The relative momentum strategy

Pine ScriptMomentum Breakout Trend following EMA SMA Volume

Author: ChaoZhang
Complexity: Intermediate · 62 lines
Parameters: 8
Backtest bar: 15m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The relative momentum strategy compares the momentum of individual stocks and indexes to judge the relative strength of stocks to the broader market. It buys when the stock momentum is higher than that of the index, and sells when the stock momentum is lower than that of the index, in order to capture the growth peak of individual stocks.

Principles

The core logic of this strategy is to judge the relative strength of individual stocks versus the market, specifically:

Calculate the return over a period of time as the momentum of the stock
Calculate index return over the same period as the index momentum
Use moving average to smooth the stock and index momentum
When the moving average of stock momentum crosses above that of the index, the stock momentum is considered stronger than the market overall - that is the buy signal
When the stock momentum moving average crosses below the index momentum moving average, the stock momentum is considered weaker, triggering the sell signal

Through this logic, we can buy into stocks when their growth is thriving and sell out as the growth momentum fades, locking in excess returns during the growth peak period of stocks.

Advantage Analysis

The main advantages of the relative momentum strategy:

Can dynamically capture the growth peak of stocks without concerning specific market conditions - just buy when the stock growth outpaces the overall market
Smoothing with moving averages filters out short-term fluctuations and enhances signal reliability
Simple direct buy and sell conditions, easy to understand and operate
Flexibility to configure the time period for calculating relative momentum and optimize

Risk Analysis

There are also some risks with the relative momentum strategy:

Stocks may pullback after growth peak ends, posing insufficient profit-taking risk
Relative momentum signals can be false, identifying a fake instead of real peak
Need to set stop loss to control maximum loss

These risks can be managed by reasonable profit-taking, stop losses, parameter tuning etc.

Optimization Directions

The relative momentum strategy can be optimized mainly from the following aspects:

Test different time periods for computing momentum to find optimum
Try out different types and lengths of moving averages for best parameters
Add volume filter to avoid false breakouts due to lack of momentum
Incorporate other indicators to confirm optimal entry timing

Conclusion

The relative momentum strategy captures the excess growth phases of individual stocks versus the overall market to generate alpha. With its simple, clear buy/sell logic and ease of operation, and when coupled with parameter optimization and risk control, this strategy can perform very well.

Parameters

Name	Default	What it controls
v_input_1	BTC_USDT:swap	index_ticker
v_input_2	40	Loopback
v_input_3	true	useStopAndIndexReturns
v_input_4	true	useStopAndIndexReturnsMa
v_input_5	0	Moving Average Type: sma \| ema \| hma \| rma \| vwma \| wma
v_input_6	10	MALength
v_input_7	timestamp(01 Jan 2010 00:00 +0000)	Backtest Start Time
v_input_8	timestamp(01 Jan 2099 00:00 +0000)	Backtest End Time

Source Pine Script

/*backtest
start: 2024-01-21 00:00:00
end: 2024-01-28 00:00:00
period: 15m
basePeriod: 5m
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/
// © HeWhoMustNotBeNamed

//@version=4
strategy("Relative Returns Strategy", overlay=false, initial_capital = 100000, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, commission_type = strategy.commission.percent, pyramiding = 1, commission_value = 0.01, calc_on_order_fills = true)

index_ticker=input("BTC_USDT:swap")
Loopback = input(40, step=20)
useStopAndIndexReturns = input(true)
useStopAndIndexReturnsMa = input(true)

useDifference = not useStopAndIndexReturns

MAType = input(title="Moving Average Type", defval="sma", options=["ema", "sma", "hma", "rma", "vwma", "wma"])
MALength = input(10, minval=10,step=10)

i_startTime = input(defval = timestamp("01 Jan 2010 00:00 +0000"), title = "Backtest Start Time", type = input.time)
i_endTime = input(defval = timestamp("01 Jan 2099 00:00 +0000"), title = "Backtest End Time", type = input.time)
inDateRange = true

f_secureSecurity(_symbol, _res, _src, _offset) => security(_symbol, _res, _src[_offset], lookahead = barmerge.lookahead_on)
f_getMovingAverage(source, MAType, length)=>
    ma = sma(source, length)
    if(MAType == "ema")
        ma := ema(source,length)
    if(MAType == "hma")
        ma := hma(source,length)
    if(MAType == "rma")
        ma := rma(source,length)
    if(MAType == "vwma")
        ma := vwma(source,length)
    if(MAType == "wma")
        ma := wma(source,length)
    ma

index = f_secureSecurity(index_ticker, '1D', close, 0)
stock_return = (close - close[Loopback])*100/close
index_return = (index - index[Loopback])*100/index

stock_return_ma = f_getMovingAverage(stock_return, MAType, MALength)
index_return_ma = f_getMovingAverage(index_return, MAType, MALength)
relativeReturns = stock_return - index_return
relativeReturns_ma = f_getMovingAverage(relativeReturns, MAType, MALength)

plot(useStopAndIndexReturns ? useStopAndIndexReturnsMa ? stock_return_ma : stock_return : na, title="StockReturn", color=color.green, linewidth=1)
plot(useStopAndIndexReturns ? useStopAndIndexReturnsMa ? index_return_ma : index_return : na, title="IndexReturn", color=color.red, linewidth=1)

plot(useDifference?relativeReturns:na, title="Relative-Returns", color=color.blue, linewidth=1)
plot(useDifference?relativeReturns_ma:na, title="MA", color=color.red, linewidth=1)

buyCondition = (useStopAndIndexReturns ? useStopAndIndexReturnsMa ? stock_return_ma > index_return_ma : stock_return > index_return : relativeReturns > relativeReturns_ma)
closeBuyCondition = (useStopAndIndexReturns ? useStopAndIndexReturnsMa ? stock_return_ma < index_return_ma : stock_return < index_return : relativeReturns < relativeReturns_ma)
strategy.entry("Buy", strategy.long, when=buyCondition and inDateRange, oca_name="oca")
strategy.close("Buy", when=closeBuyCondition)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-29. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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