# --- 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  # noqa
pd.options.mode.chained_assignment = None  # default='warn'
import technical.indicators as ftt
from technical.util import resample_to_interval, resampled_merge
from functools import reduce
from datetime import datetime, timedelta
# ObeliskIM v1.1 - 2021-03-24
#
# by Obelisk 
# https://twitter.com/brookmiles
#
# I watched a youtube video on Ichimoku and then wrote this last night at 3am while I couldn't sleep.
# Run at your own risk.
#
# Improvements and suggestions welcome!
#
# Potential areas of improvement:
# - reduce entrying small peaks during sideways ranges
# - additional entry trigger(s) for strong up trends / breakouts if tk cross happened in/below cloud

class ObeliskIM_v1_1(IStrategy):
    INTERFACE_VERSION = 3
    # Optimal timeframe for the strategy
    timeframe = '5m'
    startup_candle_count = 288  # one day @ 5m
    process_only_new_candles = True
    minimal_roi = {'0': 5}
    # Stoploss:
    stoploss = -0.04
    # Main plot indicators (Moving averages, ...)
    # plot senkou_b, too. Not only the area to it.
    plot_config = {'main_plot': {'senkou_a': {'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(0,0,0,0.2)'}, 'senkou_b': {'color': 'red'}, 'tenkan_sen': {'color': 'orange'}, 'kijun_sen': {'color': 'blue'}}, 'subplots': {'Ichimoku': {'cloud_green': {'color': 'green'}, 'cloud_red': {'color': 'red'}, 'cloud_green_strong': {'color': 'green'}, 'cloud_red_strong': {'color': 'red'}, 'tk_cross_up': {'color': 'blue'}}, 'RSI': {'rsi': {'color': 'blue'}}}}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
        # 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
        dataframe['cloud_green_strong'] = (dataframe['cloud_green'] & (dataframe['tenkan_sen'] > dataframe['kijun_sen']) & (dataframe['kijun_sen'] > dataframe['senkou_a'])).astype('int') * 2
        dataframe['cloud_red_strong'] = (dataframe['cloud_red'] & (dataframe['tenkan_sen'] < dataframe['kijun_sen']) & (dataframe['kijun_sen'] < dataframe['senkou_b'])).astype('int') * -2
        dataframe.loc[qtpylib.crossed_above(dataframe['tenkan_sen'], dataframe['kijun_sen']), 'tk_cross_up'] = 3
        dataframe['tk_cross_up'].fillna(method='ffill', inplace=True, limit=2)
        dataframe['tk_cross_up'].fillna(value=0, inplace=True)
        # dataframe['rsi_ok'] = (dataframe['rsi'] < 75).astype('int')
        dataframe['ema35_ok'] = ((dataframe['ema3'] > dataframe['ema5']) & (dataframe['ema5'] > dataframe['ema10'])).astype('int')
        dataframe['spike'] = (dataframe['close'] > dataframe['close'].shift(3) * (1 - self.stoploss * 0.9)).astype('int')
        dataframe['recent_high'] = dataframe['high'].rolling(12).max()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # params = self.entry_params
        conditions = []
        # conditions.append(dataframe['volume'] > 0)
        conditions.append(dataframe['cloud_green_strong'] > 0)
        # conditions.append( dataframe['ema3'] > dataframe['ema5'] )
        conditions.append(dataframe['tk_cross_up'] > 0)
        # conditions.append( dataframe['rsi_ok'] > 0 )
        conditions.append(dataframe['ema35_ok'] > 0)
        conditions.append(dataframe['close'] > dataframe['close'].shift())
        conditions.append(dataframe['close'] > dataframe['recent_high'].shift())  # remove me for a "good time"
        conditions.append(dataframe['spike'] < 1)
        dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # params = self.exit_params
        conditions = []
        # conditions.append(dataframe['volume'] > 0)
        conditions.append(qtpylib.crossed_below(dataframe['tenkan_sen'], dataframe['kijun_sen']) | qtpylib.crossed_below(dataframe['close'], dataframe['kijun_sen']))
        dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
        return dataframe