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Bidirectional Reversal and Momentum Moving Average Strategy

Author: ChaoZhang, Date: 2023-11-06 16:18:18
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Overview

This strategy combines reversal trading rules with momentum indicators. It integrates bidirectional reversal and Chande Momentum Oscillator to identify reversal opportunities while verifying momentum signals to generate more reliable trading signals.

Strategy Logic

The strategy consists of two parts:

The first part is bidirectional reversal trading rules. It identifies reversal opportunities by detecting close price changes in previous two days. Specifically, if close prices decreased in previous two days, and current close price is higher than previous close, and Stochastic Oscillator is below a threshold, it triggers a buy signal. On the contrary, if close prices increased in previous two days, and current close price is lower than previous close, and Stochastic Oscillator is above a threshold, it triggers a sell signal.

The second part is Chande Momentum Oscillator. It compares the magnitude of price change with the average magnitude in a certain period to determine momentum. If the momentum indicator is above a upper limit, it generates a buy signal. If below a lower limit, it generates a sell signal.

The strategy combines bidirectional reversal trading rules to locate potential reversal points, and momentum indicator to verify the validity of the reversal signals. Only when both signals agree, actual buy or sell signals will be generated.

Advantages of the Strategy

  • Dual verification mechanism improves signal reliability by avoiding false signals. Reversal trading rules identify potential reversal points, and momentum indicators verify effectiveness of the reversal signals.

  • Combining reversal strategy with trend strategy provides flexibility to capture opportunities in both reversing and trending markets.

  • Introducing momentum prevents falling into reversal traps by only trading when momentum confirms.

  • Multiple adjustable parameters can be optimized for different market conditions.

Risks of the Strategy

  • Reversal signals may face large pullbacks, requiring reasonable stop loss.

  • Precise timing of reversals is difficult, may cause misjudgments.

  • Lagging of momentum indicators may cause missing best reversal entry points.

  • Parameter tuning needs careful optimization based on specific markets, improper settings may increase risks.

Risks can be reduced by using proper stop loss, robust parameter optimization, keeping some room in reversal signal triggering conditions, etc.

Directions for Optimization

  • Test different reversal parameter combinations to find parameters sensitive to market reversals.

  • Try different momentum indicators, like RSI, volume rate of change, etc.

  • Add filters like breakouts to avoid trading non-key reversal points.

  • Evaluate stop loss strategies to find maximum drawdown-controlled methods.

  • Assess position sizing strategies to adjust position sizes based on market conditions.

Conclusion

The strategy combines the advantages of reversal and momentum strategies, with reliable signals and flexibility to capture opportunities. Parameters can be optimized, risks can be managed through stop loss and position sizing to improve stability and profitability. Overall, it explores effective integration of reversal and trend strategies, and is worth further research and application.

/*backtest
start: 2023-10-06 00:00:00
end: 2023-11-05 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 18/08/2019
// This is combo strategies for get a cumulative signal. 
//
// First strategy
// This System was created from the Book "How I Tripled My Money In The 
// Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
// The strategy buys at market, if close price is higher than the previous close 
// during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. 
// The strategy sells at market, if close price is lower than the previous close price 
// during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
//
// Second strategy
//    This indicator plots Chande Momentum Oscillator. This indicator was 
//    developed by Tushar Chande. A scientist, an inventor, and a respected 
//    trading system developer, Mr. Chande developed the CMO to capture what 
//    he calls "pure momentum". For more definitive information on the CMO and 
//    other indicators we recommend the book The New Technical Trader by Tushar 
//    Chande and Stanley Kroll.
//    The CMO is closely related to, yet unique from, other momentum oriented 
//    indicators such as Relative Strength Index, Stochastic, Rate-of-Change, 
//    etc. It is most closely related to Welles Wilder`s RSI, yet it differs 
//    in several ways:
//        - It uses data for both up days and down days in the numerator, thereby 
//          directly measuring momentum;
//        - The calculations are applied on unsmoothed data. Therefore, short-term 
//          extreme movements in price are not hidden. Once calculated, smoothing 
//          can be applied to the CMO, if desired;
//        - The scale is bounded between +100 and -100, thereby allowing you to 
//          clearly see changes in net momentum using the 0 level. The bounded scale 
//          also allows you to conveniently compare values across different securities.
//
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
Reversal123(Length, KSmoothing, DLength, Level) =>
    vFast = sma(stoch(close, high, low, Length), KSmoothing) 
    vSlow = sma(vFast, DLength)
    pos = 0.0
    pos := iff(close[2] < close[1] and close > close[1] and vFast < vSlow and vFast > Level, 1,
	         iff(close[2] > close[1] and close < close[1] and vFast > vSlow and vFast < Level, -1, nz(pos[1], 0))) 
	pos

CMO(Length, TopBand, LowBand) =>
    pos = 0
    xMom = abs(close - close[1])
    xSMA_mom = sma(xMom, Length)
    xMomLength = close - close[Length]
    nRes = 100 * (xMomLength / (xSMA_mom * Length))
    pos :=  iff(nRes > TopBand, 1,
	         iff(nRes <= LowBand, -1, nz(pos[1], 0))) 
    pos

strategy(title="Combo Backtest 123 Reversal & Chande Momentum Oscillator", shorttitle="Combo", overlay = true)
Length = input(14, minval=1)
KSmoothing = input(1, minval=1)
DLength = input(3, minval=1)
Level = input(50, minval=1)
//-------------------------
LengthCMO = input(9, minval=1)
TopBand = input(70, minval=1)
LowBand = input(-70, maxval=-1)
reverse = input(false, title="Trade reverse")
posReversal123 = Reversal123(Length, KSmoothing, DLength, Level)
posCMO = CMO(LengthCMO, TopBand, LowBand)
pos = iff(posReversal123 == 1 and posCMO == 1 , 1,
	   iff(posReversal123 == -1 and posCMO == -1, -1, 0)) 
possig = iff(reverse and pos == 1, -1,
          iff(reverse and pos == -1 , 1, pos))	   
if (possig == 1) 
    strategy.entry("Long", strategy.long)
if (possig == -1)
    strategy.entry("Short", strategy.short)	 
if (possig == 0) 
    strategy.close_all()
barcolor(possig == -1 ? #b50404: possig == 1 ? #079605 : #0536b3 )
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