Tags: ATRSMASTDHLC3

The “Double Vegas Channel Volatility-Adjusted SuperTrend Quantitative Trading Strategy” is an advanced quantitative trading system that combines two Vegas Channel Volatility-Adjusted SuperTrend indicators with different parameter settings. It aims to more accurately capture market trends and generate trades that align with the overall market direction. The strategy integrates volatility adjustments and leverages the width of the Vegas Channel to optimize the SuperTrend calculations, resulting in a dynamic and responsive trading system. Additionally, the strategy incorporates customizable take-profit and stop-loss levels, providing a robust framework for risk management.

The strategy begins by calculating the Vegas Channel, which is derived from the simple moving average (SMA) and standard deviation (STD) of the closing prices over a specified window length. This channel helps measure market volatility and forms the basis for adjusting the SuperTrend indicator. Next, the Average True Range (ATR) and the adjusted multiplier are used to determine the upper and lower thresholds of the SuperTrend. The market trend is determined by comparing the closing prices with the SuperTrend thresholds. Trade signals are generated only when both SuperTrend indicators align in the same market direction.

The main advantage of the “Double Vegas Channel Volatility-Adjusted SuperTrend Quantitative Trading Strategy” lies in its ability to dynamically adjust the SuperTrend indicator to adapt to changing market conditions. By incorporating the width of the Vegas Channel, the strategy can better respond to market volatility, improving the accuracy of trend identification. Moreover, using two SuperTrend indicators with different parameter settings provides a more comprehensive view of the market, helping to confirm trends and filter out false signals. The customizable take-profit and stop-loss levels further enhance the risk management capabilities of the strategy.

Although the strategy aims to improve the accuracy of trend identification, there are still some risks involved. Firstly, the strategy may generate false trading signals during periods of extremely high volatility or unclear market direction. Secondly, overly frequent trading can lead to high transaction costs, affecting the overall performance of the strategy. To mitigate these risks, traders can consider optimizing the strategy parameters, such as adjusting the ATR periods, Vegas Channel window lengths, and SuperTrend multipliers to suit specific market conditions. Additionally, setting appropriate take-profit and stop-loss levels is crucial to control potential losses.

The “Double Vegas Channel Volatility-Adjusted SuperTrend Quantitative Trading Strategy” can be optimized in several ways. One potential optimization direction is to incorporate additional technical indicators, such as the Relative Strength Index (RSI) or Moving Average Convergence Divergence (MACD), to enhance the reliability of trend confirmation. Another optimization direction is to introduce adaptive mechanisms that dynamically adjust the strategy parameters based on market conditions. This can be achieved using machine learning algorithms or rule-based approaches. Furthermore, optimizing the holding periods and take-profit/stop-loss levels can also improve the overall performance of the strategy.

In summary, the “Double Vegas Channel Volatility-Adjusted SuperTrend Quantitative Trading Strategy” is a powerful trading system that improves trend identification accuracy by integrating volatility adjustments and leveraging the width of the Vegas Channel. The strategy employs two SuperTrend indicators with different parameter settings to provide a more comprehensive market perspective. While the strategy shows great potential, its inherent risks should be approached with caution. By optimizing strategy parameters, incorporating additional technical indicators, and implementing adaptive mechanisms, the performance of the strategy can be further enhanced.

