XRP/USD Advanced Trading StrategyKey Features:
Triple Confirmation System combines:
Moving Average crossover (9-period vs 21-period)
RSI oversold/overbought conditions (14-period)
MACD histogram crossover
Risk Management:
Built-in stop loss/profit taking (modify via strategy settings)
Margin requirements specified (100:1 leverage)
Visual Elements:
Clean price chart overlay
Clear buy/sell arrows with labels
Moving average plots for trend identification
Optimization Tips:
Adjust MA lengths for different timeframes (shorter for day trading)
Modify RSI levels based on market volatility
Combine with Ichimoku Cloud for additional confirmation
Use Bollinger Bands® to filter false breakouts
Backtesting:
Test on multiple timeframes (4h/daily weekly)
Check performance during different market conditions
Optimize parameters using Strategy Tester
This strategy reduces false signals by requiring confirmation from three different technical indicators while maintaining clarity in signal generation. Always validate with fundamental analysis and market news before executing trade
Indicadores e estratégias
Long position strategy [75U+100x leverage]+mobile take profitAmbush against the trend, triple filtering, quantity price coordination, automated risk control
A high leverage, long position trading system based on volume surges and POC trends, combined with a mobile take profit mechanism, suitable for cryptocurrency markets (such as BTC/USDT contract trading)
逆势而行、三重过滤、量价协调、自动风险控制
基于交易量激增和POC趋势的高杠杆、多头头寸交易系统，结合移动止盈机制，适用于加密货币市场（如BTC/USDT合约交易）
多头策略[75U+100x杠杆]+移动止盈A high leverage, long position trading system based on volume surges and POC trends, combined with a mobile take profit mechanism, suitable for cryptocurrency markets (such as BTC/USDT contract trading)
Ambush against the trend, filter out fake breakthroughs
Key indicators: Volume moving average volMA/POC trend pocTrend/Price moving average priceMA
Z-Score Normalized VIX StrategyThis strategy leverages the concept of the Z-score applied to multiple VIX-based volatility indices, specifically designed to capture market reversals based on the normalization of volatility. The strategy takes advantage of VIX-related indicators to measure extreme levels of market fear or greed and adjusts its position accordingly.
1. Overview of the Z-Score Methodology
The Z-score is a statistical measure that describes the position of a value relative to the mean of a distribution in terms of standard deviations. In this strategy, the Z-score is calculated for various volatility indices to assess how far their values are from their historical averages, thus normalizing volatility levels. The Z-score is calculated as follows:
Z = \frac{X - \mu}{\sigma}
Where:
• X is the current value of the volatility index.
• \mu is the mean of the index over a specified period.
• \sigma is the standard deviation of the index over the same period.
This measure tells us how many standard deviations the current value of the index is away from its average, indicating whether the market is experiencing unusually high or low volatility (fear or calm).
2. VIX Indices Used in the Strategy
The strategy utilizes four commonly referenced volatility indices:
• VIX (CBOE Volatility Index): Measures the market’s expectations of 30-day volatility based on S&P 500 options.
• VIX3M (3-Month VIX): Reflects expectations of volatility over the next three months.
• VIX9D (9-Day VIX): Reflects shorter-term volatility expectations.
• VVIX (VIX of VIX): Measures the volatility of the VIX itself, indicating the level of uncertainty in the volatility index.
These indices provide a comprehensive view of the current volatility landscape across different time horizons.
3. Strategy Logic
The strategy follows a long entry condition and an exit condition based on the combined Z-score of the selected volatility indices:
• Long Entry Condition: The strategy enters a long position when the combined Z-score of the selected VIX indices falls below a user-defined threshold, indicating an abnormally low level of volatility (suggesting a potential market bottom and a bullish reversal). The threshold is set as a negative value (e.g., -1), where a more negative Z-score implies greater deviation below the mean.
• Exit Condition: The strategy exits the long position when the combined Z-score exceeds the threshold (i.e., when the market volatility increases above the threshold, indicating a shift in market sentiment and reduced likelihood of continued upward momentum).
4. User Inputs
• Z-Score Lookback Period: The user can adjust the lookback period for calculating the Z-score (e.g., 6 periods).
• Z-Score Threshold: A customizable threshold value to define when the market has reached an extreme volatility level, triggering entries and exits.
The strategy also allows users to select which VIX indices to use, with checkboxes to enable or disable each index in the calculation of the combined Z-score.
5. Trade Execution Parameters
• Initial Capital: The strategy assumes an initial capital of $20,000.
• Pyramiding: The strategy does not allow pyramiding (multiple positions in the same direction).
