TIANHE
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
# ============================================================
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# 多指标组合策略 (均线+RSI+MACD)
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# Multi-Indicator Composite Strategy
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# ============================================================
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#
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# 使用方法:
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# 1. 可配置均线周期、RSI阈值等参数
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# 2. 买入条件: RSI超卖 + MACD金叉 + 成交量放大
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# 3. 卖出条件: RSI超买 或 MACD死叉
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#
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# ============================================================
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# === 参数声明 ===
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# @param sma_short int 10 短期均线周期
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# @param sma_long int 30 长期均线周期
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# @param rsi_period int 14 RSI周期
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# @param rsi_oversold int 30 RSI超卖阈值
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# @param rsi_overbought int 70 RSI超买阈值
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# @param use_macd bool True 是否使用MACD过滤
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# @param use_volume bool False 是否使用成交量过滤
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# @param volume_mult float 1.5 成交量放大倍数
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# === 获取参数 ===
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sma_short_period = params.get('sma_short', 10)
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sma_long_period = params.get('sma_long', 30)
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rsi_period = params.get('rsi_period', 14)
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rsi_oversold = params.get('rsi_oversold', 30)
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rsi_overbought = params.get('rsi_overbought', 70)
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use_macd = params.get('use_macd', True)
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use_volume = params.get('use_volume', False)
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volume_mult = params.get('volume_mult', 1.5)
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# === 指标信息 ===
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my_indicator_name = "多指标组合策略"
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my_indicator_description = f"SMA{sma_short_period}/{sma_long_period} + RSI{rsi_period}"
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df = df.copy()
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# === 计算均线 ===
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sma_short = df["close"].rolling(sma_short_period).mean()
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sma_long = df["close"].rolling(sma_long_period).mean()
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# === 计算RSI ===
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delta = df["close"].diff()
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gain = delta.where(delta > 0, 0).rolling(window=rsi_period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=rsi_period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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# === 计算MACD ===
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exp1 = df["close"].ewm(span=12, adjust=False).mean()
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exp2 = df["close"].ewm(span=26, adjust=False).mean()
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macd = exp1 - exp2
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macd_signal = macd.ewm(span=9, adjust=False).mean()
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macd_hist = macd - macd_signal
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# === 计算成交量均线 ===
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volume_ma = df["volume"].rolling(20).mean()
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# === 生成信号条件 ===
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# 均线金叉
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ma_golden = (sma_short > sma_long) & (sma_short.shift(1) <= sma_long.shift(1))
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# 均线死叉
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ma_death = (sma_short < sma_long) & (sma_short.shift(1) >= sma_long.shift(1))
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# RSI超卖
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rsi_buy = rsi < rsi_oversold
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# RSI超买
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rsi_sell = rsi > rsi_overbought
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# MACD金叉
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macd_golden = (macd > macd_signal) & (macd.shift(1) <= macd_signal.shift(1))
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# MACD死叉
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macd_death = (macd < macd_signal) & (macd.shift(1) >= macd_signal.shift(1))
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# 成交量放大
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volume_up = df["volume"] > volume_ma * volume_mult
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# === 综合买卖信号 ===
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buy = ma_golden | rsi_buy # 均线金叉 或 RSI超卖
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if use_macd:
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buy = buy & (macd > macd_signal) # 需要MACD向上
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if use_volume:
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buy = buy & volume_up # 需要成交量放大
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sell = ma_death | rsi_sell # 均线死叉 或 RSI超买
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if use_macd:
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sell = sell | macd_death # MACD死叉也卖出
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df["buy"] = buy.fillna(False).astype(bool)
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df["sell"] = sell.fillna(False).astype(bool)
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# === 买卖标记点 ===
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buy_marks = [df["low"].iloc[i] * 0.995 if df["buy"].iloc[i] else None for i in range(len(df))]
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sell_marks = [df["high"].iloc[i] * 1.005 if df["sell"].iloc[i] else None for i in range(len(df))]
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# === 图表输出配置 ===
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output = {
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"name": my_indicator_name,
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"plots": [
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{"name": f"SMA{sma_short_period}", "data": sma_short.tolist(), "color": "#FF9800", "overlay": True},
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{"name": f"SMA{sma_long_period}", "data": sma_long.tolist(), "color": "#3F51B5", "overlay": True}
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],
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"signals": [
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{"type": "buy", "text": "B", "data": buy_marks, "color": "#00E676"},
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{"type": "sell", "text": "S", "data": sell_marks, "color": "#FF5252"}
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]
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}
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