/*backtest start: 2024-05-01 00:00:00 end: 2024-05-31 23:59:59 period: 3h basePeriod: 15m exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ // This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // © PresentTrading // The "Double Vegas SuperTrend Enhanced" strategy uses two SuperTrend indicators with different ATR and Vegas Channel settings // to identify market trends and generate trades. Trades are executed only when both SuperTrends align in the same direction. // The strategy includes configurable take-profit and stop-loss levels, and plots the SuperTrend levels on the chart. //@version=5 strategy("Double Vegas SuperTrend Enhanced - Strategy [presentTrading]", shorttitle="Double Vegas SuperTrend Enhanced - Strategy [presentTrading]", overlay=true, overlay = false, precision=3, commission_value= 0.1, commission_type=strategy.commission.percent, slippage= 1, currency=currency.USD, default_qty_type = strategy.percent_of_equity, default_qty_value = 10, initial_capital= 10000) // Input settings allow the user to customize the strategy's parameters. tradeDirectionChoice = input.string(title="Trade Direction", defval="Both", options=["Long", "Short", "Both"]) // Option to select the trading direction // Settings for the first Vegas SuperTrend atrPeriod1 = input(10, "ATR Period for SuperTrend 1") // Length of the ATR for volatility measurement vegasWindow1 = input(100, "Vegas Window Length 1") // Length of the moving average for the Vegas Channel superTrendMultiplier1 = input(5, "SuperTrend Multiplier Base 1") // Base multiplier for the SuperTrend calculation volatilityAdjustment1 = input.float(5, "Volatility Adjustment Factor 1") // Factor to adjust the SuperTrend sensitivity to the Vegas Channel width // Settings for the second Vegas SuperTrend atrPeriod2 = input(5, "ATR Period for SuperTrend 2") // Length of the ATR for volatility measurement vegasWindow2 = input(200, "Vegas Window Length 2") // Length of the moving average for the Vegas Channel superTrendMultiplier2 = input(7, "SuperTrend Multiplier Base 2") // Base multiplier for the SuperTrend calculation volatilityAdjustment2 = input.float(7, "Volatility Adjustment Factor 2") // Factor to adjust the SuperTrend sensitivity to the Vegas Channel width // Settings for Hold Days and TPSL Conditions useHoldDays = input.bool(true, title="Use Hold Days") holdDays = input.int(5, title="Hold Days", minval=1, maxval=60, step=1) TPSLCondition = input.string("None", "TPSL Condition", options=["TP", "SL", "Both", "None"]) takeProfitPerc = input(30.0, title="Take Profit (%)") stopLossPerc = input(20.0, title="Stop Loss (%)") // Calculate the first Vegas Channel using a simple moving average and standard deviation. vegasMovingAverage1 = ta.sma(close, vegasWindow1) vegasChannelStdDev1 = ta.stdev(close, vegasWindow1) vegasChannelUpper1 = vegasMovingAverage1 + vegasChannelStdDev1 vegasChannelLower1 = vegasMovingAverage1 - vegasChannelStdDev1 // Adjust the first SuperTrend multiplier based on the width of the Vegas Channel. channelVolatilityWidth1 = vegasChannelUpper1 - vegasChannelLower1 adjustedMultiplier1 = superTrendMultiplier1 + volatilityAdjustment1 * (channelVolatilityWidth1 / vegasMovingAverage1) // Calculate the first SuperTrend indicator values. averageTrueRange1 = ta.atr(atrPeriod1) superTrendUpper1 = hlc3 - (adjustedMultiplier1 * averageTrueRange1) superTrendLower1 = hlc3 + (adjustedMultiplier1 * averageTrueRange1) var float superTrendPrevUpper1 = na var float superTrendPrevLower1 = na var int marketTrend1 = 1 // Update SuperTrend values and determine the current trend direction for the first SuperTrend. superTrendPrevUpper1 := nz(superTrendPrevUpper1[1], superTrendUpper1) superTrendPrevLower1 := nz(superTrendPrevLower1[1], superTrendLower1) marketTrend1 := close > superTrendPrevLower1 ? 1 : close < superTrendPrevUpper1 ? -1 : nz(marketTrend1[1], 1) superTrendUpper1 := marketTrend1 == 1 ? math.max(superTrendUpper1, superTrendPrevUpper1) : superTrendUpper1 superTrendLower1 := marketTrend1 == -1 ? math.min(superTrendLower1, superTrendPrevLower1) : superTrendLower1 superTrendPrevUpper1 := superTrendUpper1 superTrendPrevLower1 := superTrendLower1 // Calculate the second Vegas Channel using a simple moving average and standard deviation. vegasMovingAverage2 = ta.sma(close, vegasWindow2) vegasChannelStdDev2 = ta.stdev(close, vegasWindow2) vegasChannelUpper2 = vegasMovingAverage2 + vegasChannelStdDev2 vegasChannelLower2 = vegasMovingAverage2 - vegasChannelStdDev2 // Adjust the second SuperTrend multiplier based on the width of the Vegas Channel. channelVolatilityWidth2 = vegasChannelUpper2 - vegasChannelLower2 adjustedMultiplier2 = superTrendMultiplier2 + volatilityAdjustment2 * (channelVolatilityWidth2 / vegasMovingAverage2) // Calculate the second SuperTrend indicator values. averageTrueRange2 = ta.atr(atrPeriod2) superTrendUpper2 = hlc3 - (adjustedMultiplier2 * averageTrueRange2) superTrendLower2 = hlc3 + (adjustedMultiplier2 * averageTrueRange2) var float superTrendPrevUpper2 = na var float superTrendPrevLower2 = na var int marketTrend2 = 1 // Update SuperTrend values and determine the current trend direction for the second SuperTrend. superTrendPrevUpper2 := nz(superTrendPrevUpper2[1], superTrendUpper2) superTrendPrevLower2 := nz(superTrendPrevLower2[1], superTrendLower2) marketTrend2 := close > superTrendPrevLower2 ? 