• Commission and Slippage: The commission is set at $0.05 per contract, and slippage is set at 1 tick.
6. Statistical Basis of the Z-Score Approach
The Z-score methodology is a standard technique in statistics and finance, commonly used in risk management and for identifying outliers or unusual events. According to Dumas, Fleming, and Whaley (1998), volatility indices like the VIX serve as a useful proxy for market sentiment, particularly during periods of high uncertainty. By calculating the Z-score, we normalize volatility and quantify the degree to which the current volatility deviates from historical norms, allowing for systematic entry and exit based on these deviations.
7. Implications of the Strategy
This strategy aims to exploit market conditions where volatility has deviated significantly from its historical mean. When the Z-score falls below the threshold, it suggests that the market has become excessively calm, potentially indicating an overreaction to past market events. Entering long positions under such conditions could capture market reversals as fear subsides and volatility normalizes. Conversely, when the Z-score rises above the threshold, it signals increased volatility, which could be indicative of a bearish shift in the market, prompting an exit from the position.
By applying this Z-score normalized approach, the strategy seeks to achieve more consistent entry and exit points by reducing reliance on subjective interpretation of market conditions.
8. Scientific Sources
• Dumas, B., Fleming, J., & Whaley, R. (1998). “Implied Volatility Functions: Empirical Tests”. The Journal of Finance, 53(6), 2059-2106. This paper discusses the use of volatility indices and their empirical behavior, providing context for volatility-based strategies.
• Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities”. Journal of Political Economy, 81(3), 637-654. The original Black-Scholes model, which forms the basis for many volatility-related strategies.
NEW Non-Directional Market StrategyFinal New Non Directional Trading Strategy! which can be used for all markets , the candles will turn grey during the choppy conditions.
Reversal Trading Bot Strategy[BullByte]Overview :
The indicator Reversal Trading Bot Strategy is crafted to capture potential market reversal points by combining momentum, volatility, and trend alignment filters. It uses a blend of technical indicators to identify both bullish and bearish reversal setups, ensuring that multiple market conditions are met before entering a trade.
Core Components :
Technical Indicators Used :
RSI (Relative Strength Index) :
Purpose : Detects divergence conditions by comparing recent lows/highs in price with the RSI.
Parameter : Length of 8.
Bollinger Bands (BB) :
Purpose : Measures volatility and identifies price levels that are statistically extreme.
Parameter : Length of 20 and a 2-standard deviation multiplier.
ADX (Average Directional Index) & DMI (Directional Movement Index) :
Purpose : Quantifies the strength of the trend. The ADX threshold is set at 20, and additional filters check for the alignment of the directional indicators (DI+ and DI–).
ATR (Average True Range) :
Purpose : Provides a volatility measure used to set stop levels and determine risk through trailing stops.
Volume SMA (Simple Moving Average of Volume ):
Purpose : Helps confirm strength by comparing the current volume against a 20-period average, with an optional filter to ensure volume is at least twice the SMA.
User-Defined Toggle Filters :
Volume Filter : Confirms that the volume is above average (or twice the SMA) before taking trades.
ADX Trend Alignment Filter : Checks that the ADX’s directional indicators support the trade direction.
BB Close Confirmation : Optionally refines the entry by requiring price to be beyond the upper or lower Bollinger Band rather than just above or below.
RSI Divergence Exit : Allows the script to close positions if RSI divergence is detected.
BB Mean Reversion Exit : Closes positions if the price reverts to the Bollinger Bands’ middle line.
Risk/Reward Filter : Ensures that the potential reward is at least twice the risk by comparing the distance to the Bollinger Band with the ATR.
Candle Movement Filter : Optional filter to require a minimum percentage move in the candle to confirm momentum.
ADX Trend Exit : Closes positions if the ADX falls below the threshold and the directional indicators reverse.
Entry Conditions :
Bullish Entry :
RSI Divergence : Checks if the current close is lower than a previous low while the RSI is above the previous low, suggesting bullish divergence.
Bollinger Confirmation : Requires that the price is above the lower (or upper if confirmation is toggled) Bollinger Band.
Volume & Trend Filters : Combines volume condition, ADX strength, and an optional candle momentum condition.
Risk/Reward Check : Validates that the trade meets a favorable risk-to-reward ratio.
Bearish Entry :
Uses a mirror logic of the bullish entry by checking for bearish divergence, ensuring the price is below the appropriate Bollinger level, and confirming volume, trend strength, candle pattern, and risk/reward criteria.
Trade Execution and Exit Strateg y:
Trade Execution :
Upon meeting the entry conditions, the strategy initiates a long or short position.
