Sunil BB Blast Heikin Ashi StrategySunil BB Blast Heikin Ashi Strategy
The Sunil BB Blast Heikin Ashi Strategy is a trend-following trading strategy that combines Bollinger Bands with Heikin-Ashi candles for precise market entries and exits. It aims to capitalize on price volatility while ensuring controlled risk through dynamic stop-loss and take-profit levels based on a user-defined Risk-to-Reward Ratio (RRR).
Key Features:
Trading Window:
The strategy operates within a user-defined time window (e.g., from 09:20 to 15:00) to align with market hours or other preferred trading sessions.
Trade Direction:
Users can select between Long Only, Short Only, or Long/Short trade directions, allowing flexibility depending on market conditions.
Bollinger Bands:
Bollinger Bands are used to identify potential breakout or breakdown zones. The strategy enters trades when price breaks through the upper or lower Bollinger Band, indicating a possible trend continuation.
Heikin-Ashi Candles:
Heikin-Ashi candles help smooth price action and filter out market noise. The strategy uses these candles to confirm trend direction and improve entry accuracy.
Risk Management (Risk-to-Reward Ratio):
The strategy automatically adjusts the take-profit (TP) level and stop-loss (SL) based on the selected Risk-to-Reward Ratio (RRR). This ensures that trades are risk-managed effectively.
Automated Alerts and Webhooks:
The strategy includes automated alerts for trade entries and exits. Users can set up JSON webhooks for external execution or trading automation.
Active Position Tracking:
The strategy tracks whether there is an active position (long or short) and only exits when price hits the pre-defined SL or TP levels.
Exit Conditions:
The strategy exits positions when either the take-profit (TP) or stop-loss (SL) levels are hit, ensuring risk management is adhered to.
Default Settings:
Trading Window:
09:20-15:00
This setting confines the strategy to the specified hours, ensuring trading only occurs during active market hours.
Strategy Direction:
Default: Long/Short
This allows for both long and short trades depending on market conditions. You can select "Long Only" or "Short Only" if you prefer to trade in one direction.
Bollinger Band Length (bbLength):
Default: 19
Length of the moving average used to calculate the Bollinger Bands.
Bollinger Band Multiplier (bbMultiplier):
Default: 2.0
Multiplier used to calculate the upper and lower bands. A higher multiplier increases the width of the bands, leading to fewer but more significant trades.
Take Profit Multiplier (tpMultiplier):
Default: 2.0
Multiplier used to determine the take-profit level based on the calculated stop-loss. This ensures that the profit target aligns with the selected Risk-to-Reward Ratio.
Risk-to-Reward Ratio (RRR):
Default: 1.0
The ratio used to calculate the take-profit relative to the stop-loss. A higher RRR means larger profit targets.
Trade Automation (JSON Webhooks):
Allows for integration with external systems for automated execution:
Long Entry JSON: Customizable entry condition for long positions.
Long Exit JSON: Customizable exit condition for long positions.
Short Entry JSON: Customizable entry condition for short positions.
Short Exit JSON: Customizable exit condition for short positions.
Entry Logic:
Long Entry:
The strategy enters a long position when:
The Heikin-Ashi candle shows a bullish trend (green close > open).
The price is above the upper Bollinger Band, signaling a breakout.
The previous candle also closed higher than it opened.
Short Entry:
The strategy enters a short position when:
The Heikin-Ashi candle shows a bearish trend (red close < open).
The price is below the lower Bollinger Band, signaling a breakdown.
The previous candle also closed lower than it opened.
Exit Logic:
Take-Profit (TP):
The take-profit level is calculated as a multiple of the distance between the entry price and the stop-loss level, determined by the selected Risk-to-Reward Ratio (RRR).
Stop-Loss (SL):
The stop-loss is placed at the opposite Bollinger Band level (lower for long positions, upper for short positions).
Exit Trigger:
The strategy exits a trade when either the take-profit or stop-loss level is hit.
Plotting and Visuals:
The Heikin-Ashi candles are displayed on the chart, with green candles for uptrends and red candles for downtrends.
Bollinger Bands (upper, lower, and basis) are plotted for visual reference.
Entry points for long and short trades are marked with green and red labels below and above bars, respectively.
Strategy Alerts:
Alerts are triggered when:
A long entry condition is met.
A short entry condition is met.
A trade exits (either via take-profit or stop-loss).
These alerts can be used to trigger notifications or webhook events for automated trading systems.
Notes:
The strategy is designed for use on intraday charts but can be applied to any timeframe.
It is highly customizable, allowing for tailored risk management and trading windows.
The Sunil BB Blast Heikin Ashi Strategy combines two powerful technical analysis tools (Bollinger Bands and Heikin-Ashi candles) with strong risk management, making it suitable for both beginners and experienced traders.
Feebacks are welcome from the users.
Indicadores e estratégias
[3Commas] Alligator StrategyThe Alligator Strategy
🔷 What it does: This script implements the Alligator Strategy, a trend-following method created by Bill Williams. It uses three customizable moving averages (SMMAs or RMAs) "Jaws," "Teeth," and "Lips" to identify market trends and potential trade opportunities. Additionally, it includes built-in stop-loss and take-profit options for enhanced risk management.
🔷 Who is it for:
Trend Traders: Those who prefer trading in markets with clear directional movement.
Advanced Users: Traders who require customizable tools and dynamic risk management features.
Beginners: Accessible to those new to trading, thanks to its intuitive visual representation of trends and pre-configured settings.
Bot Users: Supports direct signal integration for bot automation, including entries, take-profits, and stop-losses.
🔷 How does it work: The Alligator Jaws, Teeth, and Lips are smoothed moving averages (SMA, EMA, RMA, or WMA) calculated based on the selected source price ( hl2 = (high+low)/2 by default). Their lengths and offsets are customizable:
Jaws: Length 21 , offset 13.
Teeth: Length 13, offset 8.
Lips: Length 8 , offset 5.
When the lines align and spread apart (e.g., Lips > Teeth > Jaws for an uptrend), the strategy identifies a trending market.
Entry Conditions:
Long Trades: Triggered when Close > Lips > Teeth > Jaws.
Short Trades: Triggered when Close < Lips < Teeth < Jaws.
🔷 Why it’s unique:
Customization: Flexible settings for moving average types and lengths to adapt to different market conditions and strategy tester configurations.
Built-in Filters: Trend filters that can reduce false signals in certain scenarios, making it more reliable for trending markets.
Take Profit and Stop Loss:
Configurable as either percentage-based or dynamic.
Stop-loss levels adjust dynamically using the Alligator lines.
Fast exit logic moves the stop-loss closer to the price when trades are in profit.
3Commas Bot Compatibility: Designed for automated trading, allowing traders to configure and execute the strategy seamlessly.
🔷 Considerations Before Using the Indicator
🔸Why the Forward Offset: By shifting the averages forward, the Alligator helps traders focus on established trends while filtering out short-term market noise.
The standard configurations of 13-8, 8-5, and 5-3 were selected based on Bill Williams’ studies of market behavior. However, these values can be adjusted to suit different market conditions:
Volatile Markets: Faster settings (e.g., 10-6, 6-4, 3-2) may provide earlier signals.
Less Volatile Markets: Slower settings (e.g., 21-13, 13-8, 8-5) can help avoid noise and reduce false signals.
🔸Best Timeframes to Use: The Alligator can be applied across all timeframes, but certain timeframes offer better reliability.
Higher Timeframes (H4, D1, W1): Ideal for identifying significant trends and for swing or position trading.
Lower Timeframes: Not recommended due to increased noise but may work for scalping with additional confirmation tools.
🔸Disadvantages of the Alligator Strategy:
Exhausted Entry Levels: High buying levels or low selling levels can lead to momentum exhaustion and potential pullbacks.
False Signals in Ranges: Consolidating markets can produce unreliable signals.
Lagging Indicator: As it is based on moving averages, it may delay reacting to sudden price changes.
🔸Advantages of the Alligator Strategy:
Trend Focused: Simplifies the identification of trending markets.
Noise Reduction: Forward shifts and smoothed averages help filter out short-term price fluctuations.
Broad Applicability: Suitable for forex, crypto, stocks, and commodities.
🔸Important Considerations:
While the Alligator Strategy provides a systematic way to analyze markets, it does not guarantee successful outcomes. Results in trading depend on multiple factors, including market conditions, trader discipline, and risk management. Past performance of the strategy does not ensure future success, and traders should always approach the market with caution.
Risk Management: Define stop-loss levels, position size, and profit targets before entering any trade. Be prepared for the possibility of losses and ensure that your approach aligns with your overall trading plan.
