# source: https://raw.githubusercontent.com/xaemiphor/ft/4e55355517f19dc45385d23d4a5d72776db275e3/strategies/IndicatorsOr.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib from functools import reduce class Github_xaemiphor_ft__IndicatorsOr__20250111_041634(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False minimal_roi = { # "120": 0.0, # exit after 120 minutes at break even # "60": 0.01, # "30": 0.02, # "0": 0.04, } stoploss = -0.10 trailing_stop = False timeframe = "1m" # price movement timeframe informative_timeframe = '5m' # Signal timeframe process_only_new_candles = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Hyperoptable parameters buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True) buy_cci = IntParameter(low=-120, high=-90, default=-100, space="buy", optimize=True, load=True) sell_cci = IntParameter(low=90, high=120, default=100, space="sell", optimize=True, load=True) buy_stoch = IntParameter(low=1, high=50, default=20, space="buy", optimize=True, load=True) sell_stoch = IntParameter(low=50, high=100, default=80, space="sell", optimize=True, load=True) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # 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"} plot_config = { "main_plot": { "tema": {"color":"white"}, }, "subplots": { "RSI": { "rsi": {"color": "white"}, "rsi_buy": {"color": "red"}, "rsi_sell": {"color": "green"}, }, "CCI": { "cci": {"color": "white"}, "cci_buy": {"color": "red"}, "cci_sell": {"color": "green"}, }, "stochastic": { "fastd": {"color": "white"}, "fastk": {"color": "yellow"}, "stoch_buy": {"color": "red"}, "stoch_sell": {"color": "green"}, }, "macd": { "macdhist": {"color": "yellow"}, "macdhist_bar": {"color": "white"}, }, }, } def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: market = self.dp.market(metadata['pair']) dataframe["close_fee"] = (dataframe["close"] * market['maker']) dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) # rsi dataframe["rsi"] = ta.RSI(dataframe) dataframe["rsi_buy"] = self.buy_rsi.value dataframe["rsi_sell"] = self.sell_rsi.value # cci dataframe["cci"] = ta.CCI(dataframe) dataframe["cci_buy"] = self.buy_cci.value dataframe["cci_sell"] = self.sell_cci.value # stochasticfast stoch_fast = ta.STOCHF(dataframe) dataframe["fastd"] = stoch_fast["fastd"] dataframe["fastk"] = stoch_fast["fastk"] dataframe["stoch_buy"] = self.buy_stoch.value dataframe["stoch_sell"] = self.sell_stoch.value # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] dataframe["macdhist_bar"] = 0 return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['runmode'].value in ('backtest', 'hyperopt'): assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in 5m or 1m timeframe." if self.timeframe == self.informative_timeframe: dataframe = self.do_indicators(dataframe, metadata) else: if not self.dp: return dataframe informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) informative = self.do_indicators(informative.copy(), metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = { 'rsi': [], 'cci': [], 'stochfast': [], 'macd': [], } conditions['rsi'].append(qtpylib.crossed_below(dataframe["rsi"], self.buy_rsi.value)) conditions['cci'].append(qtpylib.crossed_below(dataframe["cci"], self.buy_cci.value)) conditions['stochfast'].append(dataframe["fastk"] < self.buy_stoch.value) conditions['stochfast'].append(dataframe["fastd"] < self.buy_stoch.value) conditions['stochfast'].append(qtpylib.crossed_above(dataframe["fastk"], dataframe["fastd"])) conditions['macd'].append(qtpylib.crossed_above(dataframe["macdhist"],0)) for key in conditions.keys(): for x in range(10): conditions[key].append(dataframe["volume"].shift(x) > 0) dataframe.loc[ reduce(lambda a, b: a & b, conditions[key]), f'enter_long_{key}'] = 1 dataframe.loc[ reduce(lambda x, y: x | y, (reduce(lambda a, b: a & b, conditions[key]) for key in conditions)), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = { 'rsi': [], 'cci': [], 'stochfast': [], 'macd': [], } conditions['rsi'].append(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) conditions['cci'].append(qtpylib.crossed_above(dataframe["cci"], self.sell_cci.value)) conditions['stochfast'].append(dataframe["fastk"] > self.sell_stoch.value) conditions['stochfast'].append(dataframe["fastd"] > self.sell_stoch.value) conditions['stochfast'].append(qtpylib.crossed_below(dataframe["fastk"], dataframe["fastd"])) conditions['macd'].append(qtpylib.crossed_below(dataframe["macdhist"],0)) for key in conditions.keys(): for x in range(10): conditions[key].append(dataframe["volume"].shift(x) > 0) dataframe.loc[ reduce(lambda a, b: a & b, conditions[key]), f'exit_long_{key}'] = 1 dataframe.loc[ reduce(lambda x, y: x | y, (reduce(lambda a, b: a & b, conditions[key]) for key in conditions)), 'exit_long'] = 1 return dataframe