Stop Loss & Trailing Stops :
A stop-loss is dynamically set using the ATR value, and trailing stops are implemented as a percentage of the close price.
Exit Conditions :
Additional exit filters can trigger early closures based on RSI divergence, mean reversion (via the middle Bollinger Band), or a weakening trend as signaled by ADX falling below its threshold.
This multi-layered exit strategy is designed to lock in gains or minimize losses if the market begins to reverse unexpectedly.
How the Strategy Works in Different Market Conditions :
Trending Markets :
The ADX filter ensures that trades are only taken when the trend is strong. When the market is trending, the directional movement indicators help confirm the momentum, making the reversal signal more reliable.
Ranging Markets :
In choppy markets, the Bollinger Bands expand and contract, while the RSI divergence can highlight potential turning points. The optional filters can be adjusted to avoid false signals in low-volume or low-volatility conditions.
Volatility Management :
With ATR-based stop-losses and a risk/reward filter, the strategy adapts to current market volatility, ensuring that risk is managed consistently.
Recommendation on using this Strategy with a Trading Bot :
This strategy is well-suited for high-frequency trading (HFT) due to its ability to quickly identify reversal setups and execute trades dynamically with automated stop-loss and trailing exits. By integrating this script with a TradingView webhook-based bot or an API-driven execution system, traders can automate trade entries and exits in real-time, reducing manual execution delays and capitalizing on fast market movements.
Disclaimer :
This script is provided for educational and informational purposes only. It is not intended as investment advice. Trading involves significant risk, and you should always conduct your own research and analysis before making any trading decisions. The author is not responsible for any losses incurred while using this script.
EMA 34 Crossover with Break Even Stop LossEMA 34 Crossover with Break Even Stop Loss Strategy
This trading strategy is based on the 34-period Exponential Moving Average (EMA) and aims to enter long positions when the price crosses above the EMA 34. The strategy is designed to manage risk effectively with a dynamic stop loss and take-profit mechanism.
Key Features:
EMA 34 Crossover:
The strategy generates a long entry signal when the closing price of the current bar crosses above the 34-period EMA, with the condition that the previous closing price was below the EMA. This crossover indicates a potential upward trend.
Risk Management:
Upon entering a trade, the strategy sets a stop loss at the low of the previous bar. This helps in controlling the downside risk.
A take profit level is set at a 10:1 risk-to-reward ratio, meaning the potential profit is ten times the amount risked on the trade.
Break-even Stop Loss:
As the price moves in favor of the trade and reaches a 3:1 risk-to-reward ratio, the strategy moves the stop loss to the entry price (break-even). This ensures that no loss will be incurred if the market reverses, effectively protecting profits.
Exit Conditions:
The strategy exits the trade when either the stop loss is hit (if the price drops below the stop loss level) or the take profit target is reached (if the price rises to the take profit level).
If the price reaches the break-even level (entry price), the stop loss is adjusted to lock in profits and prevent any loss.
Visualization:
The stop loss and take profit levels are plotted on the chart for easy visualization, helping traders track the status of their trade.
Trade Management Summary:
Long Entry: When price crosses above the 34-period EMA.
Stop Loss: Set to the low of the previous candle.
Take Profit: Set to a 10:1 risk-to-reward ratio.
Break-even: Stop loss is moved to entry price when a 3:1 risk-to-reward ratio is reached.
Exit: The trade is closed either when the stop loss or take profit levels are hit.
This strategy is designed to minimize losses by employing a dynamic stop loss and to maximize gains by setting a favorable risk-to-reward ratio, making it suitable for traders who prefer a structured, automated approach to risk management and trend-following.
BTC/USDC 50x Futures Strategy with Multi-TPScript in the workings for btc/usdt 50x leverage trading at 1% portfolio margin. Please do not use to save your money
ETF Rotation Strategy (India)Description of Strategy:
Suitable for ETFs or large-cap stock with very low frequency traders (2-3 trade per year). I have created for my use based on my own experience and knowledge, thought it could help more like me.
1. This ETF rotation strategy is based on trend tracking of 2 ETFs, moving averages standard,
2. One Benchmark ETF (that is selectable in Inputs) and 2nd that's you can plot on chart,
3. Strategy does comparison between the 2 ETFs in multiple time periods,
4. And prompts you for entry and exit on the plotted ETF in comparison with benchmark, suitable for investors or very low frequency traders. It may not give more than 2-3 trades per year,
5. Back testing result for plotted ETF will appear in tester, and combined both ETFs performance will appear in the table left top.
