talib 不是万能的、有些其它指标 如：BBI、PSY、BIAS等这些指标是没有的
def BBI(df, n1=3, n2=6, n3=12, n4=24): """ 多空指数 """ bbi = (MA(df["close"], n1) + MA(df["close"], n2) + MA(df["close"], n3) + MA(df["close"], n4)) / 4 new_df = pd.DataFrame(data=list(bbi), columns=["bbi"]) return new_df def PSY(df, n=12, m=6): """ 心理线 """ new_df = pd.DataFrame() new_df["psy"] = COUNT(df["close"] > df["close"].shift(1), n) / n * 100 new_df["psyma"] = MA(new_df["psy"], m) return new_df def BIAS(df, n=6): """ 乖离率 """ ma1 = MA(df["close"], n) new_df = pd.DataFrame(data=list((df["close"] - ma1) / ma1 * 100), columns=["bias"]) return new_df def MA(close, n=5): """ 简单移动平均线 """ ma_data = close.rolling(n).mean() return ma_data def COUNT(cond, n): """ 统计n周期中满足cond条件的个数 """ if n == 0: # 从第一个有效值开始统计 count_data = pd.Series(np.where(cond, 1, 0).cumsum()) else: # 统计每个n周期 count_data = pd.Series(pd.Series(np.where(cond, 1, 0)).rolling(n).sum()) return count_data 调用方法: bbi = BBI(df, 6, 12, 24, 48) if df['close'].values[-2] < bbi.values[-2] and df['close'].values[-1] >= bbi.values[-1]: order.up_cross(symbol, 'bbi 策略做多') if df['close'].values[-2] > bbi.values[-2] and df['close'].values[-1] <= bbi.values[-1]: order.down_cross(symbol, 'bbi 策略做空') psy = PSY(df, 10, 10) if (psy['psy'].iloc[-2] < 30) and (psy['psy'].iloc[-1] >= 30): order.up_cross(symbol, 'psy 策略做多') if (psy['psy'].iloc[-2] > 70) and (psy['psy'].iloc[-1] <= 70): order.down_cross(symbol, 'psy 策略做空') bias = BIAS(df, 12) if (bias['bias'].iloc[-2] > -10) and (bias['bias'].iloc[-1] <= -10): order.up_cross(symbol, 'bias 策略做多') if (bias['bias'].iloc[-2] < 10) and (bias['bias'].iloc[-1] >= 10): order.down_cross(symbol, 'bias 策略做空')
