HMA Daily Crossover Trading Strategy

This strategy enters trades based on HMA line and daily candlestick crosses and manages positions using stop loss and take profit logic. It combines different timeframe indicators for trend trading.

Pine ScriptTrend following Machine learning Momentum MACD

Author: ChaoZhang
Complexity: Starter · 31 lines
Parameters: 4
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy enters trades based on HMA line and daily candlestick crosses and manages positions using stop loss and take profit logic. It combines different timeframe indicators for trend trading.

Strategy Logic

The main signals and rules:

HMA line: Calculates Hull Moving Average to determine medium-long term trend.
Daily close price: Judges short-term price action.
Entry signal: HMA crossing above previous daily close, with price higher than previous day's price for long. Reverse for short.
Stop loss/take profit: Fixed levels to close positions when hit.

Advantages

Adjustable HMA parameters for adaptability.
Considers multi-timeframe indicators for higher quality signals.
Stop loss/take profit facilitates risk management.
Clear entry rules and position management.
Backtest parameters can be optimized for different markets.

Risks

HMA lag may miss best entry timing.
Fixed stop loss/take profit may be too aggressive or conservative.
Lacks trend strength filter, risks countertrend trades.
Simple rules prone to false signals.

Improvements:

Optimize HMA parameters for lag.
Use trailing stop loss instead of fixed.
Add volume or momentum indicators to judge trend strength.
Incorporate other indicators like MACD for signal confirmation.

Optimization

Potential ways to optimize the strategy:

Optimize HMA parameters for ideal combo.
Add trend strength filter to avoid countertrends.
Use dynamic stops instead of fixed levels.
Incorporate machine learning for auto parameter optimization.
Add simulated trading to test real-world performance.

Summary

The strategy logic is clear but has room for improvement. Adding trend filters, dynamic stops can improve stability. Overall provides a reasonable framework for catching medium-long term trends.

Parameters

Name	Default	What it controls
v_input_1	-0.05	StopLoss $
v_input_2	0.05	TargetPoint $
v_input_3_open	0	Source: open \| high \| low \| close \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_4	14	Period

Source Pine Script

/*backtest
start: 2023-08-22 00:00:00
end: 2023-09-21 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// created by SeaSide420       Enters on crossovers, exits Basket when profit $ = TP
// strategy(title="HMA & D1 crossover", overlay=true, currency="BTC", initial_capital=1, default_qty_type=strategy.percent_of_equity, default_qty_value=1, commission_type=strategy.commission.percent,commission_value=0.25,slippage=1)
SL=input(defval=-0.05,title="StopLoss $",type=input.float,step=0.01, maxval=-0.01)
TP=input(defval=0.05,title="TargetPoint $",type=input.float,step=0.01, minval=0.01)
price=input(title="Source",type=input.source,defval=open)
Period=input(14, minval=1)
hma = wma(2*wma(price, Period/2)-wma(price, Period), round(sqrt(Period)))
s1=security(syminfo.tickerid, timeframe.period, price, barmerge.gaps_off, barmerge.lookahead_off)
s2=security(syminfo.tickerid, "D", price, barmerge.gaps_off, barmerge.lookahead_off)
cp=s2<price?color.lime:color.red
cp1=plot((s2),color=color.black,title="DailyCandle1",linewidth=2,transp=0)
cp2=plot((s2[1]),color=color.black,title="DailyCandle2",linewidth=2,transp=0)
cp3=plot(hma,title="HMA",color=color.black)
fill(cp1,cp2,color=cp,transp=1)
fill(cp1,cp3,color=cp,transp=75)
closeall=strategy.openprofit<SL or strategy.openprofit>TP
if closeall
    strategy.close_all(comment = "Close All")
if (hma>hma[1] and s1>s2 and s2[1]>s2[2] and s1>s2[1])
    strategy.order("Buy", strategy.long)
if (hma<hma[1] and s1<s2 and s2[1]<s2[2] and s1<s2[1])
    strategy.order("Sell", strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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