# source: https://raw.githubusercontent.com/hamidreza07/freqtrade/2d378aa9ceb8f44e420e507903706b75ac1afb32/freqtrade/templates/FreqaiExampleStrategy3.py import logging from functools import reduce from typing import Dict, Optional import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.strategy import CategoricalParameter, IStrategy from datetime import datetime logger = logging.getLogger(__name__) class Github_hamidreza07_freqtrade__FreqaiExampleStrategy3__20231127_092233(IStrategy): """ Completely new strategy with the same function and class names. """ minimal_roi = {"0": 0.01, "60":0.01,"120":-1} plot_config = { "main_plot": {}, "subplots": { "&-s_close": {"prediction": {"color": "blue"}}, "do_predict": { "do_predict": {"color": "brown"}, }, }, } process_only_new_candles = True stoploss = -0.05 use_exit_signal = True # this is the maximum period fed to talib (timeframe independent) startup_candle_count: int = 40 can_short = True std_dev_multiplier_buy = CategoricalParameter( [0.5, 0.75, 1.0, 1.25], default=1.0, space="buy", optimize=True) std_dev_multiplier_sell = CategoricalParameter( [0.5, 0.75, 1.0, 1.25], space="sell", default=1.0, optimize=True) def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame: """ Completely different feature engineering logic. """ dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) # Calculate Stochastic Oscillator stoch = ta.STOCH(dataframe, fastk_period=period, slowk_period=3, slowd_period=3) dataframe["%-stochastic-k"] = stoch["slowk"] dataframe["%-stochastic-d"] = stoch["slowd"] dataframe["%-macd"] = ta.MACD(dataframe)['macd'] return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :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 entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :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 3.0 def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. In other words, a single feature defined in this function will automatically expand to a total of `include_timeframes` * `include_shifted_candles` * `include_corr_pairs` numbers of features added to the model. Features defined here will *not* be automatically duplicated on user defined `indicator_periods_candles` All features must be prepended with `%` to be recognized by FreqAI internals. Access metadata such as the current pair/timeframe with: `metadata["pair"]` `metadata["tf"]` More details on how these config defined parameters accelerate feature engineering in the documentation at: https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features :param dataframe: strategy dataframe which will receive the features :param metadata: metadata of current pair dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200) """ dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-raw_price"] = dataframe["close"] return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns created by all other freqai_feature_engineering_* functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is a good place for any feature that should not be auto-expanded upon (e.g. day of the week). All features must be prepended with `%` to be recognized by FreqAI internals. Access metadata such as the current pair with: `metadata["pair"]` More details about feature engineering available: https://www.freqtrade.io/en/latest/freqai-feature-engineering :param dataframe: strategy dataframe which will receive the features :param metadata: metadata of current pair usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 """ dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ Set a target with a continuous range of numbers based on a moving average crossover. """ # Calculate short-term moving average (e.g., 3-period MA) short_term_period = 3 dataframe['short_term_ma'] = dataframe['close'].rolling(window=short_term_period).mean() # Calculate long-term moving average (e.g., 10-period MA) long_term_period = 10 dataframe['long_term_ma'] = dataframe['close'].rolling(window=long_term_period).mean() short_term_ma = dataframe["short_term_ma"] # Replace with your actual short-term MA long_term_ma = dataframe["long_term_ma"] # Replace with your actual long-term MA # Calculate the difference between short-term and long-term MAs ma_crossover_diff = short_term_ma - long_term_ma # Assign the difference as the target, scaling it for better readability dataframe["&-ma_crossover_target"] = ma_crossover_diff * 100 return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # All indicators must be populated by feature_engineering_*() functions # the model will return all labels created by user in `set_freqai_targets()` # (& appended targets), an indication of whether or not the prediction should be accepted, # the target mean/std values for each of the labels created by user in # `set_freqai_targets()` for each training period. dataframe = self.freqai.start(dataframe, metadata, self) dataframe["%-sma_50"] = ta.SMA(dataframe, timeperiod=50) dataframe["%-ema_200"] = ta.EMA(dataframe, timeperiod=200) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: """ Modify entry trend logic to use the combined trailing target. :param df: DataFrame with strategy data :param metadata: Metadata of the current pair :return: Modified DataFrame with entry trend information """ enter_long_conditions = [ df["do_predict"] == 1 , df["&-ma_crossover_target"] > 10 ] if enter_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") enter_short_conditions = [ df["do_predict"] == 1, df["&-ma_crossover_target"] < -10 , ] if enter_short_conditions: df.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: """ Modify exit trend logic to use the combined trailing target. :param df: DataFrame with strategy data :param metadata: Metadata of the current pair :return: Modified DataFrame with exit trend information """ enter_long_conditions = [ df["do_predict"] == 1 , df["&-ma_crossover_target"] < 5, ] if enter_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") exit_short_conditions = [ df["do_predict"] == 1, df["&-ma_crossover_target"] > -5 , ] if exit_short_conditions: df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 return df def get_ticker_indicator(self): return int(self.config["timeframe"][:-1])