: Minimum required sentiment for entry | %: Current sentimental degree in a (Bullish/Bearish) sentimental bias
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 5% | Price must be in a bearish range
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades, Compound Annual Growth Rate (CAGR), MAR and more.
CAGR: It calculates the 'Compound Annual Growth Rate' first and last taken trades on your chart. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two strategies. Since it annualizes values, it requires a minimum 4H timeframe to display the CAGR value. annualizing returns over smaller periods of times doesn't produce very meaningful figures.
MAR: Measure of return adjusted for risk: CAGR divided by Max Drawdown. Indicates how comfortable the system might be to trade. Higher than 0.5 is ideal, 1.0 and above is very good, and anything above 3.0 should be considered suspicious and you need to make sure the total number of trades are high enough by running a Deep Backtest in strategy tester. (available for TradingView Premium users.)
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most trend-following successful strategies have a percent profitability of 15-40% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Labels:
- OFF: Hides labels in the performance table.
- PnL: Shows the profit and loss of each trade individually, providing detailed insights into the performance of each trade.
- Range: Shows the range length and Average Day Range (ADR), offering additional context about market conditions during each trade.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, MAR (Mar Ratio), CAGR (Compound Annual Growth Rate), and net profit with minimum drawdown. Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Leveraging market sentiment to construct a profitable approach.
3. Utilizing built-in market structure-based trailing stop-loss mechanisms across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Strategy Properties
This script backtest is done on 4H COINBASE:BTCUSD , using the following backtesting properties:
Balance: $5000
Order Size: 10% of the equity
Risk % per trade: 1%
Commission: 0.04% (Default commission percentage according to TradingView competitions rules)
Slippage: 75 ticks
Pyramiding: 2
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
ETH/USD 15m [iTradebot]1. General
2. Additional information
3. Access
1. This is a script for crypto trading strategies, based on following trends with moving averages.
A fast moving average and a slower getting crosschecked with MACD for buying/selling signals, those signals get crosschecked by 5 different factors, like the MACD or controlling average.
Only if all requirements will be met, the script expects the trend to change and sends an according signal.
This strategy is not made for multiple entries and keeps a position open until an opposite signal gets triggered.
It was built with ETH/USD 15min chart in mind, but can be used on any other chart, though you will have to find a different combination in inputs, to make it work with other pairs or timeframes.
Fast average: The faster of the two signal moving averages.
Slow average: The slower of the two signal moving averages.
Trend strength: Determines the crosscheck with the MACD.
Control length: Sets the period length for the control average (1=1bar, 300=300bars)
Inputs used here are:
Fast average: 5
Slow average: 19
Trend strength: 4
Control length: 306
Start capital: 1000
Trade equity: 100%
Commission: 0.036% (Binance)
Result 2020-06-11 until 2021-01-07
Net Profit: 717.56%
Percent Profitable: 54.17%
Max Drawdown: 8.79%
Profit Factor: 6.523
2. There are many combinations with good net profit outcomes, but make sure to also keep a low "Max Drawdown", to lower the risk substantially.
The combination we used, gives slightly less profit than other combinations, but also lowers the max drawdown from 20% to 9%.
In general in trading with crypto we advice to only use this strategy with Bitcoin or Ethereum , due to their high value and "stable" movements, compared to other cryptocurrencies.
The strategy is using 100% equity each trade, so make sure to have your capital management in place on the account.
To be specific, DONT put all your capital into this one strategy, but only part of it, according to your capital management.
Control lengths around 150 or 300 tend to give good combinations with the other 3 inputs. (on the 15min chart)
3. You can test this strategy 14 days for free and get access for up to 1 year on our website.
14 day free trial only requires Tradingview user name and e-mail.
Click on the link in our signature below.
Pinescript v4 - The Holy Grail (Trailing Stop)After studying several other scripts, I believe I have found the Holy Grail! (Or perhaps I've just found a bug with Tradingview's Pinescript v4 language) Anyhow, I'm publishing this script in the hope that someone smarter than myself could shed some light on the fact that adding a trailing stop to any strategy seems to make it miraculously...no that's an understatement...incredulously, stupendously, mind-bendingly profitable. I'm talking about INSANE profit factors, higher than 200x, with drawdowns of <10%. Sounds too good to be true? Maybe it is...or you could hook it up to your LIVE broker, and pray it doesn't explode. This is an upgraded version of my original Pin Bar Strategy.
Recommended Chart Settings:
Asset Class: Forex
Time Frame: H1
Long Entry Conditions:
a) Exponential Moving Average Fan up trend
b) Presence of a Bullish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Short Entry Conditions:
a) Exponential Moving Average down trend
b) Presence of a Bearish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Exit Conditions:
a) Trailing stop is hit
b) Moving Averages cross-back (optional)
c) It's the weekend
Default Robot Settings:
Equity Risk (%): 3 //how much account balance to risk per trade
Stop Loss (x*ATR, Float): 0.5 //stoploss = x * ATR, you can change x
Stop Loss Trail Points (Pips): 1 //the magic sauce, not sure how this works
Stop Loss Trail Offset (Pips): 1 //the magic sauce, not sure how this works
Slow SMA (Period): 50 //slow moving average period
Medium EMA (Period): 18 //medium exponential moving average period
Fast EMA (Period): 6 //fast exponential moving average period
ATR (Period): 14 // average true range period
Cancel Entry After X Bars (Period): 3 //cancel the order after x bars not triggered, you can change x
Backtest Results (2019 to 2020, H1, Default Settings):
AUDUSD - 1604% profit, 239.6 profit factor, 4.9% drawdown (INSANE)
NZDUSD - 1688.7% profit, 100.3 profit factor, 2.5% drawdown
GBPUSD - 1168.8% profit, 98.7 profit factor, 0% drawdown
USDJPY - 900.7% profit, 93.7 profit factor, 4.9% drawdown
USDCAD - 819% profit, 31.7 profit factor, 8.1% drawdown
EURUSD - 685.6% profit, 26.8 profit factor, 5.9% drawdown
USDCHF - 1008% profit, 18.7 profit factor, 8.6% drawdown
GBPJPY - 1173.4% profit, 16.1 profit factor, 7.9% drawdown
EURAUD - 613.3% profit, 14.4 profit factor, 9.8% drawdown
AUDJPY - 1619% profit, 11.26 profit factor, 9.1% drawdown
EURJPY - 897.2% profit, 6 profit factor, 13.8% drawdown
EURGBP - 608.9% profit, 5.3 profit factor, 9.8% drawdown (NOT TOO SHABBY)
As you can clearly see above, this forex robot is projected by the Tradingview backtester to be INSANELY profitable for all common forex pairs. So what was the difference between this strategy and my previous strategies? Check my code and look for "trail_points" and "trail_offset"; you can even look them up in the PineScript v4 documentation. They specify a trailing stop as the exit condition, which automatically closes the trade if price reverses against you.
I however suspect that the backtester is not properly calculating intra-bar price movement, and is using a simplified model. With this simplfied approach, the trailing stop code becomes some sort of "holy grail" generator, making every trade entered profitable.
Risk Warning:
This is a forex trading strategy that involves high risk of equity loss, and backtest performance will not equal future results. You agree to use this script at your own risk.
Hint:
To get more realistic results, and *maybe* overcome the intrabar simulation error, change the settings to: "Stop Loss Trail Points (pips)": 100
I am not sure if this eradicates the bug, but the entries and exits look more proper, and the profit factors are more believable.
FlowStateTrader FlowState Trader - Advanced Time-Filtered Strategy
## Overview
FlowState Trader is a sophisticated algorithmic trading strategy that combines precision entry signals with intelligent time-based filtering and adaptive risk management. Built for traders seeking to achieve their optimal performance state, FlowState identifies high-probability trading opportunities within user-defined time windows while employing dynamic trailing stops and partial position management.
## Core Strategy Philosophy
FlowState Trader operates on the principle that peak trading performance occurs when three elements align: **Focus** (precise entry signals), **Flow** (optimal time windows), and **State** (intelligent position management). This strategy excels at finding reversal opportunities at key support and resistance levels while filtering out suboptimal trading periods to keep traders in their optimal flow state.
## Key Features
### 🎯 Focus Entry System
**Support/Resistance Zone Trading**:
- Dynamic identification of key price levels using configurable lookback periods
- Entry signals triggered when price interacts with these critical zones
- Volume confirmation ensures genuine breakout/reversal momentum
- Trend filter alignment prevents counter-trend disasters
**Entry Conditions**:
- **Long Signals**: Price closes above support buffer, touches support level, with above-average volume
- **Short Signals**: Price closes below resistance buffer, touches resistance level, with above-average volume
- Optional trend filter using EMA or SMA for directional bias confirmation
### ⏰ FlowState Time Filtering System
**Comprehensive Time Controls**:
- **12-Hour Format Trading Windows**: User-friendly AM/PM time selection
- **Multi-Timezone Support**: UTC, EST, PST, CST with automatic conversion
- **Day-of-Week Filtering**: Trade only weekdays, weekends, or both
- **Lunch Hour Avoidance**: Automatically skips low-volume lunch periods (12-1 PM)
- **Visual Time Indicators**: Background coloring shows active/inactive trading periods
**Smart Time Features**:
- Handles overnight trading sessions seamlessly
- Prevents trades during historically poor performance periods
- Customizable trading hours for different market sessions
- Real-time trading window status in dashboard
### 🛡️ Adaptive Risk Management
**Multi-Level Take Profit System**:
- **TP1**: First profit target with optional partial position closure
- **TP2**: Final profit target for remaining position
- **Flexible Scaling**: Choose number of contracts to close at each level
**Dynamic Trailing Stop Technology**:
- **Three Operating Modes**:
- **Conservative**: Earlier activation, tighter trailing (protect profits)
- **Balanced**: Optimal risk/reward balance (recommended)
- **Aggressive**: Later activation, wider trailing (let winners run)
- **ATR-Based Calculations**: Adapts to current market volatility
- **Automatic Activation**: Engages when position reaches profitability threshold
### 📊 Intelligent Position Sizing
**Contract-Based Management**:
- Configurable entry quantity (1-1000 contracts)
- Partial close quantities for profit-taking
- Clear position tracking and P&L monitoring
- Real-time position status updates
### 🎨 Professional Visualization
**Enhanced Chart Elements**:
- **Entry Zone Highlighting**: Clear visual identification of trading opportunities
- **Dynamic Risk/Reward Lines**: Real-time TP and SL levels with price labels
- **Trailing Stop Visualization**: Live tracking of adaptive stop levels
- **Support/Resistance Lines**: Key level identification
- **Time Window Background**: Visual confirmation of active trading periods
**Dual Dashboard System**:
- **Strategy Dashboard**: Real-time position info, settings status, and current levels
- **Performance Scorecard**: Live P&L tracking, win rates, and trade statistics
- **Customizable Sizing**: Small, Medium, or Large display options
### ⚙️ Comprehensive Customization
**Core Strategy Settings**:
- **Lookback Period**: Support/resistance calculation period (5-100 bars)
- **ATR Configuration**: Period and multipliers for stops/targets
- **Reward-to-Risk Ratios**: Customizable profit target calculations
- **Trend Filter Options**: EMA/SMA selection with adjustable periods
**Time Filter Controls**:
- **Trading Hours**: Start/end times in 12-hour format
- **Timezone Selection**: Four major timezone options
- **Day Restrictions**: Weekend-only, weekday-only, or unrestricted
- **Session Management**: Lunch hour avoidance and custom periods
**Risk Management Options**:
- **Trailing Stop Modes**: Conservative/Balanced/Aggressive presets
- **Partial Close Settings**: Enable/disable with custom quantities
- **Alert System**: Comprehensive notifications for all trade events
### 📈 Performance Tracking
**Real-Time Metrics**:
- Net profit/loss calculation
- Win rate percentage
- Profit factor analysis
- Maximum drawdown tracking
- Total trade count and breakdown
- Current position P&L
**Trade Analytics**:
- Winner/loser ratio tracking
- Real-time performance scorecard
- Strategy effectiveness monitoring
- Risk-adjusted return metrics
### 🔔 Alert System
**Comprehensive Notifications**:
- Entry signal alerts with price and quantity
- Take profit level hits (TP1 and TP2)
- Stop loss activations
- Trailing stop engagements
- Position closure notifications
## Strategy Logic Deep Dive
### Entry Signal Generation
The strategy identifies high-probability reversal points by combining multiple confirmation factors:
1. **Price Action**: Looks for price interaction with key support/resistance levels
2. **Volume Confirmation**: Ensures sufficient market interest and liquidity
3. **Trend Alignment**: Optional filter prevents counter-trend positions
4. **Time Validation**: Only trades during user-defined optimal periods
5. **Zone Analysis**: Entry occurs within calculated buffer zones around key levels
### Risk Management Philosophy
FlowState Trader employs a three-tier risk management approach:
1. **Initial Protection**: ATR-based stop losses set at strategy entry
2. **Profit Preservation**: Trailing stops activate once position becomes profitable
3. **Scaled Exit**: Partial profit-taking allows for both security and potential
### Time-Based Edge
The time filtering system recognizes that not all trading hours are equal:
- Avoids low-volume, high-spread periods
- Focuses on optimal liquidity windows
- Prevents trading during news events (lunch hours)
- Allows customization for different market sessions
## Best Practices and Optimization
### Recommended Settings
**For Scalping (1-5 minute charts)**:
- Lookback Period: 10-20
- ATR Period: 14
- Trailing Stop: Conservative mode
- Time Filter: Major session hours only
**For Day Trading (15-60 minute charts)**:
- Lookback Period: 20-30
- ATR Period: 14-21
- Trailing Stop: Balanced mode
- Time Filter: Extended trading hours
**For Swing Trading (4H+ charts)**:
- Lookback Period: 30-50
- ATR Period: 21+
- Trailing Stop: Aggressive mode
- Time Filter: Disabled or very broad
### Market Compatibility
- **Forex**: Excellent for major pairs during active sessions
- **Stocks**: Ideal for liquid stocks during market hours
- **Futures**: Perfect for index and commodity futures
- **Crypto**: Effective on major cryptocurrencies (24/7 capability)
### Risk Considerations
- **Market Conditions**: Performance varies with volatility regimes
- **Timeframe Selection**: Lower timeframes require tighter risk management
- **Position Sizing**: Never risk more than 1-2% of account per trade
- **Backtesting**: Always test on historical data before live implementation
## Educational Value
FlowState serves as an excellent learning tool for:
- Understanding support/resistance trading
- Learning proper time-based filtering
- Mastering trailing stop techniques
- Developing systematic trading approaches
- Risk management best practices
## Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Users should thoroughly backtest the strategy and understand all risks before live trading. Always use proper position sizing and never risk more than you can afford to lose.
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*FlowState Trader represents the evolution of systematic trading - combining classical technical analysis with modern risk management and intelligent time filtering to help traders achieve their optimal performance state through systematic, disciplined execution.*
Qullamaggie [Modified] | FractalystWhat's the purpose of this strategy?
The strategy aims to identify high-probability breakout setups in trending markets, inspired by Kristjan "Qullamaggie" Kullamägi’s approach.
It focuses on capturing explosive price moves after periods of consolidation, using technical criteria like moving averages, breakouts, trailing stop-loss and momentum confirmation.
Ideal for swing traders seeking to ride strong trends while managing risk.
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How does the strategy work?
The strategy follows a systematic process to capture high-momentum breakouts:
Pre-Breakout Criteria:
Prior Price Surge: Identifies stocks that have rallied 30-100%+ in recent month(s), signaling strong underlying momentum (per Qullamaggie’s volatility expansion principles).
Consolidation Phase: Looks for a tightening price range (e.g., flag, pennant, or tight base), indicating a potential "coiling" before continuation.
Trend Confirmation: Uses moving averages (e.g., 20/50/200 EMA) to ensure the stock is trading above key averages on the daily chart, confirming an uptrend.
Price Break: Enters when price clears the consolidation high with conviction.
Risk Management:
Initial Stop Loss: Placed below the consolidation low or a recent swing point to limit downside.
Break-Even Adjustment: Moves stop loss to breakeven once the trade reaches 1.5x risk-to-reward (RR), securing a "free trade" while letting winners run.
Trailing Stop (Unique Edge):
Market Structure Trailing: Instead of trailing via moving averages, the stop is dynamically adjusted using structural invalidation level. This adapts to price action, allowing the trade to stay open during volatile retracements while locking in gains as new structure forms.
Why This Matters: Most strategies use rigid trailing stops (e.g., below the 10EMA), which often exit prematurely in choppy markets. By trailing based on structure, this strategy avoids "noise" and captures larger trends, directly boosting overall returns.
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What markets or timeframes is this suited for?
This is a long-only strategy designed for trending markets, and it performs best in:
Markets: Stocks (especially high-growth, liquid equities), cryptocurrencies (major pairs with strong volatility), commodities (e.g., oil, gold), and futures (index/commodity futures).
Timeframes: Primarily daily charts for swing trades (1-30 day holds), though weekly charts can help confirm broader trends.
Key Advantage: The TradingView script allows instant backtesting with adjustable parameters
You can:
- Test historical performance across multiple markets to identify which assets align best with the strategy.
- Optimize settings (e.g., trailing stop sensitivity, moving averages etc.) to match a market’s volatility profile.
Build a diversified portfolio by filtering for markets that show consistent profitability in backtests.
For example, you might discover cryptos require tighter trailing stops due to volatility, while stocks thrive with wider structural stops. The script automates this analysis, letting you to trade confidently.
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What indicators or tools does the strategy use?
The strategy combines customizable technical tools with strict anti-lookahead safeguards:
Core Indicators:
Moving Averages: Adjustable periods (e.g., 20/50/200 EMA or SMA) and timeframes (daily/weekly) to confirm trend alignment. Users can test combinations (e.g., 10EMA vs. 20EMA) to optimize for specific markets.
Breakout Parameters:
Consolidation Length: Adjustable window to define the "tightness" of the pre-breakout pattern.
