0bps

Strategy vault / Trend following

Multifactor Driven Trend Trading Strategy

This strategy combines the Moving Average Convergence Divergence (MACD) indicator and the Stochastic Relative Strength Index (Stoch RSI) indicator to determine market trend direction, going long when the trend is up…

Pine ScriptTrend followingMachine learningMean reversionRSIMACDEMASMAStochastic
Author
ChaoZhang
Complexity
Intermediate · 53 lines
Parameters
12
Backtest bar
1m · intraday
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

IMG

Summary

This strategy combines the Moving Average Convergence Divergence (MACD) indicator and the Stochastic Relative Strength Index (Stoch RSI) indicator to determine market trend direction, going long when the trend is up and going short when the trend is down. It belongs to the trend trading strategy category.

Strategy Logic

This strategy utilizes the MACD and Stoch RSI indicators to determine market trend direction.

The MACD indicator consists of the fast EMA line, slow EMA line and the difference between them, reflecting the convergence and divergence of short-term and long-term moving averages. When the fast line crosses above the slow line, it is a buy signal. When the fast line crosses below the slow line, it is a sell signal.

The Stoch RSI indicator combines the strengths of both the RSI and Stoch indicators to show overbought and oversold levels in the market. When Stoch RSI is greater than the Stoch RSI signal line, it is a buy signal. When it is lower than the signal line, it is a sell signal.

This strategy uses MACD and Stoch RSI on the daily and 4-hour timeframes to determine market trend. When both indicators generate buy signals on the daily and 4-hour charts, go long. When both generate sell signals, go short. This can effectively filter out false signals and improve reliability.

Advantages

  1. Combining double factors to judge market moves can filter false signals effectively and improve signal accuracy

  2. Validating signals across high and low timeframes (daily and 4H) avoids getting whipsawed

  3. Following trends avoids choppy markets

  4. Simple and clear strategy logic, easy to understand and execute

Risks and Solutions

  1. Inability to effectively determine trend reversal points may cause stop loss being triggered
  • Optimize parameters or add other indicators to judge
  1. Single contract cannot diversify market systematic risks
  • Increase other contracts or stocks to diversify
  1. Cannot determine impact of sudden big events
  • Combine fundamental analysis to enhance risk awareness

Optimization Directions

  1. Adjust MACD and Stoch RSI parameters to optimize entry and exit points

  2. Add trailing stop strategies to lock in profits

  3. Add position sizing to control per trade risk

  4. Add more factors to judge to improve signal accuracy

  5. Use machine learning methods to dynamically optimize parameters

Summary

This strategy determines trend direction via a dual factor model and validates signals across timeframes. It is a relatively stable and reliable trend following strategy, with certain risk management capabilities and room for error. Its performance can be further enhanced by adding parameters optimization, stop loss, position sizing and other modules.

Parameters

NameDefaultWhat it controls
v_input_1_close0MACD Source:: close | high | low | open | hl2 | hlc3 | hlcc4 | ohlc4
v_input_212MACD Fast Length:
v_input_326MACD Slow Length:
v_input_49MACD Signal Smoothing:
v_input_5_close0SRSI Source:: close | high | low | open | hl2 | hlc3 | hlcc4 | ohlc4
v_input_614SRSI RSI Length:
v_input_714SRSI Stoch Length:
v_input_83SRSI Smoothing:
v_input_93SRSI Signal Smoothing:
v_input_10trueTrade Size in USD:
v_input_11truePerform buy trading?
v_input_12truePerform sell trading?

Source Pine Script

/*backtest
start: 2024-01-09 00:00:00
end: 2024-01-16 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy(title='[RS]Khizon (UGAZ) 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('NGAS', '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('NGAS', '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 2024-01-17. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more
/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Multifactor-Driven-Trend-Trading-Strategy.md
  1. npx agenkit install engineering-kit in your project
  2. Paste the command into your agent and approve each phase
  3. Get tests, backtest, risk controls and a paper-trading switch
Get AgenKitHow /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.

Related specs

esc

Try , , or .

↑↓ move↵ open