EmaRsiStrategy

import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter


class EmaRsiStrategy(IStrategy):
    """
    策略逻辑：
      买入：EMA20 > EMA50（上升趋势）且 RSI < 40（回调超卖）
      卖出：RSI > 70（超买）或 EMA20 跌破 EMA50（趋势反转）
    """

    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = False

    # 持仓目标收益：持仓超过60分钟达到1%就止盈，0分钟达到4%就止盈
    minimal_roi = {
        "60": 0.01,
        "0": 0.04,
    }

    # 止损 5%
    stoploss = -0.05

    # 追踪止损：价格上涨后锁定利润
    trailing_stop = True
    trailing_stop_positive = 0.01        # 盈利1%后启动追踪
    trailing_stop_positive_offset = 0.02 # 盈利2%时才激活

    # 计算指标前需要预热的K线数量
    startup_candle_count = 50

    # Hyperopt 参数范围（后续可以用超参优化自动寻找最优值）
    buy_rsi = IntParameter(25, 45, default=40, space="buy")
    sell_rsi = IntParameter(60, 80, default=70, space="sell")

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 趋势判断：双EMA
        dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)

        # 动量判断：RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        # 成交量均线（过滤低流动性信号）
        dataframe["volume_mean"] = dataframe["volume"].rolling(20).mean()

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["ema20"] > dataframe["ema50"]) &       # 上升趋势
                (dataframe["rsi"] < self.buy_rsi.value) &         # RSI 超卖回调
                (dataframe["volume"] > dataframe["volume_mean"])   # 成交量高于均值，信号更可靠
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["rsi"] > self.sell_rsi.value) |        # RSI 超买
                (dataframe["ema20"] < dataframe["ema50"])          # 趋势反转
            ),
            "exit_long",
        ] = 1
        return dataframe