Entry Models: Flexible entry logics (Breakouts and fractals)
Anti-Lookahead Design:
All calculations (e.g., moving averages, consolidation ranges, volume averages) use only closed/confirmed data available at the time of the signal.
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How do I manage risk with this strategy?
The strategy prioritizes customizable risk controls to align with your trading style and account size:
User-Defined Risk Inputs:
Risk Per Trade: Set a % of Equity (e.g., 1-2%) to determine position size. The strategy auto-calculates shares/contracts to match your selected risk per trade.
Flexibility: Choose between fixed risk or equity-based scaling.
The script adjusts position sizing dynamically based on your selection.
Pyramiding Feature:
Customizable Entries: Adjust the number of pyramiding trades allowed (e.g., 1-3 additional positions) in the strategy settings. Each new entry is triggered only if the prior trade hits its 1.5x RR target and the trend remains intact.
Risk-Scaled Additions: New positions use profits from prior trades, compounding gains without increasing initial risk.
Risk-Free Trade Mechanic:
Once a trade reaches 1.5x RR, the stop loss is moved to breakeven, eliminating downside risk.
The strategy then opens a new position (if pyramiding is enabled) using a portion of the locked-in profit. This "snowballs" winners while keeping total capital exposure stable.
Impact on Net Profit & Drawdown:
Net Profit Boost: Pyramiding lets you ride multi-leg trends aggressively. For example, a 100% runner could generate 2-3x more profit vs. a single-entry approach.
Controlled Drawdowns: Since new positions are funded by profits (not initial capital), max drawdown stays anchored to your original risk per trade (e.g., 1-2% of account). Even if later entries fail, the breakeven stop on prior trades protects overall equity.
Why This Works: Most strategies either over-leverage (increasing drawdowns) or exit too early. By recycling profits into new positions only after securing risk-free capital, this approach mimics hedge fund "scaling in" tactics while staying retail-trader friendly.
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How does the strategy identify market structure for its trailing stoploss?
The strategy identifies market structure by utilizing an efficient logic with for loops to pinpoint the first swing candle that features a pivot of 2. This marks the beginning of the break of structure, where the market's previous trend or pattern is considered invalidated or changed.
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What are the underlying calculations?
The underlying calculations involve:
Identifying Swing Points: The strategy looks for swing highs (marked with blue Xs) and swing lows (marked with red Xs). A swing high is identified when a candle's high is higher than the highs of the candles before and after it. Conversely, a swing low is when a candle's low is lower than the lows of the candles before and after it.
Break of Structure (BOS):
Bullish BOS: This occurs when the price breaks above the swing high level of the previous structure, indicating a potential shift to a bullish trend.
Bearish BOS: This happens when the price breaks below the swing low level of the previous structure, signaling a potential shift to a bearish trend.
Structural Liquidity and Invalidation:
Structural Liquidity: After a break of structure, liquidity levels are updated to the first swing high in a bullish BOS or the first swing low in a bearish BOS.
Structural Invalidation: If the price moves back to the level of the first swing low before the bullish BOS or the first swing high before the bearish BOS, it invalidates the break of structure, suggesting a potential reversal or continuation of the previous trend.
This method provides users with a technical approach to filter market regimes, offering an advantage by minimizing the risk of overfitting to historical data, which is often a concern with traditional indicators like moving averages.
By focusing on identifying pivotal swing points and the subsequent breaks of structure, the strategy maintains a balance between sensitivity to market changes and robustness against historical data anomalies, ensuring a more adaptable and potentially more reliable market analysis tool.
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What entry criteria are used in this script?
The script uses two entry models for trading decisions: BreakOut and Fractal.
Underlying Calculations:
Breakout: The script records the most recent swing high by storing it in a variable. When the price closes above this recorded level, and all other predefined conditions are satisfied, the script triggers a breakout entry. This approach is considered conservative because it waits for the price to confirm a breakout above the previous high before entering a trade. As shown in the image, as soon as the price closes above the new candle (first tick), the long entry gets taken. The stop-loss is initially set and then moved to break-even once the price moves in favor of the trade.
Fractal: This method involves identifying a swing low with a period of 2, which means it looks for a low point where the price is lower than the two candles before and after it. Once this pattern is detected, the script executes the trade. This is an aggressive approach since it doesn't wait for further price confirmation. In the image, this is represented by the 'Fractal 2' label where the script identifies and acts on the swing low pattern.
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What type of stop-loss identification method are used in this strategy?
This strategy employs two types of stop-loss methods: Initial Stop-loss and Trailing Stop-Loss.
Underlying Calculations:
Initial Stop-loss:
ATR Based: The strategy uses the Average True Range (ATR) to set an initial stop-loss, which helps in accounting for market volatility without predicting price direction.
Calculation:
- First, the True Range (TR) is calculated for each period, which is the greatest of:
- Current Period High - Current Period Low
- Absolute Value of Current Period High - Previous Period Close
- Absolute Value of Current Period Low - Previous Period Close
- The ATR is then the moving average of these TR values over a specified period, typically 14 periods by default. This ATR value can be used to set the stop-loss at a distance from the entry price that reflects the current market volatility.
Swing Low Based:
For this method, the stop-loss is set based on the most recent swing low identified in the market structure analysis. This approach uses the lowest point of the recent price action as a reference for setting the stop-loss.
Trailing Stop-Loss:
The strategy uses structural liquidity and structural invalidation levels across multiple timeframes to adjust the stop-loss once the trade is profitable. This method involves:
Detecting Structural Liquidity: After a break of structure, the liquidity levels are updated to the first swing high in a bullish scenario or the first swing low in a bearish scenario. These levels serve as potential areas where the price might find support or resistance, allowing the stop-loss to trail the price movement.
Detecting Structural Invalidation: If the price returns to the level of the first swing low before a bullish break of structure or the first swing high before a bearish break of structure, it suggests the trend might be reversing or invalidating, prompting the adjustment of the stop-loss to lock in profits or minimize losses.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop. The ATR-based stop-loss adapts to the current market conditions by considering the volatility, ensuring that the stop-loss is not too tight during volatile periods, which could lead to premature exits, nor too loose during calm markets, which might result in larger losses. Similarly, the swing low based stop-loss provides a logical exit point if the market structure changes unfavorably.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance. This involves backtesting the strategy with different settings for the ATR period, the distance from the swing low, and how the trailing stop-loss reacts to structural liquidity and invalidation levels.
Through this process, you can tailor the strategy to perform optimally in different market environments, ensuring that the stop-loss mechanism supports the trade's longevity while safeguarding against significant drawdowns.
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What type of break-even method is used in this strategy? What are the underlying calculations?
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
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What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Strategy to Create a Profitable Edge and Systems?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What Makes This Strategy Unique?
This strategy combines flexibility, smart risk management, and momentum focus in a way that’s rare and practical:
1. Adapts to Any Market Rhythm
Works on daily, weekly, or intraday charts without code changes.
Uses two entry types: classic breakouts (like trending stocks) or fractal patterns (to avoid false starts).
2. Smarter Stop-Loss System
No rigid rules: Stops adjust based on price structure (e.g., new “higher lows”), not fixed percentages.
Avoids whipsaws: Tightens stops only when the trend strengthens, not in choppy markets.
3. Safe Profit-Boosting Pyramiding
Adds new positions only after prior trades are risk-free (stops moved above breakeven).
Scales up using locked-in profits, not new capital, to grow gains safely.
4. Built-In Momentum Check
Tracks 1/3/6-month price growth to spotlight stocks with strong, lasting momentum.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Dollar Cost Averaging (DCA) | FractalystWhat's the purpose of this strategy?
The purpose of dollar cost averaging (DCA) is to grow investments over time using a disciplined, methodical approach used by many top institutions like MicroStrategy and other institutions.
Here's how it functions:
Dollar Cost Averaging (DCA): This technique involves investing a set amount of money regularly, regardless of market conditions. It helps to mitigate the risk of investing a large sum at a peak price by spreading out your investment, thus potentially lowering your average cost per share over time.
Regular Contributions: By adding money to your investments on a pre-determined frequency and dollar amount defined by the user, you take advantage of compounding. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
Technical Analysis: The strategy employs a market trend ratio to gauge market sentiment. It calculates the ratio of bullish vs bearish breakouts across various timeframes, assigning this ratio a percentage-based score to determine the directional bias. Once this score exceeds a user-selected percentage, the strategy looks to take buy entries, signaling a favorable time for investment based on current market trends.
Fundamental Analysis: This aspect looks at the health of the economy and companies within it to determine bullish market conditions. Specifically, we consider:
Specifically, it considers:
Interest Rate: High interest rates can affect borrowing costs, potentially slowing down economic growth or making stocks less attractive compared to fixed income.
Inflation Rate: Inflation erodes purchasing power, but moderate inflation can be a sign of a healthy economy. We look for investments that might benefit from or withstand inflation.
GDP Rate: GDP growth indicates the overall health of the economy; we aim to invest in sectors poised to grow with the economy.
Unemployment Rate: Lower unemployment typically signals consumer confidence and spending power, which can boost certain sectors.
By integrating these elements, the strategy aims to:
Reduce Investment Volatility: By spreading out your investments, you're less impacted by short-term market swings.
Enhance Growth Potential: Using both technical and fundamental filters helps in choosing investments that are more likely to appreciate over time.
Manage Risk: The strategy aims to balance the risk of market timing by investing consistently and choosing assets wisely based on both economic data and market conditions.
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What are Regular Contributions in this strategy?
Regular Contributions involve adding money to your investments on a pre-determined frequency and dollar amount defined by the user. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
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How do regular contributions enhance compounding and reduce timing risk?
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
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How does the strategy integrate technical and fundamental analysis for investors?
A: The strategy combines technical and fundamental analysis in the following manner:
Technical Analysis: It uses a market trend ratio to determine the directional bias by calculating the ratio of bullish vs bearish breakouts. Once this ratio exceeds a user-selected percentage threshold, the strategy signals to take buy entries, optimizing the timing within the given timeframe(s).
Fundamental Analysis: This aspect assesses the broader economic environment to identify sectors or assets that are likely to benefit from current economic conditions. By understanding these fundamentals, the strategy ensures investments are made in assets with strong growth potential.
This integration allows the strategy to select investments that are both technically favorable for entry and fundamentally sound, providing a comprehensive approach to investment decisions in the crypto, stock, and commodities markets.
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How does the strategy identify market structure? What are the underlying calculations?
Q: How does the strategy identify market structure?
A: The strategy identifies market structure by utilizing an efficient logic with for loops to pinpoint the first swing candle that features a pivot of 2. This marks the beginning of the break of structure, where the market's previous trend or pattern is considered invalidated or changed.
What are the underlying calculations for identifying market structure?
A: The underlying calculations involve:
Identifying Swing Points: The strategy looks for swing highs (marked with blue Xs) and swing lows (marked with red Xs). A swing high is identified when a candle's high is higher than the highs of the candles before and after it. Conversely, a swing low is when a candle's low is lower than the lows of the candles before and after it.
Break of Structure (BOS):
Bullish BOS: This occurs when the price breaks above the swing high level of the previous structure, indicating a potential shift to a bullish trend.
Bearish BOS: This happens when the price breaks below the swing low level of the previous structure, signaling a potential shift to a bearish trend.
Structural Liquidity and Invalidation:
Structural Liquidity: After a break of structure, liquidity levels are updated to the first swing high in a bullish BOS or the first swing low in a bearish BOS.
Structural Invalidation: If the price moves back to the level of the first swing low before the bullish BOS or the first swing high before the bearish BOS, it invalidates the break of structure, suggesting a potential reversal or continuation of the previous trend.
This method provides users with a technical approach to filter market regimes, offering an advantage by minimizing the risk of overfitting to historical data, which is often a concern with traditional indicators like moving averages.
By focusing on identifying pivotal swing points and the subsequent breaks of structure, the strategy maintains a balance between sensitivity to market changes and robustness against historical data anomalies, ensuring a more adaptable and potentially more reliable market analysis tool.
What entry criteria are used in this script?
The script uses two entry models for trading decisions: BreakOut and Fractal.
Underlying Calculations:
Breakout: The script records the most recent swing high by storing it in a variable. When the price closes above this recorded level, and all other predefined conditions are satisfied, the script triggers a breakout entry. This approach is considered conservative because it waits for the price to confirm a breakout above the previous high before entering a trade. As shown in the image, as soon as the price closes above the new candle (first tick), the long entry gets taken. The stop-loss is initially set and then moved to break-even once the price moves in favor of the trade.
Fractal: This method involves identifying a swing low with a period of 2, which means it looks for a low point where the price is lower than the two candles before and after it. Once this pattern is detected, the script executes the trade. This is an aggressive approach since it doesn't wait for further price confirmation. In the image, this is represented by the 'Fractal 2' label where the script identifies and acts on the swing low pattern.
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How does the script calculate trend score? What are the underlying calculations?
Market Trend Ratio: The script calculates the ratio of bullish to bearish breakouts. This involves:
Counting Bullish Breakouts: A bullish breakout is counted when the price breaks above a recent swing high (as identified in the strategy's market structure analysis).
Counting Bearish Breakouts: A bearish breakout is counted when the price breaks below a recent swing low.
Percentage-Based Score: This ratio is then converted into a percentage-based score:
For example, if there are 10 bullish breakouts and 5 bearish breakouts in a given timeframe, the ratio would be 10:5 or 2:1. This could be translated into a score where 66.67% (10/(10+5) * 100) represents the bullish trend strength.
The score might be calculated as (Number of Bullish Breakouts / Total Breakouts) * 100.
User-Defined Threshold: The strategy uses this score to determine when to take buy entries. If the trend score exceeds a user-defined percentage threshold, it indicates a strong enough bullish trend to justify a buy entry. For instance, if the user sets the threshold at 60%, the script would look for a buy entry when the trend score is above this level.
Timeframe Consideration: The calculations are performed across the timeframes specified by the user, ensuring the trend score reflects the market's behavior over different periods, which could be daily, weekly, or any other relevant timeframe.
This method provides a quantitative measure of market trend strength, helping to make informed decisions based on the balance between bullish and bearish market movements.
What type of stop-loss identification method are used in this strategy?
This strategy employs two types of stop-loss methods: Initial Stop-loss and Trailing Stop-Loss.
Underlying Calculations:
Initial Stop-loss:
ATR Based: The strategy uses the Average True Range (ATR) to set an initial stop-loss, which helps in accounting for market volatility without predicting price direction.
Calculation:
- First, the True Range (TR) is calculated for each period, which is the greatest of:
- Current Period High - Current Period Low
- Absolute Value of Current Period High - Previous Period Close
- Absolute Value of Current Period Low - Previous Period Close
- The ATR is then the moving average of these TR values over a specified period, typically 14 periods by default. This ATR value can be used to set the stop-loss at a distance from the entry price that reflects the current market volatility.
Swing Low Based:
For this method, the stop-loss is set based on the most recent swing low identified in the market structure analysis. This approach uses the lowest point of the recent price action as a reference for setting the stop-loss.
Trailing Stop-Loss:
The strategy uses structural liquidity and structural invalidation levels across multiple timeframes to adjust the stop-loss once the trade is profitable. This method involves:
Detecting Structural Liquidity: After a break of structure, the liquidity levels are updated to the first swing high in a bullish scenario or the first swing low in a bearish scenario. These levels serve as potential areas where the price might find support or resistance, allowing the stop-loss to trail the price movement.
Detecting Structural Invalidation: If the price returns to the level of the first swing low before a bullish break of structure or the first swing high before a bearish break of structure, it suggests the trend might be reversing or invalidating, prompting the adjustment of the stop-loss to lock in profits or minimize losses.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop. The ATR-based stop-loss adapts to the current market conditions by considering the volatility, ensuring that the stop-loss is not too tight during volatile periods, which could lead to premature exits, nor too loose during calm markets, which might result in larger losses. Similarly, the swing low based stop-loss provides a logical exit point if the market structure changes unfavorably.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance. This involves backtesting the strategy with different settings for the ATR period, the distance from the swing low, and how the trailing stop-loss reacts to structural liquidity and invalidation levels.
Through this process, you can tailor the strategy to perform optimally in different market environments, ensuring that the stop-loss mechanism supports the trade's longevity while safeguarding against significant drawdowns.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP
- You can choose to set a take profit level at which your position gets fully closed.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Strategy to Create a Profitable Edge and Systems?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
Incorporation of Fundamental Analysis:
This strategy integrates fundamental analysis by considering key economic indicators such as interest rates, inflation, GDP growth, and unemployment rates. These fundamentals help in assessing the broader economic health, which in turn influences sector performance and market trends. By understanding these economic conditions, the strategy can identify sectors or assets that are likely to thrive, ensuring investments are made in environments conducive to growth. This approach allows for a more informed investment decision, aligning technical entries with fundamentally strong market conditions, thus potentially enhancing the strategy's effectiveness over time.
Technical Analysis Without Classical Methods:
The strategy's technical analysis diverges from traditional methods like moving averages by focusing on market structure through a trend score system.
Instead of using lagging indicators, it employs a real-time analysis of market trends by calculating the ratio of bullish to bearish breakouts. This provides several benefits:
Immediate Market Sentiment: The trend score system reacts more dynamically to current market conditions, offering insights into the market's immediate sentiment rather than historical trends, which can often lag behind real-time changes.
Reduced Overfitting: By not relying on moving averages or similar classical indicators, the strategy avoids the common pitfall of overfitting to historical data, which can lead to poor performance in new market conditions. The trend score provides a fresh perspective on market direction, potentially leading to more robust trading signals.
Clear Entry Signals: With the trend score, entry decisions are based on a clear percentage threshold, making the strategy's decision-making process straightforward and less subjective than interpreting moving average crossovers or similar signals.
Regular Contributions and Reminders:
The strategy encourages regular investments through a system of predefined frequency and amount, which could be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach:
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
Long-Term Wealth Building:
Focused on long-term wealth accumulation, this strategy:
Promotes Patience and Discipline: By emphasizing regular contributions and a disciplined approach to both entry and risk management, it aligns with the principles of long-term investing, discouraging impulsive decisions based on short-term market fluctuations.
