HMA Momentum Breakthrough Strategy

This strategy uses the HMA (Hull Moving Average) indicator and technical analysis of K-line to realize momentum judgment and breakthrough operations on prices. 1. HMA Indicator Principle The HMA indicator was created…

Pine ScriptBreakout Momentum Machine learning SMA

Author: ChaoZhang
Complexity: Starter · 54 lines
Parameters: 4
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

II. Strategy Overview

This strategy uses the HMA (Hull Moving Average) indicator and technical analysis of K-line to realize momentum judgment and breakthrough operations on prices.

III. Strategy Principle

HMA Indicator Principle

The HMA indicator was created by Alan Hull in 2005 to create a moving average that is both sensitive and smooth. Its calculation method is:

(1) Calculate the Double Smoothing average DMA of half cycle

(2) Calculate the full cycle average SMA

(3) Calculate the difference DIFF between DMA and SMA

(4) Calculate the SQRT(period) cycle average line of DIFF to get HMA

Trading Strategy

The strategy uses the upward and downward breakthroughs of the HMA indicator as signals, combined with the breakthrough of the entity part of the K-line, to generate buy and sell signals. At the same time, set stop loss and take profit principles to monitor the profit and loss situation in real time to protect profits.

IV. Advantages of Strategy

The "convergence" characteristic of the HMA indicator makes it extremely sensitive to price changes while maintaining the smoothness of the moving average to avoid false signals.
The double breakthrough mechanism improves the reliability of signals and avoids being trapped.
Dynamic stop loss and profit protection optimize risk and return.
Fully automated trading simplifies operations.

V. Risks of Strategy

In violent market fluctuations, the probability of stop loss being hit is greater.
High trading frequency increases commission costs.
Improper parameter settings can generate a lot of false signals.

Solutions

Optimize stop loss and take profit conditions and set reasonable retracements.
Adjust trading frequency to reduce commission impact.
Test and optimize HMA cycle and breakthrough conditions to determine optimal parameters.

VI. Optimization Directions of Strategy

Incorporate trend judgment indicators to avoid countertrend trading.
Increase automatic judgment of data source switching to adapt to more market environments.
Increase machine learning algorithms to achieve automatic parameter optimization.
Deploy on server to achieve 24-hour live trading verification.

VII. Summary

The HMA momentum breakout strategy utilizes the unique advantages of the Hull moving average to accurately capture market momentum. The dual breakout filtration mechanism improves signal quality, and dynamic stop profit and stop loss protect income. This strategy is easy to use, effective, and a very practical quantitative trading tool that is worth promoting.

Parameters

Name	Default	What it controls
v_input_1	5	HullMA
v_input_2	-10000	Stop Loss in $
v_input_3	500	Target Point in $
v_input_4_ohlc4	0	Price data: ohlc4 \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| close

Source Pine Script

/*backtest
start: 2022-12-28 00:00:00
end: 2024-01-03 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
//SeaSide420
strategy("Hull Moving Average and Daily Candle Crossover", shorttitle="Hull&D", overlay=true, default_qty_type=strategy.percent_of_equity, max_bars_back=720, default_qty_value=100, calc_on_order_fills= true, calc_on_every_tick=true, pyramiding=0)
// settings----------------------
q=input(title="HullMA",defval=5)
SL = input(defval=-10000.00, title="Stop Loss in $", type=float, step=1)
TP = input(defval=500.00, title="Target Point in $", type=float, step=1)
price=input(ohlc4,title="Price data")
ot=1
p=price[1]
// Daily candle crossover---------
dt = 0.0010
Daily=(p-p[1])/p[1]
//--------------------------------
// Hull MA's----------------------
n2ma=2*wma(p,round(q/2))
nma=wma(p,q)
diff=n2ma-nma
sqn=round(sqrt(q))
n2ma1=2*wma(p[1],round(q/2))
nma1=wma(p[1], q)
diff1=n2ma1-nma1
sqn1=round(sqrt(q))
n1=wma(diff,sqn)
n2=wma(diff1,sqn)
//---------------------------------
// Plotting------------------------
z1e=n1>n2?green:black
z2e=n1>n2?black:red
z3e=n1>n2?green:red
n1e=plot(n1, title="HMA1", color=z1e, linewidth=2, offset=2)
n2e=plot(n2, title="HMA2", color=z2e, linewidth=2, offset=2)
fill(n1e, n2e, color=z3e, transp=80)
// Order controls-------------------
closelong = n1<n2 and n1[1]<n2[1] and n1[2]<n2[2] or strategy.openprofit<SL or strategy.openprofit>TP
if (closelong)
    strategy.close("Long")
closeshort = n1>n2 and n1[1]>n2[1] and n1[2]>n2[2] or strategy.openprofit<SL or strategy.openprofit>TP
if (closeshort)
    strategy.close("Short")
longCondition = n1>n2 and n1[1]>n2[1] and n1[2]>n2[2] and strategy.opentrades<ot and Daily>dt and close>n1
if (longCondition)
    strategy.entry("Long",strategy.long)
shortCondition = n1<n2 and n1[1]<n2[1] and n1[2]<n2[2] and strategy.opentrades<ot and Daily<dt and close<n1 
if (shortCondition)
    strategy.entry("Short",strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-04. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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