# source: https://raw.githubusercontent.com/ring-rong/ft_strategies/aa30b7f5d43a5a6a7d97040b0ef0013def966a84/RingRong.py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from pandas import DataFrame
from freqtrade.persistence import Trade
from datetime import datetime, timedelta

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

class Github_ring_rong_ft_strategies__RingRong__20240810_213432(IStrategy):
    timeframe = "5m"
    can_short: bool = True

    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }

    startup_candle_count = 200

    def informative_pairs(self):
        return [("BTC/USDT", "5m"), ("ETH/USDT", "5m")]

    def populate_indicators(self, dataframe: pd.DataFrame, meta: dict) -> pd.DataFrame:
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)

        dataframe["short"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["long"] = ta.EMA(dataframe, timeperiod=200)

        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["mfi"] = ta.MFI(dataframe)

        dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200)

        # 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']

        for pair, timeframe in self.informative_pairs():
            informative = self.dp.get_pair_dataframe(pair, timeframe)
            informative["ema"] = ta.EMA(informative, timeperiod=50)
            informative["sma_200"] = ta.SMA(informative, timeperiod=200)

            dataframe[f"trend_{pair}"] = (
                informative["close"] > informative["sma_200"]
            )
            dataframe[f"trend_strength_{pair}"] = (
                (informative["close"] > informative["ema"])
                & (informative["ema"] > informative["sma_200"])
            )

        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, meta: dict) -> pd.DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] > 25)
                & (dataframe["short"] > dataframe["long"])
                & (dataframe["close"] > dataframe["sma_200"])
                & (dataframe["close"] <= dataframe['bb_lowerband'])
                & (dataframe["rsi"] < 30)
                & (dataframe["mfi"] < 20)
                & (dataframe["trend_BTC/USDT"] == True)
                & (dataframe["trend_strength_BTC/USDT"] == True)
                & (dataframe["trend_ETH/USDT"] == True)
                & (dataframe["trend_strength_ETH/USDT"] == True)
                & (dataframe["volume"] > 0)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "adx_cross_bullish")

        dataframe.loc[
            (
                (dataframe["adx"] > 25)
                & (dataframe["short"] < dataframe["long"])
                & (dataframe["close"] < dataframe["sma_200"])
                & (dataframe["close"] >= dataframe['bb_upperband'])
                & (dataframe["rsi"] > 70)
                & (dataframe["mfi"] > 80)
                & (dataframe["trend_BTC/USDT"] == False)
                & (dataframe["trend_ETH/USDT"] == False)
                & (dataframe["volume"] > 0)
            ),
            ["enter_short", "enter_tag"],
        ] = (1, "adx_cross_bearish")

        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, meta dict) -> pd.DataFrame:
        dataframe.loc[
            (
                (dataframe["adx"] < 25)
                | (qtpylib.crossed_below(dataframe["short"], dataframe["long"]))
                | (dataframe["close"] < dataframe["sma_200"])
                | (dataframe["close"] > dataframe['bb_middleband'])
                | (dataframe["rsi"] > 50)
                | (dataframe["mfi"] > 50)
                & (dataframe["volume"] > 0)
            ),
            ["exit_long", "exit_tag"],
        ] = (1, "exit_long")

        dataframe.loc[
            (
                (dataframe["adx"] < 25)
                | (qtpylib.crossed_above(dataframe["short"], dataframe["long"]))
                | (dataframe["close"] > dataframe["sma_200"])
                | (dataframe["close"] < dataframe['bb_middleband'])
                | (dataframe["rsi"] < 50)
                | (dataframe["mfi"] < 50)
                & (dataframe["volume"] > 0)
            ),
            ["exit_short", "exit_tag"],
        ] = (1, "exit_short")

        return dataframe