Diversification Across Asset Classes: Operating across crypto, stocks, and commodities, the strategy provides diversification, which is a key component of long-term wealth building, reducing risk through varied exposure.
Growth Over Time: The strategy's design to work with the market's natural growth cycles, supported by fundamental analysis, aims for sustainable growth rather than quick profits, aligning with the goals of investors looking to build wealth over decades.
This comprehensive approach, combining fundamental insights, innovative technical analysis, disciplined investment habits, and a focus on long-term growth, offers a unique and potentially effective pathway for investors seeking to build wealth steadily over time.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
QuantBuilder | FractalystWhat's the strategy's purpose and functionality?
QuantBuilder is designed for both traders and investors who want to utilize mathematical techniques to develop profitable strategies through backtesting on historical data.
The primary goal is to develop profitable quantitive strategies that not only outperform the underlying asset in terms of returns but also minimize drawdown.
For instance, consider Bitcoin (BTC), which has experienced significant volatility, averaging an estimated 200% annual return over the past decade, with maximum drawdowns exceeding -80%. By employing this strategy with diverse entry and exit techniques, users can potentially seek to enhance their Compound Annual Growth Rate (CAGR) while managing risk to maintain a lower maximum drawdown.
While this strategy employs quantitative techniques, including mathematical methods such as probabilities and positive expected values, it demonstrates exceptional efficacy across all markets. It particularly excels in futures, indices, stocks, cryptocurrencies, and commodities, leveraging their inherent trending behaviors for optimized performance.
In both trending and consolidating market conditions, QuantBuilder employs a combination of multi-timeframe probabilities, expected values, directional biases, moving averages and diverse entry models to identify and capitalize on bullish market movements.
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
1. Trading:
- Designed for traders looking to capitalize on bullish markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for both swing and intraday trading with a focus on probabilities and risk per trade approach.
2. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully/partially investing in the asset during bullish conditions.
How does the strategy identify market structure? What are the underlying calculations?
The strategy utilizes an efficient logic with for loops to pinpoint the first swing candle featuring a pivot of 2, establishing the point at which the break of structure begins.
What entry criteria are used in this script? What are the underlying calculations?
The script utilizes two entry models: BreakOut and fractal.
Underlying Calculations:
Breakout: The script assigns the most recent swing high to a variable. When the price closes above this level and all other conditions are met, the script executes a breakout entry (conservative approach).
Fractal: The script identifies a swing low with a period of 2. Once this condition is met, the script executes the trade (aggressive approach).
How does the script calculate probabilities? What are the underlying calculations?
The script calculates probabilities by monitoring price interactions with liquidity levels. Here’s how the underlying calculations work:
Tracking Price Hits: The script counts the number of times the price taps into each liquidity side after the EQM level is activated. This data is stored in an array for further analysis.
Sample Size Consideration: The total number of price interactions serves as the sample size for calculating probabilities.
Probability Calculation: For each liquidity side, the script calculates the probability by taking the average of the recorded hits. This allows for a dynamic assessment of the likelihood that a particular side will be hit next, based on historical performance.
Dynamic Adjustment: As new price data comes in, the probabilities are recalculated, providing real-time aduptive insights into market behavior.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
How does the script calculate expected values? What are the underlying calculations?
The script calculates expected values by leveraging the probabilities of winning and losing trades, along with their respective returns. The process involves the following steps:
This quantitative methodology provides a robust framework for assessing the expected performance of trading strategies based on historical data and backtesting results.
How is the contextual bias calculated? What are the underlying calculations?
The contextual bias in the QuantBuilder script is calculated through a structured approach that assesses market structure based on swing highs and lows. Here’s how it works:
Identification of Swing Points: The script identifies significant swing points using a defined pivot logic, focusing on the first swing high and swing low. This helps establish critical levels for determining market structure.
Break of Structure (BOS) Assessment:
Bullish BOS: The script recognizes a bullish break of structure when a candle closes above the first swing high, followed by at least one swing low.
Bearish BOS: Conversely, a bearish break of structure is identified when a candle closes below the first swing low, followed by at least one swing high.
Bias Assignment: Based on the identified break of structure, the script assigns directional biases:
A bullish bias is assigned if a bullish BOS is confirmed.
A bearish bias is assigned if a bearish BOS is confirmed.
Quantitative Evaluation: Each identified bias is quantitatively evaluated, allowing the script to assign numerical values representing the strength of each bias. This quantification aids in assessing the reliability of market sentiment across multiple timeframes.
What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
- Initial Stop-loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14)
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
- Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detect structural liquidity and structural invalidation levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
To facilitate studying historical data, all conditions and filters can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Quantitive Strategy Builder to Create a Profitable Edge and System?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
QuantBuilder stands out due to its unique combination of quantitative techniques and innovative algorithms that leverage historical data for real-time trading decisions. Unlike most algorithmic strategies that work based on predefined rules, this strategy adapts to real-time market probabilities and expected values, enhancing its reliability. Key features include:
Mathematical Framework: The strategy integrates advanced mathematical concepts, such as probabilities and expected values, to assess trade viability and optimize decision-making.
Multi-Timeframe Analysis: By utilizing multi-timeframe probabilities, QuantBuilder provides a comprehensive view of market conditions, enhancing the accuracy of entry and exit points.
Dynamic Market Structure Identification: The script employs a systematic approach to identify market structure changes, utilizing a blend of swing highs and lows to detect contextual/direction bias of the market.
Built-in Trailing Stop Loss: The strategy features a dynamic trailing stop loss based on multi-timeframe analysis of market structure. This allows traders to lock in profits while adapting to changing market conditions, ensuring that exits are executed at optimal levels without prematurely closing positions.
Robust Performance Metrics: With detailed performance tables and visualizations, users can easily evaluate strategy effectiveness and adjust parameters based on historical performance.
Adaptability: The strategy is designed to work across various markets and timeframes, making it versatile for different trading styles and objectives.
Suitability for Investors and Traders: QuantBuilder is ideal for both investors and traders looking to rely on mathematically proven data to create profitable strategies, ensuring that decisions are grounded in quantitative analysis.
These original elements combine to create a powerful tool that can help both traders and investors to build and refine profitable strategies based on algorithmic quantitative analysis.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Negroni MA & RSI Strategy, plus trade entry and SL/TP optionsI will start with the context, and some things to think about when using a strategy tool to back-test ideas.
CONTEXT
FIRST: This is derived from other people's work, but I honestly hadn't found a mixed indicator MA strategy tool that does what this now does. If it is out there, apologies!!
This tool can help back-test various MA trends (SMA, EMA, HMA, VWMA); as well as factoring in RSI levels (or not); and can factor in a fixed HTF MA (or not). You can apply a 'retest entry' or a 'breakout entry', and you can also apply various risk mgt for SL/TP orders: 1) No SL/TP; or 2) a fixed %, or 3) dynamic ATR multipliers.
Find below, some details explaining what this tool is attempting to do.
Thank you, tack, salute!
THINGS TO REVIEW (it is not just about 'profitability'!!)
Whilst discretion is always highly encouraged as a trader, and a 100% indicator-driven strategy is VERY unlikely to yield sustainable results going forward, at the very least back-testing your strategies can help provide some guidance, not just on win rate Vs profit factor, but other things including:
a) Trade frequency: if a strategy has an 75% win rate and profit factor of 4, with all your parameters and confluence checks, but only triggers 3 trades every 5 years, is that realistically implementable to your trading situation if you have a $10,000 account?
b) Trade entry type: is it consistently better to wait for a retest of an 'MA zone', or is it better to market buy/sell on breakout of the 'MA zone'?
c) Risk management (SL/TP): is it consistently better to have a fixed static % for SL/TP ("I always place my stops 2% away, whether it is EURUSD or BTCUSDT"), or would you be better placed to try using an ATR multiplier of the respective assets?
d) Moving average type: is your old faithful 100 EMA really serving you well, or is the classic SMA more reliable, or how about the HMA, or the VWMA? Is the 100/200 cross holding up, or do you need something more sensitive? Is there any significant difference between a 10 EMA/20 EMA trend zone compared to a 13 EMA /25 EMA zone?
e) Confluence: Do added confluence checks (RSI, higher timeframe MA) actually improve profitability? But even if they do, is at the cost of cutting too many trades?
INPUTS AND PARAMETERS
Choice 1) Entry Strategy: Retest or Breakout - You can select both!
[ ]:
a) RETEST entry strat: price crosses UNDER FastMA INTO the 'MA trend zone'.
b) BREAKOUT entry strat: price crosses OVER FastMA OUT the 'MA trend zone'.
Choice 2) Risk Management (SL and TP) - You can select more than 1 strategy!
a) No SL/TP: Long trades are closed when the LOW crosses back UNDER the fastMA again, and shorts are closed when the HIGH crosses back OVER the fastMA again.
b) Static % SL/TP: Your SL/TP will be a fixed % away from avg. position price... WARNING: You should change this for various asset classes; FX vol is not the same as crypto altcoin vol!
c) Dynamic ATR SL/TP: Your SL/TP is a multiple of your selected ATR range (default is 50, see 'info' when you select ATR range). ATR accounts for the change in vol of different asset classes somewhat, HOWEVER... you should probably still not have the same multiplier trading S&P500 as you would trading crypto altcoins!
Then select your preferred parameters: EMA, SMA, HMA, VWMA, etc. You can mix and match, and most options have a info/tooltip guide.
RSI note: If you don't care for RSI levels, then set buy signal at 1... i.e always buys! Similarly set sell signal at 99.
ATR note: standard ATR length is usually 14, however... your SL/TP will move POST entry, and can tighten or widen your initial SL/TP... for better AND usually for worse! Go find a trade (strat 3) on the chart, look at the SL/TP lines, now change the number to 5, you'll see.
Fixed HTF MA note: If you don't care for HTF MA confluence, just change the timeframe/options to match the 'Slow MA' options you've chosen.
Backtesting & Trading Engine [PineCoders]The PineCoders Backtesting and Trading Engine is a sophisticated framework with hybrid code that can run as a study to generate alerts for automated or discretionary trading while simultaneously providing backtest results. It can also easily be converted to a TradingView strategy in order to run TV backtesting. The Engine comes with many built-in strats for entries, filters, stops and exits, but you can also add you own.
If, like any self-respecting strategy modeler should, you spend a reasonable amount of time constantly researching new strategies and tinkering, our hope is that the Engine will become your inseparable go-to tool to test the validity of your creations, as once your tests are conclusive, you will be able to run this code as a study to generate the alerts required to put it in real-world use, whether for discretionary trading or to interface with an execution bot/app. You may also find the backtesting results the Engine produces in study mode enough for your needs and spend most of your time there, only occasionally converting to strategy mode in order to backtest using TV backtesting.
As you will quickly grasp when you bring up this script’s Settings, this is a complex tool. While you will be able to see results very quickly by just putting it on a chart and using its built-in strategies, in order to reap the full benefits of the PineCoders Engine, you will need to invest the time required to understand the subtleties involved in putting all its potential into play.
Disclaimer: use the Engine at your own risk.
Before we delve in more detail, here’s a bird’s eye view of the Engine’s features:
More than 40 built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
By combining your own strats to the built-in strats supplied with the Engine, and then tuning the numerous options and parameters in the Inputs dialog box, you will be able to play what-if scenarios from an infinite number of permutations.
USE CASES
You have written an indicator that provides an entry strat but it’s missing other components like a filter and a stop strategy. You add a plot in your indicator that respects the Engine’s External Signal Protocol, connect it to the Engine by simply selecting your indicator’s plot name in the Engine’s Settings/Inputs and then run tests on different combinations of entry stops, in-trade stops and profit taking strats to find out which one produces the best results with your entry strat.
You are building a complex strategy that you will want to run as an indicator generating alerts to be sent to a third-party execution bot. You insert your code in the Engine’s modules and leverage its trade management code to quickly move your strategy into production.
You have many different filters and want to explore results using them separately or in combination. Integrate the filter code in the Engine and run through different permutations or hook up your filtering through the external input and control your filter combos from your indicator.
You are tweaking the parameters of your entry, filter or stop strat. You integrate it in the Engine and evaluate its performance using the Engine’s statistics.
You always wondered what results a random entry strat would yield on your markets. You use the Engine’s built-in random entry strat and test it using different combinations of filters, stop and exit strats.
You want to evaluate the impact of fees and slippage on your strategy. You use the Engine’s inputs to play with different values and get immediate feedback in the detailed numbers provided in the Data Window.
You just want to inspect the individual trades your strategy generates. You include it in the Engine and then inspect trades visually on your charts, looking at the numbers in the Data Window as you move your cursor around.
You have never written a production-grade strategy and you want to learn how. Inspect the code in the Engine; you will find essential components typical of what is being used in actual trading systems.
You have run your system for a while and have compiled actual slippage information and your broker/exchange has updated his fees schedule. You enter the information in the Engine and run it on your markets to see the impact this has on your results.
FEATURES
Before going into the detail of the Inputs and the Data Window numbers, here’s a more detailed overview of the Engine’s features.
Built-in strats
The engine comes with more than 40 pre-coded strategies for the following standard system components:
Entries,
Filters,
Entry stops,
2 stage in-trade stops with kick-in rules,
Pyramiding rules,
Hard exits.
While some of the filter and stop strats provided may be useful in production-quality systems, you will not devise crazy profit-generating systems using only the entry strats supplied; that part is still up to you, as will be finding the elusive combination of components that makes winning systems. The Engine will, however, provide you with a solid foundation where all the trade management nitty-gritty is handled for you. By binding your custom strats to the Engine, you will be able to build reliable systems of the best quality currently allowed on the TV platform.
On-chart trade information
As you move over the bars in a trade, you will see trade numbers in the Data Window change at each bar. The engine calculates the P&L at every bar, including slippage and fees that would be incurred were the trade exited at that bar’s close. If the trade includes pyramided entries, those will be taken into account as well, although for those, final fees and slippage are only calculated at the trade’s exit.
You can also see on-chart markers for the entry level, stop positions, in-trade special events and entries/exits (you will want to disable these when using the Engine in strategy mode to see TV backtesting results).
Customization
You can couple your own strats to the Engine in two ways:
1. By inserting your own code in the Engine’s different modules. The modular design should enable you to do so with minimal effort by following the instructions in the code.
2. By linking an external indicator to the engine. After making the proper selections in the engine’s Settings and providing values respecting the engine’s protocol, your external indicator can, when the Engine is used in Indicator mode only:
Tell the engine when to enter long or short trades, but let the engine’s in-trade stop and exit strats manage the exits,
Signal both entries and exits,
Provide an entry stop along with your entry signal,
Filter other entry signals generated by any of the engine’s entry strats.
Conversion from strategy to study
TradingView strategies are required to backtest using the TradingView backtesting feature, but if you want to generate alerts with your script, whether for automated trading or just to trigger alerts that you will use in discretionary trading, your code has to run as a study since, for the time being, strategies can’t generate alerts. From hereon we will use indicator as a synonym for study.
Unless you want to maintain two code bases, you will need hybrid code that easily flips between strategy and indicator modes, and your code will need to restrict its use of strategy() calls and their arguments if it’s going to be able to run both as an indicator and a strategy using the same trade logic. That’s one of the benefits of using this Engine. Once you will have entered your own strats in the Engine, it will be a matter of commenting/uncommenting only four lines of code to flip between indicator and strategy modes in a matter of seconds.
Additionally, even when running in Indicator mode, the Engine will still provide you with precious numbers on your individual trades and global results, some of which are not available with normal TradingView backtesting.
Post-Exit Analysis for alternate outcomes (PEA)
While typical backtesting shows results of trade outcomes, PEA focuses on what could have happened after the exit. The intention is to help traders get an idea of the opportunity/risk in the bars following the trade in order to evaluate if their exit strategies are too aggressive or conservative.
After a trade is exited, the Engine’s PEA module continues analyzing outcomes for a user-defined quantity of bars. It identifies the maximum opportunity and risk available in that space, and calculates the drawdown required to reach the highest opportunity level post-exit, while recording the number of bars to that point.
Typically, if you can’t find opportunity greater than 1X past your trade using a few different reasonable lengths of PEA, your strategy is doing pretty good at capturing opportunity. Remember that 100% of opportunity is never capturable. If, however, PEA was finding post-trade maximum opportunity of 3 or 4X with average drawdowns of 0.3 to those areas, this could be a clue revealing your system is exiting trades prematurely. To analyze PEA numbers, you can uncomment complete sets of plots in the Plot module to reveal detailed global and individual PEA numbers.
Statistics
The Engine provides stats on your trades that TV backtesting does not provide, such as:
Average Profitability Per Trade (APPT), aka statistical expectancy, a crucial value.
APPT per bar,
Average stop size,
Traded volume .
It also shows you on a trade-by-trade basis, on-going individual trade results and data.
In-trade events
In-trade events can plot reminders and trigger alerts when they occur. The built-in events are:
Price approaching stop,
Possible tops/bottoms,
Large stop movement (for discretionary trading where stop is moved manually),
Large price movements.
Slippage and Fees
Even when running in indicator mode, the Engine allows for slippage and fees to be included in the logic and test results.
Alerts
The alert creation mechanism allows you to configure alerts on any combination of the normal or pyramided entries, exits and in-trade events.
Backtesting results
A few words on the numbers calculated in the Engine. Priority is given to numbers not shown in TV backtesting, as you can readily convert the script to a strategy if you need them.
We have chosen to focus on numbers expressing results relative to X (the trade’s risk) rather than in absolute currency numbers or in other more conventional but less useful ways. For example, most of the individual trade results are not shown in percentages, as this unit of measure is often less meaningful than those expressed in units of risk (X). A trade that closes with a +25% result, for example, is a poor outcome if it was entered with a -50% stop. Expressed in X, this trade’s P&L becomes 0.5, which provides much better insight into the trade’s outcome. A trade that closes with a P&L of +2X has earned twice the risk incurred upon entry, which would represent a pre-trade risk:reward ratio of 2.
