This strategy computes one fast and one slow EMA indicators, generating buy and sell signals based on their crossover situation, belonging to a typical trend following strategy. It goes long when the fast line crosses above the slow line, and flattens longs when the fast line crosses below the slow line. Conversely, it goes short when the fast line crosses below the slow line, and flattens shorts when the fast line crosses above the slow line.
The strategy computes one fast and one slow EMA lines, with periods of 13 and 50 respectively. When the fast line breaks out upwards crossing the slow line, a buy signal is generated to go long. When the fast line breaks downwards crossing below the slow line, a sell signal is generated to go short.
After going long, if the fast line recrosses below the slow line, a flatten long signal is generated. After going short, if the fast line recrosses above the slow line, a flatten short signal is generated.
The strategy adopts the common dual EMA system, judging trend and entry points based on crossover situations between different timeframe EMAs. The dual EMAs can effectively filter noise and identify trends when used together.
The operations are simple and intuitive, easy to automate. It only needs price information, without considering other complex factors. The EMA periods can be freely adjusted to adapt to different market environments.
The dual EMA crossover system has mediocre performance in identifying intricate trends. In ranging markets, EMA crossover signals may be frequent, risking whipsaws. Only price factors are considered without incorporating other elements.
Increasing the interval between the EMA periods could reduce crossover frequency. Volume or volatility indicators could also help provide additional insight. Optimizing stop loss strategies may also lower whipsaw risks.
Test and optimize EMA period parameters to find the optimal settings.
Add volume, volatility or other judgement rules.
Incorporate breakout signals etc to set more stringent entry conditions.
Apply machine learning to predict trends and aid EMA signal quality determination.
Optimize stop loss strategies such as trailing stops, average stops etc.
Dynamically adjust position sizing to optimize capital management.
The strategy belongs to the typical dual EMA crossover system, gauging trends by simple indicator combinations. It is easy to implement but also prone to false signals. Combining more indicators and parameter optimization can improve robustness. Overall it provides a concise trend following strategy template.
/*backtest
start: 2023-09-12 00:00:00
end: 2023-09-12 22:00:00
period: 5m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © himanshumahalle
//@version=4
strategy("CROSS_ALGO SYSTEM")
// INPUT CONTROLS
lengthSEMA= input(title="LSEMA", type = input.integer, defval=13,minval=1,maxval=100,step=1)
lengthLEMA= input(title="LLEMA", type = input.integer, defval=50,minval=1,maxval=100,step=1)
//INDICATOR
SEMA= ema(close,lengthSEMA)
LEMA= ema(close,lengthLEMA)
// BUY AND SELL
buy = crossover(SEMA,LEMA)
sell = crossunder(SEMA,LEMA)
//EXITS
buyexit = crossunder(SEMA,LEMA)
sellexit = crossover(SEMA,LEMA)
//EXECUTION
strategy.entry("long",strategy.long,when=buy,comment = "Buy")
strategy.entry("short",strategy.short,when=sell,comment = "Sell")
strategy.close("long",when= buyexit , comment= "Sell")
strategy.close("short",when= sellexit , comment= "Buy")