Dual Momentum Strategy

The Dual Momentum strategy uses fast and slow momentum indicators to generate entry and exit signals. It is a fast-reacting strategy well-suited for trending instruments on the daily and 4-hour timeframes. This…

Pine ScriptMomentum Trend following Volatility RSI MACD

Author: ChaoZhang
Complexity: Starter · 71 lines
Parameters: 5
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The strategy allows configuring long/short or long-only mode. It also allows enabling fixed stop loss or ignoring it so that the strategy acts solely on entry and exit signals.

Strategy Logic

The strategy uses fast period momentum (default 5 days) and slow period momentum (default 10 days).

When slow momentum and fast momentum are both above 0, a long entry signal is generated.

When slow momentum or fast momentum goes below 0, an exit signal is generated.

Similarly, when slow momentum and fast momentum are both below 0, a short entry signal is generated. When slow momentum or fast momentum goes above 0, an exit signal is generated.

Thus, the strategy captures trend changes using the crossover of two momentum indicators with different periods.

Advantage Analysis

Using dual momentum provides more accurate trend change detection and fewer false signals.
Fast period momentum reacts quickly to market changes, while slow period filters out noise.
Flexible long/short or long-only modes suit different trading preferences.
Optional stop loss controls risk.
Fast-reacting nature makes it suitable for trend trading on daily or higher timeframes for outsized gains.

Risk Analysis

Dual momentum relies on indicator values above/below 0, which has some lag.
The strategy is more trend-dependent and may underperform in range-bound markets with more whipsaws.
Not using stop loss risks large losses.
Wrong choice of symbols or timeframes can lead to poor results.

To control risks, tune the momentum periods, use reasonable fixed stop loss percentage, select strongly trending symbols and run on daily or higher timeframes.

Enhancement Opportunities

The strategy can be enhanced in several ways:

Add filters like MACD or RSI to avoid wrong trades at trend turning points.
Add adaptive stop loss to adjust stop distance based on market volatility.
Optimize momentum parameters for different symbols via stepwise optimization, walk forward analysis etc.
Add position sizing rules to adjust new position size based on past performance.
Differentiate long and short market conditions for asymmetric entries and exits.

Conclusion

The Dual Momentum strategy captures trend direction using fast and slow momentum crossover. Using simple indicators to detect trend changes, it is suitable for riding intraday or multi-day trends and generating excess returns. Proper risk control via stop loss, symbol/parameter optimization can improve consistency.

Parameters

Name	Default	What it controls
v_input_1	5	Fast Period
v_input_2	10	Slow Period
v_input_3	false	Long Only
v_input_4	5	Stop-loss (%)
v_input_5	false	Use Stop-Loss

Source Pine Script

/*backtest
start: 2023-08-28 00:00:00
end: 2023-09-27 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © QuantCT

//@version=4
strategy("Momentum Strategy Idea",
         shorttitle="Momentum", 
         overlay=false,
         pyramiding=0,     
         default_qty_type=strategy.percent_of_equity, 
         default_qty_value=100, 
         initial_capital=1000,           
         commission_type=strategy.commission.percent, 
         commission_value=0.075)

// ____ Inputs

fast_period = input(title="Fast Period", defval=5) 
slow_period = input(title="Slow Period", defval=10)
long_only = input(title="Long Only", defval=false)
slp = input(title="Stop-loss (%)", minval=1.0, maxval=25.0, defval=5.0)
use_sl = input(title="Use Stop-Loss", defval=false)

// ____ Logic

mom_fast = mom(close, fast_period)
mom_slow = mom(close, slow_period)
    
enter_long = (mom_slow > 0 and mom_fast > 0)
exit_long = (mom_slow < 0 or mom_fast < 0)
enter_short = (mom_slow < 0 and mom_fast < 0)
exit_short = (mom_slow > 0 or mom_fast > 0)

strategy.entry("Long", strategy.long, when=enter_long)
strategy.close("Long", when=exit_long) 
if (not long_only)
    strategy.entry("Short", strategy.short, when=enter_short)
    strategy.close("Short", when=exit_short) 
   
// ____ SL

sl_long = strategy.position_avg_price * (1- (slp/100))
sl_short = strategy.position_avg_price * (1 + (slp/100))
if (use_sl)
    strategy.exit(id="SL", from_entry="Long", stop=sl_long)
    strategy.exit(id="SL", from_entry="Short", stop=sl_short)
    
// ____ Plots

colors = 
 enter_long ? #27D600 :
 enter_short ? #E30202 :
 color.orange

mom_fast_plot = plot(mom_fast, color=colors)
mom_slow_plot = plot(mom_slow, color=colors)
fill(mom_fast_plot, mom_slow_plot, color=colors, transp=50)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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