The way to go about it when you think in X’s and that you adopt the sound risk management policy to risk a fixed percentage of your account on each trade is to equate a currency value to a unit of X. E.g. your account is 10K USD and you decide you will risk a maximum of 1% of it on each trade. That means your unit of X for each trade is worth 100 USD. If your APPT is 2X, this means every time you risk 100 USD in a trade, you can expect to make, on average, 200 USD.
By presenting results this way, we hope that the Engine’s statistics will appeal to those cognisant of sound risk management strategies, while gently leading traders who aren’t, towards them.
We trade to turn in tangible profits of course, so at some point currency must come into play. Accordingly, some values such as equity, P&L, slippage and fees are expressed in currency.
Many of the usual numbers shown in TV backtests are nonetheless available, but they have been commented out in the Engine’s Plot module.
Position sizing and risk management
All good system designers understand that optimal risk management is at the very heart of all winning strategies. The risk in a trade is defined by the fraction of current equity represented by the amplitude of the stop, so in order to manage risk optimally on each trade, position size should adjust to the stop’s amplitude. Systems that enter trades with a fixed stop amplitude can get away with calculating position size as a fixed percentage of current equity. In the context of a test run where equity varies, what represents a fixed amount of risk translates into different currency values.
Dynamically adjusting position size throughout a system’s life is optimal in many ways. First, as position sizing will vary with current equity, it reproduces a behavioral pattern common to experienced traders, who will dial down risk when confronted to poor performance and increase it when performance improves. Second, limiting risk confers more predictability to statistical test results. Third, position sizing isn’t just about managing risk, it’s also about maximizing opportunity. By using the maximum leverage (no reference to trading on margin here) into the trade that your risk management strategy allows, a dynamic position size allows you to capture maximal opportunity.
To calculate position sizes using the fixed risk method, we use the following formula: Position = Account * MaxRisk% / Stop% [, which calculates a position size taking into account the trade’s entry stop so that if the trade is stopped out, 100 USD will be lost. For someone who manages risk this way, common instructions to invest a certain percentage of your account in a position are simply worthless, as they do not take into account the risk incurred in the trade.
The Engine lets you select either the fixed risk or fixed percentage of equity position sizing methods. The closest thing to dynamic position sizing that can currently be done with alerts is to use a bot that allows syntax to specify position size as a percentage of equity which, while being dynamic in the sense that it will adapt to current equity when the trade is entered, does not allow us to modulate position size using the stop’s amplitude. Changes to alerts are on the way which should solve this problem.
In order for you to simulate performance with the constraint of fixed position sizing, the Engine also offers a third, less preferable option, where position size is defined as a fixed percentage of initial capital so that it is constant throughout the test and will thus represent a varying proportion of current equity.
Let’s recap. The three position sizing methods the Engine offers are:
1. By specifying the maximum percentage of risk to incur on your remaining equity, so the Engine will dynamically adjust position size for each trade so that, combining the stop’s amplitude with position size will yield a fixed percentage of risk incurred on current equity,
2. By specifying a fixed percentage of remaining equity. Note that unless your system has a fixed stop at entry, this method will not provide maximal risk control, as risk will vary with the amplitude of the stop for every trade. This method, as the first, does however have the advantage of automatically adjusting position size to equity. It is the Engine’s default method because it has an equivalent in TV backtesting, so when flipping between indicator and strategy mode, test results will more or less correspond.
3. By specifying a fixed percentage of the Initial Capital. While this is the least preferable method, it nonetheless reflects the reality confronted by most system designers on TradingView today. In this case, risk varies both because the fixed position size in initial capital currency represents a varying percentage of remaining equity, and because the trade’s stop amplitude may vary, adding another variability vector to risk.
Note that the Engine cannot display equity results for strategies entering trades for a fixed amount of shares/contracts at a variable price.
SETTINGS/INPUTS
Because the initial text first published with a script cannot be edited later and because there are just too many options, the Engine’s Inputs will not be covered in minute detail, as they will most certainly evolve. We will go over them with broad strokes; you should be able to figure the rest out. If you have questions, just ask them here or in the PineCoders Telegram group.
Display
The display header’s checkbox does nothing.
For the moment, only one exit strategy uses a take profit level, so only that one will show information when checking “Show Take Profit Level”.
Entries
You can activate two simultaneous entry strats, each selected from the same set of strats contained in the Engine. If you select two and they fire simultaneously, the main strat’s signal will be used.
The random strat in each list uses a different seed, so you will get different results from each.
The “Filter transitions” and “Filter states” strats delegate signal generation to the selected filter(s). “Filter transitions” signals will only fire when the filter transitions into bull/bear state, so after a trade is stopped out, the next entry may take some time to trigger if the filter’s state does not change quickly. When you choose “Filter states”, then a new trade will be entered immediately after an exit in the direction the filter allows.
If you select “External Indicator”, your indicator will need to generate a +2/-2 (or a positive/negative stop value) to enter a long/short position, providing the selected filters allow for it. If you wish to use the Engine’s capacity to also derive the entry stop level from your indicator’s signal, then you must explicitly choose this option in the Entry Stops section.
Filters
You can activate as many filters as you wish; they are additive. The “Maximum stop allowed on entry” is an important component of proper risk management. If your system has an average 3% stop size and you need to trade using fixed position sizes because of alert/execution bot limitations, you must use this filter because if your system was to enter a trade with a 15% stop, that trade would incur 5 times the normal risk, and its result would account for an abnormally high proportion in your system’s performance.
Remember that any filter can also be used as an entry signal, either when it changes states, or whenever no trade is active and the filter is in a bull or bear mode.
Entry Stops
An entry stop must be selected in the Engine, as it requires a stop level before the in-trade stop is calculated. Until the selected in-trade stop strat generates a stop that comes closer to price than the entry stop (or respects another one of the in-trade stops kick in strats), the entry stop level is used.
It is here that you must select “External Indicator” if your indicator supplies a +price/-price value to be used as the entry stop. A +price is expected for a long entry and a -price value will enter a short with a stop at price. Note that the price is the absolute price, not an offset to the current price level.
In-Trade Stops
The Engine comes with many built-in in-trade stop strats. Note that some of them share the “Length” and “Multiple” field, so when you swap between them, be sure that the length and multiple in use correspond to what you want for that stop strat. Suggested defaults appear with the name of each strat in the dropdown.
In addition to the strat you wish to use, you must also determine when it kicks in to replace the initial entry’s stop, which is determined using different strats. For strats where you can define a positive or negative multiple of X, percentage or fixed value for a kick-in strat, a positive value is above the trade’s entry fill and a negative one below. A value of zero represents breakeven.
Pyramiding
What you specify in this section are the rules that allow pyramiding to happen. By themselves, these rules will not generate pyramiding entries. For those to happen, entry signals must be issued by one of the active entry strats, and conform to the pyramiding rules which act as a filter for them. The “Filter must allow entry” selection must be chosen if you want the usual system’s filters to act as additional filtering criteria for your pyramided entries.
Hard Exits
You can choose from a variety of hard exit strats. Hard exits are exit strategies which signal trade exits on specific events, as opposed to price breaching a stop level in In-Trade Stops strategies. They are self-explanatory. The last one labelled When Take Profit Level (multiple of X) is reached is the only one that uses a level, but contrary to stops, it is above price and while it is relative because it is expressed as a multiple of X, it does not move during the trade. This is the level called Take Profit that is show when the “Show Take Profit Level” checkbox is checked in the Display section.
While stops focus on managing risk, hard exit strategies try to put the emphasis on capturing opportunity.
Slippage
You can define it as a percentage or a fixed value, with different settings for entries and exits. The entry and exit markers on the chart show the impact of slippage on the entry price (the fill).
Fees
Fees, whether expressed as a percentage of position size in and out of the trade or as a fixed value per in and out, are in the same units of currency as the capital defined in the Position Sizing section. Fees being deducted from your Capital, they do not have an impact on the chart marker positions.
In-Trade Events
These events will only trigger during trades. They can be helpful to act as reminders for traders using the Engine as assistance to discretionary trading.
Post-Exit Analysis
It is normally on. Some of its results will show in the Global Numbers section of the Data Window. Only a few of the statistics generated are shown; many more are available, but commented out in the Plot module.
Date Range Filtering
Note that you don’t have to change the dates to enable/diable filtering. When you are done with a specific date range, just uncheck “Date Range Filtering” to disable date filtering.
Alert Triggers
Each selection corresponds to one condition. Conditions can be combined into a single alert as you please. Just be sure you have selected the ones you want to trigger the alert before you create the alert. For example, if you trade in both directions and you want a single alert to trigger on both types of exits, you must select both “Long Exit” and “Short Exit” before creating your alert.
Once the alert is triggered, these settings no longer have relevance as they have been saved with the alert.
When viewing charts where an alert has just triggered, if your alert triggers on more than one condition, you will need the appropriate markers active on your chart to figure out which condition triggered the alert, since plotting of markers is independent of alert management.
Position sizing
You have 3 options to determine position size:
1. Proportional to Stop -> Variable, with a cap on size.
2. Percentage of equity -> Variable.
3. Percentage of Initial Capital -> Fixed.
External Indicator
This is where you connect your indicator’s plot that will generate the signals the Engine will act upon. Remember this only works in Indicator mode.
DATA WINDOW INFORMATION
The top part of the window contains global numbers while the individual trade information appears in the bottom part. The different types of units used to express values are:
curr: denotes the currency used in the Position Sizing section of Inputs for the Initial Capital value.
quote: denotes quote currency, i.e. the value the instrument is expressed in, or the right side of the market pair (USD in EURUSD ).
X: the stop’s amplitude, itself expressed in quote currency, which we use to express a trade’s P&L, so that a trade with P&L=2X has made twice the stop’s amplitude in profit. This is sometimes referred to as R, since it represents one unit of risk. It is also the unit of measure used in the APPT, which denotes expected reward per unit of risk.
X%: is also the stop’s amplitude, but expressed as a percentage of the Entry Fill.
The numbers appearing in the Data Window are all prefixed:
“ALL:” the number is the average for all first entries and pyramided entries.
”1ST:” the number is for first entries only.
”PYR:” the number is for pyramided entries only.
”PEA:” the number is for Post-Exit Analyses
Global Numbers
Numbers in this section represent the results of all trades up to the cursor on the chart.
Average Profitability Per Trade (X): This value is the most important gauge of your strat’s worthiness. It represents the returns that can be expected from your strat for each unit of risk incurred. E.g.: your APPT is 2.0, thus for every unit of currency you invest in a trade, you can on average expect to obtain 2 after the trade. APPT is also referred to as “statistical expectancy”. If it is negative, your strategy is losing, even if your win rate is very good (it means your winning trades aren’t winning enough, or your losing trades lose too much, or both). Its counterpart in currency is also shown, as is the APPT/bar, which can be a useful gauge in deciding between rivalling systems.
Profit Factor: Gross of winning trades/Gross of losing trades. Strategy is profitable when >1. Not as useful as the APPT because it doesn’t take into account the win rate and the average win/loss per trade. It is calculated from the total winning/losing results of this particular backtest and has less predictive value than the APPT. A good profit factor together with a poor APPT means you just found a chart where your system outperformed. Relying too much on the profit factor is a bit like a poker player who would think going all in with two’s against aces is optimal because he just won a hand that way.
Win Rate: Percentage of winning trades out of all trades. Taken alone, it doesn’t have much to do with strategy profitability. You can have a win rate of 99% but if that one trade in 100 ruins you because of poor risk management, 99% doesn’t look so good anymore. This number speaks more of the system’s profile than its worthiness. Still, it can be useful to gauge if the system fits your personality. It can also be useful to traders intending to sell their systems, as low win rate systems are more difficult to sell and require more handholding of worried customers.
Equity (curr): This the sum of initial capital and the P&L of your system’s trades, including fees and slippage.
Return on Capital is the equivalent of TV’s Net Profit figure, i.e. the variation on your initial capital.
Maximum drawdown is the maximal drawdown from the highest equity point until the drop . There is also a close to close (meaning it doesn’t take into account in-trade variations) maximum drawdown value commented out in the code.
The next values are self-explanatory, until:
PYR: Avg Profitability Per Entry (X): this is the APPT for all pyramided entries.
PEA: Avg Max Opp . Available (X): the average maximal opportunity found in the Post-Exit Analyses.
PEA: Avg Drawdown to Max Opp . (X): this represents the maximum drawdown (incurred from the close at the beginning of the PEA analysis) required to reach the maximal opportunity point.
Trade Information
Numbers in this section concern only the current trade under the cursor. Most of them are self-explanatory. Use the description’s prefix to determine what the values applies to.
PYR: Avg Profitability Per Entry (X): While this value includes the impact of all current pyramided entries (and only those) and updates when you move your cursor around, P&L only reflects fees at the trade’s last bar.
PEA: Max Opp . Available (X): It’s the most profitable close reached post-trade, measured from the trade’s Exit Fill, expressed in the X value of the trade the PEA follows.
PEA: Drawdown to Max Opp . (X): This is the maximum drawdown from the trade’s Exit Fill that needs to be sustained in order to reach the maximum opportunity point, also expressed in X. Note that PEA numbers do not include slippage and fees.
EXTERNAL SIGNAL PROTOCOL
Only one external indicator can be connected to a script; in order to leverage its use to the fullest, the engine provides options to use it as either an entry signal, an entry/exit signal or a filter. When used as an entry signal, you can also use the signal to provide the entry’s stop. Here’s how this works:
For filter state: supply +1 for bull (long entries allowed), -1 for bear (short entries allowed).
For entry signals: supply +2 for long, -2 for short.
For exit signals: supply +3 for exit from long, -3 for exit from short.
To send an entry stop level with an entry signal: Send positive stop level for long entry (e.g. 103.33 to enter a long with a stop at 103.33), negative stop level for short entry (e.g. -103.33 to enter a short with a stop at 103.33). If you use this feature, your indicator will have to check for exact stop levels of 1.0, 2.0 or 3.0 and their negative counterparts, and fudge them with a tick in order to avoid confusion with other signals in the protocol.
Remember that mere generation of the values by your indicator will have no effect until you explicitly allow their use in the appropriate sections of the Engine’s Settings/Inputs.
An example of a script issuing a signal for the Engine is published by PineCoders.
RECOMMENDATIONS TO ASPIRING SYSTEM DESIGNERS
Stick to higher timeframes. On progressively lower timeframes, margins decrease and fees and slippage take a proportionally larger portion of profits, to the point where they can very easily turn a profitable strategy into a losing one. Additionally, your margin for error shrinks as the equilibrium of your system’s profitability becomes more fragile with the tight numbers involved in the shorter time frames. Avoid <1H time frames.
Know and calculate fees and slippage. To avoid market shock, backtest using conservative fees and slippage parameters. Systems rarely show unexpectedly good returns when they are confronted to the markets, so put all chances on your side by being outrageously conservative—or a the very least, realistic. Test results that do not include fees and slippage are worthless. Slippage is there for a reason, and that’s because our interventions in the market change the market. It is easier to find alpha in illiquid markets such as cryptos because not many large players participate in them. If your backtesting results are based on moving large positions and you don’t also add the inevitable slippage that will occur when you enter/exit thin markets, your backtesting will produce unrealistic results. Even if you do include large slippage in your settings, the Engine can only do so much as it will not let slippage push fills past the high or low of the entry bar, but the gap may be much larger in illiquid markets.
Never test and optimize your system on the same dataset , as that is the perfect recipe for overfitting or data dredging, which is trying to find one precise set of rules/parameters that works only on one dataset. These setups are the most fragile and often get destroyed when they meet the real world.
Try to find datasets yielding more than 100 trades. Less than that and results are not as reliable.
Consider all backtesting results with suspicion. If you never entertained sceptic tendencies, now is the time to begin. If your backtest results look really good, assume they are flawed, either because of your methodology, the data you’re using or the software doing the testing. Always assume the worse and learn proper backtesting techniques such as monte carlo simulations and walk forward analysis to avoid the traps and biases that unchecked greed will set for you. If you are not familiar with concepts such as survivor bias, lookahead bias and confirmation bias, learn about them.
Stick to simple bars or candles when designing systems. Other types of bars often do not yield reliable results, whether by design (Heikin Ashi) or because of the way they are implemented on TV (Renko bars).
Know that you don’t know and use that knowledge to learn more about systems and how to properly test them, about your biases, and about yourself.
Manage risk first , then capture opportunity.
Respect the inherent uncertainty of the future. Cleanse yourself of the sad arrogance and unchecked greed common to newcomers to trading. Strive for rationality. Respect the fact that while backtest results may look promising, there is no guarantee they will repeat in the future (there is actually a high probability they won’t!), because the future is fundamentally unknowable. If you develop a system that looks promising, don’t oversell it to others whose greed may lead them to entertain unreasonable expectations.
Have a plan. Understand what king of trading system you are trying to build. Have a clear picture or where entries, exits and other important levels will be in the sort of trade you are trying to create with your system. This stated direction will help you discard more efficiently many of the inevitably useless ideas that will pop up during system design.
Be wary of complexity. Experienced systems engineers understand how rapidly complexity builds when you assemble components together—however simple each one may be. The more complex your system, the more difficult it will be to manage.
Play! . Allow yourself time to play around when you design your systems. While much comes about from working with a purpose, great ideas sometimes come out of just trying things with no set goal, when you are stuck and don’t know how to move ahead. Have fun!
@LucF
NOTES
While the engine’s code can supply multiple consecutive entries of longs or shorts in order to scale positions (pyramid), all exits currently assume the execution bot will exit the totality of the position. No partial exits are currently possible with the Engine.
Because the Engine is literally crippled by the limitations on the number of plots a script can output on TV; it can only show a fraction of all the information it calculates in the Data Window. You will find in the Plot Module vast amounts of commented out lines that you can activate if you also disable an equivalent number of other plots. This may be useful to explore certain characteristics of your system in more detail.