1 : close < superTrendPrevUpper2 ? -1 : nz(marketTrend2[1], 1) superTrendUpper2 := marketTrend2 == 1 ? math.max(superTrendUpper2, superTrendPrevUpper2) : superTrendUpper2 superTrendLower2 := marketTrend2 == -1 ? math.min(superTrendLower2, superTrendPrevLower2) : superTrendLower2 superTrendPrevUpper2 := superTrendUpper2 superTrendPrevLower2 := superTrendLower2 // Enhanced Visualization // Plot the SuperTrend and Vegas Channel for visual analysis for both lengths. plot(marketTrend1 == 1 ? superTrendUpper1 : na, "SuperTrend Upper 1", color=color.green, linewidth=2) plot(marketTrend1 == -1 ? superTrendLower1 : na, "SuperTrend Lower 1", color=color.red, linewidth=2) plot(marketTrend2 == 1 ? superTrendUpper2 : na, "SuperTrend Upper 2", color=color.rgb(31, 119, 130), linewidth=2) plot(marketTrend2 == -1 ? superTrendLower2 : na, "SuperTrend Lower 2", color=color.rgb(120, 42, 26), linewidth=2) // Detect trend direction changes and plot entry/exit signals for both lengths. trendShiftToBullish1 = marketTrend1 == 1 and marketTrend1[1] == -1 trendShiftToBearish1 = marketTrend1 == -1 and marketTrend1[1] == 1 trendShiftToBullish2 = marketTrend2 == 1 and marketTrend2[1] == -1 trendShiftToBearish2 = marketTrend2 == -1 and marketTrend2[1] == 1 // Define conditions for entering long or short positions, and execute trades based on these conditions for both lengths. enterLongCondition1 = marketTrend1 == 1 enterShortCondition1 = marketTrend1 == -1 enterLongCondition2 = marketTrend2 == 1 enterShortCondition2 = marketTrend2 == -1 // Entry conditions: Both conditions must be met for a trade to be executed. enterLongCondition = enterLongCondition1 and enterLongCondition2 and not na(superTrendPrevUpper1[1]) and not na(superTrendPrevUpper2[1]) enterShortCondition = enterShortCondition1 and enterShortCondition2 and not na(superTrendPrevLower1[1]) and not na(superTrendPrevLower2[1]) // Variables to track entry times var float longEntryTime = na var float shortEntryTime = na // Variables to track whether we have recently exited a trade to prevent re-entry in the same trend var bool recentlyExitedLong = false var bool recentlyExitedShort = false // Check trade direction choice before executing trade entries. if (enterLongCondition and (tradeDirectionChoice == "Long" or tradeDirectionChoice == "Both")) if (strategy.position_size < 0) strategy.close("Short Position") strategy.entry("Long Position", strategy.long) longEntryTime := time recentlyExitedLong := false recentlyExitedShort := false if (enterShortCondition and (tradeDirectionChoice == "Short" or tradeDirectionChoice == "Both")) if (strategy.position_size > 0) strategy.close("Long Position") strategy.entry("Short Position", strategy.short) shortEntryTime := time recentlyExitedShort := false recentlyExitedLong := false // Exit conditions: Either condition being met will trigger an exit. exitLongCondition = marketTrend1 == -1 or marketTrend2 == -1 exitShortCondition = marketTrend1 == 1 or marketTrend2 == 1 // Close positions based on exit conditions or hold days. if (useHoldDays and not na(longEntryTime) and (time >= longEntryTime + holdDays * 86400000) and strategy.position_size > 0) strategy.close("Long Position") longEntryTime := na recentlyExitedLong := true if (useHoldDays and not na(shortEntryTime) and (time >= shortEntryTime + holdDays * 86400000) and strategy.position_size < 0) strategy.close("Short Position") shortEntryTime := na recentlyExitedShort := true if (not useHoldDays and exitLongCondition and strategy.position_size > 0) strategy.close("Long Position") longEntryTime := na recentlyExitedLong := true if (not useHoldDays and exitShortCondition and strategy.position_size < 0) strategy.close("Short Position") shortEntryTime := na recentlyExitedShort := true // Reset recently exited flags on trend change to allow re-entry on a new trend if (trendShiftToBullish1 or trendShiftToBullish2) recentlyExitedLong := false if (trendShiftToBearish1 or trendShiftToBearish2) recentlyExitedShort := false // Conditional Profit and Loss Management if (TPSLCondition == "TP" or TPSLCondition == "Both") // Apply take profit conditions strategy.exit("TakeProfit_Long", "Long Position", limit=close * (1 + takeProfitPerc / 100)) strategy.exit("TakeProfit_Short", "Short Position", limit=close * (1 - takeProfitPerc / 100)) if (TPSLCondition == "SL" or TPSLCondition == "Both") // Apply stop loss conditions strategy.exit("StopLoss_Long", "Long Position", stop=close * (1 - stopLossPerc / 100)) strategy.exit("StopLoss_Short", "Short Position", stop=close * (1 + stopLossPerc / 100)) // Ensure that new entry signals can override the hold days condition if (enterLongCondition and (tradeDirectionChoice == "Long" or tradeDirectionChoice == "Both")) if (strategy.position_size < 0) strategy.close("Short Position") strategy.entry("Long Position", strategy.long) longEntryTime := time recentlyExitedLong := false recentlyExitedShort := false if (enterShortCondition and (tradeDirectionChoice == "Short" or tradeDirectionChoice == "Both")) if (strategy.position_size > 0) strategy.close("Long Position") strategy.entry("Short Position", strategy.short) shortEntryTime := time recentlyExitedShort := false recentlyExitedLong := false

