# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/5bd42947b5300922a342d153182906e7cdbe8a40/startegy%20test/2/momentum_tf_divergence/momentum_tf_divergence.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from datetime import datetime, timedelta, timezone from freqtrade.exchange import timeframe_to_minutes from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, merge_informative_pair) from technical.util import resample_to_interval, resampled_merge from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.pivots_points import pivots_points from typing import Any, Dict, List # 13% APR 1 year backtest class Github_hamidreza07_freqai_strategy__momentum_tf_divergence__20231226_132626(IStrategy): custom_info = {} """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # 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 # Optimal timeframe for the strategy. timeframe = '1m' informative_timeframe = '15m' # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 0.156, "311": 0.109, "708": 0.075, "1454": 0 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.167 # Trailing stoploss trailing_stop= True trailing_stop_positive=0.01 trailing_stop_positive_offset= 0.012 trailing_only_offset_is_reached= True # 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 = 50 # Optional order type mapping. order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { "MACD": { 'fastd': {'color': 'blue'}, 'fastk': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, }, "Pivot": { 'pivot': {'color': 'black'}, }, 'SMA': { 'sma15': {'color': 'white'}, 'sma50': {'color': 'yellow'}, }, }, 'subplots': { } } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 1 } ] def leverage(self, pair: str, current_time: 'datetime', current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: """ Customize leverage for each new trade. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return 20.0 def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, entry_tag: str, **kwargs) -> float: return 100 #- (self.wallets.get_total_stake_amount() / 20) def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: period = 14 smoothD = 3 SmoothK = 3 #1h DF dataframe1h = resample_to_interval(dataframe, 60) macd, macdsignal, macdhist = ta.MACD(dataframe1h['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe1h['macdf'] = macd dataframe1h['macdd'] = macdsignal dataframe1h['macdh'] = macdhist stoch_fast = ta.STOCHF(dataframe1h) dataframe1h['fastd'] = stoch_fast['fastd'] dataframe1h['fastk'] = stoch_fast['fastk'] dataframe1h['rsi'] = ta.RSI(dataframe1h, timeperiod=14) dataframe1h['cci'] = ta.CCI(dataframe1h) stochrsi = (dataframe1h['rsi'] - dataframe1h['rsi'].rolling(period).min()) / (dataframe1h['rsi'].rolling(period).max() - dataframe1h['rsi'].rolling(period).min()) dataframe1h['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100 dataframe1h['srsi_d'] = dataframe1h['srsi_k'].rolling(smoothD).mean() #1m DF dataframe15m = resample_to_interval(dataframe, 15) macd, macdsignal, macdhist = ta.MACD(dataframe15m['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe15m['macdf'] = macd dataframe15m['macdd'] = macdsignal dataframe15m['macdh'] = macdhist stoch_fast = ta.STOCHF(dataframe15m) dataframe15m['fastd'] = stoch_fast['fastd'] dataframe15m['fastk'] = stoch_fast['fastk'] dataframe15m['rsi'] = ta.RSI(dataframe15m, timeperiod=14) dataframe15m['cci'] = ta.CCI(dataframe15m) stochrsi = (dataframe15m['rsi'] - dataframe15m['rsi'].rolling(period).min()) / (dataframe15m['rsi'].rolling(period).max() - dataframe15m['rsi'].rolling(period).min()) dataframe15m['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100 dataframe15m['srsi_d'] = dataframe15m['srsi_k'].rolling(smoothD).mean() #1m DF macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe['macdf'] = macd dataframe['macdd'] = macdsignal dataframe['macdh'] = macdhist stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['cci'] = ta.CCI(dataframe) stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()) / (dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min()) dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100 dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean() #Resampling dataframe = resampled_merge(dataframe, dataframe15m, fill_na=True) dataframe = resampled_merge(dataframe, dataframe1h, fill_na=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( # LONG (dataframe["resample_15_macdh"] < 0) & (dataframe["resample_15_cci"] > 100) & (dataframe["cci"] > 100) & (dataframe["macdh"].rolling(15).sum() > 0) & # Checking 15 last exercices before entering position to verify signal (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_long'] = 0 dataframe.loc[ ( (dataframe["resample_15_macdh"] > -100) & (dataframe["resample_15_cci"] < -100) & (dataframe["cci"] > -100) & (dataframe["macdh"].rolling(15).sum() < 0) & # Checking 15 last exercices before entering position to verify signal (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['volume'] == 0) # Make sure Volume is not 0 ), 'exit_long'] = 0 # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info) dataframe.loc[ ( (dataframe['volume'] == 0) # Make sure Volume is not 0 ), 'exit_short'] = 1 return dataframe