Dual track System Momentum Trading Strategy

This strategy combines the MACD and Stoch RSI indicators to build a dual-rail trading system for trend tracking and oversold/overbought judgment. The strategy also builds indicators on the daily and 4-hour timeframes…

Pine ScriptMomentum Mean reversion Trend following RSI MACD EMA SMA Stochastic

Author: ChaoZhang
Complexity: Intermediate · 53 lines
Parameters: 12
Backtest bar: 3m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

The strategy combines the MACD and Stoch RSI indicators, which are different types of technical indicators, for configuration. MACD is a momentum indicator that judges price change velocity; Stoch RSI is an overbought/oversold indicator that judges relative price strength.

The strategy first constructs the MACD and Stoch RSI indicators on the daily and 4-hour timeframes respectively for trend and overbought/oversold judgments. When signals are triggered on both timeframes, corresponding buy/sell operations are performed.

Specifically, the MACD indicator is constructed with the DIF and DEA lines forming golden/dead crosses for judgment; the Stoch RSI indicator is constructed with the K and D lines forming golden/dead crosses for judgment. When both indicator pairs have golden crosses, buy signals are generated; when both have dead crosses, sell signals are generated.

Thus, by comprehensively applying the dual-indicator system and multi-timeframe judgments, the strategy judges price velocity and relative strength thoroughly, which helps improve decision accuracy and gain better returns.

Advantage Analysis

This strategy has the following advantages:

Combining dual-indicator system for comprehensive judgment and higher decision accuracy
Applying multi-timeframe to reduce misjudgment probability
Adopting trend tracking and overbought/oversold judgment for consideration of both price velocity and relative strength
Flexible indicator parameters adjustable for different products and market environments
Clean code structure easy to understand and expand

Risk Analysis

There are also some risks with this strategy:

There exist systemic market risks that cannot be fully avoided
Inappropriate indicator parameter settings may lead to overtrading or missing opportunities
Dual indicators may still give concurrent wrong signals, but less likely than single ones
Unable to cope with drastic market changes like black swan events

Countermeasures:

Optimize parameters and adjust trading conditions to reduce misjudgments
Incorporate more indicators for combined judgments
Add stop loss mechanisms to control single loss risk

Optimization Directions

This strategy can also be improved in the following aspects:

Incorporate more indicators for multi-indicator strategies
Add machine learning algorithms for dynamic parameter optimization
Combine sentiment indicators, news etc. for more comprehensive market condition judgments
Add stop loss, take profit strategies to optimize money management
Expand to more trading products to discover better opportunities

Conclusion

By combined application of the dual-indicator system and multi-timeframe judgments, this strategy judges price velocity and relative strength thoroughly, which can effectively capture market trends and improve deficiencies of single indicators. It also has advantages like flexible parameter tuning, easy understanding and expansion. Further expansions by multi-indicator combination, dynamic parameter optimization, sentiment indicator incorporation etc. can help boost strategy performance. [trans]

Parameters

Name	Default	What it controls
v_input_1_close	0	MACD Source:: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_2	12	MACD Fast Length:
v_input_3	26	MACD Slow Length:
v_input_4	9	MACD Signal Smoothing:
v_input_5_close	0	SRSI Source:: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_6	14	SRSI RSI Length:
v_input_7	14	SRSI Stoch Length:
v_input_8	3	SRSI Smoothing:
v_input_9	3	SRSI Signal Smoothing:
v_input_10	true	Trade Size in USD:
v_input_11	true	Perform buy trading?
v_input_12	true	Perform sell trading?

Source Pine Script

/*backtest
start: 2023-11-14 00:00:00
end: 2023-11-15 10:00:00
period: 3m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy(title='[RS]Khizon (UWTI) Strategy V0', shorttitle='K', overlay=false, pyramiding=0, initial_capital=100000, currency=currency.USD)
//  ||  Inputs:
macd_src = input(title='MACD Source:',  defval=close)
macd_fast = input(title='MACD Fast Length:',  defval=12)
macd_slow = input(title='MACD Slow Length:',  defval=26)
macd_signal_smooth = input(title='MACD Signal Smoothing:',  defval=9)
srsi_src = input(title='SRSI Source:',  defval=close)
srsi_rsi_length = input(title='SRSI RSI Length:',  defval=14)
srsi_stoch_length = input(title='SRSI Stoch Length:',  defval=14)
srsi_smooth = input(title='SRSI Smoothing:',  defval=3)
srsi_signal_smooth = input(title='SRSI Signal Smoothing:',  defval=3)
//  ||  Strategy Inputs:
trade_size = input(title='Trade Size in USD:', type=float, defval=1)
buy_trade = input(title='Perform buy trading?', type=bool, defval=true)
sel_trade = input(title='Perform sell trading?', type=bool, defval=true)
//  ||  MACD(close, 12, 26, 9):     ||---------------------------------------------||
f_macd_trigger(_src, _fast, _slow, _signal_smooth)=>
    _macd = ema(_src, _fast) - ema(_src, _slow)
    _signal = sma(_macd, _signal_smooth)
    _return_trigger = _macd >= _signal ? true : false
//  ||  Stoch RSI(close, 14, 14, 3, 3)  ||-----------------------------------------||
f_srsi_trigger(_src, _rsi_length, _stoch_length, _smooth, _signal_smooth)=>
    _rsi = rsi(_src, _rsi_length)
    _stoch = sma(stoch(_rsi, _rsi, _rsi, _stoch_length), _smooth)
    _signal = sma(_stoch, _signal_smooth)
    _return_trigger = _stoch >= _signal ? true : false
//  ||-----------------------------------------------------------------------------||
//  ||-----------------------------------------------------------------------------||
//  ||  Check Directional Bias from daily timeframe:
daily_trigger = security('USOIL', 'D', f_macd_trigger(macd_src, macd_fast, macd_slow, macd_signal_smooth) and f_srsi_trigger(srsi_src, srsi_rsi_length, srsi_stoch_length, srsi_smooth, srsi_signal_smooth))
h4_trigger = security('USOIL', '240', f_macd_trigger(macd_src, macd_fast, macd_slow, macd_signal_smooth) and f_srsi_trigger(srsi_src, srsi_rsi_length, srsi_stoch_length, srsi_smooth, srsi_signal_smooth))

plot(title='D1T', series=daily_trigger?0:na, style=circles, color=blue, linewidth=4, transp=65)
plot(title='H4T', series=h4_trigger?0:na, style=circles, color=navy, linewidth=2, transp=0)

sel_open = sel_trade and not daily_trigger and not h4_trigger
buy_open = buy_trade and daily_trigger and h4_trigger
sel_close = not buy_trade and daily_trigger and h4_trigger
buy_close = not sel_trade and not daily_trigger and not h4_trigger
strategy.entry('sel', long=false, qty=trade_size, comment='sel', when=sel_open)
strategy.close('sel', when=sel_close)
strategy.entry('buy', long=true, qty=trade_size, comment='buy', when=buy_open)
strategy.close('buy', when=buy_close)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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