- Follow Line Indicator
- Supertrend+4moving
- ATR Average Breakout Strategy
- AlphaTrend
- Concept Dual SuperTrend
- Intraday Scalable Volatility Trading Strategy
- Ichimoku Cloud and ATR Strategy
- Bollinger Bands Momentum Crossover Strategy
- Dynamic ATR Stop Loss and Take Profit Moving Average Crossover Strategy
- Bollinger Band ATR Trend Following Strategy

- Moving Average Crossover Strategy Based on Dual Moving Averages
- MACD and Supertrend Combination Strategy
- Buy/Sell Strategy Based on Volume & Candlestick Patterns
- SMA Trend Following Strategy with Trailing Stop-Loss and Disciplined Re-Entry
- EMA and Bollinger Bands Breakout Strategy
- CDC Action Zone Trading Bot Strategy with ATR for Take Profit and Stop Loss
- Continuous Candle Based Dynamic Grid Adaptive Moving Average with Dynamic Stop Loss Strategy
- MA Cross Strategy
- Trend-Following Trading Strategy with Momentum Filtering
- RSI and Linear Regression Channel Trading Strategy
- EMA RSI Crossover Strategy
- Moving Average Convergence Momentum Cloud Strategy
- Dual Moving Average Crossover Stop Loss and Take Profit Strategy
- TEMA Dual Moving Average Crossover Strategy
- Multi-Timeframe SMA Trend Following Strategy with Dynamic Stop Loss
- Bollinger Bands Accurate Entry And Risk Control Strategy
- Bollinger Bands + RSI + Stochastic RSI Strategy Based on Volatility and Momentum Indicators
- TURTLE-ATR Bollinger Bands Breakout Strategy
- VWAP and Super Trend Buy/Sell Strategy
- Advanced MACD Strategy with Limited Martingale