Recursive Momentum Trading Strategy

Pine ScriptMomentum Machine learning Breakout SMA ATR Pivots / S&R

Author: ChaoZhang
Complexity: Starter · 75 lines
Parameters: 5
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend-following and breakout strategy based on the Recursive Bands indicator developed by alexgrover. It uses the Recursive Bands indicator to determine price trends and key support/resistance levels, combined with momentum conditions to filter out false breakouts, achieving low-frequency but high-quality entry signals.

Strategy Logic

Recursive Bands Indicator Calculation

The Recursive Bands indicator consists of an upper band, lower band and middle line. The indicator is calculated as:

Upper Band = Max(Previous bar's upper band, Close price + nVolatility) Lower Band = Min(Previous bar's lower band, Close price - nVolatility) Middle Line = (Upper Band + Lower Band)/2

Here n is a scaling coefficient, and volatility can be chosen from ATR, standard deviation, average true range or a special RFV method. The length parameter controls the sensitivity, larger values make the indicator trigger less frequently.

Trading Rules

The strategy first checks if the lower band and upper band are trending in the same direction to avoid false breakouts.

When price breaks below the lower band, go long. When price breaks above the upper band, go short.

In addition, stop loss logic is implemented.

Advantage Analysis

The advantages of this strategy are:

Efficient indicator calculation using recursive framework, avoiding repeated computation
Flexible parameter tuning to adapt to different market regimes
Combining trend and breakout, avoiding false breakouts
Momentum condition filters ensure signal quality

Risk Analysis

There are also some risks with this strategy:

Improper parameter settings may lead to overtrading or poor signal quality
May face large losses when major trend changes
Insufficient slippage control in extreme moves may amplify losses

These risks can be managed by parameter optimization, implementing stop loss, increasing slippage threshold etc.

Optimization Directions

Some directions to optimize the strategy further:

Incorporate indicators across multiple timeframes for robustness
Add machine learning module for adaptive parameter optimization
Conduct quantitative correlation analysis to find optimal parameter combinations
Use deep learning to forecast price paths and improve signal accuracy

Conclusion

In summary, this is a very practical and efficient trend-following strategy. It combines the recursive framework for computational efficiency, uses trend support/resistance to determine major trends, adds momentum conditions to filter false breakouts and ensure signal quality. With proper parameter tuning and risk control, it can achieve good results. Worthy of further research and optimization to adapt to more complex market regimes.

Parameters

Name	Default	What it controls
v_input_int_1	260	Length
v_input_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_string_1	0	Method: Classic \| Atr \| Stdev \| Ahlr \| Rfv
v_input_bool_1	true	Bands Hold Direction
v_input_2	3	lookback

Source Pine Script

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// @version=5
// Original indicator by alexgrover
strategy('Extended Recursive Bands Strategy', overlay=true, commission_type=strategy.commission.percent,commission_value=0.06,default_qty_type =strategy.percent_of_equity,default_qty_value = 100,initial_capital =1000)
length = input.int(260, step=10, title='Length')
src = input(close, title='Source')
method = input.string('Classic', options=['Classic', 'Atr', 'Stdev', 'Ahlr', 'Rfv'], title='Method')
bandDirectionCheck = input.bool(true, title='Bands Hold Direction')
lookback = input(3)
//----
atr = ta.atr(length)
stdev = ta.stdev(src, length)
ahlr = ta.sma(high - low, length)
rfv = 0.
rfv := ta.rising(src, length) or ta.falling(src, length) ? math.abs(ta.change(src)) : rfv[1]
//-----
f(a, b, c) =>
    method == a ? b : c
v(x) =>
    f('Atr', atr, f('Stdev', stdev, f('Ahlr', ahlr, f('Rfv', rfv, x))))
//----
sc = 2 / (length + 1)
a = 0.
a := math.max(nz(a[1], src), src) - sc * v(math.abs(src - nz(a[1], src)))
b = 0.
b := math.min(nz(b[1], src), src) + sc * v(math.abs(src - nz(b[1], src)))
c = (a+b)/2

// Colors
beColor = #675F76
buColor = #a472ff

// Plots
pA = plot(a, color=color.new(beColor, 0), linewidth=2, title='Upper Band')
pB = plot(b, color=color.new(buColor, 0), linewidth=2, title='Lower Band')
pC = plot(c, color=color.rgb(120,123,134,0), linewidth=2, title='Middle Band')
fill(pC, pA, color=color.new(beColor,90))
fill(pC, pB, color=color.new(buColor,90))

// Band keeping direction
// By Adulari
longc = 0
shortc = 0
for i = 0 to lookback-1
    if b[i] > b[i+1]
        longc:=longc+1
    if a[i] < a[i+1]
        shortc:=shortc+1
bhdLong = if bandDirectionCheck
    longc==lookback
else
    true
bhdShort = if bandDirectionCheck
    shortc==lookback
else
    true

// Strategy
if b>=low and bhdLong
    strategy.entry(id='Long',direction=strategy.long)
if high>=a and bhdShort
    strategy.entry(id='Short',direction=strategy.short)

// TP at middle line
//if low<=c and strategy.position_size<0 and strategy.position_avg_price>close
    //strategy.exit(id="Short",limit=close)
//if high>=c and strategy.position_size>0 and strategy.position_avg_price<close
    //strategy.exit(id="Long",limit=close)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-31. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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