6. Combined back testing is done for past 5 years,
7. Option to select start year of back test is available in Input for combined result in table,
8. Its tested mostly on India liquid ETFs (12-15), included in table right bottom, Input has option to select or deselect this table to appear or disappear. When deselected script speed is better. Although global ETFs can be tested by changing benchmark ETF is Input and select watchlist accordingly,
9. May be tried on Large cap stocks, however tested for ETFs due less volatility in comparison to stocks,
10. User need to add most liquid ETF in watchlist and then when plot any ETF it will show its performance with benchmark and show entry /exit,
11. Future performance obviously will depend on market conditions time to time.
For Access to it, please contact me on email "ssukhjitkd@gmail.com" with your Tradingview account name and brief description of you, markets you trade and your trading interest.
BTC Trading RobotOverview
This Pine Script strategy is designed for trading Bitcoin (BTC) by placing pending orders (BuyStop and SellStop) based on local price extremes. The script also implements a trailing stop mechanism to protect profits once a position becomes sufficiently profitable.
________________________________________
Inputs and Parameter Setup
1. Trading Profile:
o The strategy is set up specifically for BTC trading.
o The systemType input is set to 1, which means the strategy will calculate trade parameters using the BTC-specific inputs.
2. Common Trading Inputs:
o Risk Parameters: Although RiskPercent is defined, its actual use (e.g., for position sizing) isn’t implemented in this version.
o Trading Hours Filter:
SHInput and EHInput let you restrict trading to a specific hour range. If these are set (non-zero), orders will only be placed during the allowed hours.
3. BTC-Specific Inputs:
o Take Profit (TP) and Stop Loss (SL) Percentages:
TPasPctBTC and SLasPctBTC are used to determine the TP and SL levels as a percentage of the current price.
o Trailing Stop Parameters:
TSLasPctofTPBTC and TSLTgrasPctofTPBTC determine when and by how much a trailing stop is applied, again as percentages of the TP.
4. Other Parameters:
o BarsN is used to define the window (number of bars) over which the local high and low are calculated.
o OrderDistPoints acts as a buffer to prevent the entry orders from being triggered too early.
________________________________________
Trade Parameter Calculation
• Price Reference:
o The strategy uses the current closing price as the reference for calculations.
• Calculation of TP and SL Levels:
o If the systemType is set to BTC (value 1), then:
Take Profit Points (Tppoints) are calculated by multiplying the current price by TPasPctBTC.
Stop Loss Points (Slpoints) are calculated similarly using SLasPctBTC.
A buffer (OrderDistPoints) is set to half of the take profit points.
Trailing Stop Levels:
TslPoints is calculated as a fraction of the TP (using TSLTgrasPctofTPBTC).
TslTriggerPoints is similarly determined, which sets the profit level at which the trailing stop will start to activate.
________________________________________
Time Filtering
• Session Control:
o The current hour is compared against SHInput (start hour) and EHInput (end hour).
o If the current time falls outside the allowed window, the script will not place any new orders.
________________________________________
Entry Orders
• Local Price Extremes:
o The strategy calculates a local high and local low using a window of BarsN * 2 + 1 bars.
• Placing Stop Orders:
o BuyStop Order:
A long entry is triggered if the current price is less than the local high minus the order distance buffer.
The BuyStop order is set to trigger at the level of the local high.
o SellStop Order:
A short entry is triggered if the current price is greater than the local low plus the order distance buffer.
The SellStop order is set to trigger at the level of the local low.
Note: Orders are only placed if there is no current open position and if the session conditions are met.
________________________________________
Trailing Stop Logic
Once a position is open, the strategy monitors profit levels to protect gains:
• For Long Positions:
o The script calculates the profit as the difference between the current price and the average entry price.
o If this profit exceeds the TslTriggerPoints threshold, a trailing stop is applied by placing an exit order.
o The stop price is set at a distance below the current price, while a limit (profit target) is also defined.
• For Short Positions:
o The profit is calculated as the difference between the average entry price and the current price.
o A similar trailing stop exit is applied if the profit exceeds the trigger threshold.
________________________________________
Summary
In essence, this strategy works by:
• Defining entry levels based on recent local highs and lows.
• Placing pending stop orders to enter the market when those levels are breached.
• Filtering orders by time, ensuring trades are only taken during specified hours.
• Implementing a trailing stop mechanism to secure profits once the trade moves favorably.
This approach is designed to automate BTC trading based on price action and dynamic risk management, although further enhancements (like dynamic position sizing based on RiskPercent) could be added for a more complete risk management system.
