The dual timeframe volatility spread trading strategy judges the overbought/oversold status of the market by calculating the spread between RSI indicators of two different time cycles to implement low risk trend trading.
The core indicators of this strategy are shortTermXtrender and longTermXtrender. shortTermXtrender calculates the RSI spread on the short-term timeframe, and longTermXtrender calculates the RSI spread on the long-term timeframe.
The short-term timeframe adopts the price difference between 7-day EMA and 4-day LMA to calculate RSI, and then the price difference with 50 constitutes shortTermXtrender. The long-term timeframe adopts the price difference between RSI of 4-day EMA and 50 to constitute longTermXtrender.
When shortTermXtrender crosses above 0, go long; when longTermXtrender crosses above 0, also go long. The stop loss principle after going long is to stop loss when shortTermXtrender crosses below 0; when longTermXtrender crosses below 0, stop loss too.
In this way, by judging on dual timeframes, more false breakouts can be filtered out.
The biggest advantage of this strategy is that the trend judgment is accurate. The combination of dual timeframes can effectively filter out noise and lock in the target trend direction. This provides a guarantee for low-risk trend tracking trading.
In addition, the strategy provides room for parameter optimization. Users can adjust parameters such as SMA cycle and RSI parameters according to different varieties and time cycles to optimize strategy results.
The main risk of this strategy is the wrong judgment of long and short. In oscillating markets, it is easy to generate wrong signals. If the position is still opened at this time, there will be the risk of loss.
In addition, improper parameter settings can also lead to poor results. If the time cycle parameter is set too short, the probability of misjudgment will increase; if the time cycle parameter is set too long, the opportunity for the trend will be missed. This requires users to test and optimize parameters for different markets.
The strategy can be optimized in the following aspects:
Increase profit taking mechanism. Currently there is no profit taking setting in the strategy. Profit can be taken in time after reaching the target profit.
Increase position management. Positions can be dynamically adjusted based on capital size, volatility and other indicators.
Test parameter settings for different varieties. Users can test the optimal parameter combination by backtesting different timeframes such as daily and 60 minutes.
Increase machine learning assisted judgment. Models can be trained to determine market conditions and dynamically adjust strategy parameters to improve win rate.
The dual timeframe volatility spread trading strategy achieves efficient trend capturing by constructing dual timeframe indicators. The strategy has large optimization space. Users can optimize through parameter adjustment, profit taking management, position management, etc. to obtain better strategy results. This strategy is suitable for users with some trading experience.
/*backtest start: 2024-01-18 00:00:00 end: 2024-02-17 00:00:00 period: 2h basePeriod: 15m exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}] */ //@version=4 //study("MavXtrender") strategy("MavXtrender") ShortTermSMA = input(7) ShortTermLMA = input(4) ShortTermRSI = input(2) LongTermMA = input(4) LongTermRSI = input(2) UseFactors = input(true) TradeShortTerm = input(true) TradeLongTerm = input(true) count = TradeShortTerm == true ? 1 : 0 count := TradeLongTerm == true ? count + 1 : count // set position size Amount = strategy.equity / (close * count) ShortTermLMA := UseFactors == true ? round(ShortTermSMA * ShortTermLMA) : ShortTermLMA ShortTermRSI := UseFactors == true ? round(ShortTermSMA * ShortTermRSI) : ShortTermRSI LongTermMA := UseFactors == true ? round(ShortTermSMA * LongTermMA) : LongTermMA LongTermRSI := UseFactors == true ? round(ShortTermSMA * LongTermRSI) : LongTermRSI shortTermXtrender = rsi(ema(close, ShortTermSMA) - ema(close, ShortTermLMA), ShortTermRSI ) - 50 longTermXtrender = rsi( ema(close, LongTermMA), LongTermRSI ) - 50 // === INPUT BACKTEST RANGE === FromDay = input(defval = 1, title = "From Day", minval = 1, maxval = 31) FromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12) FromYear = input(defval = 2018, title = "From Year", minval = 2012) ToDay = input(defval = 1, title = "To Day", minval = 1, maxval = 31) ToMonth = input(defval = 1, title = "To Month", minval = 1, maxval = 12) ToYear = input(defval = 2020, title = "To Year", minval = 2012) // === FUNCTION EXAMPLE === start = timestamp(FromYear, FromMonth, FromDay, 00, 00) // backtest start window finish = timestamp(ToYear, ToMonth, ToDay, 23, 59) // backtest finish window window() => true strategy.entry("ShortTerm", strategy.long, qty = Amount, when = window() and crossover(shortTermXtrender,0) and TradeShortTerm) strategy.entry("LongTerm", strategy.long, qty = Amount, when = window() and crossover(longTermXtrender,0) and TradeLongTerm) strategy.close("ShortTerm", when = crossunder(shortTermXtrender,0) or time > finish) strategy.close("LongTerm", when = crossunder(longTermXtrender,0) or time > finish) shortXtrenderCol = shortTermXtrender > 0 ? shortTermXtrender > shortTermXtrender[1] ? color.lime : #228B22 : shortTermXtrender > shortTermXtrender[1] ? color.red : #8B0000 plot(shortTermXtrender, color=shortXtrenderCol, style=plot.style_columns, linewidth=1, title="B-Xtrender Osc. - Histogram", transp = 50) longXtrenderCol = longTermXtrender> 0 ? longTermXtrender > longTermXtrender[1] ? color.lime : #228B22 : longTermXtrender > longTermXtrender[1] ? color.red : #8B0000 plot(longTermXtrender , color=longXtrenderCol, style=plot.style_histogram, linewidth=2, title="B-Xtrender Trend - Histogram", transp = 80) plot(longTermXtrender , color=color.white, style=plot.style_line, linewidth=1, title="B-Xtrender Trend - Line", transp = 80)