# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
from datetime import datetime

import numpy
import numpy as np  # noqa
import pandas as pd  # noqa
from freqtrade.strategy.parameters import  DecimalParameter, BooleanParameter, IntParameter
from pandas import DataFrame
import math
from functools import reduce

from freqtrade.strategy.interface import IStrategy

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


# This class is a sample. Feel free to customize it.
class StrategyPierrick41222(IStrategy):
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 2

    # valeur de bbwidth pour démarrer
    buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.07, space="buy")
    # pourcentage sma à dépasser
    buy_sma_percent = DecimalParameter(0.95, 1.05, decimals=2, default=0.97, space="buy")

    # buy_rsi = IntParameter(20, 40, default=30, space="buy")
    # buy_adx_enabled = BooleanParameter(default=True, space="buy")
    # buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
    # buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")

    # ROI table:
    minimal_roi = {
        # "0": 0.015
        "0": 5
    }

    # Stoploss:
    stoploss = -1
    trailing_stop = False
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.0275  # 0.015
    trailing_only_offset_is_reached = True

    # max_open_trades = 3

    # Optimal ticker interval for the strategy.
    timeframe = '5m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'bb_lowerband': {'color': 'red'},
            'bb_upperband': {'color': 'green'},
            'sma100': {'color': 'blue'},
            'sma10': {'color': 'yellow'},
            'min': {'color': 'white'},
            'max': {'color': 'white'},
            'sma20': {'color': 'cyan'}
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "BB": {
                'bb_width': {'color': 'green'},
                'bb_min': {'color': 'red'},
            },
            "Volume": {
                'volume5': {'color': 'yellow'},
            },
            "Rsi": {
                'rsi': {'color': 'pink'},
            },
            "rolling": {
                'bb_rolling': {'color': '#87e470'},
                "bb_rolling_min": {'color': '#ac3e2a'}
            },
            "percent": {
                "percent": {'color': 'green'},
                "percent5": {'color': 'red'}
            }
        }
    }

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

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

        # print("proposed_stake=", proposed_stake, " max_stake=", max_stake)
        if current_candle['bb_width'] > 0.012:
            print("use more stake", pair, " ", proposed_stake * 2)
            return min(max_stake, proposed_stake * 2)

        if current_candle['bb_width'] > 0.09:
            print("use more stake", pair, " ", proposed_stake * 1.5)
            return min(max_stake, proposed_stake * 1.5)

        # if current_candle['bb_width'] < 0.020:
        #     print("use less stake", pair, " ", proposed_stake / 2)
        #     return min(max_stake, proposed_stake / 2)
        # if self.config['stake_amount'] == 'unlimited':
        #     # Use entire available wallet during favorable conditions when in compounding mode.
        #     return max_stake
        # else:
        #     # Compound profits during favorable conditions instead of using a static stake.
        #     return self.wallets.get_total_stake_amount() / self.config['max_open_trades']

        # Use default stake amount.
        return proposed_stake

    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        previous_last_candle = dataframe.iloc[-2].squeeze()
        previous_5_candle = dataframe.iloc[-5].squeeze()

        # (last_candle['percent5'] < -0.005) \
        # if (0 < current_profit < 0.005) \
        #         & ((current_time - trade.open_date_utc).seconds >= 3600 * 2):
        #     # & (previous_last_candle['sma10'] > last_candle['sma10']):
        #     print("too_small_gain", pair, trade, " profit=", current_profit, " rate=", current_rate, " percent5=",
        #           last_candle['percent5'])
        #     return 'too_small_gain'

        # if (current_profit < -0.05) \
        #         & ((current_time - trade.open_date_utc).days >= 3):
        #     print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate)
        #     return 'stop_loss_profit'

        # if (current_profit > 0.02) \
        #         & (last_candle['percent'] < 0.01) \
        #         & ((current_time - trade.open_date_utc).seconds >= 3600):
        #     print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate)
        #     return 'lost_half_profit'

        if (current_profit > 0) \
                & ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \
                & (previous_5_candle['sma20'] > last_candle['sma20']) \
                & (last_candle['percent'] < 0) \
                & (last_candle['percent5'] < 0):
            print("over_bb_band_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
            return 'over_bb_band_sma20_desc'

        if (current_profit > 0) \
                & ((current_time - trade.open_date_utc).seconds >= 3600) \
                & (last_candle['rsi'] > 75):
            print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
            return 'over_rsi'

