# source: https://raw.githubusercontent.com/reuniware/FreqTrade_Work/dceb9e2ad7a67019eb3bfb634adb8ff28b692711/202210-freqtrade-work/ETUDE001/strat-ichimoku-003-gateio.py # 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 Github_reuniware_FreqTrade_Work__strat_ichimoku_003_gateio__20221023_122014(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 = True #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": 0.25 } # 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 = 0.0025 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '15m' # 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): # get access to all pairs available in whitelist. #pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. #informative_pairs = [(pair, '1h') for pair in pairs] # Optionally Add additional "static" pairs informative_pairs = [("BTC/USDT:USDT", "30m"), ("BTC/USDT:USDT", "1h"), ("BTC/USDT:USDT", "4h"),] return informative_pairs informativeBTC30M : DataFrame informativeBTC1H : DataFrame informativeBTC4H : DataFrame def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: global informativeBTC30M global informativeBTC1H global informativeBTC4H #log_to_results(metadata['pair']) inf_tf = '30m' informativeBTC30M = self.dp.get_pair_dataframe(pair="BTC/USDT:USDT", timeframe=inf_tf) inf_tf = '1h' informativeBTC1H = self.dp.get_pair_dataframe(pair="BTC/USDT:USDT", timeframe=inf_tf) inf_tf = '4h' informativeBTC4H = self.dp.get_pair_dataframe(pair="BTC/USDT:USDT", timeframe=inf_tf) #Ichimoku calculations for the BTC in 30m informativeBTC30M['BTC_ICH_SSB_30M'] = taichi.trend.ichimoku_b(informativeBTC30M['high'], informativeBTC30M['low'], window2=26, window3=52).shift(26) informativeBTC30M['BTC_ICH_SSA_30M'] = taichi.trend.ichimoku_a(informativeBTC30M['high'], informativeBTC30M['low'], window1=9, window2=26).shift(26) informativeBTC30M['BTC_ICH_KS_30M'] = taichi.trend.ichimoku_base_line(informativeBTC30M['high'], informativeBTC30M['low']) informativeBTC30M['BTC_ICH_TS_30M'] = taichi.trend.ichimoku_conversion_line(informativeBTC30M['high'], informativeBTC30M['low']) informativeBTC30M['BTC_ICH_CS_30M'] = informativeBTC30M['close'] informativeBTC30M['BTC_ICH_CS_HIGH_30M'] = informativeBTC30M['high'].shift(26) informativeBTC30M['BTC_ICH_CS_LOW_30M'] = informativeBTC30M['low'].shift(26) informativeBTC30M['BTC_ICH_CS_KS_30M'] = informativeBTC30M['BTC_ICH_KS_30M'].shift(26) informativeBTC30M['BTC_ICH_CS_TS_30M'] = informativeBTC30M['BTC_ICH_TS_30M'].shift(26) informativeBTC30M['BTC_ICH_CS_SSA_30M'] = informativeBTC30M['BTC_ICH_SSA_30M'].shift(26) informativeBTC30M['BTC_ICH_CS_SSB_30M'] = informativeBTC30M['BTC_ICH_SSB_30M'].shift(26) #Ichimoku calculations for the BTC in 1h informativeBTC1H['BTC_ICH_SSB_1H'] = taichi.trend.ichimoku_b(informativeBTC1H['high'], informativeBTC1H['low'], window2=26, window3=52).shift(26) informativeBTC1H['BTC_ICH_SSA_1H'] = taichi.trend.ichimoku_a(informativeBTC1H['high'], informativeBTC1H['low'], window1=9, window2=26).shift(26) informativeBTC1H['BTC_ICH_KS_1H'] = taichi.trend.ichimoku_base_line(informativeBTC1H['high'], informativeBTC1H['low']) informativeBTC1H['BTC_ICH_TS_1H'] = taichi.trend.ichimoku_conversion_line(informativeBTC1H['high'], informativeBTC1H['low']) informativeBTC1H['BTC_ICH_CS_1H'] = informativeBTC1H['close'] informativeBTC1H['BTC_ICH_CS_HIGH_1H'] = informativeBTC1H['high'].shift(26) informativeBTC1H['BTC_ICH_CS_LOW_1H'] = informativeBTC1H['low'].shift(26) informativeBTC1H['BTC_ICH_CS_KS_1H'] = informativeBTC1H['BTC_ICH_KS_1H'].shift(26) informativeBTC1H['BTC_ICH_CS_TS_1H'] = informativeBTC1H['BTC_ICH_TS_1H'].shift(26) informativeBTC1H['BTC_ICH_CS_SSA_1H'] = informativeBTC1H['BTC_ICH_SSA_1H'].shift(26) informativeBTC1H['BTC_ICH_CS_SSB_1H'] = informativeBTC1H['BTC_ICH_SSB_1H'].shift(26) #Ichimoku