# source: https://raw.githubusercontent.com/atlaslejon/atlasExAi/52d43f5294f7d191663282fa70da669777421ba5/ExampleLSTMStrategy.py
import logging
from functools import reduce

import numpy as np
import pandas as pd
from typing import dict
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib

from freqtrade.exchange.exchange_utils import *
from freqtrade.strategy import IStrategy, RealParameter

logger = logging.getLogger(__name__)


class Github_atlaslejon_atlasExAi__ExampleLSTMStrategy__20250126_091912(IStrategy):
    """
    This is an example strategy that uses the LSTMRegressor model to predict the target score.
    Use at your own risk.
    This is a simple example strategy and should be used for educational purposes only.
    """
    # Hyperspace parameters:
    buy_params = {
        "threshold_buy": 0.59453,
        "w0": 0.54347,
        "w1": 0.82226,
        "w2": 0.56675,
        "w3": 0.77918,
        "w4": 0.98488,
        "w5": 0.31368,
        "w6": 0.75916,
        "w7": 0.09226,
        "w8": 0.85667,
    }

    sell_params = {
        "threshold_sell": 0.80573,
    }

    # ROI table:
    minimal_roi = {
        "600": 0  # we let the model decide when to exit
    }

    # Stoploss:
    stoploss = -1  # Were letting the model decide when to sell

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.0139
    trailing_only_offset_is_reached = True

    timeframe = "1h"
    can_short = True
    use_exit_signal = True
    process_only_new_candles = True

    startup_candle_count = 20

    threshold_buy = RealParameter(-1, 1, default=0, space='buy')
    threshold_sell = RealParameter(-1, 1, default=0, space='sell')

    # Weights for calculating the aggregate score - the sum of all weighted normalized indicators has to be 1!
    w0 = RealParameter(0, 1, default=0.10, space='buy')
    w1 = RealParameter(0, 1, default=0.15, space='buy')
    w2 = RealParameter(0, 1, default=0.10, space='buy')
    w3 = RealParameter(0, 1, default=0.15, space='buy')
    w4 = RealParameter(0, 1, default=0.10, space='buy')
    w5 = RealParameter(0, 1, default=0.10, space='buy')
    w6 = RealParameter(0, 1, default=0.10, space='buy')
    w7 = RealParameter(0, 1, default=0.05, space='buy')
    w8 = RealParameter(0, 1, default=0.15, space='buy')

    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
                                       metadata: dict, **kwargs):

        dataframe["%-cci-period"] = ta.CCI(dataframe, timeperiod=20)
        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=10)
        dataframe["%-momentum-period"] = ta.MOM(dataframe, timeperiod=4)
        dataframe['%-ma-period'] = ta.SMA(dataframe, timeperiod=10)
        dataframe['%-macd-period'], dataframe['%-macdsignal-period'], dataframe['%-macdhist-period'] = ta.MACD(
            dataframe['close'], slowperiod=12,
            fastperiod=26)
        dataframe['%-roc-period'] = ta.ROC(dataframe, timeperiod=2)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=period, stds=2.2
        )
        dataframe["bb_lowerband-period"] = bollinger["lower"]
        dataframe["bb_middleband-period"] = bollinger["mid"]
        dataframe["bb_upperband-period"] = bollinger["upper"]
        dataframe["%-bb_width-period"] = (
                                                 dataframe["bb_upperband-period"]
                                                 - dataframe["bb_lowerband-period"]
                                         ) / dataframe["bb_middleband-period"]
        dataframe["%-close-bb_lower-period"] = (
                dataframe["close"] / dataframe["bb_lowerband-period"]
        )

        return dataframe

    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):

        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-raw_volume"] = dataframe["volume"]
        dataframe["%-raw_price"] = dataframe["close"]
        return dataframe

    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs):

        dataframe['date'] = pd.to_datetime(dataframe['date'])
        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
        return dataframe