🔷 STRATEGY PROPERTIES
Symbol: BINANCE:BTCUSDT (Spot).
Timeframe: 1D (Daily Timeframe).
Test Period: All historical data available.
Initial Capital: 10000 USDT.
Order Size per Trade: 1% of Capital, you can use a higher value e.g. 5%, be cautious that the Max Drawdown does not exceed 10%, as it would indicate a very risky trading approach.
Commission: Binance commission 0.1%, adjust according to the exchange being used, lower numbers will generate unrealistic results. By using low values e.g. 5%, it allows us to adapt over time and check the functioning of the strategy.
Slippage: 5 ticks, for pairs with low liquidity or very large orders, this number should be increased as the order may not be filled at the desired level.
Margin for Long and Short Positions: 100%.
Indicator Settings: Default Configuration.
Alligator: Source hl2 | Calculation RMA | Jaw 21-13, Teeth 13-8, Lips 8-5.
Strategy: Long & Short.
Max Stop Loss per Trade: 10% of Trade Size.
Exit trades on opposite signal: Enable.
Alligator Stop Loss: Enable.
Alligator Fast Exit: Enable.
🔷 STRATEGY RESULTS
⚠️ Remember, past results do not guarantee future performance.
Net Profit: +355.68 USDT (+3.56%).
Total Closed Trades: 103.
Percent Profitable: 47.57%.
Profit Factor: 1.927.
Max Drawdown: -57.99 USDT (-0.56%).
Average Trade: +3.45 USDT (+3.41%).
Average # Bars in Trades: 16.
🔷 HOW TO USE
🔸Adjust the Alligator Settings:
The default values generally work well: Source hl2 | Calculation RMA | Jaw 21-13, Teeth 13-8, Lips 8-5. However, if you want to use it on timeframes smaller than 4H (4 hours), consider increasing the values to better filter market noise.
Please review the "Indicator Settings" section for configuration.
🔸Choose a Symbol that Typically Trends:
Select an asset that tends to create trends. However, the Strategy Tester results may display poor performance, making it less suitable for sending signals to bots.
🔸Add Trend Filters:
You can enable trend filters like MA and SuperTrend. By default, these are disabled as they are often unnecessary, but you can experiment with their configuration to see if they optimize the strategy's results.
Please review the "Indicator Settings" section for configuration.
🔸Enable Stop Loss Levels:
Activate Stop Loss features, such as Stop Loss % or Alligator Stop Loss. If both are enabled, the one closest to the price during the trade will be applied.
Please review the "Indicator Settings" section for configuration.
🔸Enable Take Profit Levels:
Activate Take Profit options, such as Take Profit % or Alligator Fast Exit. If both are enabled, the one that triggers first will be executed.
Please review the "Indicator Settings" section for configuration.
This is an example with the default settings and how Alligator Stop Loss and Alligator Fast Exit are activated:
In this example, we additionally enable the Take Profit at 10%. We can observe that the Alligator Stop Loss is the active one since it is closer to the price. When the price moves 10% in favor or against the trade, the position is closed. Although the Alligator Fast Exit is enabled, it does not activate because the trades are closed beforehand.
🔸Results Review:
It is important to check the Max Drawdown. This value should ideally not exceed 10% of your capital. Consider adjusting the trade size to ensure this threshold is not surpassed.
Remember to include the correct values for commission and slippage according to the symbol and exchange where you are conducting the tests. Otherwise, the results will not be realistic.
If you are satisfied with the results, you may consider automating your trades. However, it is strongly recommended to use a small amount of capital or a demo account to test proper execution before committing real funds.
🔸Create alerts to trigger the DCA Bot
Verify Messages: Ensure the message matches the one specified by the DCA Bot.
Multi-Pair Configuration: For multi-pair setups, enable the option to add the symbol in the correct format.
Signal Settings: Enable whether you want to receive long or short signals (Entry | TP | SL), copy and paste the the messages for the DCA Bots configured in 3Commas.
Alert Setup:
When creating an alert, set the condition to the indicator and choose "alert() function call only.
Enter any desired Alert Name.
Open the Notifications tab, enable Webhook URL, and paste the Webhook URL from 3Commas.
For more details, refer to the 3Commas section: "How to use TradingView Custom Signals.
Finalize Alerts: Click Create, you're done! Alerts will now be sent automatically in the correct format to 3Commas.
🔷 INDICATOR SETTINGS
🔸Alligator Settings
MA's source: Source price for Alligator moving averages.
MA's Type: Type of calculation for MA's.
Jaw and Offset: Jaw length and offset to the right.
Teeth and Offset: Teethlength and offset to the right.
Lips and Offset: Lips length and offset to the right.
🔸Alligator Style
Plot Alligator: Show Alligator Ribbon.
Plot MA's: Show Alligator MA's.
Colors: Main and Gradient Colors for Bullish Alligator, Berish Alligator, Neutral Alligator. For gradient colors it is recommended to use an opacity of 15.
🔸MA & SuperTrend Filters
MA & Plot: Activate MA Filter and Plot MA on the chart.
Long Entries: When activated, it will only execute entries if the price is above the MA
Short Entries: When activated, it will only execute entries if the price is below the MA.
Source: Source price for moving average calculations.
Length: Candles to be used by the MA calculations.
Type: Type of calculation for MA.
Timeframe: Here you can select a larger timeframe for the filter.
ST & Plot: Activate SuperTrend Filter and Plot SuperTrend on the chart.
Long Entries: When activated, it will only execute entries if the price is above the SuperTrend.
Short Entries: When activated, it will only execute entries if the price is below the SuperTrend.
Source: Source price for SuperTrend calculations.
Length: Candles to be used by the SuperTrend calculations.
Factor: ATR multiplier of the SuperTrend.
Timeframe: Here you can select a larger timeframe for the filter.
🔸Strategy Tester
Strategy: Order Type direction in which trades are executed.
Take Profit %: When activated, the entered value will be used as the Take Profit in percentage from the entry price level.
Stop Loss %: When activated, the entered value will be used as the Stop Loss in percentage from the entry price level. If Alligator Stop Loss is activated, the closest one to the price will be used.
Exit trades on opposite signal: This option closes the trade if the opposite condition is met. For instance, if we are in a long position and a sell signal is triggered, the long position will be closed, and a short position will be opened. The same applies inversely.
Alligator Stop Loss: In a long trade, the lower part of the Alligator indicator will be used as a dynamic stop loss. Similarly, in a short trade, the upper part of the indicator will be used.
Alligator Fast Exit: Its purpose is to attempt to protect movements in favor of the trade's direction. In the case of long trades, once the price and the upper part of the Alligator indicator are above the trade's entry price, the stop loss will be moved to the upper part. For short trades, once the price and the lower part of the Alligator indicator are below the trade's entry price, the stop loss will be moved to the lower part of the Alligator indicator.
Alligator Squeeze Entry: When activated, entries will only be executed if they meet the condition after a neutral zone of the Alligator indicator.
Alligator Squeeze Exit: When this option is activated, any open trades will be closed when the Alligator indicator enters a neutral mode.
Use Custom Test Period: When enabled signals only works in the selected time window. If disabled it will use all historical data available on the chart.
Test Start and End: Once the Custom Test Period is enabled, here you select the start and end date that you want to analyze.
🔸3Commas DCA Bot Signals
Check Messages: Enable the table to review the messages to be sent to the bot.
Entry | TP | SL: Enable this options to send Buy Entry, Take Profit (TP), and Stop Loss (SL) signals to 3Commas.
Deal Entry and Deal Exit : Copy and paste the message for the deal start signal and close order at Market Price of the DCA Bot you created in 3Commas. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the 3Commas bot so that it can process properly so that it executes and starts the trade.
DCA Bot Multi-Pair: You must activate it if you want to use the signals in a DCA Bot Multi-pair in the text box you must enter (using the 3Commas format) the symbol in which you are creating the alert, you can check the format of each symbol when you create the bot.
🔷 CONCLUSION
The Alligator Strategy is a valuable tool for identifying potential trends and improving decision-making. However, no trading strategy is foolproof. Careful consideration of market conditions, proper risk management, and personal trading goals are essential. Use the Alligator as part of a broader trading system, and remember that consistent learning and discipline are key to success in trading.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
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The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Multi-Band Comparison Strategy (CRYPTO)Multi-Band Comparison Strategy (CRYPTO)
Optimized for Cryptocurrency Trading
This Pine Script strategy is built from the ground up for traders who want to take advantage of cryptocurrency volatility using a confluence of advanced statistical bands. The strategy layers Bollinger Bands, Quantile Bands, and a unique Power-Law Band to map out crucial support/resistance zones. It then focuses on a Trigger Line—the lower standard deviation band of the upper quantile—to pinpoint precise entry and exit signals.
