LitmusMinMaxClassificationStrategy

from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, merge_informative_pair
from functools import reduce
from pandas import DataFrame
from technical import qtpylib
from typing import Optional

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

logger = logging.getLogger(__name__)


class LitmusMinMaxClassificationStrategy(IStrategy):
    """
    to run this:
      freqtrade trade --strategy LitmusMinMaxClassificationStrategy
      --config user_data/strategies/config.LitmusMinMaxClassification.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": {
                "missed2_minima": {"color": "PaleGreen"},
                "missed1_minima": {"color": "ForestGreen"},
                "missed1_long_entry_target": {"color": "ForestGreen"},
                "missed2_maxima": {"color": "Salmon"},
                "missed1_maxima": {"color": "Crimson"},
                "missed1_long_exit_target": {"color": "Crimson"},
            },
            "Short": {
                "missed2_maxima": {"color": "PaleGreen"},
                "missed1_maxima": {"color": "ForestGreen"},
                "missed1_short_entry_target": {"color": "ForestGreen"},
                "missed2_minima": {"color": "Salmon"},
                "missed1_minima": {"color": "Crimson"},
                "missed1_short_exit_target": {"color": "Crimson"},
            },
            "Segment": {
                "long_segment": {"color": "ForestGreen"},
                "short_segment": {"color": "Crimson"}
            },
            "SegT": {
                "segment_delta_cum": {"color": "#4e8b88"},
                "segment_delta": {"color": "#3c6864"}
            },
            "Labels": {
                "real_segment_peaks": {"color": "#E8D4F7"},
                "real_peaks": {"color": "#700CBC"},
            },
            "Other": {
                "time_to_train": {"color": "DarkGray"},
                "num_trees_&target": {"color": "#700CBC"},
                "num_trees_&segments": {"color": "#E8D4F7"},
                "num_features_excluded_&target": {"color": "#700CBC"},
                "num_features_excluded_&segments": {"color": "#E8D4F7"},
            },
        },
    }

    # 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
        """

        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 pivot positions
            min_growth = self.freqai_info["labeling_parameters"].get(
                "min_growth", -1)
            peaks = zigzag.peak_valley_pivots(
                df["close"].values, min_growth, -min_growth)
            name_map = {0: "not_minmax", 1: "maxima", -1: "minima",
                        2: "missed1_maxima", -2: "missed1_minima",
                        3: "missed2_maxima", -3: "missed2_minima"}
            peaks[0] = 0  # Set first value of peaks = 0
            peaks[-1] = 0  # Set last value of peaks = 0
            df["&target"] = peaks
            df["&target"] = df["&target"].map(name_map)
            df["real_peaks"] = peaks

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

            df.loc[(df["&target"].shift(2) == name_map[1]), "&target"] = name_map[3]
            df.loc[(df["&target"].shift(2) == name_map[-1]), "&target"] = name_map[-3]

            # Reset minima / maxima label back to not_minmax (predictions not used)
            df.loc[(df["&target"] == name_map[1]), "&target"] = name_map[0]
            df.loc[(df["&target"] == name_map[-1]), "&target"] = name_map[0]

            """# Segment Labels to bail on bad trades
            segment_min_growth = self.freqai_info["labeling_parameters"].get(
                "segment_min_growth", -1)
            segment_peaks = zigzag.peak_valley_pivots(
                df["close"].values, segment_min_growth, -segment_min_growth)
            segments = zigzag.pivots_to_modes(segment_peaks)
            df["&segments"] = segments
            df["&segments"] = df["&segments"].map(
                {1: "long_segment", -1: "short_segment"})
            df["real_segment_peaks"] = segment_peaks"""

        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 = 3
        exit_mul = 2

        # Long entry (missed1)
        dataframe["missed1_long_entry_target"] = (
            dataframe["missed1_minima_mean"] + dataframe["missed1_minima_std"] * enter_mul)

        # Long exit (missed1))
        dataframe["missed1_long_exit_target"] = (
            dataframe["missed1_maxima_mean"] + dataframe["missed1_maxima_std"] * exit_mul)

