# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-futures-k8s-namespace/DIV_v1.py
from pandas import DataFrame
import talib.abstract as ta
from functools import reduce
import numpy as np
from freqtrade.strategy import IStrategy
# DIV v1.0 - 2021-09-07
# by Sanka 

class Github_DerSalvador_freqtrade_helm_chart__DIV_v1__20260416_224245(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.10347601757573865, '3': 0.050495605759981035, '5': 0.03350898081823659, '61': 0.0275218557571848, '292': 0.005185372158403069, '399': 0}
    stoploss = -0.15
    timeframe = '5m'
    startup_candle_count = 200
    process_only_new_candles = True
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True
    plot_config = {'main_plot': {'ohlc_bottom': {'type': 'scatter', 'plotly': {'mode': 'markers', 'name': 'a', 'text': 'aa', 'marker': {'symbol': 'cross-dot', 'size': 3, 'color': 'black'}}}}, 'subplots': {'rsi': {'rsi': {'color': 'blue'}, 'rsi_bottom': {'type': 'scatter', 'plotly': {'mode': 'markers', 'name': 'b', 'text': 'bb', 'marker': {'symbol': 'cross-dot', 'size': 3, 'color': 'black'}}}}}}
    #############################################################

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Divergence
        dataframe = divergence(dataframe, 'rsi')
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['bullish_divergence'] == True) & (dataframe['rsi'] < 30) & (dataframe['volume'] > 0), 'entry'] = 1
        return dataframe

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

def divergence(dataframe: DataFrame, source='rsi'):
    # Detect divergence between close price and source
    # Detect HL or LL
    dataframe['ohlc_bottom'] = np.NaN
    dataframe['rsi_bottom'] = np.NaN
    dataframe.loc[(dataframe['close'].shift() <= dataframe['close'].shift(2)) & (dataframe['close'] >= dataframe['close'].shift()), 'ohlc_bottom'] = dataframe['close'].shift()
    dataframe.loc[(dataframe[source].shift() <= dataframe[source].shift(2)) & (dataframe[source] >= dataframe[source].shift()), 'rsi_bottom'] = dataframe[source].shift()
    dataframe['ohlc_bottom'].fillna(method='ffill', inplace=True)
    dataframe['rsi_bottom'].fillna(method='ffill', inplace=True)
    # Detect divergence
    dataframe['bullish_divergence'] = np.NaN
    dataframe['hidden_bullish_divergence'] = np.NaN
    for i in range(2, 15):
        # Check there is nothing between the 2 diverging points
        conditional_array = []
        for ii in range(1, i):
            conditional_array.append(dataframe['ohlc_bottom'].shift(i).le(dataframe['ohlc_bottom'].shift(ii)))
        res = reduce(lambda x, y: x & y, conditional_array)
        dataframe.loc[dataframe['ohlc_bottom'].lt(dataframe['ohlc_bottom'].shift(i)) & dataframe['rsi_bottom'].gt(dataframe['rsi_bottom'].shift(i)) & dataframe['ohlc_bottom'].le(dataframe['ohlc_bottom'].shift()) & res, 'bullish_divergence'] = True
    return dataframe