Key Features
Bollinger Band Overlay
The upper Bollinger Band visually shifts to yellow when price exceeds it, turning black otherwise. This offers a straightforward way to gauge heightened momentum or potential market slowdowns.
Quantile & Power-Law Integration
The script calculates upper and lower quantile bands to assess probabilistic price extremes.
A Power-Law Band is also available to measure historically significant return levels, providing further insight into overbought or oversold conditions in fast-moving crypto markets.
Standard Deviation Trigger
The lower standard deviation band of the upper quantile acts as the strategy’s trigger. If price consistently holds above this line, the strategy interprets it as a strong bullish signal (“green” zone). Conversely, dipping below indicates a “red” zone, signaling potential reversals or exits.
Consecutive Bar Confirmation
To reduce choppy signals, you can fine-tune the number of consecutive bars required to confirm an entry or exit. This helps filter out noise and false breaks—critical in the often-volatile crypto realm.
Adaptive for Multiple Timeframes
Whether you’re scalping on a 5-minute chart or swing trading on daily candles, the strategy’s flexible confirmation and overlay options cater to different market conditions and trading styles.
Complete Plot Customization
Easily toggle visibility of each band or line—Bollinger, Quantile, Power-Law, and more.
Built-in Simple and Exponential Moving Averages can be enabled to further contextualize market trends.
Why It Excels at Crypto
Cryptocurrencies are known for rapid price swings, and this strategy addresses exactly that by combining multiple statistical methods. The quantile-based confirmation reduces noise, while Bollinger and Power-Law bands help highlight breakout regions in trending markets. Traders have reported that it works seamlessly across various coins and tokens, adapting its triggers to each asset’s unique volatility profile.
Give it a try on your favorite cryptocurrency pairs. With advanced data handling, crisp visual cues, and adjustable confirmation logic, the Multi-Band Comparison Strategy provides a robust framework to capture profitable moves and mitigate risk in the ever-evolving crypto space.
Falcon Liquidity Grab StrategyHow to Use This Script for Commodities and Indices
Best Timeframes: Start with 15-minute charts but test on higher timeframes like 1 hour for indices.
Risk Settings: Adjust the stop_loss_points and take_profit_multiplier to match the volatility of the chosen instrument.
00 Averaging Down Backtest Strategy by RPAlawyer v21FOR EDUCATIONAL PURPOSES ONLY! THE CODE IS NOT YET FULLY DEVELOPED, BUT IT CAN PROVIDE INTERESTING DATA AND INSIGHTS IN ITS CURRENT STATE.
This strategy is an 'averaging down' backtester strategy. The goal of averaging/doubling down is to buy more of an asset at a lower price to reduce your average entry price.
This backtester code proves why you shouldn't do averaging down, but the code can be developed (and will be developed) further, and there might be settings even in its current form that prove that averaging down can be done effectively.
Different averaging down strategies exist:
- Linear/Fixed Amount: buy $1000 every time price drops 5%
- Grid Trading: Placing orders at price levels, often with increasing size, like $1000 at -5%, $2000 at -10%
- Martingale: doubling the position size with each new entry
- Reverse Martingale: decreasing position size as price falls: $4000, then $2000, then $1000
- Percentage-Based: position size based on % of remaining capital, like 10% of available funds at each level
- Dynamic/Adaptive: larger entries during high volatility, smaller during low
- Logarithmic: position sizes increase logarithmically as price drops
Unlike the above average costing strategies, it applies averaging down (I use DCA as a synonym) at a very strong trend reversal. So not at a certain predetermined percentage negative PNL % but at a trend reversal signaled by an indicator - hence it most closely resembles a dynamically moving grid DCA strategy.
Both entering the trade and averaging down assume a strong trend. The signals for trend detection are provided by an indicator that I published under the name '00 Parabolic SAR Trend Following Signals by RPAlawyer', but any indicator that generates numeric signals of 1 and -1 for buy and sell signals can be used.
The indicator must be connected to the strategy: in the strategy settings under 'External Source' you need to select '00 Parabolic SAR Trend Following Signals by RPAlawyer: Connector'. From this point, the strategy detects when the indicator generates buy and sell signals.
The strategy considers a strong trend when a buy signal appears above a very conservative ATR band, or a sell signal below the ATR band. The conservative ATR is chosen to filter ranging markets. This very conservative ATR setting has a default multiplier of 8 and length of 40. The multiplier can be increased up to 10, but there will be very few buy and sell signals at that level and DCA requirements will be very high. Trade entry and DCA occur at these strong trends. In the settings, the 'ATR Filter' setting determines the entry condition (e.g., ATR Filter multiplier of 9), and the 'DCA ATR' determines when DCA will happen (e.g., DCA ATR multiplier of 6).
The DCA levels and DCA amounts are determined as follows:
The first DCA occurs below the DCA Base Deviation% level (see settings, default 3%) which acts as a threshold. The thick green line indicates the long position avg price, and the thin red line below the green line indicates the 3% DCA threshold for long positions. The thick red line indicates the short position avg price, and the thin red line above the thick red line indicates the short position 3% DCA threshold. DCA size multiplier defines the DCA amount invested.
If the loss exceeds 3% AND a buy signal arrives below the lower ATR band for longs, or a sell signal arrives above the upper ATR band for shorts, then the first DCA will be executed. So the first DCA won't happen at 3%, rather 3% is a threshold where the additional condition is that the price must close above or below the ATR band (let's say the first DCA occured at 8%) – this is why the code resembles a dynamic grid strategy, where the grid moves such that alongside the first 3% threshold, a strong trend must also appear for DCA. At this point, the thick green/red line moves because the avg price is modified as a result of the DCA, and the thin red line indicating the next DCA level also moves. The next DCA level is determined by the first DCA level, meaning modified avg price plus an additional +8% + (3% * the Step Scale Multiplier in the settings). This next DCA level will be indicated by the modified thin red line, and the price must break through this level and again break through the ATR band for the second DCA to occur.
Since all this wasn't complicated enough, and I was always obsessed by the idea that when we're sitting in an underwater position for days, doing DCA and waiting for the price to correct, we can actually enter a short position on the other side, on which we can realize profit (if the broker allows taking hedge positions, Binance allows this in Europe).
This opposite position in this strategy can open from the point AFTER THE FIRST DCA OF THE BASE POSITION OCCURS. This base position first DCA actually indicates that the price has already moved against us significantly so time to earn some money on the other side. Breaking through the ATR band is also a condition for entry here, so the hedge position entry is not automatic, and the condition for further DCA is breaking through the DCA Base Deviation (default 3%) and breaking through the ATR band. So for the 'hedge' or rather opposite position, the entry and further DCA conditions are the same as for the base position. The hedge position avg price is indicated by a thick black line and the Next Hedge DCA Level is indicated by a thin black line.
The TPs are indicated by green labels for base positions and red labels for hedge positions.
No SL built into the strategy at this point but you are free to do your coding.
Summary data can be found in the upper right corner.
The fantastic trend reversal indicator Machine learning: Lorentzian Classification by jdehorty can be used as an external indicator, choose 'backtest stream' for the external source. The ATR Band multiplicators need to be reduced to 5-6 when using Lorentz.
The code can be further developed in several aspects, and as I write this, I already have a few ideas 😊
Optimized Engulfing StrategyOptimized Engulfing Strategy
The Optimized Engulfing Strategy is a trend-following system designed to capitalize on bullish and bearish engulfing patterns in the market. It uses a combination of price action, trend direction, and volatility-based risk management to execute high-probability trades.
Key Components:
Bullish Engulfing Pattern:
A bullish engulfing candle is identified when:
The current candle closes above its open (bullish).
The previous candle closes below its open (bearish).
The current candle's close is higher than the previous candle's open.
The current candle's open is lower than the previous candle's close.
This pattern signals potential bullish momentum.
Bearish Engulfing Pattern:
A bearish engulfing candle is identified when:
The current candle closes below its open (bearish).
The previous candle closes above its open (bullish).
The current candle's close is lower than the previous candle's open.
The current candle's open is higher than the previous candle's close.
This pattern signals potential bearish momentum.
Trend Confirmation:
Trades are only taken in the direction of the trend:
Buy: When the 50-period SMA (simple moving average) is above the 200-period SMA, indicating an uptrend.
Sell: When the 50-period SMA is below the 200-period SMA, indicating a downtrend.
Risk Management:
Stop Loss: Placed below the low of the engulfing candle (for buys) or above the high (for sells), with an additional buffer based on the ATR (Average True Range) multiplied by a user-defined factor (default: 1.5).
