from freqtrade.strategy import IStrategy
from pandas_ta import ema
import pandas as pd
import pandas_ta as ta
import numpy as np
from datetime import datetime, timedelta
from freqtrade.persistence import Trade, Order
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy HammerRsiStrategy --strategy-path ./user_data/Chan/strategies --timerange=20250520-


class HammerRsiStrategy(IStrategy):
    timeframe = "1m"  # 1分钟K线
    minimal_roi = {"0": 0.005}  # 0.5% 止盈
    stoploss = -0.002  # 0.2% 固定止损
    trailing_stop = True
    trailing_stop_positive = 0.001  # 0.1% 追踪止损
    trailing_stop_positive_offset = 0.002  # 0.2% 触发追踪止损
    startup_candle_count = 20  # 启动K线数

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        dataframe['rsi'] = ta.rsi(dataframe['close'], length=14)
        dataframe['ema_fast'] = ta.ema(dataframe['close'], length=5)
        dataframe['ema_slow'] = ta.ema(dataframe['close'], length=20)
        dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        conditions = (
            (dataframe['ema_fast'] > dataframe['ema_slow']) &  # 快EMA上穿慢EMA
            (dataframe['rsi'] < 45)  # RSI < 45
        )
        print(f"Signal check: ema_fast={dataframe['ema_fast'].iloc[-1]}, ema_slow={dataframe['ema_slow'].iloc[-1]}, rsi={dataframe['rsi'].iloc[-1]}")
        dataframe.loc[conditions, ['enter_long', 'enter_tag']] = (1, 'ema_rsi_entry')
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        conditions = (
            (dataframe['ema_fast'] < dataframe['ema_slow']) |  # 快EMA下穿慢EMA
            (dataframe['rsi'] > 60)  # RSI > 60
        )
        dataframe.loc[conditions, ['exit_long', 'exit_tag']] = (1, 'ema_rsi_exit')
        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)
        atr = dataframe['atr'].iloc[-1]
        return -1.5 * atr / current_rate  # 止损为1.5倍ATR

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                           proposed_stake: float, min_stake: float, max_stake: float,
                           entry_tag: str) -> float:
        return proposed_stake * 0.01  # 1%账户余额