EMA Crossover (Short Focus with Trailing Stop)This strategy utilizes a combination of Exponential Moving Averages (EMA) and Simple Moving Averages (SMA) to generate entry and exit signals for both long and short positions. The core of the strategy is based on the 13-period EMA (short EMA) crossing the 33-period EMA (long EMA) for entering long trades, while a 13-period EMA crossing the 25-period EMA (mid EMA) generates short trade signals. The 100-period SMA and 200-period SMA serve as additional trend indicators to provide context for the market conditions. The strategy aims to capitalize on trend reversals and momentum shifts in the market.
The strategy is designed to execute trades swiftly with an emphasis on entering positions when conditions align in real time. For long entries, the strategy initiates a buy when the 13 EMA is greater than the 33 EMA, indicating a bullish trend. For short entries, the 13 EMA crossing below the 33 EMA signals a bearish trend, prompting a short position. Importantly, the code includes built-in exit conditions for both long and short positions. Long positions are exited when the 13 EMA falls below the 33 EMA, while short positions are closed when the 13 EMA crosses above the 25 EMA.
A key feature of the strategy is the use of trailing stops for both long and short positions. This dynamic exit method adjusts the stop level as the market moves in favor of the trade, locking in profits while reducing the risk of losses. The trailing stop for long positions is based on the high price of the current bar, while the trailing stop for short positions is set using the low price, providing more flexibility in managing risk. This trailing stop mechanism helps to capture profits from favorable market moves while ensuring that positions are exited if the market moves against them.
This strategy works best on the 4H timeframe and is optimized for major cryptocurrency pairs. The daily chart allows for the EMAs to provide more reliable signals, as the strategy is designed to capture broader trends rather than short-term market fluctuations. Using it on major crypto pairs increases its effectiveness as these assets tend to have strong and sustained trends, providing better opportunities for the strategy to perform well.
EMA Crossover (Short Focus with Trailing Stop)This strategy utilizes a combination of Exponential Moving Averages (EMA) and Simple Moving Averages (SMA) to generate entry and exit signals for both long and short positions. The core of the strategy is based on the 13-period EMA (short EMA) crossing the 33-period EMA (long EMA) for entering long trades, while a 13-period EMA crossing the 25-period EMA (mid EMA) generates short trade signals. The 100-period SMA and 200-period SMA serve as additional trend indicators to provide context for the market conditions. The strategy aims to capitalize on trend reversals and momentum shifts in the market.
The strategy is designed to execute trades swiftly with an emphasis on entering positions when conditions align in real time. For long entries, the strategy initiates a buy when the 13 EMA is greater than the 33 EMA, indicating a bullish trend. For short entries, the 13 EMA crossing below the 33 EMA signals a bearish trend, prompting a short position. Importantly, the code includes built-in exit conditions for both long and short positions. Long positions are exited when the 13 EMA falls below the 33 EMA, while short positions are closed when the 13 EMA crosses above the 25 EMA.
A key feature of the strategy is the use of trailing stops for both long and short positions. This dynamic exit method adjusts the stop level as the market moves in favor of the trade, locking in profits while reducing the risk of losses. The trailing stop for long positions is based on the high price of the current bar, while the trailing stop for short positions is set using the low price, providing more flexibility in managing risk. This trailing stop mechanism helps to capture profits from favorable market moves while ensuring that positions are exited if the market moves against them.
This strategy works best on the daily timeframe and is optimized for major cryptocurrency pairs. The daily chart allows for the EMAs to provide more reliable signals, as the strategy is designed to capture broader trends rather than short-term market fluctuations. Using it on major crypto pairs increases its effectiveness as these assets tend to have strong and sustained trends, providing better opportunities for the strategy to perform well.
D.J. XAU 1MIN. SCALPING - London SessionThis is a scalping strategy designed for XAU (Gold) on a 1-minute timeframe, optimized for the London trading session (02:00 - 09:00 UTC). It uses a combination of EMA crossovers, an adaptive EMA filter, and Chandelier Exit for dynamic stop-loss management.
Key Components
EMA Crossover System
Short EMA (12) & Long EMA (26) determine trend direction.
A bullish crossover (Short EMA > Long EMA) signals a long entry.
A bearish crossover (Short EMA < Long EMA) signals a short entry.
Adaptive EMA Filter (50-period)
Confirms trend strength:
Longs only if price is above the 50 EMA.
Shorts only if price is below the 50 EMA.
Chandelier Exit (CE) for Stop Management
Uses ATR (22-period, 3x multiplier) to set dynamic trailing stops.
Long trades: Exit when price closes below the CE stop.
Short trades: Exit when price closes above the CE stop.