When backtesting using the TV backtesting feature, you will need to provide the strategy parameters you wish to use through either Settings/Properties or by changing the default values in the code’s header. These values are defined in variables and used not only in the strategy() statement, but also as defaults in the Engine’s relevant Inputs.
If you want to test using pyramiding, then both the strategy’s Setting/Properties and the Engine’s Settings/Inputs need to allow pyramiding.
If you find any bugs in the Engine, please let us know.
THANKS
To @glaz for allowing the use of his unpublished MA Squize in the filters.
To @everget for his Chandelier stop code, which is also used as a filter in the Engine.
To @RicardoSantos for his pseudo-random generator, and because it’s from him that I first read in the Pine chat about the idea of using an external indicator as input into another. In the PineCoders group, @theheirophant then mentioned the idea of using it as a buy/sell signal and @simpelyfe showed a piece of code implementing the idea. That’s the tortuous story behind the use of the external indicator in the Engine.
To @admin for the Volatility stop’s original code and for the donchian function lifted from Ichimoku .
To @BobHoward21 for the v3 version of Volatility Stop .
To @scarf and @midtownsk8rguy for the color tuning.
To many other scripters who provided encouragement and suggestions for improvement during the long process of writing and testing this piece of code.
To J. Welles Wilder Jr. for ATR, used extensively throughout the Engine.
To TradingView for graciously making an account available to PineCoders.
And finally, to all fellow PineCoders for the constant intellectual stimulation; it is a privilege to share ideas with you all. The Engine is for all TradingView PineCoders, of course—but especially for you.
Look first. Then leap.
VoVix DEVMA🌌 VoVix DEVMA: A Deep Dive into Second-Order Volatility Dynamics
Welcome to VoVix+, a sophisticated trading framework that transcends traditional price analysis. This is not merely another indicator; it is a complete system designed to dissect and interpret the very fabric of market volatility. VoVix+ operates on the principle that the most powerful signals are not found in price alone, but in the behavior of volatility itself. It analyzes the rate of change, the momentum, and the structure of market volatility to identify periods of expansion and contraction, providing a unique edge in anticipating major market moves.
This document will serve as your comprehensive guide, breaking down every mathematical component, every user input, and every visual element to empower you with a profound understanding of how to harness its capabilities.
🔬 THEORETICAL FOUNDATION: THE MATHEMATICS OF MARKET DYNAMICS
VoVix+ is built upon a multi-layered mathematical engine designed to measure what we call "second-order volatility." While standard indicators analyze price, and first-order volatility indicators (like ATR) analyze the range of price, VoVix+ analyzes the dynamics of the volatility itself. This provides insight into the market's underlying state of stability or chaos.
1. The VoVix Score: Measuring Volatility Thrust
The core of the system begins with the VoVix Score. This is a normalized measure of volatility acceleration or deceleration.
Mathematical Formula:
VoVix Score = (ATR(fast) - ATR(slow)) / (StDev(ATR(fast)) + ε)
Where:
ATR(fast) is the Average True Range over a short period, representing current, immediate volatility.
ATR(slow) is the Average True Range over a longer period, representing the baseline or established volatility.
StDev(ATR(fast)) is the Standard Deviation of the fast ATR, which measures the "noisiness" or consistency of recent volatility.
ε (epsilon) is a very small number to prevent division by zero.
Market Implementation:
Positive Score (Expansion): When the fast ATR is significantly higher than the slow ATR, it indicates a rapid increase in volatility. The market is "stretching" or expanding.
Negative Score (Contraction): When the fast ATR falls below the slow ATR, it indicates a decrease in volatility. The market is "coiling" or contracting.
Normalization: By dividing by the standard deviation, we normalize the score. This turns it into a standardized measure, allowing us to compare volatility thrust across different market conditions and timeframes. A score of 2.0 in a quiet market means the same, relatively, as a score of 2.0 in a volatile market.
2. Deviation Analysis (DEV): Gauging Volatility's Own Volatility
The script then takes the analysis a step further. It calculates the standard deviation of the VoVix Score itself.
Mathematical Formula:
DEV = StDev(VoVix Score, lookback_period)
Market Implementation:
This DEV value represents the magnitude of chaos or stability in the market's volatility dynamics. A high DEV value means the volatility thrust is erratic and unpredictable. A low DEV value suggests the change in volatility is smooth and directional.
3. The DEVMA Crossover: Identifying Regime Shifts
This is the primary signal generator. We take two moving averages of the DEV value.
Mathematical Formula:
fastDEVMA = SMA(DEV, fast_period)
slowDEVMA = SMA(DEV, slow_period)
The Core Signal:
The strategy triggers on the crossover and crossunder of these two DEVMA lines. This is a profound concept: we are not looking at a moving average of price or even of volatility, but a moving average of the standard deviation of the normalized rate of change of volatility.
Bullish Crossover (fastDEVMA > slowDEVMA): This signals that the short-term measure of volatility's chaos is increasing relative to the long-term measure. This often precedes a significant market expansion and is interpreted as a bullish volatility regime.
Bearish Crossunder (fastDEVMA < slowDEVMA): This signals that the short-term measure of volatility's chaos is decreasing. The market is settling down or contracting, often leading to trending moves or range consolidation.
⚙️ INPUTS MENU: CONFIGURING YOUR ANALYSIS ENGINE
Every input has been meticulously designed to give you full control over the strategy's behavior. Understanding these settings is key to adapting VoVix+ to your specific instrument, timeframe, and trading style.
🌀 VoVix DEVMA Configuration
🧬 Deviation Lookback: This sets the lookback period for calculating the DEV value. It defines the window for measuring the stability of the VoVix Score. A shorter value makes the system highly reactive to recent changes in volatility's character, ideal for scalping. A longer value provides a smoother, more stable reading, better for identifying major, long-term regime shifts.
⚡ Fast VoVix Length: This is the lookback period for the fastDEVMA. It represents the short-term trend of volatility's chaos. A smaller number will result in a faster, more sensitive signal line that reacts quickly to market shifts.
🐌 Slow VoVix Length: This is the lookback period for the slowDEVMA. It represents the long-term, baseline trend of volatility's chaos. A larger number creates a more stable, slower-moving anchor against which the fast line is compared.
How to Optimize: The relationship between the Fast and Slow lengths is crucial. A wider gap (e.g., 20 and 60) will result in fewer, but potentially more significant, signals. A narrower gap (e.g., 25 and 40) will generate more frequent signals, suitable for more active trading styles.
🧠 Adaptive Intelligence
🧠 Enable Adaptive Features: When enabled, this activates the strategy's performance tracking module. The script will analyze the outcome of its last 50 trades to calculate a dynamic win rate.
⏰ Adaptive Time-Based Exit: If Enable Adaptive Features is on, this allows the strategy to adjust its Maximum Bars in Trade setting based on performance. It learns from the average duration of winning trades. If winning trades tend to be short, it may shorten the time exit to lock in profits. If winners tend to run, it will extend the time exit, allowing trades more room to develop. This helps prevent the strategy from cutting winning trades short or holding losing trades for too long.
⚡ Intelligent Execution
📊 Trade Quantity: A straightforward input that defines the number of contracts or shares for each trade. This is a fixed value for consistent position sizing.
🛡️ Smart Stop Loss: Enables the dynamic stop-loss mechanism.
🎯 Stop Loss ATR Multiplier: Determines the distance of the stop loss from the entry price, calculated as a multiple of the current 14-period ATR. A higher multiplier gives the trade more room to breathe but increases risk per trade. A lower multiplier creates a tighter stop, reducing risk but increasing the chance of being stopped out by normal market noise.
💰 Take Profit ATR Multiplier: Sets the take profit target, also as a multiple of the ATR. A common practice is to set this higher than the Stop Loss multiplier (e.g., a 2:1 or 3:1 reward-to-risk ratio).
🏃 Use Trailing Stop: This is a powerful feature for trend-following. When enabled, instead of a fixed stop loss, the stop will trail behind the price as the trade moves into profit, helping to lock in gains while letting winners run.
🎯 Trail Points & 📏 Trail Offset ATR Multipliers: These control the trailing stop's behavior. Trail Points defines how much profit is needed before the trail activates. Trail Offset defines how far the stop will trail behind the current price. Both are based on ATR, making them fully adaptive to market volatility.
⏰ Maximum Bars in Trade: This is a time-based stop. It forces an exit if a trade has been open for a specified number of bars, preventing positions from being held indefinitely in stagnant markets.
⏰ Session Management
These inputs allow you to confine the strategy's trading activity to specific market hours, which is crucial for day trading instruments that have defined high-volume sessions (e.g., stock market open).
🎨 Visual Effects & Dashboard
These toggles give you complete control over the on-chart visuals and the dashboard. You can disable any element to declutter your chart or focus only on the information that matters most to you.
📊 THE DASHBOARD: YOUR AT-A-GLANCE COMMAND CENTER
The dashboard centralizes all critical information into one compact, easy-to-read panel. It provides a real-time summary of the market state and strategy performance.
🎯 VOVIX ANALYSIS
Fast & Slow: Displays the current numerical values of the fastDEVMA and slowDEVMA. The color indicates their direction: green for rising, red for falling. This lets you see the underlying momentum of each line.
Regime: This is your most important environmental cue. It tells you the market's current state based on the DEVMA relationship. 🚀 EXPANSION (Green) signifies a bullish volatility regime where explosive moves are more likely. ⚛️ CONTRACTION (Purple) signifies a bearish volatility regime, where the market may be consolidating or entering a smoother trend.
Quality: Measures the strength of the last signal based on the magnitude of the DEVMA difference. An ELITE or STRONG signal indicates a high-conviction setup where the crossover had significant force.
PERFORMANCE
Win Rate & Trades: Displays the historical win rate of the strategy from the backtest, along with the total number of closed trades. This provides immediate feedback on the strategy's historical effectiveness on the current chart.
EXECUTION
Trade Qty: Shows your configured position size per trade.
Session: Indicates whether trading is currently OPEN (allowed) or CLOSED based on your session management settings.
POSITION
Position & PnL: Displays your current position (LONG, SHORT, or FLAT) and the real-time Profit or Loss of the open trade.
🧠 ADAPTIVE STATUS
Stop/Profit Mult: In this simplified version, these are placeholders. The primary adaptive feature currently modifies the time-based exit, which is reflected in how long trades are held on the chart.
🎨 THE VISUAL UNIVERSE: DECIPHERING MARKET GEOMETRY
The visuals are not mere decorations; they are geometric representations of the underlying mathematical concepts, designed to give you an intuitive feel for the market's state.
The Core Lines:
FastDEVMA (Green/Maroon Line): The primary signal line. Green when rising, indicating an increase in short-term volatility chaos. Maroon when falling.
SlowDEVMA (Aqua/Orange Line): The baseline. Aqua when rising, indicating a long-term increase in volatility chaos. Orange when falling.
🌊 Morphism Flow (Flowing Lines with Circles):
What it represents: This visualizes the momentum and strength of the fastDEVMA. The width and intensity of the "beam" are proportional to the signal strength.
Interpretation: A thick, steep, and vibrant flow indicates powerful, committed momentum in the current volatility regime. The floating '●' particles represent kinetic energy; more particles suggest stronger underlying force.
📐 Homotopy Paths (Layered Transparent Boxes):
What it represents: These layered boxes are centered between the two DEVMA lines. Their height is determined by the DEV value.
Interpretation: This visualizes the overall "volatility of volatility." Wider boxes indicate a chaotic, unpredictable market. Narrower boxes suggest a more stable, predictable environment.
🧠 Consciousness Field (The Grid):
What it represents: This grid provides a historical lookback at the DEV range.
Interpretation: It maps the recent "consciousness" or character of the market's volatility. A consistently wide grid suggests a prolonged period of chaos, while a narrowing grid can signal a transition to a more stable state.
📏 Functorial Levels (Projected Horizontal Lines):
What it represents: These lines extend from the current fastDEVMA and slowDEVMA values into the future.
Interpretation: Think of these as dynamic support and resistance levels for the volatility structure itself. A crossover becomes more significant if it breaks cleanly through a prior established level.
🌊 Flow Boxes (Spaced Out Boxes):
What it represents: These are compact visual footprints of the current regime, colored green for Expansion and red for Contraction.
Interpretation: They provide a quick, at-a-glance confirmation of the dominant volatility flow, reinforcing the background color.
Background Color:
This provides an immediate, unmistakable indication of the current volatility regime. Light Green for Expansion and Light Aqua/Blue for Contraction, allowing you to assess the market environment in a split second.
📊 BACKTESTING PERFORMANCE REVIEW & ANALYSIS
The following is a factual, transparent review of a backtest conducted using the strategy's default settings on a specific instrument and timeframe. This information is presented for educational purposes to demonstrate how the strategy's mechanics performed over a historical period. It is crucial to understand that these results are historical, apply only to the specific conditions of this test, and are not a guarantee or promise of future performance. Market conditions are dynamic and constantly change.
Test Parameters & Conditions
To ensure the backtest reflects a degree of real-world conditions, the following parameters were used. The goal is to provide a transparent baseline, not an over-optimized or unrealistic scenario.
Instrument: CME E-mini Nasdaq 100 Futures (NQ1!)
Timeframe: 5-Minute Chart
Backtesting Range: March 24, 2024, to July 09, 2024
Initial Capital: $100,000
Commission: $0.62 per contract (A realistic cost for futures trading).
Slippage: 3 ticks per trade (A conservative setting to account for potential price discrepancies between order placement and execution).
Trade Size: 1 contract per trade.
Performance Overview (Historical Data)
The test period generated 465 total trades , providing a statistically significant sample size for analysis, which is well above the recommended minimum of 100 trades for a strategy evaluation.
Profit Factor: The historical Profit Factor was 2.663 . This metric represents the gross profit divided by the gross loss. In this test, it indicates that for every dollar lost, $2.663 was gained.
Percent Profitable: Across all 465 trades, the strategy had a historical win rate of 84.09% . While a high figure, this is a historical artifact of this specific data set and settings, and should not be the sole basis for future expectations.
Risk & Trade Characteristics
Beyond the headline numbers, the following metrics provide deeper insight into the strategy's historical behavior.
Sortino Ratio (Downside Risk): The Sortino Ratio was 6.828 . Unlike the Sharpe Ratio, this metric only measures the volatility of negative returns. A higher value, such as this one, suggests that during this test period, the strategy was highly efficient at managing downside volatility and large losing trades relative to the profits it generated.
Average Trade Duration: A critical characteristic to understand is the strategy's holding period. With an average of only 2 bars per trade , this configuration operates as a very short-term, or scalping-style, system. Winning trades averaged 2 bars, while losing trades averaged 4 bars. This indicates the strategy's logic is designed to capture quick, high-probability moves and exit rapidly, either at a profit target or a stop loss.
Conclusion and Final Disclaimer
This backtest demonstrates one specific application of the VoVix+ framework. It highlights the strategy's behavior as a short-term system that, in this historical test on NQ1!, exhibited a high win rate and effective management of downside risk. Users are strongly encouraged to conduct their own backtests on different instruments, timeframes, and date ranges to understand how the strategy adapts to varying market structures. Past performance is not indicative of future results, and all trading involves significant risk.
🔧 THE DEVELOPMENT PHILOSOPHY: FROM VOLATILITY TO CLARITY
The journey to create VoVix+ began with a simple question: "What drives major market moves?" The answer is often not a change in price direction, but a fundamental shift in market volatility. Standard indicators are reactive to price. We wanted to create a system that was predictive of market state. VoVix+ was designed to go one level deeper—to analyze the behavior, character, and momentum of volatility itself.
The challenge was twofold. First, to create a robust mathematical model to quantify these abstract concepts. This led to the multi-layered analysis of ATR differentials and standard deviations. Second, to make this complex data intuitive and actionable. This drove the creation of the "Visual Universe," where abstract mathematical values are translated into geometric shapes, flows, and fields. The adaptive system was intentionally kept simple and transparent, focusing on a single, impactful parameter (time-based exits) to provide performance feedback without becoming an inscrutable "black box." The result is a tool that is both profoundly deep in its analysis and remarkably clear in its presentation.
⚠️ RISK DISCLAIMER AND BEST PRACTICES
VoVix+ is an advanced analytical tool, not a guarantee of future profits. All financial markets carry inherent risk. The backtesting results shown by the strategy are historical and do not guarantee future performance. This strategy incorporates realistic commission and slippage settings by default, but market conditions can vary. Always practice sound risk management, use position sizes appropriate for your account equity, and never risk more than you can afford to lose. It is recommended to use this strategy as part of a comprehensive trading plan. This was developed specifically for Futures
"The prevailing wisdom is that markets are always right. I take the opposite view. I assume that markets are always wrong. Even if my assumption is occasionally wrong, I use it as a working hypothesis."
— George Soros
— Dskyz, Trade with insight. Trade with anticipation.
Kijun Shifting Band Oscillator | QuantMAC🎯 Kijun Shifting Band Oscillator | QuantMAC
📊 **Revolutionary Technical Analysis Tool Combining Ancient Ichimoku Wisdom with Cutting-Edge Statistical Methods**
🌟 Overview
The Kijun Shifting Band Oscillator represents a sophisticated fusion of traditional Japanese technical analysis and modern statistical theory. Built upon the foundational concepts of the Ichimoku Kinko Hyo system, this indicator transforms the classic Kijun-sen (base line) into a dynamic, multi-dimensional analysis tool that provides traders with unprecedented market insights.
This advanced oscillator doesn't just show you where price has been – it reveals the underlying momentum dynamics and volatility patterns that drive market movements, giving you a statistical edge in your trading decisions.
