import logging from functools import reduce import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.strategy import CategoricalParameter, IStrategy logger = logging.getLogger(__name__) class FreqaiExampleStrategy(IStrategy): """ Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.freqai.start(dataframe, metadata) to make predictions on their data. feature_engineering_*() automatically generate the variety of features indicated by the user in the canonical freqtrade configuration file under config['freqai']. """ minimal_roi = {"0": 0.1, "240": -1} plot_config = { "main_plot": {}, "subplots": { "prediction": {"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 = False std_dev_multiplier_buy = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True) std_dev_multiplier_sell = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True) def feature_engineering_expand_all(self, dataframe, period, **kwargs): """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined `indicator_periods_candles`, `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 `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` * `include_corr_pairs` numbers of features added to the model. All features must be prepended with `%` to be recognized by FreqAI internals. 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 df: strategy dataframe which will receive the features :param period: period of the indicator - usage example: dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) """ dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe def feature_engineering_expand_basic(self, dataframe, **kwargs): """ *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. 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 df: strategy dataframe which will receive the features 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, **kwargs): """ *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. More details about feature engineering available: https://www.freqtrade.io/en/latest/freqai-feature-engineering :param df: strategy dataframe which will receive the features 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, **kwargs): """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals. More details about feature engineering available: https://www.freqtrade.io/en/latest/freqai-feature-engineering :param df: strategy dataframe which will receive the targets usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"] """ dataframe["&-s_close"] = ( dataframe["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) .mean() / dataframe["close"] - 1 ) # Classifiers are typically set up with strings as targets: # df['&s-up_or_down'] = np.where( df["close"].shift(-100) > # df["close"], 'up', 'down') # If user wishes to use multiple targets, they can add more by # appending more columns with '&'. User should keep in mind that multi targets # requires a multioutput prediction model such as # templates/CatboostPredictionMultiModel.py, # df["&-s_range"] = ( # df["close"] # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) # .max() # - # df["close"] # .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) # .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) # .min() # ) 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 `feature_engineering_*` # (& 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) for val in self.std_dev_multiplier_buy.range: dataframe[f'target_roi_{val}'] = ( dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val ) for val in self.std_dev_multiplier_sell.range: dataframe[f'sell_roi_{val}'] = ( dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * val ) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [ df["do_predict"] == 1, df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"], ] 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["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"], ] 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: exit_long_conditions = [ df["do_predict"] == 1, df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25, ] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 exit_short_conditions = [ df["do_predict"] == 1, df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25, ] 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]) def confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time, entry_tag, side: str, **kwargs, ) -> bool: df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = df.iloc[-1].squeeze() if side == "long": if rate > (last_candle["close"] * (1 + 0.0025)): return False else: if rate < (last_candle["close"] * (1 - 0.0025)): return False return True