RSI双底选股公式：精准捕捉股价底部反转信号

{同花顺版 RSI双底}
RSI1:EMA(CLOSE,12)-EMA(CLOSE,26);
RSI2:EMA(RSI1,9);
{注意：同花顺默认RSI算法可能不同，此处使用MACD的DIF作为近似替代或自定义RSI}
{自定义RSI计算}
LC:=REF(CLOSE,1);
RSI_VAL:=SMA(MAX(CLOSE-LC,0),14,1)/SMA(ABS(CLOSE-LC),14,1)100;
{查找前低}
PRE_LOW_REF:=REF(LOW,10); {示例：10日前的低点，实际需动态查找}
{简化逻辑：RSI小于30且股价开始反弹}
CONDITION1:=RSI_VAL < 30 AND REF(RSI_VAL,1) < 30 AND RSI_VAL > REF(RSI_VAL,1);
{股价W底简化：今日收盘高于5日均线，且昨日低于5日均线}
CONDITION2:=CROSS(CLOSE,MA(CLOSE,5));
{最终信号}
XG: CONDITION1 AND CONDITION2;

import pandas as pd
import numpy as np
def calculate_rsi(data, period=14):
    delta = data['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi
def find_rsi_double_bottom(df, rsi_col='RSI'):
    """
    查找RSI双底背离信号
    """
    # 1. 计算RSI
    df['RSI'] = calculate_rsi(df)
    # 2. 寻找局部低点 (简化版：使用滚动窗口最小值)
    # 实际应用中需使用更复杂的极值点检测算法，如scipy.signal.argrelextrema
    low_points = []
    window = 10
    for i in range(window, len(df) - window):
        if df.iloc[i]['Low'] < df.iloc[i-window:i+window]['Low'].min():
            if df.iloc[i][rsi_col] < 30: # 仅在低位寻找
                low_points.append(i)
    # 3. 检测双底结构
    signals = []
    for i in range(len(low_points) - 1):
        idx1 = low_points[i]
        idx2 = low_points[i+1]
        # 条件1：第二个低点高于第一个低点 (价格底抬高)
        price_higher = df.iloc[idx2]['Low'] > df.iloc[idx1]['Low']
        # 条件2：第二个RSI低点高于第一个RSI低点 (RSI底背离)
        rsi_higher = df.iloc[idx2][rsi_col] > df.iloc[idx1][rsi_col]
        # 条件3：当前价格在第二个低点后开始反弹
        current_rise = df.iloc[idx2]['Close'] > df.iloc[idx2-2]['Close']
        if price_higher and rsi_higher and current_rise:
            signals.append({
                'date': df.iloc[idx2]['Date'],
                'price': df.iloc[idx2]['Close'],
                'rsi': df.iloc[idx2][rsi_col]
            })
    return pd.DataFrame(signals)
使用示例:
df = pd.read_csv('stock_data.csv')
results = find_rsi_double_bottom(df)
print(results)