统计了13种均线战法的胜率，5分钟带你看看谁的胜率更高

相信很多新手和资深交易员都会用均线来判断趋势，但你有没有想过，不同的均线策略，其准确率和收益可能差别很大？
这篇文章将系统整理13种最常见的均线策略，从经典的短期均线交叉到长期趋势判断，一步步帮你量化这些策略的买卖信号，并最终比较它们的实战效果。

当出现买入和卖出信号以后，分别对1、2、3、5、10、20日后涨跌做了统计分析，包括概率（N日后涨跌幅为正的概率）

1.短期均线金叉/死叉（MA5 & MA20）

这是最经典的均线交叉策略，短期均线（5日）上穿长期均线（20日）为金叉买入信号，下穿为死叉卖出信号。适合捕捉短期趋势起点和转折点。

def technical_ma_cross(df):
    df = df.copy()
    df['ma5'] = df['close'].rolling(5).mean()
    df['ma20'] = df['close'].rolling(20).mean()
    
    con1 = (df['ma5'] > df['ma20']) & (df['ma5'].shift(1) <= df['ma20'].shift(1))
    con2 = (df['ma5'] < df['ma20']) & (df['ma5'].shift(1) >= df['ma20'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

2.双均线交叉策略（MA10 & MA50）

中短期均线交叉，用于捕捉中期趋势。MA10上穿MA50为买入，下穿为卖出，信号比短期金叉更平滑，噪声更少。

def technical_double_ma(df):
    df = df.copy()
    df['ma10'] = df['close'].rolling(10).mean()
    df['ma50'] = df['close'].rolling(50).mean()
    
    con1 = (df['ma10'] > df['ma50']) & (df['ma10'].shift(1) <= df['ma50'].shift(1))
    con2 = (df['ma10'] < df['ma50']) & (df['ma10'].shift(1) >= df['ma50'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

3.三均线系统（MA5, MA20, MA60）

三条均线组合用于判断多空排列：短期>中期>长期为多头排列，反之为空头排列。买卖信号更严格，减少震荡行情误操作。

def technical_triple_ma(df):
    df = df.copy()
    df['ma5'] = df['close'].rolling(5).mean()
    df['ma20'] = df['close'].rolling(20).mean()
    df['ma60'] = df['close'].rolling(60).mean()
    
    con1 = (df['ma5'] > df['ma20']) & (df['ma20'] > df['ma60']) & ~((df['ma5'].shift(1) > df['ma20'].shift(1)) & (df['ma20'].shift(1) > df['ma60'].shift(1)))
    con2 = (df['ma5'] < df['ma20']) & (df['ma20'] < df['ma60']) & ~((df['ma5'].shift(1) < df['ma20'].shift(1)) & (df['ma20'].shift(1) < df['ma60'].shift(1)))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

4.均线乖离率策略（MA20 + 价格偏离）

当价格偏离均线过大（如上下5%），可能存在回调或反弹的机会。价格低于MA20一定比例买入，高于一定比例卖出。适合捕捉短期极端波动。

def technical_bias(df, ma=20, threshold=0.05):
    df = df.copy()
    df['ma'] = df['close'].rolling(ma).mean()
    
    con1 = (df['close'] < df['ma'] * (1 - threshold)) & ~(df['close'].shift(1) < df['ma'].shift(1) * (1 - threshold))
    con2 = (df['close'] > df['ma'] * (1 + threshold)) & ~(df['close'].shift(1) > df['ma'].shift(1) * (1 + threshold))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

5.均线突破策略（价格上穿MA50）

价格突破长期均线（如MA50）通常意味着趋势开始。上穿买入，下穿卖出，适合追随趋势行情。

def technical_ma_break(df, ma=50):
    df = df.copy()
    df['ma'] = df['close'].rolling(ma).mean()
    
    con1 = (df['close'] > df['ma']) & (df['close'].shift(1) <= df['ma'].shift(1))
    con2 = (df['close'] < df['ma']) & (df['close'].shift(1) >= df['ma'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

6.均线支撑/阻力策略（价格回到MA支撑）

均线可作为动态支撑和阻力。价格回调到均线支撑附近买入，碰到均线阻力卖出，适合顺势做波段交易。

def technical_ma_support(df, ma=20):
    df = df.copy()
    df['ma'] = df['close'].rolling(ma).mean()
    
    con1 = (df['close'] > df['ma']) & (df['close'].shift(1) <= df['ma'].shift(1))
    con2 = (df['close'] < df['ma']) & (df['close'].shift(1) >= df['ma'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

7.均线回调买入策略（价格回调至MA且MA上行）

当价格回调至均线附近，同时均线呈上升趋势时买入，下跌趋势卖出。这种策略结合趋势和回调，降低误操作概率。

def technical_ma_pullback(df, ma=20):
    df = df.copy()
    df['ma'] = df['close'].rolling(ma).mean()
    df['ma_diff'] = df['ma'].diff()
    
    con1 = (df['close'] < df['ma']) & (df['ma_diff'] > 0) & ~((df['close'].shift(1) < df['ma'].shift(1)) & (df['ma_diff'].shift(1) > 0))
    con2 = (df['close'] > df['ma']) & (df['ma_diff'] < 0) & ~((df['close'].shift(1) > df['ma'].shift(1)) & (df['ma_diff'].shift(1) < 0))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

