Trend Trading Strategy Based on Dynamic Moving Average

This strategy generates trading signals based on the dynamic moving average to go long when stock prices rise and close positions when prices fall. By combining the advantages of momentum indicators and moving…

Pine ScriptTrend following Mean reversion Momentum MACD EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 78 lines
Parameters: 15
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Principle

The strategy mainly relies on three Hull Moving Average (HMA) variants – regular HMA, Weighted HMA (WHMA) and Exponential HMA (EHMA). As the code shows, it allows users to switch between the three Hull MAs.

The formula for HMA is:

HMA = WMA(2*WMA(close, n/2)-WMA(close, n), sqrt(n))

Where WMA is the Weighted Moving Average and n is the period parameter. Compared to SMA, HMA responds faster to price changes.

The formulas for WHMA and EHMA are similar. HMA is chosen as the default option.

After calculating the HMA, the strategy uses the midline value of HMA as trading signals. It goes long when the price crosses above the HMA midline and closes positions when the price falls below the line. Thus it tracks medium-term trends using the HMA midline for profits.

Advantages

Compared to traditional MA strategies, this strategy has the following edges:

Faster response and stronger trend-following ability for timely entries and stops
Lower unnecessary trade frequency, avoiding chasing surges and stops
Flexible HMA parameters for adapting to more market environments
Switchable HMA variants to expand applicability

Risks

There are also some risks:

Generating multiple false signals during range-bound markets, increasing trade frequency and slippage costs
Missing trend reversal points if HMA parameters are not set properly, leading to greater loss risks
Liquidity risk and huge slippage when trading low liquidity stocks

Solutions:

Optimize HMA parameters for best values
Add other indicators to determine trend reversal points
Select liquid stocks with large average daily volume

Improvements

The strategy can also be enhanced from the following aspects:

Add volume or other filters to ensure signal reliability
Combine MACD, KDJ for better timing, improving win rate
Adjust HMA periods based on real-trade backtests
Switch to WHMA or EHMA that performs the best for specific stocks
Add stop loss mechanisms to control single trade loss

Summary

The dynamic MA trading strategy integrates the fast response of HMA to effectively track medium-term price trends. By opening long positions at appropriate timing and closing stops, it has demonstrated good backtest results. Further improvements in parameter tuning and stock filtering would lead to more steady excess returns. It is an easy-to-implement, risk-controllable quantitative strategy.

Parameters

Name	Default	What it controls
v_input_string_1	0	Strategy Direction: long \| short \| all
v_input_1	2000	Backtest Start Year
v_input_2	true	Backtest Start Month
v_input_3	true	Backtest Start Day
v_input_4	2030	Backtest Stop Year
v_input_5	12	Backtest Stop Month
v_input_6	30	Backtest Stop Day
v_input_7_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_string_2	0	Hull Variation: Hma \| Thma \| Ehma
v_input_8	55	Length(180-200 for floating S/R , 55 for swing entry)
v_input_9	true	Color Hull according to trend?
v_input_10	false	Color candles based on Hull's Trend?
v_input_11	true	Show as a Band?
v_input_12	true	Line Thickness
v_input_int_1	40	Band Transparency

Source Pine Script

/*backtest
start: 2022-12-14 00:00:00
end: 2023-12-20 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy('Position Investing by SirSeff', overlay=true, pyramiding=1, default_qty_type=strategy.percent_of_equity, default_qty_value=100, calc_on_order_fills=false, slippage=0, commission_type=strategy.commission.percent, commission_value=0)
strat_dir_input = input.string(title='Strategy Direction', defval='long', options=['long', 'short', 'all'])
strat_dir_value = strat_dir_input == 'long' ? strategy.direction.long : strat_dir_input == 'short' ? strategy.direction.short : strategy.direction.all
strategy.risk.allow_entry_in(strat_dir_value)
//////////////////////////////////////////////////////////////////////
// Testing Start dates
testStartYear = input(2000, 'Backtest Start Year')
testStartMonth = input(1, 'Backtest Start Month')
testStartDay = input(1, 'Backtest Start Day')
testPeriodStart = timestamp(testStartYear, testStartMonth, testStartDay, 0, 0)
//Stop date if you want to use a specific range of dates
testStopYear = input(2030, 'Backtest Stop Year')
testStopMonth = input(12, 'Backtest Stop Month')
testStopDay = input(30, 'Backtest Stop Day')
testPeriodStop = timestamp(testStopYear, testStopMonth, testStopDay, 0, 0)


testPeriod() => true
// Component Code Stop
//////////////////////////////////////////////////////////////////////
//INPUT
src = input(close, title='Source')
modeSwitch = input.string('Hma', title='Hull Variation', options=['Hma', 'Thma', 'Ehma'])
length = input(55, title='Length(180-200 for floating S/R , 55 for swing entry)')
switchColor = input(true, 'Color Hull according to trend?')
candleCol = input(false, title='Color candles based on Hull\'s Trend?')
visualSwitch = input(true, title='Show as a Band?')
thicknesSwitch = input(1, title='Line Thickness')
transpSwitch = input.int(40, title='Band Transparency', step=5)

//FUNCTIONS
//HMA
HMA(_src, _length) =>
    ta.wma(2 * ta.wma(_src, _length / 2) - ta.wma(_src, _length), math.round(math.sqrt(_length)))
//EHMA    
EHMA(_src, _length) =>
    ta.ema(2 * ta.ema(_src, _length / 2) - ta.ema(_src, _length), math.round(math.sqrt(_length)))
//THMA    
THMA(_src, _length) =>
    ta.wma(ta.wma(_src, _length / 3) * 3 - ta.wma(_src, _length / 2) - ta.wma(_src, _length), _length)

//SWITCH
Mode(modeSwitch, src, len) =>
    modeSwitch == 'Hma' ? HMA(src, len) : modeSwitch == 'Ehma' ? EHMA(src, len) : modeSwitch == 'Thma' ? THMA(src, len / 2) : na

//OUT
HULL = Mode(modeSwitch, src, length)
MHULL = HULL[0]
SHULL = HULL[2]

//COLOR
hullColor = switchColor ? HULL > HULL[2] ? #00ff00 : #ff0000 : #ff9800

//PLOT
///< Frame
Fi1 = plot(MHULL, title='MHULL', color=hullColor, linewidth=thicknesSwitch, transp=50)
Fi2 = plot(visualSwitch ? SHULL : na, title='SHULL', color=hullColor, linewidth=thicknesSwitch, transp=50)
///< Ending Filler
fill(Fi1, Fi2, title='Band Filler', color=hullColor, transp=transpSwitch)
///BARCOLOR
barcolor(color=candleCol ? switchColor ? hullColor : na : na)


if HULL[0] > HULL[2] and testPeriod()
    strategy.entry('Invest', strategy.long)
if HULL[0] < HULL[2] and testPeriod()
    strategy.entry('Pause', strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-21. 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/Trend-Trading-Strategy-Based-on-Dynamic-Moving-Average.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.