🔥 Key Features & Innovations
Dual Trading Modes for Maximum Flexibility: 🚀
Long/Short Mode: Full bidirectional trading capability for aggressive traders seeking to capitalize on both bullish and bearish market conditions
Long/Cash Mode: Conservative approach perfect for risk-averse traders, taking long positions during uptrends and moving to cash during downtrends (avoiding short exposure)
Advanced Visual Intelligence: 🎨
9 Professional Color Schemes: From classic blue/navy to vibrant orange/purple combinations, each optimized for different chart backgrounds and personal preferences
Dynamic Gradient Histogram: Color intensity reflects oscillator strength, providing instant visual feedback on momentum magnitude
Intelligent Overlay Bands: Semi-transparent fills create clear visual boundaries without cluttering your chart
Smart Candle Coloring: Real-time color changes reflect current market state and trend direction
Customizable Threshold Lines: Clearly marked entry and exit levels with contrasting colors
Professional-Grade Analytics: 📊
Real-Time Performance Metrics: Live calculation of 9 key performance indicators
Risk-Adjusted Returns: Sharpe, Sortino, and Omega ratios for comprehensive performance evaluation
Position Sizing Guidance: Half-Kelly percentage for optimal risk management
Drawdown Analysis: Maximum drawdown tracking for risk assessment
📈 Deep Technical Foundation
Kijun-Based Mathematical Framework: 🧮
The indicator begins with the traditional Kijun-sen calculation but extends it significantly:
Statistical Enhancements: 📉
Adaptive Volatility: Bands expand and contract based on market volatility
Momentum Filtering: EMA smoothing of oscillator for trend confirmation
State Management: Intelligent signal filtering prevents whipsaws and false signals
Multi-Timeframe Compatibility: Optimized algorithms work across all timeframes
⚙️ Comprehensive Parameter Control
Kijun Core Settings: 🎛️
Kijun Length (Default: 30): Controls the lookback period for the base calculation. Shorter periods = more responsive, longer periods = smoother signals
Source Selection: Choose from Close, Open, High, Low, or HL2. Close price recommended for most applications
Calculation Method: Uses traditional Ichimoku methodology ensuring compatibility with classic analysis
Advanced Oscillator Configuration: 📊
Standard Deviation Length (Default: 36): Determines volatility measurement period. Affects band width and sensitivity
SD Multiplier (Default: 2.1): Fine-tune band distance from basis line. Higher values = wider bands, lower values = tighter bands
Oscillator Multiplier (Default: 100): Scales the final oscillator output. Useful for matching other indicators or personal preference
Smoothing Algorithm: Built-in EMA smoothing prevents noise while maintaining responsiveness
Signal Threshold Optimization: 🎯
Long Threshold (Default: 83): Oscillator level that triggers long entries. Higher values = fewer but stronger signals
Short Threshold (Default: 42): Oscillator level that triggers short entries. Lower values = fewer but stronger signals
Threshold Logic: Crossover-based system with state management prevents signal overlap
Customization Range: Fully adjustable to match your trading style and risk tolerance
Precision Date Control: 📅
Start Date/Month/Year: Precise backtesting control down to the day
Historical Analysis: Test strategies on specific market periods or events
Strategy Validation: Isolate performance during different market conditions
📊 Professional Metrics Dashboard
Risk Assessment Metrics: 💼
Maximum Drawdown %: Largest peak-to-trough decline in portfolio value. Critical for understanding worst-case scenarios and position sizing
Sortino Ratio: Risk-adjusted return measure focusing only on downside volatility. Superior to Sharpe ratio for asymmetric return distributions
Sharpe Ratio: Classic risk-adjusted performance metric. Values above 1.0 considered good, above 2.0 excellent
Omega Ratio: Probability-weighted ratio capturing all moments of return distribution. More comprehensive than Sharpe or Sortino
Performance Analytics: 📈
Profit Factor: Gross Profit ÷ Gross Loss. Values above 1.0 indicate profitability, above 2.0 considered excellent
Win Rate %: Percentage of profitable trades. Consider alongside average win/loss size for complete picture
Net Profit %: Total return on initial capital. Accounts for compounding effects
Total Trades: Sample size for statistical significance assessment
Advanced Position Sizing: 🎯
Half Kelly %: Optimal position size based on Kelly Criterion, reduced by 50% for safety margin
Risk Management: Helps determine appropriate position size relative to account equity
Mathematical Foundation: Based on win probability and profit factor calculations
Practical Application: Directly usable percentage for position sizing decisions
🎨 Advanced Display Options
Flexible Interface Design: 🖥️
6 Positioning Options: Top/Bottom/Middle × Left/Right combinations for optimal chart organization
Toggle Functionality: Show/hide metrics table for clean chart presentation during analysis
Color Coordination: Metrics table colors match selected oscillator color scheme
Professional Styling: Clean, readable format with proper spacing and alignment
Visual Hierarchy: 🎭
Oscillator Histogram: Primary focus with gradient intensity showing momentum strength
Threshold Lines: Clear horizontal references for entry/exit levels
Zero Line: Neutral reference point for trend bias determination
Background Bands: Subtle overlay context without chart clutter
🚀 Advanced Signal Generation System
Multi-Layer Signal Logic: ⚡
Primary Signal Generation: Oscillator crossover above Long Threshold (default 83) triggers long entries
Exit Signal Processing: Oscillator crossunder below Short Threshold (default 42) triggers position exits
State Management System: Prevents duplicate signals and ensures clean position transitions
Mode-Specific Logic: Different behavior for Long/Short vs Long/Cash modes
Date Range Filtering: Signals only generated within specified backtesting period
Confirmation Requirements: Bar confirmation prevents false signals from intrabar price spikes
Intelligent Position Management: 🧠
Entry Tracking: Precise entry price recording for accurate P&L calculations
Position State Monitoring: Continuous tracking of long/short/cash positions
Automatic Exit Logic: Seamless position closure and new position initiation
Performance Calculation: Real-time P&L tracking with compounding effects
📉📈 Comprehensive Band Interpretation Guide
Dynamic Band Analysis: 🔍
Upper Band Function: Represents dynamic resistance based on recent volatility. Price approaching upper band suggests potential reversal or breakout
Lower Band Function: Represents dynamic support with volatility adjustment. Price near lower band indicates oversold conditions or support testing
Middle Line (Basis): Trend direction indicator. Price above = bullish bias, price below = bearish bias
Band Width Interpretation: Wide bands = high volatility, narrow bands = low volatility/potential breakout setup
Band Slope Analysis: Rising bands = strengthening trend, falling bands = weakening trend
Oscillator Interpretation: 📊
Values Above 50: Price in upper half of recent range, bullish momentum
Values Below 50: Price in lower half of recent range, bearish momentum
Extreme Values (>80 or <20): Overbought/oversold conditions, potential reversal zones
Momentum Divergence: Oscillator direction vs price direction for early reversal signals
Trend Confirmation: Oscillator direction confirming or contradicting price trends
💡 Strategic Trading Applications
Primary Trading Strategies: 🎯
Trend Following: Use threshold crossovers to capture major directional moves. Best in trending markets with clear directional bias
Mean Reversion: Identify extreme oscillator readings for counter-trend opportunities. Effective in range-bound markets
Breakout Trading: Monitor band compressions followed by expansions for breakout signals
Swing Trading: Combine oscillator signals with band interactions for swing position entries/exits
Risk Management: Use metrics dashboard for position sizing and risk assessment
Market Condition Optimization: 🌊
Trending Markets: Increase threshold separation for fewer, stronger signals
Choppy Markets: Decrease threshold separation for more responsive signals
High Volatility: Increase SD multiplier for wider bands
Low Volatility: Decrease SD multiplier for tighter bands and earlier signals
⚙️ Advanced Configuration Tips
Parameter Optimization Guidelines: 🔧
Kijun Length Adjustment: Shorter periods (10-20) for faster signals, longer periods (50-100) for smoother trends
SD Length Tuning: Match to your trading timeframe - shorter for responsive, longer for stability
Threshold Calibration: Backtest different levels to find optimal entry/exit points for your market
Color Scheme Selection: Choose schemes that provide best contrast with your chart background and other indicators
Integration with Other Indicators: 🔗
Volume Indicators: Confirm oscillator signals with volume spikes
Support/Resistance: Use key levels to filter oscillator signals
Momentum Indicators: RSI, MACD confirmation for signal strength
Trend Indicators: Moving averages for overall trend bias confirmation
⚠️ Important Usage Notes & Limitations
Indicator Characteristics: ⚡
Lagging Nature: Based on historical price data - signals occur after moves have begun
Best Practice: Combine with leading indicators and price action analysis
Market Dependency: Performance varies across different market conditions and instruments
Backtesting Essential: Always validate parameters on historical data before live implementation
Optimization Recommendations: 🎯
Parameter Testing: Systematically test different combinations on your preferred instruments
Walk-Forward Analysis: Regularly re-optimize parameters to maintain effectiveness
Market Regime Awareness: Adjust parameters for different market conditions (trending vs ranging)
Risk Controls: Implement maximum drawdown limits and position size controls
🔧 Technical Specifications
Performance Optimization: ⚡
Efficient Algorithms: Optimized calculations for smooth real-time operation
Memory Management: Smart array handling for metrics calculations
Visual Optimization: Balanced detail vs performance for responsive charts
Multi-Symbol Ready: Consistent performance across different assets
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The Kijun Shifting Band Oscillator represents the evolution of technical analysis, bridging the gap between traditional methods and modern quantitative approaches. This indicator provides traders with a comprehensive toolkit for market analysis, combining the intuitive wisdom of Japanese candlestick analysis with the precision of statistical mathematics.
🎯 Designed for serious traders who demand professional-grade analysis tools with institutional-quality metrics and risk management capabilities. Whether you're a discretionary trader seeking visual confirmation or a systematic trader building quantitative strategies, this indicator provides the foundation for informed trading decisions.
⚠️ IMPORTANT DISCLAIMER
Past Performance Warning: 📉⚠️
PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. Historical backtesting results, while useful for strategy development and parameter optimization, do not guarantee similar performance in live trading conditions. Market conditions change continuously, and what worked in the past may not work in the future.
Remember: Successful trading requires discipline, continuous learning, and adaptation to changing market conditions. No indicator or strategy guarantees profits, and all trading involves substantial risk of loss.
Slark Signal XtremeStrategy Description: Slark Signal Xtreme
The Slark Signal Xtreme is an innovative trading strategy designed to identify and capitalize on market opportunities by leveraging pivots, trend breakouts, and dynamic risk management. This strategy combines day-of-week and time filters with a ticks-based Stop Loss (SL) and Take Profit (TP) system, delivering customized signals and real-time alerts. Ideal for traders seeking a structured and highly customizable approach, Slark Signal Xtreme also incorporates advanced visual tools for efficient trade management.
Key Features:
Pivot- and Breakout-Based Signals: Utilizes pivot detection (highs/lows) combined with an ATR-based slope calculation to pinpoint trend changes and potential entry or exit points.
Dynamic Stop-Loss (SL) and Take-Profit (TP) Levels: Automatically calculates SL and TP based on the entry price and user-defined tick settings, adapting to volatility and optimizing risk management.
Time and Day Filters: Allows you to select specific days of the week and trading sessions during which signals are generated, avoiding low-liquidity periods or unwanted high volatility.
Customizable Risk Management: Lets you define the number of ticks for SL and TP, trading hours, initial capital, pyramiding, and commissions, tailoring the strategy to various risk profiles and assets.
Enhanced Visualization:
- SL and TP Boxes: Displays rectangular boxes on the chart indicating SL and TP levels, streamlining trade management.
- Candle Color Changes: Candles can be colored according to price position relative to pivot lines (bullish, bearish, or neutral).
- Session Highlight: Shades the chart background during the selected trading hours, providing immediate context on when the strategy is active.
Automated Alerts: Generates customizable alerts in TradingView whenever a buy or sell signal is triggered, detailing the timing, instrument, and SL/TP levels.
How the Strategy Works:
Technical Indicator Calculations:
- Pivot High/Low and Slope: Identifies price pivot points and calculates slope (based on ATR) to measure trend strength.
- Time and Day Filters: Signals only trigger within the specified days and hours, helping avoid undesirable market conditions.
Generating Buy and Sell Signals:
- Buy Signal (Long): Activated when price breaks above a downward pivot-based trendline or meets the condition for higher pivots.
- Sell Signal (Short): Activated when price breaks below an upward pivot-based trendline or meets the condition for lower pivots.
- Operation Conditions: Signals are only generated on selected days and during chosen trading hours, avoiding periods of low liquidity or excessive volatility.
Dynamic SL and TP Calculation:
- Stop-Loss (SL) and Take-Profit (TP): Determined by the entry price ± a user-defined number of ticks.
- SL and TP Visualization: Boxes are drawn on the chart from the entry price to SL/TP levels, enabling clear visual reference for trade management.
Order Execution and Alerts:
- Order Execution: When a signal is generated, Slark Signal Xtreme automatically opens a long or short position in TradingView’s backtesting environment.
- Alerts: Customizable alerts can be set up to provide real-time notifications (via TradingView or third-party integrations), offering essential details like instrument, time, SL/TP, etc.
Trade Management and Monitoring:
- Automatic Closure: Each trade is automatically closed upon reaching its SL or TP, ensuring disciplined risk control.
- Trade Summary: TradingView’s built-in reporting tools list all trades with cumulative results, simplifying performance evaluation.
Additional Visualization:
- Candle Coloring by Trend: Candles can be colored bullish, bearish, or neutral based on the pivot-driven trend detection.
- Operational Range Highlighting: The chart background is shaded during the permitted trading hours, clarifying when the strategy is active and enhancing visibility.
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Strategy Properties (Important)
This backtest was conducted in TradingView under the following configuration:
Initial Capital: 1000 USD
Order Size: 10,000 contracts (adjust according to the traded asset)
Commission: 0.05 USD per order
Slippage: 1 tick
Pyramiding: 1 order
Price Verification for Limit Orders: 0 ticks
Recalculate on Every Tick & On Bar Close: Enabled
Bar Magnifier for Backtesting Precision: Enabled
These properties provide a realistic view of the strategy’s performance. However, default parameters may vary depending on each user or market:
Order Size: Should be calculated according to the asset traded and your desired risk level.
Commission and Slippage: Costs can vary by market and instrument; there is no universal default that guarantees realistic results.
All users are strongly recommended to adjust these properties within the script settings to match their own trading accounts and platforms, ensuring the most accurate backtest results.
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Backtesting Results:
- Net Profit: +28.70
- Total Trades: 397
- Winning Trades: 138
- Win Rate: 34.76%
- Profit Factor: 1.07
- Sharpe Ratio: 1.25
- Sortino Ratio: 1.45
- Average Bars per Trade: 24
- Average Profit per Trade: 1.45
These numbers provide an overview of the strategy’s historical performance, demonstrating its potential for profitability given appropriate risk management.
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Interpretation of Results:
- The strategy can be profitable despite a relatively modest win rate, thanks to a suitable risk-reward ratio.
- A profit factor of 1.07 indicates that total profits slightly exceed total losses.
- It is essential to monitor drawdown and ensure it aligns with your personal risk tolerance.
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Risk Warning:
Trading leveraged financial instruments carries a high level of risk and may not be suitable for all investors. Before trading, carefully consider your investment objectives, experience level, and risk tolerance. Past performance does not guarantee future results. Always perform additional testing and adjust the strategy to your specific needs.
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What Makes This Strategy Original?
Focus on Pivots and Time/Day Filters: Rather than purely relying on momentum indicators, Slark Signal Xtreme uses pivot-based signals and scheduling filters to capture higher-liquidity, directional market moves.
Dynamic Risk Management: Ticks-based SL/TP and customizable trading sessions enable precise adaptation to various markets and trading styles.
Advanced Visualization Tools: SL/TP boxes, candle coloring, and session highlights streamline market interpretation and facilitate real-time decision-making.
Seamless Alert Integration: Although native TradingView alerts are provided, it can be integrated with third-party messaging services (Telegram, Discord, etc.) for enhanced automation.
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Additional Considerations
Continuous Testing and Optimization: Regularly backtest and fine-tune parameters (SL, TP, time filters, etc.) to accommodate changing market conditions.
Complementary Analysis: Combine this strategy with other technical or fundamental tools to confirm signals.
Rigorous Risk Management: Ensure SL/TP levels and position sizes conform to your overall risk management plan.
Updates and Support: Future updates and improvements may be released based on community feedback. For questions or suggestions, feel free to reach out.
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Example Configuration
Assume you want to run Slark Signal Xtreme with these settings:
Trading Days: Monday to Friday
Trading Hours: 8:00 to 11:00 (exchange or broker time)
Stop Loss (SL) in Ticks: 100
Take Profit (TP) in Ticks: 300
SL/TP Box Extension: 20 bars
Initial Capital: 1000 USD
Risk per Trade: 1% of capital
Commissions & Slippage: 0.05 USD commission, 1 tick slippage
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Conclusion
The Slark Signal Xtreme strategy delivers a robust and adaptable solution by merging pivots, time/day filters, flexible risk parameters, and advanced visualization. Its distinctive and customizable design makes it a powerful resource for traders aiming to diversify their methods and exploit trend breakouts under specific conditions. Fully compatible with TradingView, Slark Signal Xtreme can enhance your trading toolkit and foster a more systematic approach to your operations.
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Final Disclaimer:
Financial markets are inherently volatile and pose significant risks. This strategy should be employed as part of a comprehensive trading plan and does not guarantee positive outcomes. Always consult a qualified financial advisor before making investment decisions. The use of Slark Signal Xtreme is solely at the user’s discretion, who must evaluate personal risk tolerance and financial objectives.
Liquid Pours XtremeStrategy Description: Liquid Pours Xtreme
The Liquid Pours Xtreme is an innovative trading strategy that combines the analysis of specific time-based patterns with price comparisons to identify potential opportunities in the forex market. Designed for traders seeking a structured methodology based on clear rules, this strategy offers integration with Telegram for real-time alerts and provides visual tools to enhance trade management.
Key Features:
Analysis of Specific Time Patterns: The strategy captures and compares closing prices at two key moments during the trading day, identifying recurring patterns that may indicate future market movements.
Dynamic SL and TP Levels Implementation: Utilizes tick-based calculations to set Stop-Loss and Take-Profit levels, adapting to the current market volatility.