Take Profit: Calculated using a fixed risk-to-reward ratio (default: 1:2), ensuring a potential reward that is double the risk.
Session Filtering:
Trades can be limited to specific trading hours using a customizable session filter (default: 24 hours).
Trade Execution:
Separate logic is implemented for buy and sell trades, allowing independent toggling of long or short positions via user inputs.
Visualization:
Bullish and bearish engulfing candles are highlighted on the chart for clarity.
The ATR value is displayed in the top-right corner of the chart for reference.
How It Works:
Identify a bullish or bearish engulfing pattern.
Confirm the direction of the trend using the 50 SMA and 200 SMA.
Ensure the market is within the allowed session filter (e.g., London or New York sessions).
Enter a trade if all conditions are met:
Long trades for bullish engulfing patterns in an uptrend.
Short trades for bearish engulfing patterns in a downtrend.
Manage the trade using a stop loss and take profit based on ATR and the risk-reward ratio.
Bitcoin 1H-15M Breakout StrategyKey Features
1H and 15M Timeframes:
The script uses the 1-hour timeframe for the range and 15-minute timeframe for breakout conditions.
request.security is used to fetch the higher timeframe data.
Risk Management:
Variables entry_price, sl_price, and tp_price are declared explicitly as float with na initialization to handle dynamic assignment.
Stop-loss and take-profit levels are calculated based on the specified Risk-Reward Ratio (RRR) and buffer (in pips).
Trade Logic:
Long trade triggered when the 15-minute candle closes above the 1-hour high.
Short trade triggered when the 15-minute candle closes below the 1-hour low.
Visualization:
The range_high and range_low (previous 1-hour high and low) are plotted on the chart using dashed lines.
Debugging:
Enabling the show_debug input displays labels showing stop-loss and take-profit values for easier troubleshooting.
Smart DCA Invest LiteEnglish description:
📊 Smart DCA Invest – Features Overview
✅ Automated DCA strategy with dynamic profit targets, optimized risk management.
⚙️ Functionality:
🕒 Time Interval Settings
• 📅 Start Date and Time: The strategy activates only after the specified start time.
• 🔄 Auto Restart: Automatically restarts the strategy after a position is closed.
💵 Investment Amounts
• 🟢 Initial Investment Amount: The amount invested when the first position is opened.
• 🔄 Recurring Investment Amount: The amount invested periodically for subsequent purchases.
📊 Purchase Frequency
• ⏱ Interval Between Purchases: Specifies the minimum number of candles between two purchases to avoid overly frequent position expansions.
🛡️ Risk Management
• 📉 Loss Limit: The strategy halts additional purchases if the price does not drop below a predefined loss level, optimizing the average cost reduction.
• 🎯 Take Profit: A predefined profit target percentage, triggering position closure upon reaching it.
📈 Dynamic Take Profit (TP) Settings
• ⏳ TP Increase Frequency: The interval in days for dynamic TP growth.
• 📊 TP Growth Rate: The percentage by which the TP level increases at the end of each interval.
• ⚙️ Enable Dynamic TP: Allows the TP level to increase dynamically over time based on holding duration.
• 🧠 Smart Invest: Accumulates skipped purchases above the average entry or loss limit price and invests them when the price drops below the loss limit.
🎨 Visual Representation
• 📏 Average Price Line: Displays the average entry price in yellow.
• 🛑 Stop Limit Line: Displays the loss limit in red.
• ✅ Take Profit Line: Displays the dynamically updated profit target in green.
🎨 Visual Elements
• 📏 Average Price Line: Visualizes the average cost on the chart.
• 🛑 Stop Limit Line: Visualizes the loss limit level.
• ✅ Take Profit Line: Displays the TP level graphically.
• 📊 Statistics Table: Detailed data summary presented in a table at the end of the strategy.
📊 Statistics Table
• 📈 Average Price: The average entry price of the current position.
• 🛑 Stop Limit: The loss limit value.
• ✅ Take Profit: The profit target value.
• 📦 Position Size: The size of the current position.
• 💵 Max Invested Amount: The highest amount invested.
• ⏳ Longest DCA Period: The longest duration a DCA position was open.
• 💼 Current Investment: The amount currently invested.
• 🔄 Multiplier: Purchase multiplier value.
• 📊 Dynamically Adjusted TP %: The current dynamic Take Profit percentage.
- Recommended for retesting
Hungarian description:
📊 Smart DCA Invest – Funkciók Leírása
✅ Automatizált DCA stratégia dinamikus profitcélokkal, optimalizált kockázatkezeléssel.
⚙️ Működés:
🕒 Időintervallum Beállítások
• 📅 Kezdési dátum és idő: A stratégia csak a meghatározott kezdési időpont után aktiválódik.
• ⏳ Befejezési dátum és idő: A stratégia a meghatározott időpontig működik.
• 🔄 Automatikus újraindítás: Pozíciózárás után a stratégia automatikusan újraindulhat.
💵 Befektetési Összegek
• 🟢 Első befektetési összeg: Az első pozíció nyitásakor befektetett összeg.
• 🔄 Napi vásárlási összeg: Ismételt periódusonkénti vásárlások összege.
📊 Vásárlási Gyakoriság
• ⏱ Intervallum két vásárlás között: Meghatározza a minimális gyertya intervallumot két vásárlás között, elkerülve a túl gyakori pozícióbővítéseket.
🛡️ Kockázatkezelés
• 📉 Loss Limit: Ha az ár nem csökken egy meghatározott veszteségi szint alá, a stratégia nem vásárol tovább, hogy hatékonyabban csökkentse az átlagárat.
• 🎯 Take Profit: Előre meghatározott profitcél százalékos értéke, amely elérésekor a pozíció lezárul.
📈 Dinamikus Take Profit (TP) Beállítások
• ⏳ TP növelési gyakoriság: A dinamikus TP növekedésének időszaka napokban.
• 📊 TP növekedés mértéke: A TP szint százalékos növekedése az intervallum végén.
• ⚙️ Dinamikus TP engedélyezése: A TP szint dinamikusan növekszik a tartási idő függvényében.
• 🧠 Smart Invest: Kihagyott vásárlások felhalmozása (átlagos bekerülési vagy „Loss limit” feletti árfolyamnál), amelyek a „Loss limit” árszint alatt befektetésre kerülnek.
🎨 Vizuális Megjelenítés
• 📏 Átlagár vonal: Sárga színnel jelzi az átlagárat.
• 🛑 Stop Limit vonal: Piros színnel jelzi a veszteségi korlátot.
• ✅ Take Profit vonal: Zöld színnel jelzi a dinamikusan frissülő profitcélt.
🎨 Vizuális Elemek
• 📏 Átlagár vonal: Az átlagár megjelenítése a grafikonon.
• 🛑 Stop Limit vonal: A veszteségkorlátozási szint megjelenítése.
• ✅ Take Profit vonal: A Take Profit szint grafikai megjelenítése.
• 📊 Statisztikai táblázat megjelenítése: A stratégia végén részletes adatok jelennek meg egy táblázatban.
📊 Statisztikai Táblázat
• 📈 Átlagár: Az aktuális pozíció átlagos bekerülési ára.
• 🛑 Stop Limit: A veszteségkorlátozási szint értéke.
• ✅ Take Profit: A profitcél értéke.
• 📦 Pozícióméret: Az aktuális pozíció nagysága.
• 💵 Maximális befektetett összeg: A legnagyobb befektetett érték.
• ⏳ Leghosszabb DCA időszak: A leghosszabb időtartam, amíg egy DCA pozíció nyitva maradt.
• 💼 Aktuális befektetés: Az aktuálisan befektetett összeg.
• 🔄 Multiplikátor: Vásárlási szorzó érték.
• 📊 Dinamikusan beállított TP %: Az aktuálisan érvényes Take Profit százalékos értéke.
Big Candle Identifier with RSI Divergence and Advanced Stops1. Strategy Objective
The main goal of this strategy is to:
Identify significant price momentum (big candles).
Enter trades at opportune moments based on market signals (candlestick patterns and RSI divergence).
Limit initial risk through a fixed stop loss.
Maximize profits by using a trailing stop that activates only after the trade moves a specified distance in the profitable direction.
2. Components of the Strategy
A. Big Candle Identification
The strategy identifies big candles as indicators of strong momentum.
A big candle is defined as:
The body (absolute difference between close and open) of the current candle (body0) is larger than the bodies of the last five candles.
The candle is:
Bullish Big Candle: If close > open.
Bearish Big Candle: If open > close.
