Double Moving Average Crossover Trading Strategy 436775

Author: ChaoZhang
Complexity: Intermediate · 65 lines
Parameters: 9
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy generates buy and sell signals based on the golden cross and death cross of moving averages. Specifically, it uses a 5-day exponential moving average (EMA) and a 34-day double exponential moving average (DEMA). When the short-term 5-day EMA crosses above the long-term 34-day DEMA, a buy signal is generated. When the short-term 5-day EMA crosses below the long-term 34-day DEMA, a sell signal is generated.

Strategy Logic

Calculate the 5-day EMA and 34-day DEMA
Generate a buy signal when the short-term 5-day EMA crosses above the long-term 34-day DEMA
Generate a sell signal when the short-term 5-day EMA crosses below the long-term 34-day DEMA
Option to trade only during specific trading sessions
Option to use trailing stop loss

This strategy combines both trend following and moving average crossover factors for stable performance. Moving averages as a trend following indicator can effectively identify market trends; The EMA and DEMA combination can effectively smooth price data to generate trading signals; The crossovers between short-term and long-term moving averages can provide early trading signals when major trend changes.

Advantage Analysis

Simple and clear strategy logic, easy to understand and implement
Combination use of moving averages considers both trend judgment and price data smoothing
Crossovers between short-term and long-term moving averages can provide early signals at major turning points
Parameters can be optimized to adjust moving average lengths for different products and timeframes
Integrating two factors can improve strategy stability

Risk Analysis

More false signals may occur in ranging markets
Inappropriate moving average lengths may cause signal lagging
Improper trading hours and stop loss settings may affect strategy profitability

These risks can be reduced by adjusting moving average lengths, optimizing trading hours, and setting reasonable stop loss.

Optimization Directions

Adjust moving average length parameters for different trading products and timeframes
Optimize trading session parameters to trade during most active periods
Compare fixed stop loss vs trailing stop loss
Test impact of different price source options on strategy

Conclusion

This strategy generates trading signals through double moving average crossovers, combined with trend following and data smoothing techniques. It is a simple and practical trend following strategy. Through parameter tuning and logic refinement, it can adapt to different products and timeframes, provide early signals at major trend changes, and avoid false signals. Worth recommending and applying.

Parameters

Name	Default	What it controls
v_input_1	true	Use Trading Session?
v_input_2	true	Use Trailing Stop?
v_input_3	0400-1500	Trade Session:
v_input_4	true	Trade Size:
v_input_5	55	Take profit in pips:
v_input_6	22	Stop loss in pips:
v_input_7	5	EMA length:
v_input_8	34	DEMA length:
v_input_9_open	0	Price source:: open \| high \| low \| close \| hl2 \| hlc3 \| hlcc4 \| ohlc4

Source Pine Script

/*backtest
start: 2023-11-01 00:00:00
end: 2023-11-30 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
args: [["v_input_1",false]]
*/

//@version=2
strategy(title='[STRATEGY][RS]MicuRobert EMA cross V2', shorttitle='S', overlay=true)
USE_TRADESESSION = input(title='Use Trading Session?', type=bool, defval=true)
USE_TRAILINGSTOP = input(title='Use Trailing Stop?', type=bool, defval=true)
trade_session = input(title='Trade Session:',  defval='0400-1500', confirm=false)
istradingsession = not USE_TRADESESSION ? false : not na(time('1', trade_session))
bgcolor(istradingsession?gray:na)
trade_size = input(title='Trade Size:', type=float, defval=1)
tp = input(title='Take profit in pips:', type=float, defval=55.0) * (syminfo.mintick*10)
sl = input(title='Stop loss in pips:', type=float, defval=22.0) * (syminfo.mintick*10)
ma_length00 = input(title='EMA length:', defval=5)
ma_length01 = input(title='DEMA length:',  defval=34)
price = input(title='Price source:', defval=open)

//  ||--- NO LAG EMA, Credit LazyBear:  ---||
f_LB_zlema(_src, _length)=>
    _ema1=ema(_src, _length)
    _ema2=ema(_ema1, _length)
    _d=_ema1-_ema2
    _zlema=_ema1+_d
//  ||-------------------------------------||

ma00 = f_LB_zlema(price, ma_length00)
ma01 = f_LB_zlema(price, ma_length01)
plot(title='M0', series=ma00, color=black)
plot(title='M1', series=ma01, color=black)

isnewbuy = change(strategy.position_size)>0 and change(strategy.opentrades)>0
isnewsel = change(strategy.position_size)<0 and change(strategy.opentrades)>0

buy_entry_price = isnewbuy ? price : buy_entry_price[1]
sel_entry_price = isnewsel ? price : sel_entry_price[1]
plot(title='BE', series=buy_entry_price, style=circles, color=strategy.position_size <= 0 ? na : aqua)
plot(title='SE', series=sel_entry_price, style=circles, color=strategy.position_size >= 0 ? na : aqua)
buy_appex = na(buy_appex[1]) ? price : isnewbuy ? high : high >= buy_appex[1] ? high : buy_appex[1]
sel_appex = na(sel_appex[1]) ? price : isnewsel ? low : low <= sel_appex[1] ? low : sel_appex[1]
plot(title='BA', series=buy_appex, style=circles, color=strategy.position_size <= 0 ? na : teal)
plot(title='SA', series=sel_appex, style=circles, color=strategy.position_size >= 0 ? na : teal)
buy_ts = buy_appex - sl
sel_ts = sel_appex + sl
plot(title='Bts', series=buy_ts, style=circles, color=strategy.position_size <= 0 ? na : red)
plot(title='Sts', series=sel_ts, style=circles, color=strategy.position_size >= 0 ? na : red)

buy_cond1 = crossover(ma00, ma01) and (USE_TRADESESSION ? istradingsession : true)
buy_cond0 = crossover(price, ma00) and ma00 > ma01 and (USE_TRADESESSION ? istradingsession : true)
buy_entry = buy_cond1 or buy_cond0
buy_close = (not USE_TRAILINGSTOP ? low <= buy_entry_price - sl: low <= buy_ts) or high>=buy_entry_price+tp//high>=last_traded_price + tp or low<=last_traded_price - sl //high >= hh or 
sel_cond1 = crossunder(ma00, ma01) and (USE_TRADESESSION ? istradingsession : true)
sel_cond0 = crossunder(price, ma00) and ma00 < ma01 and (USE_TRADESESSION ? istradingsession : true)
sel_entry = sel_cond1 or sel_cond0
sel_close = (not USE_TRAILINGSTOP ? high >= sel_entry_price + sl : high >= sel_ts) or low<=sel_entry_price-tp//low<=last_traded_price - tp or high>=last_traded_price + sl //low <= ll or 

strategy.entry('buy', long=strategy.long, qty=trade_size, comment='buy', when=buy_entry)
strategy.close('buy', when=buy_close)
strategy.entry('sell', long=strategy.short, qty=trade_size, comment='sell', when=sel_entry)
strategy.close('sell', when=sel_close)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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