Advanced Telegram Integration: Provides detailed alerts including information such as the asset, signal time, entry price, and SL/TP levels, facilitating real-time decision-making.
Complete Customization: Allows users to adjust key parameters, including operation schedules, weekdays, and visual settings, adapting to different trading styles.
Enhanced Chart Visualization: Includes visual elements like candle color changes based on signal state, event markers, and halos to highlight important moments.
Default Strategy Properties: Specific configuration for optimal risk management and simulation.
How the Strategy Works
Capturing Prices at Key Moments:
- The strategy records the closing price at two user-defined specific times. These times typically correspond to periods of high market volatility, such as the opening of the European session and the US pre-market.
- Rationale: Volatility and trading volume usually increase during these times, presenting opportunities for significant price movements.
Generating Signals Based on Price Comparison:
- Buy Signal: If the second closing price is lower than the first, it indicates possible accumulation and is interpreted as a bullish signal.
- Sell Signal: If the second closing price is higher than the first, it suggests possible distribution and is interpreted as a bearish signal.
- Signals are only generated on selected trading days, allowing you to avoid days with lower liquidity or higher risk.
Calculating Dynamic SL and TP Levels:
- Stop-Loss (SL) and Take-Profit (TP) levels are calculated based on the entry price and a user-defined number of ticks, adapting to market volatility.
- The strategy offers the option to base these levels on the close of the signal candle or the open of the next candle, providing flexibility according to the trader's preference.
- SL and TP boxes are drawn on the chart for visual reference, facilitating trade management.
Automatic Execution and Alerts:
- Upon signal generation, the strategy automatically executes a market order (buy or sell).
- Sends a detailed alert to your Telegram channel, including essential information for quick decision-making.
Visual Elements:
- Colors candles based on the signal state: buy, sell, or neutral, allowing for quick trend identification.
- Provides a smooth color transition between signal states and uses markers and halos to highlight important events and signals on the chart.
Trade Management:
- Manages open trades with automatic exit conditions based on the established SL and TP levels.
- Includes mechanisms to prevent exceeding TradingView's limitations on boxes and labels, ensuring optimal script performance.
Originality and utility:
- This strategy incorporates a unique approach focusing on specific time patterns and their relationship to institutional activity in the market.
How to Use the Strategy
Add the Script to the Chart:
- Go to the indicators menu in TradingView.
- Search for " Liquid Pours Xtreme " and add it to your chart.
Set Up Telegram Alerts:
- Enter your Telegram Chat ID in the script parameters to receive alerts.
- Customize the Buy and Sell alert messages as desired.
Configure Time Patterns:
- Set the hours and minutes for the two times you want to compare closing prices, aligning them with relevant market sessions or events.
Set SL and TP Parameters:
- Define the number of ticks for the Stop-Loss and Take-Profit levels, adapting them to the asset you're trading and your risk tolerance.
- Choose the basis for SL and TP calculation (close of the signal candle or open of the next candle).
Select Trading Days:
- Enable or disable trading on specific days of the week, allowing you to avoid days with lower activity or unexpected volatility.
Customize Visual Elements:
- Adjust the colors and styles of visual elements to enhance readability and suit your personal preferences.
Monitor the Strategy:
- Observe the chart for signals and use Telegram alerts to stay informed of new opportunities, even when you're not at your terminal.
Testing and Optimization:
- Use TradingView's backtesting features to evaluate the historical performance of the strategy with different parameters.
- Adjust and optimize the parameters based on the results and your own analysis.
Adjust the Strategy Properties:
- Ensure that the strategy properties (order size, commission, slippage) are aligned with your trading account and platform to obtain realistic results.
Strategy Properties (Important)
This script backtest is conducted on M30 EURUSD , using the following backtesting properties:
Initial Capital: $10,000
Order Size: 50,000 Contracts (equivalent to 0.5% of the capital)
Commission: $0.20 per order
Slippage: 1 tick
Pyramiding: 1 order
Verify Price for Limit Orders: 0 ticks
Recalculate on Order Execution: Enabled
Recalculate on Every Tick: Enabled
Recalculate After Order Filled: Enabled
Bar Magnifier for Backtesting Precision: Enabled
We use these properties to ensure a realistic preview of the backtesting system. Note that default properties may vary for different reasons:
- Order Size: It is essential to calculate the contract size according to the traded asset and desired risk level.
- Commission and Slippage: These costs can vary depending on the market and instrument; there is no default value that might return realistic results.
We strongly recommend all users adjust the Properties within the script settings to align with their accounts and trading platforms to ensure the results from the strategies are realistic.
Backtesting Results:
- Net Profit: $4,037.50 (40.37%)
- Total Closed Trades : 292
- Profitability Percentage: 26.71%
- Profit Factor: 1.369
- Max Drawdown: $769.30 (6.28%)
- Average Trade: $13.83 (0.03%)
- Average Bars in Trades: 11
These results were obtained under the mentioned conditions and properties, providing an overview of the strategy's historical performance.
Interpreting Results:
- The strategy has demonstrated profitability in the analyzed period, although with a win rate of 26.71%, indicating that success relies on a favorable risk-reward ratio.
- The profit factor of 1.369 suggests that total gains exceed total losses by that proportion.
- It is crucial to consider the maximum drawdown of 6.28% when evaluating the strategy's suitability to your risk tolerance.
Risk Warning:
Trading leveraged financial instruments carries a high level of risk and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk tolerance. Past performance does not guarantee future results. It is essential to conduct additional testing and adjust the strategy according to your needs.
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What Makes This Strategy Original?
Time-Based Pattern Approach: Unlike conventional strategies, this strategy focuses on identifying time patterns that reflect institutional activity and macroeconomic events that can influence the market.
Advanced Technological Integration: The combination of automatic execution and customized alerts via Telegram provides an efficient and modern tool for active traders.
Customization and Adaptability: The wide range of adjustable parameters allows the strategy to be tailored to different assets, time zones, and trading styles.
Enhanced Visual Tools: Incorporated visual elements facilitate quick market interpretation and informed decision-making.
Additional Considerations
Continuous Testing and Optimization: Users are encouraged to perform additional backtesting and optimize parameters according to their own observations and requirements.
Complementary Analysis: Use this strategy in conjunction with other indicators and fundamental analysis to reinforce decision-making.
Rigorous Risk Management: Ensure that SL and TP levels, as well as position sizing, align with your risk management plan.
Updates and Support: I am committed to providing updates and improvements based on community feedback. For inquiries or suggestions, feel free to contact me.
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Example Configuration
Assuming you want to use the strategy with the following parameters:
Telegram Chat ID: Your unique Telegram Chat ID
First Time (Hour:Minute): 6:30
Second Time (Hour:Minute): 7:30
SL Ticks: 100
TP Ticks: 400
SL and TP Basis: Close of the Signal Candle
Trading Days: Tuesday, Wednesday, Thursday
Simulated Initial Capital: $10,000
Risk per Trade in Simulation: $50 (-0.5% of capital)
Slippage and Commissions in Simulation: 1 tick of slippage and $0.20 commission per trade
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Conclusion
The Liquid Pours Xtreme strategy offers an innovative approach by combining specific time analysis with robust risk management and modern technological tools. Its original and adaptable design makes it valuable for traders looking to diversify their methods and capitalize on opportunities based on less conventional patterns.
Ready for immediate implementation in TradingView, this strategy can enrich your trading arsenal and contribute to a more informed and structured approach to your operations.
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Final Disclaimer:
Financial markets are volatile and can present significant risks. This strategy should be used as part of a comprehensive trading approach and does not guarantee positive results. It is always advisable to consult with a professional financial advisor before making investment decisions.
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Universal Trend Following Strategy | QuantumRsearchUniversal All Assets Strategy by Rocheur
The Universal All Assets Strategy is a cutting-edge, trend-following algorithm designed to operate seamlessly across multiple asset classes, including equities, commodities, forex, and cryptocurrencies. This strategy leverages the power of eight unique indicators, offering traders robust, adaptive signals. Its dynamic logic, combined with a comprehensive risk management framework, allows for precision trading in a variety of market conditions.
Core Methodologies and Features
1. Eight Integrated Trend Indicators
At the heart of the Universal All Assets Strategy are eight sophisticated trend-following indicators, each designed to capture different facets of market behavior. These indicators work together to provide a multi-dimensional analysis of price trends, filtering out noise and reacting only to significant movements:
Directional Moving Averages : Tracks the primary market trend, offering a clear indication of long-term price direction, ideal for identifying sustained upward or downward movements.
Smoothed Moving Averages : Reduces short-term volatility and noise to reveal the underlying trend, enhancing signal clarity and helping traders avoid reacting to temporary price spikes.
RSI Loops : Utilizes the Relative Strength Index (RSI) to assess market momentum, using a unique for loop mechanism to smooth out data and enhance precision.
Supertrend Filters : This indicator dynamically adjusts to market volatility, closely following price action to detect significant breakouts or reversals. The Supertrend is a core component for identifying shifts in trend direction with minimal lag.
RVI for Loop : The Relative Volatility Index (RVI) measures the strength of market volatility. It is optimized with a for loop mechanism, which smooths out the data and improves directional cues, especially in choppy or sideways markets.
Hull for Loop : The Hull Moving Average is designed to minimize lag while offering a smooth, responsive trend line. The for loop mechanism further enhances this by making the Hull even more sensitive to trend shifts, ensuring faster reaction to market movements without generating excessive noise.
These indicators evaluate market conditions independently, assigning a score of 1 for bullish trends and -1 for bearish trends. The average score across all eight indicators is calculated for each time frame (or bar), and this score determines whether the strategy should enter, exit, or remain neutral in a trade.
2. Scoring and Signal Confirmation
The strategy’s confirmation system ensures that trades are initiated only when there is strong alignment across multiple indicators:
A Long Position (Buy) is initiated when the majority of indicators generate a bullish signal, i.e., the average score exceeds a predefined upper threshold.
A Short Position or Exit is triggered when the average score falls below a lower threshold, signaling a bearish trend or neutral market.
By using a majority-rule confirmation system, the strategy filters out weak signals, reducing the chances of reacting to market noise or false positives. This ensures that only robust trends—those supported by multiple indicators—trigger trades.
Adaptive Logic for All Asset Classes
The Universal All Assets Strategy stands out for its ability to adapt dynamically across different asset classes. Whether it’s applied to highly volatile assets like cryptocurrencies or more stable instruments like equities, the strategy fine-tunes its behavior to match the asset’s volatility profile and price behavior.
Volatility Filters : The system incorporates volatility-sensitive filters, such as the Average True Range (ATR) and standard deviation metrics, which dynamically adjust its sensitivity based on market conditions. This ensures the strategy remains responsive to significant price movements while filtering out inconsequential fluctuations.
This adaptability makes the Universal All Assets Strategy effective across diverse markets, providing consistent performance whether the market is trending, range-bound, or experiencing high volatility.
Customization and Flexibility
1. Directional Bias
The strategy offers traders the flexibility to set a customizable directional bias, allowing it to focus on:
Long-only trades during bullish markets.
Short-only trades during bear markets.
Bi-directional trades for those looking to capitalize on both uptrends and downtrends.
This bias can be fine-tuned based on market conditions, trader preference, or risk tolerance, without compromising the integrity of the overall signal-generation process.
2. Volatility Sensitivity
Traders can adjust the strategy’s volatility sensitivity through customizable settings. By modifying how the system reacts to volatility, traders can make the strategy more aggressive in high-volatility environments or more conservative in quieter markets, depending on their individual trading style.
Visual Representation of Component Behavior
One of the unique features of the strategy is its real-time visual representation of the eight indicators through a component table displayed on the chart. This table provides a clear overview of the current status of each indicator:
A score of 1 indicates a bullish signal.
A score of -1 indicates a bearish signal.
The table is updated at each time frame (bar), showing how each indicator is contributing to the overall trend decision. This real-time feedback allows traders to monitor the exact composition of the strategy’s signal, helping them better understand market dynamics.
Oscillator Visualization for Trend Detection
To complement the component table, the strategy includes a trend oscillator displayed beneath the price chart, offering a visual summary of the overall market direction:
Green bars represent bullish trends when the majority of indicators signal an uptrend.
Red bars represent bearish trends or a neutral (cash) position when the majority of indicators detect a downtrend.
This oscillator allows traders to quickly assess the market’s overall direction at a glance, without needing to analyze each individual indicator, providing a clear and immediate visual of the market trend.
Backtested and Forward-Tested for Real-World Conditions
The Universal All Assets Strategy has been thoroughly tested under real-world trading conditions, incorporating key factors like:
Slippage : Set at 20 ticks to represent real market fluctuations.
Order Size : Calculated as 10% of equity, ensuring appropriate risk exposure for realistic capital management.
Commission : A fee of 0.05% has been factored in to account for trading costs.
These settings ensure that the strategy’s performance metrics—such as the Sortino Ratio , Sharpe Ratio , Omega Ratio , and Profit Factor —are reflective of actual trading environments. The rigorous backtesting and forward-testing processes ensure that the strategy produces realistic results, making it compatible with the markets it is written for and demonstrating how the system would behave in live conditions. It also includes robust risk management tools to minimize drawdowns and preserve capital, making it suitable for both professional and retail traders.
Anti-Fragile Design and Realistic Expectations
The Universal All Assets Strategy is engineered to be anti-fragile, thriving in volatile markets by adjusting to turbulence rather than being damaged by it. This is a crucial feature that ensures the strategy remains effective even during times of significant market instability.
Moreover, the strategy is transparent about realistic expectations, acknowledging that no system can guarantee a 100% win rate and that past performance is not indicative of future results. This transparency fosters trust and provides traders with a realistic framework for long-term success, making it an ideal choice for traders looking to navigate complex market conditions with confidence.
Acknowledgment of External Code
Special credit goes to bii_vg, whose invite-only code was used with permission in the development of the Universal All Assets Strategy. Their contributions have been instrumental in refining certain aspects of this strategy, ensuring its robustness and adaptability across various markets.
Conclusion
The Universal All Assets Strategy by Rocheur offers traders a powerful, adaptable tool for capturing trends across a wide range of asset classes. Its eight-indicator confirmation system, combined with customizable settings and real-time visual representations, provides a comprehensive solution for traders seeking precision, flexibility, and consistency. Whether used in high-volatility markets or more stable environments, the strategy’s dynamic adaptability, transparent logic, and robust testing make it an excellent choice for traders aiming to maximize performance while managing risk effectively.
Supertrend (Buy/Sell) With TP & SLSupertrend (Buy/Sell) with TP & SL: An Enhanced Trading Tool
This Pine Script indicator combines the popular Supertrend indicator with multiple take-profit (TP) and stop-loss (SL) levels, providing traders with a comprehensive visual aid for potential entries, exits, and risk management.
Originality
Buffer Zones for Precision: Instead of relying solely on the Supertrend line, this script incorporates buffer zones around it. This helps filter out false signals, especially in volatile markets, leading to more accurate buy/sell signals.
Flexible Stop-Loss: Offers the choice between a fixed or trailing stop-loss, allowing traders to tailor their risk management approach based on their preferences and market conditions.
Multiple Take-Profit Levels: Provides three potential take-profit levels, giving traders the flexibility to secure profits at different stages of a trend.
Heikin Ashi Candles & VWAP: Incorporates Heikin Ashi candles for smoother trend visualization and adds a VWAP line for potential support/resistance levels.
Clear Table Display: Presents key information like Stop Loss and Take Profit levels in a user-friendly table, making it easier to track trade targets.
How It Works
Supertrend Calculation: The Supertrend is calculated using ATR (Average True Range) to gauge market volatility. The script then creates buffer zones around the Supertrend line for refined signal generation.
Buy/Sell Signals:
Buy: When the close price crosses above the upper buffer zone, indicating a potential uptrend.
Sell: When the close price crosses below the lower buffer zone, suggesting a potential downtrend.
Take Profit & Stop Loss:
Take Profits: Three TP levels are calculated based on ATR and a customizable profit factor.
Stop Loss: The stop-loss can be set as either a fixed value based on ATR or as a trailing stop-loss that dynamically adjusts to lock in profits.
How To Use
Add the Indicator: Search for "Supertrend (Buy/Sell) With TP & SL" in the TradingView indicators list and add it to your chart.
Customize Inputs: Adjust parameters like ATR Period, Factor, Take Profit Factor, Stop Loss Factor, Stop Loss Type, etc., based on your trading style and preferences.
Interpret Signals: Look for buy signals when the price crosses above the upper buffer and sell signals when it crosses below the lower buffer.
Manage Risk: Use the plotted Take Profit and Stop Loss levels to manage your risk and potential rewards.
Concepts
Supertrend: A trend-following indicator that helps identify the direction of the prevailing trend.
ATR (Average True Range): A measure of market volatility.
Buffer Zones: Used to filter out false signals by creating a zone around the Supertrend line.
Trailing Stop Loss: A dynamic stop-loss that moves with the price to protect profits.
Heikin Ashi: A type of candlestick chart designed to filter out market noise and make trends easier to identify.
VWAP (Volume Weighted Average Price): An indicator that shows the average price at which a security has traded throughout the day, based on both volume and price.
Important Note: This script is for educational and informational purposes only. Backtest thoroughly and use with caution in live trading. Always manage your risk appropriately.
GKD-BT Optimizer SCSC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCSC Backtest (Solo Confirmation Super Complex) is a Backtest module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System." (see the section Giga Kaleidoscope (GKD) Modularized Trading System below for an explanation of the GKD trading system)
**the backtest data rendered to the chart above and all screenshots below use $5 commission per trade and 10% equity per trade with $1 million initial capital**
█ GKD-BT Optimizer SCSC Backtest
The GKD-BT Optimizer SCSC Backtest is a comprehensive backtesting module designed to optimize the combination of key GKD indicators within AlgxTrading's "Giga Kaleidoscope Modularized Trading System." This module facilitates precise strategy refinement by allowing traders to configure and optimize the following critical GKD indicators:
GKD-B Baseline
GKD-V Volatility/Volume
GKD-C Confirmation 1
GKD-C Continuation
Each indicator is equipped with an "Optimizer" mode, enabling dynamic feedback and iterative improvements directly into the backtesting environment. This integrated approach ensures that each component contributes effectively to the overall strategy, providing a robust framework for achieving optimized trading outcomes.