        # Long exit (missed2))
        dataframe["missed2_long_exit_target"] = (
                dataframe["missed2_maxima_mean"] + dataframe["missed2_maxima_std"] * exit_mul)

        # Short entry (missed1)
        dataframe["missed1_short_entry_target"] = (
                dataframe["missed1_maxima_mean"] + dataframe["missed1_maxima_std"] * enter_mul)

        # Short exit (missed1)
        dataframe["missed1_short_exit_target"] = (
                dataframe["missed1_minima_mean"] + dataframe["missed1_minima_std"] * exit_mul)

        # Short exit (missed2)
        dataframe["missed2_short_exit_target"] = (
                dataframe["missed2_minima_mean"] + dataframe["missed2_minima_std"] * exit_mul)

        # Segment Indicator Cumulative
        """ewm_span = 5
        dataframe["segment_delta"] = dataframe["long_segment"] - dataframe["short_segment"]
        dataframe["segment_delta_cum"] = dataframe["segment_delta"].ewm(span=ewm_span).sum()"""

        return dataframe

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

        # Missed Long Entry
        conditions = [
            qtpylib.crossed_above(df["missed1_minima"], df["missed1_long_entry_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["enter_long", "enter_tag"]
            ] = (1, "missed1_minima")

        # Missed Short Entry
        conditions = [
            qtpylib.crossed_above(df["missed1_maxima"], df["missed1_short_entry_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["enter_short", "enter_tag"]
            ] = (1, "missed1_maxima")

        return df

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

        # Long Exit (missed1)
        conditions = [
            qtpylib.crossed_above(df["missed1_maxima"], df["missed1_long_exit_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["exit_long", "exit_tag"]
            ] = (1, "missed1_maxima")

        # Long Exit (missed2)
        conditions = [
            qtpylib.crossed_above(df["missed2_maxima"], df["missed2_long_exit_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["exit_long", "exit_tag"]
            ] = (1, "missed2_maxima")

        # Short Exit (missed1)
        conditions = [
            qtpylib.crossed_above(df["missed1_minima"], df["missed1_short_exit_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["exit_short", "exit_tag"]
            ] = (1, "missed1_minima")

        # Short Exit (missed2)
        conditions = [
            qtpylib.crossed_above(df["missed2_minima"], df["missed2_short_exit_target"])]
        if conditions:
            df.loc[
                reduce(lambda x, y: x & y, conditions), ["exit_short", "exit_tag"]
            ] = (1, "missed2_minima")

        return df

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

    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:
        """
        Customize leverage for each new trade. This method is only called in futures mode.

        :param pair: Pair that's currently analyzed
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
        :param proposed_leverage: A leverage proposed by the bot.
        :param max_leverage: Max leverage allowed on this pair
        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
        :param side: 'long' or 'short' - indicating the direction of the proposed trade
        :return: A leverage amount, which is between 1.0 and max_leverage.
        """

        fixed_leverage = self.freqai_info.get("fixed_leverage", 0)
        if fixed_leverage > 0:
            return fixed_leverage
        else:
            return 1.0

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
                            side: str, **kwargs) -> bool:

        open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True))

        # Balance longs vs shorts to help protect against black swan event
        max_open_trades = self.config.get("max_open_trades", 0)
        if max_open_trades > 0:
            num_shorts, num_longs = 0, 0
            for trade in open_trades:
                if trade.enter_tag == "short":
                    num_shorts += 1
                elif trade.enter_tag == "long":
                    num_longs += 1

            if side == "long" and num_longs >= max_open_trades / 2.0:
                return False

            if side == "short" and num_shorts >= max_open_trades / 2.0:
                return False

        # Prevent taking trades that have already moved too far in predicted direction
        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

    # use_custom_stoploss = True

    """def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        if current_profit < 0.005:
            return -1  # keep using the inital stoploss

        # After reaching the desired offset, allow the stoploss to trail by half the profit
        desired_stoploss = current_profit / 2.0

        # Use a minimum of 2.5% and a maximum of 5%
        return max(min(desired_stoploss, 0.03), 0.01)"""

    """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
        """