8.均线多空排列策略（短期>中期>长期）

通过判断均线排列顺序来捕捉趋势，短期>中期>长期为买入信号，反之为卖出。比单纯均线交叉更稳健，适合趋势明显的行情。

def technical_ma_order(df, short=5, mid=20, long=60):
    df = df.copy()
    df['ma_short'] = df['close'].rolling(short).mean()
    df['ma_mid'] = df['close'].rolling(mid).mean()
    df['ma_long'] = df['close'].rolling(long).mean()
    
    con1 = (df['ma_short'] > df['ma_mid']) & (df['ma_mid'] > df['ma_long']) & ~((df['ma_short'].shift(1) > df['ma_mid'].shift(1)) & (df['ma_mid'].shift(1) > df['ma_long'].shift(1)))
    con2 = (df['ma_short'] < df['ma_mid']) & (df['ma_mid'] < df['ma_long']) & ~((df['ma_short'].shift(1) < df['ma_mid'].shift(1)) & (df['ma_mid'].shift(1) < df['ma_long'].shift(1)))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

9.EMA短期突破策略

使用指数均线（EMA），对近期价格变化反应更快。短期EMA上穿长期EMA买入，下穿卖出，可捕捉更敏感的趋势变化。

def technical_ema_cross(df, short=12, long=26):
    df = df.copy()
    df['ema_short'] = df['close'].ewm(span=short).mean()
    df['ema_long'] = df['close'].ewm(span=long).mean()
    
    con1 = (df['ema_short'] > df['ema_long']) & (df['ema_short'].shift(1) <= df['ema_long'].shift(1))
    con2 = (df['ema_short'] < df['ema_long']) & (df['ema_short'].shift(1) >= df['ema_long'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

10.均线加趋势过滤策略

在短期均线上穿长期均线的基础上，要求价格在长期均线之上，进一步过滤震荡信号。适合追踪主升趋势，减少横盘误操作。

def technical_ma_trend_filter(df, short=10, long=50):
    df = df.copy()
    df['ma_short'] = df['close'].rolling(short).mean()
    df['ma_long'] = df['close'].rolling(long).mean()
    
    con1 = (df['ma_short'] > df['ma_long']) & (df['close'] > df['ma_long']) & ~((df['ma_short'].shift(1) > df['ma_long'].shift(1)) & (df['close'].shift(1) > df['ma_long'].shift(1)))
    con2 = (df['ma_short'] < df['ma_long']) & (df['close'] < df['ma_long']) & ~((df['ma_short'].shift(1) < df['ma_long'].shift(1)) & (df['close'].shift(1) < df['ma_long'].shift(1)))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

11.均线突破回调策略（价格突破MA后回调买入）

价格突破均线形成趋势后，回调到均线附近买入，跌破均线卖出。结合突破和回调，能更好控制入场时机。

def technical_ma_break_pullback(df, ma=20):
    df = df.copy()
    df['ma'] = df['close'].rolling(ma).mean()
    
    con1 = (df['close'] < df['ma']) & (df['close'].shift(1) > df['ma'].shift(1))
    con2 = (df['close'] > df['ma']) & (df['close'].shift(1) < df['ma'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

12.均线短期加速金叉（MA5加速上穿MA20）

短期均线快速上穿长期均线，且上升加速时发出买入信号，反之卖出。适合捕捉趋势初期的加速行情。

def technical_ma_acceleration(df):
    df = df.copy()
    df['ma5'] = df['close'].rolling(5).mean()
    df['ma20'] = df['close'].rolling(20).mean()
    df['ma5_diff'] = df['ma5'].diff()
    
    con1 = (df['ma5'] > df['ma20']) & (df['ma5'].shift(1) <= df['ma20'].shift(1)) & (df['ma5_diff'] > df['ma5_diff'].shift(1))
    con2 = (df['ma5'] < df['ma20']) & (df['ma5'].shift(1) >= df['ma20'].shift(1)) & (df['ma5_diff'] < df['ma5_diff'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

13.均线高低点突破策略（突破近期高点MA）

结合均线与近期高低点突破，当价格突破MA附近的5日高点买入，跌破5日低点卖出。适合捕捉连续上涨或下跌趋势的初期阶段。

def technical_ma_high_low(df, ma=20):
    df = df.copy()
    df['ma'] = df['close'].rolling(ma).mean()
    df['ma_high'] = df['ma'].rolling(5).max()
    df['ma_low'] = df['ma'].rolling(5).min()
    
    con1 = (df['close'] > df['ma_high']) & (df['close'].shift(1) <= df['ma_high'].shift(1))
    con2 = (df['close'] < df['ma_low']) & (df['close'].shift(1) >= df['ma_low'].shift(1))
    
    df['signal'] = None
    df.loc[con1, 'signal'] = 1
    df.loc[con2, 'signal'] = 0
    return df

总结

统计完以后，结果真的让人吃惊，看跌信号的成功率居然完胜看涨信号，以后做均线信号是不是可以反着来

technical_ema_cross（EMA短期突破策略）和technical_ma_cross（短期均线金叉/死叉（MA5 & MA20））这2个均线指标的反向指标胜率居然排在了第一位。
整体而言，信号出现后，随着天数的增加，胜率是逐步上升的，普遍在第10天和20天能达到峰值，想做均线策略的，20天作为一个周期效果应该不错。
technical_ma_pullback（均线回调买入策略）策略虽然胜率低，但是平均收益是最高的