LitmusMinMaxRegretClassificationStrategy

from freqtrade.strategy import IStrategy, merge_informative_pair
from functools import reduce
from pandas import DataFrame
from technical import qtpylib

import logging
import pandas as pd
import talib.abstract as ta
import zigzag

logger = logging.getLogger(__name__)


class LitmusMinMaxRegretClassificationStrategy(IStrategy):
    """
    to run this:
      freqtrade trade --strategy LitmusMinMaxRegretClassificationStrategy
      --config user_data/strategies/config.LitmusMinMaxRegretClassification.json
      --freqaimodel LitmusMultiTargetClassifier --verbose
    """

    minimal_roi = {"0": 0.1, "240": -1}

    plot_config = {
        "main_plot": {},
        "subplots": {
            "do_predict": {
                "do_predict": {"color": "brown"},
                "DI_values": {"color": "grey"},
            },
            "Long": {
                "long_entry": {"color": "PaleGreen"},
                "missed_long_entry": {"color": "ForestGreen"},
                "long_entry_target": {"color": "PaleGreen"},
                "missed_long_entry_target": {"color": "ForestGreen"},
                "long_exit": {"color": "Salmon"},
                "missed_long_exit": {"color": "Crimson"},
                "missed_long_exit_target": {"color": "Crimson "},
            },
            "Segment": {
                "long_segment": {"color": "ForestGreen"},
                "not_long_segment": {"color": "Crimson"}
            },
            "After": {
                "after_long_top": {"color": "Crimson"},
                "after_long_bottom": {"color": "ForestGreen"},
                "not_after": {"color": "DarkGray"}
            },
            "Labels": {
                "real_long_peaks": {"color": "Blue"},
                "tripple_barrier_int": {"color": "Orange"}
            },
            "F1": {
                "max_f1_long_entry": {"color": "PaleGreen"},
                "max_f1_long_exit": {"color": "Salmon"},
                "max_f1_missed_long_entry": {"color": "ForestGreen"},
                "max_f1_missed_long_exit": {"color": "Crimson"}
            },
            "Time": {
                "time_to_train": {"color": "DarkGray"}
            },
        },
    }

    # Stop loss config
    stoploss = -0.03
    """trailing_stop = True
    trailing_stop_positive_offset = 0.01
    trailing_stop_positive = 0.005
    trailing_only_offset_is_reached = True"""

    process_only_new_candles = True
    use_exit_signal = True
    startup_candle_count = 300
    can_short = True

    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
        """
        """try:
            print(df["%-long_entry_pred"].columns)
        except:
            pass"""

        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}-close_over_sma-period_{t}"] = (
                informative["close"] / informative[f"{coin}-sma-period_{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
            df["%-hour_of_day"] = df["date"].dt.hour

            # Zigzag min/max for long pivot positions
            min_growth_long = self.freqai_info["labeling_parameters"].get(
                "min_growth_long", -1)
            min_retraction_long = self.freqai_info["labeling_parameters"].get(
                "min_retraction_long", -1)
            long_peaks = zigzag.peak_valley_pivots(
                df["close"].values, min_growth_long, -min_retraction_long)
            long_segments = zigzag.pivots_to_modes(long_peaks)

            # Set start and end as not peaks
            long_peaks[0] = 0  # Set first value of peaks = 0
            long_peaks[-1] = 0  # Set last value of peaks = 0
            df["real_long_peaks"] = long_peaks

            df["&long_target"] = long_peaks
            name_map = {0: "not_minmax", 1: "long_exit", -1: "long_entry",
                        2: "missed_long_exit", -2: "missed_long_entry"}
            df["&long_target"] = df["&long_target"].map(name_map)

            # Missed entries & exits (labels)
            df.loc[(df["&long_target"].shift(1) == name_map[1]), "&long_target"] = name_map[2]
            df.loc[(df["&long_target"].shift(1) == name_map[-1]), "&long_target"] = name_map[-2]

            df.loc[(df["&long_target"].shift(2) == name_map[1]), "&long_target"] = name_map[2]
            df.loc[(df["&long_target"].shift(2) == name_map[-1]), "&long_target"] = name_map[-2]

            # Long / Not Long Segments Classifier
            df["&long_segment"] = long_segments
            segment_name_map = {1: "long_segment", -1: "not_long_segment"}
            df["&long_segment"] = df["&long_segment"].map(segment_name_map)

            # Just after peak classifier
            segment_length = 8
            df["&after_segment"] = long_peaks
            df["&after_segment"] = df["&after_segment"].shift(1)
            df["&after_segment"] = df["&after_segment"].rolling(segment_length).mean()
            df["&after_segment"] = df["&after_segment"].apply(
                lambda x: 1 if x > 0 else (-1 if x < 0 else 0))
            after_name_map = {1: "after_long_top", -1: "after_long_bottom", 0: "not_after"}
            df["&after_segment"] = df["&after_segment"].map(after_name_map)
            print(df[["&after_segment", "&long_target"]].head(50))

        return df

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        self.freqai_info = self.config["freqai"]

        dataframe = self.freqai.start(dataframe, metadata, self)

        enter_mul = 2.6
        exit_mul = 1.7

        # Long entry targets
        dataframe["long_entry_target"] = (
            dataframe["long_entry_mean"] + dataframe["long_entry_std"] * enter_mul)
        dataframe["missed_long_entry_target"] = (
            dataframe["missed_long_entry_mean"] + dataframe["missed_long_entry_std"] * enter_mul)

        # Long exit targets
        dataframe["long_exit_target"] = (
            dataframe["long_exit_mean"] + dataframe["long_exit_std"] * exit_mul)
        dataframe["missed_long_exit_target"] = (
            dataframe["missed_long_exit_mean"] + dataframe["missed_long_exit_std"] * exit_mul)

        # Long segment rolling metric
        # TODO

        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        # Missed Long Entry
        conditions = [
            1 == 1,
            qtpylib.crossed_above(df["missed_long_entry"], df["missed_long_entry_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["enter_long", "enter_tag"]
            ] = (1, "missed_long_entry")

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        # Long Exit
        conditions = [
            1 == 1,
            qtpylib.crossed_above(df["missed_long_exit"], df["missed_long_exit_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["exit_long", "exit_tag"]
            ] = (1, "missed_long_exit")

        return df

    def get_ticker_indicator(self):
        return int(self.config["timeframe"][:-1])

    """def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            leverage: float, entry_tag: str, side: str,
                            **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        bid = self.wallets.get_available_stake_amount() * current_candle["missed_long_entry"]

        return bid"""