Purpose: Big candles signal potential continuation or reversal of trends, serving as the primary entry trigger.
B. RSI Divergence
Relative Strength Index (RSI): A momentum oscillator used to detect overbought/oversold conditions and divergence.
Fast RSI: A 5-period RSI, which is more sensitive to short-term price movements.
Slow RSI: A 14-period RSI, which smoothens fluctuations over a longer timeframe.
Divergence: The difference between the fast and slow RSIs.
Positive divergence (divergence > 0): Bullish momentum.
Negative divergence (divergence < 0): Bearish momentum.
Visualization: The divergence is plotted on the chart, helping traders confirm momentum shifts.
C. Stop Loss
Initial Stop Loss:
When entering a trade, an immediate stop loss of 200 points is applied.
This stop loss ensures the maximum risk is capped at a predefined level.
Implementation:
Long Trades: Stop loss is set below the entry price at low - 200 points.
Short Trades: Stop loss is set above the entry price at high + 200 points.
Purpose:
Prevents significant losses if the price moves against the trade immediately after entry.
D. Trailing Stop
The trailing stop is a dynamic risk management tool that adjusts with price movements to lock in profits. Here’s how it works:
Activation Condition:
The trailing stop only starts trailing when the trade moves 200 ticks (profit) in the right direction:
Long Position: close - entry_price >= 200 ticks.
Short Position: entry_price - close >= 200 ticks.
Trailing Logic:
Once activated, the trailing stop:
For Long Positions: Trails behind the price by 150 ticks (trail_stop = close - 150 ticks).
For Short Positions: Trails above the price by 150 ticks (trail_stop = close + 150 ticks).
Exit Condition:
The trade exits automatically if the price touches the trailing stop level.
Purpose:
Ensures profits are locked in as the trade progresses while still allowing room for price fluctuations.
E. Trade Entry Logic
Long Entry:
Triggered when a bullish big candle is identified.
Stop loss is set at low - 200 points.
Short Entry:
Triggered when a bearish big candle is identified.
Stop loss is set at high + 200 points.
F. Trade Exit Logic
Trailing Stop: Automatically exits the trade if the price touches the trailing stop level.
Fixed Stop Loss: Exits the trade if the price hits the predefined stop loss level.
G. 21 EMA
The strategy includes a 21-period Exponential Moving Average (EMA), which acts as a trend filter.
EMA helps visualize the overall market direction:
Price above EMA: Indicates an uptrend.
Price below EMA: Indicates a downtrend.
H. Visualization
Big Candle Identification:
The open and close prices of big candles are plotted for easy reference.
Trailing Stop:
Plotted on the chart to visualize its progression during the trade.
Green Line: Indicates the trailing stop for long positions.
Red Line: Indicates the trailing stop for short positions.
RSI Divergence:
Positive divergence is shown in green.
Negative divergence is shown in red.
3. Key Parameters
trail_start_ticks: The number of ticks required before the trailing stop activates (default: 200 ticks).
trail_distance_ticks: The distance between the trailing stop and price once the trailing stop starts (default: 150 ticks).
initial_stop_loss_points: The fixed stop loss in points applied at entry (default: 200 points).
tick_size: Automatically calculates the minimum tick size for the trading instrument.
4. Workflow of the Strategy
Step 1: Entry Signal
The strategy identifies a big candle (bullish or bearish).
If conditions are met, a trade is entered with a fixed stop loss.
Step 2: Initial Risk Management
The trade starts with an initial stop loss of 200 points.
Step 3: Trailing Stop Activation
If the trade moves 200 ticks in the profitable direction:
The trailing stop is activated and follows the price at a distance of 150 ticks.
Step 4: Exit the Trade
The trade is exited if:
The price hits the trailing stop.
The price hits the initial stop loss.
5. Advantages of the Strategy
Risk Management:
The fixed stop loss ensures that losses are capped.
The trailing stop locks in profits after the trade becomes profitable.
Momentum-Based Entries:
The strategy uses big candles as entry triggers, which often indicate strong price momentum.
Divergence Confirmation:
RSI divergence helps validate momentum and avoid false signals.
Dynamic Profit Protection:
The trailing stop adjusts dynamically, allowing the trade to capture larger moves while protecting gains.
6. Ideal Market Conditions
This strategy performs best in:
Trending Markets:
Big candles and momentum signals are more effective in capturing directional moves.
High Volatility:
Larger price swings improve the probability of reaching the trailing stop activation level (200 ticks).
Systematic Risk Aggregation ModelThe “Systematic Risk Aggregation Model” is a quantitative trading strategy implemented in Pine Script™ designed to assess and visualize market risk by aggregating multiple financial risk factors. This model uses a multi-dimensional scoring approach to quantify systemic risk, incorporating volatility, drawdowns, put/call ratios, tail risk, volume spikes, and the Sharpe ratio. It derives a composite risk score, which is dynamically smoothed and plotted alongside adaptive Bollinger Bands to identify trading opportunities. The strategy’s theoretical framework aligns with modern portfolio theory and risk management literature (Markowitz, 1952; Taleb, 2007).
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Key Components of the Model
1. Volatility as a Risk Proxy
The model calculates the standard deviation of the closing price over a specified period (volatility_length) to quantify market uncertainty. Volatility is normalized to a score between 0 and 100, using its historical minimum and maximum values.
Reference: Volatility has long been regarded as a critical measure of financial risk and uncertainty in capital markets (Hull, 2008).
2. Drawdown Assessment
The drawdown metric captures the relative distance of the current price from the highest price over the specified period (drawdown_length). This is converted into a normalized score to reflect the magnitude of recent losses.
Reference: Drawdown is a key metric in risk management, often used to measure potential downside risk in portfolios (Maginn et al., 2007).
3. Put/Call Ratio as a Sentiment Indicator
The strategy integrates the put/call ratio, sourced from an external symbol, to assess market sentiment. High values often indicate bearish sentiment, while low values suggest bullish sentiment (Whaley, 2000). The score is normalized similarly to other metrics.
4. Tail Risk via Modified Z-Score
Tail risk is approximated using the modified Z-score, which measures the deviation of the closing price from its moving average relative to its standard deviation. This approach captures extreme price movements and potential “black swan” events.
Reference: Taleb (2007) discusses the importance of considering tail risks in financial systems.
5. Volume Spikes as a Proxy for Market Activity
A volume spike is defined as the ratio of current volume to its moving average. This ratio is normalized into a score, reflecting unusual trading activity, which may signal market turning points.
Reference: Volume analysis is a foundational tool in technical analysis and is often linked to price momentum (Murphy, 1999).
6. Sharpe Ratio for Risk-Adjusted Returns
The Sharpe ratio measures the risk-adjusted return of the asset, using the mean log return divided by its standard deviation over the same period. This ratio is transformed into a score, reflecting the attractiveness of returns relative to risk.
Reference: Sharpe (1966) introduced the Sharpe ratio as a standard measure of portfolio performance.
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Composite Risk Score
The composite risk score is calculated as a weighted average of the individual risk factors:
• Volatility: 30%
• Drawdown: 20%
• Put/Call Ratio: 20%
• Tail Risk (Z-Score): 15%
• Volume Spike: 10%
• Sharpe Ratio: 5%
This aggregation captures the multi-dimensional nature of systemic risk and provides a unified measure of market conditions.
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Dynamic Bands with Bollinger Bands
The composite risk score is smoothed using a moving average and bounded by Bollinger Bands (basis ± 2 standard deviations). These bands provide dynamic thresholds for identifying overbought and oversold market conditions:
• Upper Band: Signals overbought conditions, where risk is elevated.
• Lower Band: Indicates oversold conditions, where risk subsides.
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Trading Strategy
The strategy operates on the following rules:
1. Entry Condition: Enter a long position when the risk score crosses above the upper Bollinger Band, indicating elevated market activity.
2. Exit Condition: Close the long position when the risk score drops below the lower Bollinger Band, signaling a reduction in risk.
These conditions are consistent with momentum-based strategies and adaptive risk control.
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Conclusion
This script exemplifies a systematic approach to risk aggregation, leveraging multiple dimensions of financial risk to create a robust trading strategy. By incorporating well-established risk metrics and sentiment indicators, the model offers a comprehensive view of market dynamics. Its adaptive framework makes it versatile for various market conditions, aligning with contemporary advancements in quantitative finance.
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References
1. Hull, J. C. (2008). Options, Futures, and Other Derivatives. Pearson Education.
2. Maginn, J. L., Tuttle, D. L., McLeavey, D. W., & Pinto, J. E. (2007). Managing Investment Portfolios: A Dynamic Process. Wiley.
3. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.
4. Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
5. Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business, 39(1), 119–138.
6. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
7. Whaley, R. E. (2000). The Investor Fear Gauge. The Journal of Portfolio Management, 26(3), 12–17.
FON60DK by leventsahThe strategy generates buy and sell signals using the Tillson T3 and TOTT (Twin Optimized Trend Tracker) indicators. Additionally, the Williams %R indicator is used to filter the signals. Below is an explanation of the main components of the code:
1. Input Parameters:
Tillson T3 and TOTT parameters: Separate parameters are defined for both buy (AL) and sell (SAT) conditions. These parameters control the sensitivity and behavior of the indicators.
Williams %R period: The period for the Williams %R indicator is set to determine overbought and oversold levels.
2. Tillson T3 Calculation:
The Tillson T3 indicator is a smoothed moving average that uses an exponential moving average (EMA) with additional smoothing. The formula calculates a weighted average of multiple EMAs to produce a smoother line.
The t3 function computes the Tillson T3 value based on the close price and the input parameters.
3. TOTT Calculation (Twin Optimized Trend Tracker):
The TOTT indicator is a trend-following tool that adjusts its sensitivity based on market conditions. It uses a combination of price action and a volatility coefficient to determine trend direction.
The Var_Func function calculates the TOTT value, which is then used to derive the OTT (Optimized Trend Tracker) levels for both buy and sell conditions.
4. Williams %R Calculation:
Williams %R is a momentum oscillator that measures overbought and oversold levels. It is calculated using the highest high and lowest low over a specified period.
5. Buy and Sell Conditions:
Buy Condition: A buy signal is generated when the Tillson T3 value crosses above the TOTT upper band (OTTup) and the Williams %R is above -20 (indicating an oversold condition).
Sell Condition: A sell signal is generated when the Tillson T3 value crosses below the TOTT lower band (OTTdnS) and the Williams %R is above -70 (used to close long positions).
6. Strategy Execution:
The strategy.entry function is used to open a long position when the buy condition is met.
The strategy.close function is used to close the long position when the sell condition is met.
7. Visualization:
The bars on the chart are colored green when a long position is open.
The Tillson T3, TOTT upper band (OTTup), and TOTT lower band (OTTdn) are plotted on the chart for both buy and sell conditions.
8. Plots:
The Tillson T3 values for buy and sell conditions are plotted in blue.
The TOTT upper and lower bands are plotted in green and red, respectively, for both buy and sell conditions.
Summary:
This strategy combines trend-following indicators (Tillson T3 and TOTT) with a momentum oscillator (Williams %R) to generate buy and sell signals. The use of separate parameters for buy and sell conditions allows for fine-tuning the strategy based on market behavior. The visual elements, such as colored bars and plotted indicators, help traders quickly identify signals and trends on the chart.
Outside Bar Strategy % (Alessio)Outside Bar Strategy %
This strategy is based on identifying Outside Bars, which occur when the current bar's high is higher than the previous bar's high and its low is lower than the previous bar's low. The strategy enters trades in the direction of the Outside Bar, offering a powerful way to capture price moves following a strong price expansion.
Key Features:
Long and Short Entries: The strategy enters a Long trade when the Outside Bar closes bullish (current close > open), and a Short trade when the Outside Bar closes bearish (current close < open).
Customizable Entry Levels: The entry point is calculated based on a customizable percentage of the Outside Bar's range, allowing flexibility for traders to fine-tune their entries at 50% or 70% of the bar's range.
Stop Loss (SL) and Take Profit (TP):
Stop Loss (SL) is automatically placed at the Outside Bar's low for Long trades and at its high for Short trades.
Take Profit (TP) is calculated as a percentage of the Outside Bar's range, with customizable settings for take-profit levels.
Visual Indicators:
Entry, Stop Loss, and Take Profit levels are plotted as lines on the chart, with customizable colors and widths for easy identification.
Labels are placed on the chart to indicate whether the trade is Long or Short, positioned above or below the Outside Bar's candlestick.
Alerts: Users can enable alerts to receive notifications when a trade is triggered, including details such as entry points and stop loss levels.
Strategy Parameters:
Entry Percentage: Set the entry level as a percentage of the Outside Bar's range (e.g., 50%, 70%).
Take Profit Percentage: Customize the Take Profit level as a percentage of the Outside Bar's range.
Customizable Colors and Line Widths: Adjust the colors and thickness of the entry, stop loss, and take profit lines to fit your preferences.
Alerts: Enable alerts to be notified when a trade is executed or when the entry level is reached.
This strategy is ideal for traders who want to capitalize on significant price moves after a breakout, with clear risk management through Stop Loss and Take Profit levels. The customizable features make it suitable for various market conditions and trading styles.
Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures).
Key Components of the DVDM:
1. Implied Volatility (IV):
The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000).
2. Historical Volatility (HV):
The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012).
3. Volatility Spread:
The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior.
4. Dynamic Thresholds:
Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004).
Trading Logic:
1. Long Entry:
A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums.
2. Short Entry:
A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty.
3. Exit Conditions:
Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence.
Advantages of the DVDM:
1. Adaptability:
Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments.
2. Quantitative Precision:
The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making.
3. Market Versatility:
The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance.
Scientific Relevance:
The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982).
References:
1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397.
2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education.
4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539.
5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
Fibonacci Trend - Aynet1. Inputs
lookbackPeriod: Defines the number of bars to consider for calculating swing highs and lows. Default is 20.
fibLevel1 to fibLevel5: Fibonacci retracement levels to calculate price levels (23.6%, 38.2%, 50%, 61.8%, 78.6%).
useTime: Enables or disables time-based Fibonacci projections.
riskPercent: Defines the percentage of risk for trading purposes (currently not used in calculations).
2. Functions
isSwingHigh(index): Identifies a swing high at the given index, where the high of that candle is higher than both its previous and subsequent candles.
isSwingLow(index): Identifies a swing low at the given index, where the low of that candle is lower than both its previous and subsequent candles.
3. Variables
swingHigh and swingLow: Store the most recent swing high and swing low prices.
swingHighTime and swingLowTime: Store the timestamps of the swing high and swing low.
fib1 to fib5: Fibonacci levels based on the difference between swingHigh and swingLow.
4. Swing Point Detection
The script checks if the last bar is a swing high or swing low using the isSwingHigh() and isSwingLow() functions.
If a swing high is detected:
The high price is stored in swingHigh.
The timestamp of the swing high is stored in swingHighTime.
If a swing low is detected:
The low price is stored in swingLow.
The timestamp of the swing low is stored in swingLowTime.
5. Fibonacci Levels Calculation
If both swingHigh and swingLow are defined, the script calculates the Fibonacci retracement levels (fib1 to fib5) based on the price difference (priceDiff = swingHigh - swingLow).
6. Plotting Fibonacci Levels
Fibonacci levels (fib1 to fib5) are plotted as horizontal lines using the line.new() function.
Labels (e.g., "23.6%") are added near the lines to indicate the level.
Lines and labels are color-coded:
23.6% → Blue
38.2% → Green
50.0% → Yellow
61.8% → Orange
78.6% → Red
7. Filling Between Fibonacci Levels
The plot() function creates lines for each Fibonacci level.
The fill() function is used to fill the space between two levels with semi-transparent colors:
Blue → Between fib1 and fib2
Green → Between fib2 and fib3
Yellow → Between fib3 and fib4
Orange → Between fib4 and fib5
8. Time-Based Fibonacci Projections
If useTime is enabled:
The time difference (timeDiff) between the swing high and swing low is calculated.
Fibonacci time projections are added based on multiples of 23.6%.
If the current time reaches a projected time, a label (e.g., "T1", "T2") is displayed near the high price.
9. Trading Logic
Two placeholder variables are defined for trading logic:
longCondition: Tracks whether a condition for a long trade is met (currently not implemented).
shortCondition: Tracks whether a condition for a short trade is met (currently not implemented).
These variables can be extended to define entry/exit signals based on Fibonacci levels.
How It Works
Detect Swing Points: It identifies recent swing high and swing low points on the chart.
Calculate Fibonacci Levels: Based on the swing points, it computes retracement levels.
Visualize Levels: Plots the levels on the chart with labels and fills between them.
Time Projections: Optionally calculates time-based projections for future price movements.
Trading Opportunities: The framework provides tools for detecting potential reversal or breakout zones using Fibonacci levels.
KB Dinamik Grid Bot V8 TrailingThis Pine Script code aims to create a "Dynamic Grid Trading Bot" and perform automatic trading between price ranges. Let's break it down into sections to better understand its functions:
1. Settings and User Inputs
The user can specify the following parameters for the bot:
Lower and Upper Price Limit: Determines the price range where the grid levels are defined.