Session-Based Filter
Trades are only taken during the London session (02:00 - 09:00 UTC).
Risk Management
Fixed Risk-Reward Ratio (configurable: 1:1, 1:1.5, 1:2, etc.).
Trailing Stop Option (adjustable points).
Swing High/Low used for initial stop-loss placement.
Visual Indicators
EMA lines (12, 26, 50) plotted on the chart.
Chandelier Exit stops (green for long, red for short).
Background highlight during the London session.
Trade signals marked with circles (green for long, red for short).
Best Suited For
Fast scalping in high-liquidity conditions.
Gold (XAU/USD) during London hours (high volatility).
Traders who prefer EMA-based trend-following with dynamic exits.
XAUUSD ATR Auto-Setup StrategyHigh-probability trading strategy for XAUUSD.
Designed to enter during the most active sessions and capture profitable moves.
Built to reduce drawdown and improve trade quality.
✅ Tested for consistent performance.
📈 Focused on profitability over quantity.
Adaptive Fibonacci Pullback System -FibonacciFluxAdaptive Fibonacci Pullback System (AFPS) - FibonacciFlux
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Original concepts by FibonacciFlux.
Abstract
The Adaptive Fibonacci Pullback System (AFPS) presents a sophisticated, institutional-grade algorithmic strategy engineered for high-probability trend pullback entries. Developed by FibonacciFlux, AFPS uniquely integrates a proprietary Multi-Fibonacci Supertrend engine (0.618, 1.618, 2.618 ratios) for harmonic volatility assessment, an Adaptive Moving Average (AMA) Channel providing dynamic market context, and a synergistic Multi-Timeframe (MTF) filter suite (RSI, MACD, Volume). This strategy transcends simple indicator combinations through its strict, multi-stage confluence validation logic. Historical simulations suggest that specific MTF filter configurations can yield exceptional performance metrics, potentially achieving Profit Factors exceeding 2.6 , indicative of institutional-level potential, while maintaining controlled risk under realistic trading parameters (managed equity risk, commission, slippage).
4 hourly MTF filtering
1. Introduction: Elevating Pullback Trading with Adaptive Confluence
Traditional pullback strategies often struggle with noise, false signals, and adapting to changing market dynamics. AFPS addresses these challenges by introducing a novel framework grounded in Fibonacci principles and adaptive logic. Instead of relying on static levels or single confirmations, AFPS seeks high-probability pullback entries within established trends by validating signals through a rigorous confluence of:
Harmonic Volatility Context: Understanding the trend's stability and potential turning points using the unique Multi-Fibonacci Supertrend.
Adaptive Market Structure: Assessing the prevailing trend regime via the AMA Channel.
Multi-Dimensional Confirmation: Filtering signals with lower-timeframe Momentum (RSI), Trend Alignment (MACD), and Market Conviction (Volume) using the MTF suite.
The objective is to achieve superior signal quality and adaptability, moving beyond conventional pullback methodologies.
2. Core Methodology: Synergistic Integration
AFPS's effectiveness stems from the engineered synergy between its core components:
2.1. Multi-Fibonacci Supertrend Engine: Utilizes specific Fibonacci ratios (0.618, 1.618, 2.618) applied to ATR, creating a multi-layered volatility envelope potentially resonant with market harmonics. The averaged and EMA-smoothed result (`smoothed_supertrend`) provides a robust, dynamic trend baseline and context filter.
// Key Components: Multi-Fibonacci Supertrend & Smoothing
average_supertrend = (supertrend1 + supertrend2 + supertrend3) / 3
smoothed_supertrend = ta.ema(average_supertrend, st_smooth_length)
2.2. Adaptive Moving Average (AMA) Channel: Provides dynamic market context. The `ama_midline` serves as a key filter in the entry logic, confirming the broader trend bias relative to adaptive price action. Extended Fibonacci levels derived from the channel width offer potential dynamic S/R zones.
// Key Component: AMA Midline
ama_midline = (ama_high_band + ama_low_band) / 2
2.3. Multi-Timeframe (MTF) Filter Suite: An optional but powerful validation layer (RSI, MACD, Volume) assessed on a lower timeframe. Acts as a **validation cascade** – signals must pass all enabled filters simultaneously.
2.4. High-Confluence Entry Logic: The core innovation. A pullback entry requires a specific sequence and validation:
Price interaction with `average_supertrend` and recovery above/below `smoothed_supertrend`.
Price confirmation relative to the `ama_midline`.
Simultaneous validation by all enabled MTF filters.