calculations for the BTC in 4h informativeBTC4H['BTC_ICH_SSB_4H'] = taichi.trend.ichimoku_b(informativeBTC4H['high'], informativeBTC4H['low'], window2=26, window3=52).shift(26) informativeBTC4H['BTC_ICH_SSA_4H'] = taichi.trend.ichimoku_a(informativeBTC4H['high'], informativeBTC4H['low'], window1=9, window2=26).shift(26) informativeBTC4H['BTC_ICH_KS_4H'] = taichi.trend.ichimoku_base_line(informativeBTC4H['high'], informativeBTC4H['low']) informativeBTC4H['BTC_ICH_TS_4H'] = taichi.trend.ichimoku_conversion_line(informativeBTC4H['high'], informativeBTC4H['low']) informativeBTC4H['BTC_ICH_CS_4H'] = informativeBTC4H['close'] informativeBTC4H['BTC_ICH_CS_HIGH_4H'] = informativeBTC4H['high'].shift(26) informativeBTC4H['BTC_ICH_CS_LOW_4H'] = informativeBTC4H['low'].shift(26) informativeBTC4H['BTC_ICH_CS_KS_4H'] = informativeBTC4H['BTC_ICH_KS_4H'].shift(26) informativeBTC4H['BTC_ICH_CS_TS_4H'] = informativeBTC4H['BTC_ICH_TS_4H'].shift(26) informativeBTC4H['BTC_ICH_CS_SSA_4H'] = informativeBTC4H['BTC_ICH_SSA_4H'].shift(26) informativeBTC4H['BTC_ICH_CS_SSB_4H'] = informativeBTC4H['BTC_ICH_SSB_4H'].shift(26) #log_to_results(informativeBTC4H.to_string()) #Ichimoku calculations for the strategy's timeframe 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) dataframe['ICH_KS'] = taichi.trend.ichimoku_base_line(dataframe['high'], dataframe['low']) dataframe['ICH_TS'] = taichi.trend.ichimoku_conversion_line(dataframe['high'], dataframe['low']) dataframe['ICH_CS'] = dataframe['close'] dataframe['ICH_CS_HIGH'] = dataframe['high'].shift(26) dataframe['ICH_CS_LOW'] = dataframe['low'].shift(26) dataframe['ICH_CS_KS'] = dataframe['ICH_KS'].shift(26) dataframe['ICH_CS_TS'] = dataframe['ICH_TS'].shift(26) dataframe['ICH_CS_SSA'] = dataframe['ICH_SSA'].shift(26) dataframe['ICH_CS_SSB'] = dataframe['ICH_SSB'].shift(26) # RSI dataframe['rsi'] = ta.RSI(dataframe) #log_to_results(dataframe.to_string()) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: global informativeBTC30M global informativeBTC1H global informativeBTC4H dataframe.loc[ ( #Conditions for the current asset being calculated in the strategy's timeframe (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['ICH_SSA']) & (dataframe['close'] > dataframe['ICH_SSB']) & (dataframe['close'] > dataframe['ICH_KS']) & (dataframe['close'] > dataframe['ICH_TS']) & (dataframe['open'] > dataframe['ICH_SSA']) & (dataframe['open'] > dataframe['ICH_SSB']) & (dataframe['open'] > dataframe['ICH_KS']) & (dataframe['open'] > dataframe['ICH_TS']) & (dataframe['ICH_CS'] > dataframe['ICH_CS_HIGH']) & (dataframe['ICH_CS'] > dataframe['ICH_CS_KS']) & (dataframe['ICH_CS'] > dataframe['ICH_CS_TS']) & (dataframe['ICH_CS'] > dataframe['ICH_CS_SSA']) & (dataframe['ICH_CS'] > dataframe['ICH_CS_SSB']) #Conditions for the bitcoin in 30m & (informativeBTC30M['BTC_ICH_CS_30M'] > informativeBTC30M['BTC_ICH_CS_HIGH_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] > informativeBTC30M['BTC_ICH_CS_KS_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] > informativeBTC30M['BTC_ICH_CS_TS_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] > informativeBTC30M['BTC_ICH_CS_SSA_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] > informativeBTC30M['BTC_ICH_CS_SSB_30M']) #Conditions for the bitcoin in 1h & (informativeBTC1H['BTC_ICH_CS_1H'] > informativeBTC1H['BTC_ICH_CS_HIGH_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] > informativeBTC1H['BTC_ICH_CS_KS_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] > informativeBTC1H['BTC_ICH_CS_TS_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] > informativeBTC1H['BTC_ICH_CS_SSA_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] > informativeBTC1H['BTC_ICH_CS_SSB_1H']) #Conditions