        # description trade
        # Trade(id=0, pair=CAKE/USDT, amount=4.19815281, open_rate=11.91000000, open_since=2021-12-22 17:55:00)
        # print(last_candle)
        if 0.05 < current_profit < 1:
            if (
                    (previous_last_candle['sma10'] > last_candle['sma10'] * 1.005) &
                    (current_time - trade.open_date_utc).seconds >= 3600 * 3
            # ) | (
            #         (current_time - trade.open_date_utc).seconds >= 3600 * 6
            ):
                self.lock_pair(pair, until=current_time + timedelta(hours=3))

                print("profit_3h_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
                return 'profit_3h_sma10_desc'

        if (0 < current_profit < 0.1) \
                & (previous_last_candle['sma20'] > last_candle['sma20']) \
                & ((current_time - trade.open_date_utc).seconds >= 3600 * 5):
            print("profit_5h_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
            return 'profit_5h_sma20_desc'

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # MACD
        # macd = ta.MACD(dataframe)
        # dataframe['macd'] = macd['macd']
        # dataframe['macdsignal'] = macd['macdsignal']
        # dataframe['macdhist'] = macd['macdhist']

        # # # Plus Directional Indicator / Movement
        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
        #
        # # Minus Directional Indicator / Movement
        # dataframe['adx'] = ta.ADX(dataframe)
        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)

        # dataframe['min'] = ta.MIN(dataframe)
        # dataframe['max'] = ta.MAX(dataframe)

        # # Aroon, Aroon Oscillator
        # aroon = ta.AROON(dataframe)
        # dataframe['aroonup'] = aroon['aroonup']
        # dataframe['aroondown'] = aroon['aroondown']
        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)

        # # EMA - Exponential Moving Average
        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        # # SMA - Simple Moving Average
        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
        dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20)
        dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
        dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
        # dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
        # dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95
        # dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98
        # dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500)
        # dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9
        # dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95
        # dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2
        dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
        dataframe["percent5"] = dataframe["percent"].rolling(5).sum()
        dataframe["percent20"] = dataframe["percent"].rolling(20).sum()
        dataframe['min'] = ta.MIN(dataframe['close'], timeperiod=200)
        dataframe['min20'] = ta.MIN(dataframe['close'], timeperiod=20)

        dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200)
        dataframe['max_min'] = dataframe['max'] / dataframe['min']
        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe["bb_percent"] = (
                (dataframe["close"] - dataframe["bb_lowerband"]) /
                (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
                (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )
        dataframe['bb_min'] = ta.MIN(dataframe['bb_lowerband'], timeperiod=36)
        dataframe['volume5'] = dataframe["volume"].rolling(5).mean()

        dataframe["rolling"] = (
                100 * (dataframe["close"] - dataframe["bb_lowerband"]) / dataframe["bb_lowerband"]).rolling(
            3).mean()
        dataframe["bb_rolling"] = dataframe["rolling"] / dataframe["bb_width"]
        dataframe['bb_rolling_min'] = ta.MIN(dataframe['bb_rolling'], timeperiod=10)
        dataframe['bb_buy'] = (dataframe['min'] + (dataframe['max'] - dataframe['min']) / 3)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = [
            dataframe['bb_width'] >= self.buy_bollinger.value,
            (dataframe['close'] < dataframe['bb_lowerband']),
            (dataframe['close'] < dataframe['sma100'] * self.buy_sma_percent.value)
        ]
        # GUARDS AND TRENDS

        if conditions:
            dataframe.loc[(reduce(lambda x, y: x & y, conditions)), 'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['close'] < dataframe['open']) &
                (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
                (dataframe['close'].shift(2) < dataframe['open'].shift(2)) &
                (dataframe['close'] < dataframe['bb_lowerband']) &
                (((dataframe['bb_lowerband'].shift(2) - dataframe['bb_lowerband']) / dataframe['bb_lowerband']) >= 0.02)
                # (((dataframe['close'].shift(1) - dataframe['close']) / dataframe['close']) >= 0.025)
            ), 'sell'] = 1
        return dataframe