// Simplified Long Entry Logic Example (incorporates key elements)
long_entry_condition = enable_long_positions and
(low < average_supertrend and close > smoothed_supertrend) and // Pullback & Recovery
(close > ama_midline and close > ama_midline) and // AMA Confirmation
(rsi_filter_long_ok and macd_filter_long_ok and volume_filter_ok) // MTF Validation
This strict, multi-stage confluence significantly elevates signal quality compared to simpler pullback approaches.
1hourly filtering
3. Realistic Implementation and Performance Potential
AFPS is designed for practical application, incorporating realistic defaults and highlighting performance potential with crucial context:
3.1. Realistic Default Strategy Settings:
The script includes responsible default parameters:
strategy('Adaptive Fibonacci Pullback System - FibonacciFlux', shorttitle = "AFPS", ...,
initial_capital = 10000, // Accessible capital
default_qty_type = strategy.percent_of_equity, // Equity-based risk
default_qty_value = 4, // Default 4% equity risk per initial trade
commission_type = strategy.commission.percent,
commission_value = 0.03, // Realistic commission
slippage = 2, // Realistic slippage
pyramiding = 2 // Limited pyramiding allowed
)
Note: The default 4% risk (`default_qty_value = 4`) requires careful user assessment and adjustment based on individual risk tolerance.
3.2. Historical Performance Insights & Institutional Potential:
Backtesting provides insights into historical behavior under specific conditions (always specify Asset/Timeframe/Dates when sharing results):
Default Performance Example: With defaults, historical tests might show characteristics like Overall PF ~1.38, Max DD ~1.16%, with potential Long/Short performance variance (e.g., Long PF 1.6+, Short PF < 1).
Optimized MTF Filter Performance: Crucially, historical simulations demonstrate that meticulous configuration of the MTF filters (particularly RSI and potentially others depending on market) can significantly enhance performance. Under specific, optimized MTF filter settings combined with appropriate risk management (e.g., 7.5% risk), historical tests have indicated the potential to achieve **Profit Factors exceeding 2.6**, alongside controlled drawdowns (e.g., ~1.32%). This level of performance, if consistently achievable (which requires ongoing adaptation), aligns with metrics often sought in institutional trading environments.
Disclaimer Reminder: These results are strictly historical simulations. Past performance does not guarantee future results. Achieving high performance requires careful parameter tuning, adaptation to changing markets, and robust risk management.
3.3. Emphasizing Risk Management:
Effective use of AFPS mandates active risk management. Utilize the built-in Stop Loss, Take Profit, and Trailing Stop features. The `pyramiding = 2` setting requires particularly diligent oversight. Do not rely solely on default settings.
4. Conclusion: Advancing Trend Pullback Strategies
The Adaptive Fibonacci Pullback System (AFPS) offers a sophisticated, theoretically grounded, and highly adaptable framework for identifying and executing high-probability trend pullback trades. Its unique blend of Fibonacci resonance, adaptive context, and multi-dimensional MTF filtering represents a significant advancement over conventional methods. While requiring thoughtful implementation and risk management, AFPS provides discerning traders with a powerful tool potentially capable of achieving institutional-level performance characteristics under optimized conditions.
Acknowledgments
Developed by FibonacciFlux. Inspired by principles of Fibonacci analysis, adaptive averaging, and multi-timeframe confirmation techniques explored within the trading community.
Disclaimer
Trading involves substantial risk. AFPS is an analytical tool, not a guarantee of profit. Past performance is not indicative of future results. Market conditions change. Users are solely responsible for their decisions and risk management. Thorough testing is essential. Deploy at your own considered risk.
ICT V2 Scalping Bot - Nasdaq & DAX✅ Fibonacci zone logic
✅ RSI filter
✅ Engulfing candle confirmation
✅ Session restriction (Euro/US)
✅ Wyckoff logic placeholder
✅ HTF trend via moving averages
✅ ATR-based stop-loss and 2.5x TP
✅ Signal plotting on chart
Gaussian Channel Strategy v2.0Gaussian Channel Strategy
A mean-reversion trading system using standard deviation channels
Strategy Logic:
Plots Gaussian channels (moving average ± standard deviation multiples)
Enters long when price crosses below lower channel boundary
Enters short when price crosses above upper channel boundary
Optional EMA filter to align with trend direction
Risk Management:
Configurable stop loss (fixed percentage or ATR-based)
Trailing stop option
Dynamic take profit based on risk-reward ratio
Customization Options:
Adjustable channel length (default 20 periods)
Standard deviation multiplier (default 2.0)
EMA filter length (default 200)
ATR period for volatility measurement (default 14)
Visual Features:
Displays Gaussian channel boundaries
Shows EMA trend line
Marks entry/exit points
Plots current stop loss and take profit levels
Usage Notes:
Works on any timeframe
Includes basic alert functionality
Designed for instruments with mean-reverting tendencies
Alpha Beast – Max Performance ModeTest strategy.
