# source: https://raw.githubusercontent.com/cyberjunky/willemstijn_user_data/758a23f80ce7a5dca176e939c4f3f8562d11a304/strategies/2023_freqtrade-stuff-main_brookmiles/strategies/archive/Obelisk_TradePro_Ichi_v2.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

# --------------------------------
import pandas as pd
import numpy as np
import technical.indicators as ftt

pd.options.mode.chained_assignment = None  # default='warn'

# Obelisk_TradePro_Ichi v2 - 2021-03-27
#
# by Obelisk 
# https://github.com/brookmiles/
#
# Based on "Crazy Results Best Ichimoku Cloud Trading Strategy Proven 100 Trades" by Trade Pro
# https://www.youtube.com/watch?v=8gWIykJgMNY
#
# Does not attempt to emulate the risk/reward take-profit/stop-loss, so the sell criteria are mine.
#
# Contributions:
# JimmyNixx - SSL Channel confirmation
#
#
# Backtested with pairlist generated with:
#     "pairlists": [
#         {
#             "method": "VolumePairList",
#             "number_assets": 50,
#             "sort_key": "quoteVolume",
#             "refresh_period": 1800
#         },
#         {"method": "AgeFilter", "min_days_listed": 10},
#         {"method": "PrecisionFilter"},
#         {"method": "PriceFilter", "low_price_ratio": 0.001},
#         {"method": "SpreadFilter", "max_spread_ratio": 0.002},
#         {
#             "method": "RangeStabilityFilter",
#             "lookback_days": 3,
#             "min_rate_of_change": 0.2,
#             "refresh_period": 1440
#         },
#         {
#             "method": "VolumePairList",
#             "number_assets": 25,
#             "sort_key": "quoteVolume",
#         },
#     ],

def SSLChannels(dataframe, length = 7):
    df = dataframe.copy()
    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
    return df['sslDown'], df['sslUp']

class Github_cyberjunky_willemstijn_user_data__Obelisk_TradePro_Ichi_v2__20230822_152729(IStrategy):

    # Optimal timeframe for the strategy
    timeframe = '1h'

    startup_candle_count = 180
    process_only_new_candles = True

    minimal_roi = {
        "0": 10,
    }

    # Stoploss:
    stoploss = -0.04

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'senkou_a': {
                'color': 'green',
                'fill_to': 'senkou_b',
                'fill_label': 'Ichimoku Cloud',
                'fill_color': 'rgba(0,0,0,0.2)',
            },
            # plot senkou_b, too. Not only the area to it.
            'senkou_b': {
                'color': 'red',
            },
            'tenkan_sen': { 'color': 'orange' },
            'kijun_sen': { 'color': 'blue' },

            'chikou_span': { 'color': 'lightgreen' },

            # 'ssl_up': { 'color': 'green' },
            # 'ssl_down': { 'color': 'red' },
        },
        'subplots': {
            "Signals": {
                'go_long': {'color': 'blue'},
                'future_green': {'color': 'green'},
                'chikou_high': {'color': 'lightgreen'},
                'ssl_high': {'color': 'orange'},
            },
        }
    }

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

        # # Standard Settings
        # displacement = 26
        # ichimoku = ftt.ichimoku(dataframe, 
        #     conversion_line_period=9, 
        #     base_line_periods=26,
        #     laggin_span=52, 
        #     displacement=displacement
        #     )

        # Crypto Settings
        displacement = 30
        ichimoku = ftt.ichimoku(dataframe, 
            conversion_line_period=20, 
            base_line_periods=60,
            laggin_span=120, 
            displacement=displacement
            )

        dataframe['chikou_span'] = ichimoku['chikou_span']

        # cross indicators
        dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
        dataframe['kijun_sen'] = ichimoku['kijun_sen']

        # cloud, green a > b, red a < b
        dataframe['senkou_a'] = ichimoku['senkou_span_a']
        dataframe['senkou_b'] = ichimoku['senkou_span_b']
        dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
        dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
        dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
        dataframe['cloud_red'] = ichimoku['cloud_red'] * -1

        # DANGER ZONE START

        # NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud
        dataframe['future_green'] = (dataframe['leading_senkou_span_a'] > dataframe['leading_senkou_span_b']).astype('int') * 2

        # The chikou_span is shifted into the past, so we need to be careful not to read the
        # current value.  But if we shift it forward again by displacement it should be safe to use.
        # We're effectively "looking back" at where it normally appears on the chart.
        dataframe['chikou_high'] = (
                (dataframe['chikou_span'] > dataframe['senkou_a']) &
                (dataframe['chikou_span'] > dataframe['senkou_b'])
            ).shift(displacement).fillna(0).astype('int')

        # DANGER ZONE END

        ssl_down, ssl_up = SSLChannels(dataframe, 10)
        dataframe['ssl_down'] = ssl_down
        dataframe['ssl_up'] = ssl_up
        dataframe['ssl_high'] = (ssl_up > ssl_down).astype('int') * 3

        dataframe['go_long'] = (
                (dataframe['tenkan_sen'] > dataframe['kijun_sen']) &
                (dataframe['close'] > dataframe['senkou_a']) &
                (dataframe['close'] > dataframe['senkou_b']) &
                (dataframe['future_green'] > 0) &
                (dataframe['chikou_high'] > 0) &
                (dataframe['ssl_high'] > 0)
                ).astype('int') * 4

        return dataframe


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

        dataframe.loc[
            qtpylib.crossed_above(dataframe['go_long'], 0) 
        ,
        'buy'] = 1

        return dataframe

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

        dataframe.loc[
                (dataframe['ssl_high'] == 0)
                &
                (
                    (dataframe['tenkan_sen'] < dataframe['kijun_sen'])
                    |
                    (dataframe['close'] < dataframe['kijun_sen'])
                )
        ,
        'sell'] = 1

        return dataframe