# source: https://raw.githubusercontent.com/gtudor33/SwingHighToSky/47d6c0badacdd9878fe02befada655e2a638410b/ft_userdata/user_data/strategies/my_strategy_example.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import numpy as np
# --------------------------------

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)

    return rolling_mean, lower_band


class github_gtudor33_SwingHighToSky__my_strategy_example__20230411_233034(IStrategy):
    minimal_roi = {
      "0": 0.15437,
      "67": 0.10256,
      "106": 0.03905,
      "375": 0
    }

    stoploss = -0.34338
    timeframe = '15m'
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive_offset = 0.00375  # Trigger positive stoploss once crosses above this percentage
    trailing_stop_positive = 0.00175 # Sell asset if it dips down this much

    @property
    def protections(self):
        return [
            #{
            #    "method": "CooldownPeriod",
            #    "stop_duration_candles": 5
            #},
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": 48,
                "trade_limit": 20,
                "stop_duration_candles": 4,
                "max_allowed_drawdown": 0.2
            },
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "only_per_pair": False
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 6,
                "trade_limit": 2,
                "stop_duration_candles": 60,
                "required_profit": 0.02
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "required_profit": 0.01
            }
        ]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe)

        # Bollinger bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=40, stds=2)
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_midband'] = bollinger['mid']
        dataframe['bb_lowerband'] = bollinger['lower']
        mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
        dataframe['mid'] = np.nan_to_num(mid)
        dataframe['lower'] = np.nan_to_num(lower)
        dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs()
        dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe['lower'].shift().gt(0) &
                dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
                dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
                dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift())
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        no sell signal
        """
        dataframe.loc[:, 'sell'] = 0
        return dataframe