for the bitcoin in 4h #& (informativeBTC4H['BTC_ICH_CS_4H'] > informativeBTC4H['BTC_ICH_CS_HIGH_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] > informativeBTC4H['BTC_ICH_CS_KS_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] > informativeBTC4H['BTC_ICH_CS_TS_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] > informativeBTC4H['BTC_ICH_CS_SSA_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] > informativeBTC4H['BTC_ICH_CS_SSB_4H']) #& ( # (informativeBTC4H['BTC_ICH_SSB_4H'] > informativeBTC4H['BTC_ICH_SSA_4H']) # #& (informativeBTC4H['open'] < informativeBTC4H['BTC_ICH_SSB_4H']) # #& (informativeBTC4H['close'] > informativeBTC4H['BTC_ICH_SSB_4H']) # & (informativeBTC4H['close'] > informativeBTC4H['BTC_ICH_KS_4H']) # & (informativeBTC4H['close'] > informativeBTC4H['BTC_ICH_TS_4H']) #) ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe['close'] < dataframe['open']) & (dataframe['close'] < dataframe['ICH_SSA']) & (dataframe['close'] < dataframe['ICH_SSB']) & (dataframe['close'] < dataframe['ICH_KS']) & (dataframe['close'] < dataframe['ICH_TS']) & (dataframe['open'] < dataframe['ICH_SSA']) & (dataframe['open'] < dataframe['ICH_SSB']) & (dataframe['open'] < dataframe['ICH_KS']) & (dataframe['open'] < dataframe['ICH_TS']) & (dataframe['ICH_CS'] < dataframe['ICH_CS_LOW']) & (dataframe['ICH_CS'] < dataframe['ICH_CS_KS']) & (dataframe['ICH_CS'] < dataframe['ICH_CS_TS']) & (dataframe['ICH_CS'] < dataframe['ICH_CS_SSA']) & (dataframe['ICH_CS'] < dataframe['ICH_CS_SSB']) #Conditions for the bitcoin in 30m & (informativeBTC30M['BTC_ICH_CS_30M'] < informativeBTC30M['BTC_ICH_CS_LOW_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] < informativeBTC30M['BTC_ICH_CS_KS_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] < informativeBTC30M['BTC_ICH_CS_TS_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] < informativeBTC30M['BTC_ICH_CS_SSA_30M']) & (informativeBTC30M['BTC_ICH_CS_30M'] < informativeBTC30M['BTC_ICH_CS_SSB_30M']) & (informativeBTC1H['BTC_ICH_CS_1H'] < informativeBTC1H['BTC_ICH_CS_LOW_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] < informativeBTC1H['BTC_ICH_CS_KS_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] < informativeBTC1H['BTC_ICH_CS_TS_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] < informativeBTC1H['BTC_ICH_CS_SSA_1H']) & (informativeBTC1H['BTC_ICH_CS_1H'] < informativeBTC1H['BTC_ICH_CS_SSB_1H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] < informativeBTC4H['BTC_ICH_CS_LOW_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] < informativeBTC4H['BTC_ICH_CS_KS_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] < informativeBTC4H['BTC_ICH_CS_TS_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] < informativeBTC4H['BTC_ICH_CS_SSA_4H']) #& (informativeBTC4H['BTC_ICH_CS_4H'] < informativeBTC4H['BTC_ICH_CS_SSB_4H']) #& ( # (informativeBTC4H['BTC_ICH_SSB_4H'] < informativeBTC4H['BTC_ICH_SSA_4H']) # #& (informativeBTC4H['open'] > informativeBTC4H['BTC_ICH_SSB_4H']) # #& (informativeBTC4H['close'] < informativeBTC4H['BTC_ICH_SSB_4H']) # & (informativeBTC4H['close'] < informativeBTC4H['BTC_ICH_KS_4H']) # & (informativeBTC4H['close'] < informativeBTC4H['BTC_ICH_TS_4H']) #) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #dataframe.loc[ # ( # (dataframe['close'] >= dataframe['ICH_KS']) # | (dataframe['close'] <= dataframe['ICH_SSB']) # ), # 'exit_long'] = 1 # #dataframe.loc[ # ( # (dataframe['close'] <= dataframe['ICH_KS']) # | (dataframe['close'] >= dataframe['ICH_SSB']) # ), # 'exit_short'] = 1 return dataframe