# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame import os from datetime import datetime from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter) from freqtrade.strategy import merge_informative_pair # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import ta as taichi pd.set_option('display.max_columns', 100) pd.set_option('display.max_rows', None) pd.set_option('display.expand_frame_repr', True) def delete_log_results(): if os.path.exists("mylogs.txt"): os.remove("mylogs.txt") def log_to_results(str_to_log): fr = open("mylogs.txt", "a") #fr.write(str(datetime.now()) + " : " + str_to_log + "\n") fr.write(str_to_log + "\n") fr.close() # This class is a sample. Feel free to customize it. class SampleStrategy(IStrategy): delete_log_results() # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Can this strategy go short? can_short: bool = False #roi0 = RealParameter(0.01, 0.09, decimals=1, default=0.04, space="buy") # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { #"60": 0.01, #"30": 0.01, "0": 0.02, } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.25 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 26 # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #log_to_results("populate_indicators" + str(metadata)) dataframe['ICH_SSB'] = taichi.trend.ichimoku_b(dataframe['high'], dataframe['low'], window2=26, window3=52).shift(26) dataframe['ICH_SSA'] = taichi.trend.ichimoku_a(dataframe['high'], dataframe['low'], window1=9, window2=26).shift(26) #print(dataframe['ICH_SSA']) dataframe['ICH_KS'] = taichi.trend.ichimoku_base_line(dataframe['high'], dataframe['low']) #print(dataframe['ICH_KS']) dataframe['ICH_TS'] = taichi.trend.ichimoku_conversion_line(dataframe['high'], dataframe['low']) #print(dataframe['ICH_TS']) dataframe['ICH_CS'] = dataframe['close'].shift(26) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # Définition de l'indicateur Bollinger bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # Ajout de la bande supérieure dataframe['bb_lowerband'] = bollinger['lower'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Lorsque le RSI est inférieur à 30 (dataframe['rsi'] < 30) & # Lorsque la clôture du cours est sous la bande inférieure (dataframe['close'] < dataframe['bb_lowerband']) ), 'enter_long'] = 1 #log_to_results(str(metadata)) #log_to_results(str(dataframe)) #dataframe.loc[ #( - # (qtpylib.crossed_above(dataframe['ICH_KS'], dataframe['ICH_TS'])) #), #'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Sortie lorsque le RSI est supérieur à 70 (dataframe['rsi'] > 70) ), 'exit_long'] = 1 #dataframe.loc[ # ( # Signal: RSI crosses above 70 # (qtpylib.crossed_above(dataframe['ICH_TS'], dataframe['ICH_KS'])) # ), # 'exit_short'] = 1 return dataframe #=================== SUMMARY METRICS =================== #| Metric | Value | #|-----------------------------+-----------------------| #| Backtesting from | 2022-07-01 00:00:00 | #| Backtesting to | 2022-10-06 00:00:00 | #| Max open trades | 2 | #| | | #| Total/Daily Avg Trades | 131 / 1.35 | #| Starting balance | 1000 USDT | #| Final balance | 2698.751 USDT | #| Absolute profit | 1698.751 USDT | #| Total profit % | 169.88% | #| CAGR % | 4091.96% | #| Profit factor | 2.22 | #| Trades per day | 1.35 | #| Avg. daily profit % | 1.75% | #| Avg. stake amount | 918.419 USDT | #| Total trade volume | 120312.861 USDT | #| | | #| Best Pair | DEFI/USDT:USDT 41.84% | #| Worst Pair | TRB/USDT:USDT -23.00% | #| Best trade | WSB/USDT:USDT 16.22% | #| Worst trade | TRB/USDT:USDT -25.01% | #| Best day | 238.663 USDT | #| Worst day | -258.538 USDT | #| Days win/draw/lose | 48 / 41 / 9 | #| Avg. Duration Winners | 15:53:00 | #| Avg. Duration Loser | 5 days, 10:17:00 | #| Rejected Entry signals | 551992 | #| Entry/Exit Timeouts | 60 / 359 | #| | | #| Min balance | 1010.034 USDT | #| Max balance | 2699.274 USDT | #| Max % of account underwater | 24.46% | #| Absolute Drawdown (Account) | 24.46% | #| Absolute Drawdown | 587.996 USDT | #| Drawdown high | 1404.339 USDT | #| Drawdown low | 816.343 USDT | #| Drawdown Start | 2022-08-11 04:00:00 | #| Drawdown End | 2022-08-30 16:00:00 | #| Market change | -1.21% | #======================================================= # #freqtrade backtesting -c config-gateio.json -s SampleStrategy --timeframe=1h --timerange=20220701-20221006