Single Point Moving Average Breakout Strategy

Pine ScriptBreakout Momentum Trend following

Author: ChaoZhang
Complexity: Intermediate · 55 lines
Parameters: 10
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The single point moving average breakout strategy is a quantitative trading strategy based on the Chande Momentum Oscillator. It detects when the market is in a consolidation phase by calculating the momentum changes of price. When the Chande Momentum line crosses above the buy line or falls below the sell line, long or short trades will be executed accordingly.

Strategy Logic

The strategy first calculates the price momentum change momm, then separates it into positive momentum m1 and negative momentum m2. Next, it sums the positive and negative momentum over a lookback period into sm1 and sm2. Finally, the Chande Momentum Oscillator chandeMO is derived. The indicator oscillates around the zero line. Readings above zero indicate stronger upward momentum, while readings below zero indicate stronger downward momentum.

When the Chande Momentum line crosses above the buy line from lower levels, it signals that price is breaking out of a downtrend and ready to start an uptrend. The strategy will go long. When the line falls below the sell line from higher levels, short positions will be initiated.

Advantage Analysis

The strategy is able to identify turning points from downtrend to consolidation to uptrend, allowing entries at low prices and exits at high prices.
The Chande Momentum Oscillator considers both magnitude and rate of price changes, making it very effective for trend detection.
The strategy logic is simple and easy to implement.

Risk Analysis

The Chande Momentum Oscillator is sensitive to input parameters. Different parameter tuning can lead to vastly different trading signals and results.
Static buy and sell line settings may also introduce excessive false signals.
The lack of stop loss means that losing trades can accumulate large losses.

Some ways to improve include using dynamic buy/sell lines, filtering signals with other indicators, and implementing stop loss to control risks.

Optimization Directions

Test different parameter settings to find optimal values
Adopt dynamic buy and sell lines
Add additional filters with other indicators
Incorporate stop loss logic to cut losses

Conclusion

The single point moving average breakout strategy identifies trend turning points from downtrend to consolidation to uptrend using the Chande Momentum Oscillator, allowing low buy high sell trading. Despite being simple and intuitive, enhancements in parameter tuning, signal filtering and risk control can further improve performance. Overall, it serves as an effective tool for quantitative traders to determine trend reversals.

Parameters

Name	Default	What it controls
v_input_1	2021	Backtest Start Year
v_input_2	true	Backtest Start Month
v_input_3	10	Backtest Start Day
v_input_4	999999	Backtest Stop Year
v_input_5	9	Backtest Stop Month
v_input_6	26	Backtest Stop Day
v_input_7	9	length
v_input_8_close	0	Price: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_9	-80	buyline
v_input_10	80	sellline

Source Pine Script

/*backtest
start: 2024-01-02 00:00:00
end: 2024-02-01 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4

//* Backtesting Period Selector | Component *//
//* https://www.tradingview.com/script/eCC1cvxQ-Backtesting-Period-Selector-Component *//
//* https://www.tradingview.com/u/pbergden/ *//
//* Modifications made *//
testStartYear = input(2021, "Backtest Start Year") 
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(10, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(999999, "Backtest Stop Year")
testStopMonth = input(9, "Backtest Stop Month")
testStopDay = input(26, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

testPeriod() => true
/////////////// END - Backtesting Period Selector | Component ///////////////
strategy(title="Chande Momentum Strat", shorttitle="ChandeMO Strat", format=format.price, precision=2)
length = input(9, minval=1)
src = input(close, "Price", type = input.source)
momm = change(src)
f1(m) => m >= 0.0 ? m : 0.0
f2(m) => m >= 0.0 ? 0.0 : -m
m1 = f1(momm)
m2 = f2(momm)
sm1 = sum(m1, length)
sm2 = sum(m2, length)
percent(nom, div) => 100 * nom / div
chandeMO = percent(sm1-sm2, sm1+sm2)
plot(chandeMO, "Chande MO", color=color.blue)
hline(0, color=#C0C0C0, linestyle=hline.style_dashed, title="Zero Line")
buyline= input(-80)
sellline= input(80)
hline(buyline, color=color.gray)
hline(sellline, color=color.gray)

if testPeriod()
    if crossover(chandeMO, buyline)
        strategy.entry("Long", strategy.long, alert_message="a=ABCD b=buy e=binanceus q=1.2 s=uniusd")
    //    strategy.exit(id="Long Stop Loss", stop=strategy.position_avg_price*0.8) //20% stop loss 
        
    if crossunder(chandeMO, sellline)
        strategy.entry("Short", strategy.short, alert_message="a=ABCD b=sell e=binanceus q=1.2 s=uniusd")
    //    strategy.exit(id="Short Stop Loss", stop=strategy.position_avg_price*1.2) //20% stop loss

//      remember to alert as    {{strategy.order.alert_message}}

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-02. 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/Single-Point-Moving-Average-Breakout-Strategy.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /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.