import logging from functools import reduce import pandas as pd import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.strategy import CategoricalParameter, IStrategy, merge_informative_pair 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. populate_any_indicators() automatically generates 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 populate_any_indicators( self, pair, df, tf, informative=None, set_generalized_indicators=False ): """ Function designed to automatically generate, name and merge features from user indicated timeframes in the configuration file. User controls the indicators passed to the training/prediction by prepending indicators with `'%-' + coin ` (see convention below). I.e. user should not prepend any supporting metrics (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the model. :param pair: pair to be used as informative :param df: strategy dataframe which will receive merges from informatives :param tf: timeframe of the dataframe which will modify the feature names :param informative: the dataframe associated with the informative pair """ coin = pair.split('/')[0] if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) # first loop is automatically duplicating indicators for time periods for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: t = int(t) informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t) informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(informative), window=t, stds=2.2 ) informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] informative[f"%-{coin}bb_width-period_{t}"] = ( informative[f"{coin}bb_upperband-period_{t}"] - informative[f"{coin}bb_lowerband-period_{t}"] ) / informative[f"{coin}bb_middleband-period_{t}"] informative[f"%-{coin}close-bb_lower-period_{t}"] = ( informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] ) informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t) informative[f"%-{coin}relative_volume-period_{t}"] = ( informative["volume"] / informative["volume"].rolling(t).mean() ) informative[f"%-{coin}pct-change"] = informative["close"].pct_change() informative[f"%-{coin}raw_volume"] = informative["volume"] informative[f"%-{coin}raw_price"] = informative["close"] indicators = [col for col in informative if col.startswith("%")] # This loop duplicates and shifts all indicators to add a sense of recency to data for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): if n == 0: continue informative_shift = informative[indicators].shift(n) informative_shift = informative_shift.add_suffix("_shift-" + str(n)) informative = pd.concat((informative, informative_shift), axis=1) df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) skip_columns = [ (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] ] df = df.drop(columns=skip_columns) # Add generalized indicators here (because in live, it will call this # function to populate indicators during training). Notice how we ensure not to # add them multiple times if set_generalized_indicators: df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 # user adds targets here by prepending them with &- (see convention below) df["&-s_close"] = ( df["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) .mean() / df["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 df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # All indicators must be populated by populate_any_indicators() for live functionality # to work correctly. # the model will return all labels created by user in `populate_any_indicators` # (& 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 # `populate_any_indicators()` 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