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 RingRongv2(IStrategy):
    timeframe = "5m"
    can_short: bool = True

    minimal_roi = {
        "60": 0.50,
        "30": 0.20,
        "0": 100
    }

    startup_candle_count = 200

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

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