Quantitative Trading Strategy Based on High Low Point Moving Averages

This strategy uses simple moving averages of high points and low points compared to current close price to determine entries and exits. It aims to capture price breakout signals from moving average crossovers to…

Pine ScriptBreakout Momentum Trend following SMA

Author: ChaoZhang
Complexity: Intermediate · 57 lines
Parameters: 9
Backtest bar: 30m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculate 4-period simple moving average of high prices.
Calculate 4-period simple moving average of low prices.
Go long when close price breaks above high point SMA.
Go short when close price breaks below low point SMA.
Use fixed stop loss and take profit for risk management.

Advantage Analysis

Uses simple indicators, easy to understand and implement.
Timely captures price breakout signals from SMA crossovers.
Can quickly filter out noise and identify trends.
Lightweight calculations reduce strategy overhead.
Suitable as base strategy for extensions.

Risk Analysis

Requires reasonable parameters to avoid oversensitivity.
Unable to handle risks from huge breakouts.
Possibilities of whipsaw losses in ranges.
Cannot automatically adjust stops and limits.
Hard to judge longer-term trend context.

Optimization Directions

Test different parameters for impact on signal quality.
Add filters to validate effectiveness of breakouts.
Incorporate trend analysis to avoid traps.
Develop dynamic stops and limits.
Optimize stops to improve win rate.
Test robustness across different timeframes.

Summary

This strategy uses simple indicators to gauge price momentum and provides a basic trend trading framework. With further improvements like parameter optimization and risk controls, the trading logic is highly extensible into a robust quant system. Overall an easy to use strategy suitable for beginners to start quant trading.

Parameters

Name	Default	What it controls
v_input_1	2017	Backtest Start Year
v_input_2	4	Backtest Start Month
v_input_3	true	Backtest Start Day
v_input_4	2017	Backtest Stop Year
v_input_5	5	Backtest Stop Month
v_input_6	true	Backtest Stop Day
v_input_7	true	Color Background?
v_input_8	4	length
v_input_9	false	displace

Source Pine Script

/*backtest
start: 2023-09-11 00:00:00
end: 2023-09-13 00:00:00
period: 30m
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("HiLo", overlay=true)

// Testing a specific period
testStartYear = input(2017, "Backtest Start Year")
testStartMonth = input(4, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(2017, "Backtest Stop Year")
testStopMonth = input(5, "Backtest Stop Month")
testStopDay = input(1, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

// A switch to control background coloring of the test period
testPeriodBackground = input(title="Color Background?", type=bool, defval=true)
testPeriodBackgroundColor = testPeriodBackground and (time >= testPeriodStart) and (time <= testPeriodStop) ? #00FF00 : na
bgcolor(testPeriodBackgroundColor, transp=97)

testPeriod() =>
    time >= testPeriodStart and time <= testPeriodStop ? true : false


//HiLo Strategy
length = input(4, minval=0)
displace = input(0, minval=0)
highsma = sma(high, length)
lowsma = sma(low, length)

longCondition = close > highsma[displace]
if (longCondition)
    strategy.entry("long", true)

shortCondition = close < lowsma[displace]
if (shortCondition)
    strategy.entry("short", false)

// Exit seems with a problem. it keeps saying the order's limit (2000) was reached even if I back test it just for a day. 
// If the two lines bellow are commented, then it it works. Anyone? Any idea what's wrong?

// strategy.exit("exit", "long", profit=10, loss=5)
// strategy.exit("exit", "short", profit=10, loss=5)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-19. 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/Quantitative-Trading-Strategy-Based-on-High-Low-Point-Moving-Averages.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.