LitmusMinMaxStrategy

from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
# from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
from functools import reduce
from pandas import DataFrame
from scipy.signal import argrelextrema
from technical import qtpylib
from freqtrade.litmus import indicator_helpers

import logging
import numpy as np
import pandas as pd
import talib.abstract as ta


logger = logging.getLogger(__name__)


class LitmusMinMaxStrategy(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, "600": -1}

    plot_config = {
        "main_plot": {},
        "subplots": {
            "do_predict": {
                "do_predict": {"color": "brown"},
                "DI_values": {"color": "grey"},
            },
            "Maxima": {
                "&s-maxima": {"color": "CornflowerBlue"},
                "long_exit_target": {"color": "FireBrick"},
                "short_entry_target": {"color": "DarkOliveGreen"},
                "&s-maxima-shift-2": {"color": "LightPink"},
            },
            "Minima": {
                "&s-minima": {"color": "CornflowerBlue"},
                "long_entry_target": {"color": "DarkOliveGreen"},
                "short_exit_target": {"color": "FireBrick"},
                "&s-minima-shift-2": {"color": "LightPink"},
            },

            "Max Forecast": {
                "&s-max-close": {"color": "MediumSeaGreen"},
            },
            "Real": {
                "real-minima": {"color": "blue"},
                "real-maxima": {"color": "yellow"},
                "price_forecast": {"color": "brown"}
            },
        },
    }

    process_only_new_candles = True
    stoploss = -0.05
    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)

    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]

        with self.freqai.lock:
            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

                # Max extreme value
                df["s-max-close"] = (
                        df["close"]
                        .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
                        .rolling(self.freqai_info["feature_parameters"]["label_period_candles"] + 1)
                        .apply(indicator_helpers.max_extreme_value)
                        / df["close"]
                        - 1
                )

                # Define Min & Max binary indicators
                min_peaks = argrelextrema(df["close"].values, np.less, order=30)
                max_peaks = argrelextrema(df["close"].values, np.greater, order=30)

                df["&s-minima"] = 0
                df["&s-maxima"] = 0
                df["real-minima"] = 0
                df["real-maxima"] = 0

                # proximity_tolerance = 0.01

                for mp in min_peaks[0]:
                    df.at[mp, "real-minima"] = 1
                    df.at[mp, "&s-minima"] = 1
                    """min_peak_value = df.at[mp, "close"]
                    if abs(df.at[mp - 1, "close"] / min_peak_value - 1) < proximity_tolerance:
                        df.at[mp - 1, "&s-minima"] = 1
                        df.at[mp - 1, "real-minima"] = 0.5
                    if abs(df.at[mp + 1, "close"] / min_peak_value - 1) < proximity_tolerance:
                        df.at[mp + 1, "&s-minima"] = 1
                        df.at[mp + 1, "real-minima"] = 0.5"""

                for mp in max_peaks[0]:
                    df.at[mp, "real-maxima"] = 1
                    df.at[mp, "&s-maxima"] = 1
                    """max_peak_value = df.at[mp, "close"]
                    if abs(df.at[mp - 1, "close"] / max_peak_value - 1) < proximity_tolerance:
                        df.at[mp - 1, "&s-maxima"] = 1
                        df.at[mp - 1, "real-maxima"] = 0.5
                    if abs(df.at[mp + 1, "close"] / max_peak_value - 1) < proximity_tolerance:
                        df.at[mp + 1, "&s-maxima"] = 1
                        df.at[mp + 1, "real-maxima"] = 0.5"""

                # Create shifted target for predicting missing max/min
                """lookback_period = 5
                df["&s-minima-missed"] = (
                    df["&s-minima"]
                    .shift(1)
                    .rolling(lookback_period)
                    .apply(some_func)
                )
                df["&s-maxima-missed"] = (
                    df["&s-maxima"]
                    .shift(1)
                    .rolling(lookback_period)
                    .apply(some_func)
                )"""

        return df

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

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

        # 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)

        # Standard deviation entry / exists
        entry_std = 1.4
        exit_std = 1.3

        # Short
        dataframe["short_entry_target"] = (
                dataframe["&s-maxima_mean"] + dataframe["&s-maxima_std"] * entry_std)
        dataframe["short_exit_target"] = (
                dataframe["&s-minima_mean"] + dataframe["&s-minima_std"] * exit_std)

        # Long
        dataframe["long_entry_target"] = (
                dataframe["&s-minima_mean"] + dataframe["&s-minima_std"] * entry_std)
        dataframe["long_exit_target"] = (
                dataframe["&s-maxima_mean"] + dataframe["&s-maxima_std"] * exit_std)

        return dataframe

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

        enter_long_conditions = [df["do_predict"] == 1, df["&s-minima"] > df["long_entry_target"]]

        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-maxima"] > df["short_entry_target"]]

        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 = [1 == 1, df["&s-maxima"] > df["long_exit_target"]]

        if exit_long_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1

        exit_short_conditions = [1 == 1, df["&s-minima"] > df["short_exit_target"]]

        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 custom_exit(
        self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
    ):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
        trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
        if trade_candle.empty:
            return None
        trade_candle = trade_candle.squeeze()
        follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
        if not follow_mode:
            pair_dict = self.freqai.dd.pair_dict
        else:
            pair_dict = self.freqai.dd.follower_dict
        entry_tag = trade.enter_tag
        if (
            "prediction" + entry_tag not in pair_dict[pair]
            or pair_dict[pair]["prediction" + entry_tag] > 0
        ):
            with self.freqai.lock:
                pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
                if not follow_mode:
                    self.freqai.dd.save_drawer_to_disk()
                else:
                    self.freqai.dd.save_follower_dict_to_disk()
        roi_price = pair_dict[pair]["prediction" + entry_tag]
        roi_time = self.max_roi_time_long.value
        roi_decay = roi_price * (
            1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
        )
        if roi_decay < 0:
            roi_decay = self.linear_roi_offset.value
        else:
            roi_decay += self.linear_roi_offset.value
        if current_profit > roi_decay:
            return "roi_custom_win"
        if current_profit < -roi_decay:
            return "roi_custom_loss"
            """

    """def confirm_trade_exit(
        self,
        pair: str,
        trade: Trade,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        exit_reason: str,
        current_time,
        **kwargs,
    ) -> bool:

        entry_tag = trade.enter_tag
        follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
        if not follow_mode:
            pair_dict = self.freqai.dd.pair_dict
        else:
            pair_dict = self.freqai.dd.follower_dict

        pair_dict[pair]["prediction" + entry_tag] = 0
        if not follow_mode:
            self.freqai.dd.save_drawer_to_disk()
        else:
            self.freqai.dd.save_follower_dict_to_disk()

        return True"""

    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 analyze_trade_database(self, dk: FreqaiDataKitchen, pair: str) -> None:
        """
        User analyzes the trade database here and returns summary stats which will be passed back
        to the strategy for reinforcement learning or for additional adaptive metrics for use
        in entry/exit signals. Store these metrics in dk.data['extra_returns_per_train'] and
        they will format themselves into the dataframe as an additional column in the user
        strategy. User has access to the current trade database in dk.trade_database_df.
        """
        # total_profit = dk.trade_database_df['close_profit_abs'].sum()
        # dk.data['extra_returns_per_train']['total_profit'] = total_profit

        return