Example Classifier strat

freqtrade/templates/FreqaiHybridExampleStrategy.py

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+import logging
+from datetime import datetime, timedelta
+from functools import reduce
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from freqtrade.exchange import timeframe_to_prev_date
+from freqtrade.persistence import Trade
+from freqtrade.strategy import (DecimalParameter, IntParameter, IStrategy,
+                                merge_informative_pair)
+from numpy.lib import math
+from pandas import DataFrame
+from technical import qtpylib
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiExampleHybridStrategy(IStrategy):
+    """
+    Example classifier hybrid 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'].
+    
+    The underlying original supertrend strat is authored by @juankysoriano (Juan Carlos Soriano)
+    * github: https://github.com/juankysoriano/
+    
+    This strategy is not designed to be used live
+    """
+
+    minimal_roi = {"0": 0.1, "30": 0.75, "60": 0.05, "120": 0.025, "240": -1}
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "prediction": {"prediction": {"color": "blue"}},
+            "target_roi": {
+                "target_roi": {"color": "brown"},
+            },
+            "do_predict": {
+                "do_predict": {"color": "brown"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.1
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = True
+
+    linear_roi_offset = DecimalParameter(
+        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
+    )
+    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
+    
+    buy_params = {
+        "buy_m1": 4,
+        "buy_m2": 7,
+        "buy_m3": 1,
+        "buy_p1": 8,
+        "buy_p2": 9,
+        "buy_p3": 8,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_m1": 1,
+        "sell_m2": 3,
+        "sell_m3": 6,
+        "sell_p1": 16,
+        "sell_p2": 18,
+        "sell_p3": 18,
+    }
+    
+    buy_m1 = IntParameter(1, 7, default=1)
+    buy_m2 = IntParameter(1, 7, default=3)
+    buy_m3 = IntParameter(1, 7, default=4)
+    buy_p1 = IntParameter(7, 21, default=14)
+    buy_p2 = IntParameter(7, 21, default=10)
+    buy_p3 = IntParameter(7, 21, default=10)
+
+    sell_m1 = IntParameter(1, 7, default=1)
+    sell_m2 = IntParameter(1, 7, default=3)
+    sell_m3 = IntParameter(1, 7, default=4)
+    sell_p1 = IntParameter(7, 21, default=14)
+    sell_p2 = IntParameter(7, 21, default=10)
+    sell_p3 = IntParameter(7, 21, default=10)
+
+    def informative_pairs(self):
+        whitelist_pairs = self.dp.current_whitelist()
+        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        informative_pairs = []
+        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+            for pair in whitelist_pairs:
+                informative_pairs.append((pair, tf))
+            for pair in corr_pairs:
+                if pair in whitelist_pairs:
+                    continue  # avoid duplication
+                informative_pairs.append((pair, tf))
+        return informative_pairs
+
+    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, window=t)
+            informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+            informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+
+            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(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'] = np.where( df["close"].shift(-50) >
+                                            df["close"], 'up', 'down')
+
+            # REGRESSOR Model: Can use single or multi traget
+            # 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
+            #)
+            # 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.
+        
+        for multiplier in self.buy_m1.range:
+            for period in self.buy_p1.range:
+                dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend(
+                    dataframe, multiplier, period
+                )["STX"]
+
+        for multiplier in self.buy_m2.range:
+            for period in self.buy_p2.range:
+                dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend(
+                    dataframe, multiplier, period
+                )["STX"]
+
+        for multiplier in self.buy_m3.range:
+            for period in self.buy_p3.range:
+                dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend(
+                    dataframe, multiplier, period
+                )["STX"]
+
+        for multiplier in self.sell_m1.range:
+            for period in self.sell_p1.range:
+                dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend(
+                    dataframe, multiplier, period
+                )["STX"]
+
+        for multiplier in self.sell_m2.range:
+            for period in self.sell_p2.range:
+                dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend(
+                    dataframe, multiplier, period
+                )["STX"]
+
+        for multiplier in self.sell_m3.range:
+            for period in self.sell_p3.range:
+                dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend(
+                    dataframe, multiplier, period
+                )["STX"]
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (df[f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"] == "up") &
+            (df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up") &
+            (df[f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"] == "up") &
+            (df["do_predict"] == 1) &
+            (df['&s-up_or_down'] == 'up'), 
+            "enter_long",
+        ] = 1
+
+        df.loc[
+            (df[f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}"] == "down") & 
+            (df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down") & 
+            (df[f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}"] == "down") & 
+            (df["do_predict"] == 1) & 
+            (df['&s-up_or_down'] == 'down'),
+            "enter_short",
+        ] = 1
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        
+        df.loc[
+            (df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down"),
+            "exit_long",
+        ] = 1
+
+        df.loc[
+            (df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up"),
+            "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
+    
+    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:
+
+        return 1
+
+    """
+    Supertrend Indicator; adapted for freqtrade, optimized by the math genius.
+    from: Perkmeister#2394
+    """
+
+    def supertrend(self, dataframe: DataFrame, multiplier, period):
+        df = dataframe.copy()
+        last_row = dataframe.tail(1).index.item()
+
+        df['TR'] = ta.TRANGE(df)
+        df['ATR'] = ta.SMA(df['TR'], period)
+
+        st = 'ST_' + str(period) + '_' + str(multiplier)
+        stx = 'STX_' + str(period) + '_' + str(multiplier)
+
+        # Compute basic upper and lower bands
+        BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values
+        BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values
+        FINAL_UB = np.zeros(last_row + 1)
+        FINAL_LB = np.zeros(last_row + 1)
+        ST = np.zeros(last_row + 1)
+        CLOSE = df['close'].values
+
+        # Compute final upper and lower bands
+        for i in range(period, last_row + 1):
+            FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1] or CLOSE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1]
+            FINAL_LB[i] = BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1] or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1]
+
+        # Set the Supertrend value
+        for i in range(period, last_row + 1):
+            ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \
+                    FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] >  FINAL_UB[i] else \
+                    FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \
+                    FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] <  FINAL_LB[i] else 0.00
+        df_ST = pd.DataFrame(ST, columns=[st])
+        df = pd.concat([df, df_ST],axis=1)
+
+        # Mark the trend direction up/down
+        df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down',  'up'), np.NaN)
+
+        df.fillna(0, inplace=True)
+
+        return DataFrame(index=df.index, data={
+            'ST' : df[st],
+            'STX' : df[stx]
+        })