Number of Grid Lines: Defines how many levels the grid will consist of.
Transaction Amount: Specifies the trading volume for each trading transaction.
Start Date: The date when the bot will start trading.
Price Step (priceStep): Specifies specific steps after the comma to adjust the grid levels more precisely.
Trailing: A feature that activates dynamic selling by following price movements.
2. Calculating Grid Levels
Grid levels: Divides the specified price range into user-defined levels and rounds each level with priceStep.
Lines and labels: Lines and labels are created to visually represent grid levels.
3. Buying and Selling Logic
Buying Transaction: When the price approaches a lower grid level (as much as the offset) and the position is empty, a purchase is made.
Trailing Selling: If Trailing is active, a sale is made when the price passes the specified "trailing step" level.
Normal Selling: If Trailing is not active, a sale is made when the price approaches an upper grid level.
4. Profit and Statistics Tracking
The bot tracks the profit-loss status per transaction and in total.
The number of purchases and sales and net profit information are calculated from the start date.
5. Table Display
The bot places statistical data in a table:
Number of purchases and sales.
Starting date.
Total number of transactions.
Net profit.
Amount of open positions.
6. Drawing and Tracking
Each price movement is updated and the color of the grid lines (green or red) is changed depending on the price's status relative to the level.
This code is a strategy that aims to make a profit by continuously buying and selling in the event of price fluctuations within a range. The "Trailing" feature allows you to keep your profits when the price moves upwards. Net profit, open positions and other statistics are displayed in the table.
TDGS Dynamic Grid Trading Strategy [CoinFxPro]Advanced Dynamic Grid Trading Strategy
Logic and Working Principle:
This strategy uses a dynamic grid system to support both long and short trades. Grid trading aims to capitalize on price fluctuations within a predefined range by executing buy and sell orders systematically. The system calculates grid levels based on a base price and dynamically trades within these levels.
Grid Levels:
Grid levels are calculated based on the initial price and the user-defined grid spacing percentage.
Long Mode: Buys when the price decreases and sells when the price increases.
Short Mode: Sells when the price increases and buys when the price decreases.
Grid Updates:
Grid levels are recalculated based on the market price when the price moves by a user-defined update percentage.
For example;
In Long mode, when the price shows an upward trend, that is, when it rises by the Grid Update Percentage specified by the user, Grid levels are recreated and trades are made according to the new grid levels. While the price and grid levels are updated according to the new price, the Stop level is also updated upwards and the stop is followed with the TrailingStop logic.
In short mode, the same system operates with reverse logic. In other words, as prices decrease downwards, the grids are updated downwards when the Grid update percentage determined by the user decreases. The stop level is also updated accordingly.
The difference of the strategy from other Gridbots is that the grid levels are automatically updated and the levels are recreated with the price percentage difference determined by the user. Old levels can be tracked on the chart.
As the price updates, the self-updating grid levels are updated upwards in long mode and downwards in short mode.
The number of buying lots and selling lots are separated, allowing both trading within the position and the opportunity to collect lots and increase the position.
When trading with the grid trading logic, when buying and selling between grids, there is no repeated purchase at the same level unless there is a sale at the upper grid level. In this way, each level will be traded within itself.
For example, in a long condition, when the price is going up, after deducting the selling lot from the buying lot at each level, the remaining lots will be collected while the price is going up and an opportunity will be provided from the price rise.
Different preferences have been added to the profit taking conditions, allowing the robot to continue or stop after profit taking, if desired.
The system, which acts entirely according to user parameters, constantly updates itself as long as it moves in the direction determined by itself, and in these conditions, transactions are carried out according to profit or stop conditions.
Parameters:
Grid Parameters:
Settings such as buy lot size, sell lot size, grid count, and grid spacing percentage allow flexibility and customization.
Risk Management:
Stop loss (%) and take profit (%) levels help limit potential losses and secure profits at predefined thresholds.
Objective:
The goal of this strategy is to systematically capitalize on market price fluctuations through automated grid trading. This method is particularly effective in volatile markets where the price oscillates within a specific range.
The strategy works with a complete algorithm logic, and in appropriate instruments (especially instruments with depth and transaction volume should be preferred), buying and selling transactions are made according to the parameters determined at the beginning, and if the conditions go beyond the conditions, the stop is made, and when the profit taking conditions are met, it takes profit and prices according to the determined value. When it is updated, the values are updated again and the parameter works algorithmically.
Risk Management Recommendations:
Initial Capital: Grid trading involves frequent transactions, so sufficient initial capital is essential.
Stop Loss: Always set stop loss levels to prevent significant losses.
Grid Count and Spacing: A higher number of grids provides more trading opportunities but using grids that are too close may increase transaction costs due to small price movements.
First of all, it is important for risk management that you choose instruments that have depth and high transaction volume.
Strategy results may differ as a result of the parameters entered. Therefore, before trading in your real account, it is recommended that you start real transactions after backtesting with different parameters.
If you are stuck on something, you can mention it in the comments.
Autonomous 5-Minute RobotKey Components of the Strategy:
Trend Detection:
A 50-period simple moving average (SMA) is used to define the market trend. If the current close is above the SMA, the market is considered to be in an uptrend (bullish), and if it's below, it's considered a downtrend (bearish).
The strategy also looks at the trend over the last 30 minutes (6 candles in a 5-minute chart). The strategy compares the previous close with the current close to detect an uptrend or downtrend.
Volume Analysis:
The strategy calculates buyVolume and sellVolume based on price movement within each candle.
The condition for entering a long position is when the market is in an uptrend, and the buy volume is greater than the sell volume.
The condition for entering a short position is when the market is in a downtrend, and the sell volume is greater than the buy volume.
Trade Execution:
The strategy enters a long position when the trend is up and the buy volume is higher than the sell volume.
The strategy enters a short position when the trend is down and the sell volume is higher than the buy volume.
Positions are closed based on stop-loss and take-profit conditions.
Stop-loss is set at 3% below the entry price.
Take-profit is set at 29% above the entry price.
Exit Conditions:
Long trades will be closed if the price falls 3% below the entry price or rises 29% above the entry price.
Short trades will be closed if the price rises 3% above the entry price or falls 29% below the entry price.
Visuals:
The SMA (50-period) is plotted on the chart to show the trend.
Buy and sell signals are marked with labels on the chart for easy identification.
With this being said this algo is still being worked on to be autonomous
Analyze the Market Direction: Determine whether the market is in an uptrend or downtrend over the past 30 minutes (using the last 6 candles in a 5-minute chart).
Use Trend Indicators and Volume: Implement trend-following indicators like moving averages or the SMA/EMA crossover and consider volume to decide when to enter or exit a trade.
Enter and Exit Trades: The robot will enter long positions when the trend is up and short positions when the trend is down. Additionally, it will close positions based on volume signals and price action (e.g., volume spikes, price reversals).
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role.
1. Hammer:
• Typically occurs after a downtrend.
• Signals a potential trend reversal to the upside.
• A Hammer has:
• A small body (close and open are close to each other).
• A long lower shadow, at least twice as long as the body.
• No or a very short upper shadow.
2. Hanging Man:
• Typically occurs after an uptrend.
• Signals a potential reversal to the downside.
• A Hanging Man has:
• A small body, similar to the Hammer.
• A long lower shadow, at least twice as long as the body.
• A small or no upper shadow.
These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control.
Relevance of Chart Patterns in Forex
In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants.
Scientific Literature on the Relevance of Candlestick Patterns
1. Behavioral Finance and Candlestick Patterns:
Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends.
2. Statistical Validation of Chart Patterns:
Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well.
3. Market Efficiency and Candlestick Patterns:
The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading.
Testing the Strategy in Forex Using the Provided Script
The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes.
1. Testing on Different Timeframes:
• The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness.
2. Exit Strategies:
• The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior.
Conclusion
The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness.
For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades.
References:
• Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
• Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
Bullish Reversal Bar Strategy [Skyrexio]Overview
Bullish Reversal Bar Strategy leverages the combination of candlestick pattern Bullish Reversal Bar (description in Methodology and Justification of Methodology), Williams Alligator indicator and Williams Fractals to create the high probability setups. Candlestick pattern is used for the entering into trade, while the combination of Williams Alligator and Fractals is used for the trend approximation as close condition. Strategy uses only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator or the candlestick pattern invalidation to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Trend Trade Filter: strategy uses Alligator and Fractal combination as high probability trend filter.
