Trend Trading Strategy Based on Dynamic Moving Average

Author: ChaoZhang, Date: 2023-12-21 11:33:50
Tags:

Overview

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 averages, it aims at tracking medium-term price trends for steady profits.

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.

/*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)

More

Forums
|
API
|
Github
|
About
|
SiteMap