from freqtrade.strategy.interface import IStrategy
import pandas as pd
import numpy as np
import talib as ta
import logging
import datetime
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter

logger = logging.getLogger(__name__)

class MyOptimizedStrategy(IStrategy):
    # --- Hyperoptables ---
    buy_rsi = IntParameter(20, 50, default=30, space="buy")
    sell_rsi = IntParameter(50, 80, default=70, space="sell")

    # --- FreqAI 相关 ---
    minimal_roi = {"0": 0.05, "30": 0.02, "60": 0}
    stoploss = -0.1
    trailing_stop = True
    trailing_stop_positive = 0.03
    trailing_stop_positive_offset = 0.05

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
        dataframe["up_or_down"] = np.where(
            dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
        )
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        # 添加 volume_ma
        dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()

        # 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"]

        dataframe.replace([np.inf, -np.inf], 0, inplace=True)
        dataframe.ffill(inplace=True)
        dataframe.fillna(0, inplace=True)

        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        conditions = [
            (dataframe['do_predict'] == 1),
            (dataframe['rsi'] < dataframe['&-buy_rsi_pred']),
            (dataframe['close'] < dataframe['bb_lowerband'])
        ]
        dataframe.loc[reduce(np.logical_and, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        conditions = [
            (dataframe['do_predict'] == 1),
            (dataframe['rsi'] > dataframe['&-sell_rsi_pred'])
        ]
        dataframe.loc[reduce(np.logical_and, conditions), 'exit_long'] = 1
        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float,
                        current_profit: float, **kwargs) -> float:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_row = dataframe.iloc[-1]
        if "&-stoploss" in last_row:
            return float(last_row["&-stoploss"])
        return self.stoploss