Methodology
The strategy opens long trade when the following price met the conditions:
1.Current candle's high shall be below the Williams Alligator's lines (Jaw, Lips, Teeth)(all details in "Justification of Methodology" paragraph)
2.Price shall create the candlestick pattern "Bullish Reversal Bar". Optionally if MFI and AO filters are enabled current candle shall have the decreasing AO and at least one of three recent bars shall have the squat state on the MFI (all details in "Justification of Methodology" paragraph)
3.If price breaks through the high of the candle marked as the "Bullish Reversal Bar" the long trade is open at the price one tick above the candle's high
4.Initial stop loss is placed at the Bullish Reversal Bar's candle's low
5.If price hit the Bullish Reversal Bar's low before hitting the entry price potential trade is cancelled
6.If trade is active and initial stop loss has not been hit, trade is closed when the combination of Alligator and Williams Fractals shall consider current trend change from upward to downward.
Strategy settings
In the inputs window user can setup strategy setting:
Enable MFI (if true trades are filtered using Market Facilitation Index (MFI) condition all details in "Justification of Methodology" paragraph), by default = false)
Enable AO (if true trades are filtered using Awesome Oscillator (AO) condition all details in "Justification of Methodology" paragraph), by default = false)
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. The first and key concept is the Bullish Reversal Bar candlestick pattern. This is just the single bar pattern. The rules are simple:
Candle shall be closed in it's upper half
High of this candle shall be below all three Alligator's lines (Jaw, Lips, Teeth)
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
How we can use all these indicators in this strategy? This strategy is a counter trend one. Candle's high shall be below all Alligator's lines. During this market stage the bullish reversal bar candlestick pattern shall be printed. This bar during the downtrend is a high probability setup for the potential reversal to the upside: bulls were able to close the price in the upper half of a candle. The breaking of its high is a high probability signal that trend change is confirmed and script opens long trade. If market continues going down and break down the bullish reversal bar's low potential trend change has been invalidated and strategy close long trade.
If market really reversed and started moving to the upside strategy waits for the trend change form the downtrend to the uptrend according to approximation of Alligator and Fractals combination. If this change happens strategy close the trade. This approach helps to stay in the long trade while the uptrend continuation is likely and close it if there is a high probability of the uptrend finish.
Optionally users can enable MFI and AO filters. First of all, let's briefly explain what are these two indicators. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
This indicator is filtering signals in the following way: if current AO bar is decreasing this candle can be interpreted as a bullish reversal bar. This logic is applicable because initially this strategy is a trend reversal, it is searching for the high probability setup against the current trend. Decreasing AO is the additional high probability filter of a downtrend.
Let's briefly look what is MFI. The Market Facilitation Index (MFI) is a technical indicator that measures the price movement per unit of volume, helping traders gauge the efficiency of price movement in relation to trading volume. Here's how you can calculate it:
MFI = (High−Low)/Volume
MFI can be used in combination with volume, so we can divide 4 states. Bill Williams introduced these to help traders interpret the interaction between volume and price movement. Here’s a quick summary:
Green Window (Increased MFI & Increased Volume): Indicates strong momentum with both price and volume increasing. Often a sign of trend continuation, as both buying and selling interest are rising.
Fake Window (Increased MFI & Decreased Volume): Shows that price is moving but with lower volume, suggesting weak support for the trend. This can signal a potential end of the current trend.
Squat Window (Decreased MFI & Increased Volume): Shows high volume but little price movement, indicating a tug-of-war between buyers and sellers. This often precedes a breakout as the pressure builds.
Fade Window (Decreased MFI & Decreased Volume): Indicates a lack of interest from both buyers and sellers, leading to lower momentum. This typically happens in range-bound markets and may signal consolidation before a new move.
For our purposes we are interested in squat bars. This is the sign that volume cannot move the price easily. This type of bar increases the probability of trend reversal. In this indicator we added to enable the MFI filter of reversal bars. If potential reversal bar or two preceding bars have squat state this bar can be interpret as a reversal one.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -5.29%
Maximum Single Profit: +29.99%
Net Profit: +5472.66 USDT (+54.73%)
Total Trades: 103 (33.98% win rate)
Profit Factor: 1.634
Maximum Accumulated Loss: 1231.15 USDT (-8.32%)
Average Profit per Trade: 53.13 USDT (+0.94%)
Average Trade Duration: 76 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h ETH/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
MEERU-72-FX-ALGO"Unlock Your Trading Potential with MEERU-72-FX-ALGO! 🚀💹
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MEERU-72-FX-ALGO"Unlock Your Trading Potential with MEERU-72-FX-ALGO! 🚀💹
Are you ready to take your trading to the next level? Introducing *MEERU-72-FX-ALGO* — a powerful, automated trading algorithm designed for success. Whether you're a beginner or an experienced trader, MEERU-72-FX-ALGO is built to optimize your trades, increase accuracy, and maximize profits. Say goodbye to emotional trading and hello to consistent, data-driven results.
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Dynamic Support and Resistance Pivot Strategy The Dynamic Support and Resistance Pivot Strategy is a flexible and adaptive tool designed to identify short-term support and resistance levels using the concept of price pivots.
### Key Elements of the Strategy
1. Pivot points as support and resistance levels
Pivots are significant turning points on the price chart, often marking local highs and lows where the price has reversed direction. A pivot high occurs when the price forms a local peak, while a pivot low occurs when the price forms a local trough. When a new pivot high is formed, it creates a resistance level. Conversely, when a new pivot low is formed, it creates a support level.
The strategy continuously updates these levels as new pivots are detected, ensuring they remain relevant to the current market conditions. By identifying these price levels, the strategy dynamically adjusts to market conditions, allowing it to adapt to both trending and ranging markets, since it has a long target and can perform reversal operations.
2. Entry Criteria
- Buy (Long): A long position is triggered when the price is near the support level and then crosses it from below to above. This suggests that the price has found support and may start moving upwards.
- Sell (Short): A short position is triggered when the price is near the resistance level and then crosses it from above to below. This indicates that the price may be reversing and moving downward.
3. Support/Resistance distance (%)
- This parameter establishes a percentage range around the identified support and resistance level. For example, if the Support Resistance Distance is 0.4% (default), the closing price must be within a range of 0.4% above support or below the resistance to be considered "close" and trigger a trade.
4. Exit criteria
- Take profit = 27 %
- Stop loss = 10 %
- Reversal if a new entry point is identified in the opposite direction
5. No Repainting
- The Dynamic Support and Resistance Pivot Strategy is not subject to repainting.
6. Position Sizing by Equity and risk management
- This strategy has a default configuration to operate with 35% of the equity. The stop loss is set to 10% from the entry price. This way, the strategy is putting at risk about 10% of 35% of equity, that is, around 3.5% of equity for each trade. The percentage of equity and stop loss can be adjusted by the user according to their risk management.
7. Backtest results
- This strategy was subjected to backtest and operations in replay mode on **1000000MOGUSDT.P**, with the inclusion of transaction fees at 0.12% and slipagge of 5 ticks, and the past results have shown consistent profitability. Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
8. Chart Visualization
- Support and resistance levels are displayed as green (support) and red (resistance) lines.
- Pivot prices are displayed as green (pivot low) and red (pivot high) labels.
In this image above, the Support/Resistance distance (%) parameter was set to 0.8.
9. Default Configuration
Chart Timeframe: 1h
Pivot Lengh: 2
Support/Resistance distance (%): 0.4*
Stop Loss: 10 %
Take Profit: 27 %
* This parameter can alternatively be set to 0.8.
10. Alternative Configuration
Chart Timeframe: 20 min
Pivot Lengh: 4
Support/Resistance distance (%): 0.1
Stop Loss: 10 %
Take Profit: 25 %
BYBIT:1000000MOGUSDT.P
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior
The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean.
Theoretical Framework
Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend.
Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration.
Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002).
The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers.
Strategy Construction
Indicator Inputs:
ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations.
Bollinger Bands:
Lower Band: Calculated with a 18-period length and a standard deviation of 1.7.
Upper Band: Calculated with a 21-period length and a standard deviation of 2.1.
Calculations:
ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage.
Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it.
Trading Conditions:
Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement.
Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum.
Visual Indicators:
ROC Plot: The ROC is plotted as a line to visualize the momentum direction.
Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC.
Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones.
Strategy Performance Considerations
The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions.
The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions.
Empirical Evidence and Relevance
The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior.
Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context.
Conclusion
The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches.
References
Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw-Hill Professional.
Chan, L. K. C. (1996). Momentum, Mean Reversion, and the Cross-Section of Stock Returns. Journal of Finance, 51(5), 1681-1713.
De Bondt, W. F., & Thaler, R. H. (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), 793-805.
Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