    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:

        dataframe['ma'] = ta.SMA(dataframe, timeperiod=10)
        dataframe['roc'] = ta.ROC(dataframe, timeperiod=2)
        dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = ta.MACD(dataframe['close'], slowperiod=12,
                                                                                    fastperiod=26)
        dataframe['momentum'] = ta.MOM(dataframe, timeperiod=4)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10)
        bollinger = ta.BBANDS(dataframe, timeperiod=20)
        dataframe['bb_upperband'] = bollinger['upperband']
        dataframe['bb_middleband'] = bollinger['middleband']
        dataframe['bb_lowerband'] = bollinger['lowerband']
        dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
        dataframe['stoch'] = ta.STOCH(dataframe)['slowk']
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['obv'] = ta.OBV(dataframe)

        # Step 1: Normalize Indicators:
        # Why? Normalizing the indicators will make them comparable and allow us to assign weights to them.
        # How? We will calculate the z-score of each indicator by subtracting the rolling mean and dividing by the
        # rolling standard deviation. This will give us a normalized value that is centered around 0 with a standard
        # deviation of 1.
        dataframe['normalized_stoch'] = (dataframe['stoch'] - dataframe['stoch'].rolling(window=14).mean()) / dataframe[
            'stoch'].rolling(window=14).std()
        dataframe['normalized_atr'] = (dataframe['atr'] - dataframe['atr'].rolling(window=14).mean()) / dataframe[
            'atr'].rolling(window=14).std()
        dataframe['normalized_obv'] = (dataframe['obv'] - dataframe['obv'].rolling(window=14).mean()) / dataframe[
            'obv'].rolling(window=14).std()
        dataframe['normalized_ma'] = (dataframe['close'] - dataframe['close'].rolling(window=10).mean()) / dataframe[
            'close'].rolling(window=10).std()
        dataframe['normalized_macd'] = (dataframe['macd'] - dataframe['macd'].rolling(window=26).mean()) / dataframe[
            'macd'].rolling(window=26).std()
        dataframe['normalized_roc'] = (dataframe['roc'] - dataframe['roc'].rolling(window=2).mean()) / dataframe[
            'roc'].rolling(window=2).std()
        dataframe['normalized_momentum'] = (dataframe['momentum'] - dataframe['momentum'].rolling(window=4).mean()) / \
                                           dataframe['momentum'].rolling(window=4).std()
        dataframe['normalized_rsi'] = (dataframe['rsi'] - dataframe['rsi'].rolling(window=10).mean()) / dataframe[
            'rsi'].rolling(window=10).std()
        dataframe['normalized_bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']).rolling(
            window=20).mean() / (dataframe['bb_upperband'] - dataframe['bb_lowerband']).rolling(window=20).std()
        dataframe['normalized_cci'] = (dataframe['cci'] - dataframe['cci'].rolling(window=20).mean()) / dataframe[
            'cci'].rolling(window=20).std()

        # Dynamic Weights (Example: Increase the weight of momentum in a strong trend)
        trend_strength = abs(dataframe['ma'] - dataframe['close'])

        # Calculate the rolling mean and standard deviation of the trend strength to determine a strong trend
        # The threshold is set to 1.5 times the standard deviation above the mean, but can be adjusted as needed
        strong_trend_threshold = trend_strength.rolling(window=14).mean() + 1.5 * trend_strength.rolling(
            window=14).std()

        # Assign a higher weight to momentum if the trend is strong
        is_strong_trend = trend_strength > strong_trend_threshold

        # Assign the dynamic weights to the dataframe
        dataframe['w_momentum'] = np.where(is_strong_trend, self.w3.value * 1.5, self.w3.value)

