🎯 this is a optimized version based on ATR_RSI_Strategy with no-repaint. Sharpe ratio: 1.4, trade times: 116 , trade symbol: BTCUSDTPERP 15M you can get same backtesting result with the correct settings.
🎲 Strategy Logic
🎯 the core logic is quite simple, use ATR and RSI and SMA 1. when price is in high volatility ( atr_value > atr_ma); 2. wait for a break signal (rsi_value > rsi_buy or rsi_value < rsi_sell); 3. entry Long or Short,use trailing stop-loss to max security and percent TP to keep profit.
🎲 Settings
🎯 there are 7 input properties in script, but I only finetune 4 of them (bold field below), you may change other parameter to get better result by yourself.
atr_length: length to get atr value
atr_ma_length: length of smoothing atr value
atr_ma_norm_min: atr_ma normalized min value, filter high volatility ranges
atr_ma_norm_max: atr_ma normalized max value, filter high volatility ranges
rsi_length: length to get rsi value
rsi_entry: 50 +/- rsi_entry to get entry threshold
trailing_percent: trailing stop-loss percent
🎲 Usage
🎯 the commission set to 0.05%, part of exchange the commission is less than 0.05% in reality, but I will still use 0.05% in my next script.
🎯 this script use 50% of equity to size positions follow general script position, you can adjust the value to fix size or 100% of equity to compare result with other strategy, but I still suggest you use 5-10% of equity for each strategy in reality.
🎯any questions please comment below. if there are any words violate House Rule, please tell me below and i will revise immediately don't want be hiddened again 😂😂
Additionally, I plan to publish 20 profitable strategies in 2023; let‘s witness it together!
Hope this strategy will be usefull for you :)
enjoy! 🚀🚀🚀
發行說明
update Order size from 50% to 10% of equity, is usually more realistic for each strategy.
發行說明
update script format and remarks for properties.
發行說明
add alert. 1. when in living trading, Enter after freq_once_per_bar_close 2. when in living trading, SL and TP should use freq_once_per_bar 3. you can compare with trade_list of backtest result thx for @jeroenderouw92 feedback. 🚀