Multi Period Dynamic Moving Average Strategy

This strategy dynamically selects different types of moving averages across multiple timeframes to generate trading signals. The strategy allows selecting from SMA, EMA, TEMA, WMA and HMA moving averages, with…

Pine ScriptBreakout Trend following Multi-timeframe EMA SMA Volume

Author: ChaoZhang
Complexity: Intermediate · 83 lines
Parameters: 12
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

This strategy dynamically selects different types of moving averages across multiple timeframes to generate trading signals.

Strategy Logic

The strategy allows selecting from SMA, EMA, TEMA, WMA and HMA moving averages, with customizable period lengths. Different types of moving averages will be plotted dynamically based on selection. It goes long when the closing price breaks above the moving average, and goes short when the closing price breaks below.

Specifically, the strategy first defines the backtest period based on input parameters. It then calculates five types of moving averages:

SMA Simple Moving Average
EMA Exponential Moving Average
TEMA Triple Exponential Moving Average
WMA Weighted Moving Average
HMA Hull Moving Average

The corresponding moving average is plotted based on selection. It goes long when the closing price is above the moving average, and goes short when below.

By combining different types of moving averages, the strategy can smooth price data and filter out market noise to generate more reliable trading signals. Customizable period lengths allow trading different trends across timeframes.

Advantages

Combines multiple moving averages for greater reliability
Customizable periods suit different trading timeframes
Dynamic switching of averages allows flexible optimization
Simple and intuitive trend following suitable for beginners

Risks

Lagging of moving averages may miss trend turning points
Overfitting with fixed parameters, underperformance in live trading likely
Aggressive long/short signals impact capital usage efficiency

Risks can be reduced by:

Adding other indicators to determine entries more precisely
Real-trade optimization of parameters for different market regimes
Optimizing position sizing based on account size and risk management

Enhancement Opportunities

The strategy can be improved in several aspects:

Add other filters for more stable signals

e.g. volume indicators to avoid false breakouts without volume confirmation.
Optimize entry and exit logic

Set price channels and stop losses to reduce unnecessary losses.
Dynamic moving average periods

Use longer periods in strong trends and shorter periods during consolidations.
Improve money management

Adjust position sizes based on drawdowns and profit taking.

Conclusion

The strategy combines various moving averages across timeframes to generate relatively stable trend following effects. With ample room for optimizations in entries, exits, parameters and money management, it can be improved for better real-world performance.

Parameters

Name	Default	What it controls
v_input_1	100000000	Buy quantity
v_input_2	2017	Backtest Start Year
v_input_3	true	Backtest Start Month
v_input_4	true	Backtest Start Day
v_input_5	false	Backtest Start Hour
v_input_6	false	Backtest Start Minute
v_input_7	2099	Backtest Stop Year
v_input_8	true	Backtest Stop Month
v_input_9	30	Backtest Stop Day
v_input_10	true	Color Background?
v_input_11	0	Select MA: SMA \| EMA \| TEMA \| WMA \| HMA
v_input_12	7	Period

Source Pine Script

/*backtest
start: 2022-10-20 00:00:00
end: 2023-10-26 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("MA_strategy ", shorttitle="MA_strategy", overlay=true, initial_capital=100000)

qty = input(100000000, "Buy quantity")

testStartYear = input(2017, "Backtest Start Year")
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testStartHour = input(0, "Backtest Start Hour")
testStartMin = input(0, "Backtest Start Minute")

testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,testStartHour,testStartMin)

testStopYear = input(2099, "Backtest Stop Year")
testStopMonth = input(1, "Backtest Stop Month")
testStopDay = input(30, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)


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


ma1 = input( "SMA",title="Select MA", options=["SMA", "EMA","TEMA", "WMA","HMA"])


len1 = input(7, minval=1, title="Period")

s=sma(close,len1)

e=ema(close,len1)


xEMA1 = ema(close, len1)
xEMA2 = ema(xEMA1, len1)
xEMA3 = ema(xEMA2, len1)
t = 3 * xEMA1 - 3 * xEMA2 + xEMA3


f_hma(_src, _length)=>
    _return = wma((2 * wma(_src, _length / 2)) - wma(_src, _length), round(sqrt(_length)))

h = f_hma(close, len1)

w = wma(close, len1)

ma = ma1 == "SMA"?s:ma1=="EMA"?e:ma1=="WMA"?w:ma1=="HMA"?h:ma1=="TEMA"?t:na

buy= close>ma
sell= close<ma

alertcondition(buy, title='buy', message='buy')
alertcondition(sell, title='sell', message='sell')

ordersize=floor(strategy.equity/close)

if testPeriod()
    strategy.entry("long",strategy.long,ordersize,when=buy)
    strategy.close("long", when = sell )

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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