        # Step 2: Calculate aggregate score S
        w = [self.w0.value, self.w1.value, self.w2.value, self.w3.value, self.w4.value, self.w5.value,
             self.w6.value, self.w7.value, self.w8.value]

        dataframe['S'] = w[0] * dataframe['normalized_ma'] + w[1] * dataframe['normalized_macd'] + w[2] * dataframe[
            'normalized_roc'] + w[3] * dataframe['normalized_rsi'] + w[4] * \
                         dataframe['normalized_bb_width'] + w[5] * dataframe['normalized_cci'] + dataframe[
                             'w_momentum'] * dataframe['normalized_momentum'] + self.w8.value * dataframe[
                             'normalized_stoch'] + self.w7.value * dataframe['normalized_atr'] + self.w6.value * \
                         dataframe['normalized_obv']

        # Step 3: Market Regime Filter R
        # EXPLANATION: If the price is above the upper Bollinger Band, assign a value
        # of 1 to R. If the price is below the lower Bollinger Band, assign a value of -1 to R. Otherwise,
        # the value R stays 0.
        # What's basically happening here is that we are assigning a value of 1 to R when
        # the price is in the upper band, -1 when the price is in the lower band, and 0 when the price is in the
        # middle band. This is a simple way to determine the market regime based on Bollinger Bands. What is market
        # regime? Market regime is the state of the market. It can be trending, ranging, or reversing. So we are
        # using Bollinger Bands to determine the market regime. You can use other indicators to determine the market
        # regime as well. For example, you can use moving averages, RSI, MACD, etc.
        dataframe['R'] = 0
        dataframe.loc[(dataframe['close'] > dataframe['bb_middleband']) & (
                dataframe['close'] > dataframe['bb_upperband']), 'R'] = 1
        dataframe.loc[(dataframe['close'] < dataframe['bb_middleband']) & (
                dataframe['close'] < dataframe['bb_lowerband']), 'R'] = -1

        # Additional Market Regime Filter based on long-term MA
        dataframe['ma_100'] = ta.SMA(dataframe, timeperiod=100)
        dataframe['R2'] = np.where(dataframe['close'] > dataframe['ma_100'], 1, -1)

        # Step 4: Volatility Adjustment V
        # EXPLANATION: Calculate the Bollinger Band width and assign it to V. The Bollinger Band width is the
        # difference between the upper and lower Bollinger Bands divided by the middle Bollinger Band. The idea is
        # that when the Bollinger Bands are wide, the market is volatile, and when the Bollinger Bands are narrow,
        # the market is less volatile. So we are using the Bollinger Band width as a measure of volatility. You can
        # use other indicators to measure volatility as well. For example, you can use the ATR (Average True Range)
        bb_width = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
        dataframe['V'] = 1 / bb_width  # example, assuming V is inversely proportional to BB width

        # Another Volatility Adjustment using ATR
        dataframe['V2'] = 1 / dataframe['atr']

        # Get Final Target Score to incorporate new calculations
        dataframe['T'] = dataframe['S'] * dataframe['R'] * dataframe['V'] * dataframe['R2'] * dataframe['V2']

        # Assign the target score T to the AI target column
        dataframe['&-target'] = dataframe['T']

        return dataframe

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

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

        dataframe = self.freqai.start(dataframe, metadata, self)
        # One can define indicators here if needed and add logic to populate_entry_trend and populate_exit_trend
        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        enter_long_conditions = [
            df["do_predict"] == 1,
            df['&-target'] > self.threshold_buy.value,  #
            df['volume'] > 0
        ]

        enter_short_conditions = [
            df["do_predict"] == 1,
            df['&-target'] < self.threshold_sell.value,
            df["volume"] > 0

        ]

        df.loc[
            reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
        ] = (1, "long")

        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['&-target'] < self.threshold_sell.value
        ]

        exit_short_conditions = [
            df["do_predict"] == 1,
            df['&-target'] > self.threshold_buy.value
        ]

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

        if exit_short_conditions:
            df.loc[
                reduce(lambda x, y: x & y, exit_short_conditions), ["exit_short", "exit_tag"]
            ] = (1, "exit_short")

        return df