The GKD-BT Optimizer supports granular test configurations including a single take profit and stop loss setting, and allows for targeted testing within specified date ranges to simulate forward testing with historical data. This feature is essential for evaluating the resilience and effectiveness of trading strategies under various market conditions.
Furthermore, the module is designed with user-centric features such as:
Customizable Trading Panel: Displays critical backtest results and trade statistics, which can be shown or hidden as per user preference.
Highlighting Thresholds: Users can set thresholds for Total Percent Wins, Percent Profitable, and Profit Factor, which helps in quickly identifying the most relevant metrics for analysis.
The detailed setup ensures that traders can not only adjust their strategies based on historical performance but also fine-tune their approach to meet specific trading objectives.
🔶 To configure this indicator: ***all GKD indicators listed below are all included in the AlgxTrading trading system package***
1. Add GKD-C Confirmation, GKD-B Baseline, GKD-V Volatility/Volume, and GKD-C Continuation to your chart
2. In the GKD-B Baseline indicator, change "Baseline Type" to "Optimizer"
3. In the GKD-V Volatility/Volume indicator, change "Volatility/Volume Type" to "Optimizer"
4. In the GKD-C Confirmation 1 indicator, change "Confirmation Type" to "Optimizer"
5. In the GKD-C Continuation indicator, change "Confirmation Type" to "Optimizer"
An example of steps 2-5. In the screenshot example below, we change the value "Confirmation Type" in the GKD-C Fisher Transform indicator to "Optimizer"
6. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the field "Import GKD-B Baseline indicator"
7. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the field "Import GKD-V Volatility/Volume indicator"
8. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the field "Import GKD-C Confirmation 1 indicator"
9. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation indicator into the field "Import GKD-C Continuation indicator"
An example of steps 6-9. In the screenshot example below, we import the value "Input into NEW GKD-BT Backtest" from the GKD-C Fisher Transform indicator into the GKD-BT Optimizer SCSC Backtest
10. Decide which of the 5 indicators you wish to optimize in first in the GKD-BT Optimizer SCSC Backtest. Change the value of the import from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
An example of step 10. In the screenshot example below, we chose to optimize the Confirmation 1 indicator, the GKD-C Fisher Transform. We change the value of the field "Import GKD-C Confirmation 1 indicator" from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
11. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", use the dropdown menu "Optimization Indicator" to select the type of indicator you selected from step 12 above: "Baseline", "Volatility/Volume", "Confirmation 1", or "Continuation"
12. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Start" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Start"
13. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Skip" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Skip"
An example of step 11. In the screenshot example below, we select "Confirmation 1" from the "Optimization Indicator" dropdown menu
An example of steps 12 and 13. In the screenshot example below, we import "Import Optimization Indicator Start" and "Import Optimization Indicator Skip" from the GKD-C Fisher Transform indicator into their respective fields
🔶 This backtest includes the following metrics
Net profit: Overall profit or loss achieved.
Total Closed Trades: Total number of closed trades, both winning and losing.
Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
🔶 Summary of notable settings not already explained above
🔹 Backtest Properties
These settings define the financial and logistical parameters of the trading simulation, including:
Initial Capital: Specifies the starting balance for the backtest, setting the baseline for measuring profitability and loss.
Order Size: Determines the size of trades, which can be fixed or a percentage of the equity, affecting risk and return.
Order Type: Chooses between fixed contract sizes or a percentage-based order size, allowing for static or dynamic trading volumes.
Commission per Order: Accounts for trading costs, subtracting these from profits to provide a more accurate net performance result.
🔹 Signal Qualifiers
This group of settings establishes criteria related to the strategy's Baseline, and Volatility/Volume indicators in relation to the GKD-C Confirmation 1 indicator, which is crucial for validating trade signals. These include:
Maximum Allowable Post Signal Baseline Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Baseline, then should the Baseline "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Volatility/Volume Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the position of the Volatility/Volume, then should the Volatility/Volume "catch-up" with the long/short of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
🔹 Signal Settings
Signal Options: These settings allow users to toggle the visibility of different types of entries based on the strategy criteria, such as standard entries, baseline entries, and continuation entries.
Standard Entry Rules Settings: Detailed criteria for standard entries can be customized here, including conditions on baseline agreement, price within specific zones, and agreement with other confirmation indicators.
1-Candle Rule Standard Entry Rules Settings: Similar to standard entries, but with a focus on conditions that must be met within a one-candle timeframe.
Baseline Entry Rules Settings: Specifies rules for entries based on the baseline, including conditions on confirmation agreement and price zones.
Volatility/Volume Entry Rules Settings: This includes settings for entries based on volatility or volume conditions, with specific rules on confirmation agreement and baseline agreement.
Continuation Entry Rules Settings: This group outlines the conditions for continuation entries, focusing on agreement with baseline and confirmation indicators since the entry signal trigger.
🔹 Volatility Settings
Volatility PnL Settings: Parameters for defining the type of volatility measure to use, its period, and multipliers for profit and stop levels.
Volatility Types Included
Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature.
Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility.
Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics.
Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior.
Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements.
Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity.
Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics.
Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions.
Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact.
ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions.
Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms.
Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers.
Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility.
ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility.
True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
🔹 Other Settings
Backtest Dates: Users can specify the timeframe for the backtest, including start and end dates, as well as the acceptable entry time window.
Volatility Inputs: Additional settings related to volatility calculations, such as static percent, internal filter period for median absolute deviation, and parameters for specific volatility models.
UI Options: Settings to customize the user interface, including table activation, date panel visibility, and aesthetics like color and text size.
Export Options: Allows users to select the type of data to export from the backtest, focusing on metrics like net profit, total closed trades, and average profit per trade.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*see the section “GKD Trading System Signals” below
**not a required component of the GKD algorithm
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ Conclusion
The GKD-BT Optimizer SCSC Backtest is a critical tool within the Giga Kaleidoscope Modularized Trading System, designed for precise strategy refinement and evaluation within the GKD framework. It enables the optimization and testing of various trading indicators and strategies under different market conditions. The module's design facilitates detailed analysis of individual trading components' performance, allowing for the optimization of indicators like Baseline, Volatility/Volume, Confirmation, and Continuation. This optimization process aids traders in identifying the most effective configurations, thereby enhancing trading outcomes and strategy efficiency within the GKD ecosystem.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
GKD-BT Optimizer Full GKD Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer Full GKD Backtest is a Backtest module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System." (see the section Giga Kaleidoscope (GKD) Modularized Trading System below for an explanation of the GKD trading system)
**the backtest data rendered to the chart above and all screenshots below use $5 commission per trade and 10% equity per trade with $1 million initial capital**
█ GKD-BT Optimizer Full GKD Backtest
The GKD-BT Optimizer Full GKD Backtest is a comprehensive backtesting module designed to optimize the combination of key GKD indicators within AlgxTrading's "Giga Kaleidoscope Modularized Trading System." This module facilitates precise strategy refinement by allowing traders to configure and optimize the following critical GKD indicators:
GKD-B Baseline
GKD-V Volatility/Volume
GKD-C Confirmation 1
GKD-C Confirmation 2
GKD-C Continuation
Each indicator is equipped with an "Optimizer" mode, enabling dynamic feedback and iterative improvements directly into the backtesting environment. This integrated approach ensures that each component contributes effectively to the overall strategy, providing a robust framework for achieving optimized trading outcomes.
The GKD-BT Optimizer supports granular test configurations including a single take profit and stop loss setting, and allows for targeted testing within specified date ranges to simulate forward testing with historical data. This feature is essential for evaluating the resilience and effectiveness of trading strategies under various market conditions.
Furthermore, the module is designed with user-centric features such as:
Customizable Trading Panel: Displays critical backtest results and trade statistics, which can be shown or hidden as per user preference.
Highlighting Thresholds: Users can set thresholds for Total Percent Wins, Percent Profitable, and Profit Factor, which helps in quickly identifying the most relevant metrics for analysis.
The detailed setup ensures that traders can not only adjust their strategies based on historical performance but also fine-tune their approach to meet specific trading objectives.
🔶 To configure this indicator: ***all GKD indicators listed below are all included in the AlgxTrading trading system package***
1. Add GKD-C Confirmation, GKD-B Baseline, GKD-V Volatility/Volume, GKD-C Confirmation 2, and GKD-C Continuation to your chart
2. In the GKD-B Baseline indicator, change "Baseline Type" to "Optimizer"
3. In the GKD-V Volatility/Volume indicator, change "Volatility/Volume Type" to "Optimizer"
4. In the GKD-C Confirmation 1 indicator, change "Confirmation Type" to "Optimizer"
5. In the GKD-C Confirmation 2 indicator, change "Confirmation Type" to "Optimizer"
6. In the GKD-C Continuation indicator, change "Confirmation Type" to "Optimizer"
An example of steps 2-6. In the screenshot example below, we change the value "Confirmation Type" in the GKD-C Fisher Transform indicator to "Optimizer"
7. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the field "Import GKD-B Baseline indicator"
8. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the field "Import GKD-V Volatility/Volume indicator"
9. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the field "Import GKD-C Confirmation 1 indicator"
10. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 indicator into the field "Import GKD-C Confirmation 2 indicator"
11. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation indicator into the field "Import GKD-C Continuation indicator"
An example of steps 7-11. In the screenshot example below, we import the value "Input into NEW GKD-BT Backtest" from the GKD-C Coppock Curve indicator into the GKD-BT Optimizer Full GKD Backtest
12. Decide which of the 5 indicators you wish to optimize in first in the GKD-BT Optimizer Full GKD Backtest. Change the value of the import from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
An example of step 12. In the screenshot example below, we chose to optimize the Confirmation 1 indicator, the GKD-C Fisher Transform. We change the value of the field "Import GKD-C Confirmation 1 indicator" from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
13. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", use the dropdown menu "Optimization Indicator" to select the type of indicator you selected from step 12 above: "Baseline", "Volatility/Volume", "Confirmation 1", "Confirmation 2", or "Continuation"
14. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Start" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Start"
15. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Skip" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Skip"
An example of step 13. In the screenshot example below, we select "Confirmation 1" from the "Optimization Indicator" dropdown menu
An example of steps 14 and 15. In the screenshot example below, we import "Import Optimization Indicator Start" and "Import Optimization Indicator Skip" from the GKD-C Fisher Transform indicator into their respective fields
🔶 This backtest includes the following metrics
Net profit: Overall profit or loss achieved.
Total Closed Trades: Total number of closed trades, both winning and losing.
Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
🔶 Summary of notable settings not already explained above
🔹 Backtest Properties
These settings define the financial and logistical parameters of the trading simulation, including:
Initial Capital: Specifies the starting balance for the backtest, setting the baseline for measuring profitability and loss.
Order Size: Determines the size of trades, which can be fixed or a percentage of the equity, affecting risk and return.
Order Type: Chooses between fixed contract sizes or a percentage-based order size, allowing for static or dynamic trading volumes.
Commission per Order: Accounts for trading costs, subtracting these from profits to provide a more accurate net performance result.
🔹 Signal Qualifiers
This group of settings establishes criteria related to the strategy's Baseline, Volatility/Volume, and Confirmation 2 indicators in relation to the GKD-C Confirmation 1 indicator, which is crucial for validating trade signals. These include:
Maximum Allowable Post Signal Baseline Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Baseline, then should the Baseline "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Volatility/Volume Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the position of the Volatility/Volume, then should the Volatility/Volume "catch-up" with the long/short of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Confirmation 2 Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Confirmation 2, then should the Confirmation 2 "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
🔹 Signal Settings
Signal Options: These settings allow users to toggle the visibility of different types of entries based on the strategy criteria, such as standard entries, baseline entries, and continuation entries.
Standard Entry Rules Settings: Detailed criteria for standard entries can be customized here, including conditions on baseline agreement, price within specific zones, and agreement with other confirmation indicators.
1-Candle Rule Standard Entry Rules Settings: Similar to standard entries, but with a focus on conditions that must be met within a one-candle timeframe.
Baseline Entry Rules Settings: Specifies rules for entries based on the baseline, including conditions on confirmation agreement and price zones.
Volatility/Volume Entry Rules Settings: This includes settings for entries based on volatility or volume conditions, with specific rules on confirmation agreement and baseline agreement.
Confirmation 2 Entry Rules Settings: Settings here define the rules for entries based on a second confirmation indicator, detailing the required agreements and conditions.
Continuation Entry Rules Settings: This group outlines the conditions for continuation entries, focusing on agreement with baseline and confirmation indicators since the entry signal trigger.
🔹 Volatility Settings
Volatility PnL Settings: Parameters for defining the type of volatility measure to use, its period, and multipliers for profit and stop levels.
Volatility Types Included
Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature.
Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility.
Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics.
Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior.
Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements.
Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity.
Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics.
Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions.
Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact.
ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions.
Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms.
Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers.
Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility.
ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility.
True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
🔹 Other Settings
Backtest Dates: Users can specify the timeframe for the backtest, including start and end dates, as well as the acceptable entry time window.
Volatility Inputs: Additional settings related to volatility calculations, such as static percent, internal filter period for median absolute deviation, and parameters for specific volatility models.
UI Options: Settings to customize the user interface, including table activation, date panel visibility, and aesthetics like color and text size.
Export Options: Allows users to select the type of data to export from the backtest, focusing on metrics like net profit, total closed trades, and average profit per trade.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*see the section “GKD Trading System Signals” below
**not a required component of the GKD algorithm
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ Conclusion
The GKD-BT Optimizer Full GKD Backtest is a critical tool within the Giga Kaleidoscope Modularized Trading System, designed for precise strategy refinement and evaluation within the GKD framework. It enables the optimization and testing of various trading indicators and strategies under different market conditions. The module's design facilitates detailed analysis of individual trading components' performance, allowing for the optimization of indicators like Baseline, Volatility/Volume, Confirmation, and Continuation. This optimization process aids traders in identifying the most effective configurations, thereby enhancing trading outcomes and strategy efficiency within the GKD ecosystem.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
TUE ADX/MACD Confluence Algorithm V1This algorithm is designed to look at the ADX/MACD confluence for opening conditions, apply various filters including volume and volatility, then determine stop outs, break evens, and take profits.
The ADX and MACD confluence can be a powerful predictor in stock movements. Both of these indicators find trend but do it in different ways. When they're combined they have a high success rate of finding openings. That's done by finding the bar in which both show the same direction - that bar is the beginning of the confluence. I have a free indicator called the TUE ADX/MACD Confluence that you can use to see this in action.
This script will help you find those confluences in an easy to understand manner. It will open a trade on a detected confluence, using the rest of the variables available in the algorithm as filters. You can filter based on cumulative volume delta, volatility, and trend. It includes settings for either trend following or contrarian trades, and the ability to go long, short, or both.
It includes Buy and Sell signals for detected confluences, and will show colored candles to help you determine when to exit a trade if you don't want to follow the included take profit areas. When the candles turn to white that means the detected confluence is no longer in play. The Buy and Sell signals will display on the first occurrence of each confluence.
The take profit areas are based on proprietary math that help find peaks and valleys. You can adjust the size of the take profits as a percentage of the position, change to static take profits (i.e. take profit in 16 ticks), or use both. You can also disable them and use the natural closing conditions (reversal of MACD/ADX confluence).
Our algo works in any market and will allow user to adjust input settings to be used on any ticker they'd like. It is built as a strategy so you can back test on any ticker to find the exact right settings to dial it in and then switch to live trading mode to see signals. Can be used for day trades or swing trades.
Automated Trading
This algo has been tested and certified to work for automated trading.
Works on Forex
It's confirmed to work on forex so you can trade that market.
Gets you into long successful trades, and gets out of poor ones quickly
It keeps you in the long trades taking small profits along the way, but cuts losers quickly in comparison. This style leads to a high profit factor, as you can see over 3.0 in the included ES 3 Min chart.
It looks at many variables so you don't have to
- Uses ADX/MACD confluence for opening/closing conditions.
- It uses volume, if the user wants, to help filter entries. The volume calculation is based off of my proprietary cumulative volume delta indicator and helps find if the volume is moving long or short.
- It uses proprietary take profit math to help find peaks and valleys to peel off profits. It is based on the changes in momentum of the underlying.
- It allows for stop outs and break evens based on volatility so they'll always adjust with the movement of the underlying ticker (see the blue lines above and below the opening in the chart).
- It allows for offset break evens to keep a portion of the profit.
Strategy for the Algo
Included so you can understand how to trade with it.
ONE: After loading this strategy onto a ticker turn off volume if it's a ticker with no volume , set the dates at the bottom to when the stock is active (you want to start backtesting when a stock started trading like it trades currently).
TWO: From there adjust the ADX/MACD to find the highest win rate and profit factor.
THREE: Then adjust the volume length to find the highest win rate and profit factor. It's important while doing these that you pay attention to a smooth upward equity curve.
FOUR: After this has been done now adjust the long and short risk multipliers. This determines your stop out.
FIVE: Then adjust breakeven multipliers - this is the level at which it changes to a breakeven stop out instead of the previous one. You can also set an offset to keep a small part of the profit.
SIX: Finally adjust the take profit sizes.
SEVEN: Once this is all done go back through the list and adjust up and down by one or two clicks and see if a better curve can be obtained. Very frequently long and short trades have different settings.
EIGHT: When you are finished save the settings in a custom indicator template and put it with it's own chart.
Additional
The settings shown on screen are not the default settings, but are settings chosen for this ticker and timeframe based on the process above. Nearly every ticker and timeframe will require adjustment from default, that's why the algorithm is built to be highly flexible. It can fit any ticker and timeframe, as well as market environment.
Also included in the chart above is a $1.25 commission per contract, and a tick of slippage (which on average is about right for automated trading on ES).
