From 1c98640129cc44337da32f1488d5faae8211c70f Mon Sep 17 00:00:00 2001 From: Mark Regan Date: Wed, 26 Oct 2022 13:11:10 +0100 Subject: [PATCH] Delete MultiTargetClassifierTestStrategy.py --- .../MultiTargetClassifierTestStrategy.py | 244 ------------------ 1 file changed, 244 deletions(-) delete mode 100644 user_data/strategies/MultiTargetClassifierTestStrategy.py diff --git a/user_data/strategies/MultiTargetClassifierTestStrategy.py b/user_data/strategies/MultiTargetClassifierTestStrategy.py deleted file mode 100644 index 6ca2567c3..000000000 --- a/user_data/strategies/MultiTargetClassifierTestStrategy.py +++ /dev/null @@ -1,244 +0,0 @@ -import logging -from functools import reduce - -import numpy as np -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 MultiTargetClassifierTestStrategy(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 - - # Classifiers are typically set up with strings as targets: - df['&s-up_or_down_long'] = np.where( - df["close"].shift(-100) > df["close"], 'up_long', 'down_long') - df['&s-up_or_down_medium'] = np.where( - df["close"].shift(-50) > df["close"], 'up_medium', 'down_medium') - df['&s-up_or_down_short'] = np.where( - df["close"].shift(-20) > df["close"], 'up_short', 'down_short') - - # 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["up_long_mean"] + dataframe["up_long_std"] * val - ) - for val in self.std_dev_multiplier_sell.range: - dataframe[f'sell_roi_{val}'] = ( - dataframe["down_long_mean"] - dataframe["down_long_std"] * val - ) - return dataframe - - def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: - - enter_long_conditions = [ - df["do_predict"] == 1, - df["up_long"] > 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["down_long"] < 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["down_long"] < 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["up_long"] > 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