This strategy was created as a test, but shows good results in the 1-day chart.
Not So Simple Donchian v1Not So Simple Donchian 3.0: Advanced trading system based on Donchian channels with intelligent risk management.
Complete strategy for automated trading with direct integration to 3Commas. Ideal for traders of all levels looking for a robust system, backtested and optimized for multiple pairs and timeframes.
KEY FEATURES:
- Based on powerful and effective Donchian channels to identify entry and exit points
- Configurable EMA filter with "yellow zone" detection to avoid trading during indecision periods
- Enhanced stop loss system with fixed percentage capital protection
- Pattern recognition including Donchian Base and Hammer patterns
- 16 preconfigured presets (or configure your own parameters)
- Dynamic drawdown protection with automatic risk reduction after consecutive losses
- Signal cooldown periods to prevent overtrading after wins/losses
- Independent capital allocation for long and short positions
- Configurable profit currency selection for both long and short positions
3COMMAS INTEGRATION:
Seamless integration with 3Commas through a simplified alert system that allows:
- Independent configuration of order currency for long/short positions
- Customizable capital allocation for different position types
- Clear visualization of signals through markers on the chart
- Alert messages with complete operation parameters
RISK MANAGEMENT:
- Different capital allocation for long vs short positions
- Limit maximum risk per trade with fixed percentage stop loss
- Intra-candle stop loss verification for immediate reaction
- Dynamic trailing stop system with updates based on R:R levels
- Automatic drawdown protection that reduces risk after losses
- Signal cooldown periods after wins or losses to avoid emotional trading
ADVANCED TECHNICAL FEATURES:
- Yellow EMA state detection to avoid trading during indecision periods
- Improved stop loss management for short positions
- Separate profit currency options for long and short positions
- Signal prevention during market indecision
ANALYSIS AND BACKTESTING:
This strategy has been extensively tested with years of data across multiple pairs and timeframes.
Version 3.0 incorporates all improvements and corrections based on extensive testing:
- Implementation of signal cooling periods to prevent overtrading
- Advanced EMA yellow state detection to avoid trading during uncertain market conditions
- Independent capital and profit currency management
- Enhanced visualization with status tables and dynamic labels
Includes complete user interface with trade visualization, trend lines, and informative labels.
NOTE: As with any strategy, perform your own backtesting before implementing with real capital.
EZLIN-Tabish-Short-Trade-only-4APR25-8PMonly for educationonly for education only for education only for education only for education
BTC Swing Strategy (v1.1 Core Edition)This strategy is a trend-following and volatility-breakout swing trading system optimized for the BTCUSDT 1-hour timeframe.
【 Overview 】
Market: BTCUSDT (Cryptocurrency - Bitcoin)
Timeframe: 1-hour (optimized; no guarantee for other timeframes)
Strategy Type: Trend-following + Volatility Breakout
Trade Direction: Long (Buy) only
【 Entry Logic 】
Confirm trend direction with EMA (20/50/100) and 4H EMA alignment
Filter strong trends using ADX and Volume
Detect "quiet accumulation phases" with ATR, VWAP, and candlestick patterns
Capture momentum with MACD crossovers
Avoid overbought conditions using RSI filters
【 Exit & Risk Management 】
Detect exit signals via MACD bearish crossovers
Use ATR-based dynamic trailing stops to lock in profits
Automatically tighten the trailing stop during high volatility (ATR spikes)
Automatically adjust position size based on risk (default 0.3% of account equity per trade)
Default settings use an ATR ×1.8 stop-loss and ATR ×0.8 trailing stop, maintaining max drawdown within approximately 1.7%.
【 Features 】
Maximum drawdown: within 1.7% (backtested from 2013–2025)
Win rate: approx. 43.6%
Payoff ratio: 2.62
Profit factor: 2.02
Fully automated logic — no manual decision-making required
Compatible with TradingView Alerts + Webhook for auto-trading (e.g., via 3Commas)
【 Important Notes 】
This strategy is based on historical data and does not guarantee future performance.
Due to the volatile nature of cryptocurrency markets, unexpected losses may occur.
Please use at your own discretion.
【 Access Information 】
This is an Invite-only script.
If you are interested, please visit:
→ btcstrategy.base.shop
Swing Trade Strategy - Long Entry (100% Equity)Long swing trade on Daily chart using Elder's indicators
