MA線

• 收盤 > MA，看多
• 收盤 < MA，看空

data_df.loc[:, "X_SMA"] = ta.sma(close=data_df.Close, length=10)
data_df.loc[:, "X_WMA"] = ta.wma(close=data_df.Close, length=10)
data_df.loc[:, "X_EMA"] = ta.ema(close=data_df.Close, length=10)

data_df.loc[:, "T_SMA"] = data_df.X_SMA < data_df.Close
data_df.loc[:, "T_WMA"] = data_df.X_WMA < data_df.Close
data_df.loc[:, "T_EMA"] = data_df.X_EMA < data_df.Close

隨機指標

• 指標成長，看多
• 指標降低，看空

kdj = ta.kdj(high=data_df.High, low=data_df.Low, close=data_df.Close, length=14)

data_df.loc[:, "X_WILLR"] = ta.willr(
    high=data_df.High, low=data_df.Low, close=data_df.Close, length=14
)
data_df.loc[:, "X_STCK"] = kdj[kdj.columns[0]]
data_df.loc[:, "X_STCD"] = kdj[kdj.columns[1]]

data_df.loc[:, "T_WILLR"] = data_df.X_WILLR > data_df.X_WILLR.shift(1)
data_df.loc[:, "T_STCK"] = data_df.X_STCK > data_df.X_STCK.shift(1)
data_df.loc[:, "T_STCD"] = data_df.X_STCD > data_df.X_STCD.shift(1)

RSI、CCI

• 指標過高，看空
• 指標過低，看多
• 指標位於中間值
  • 指標成長，看多
  • 指標降低，看空

data_df.loc[:, "X_RSI"] = ta.rsi(close=data_df.Close, length=6)

def rsi_trend(x):
    if x[0] > 70:
        return False
    elif x[0] < 30:
        return True
    else:
        return x[0] > x[1]


data_df.loc[:, "T_RSI"] = pd.concat(
    [data_df.X_RSI, data_df.X_RSI.shift(1)], axis=1
).apply(rsi_trend, axis=1)

data_df.loc[:, "X_CCI"] = ta.cci(
    high=data_df.High, low=data_df.Low, close=data_df.Close, length=14
)


def cci_trend(x):
    if x[0] > 200:
        return False
    elif x[0] < -200:
        return True
    else:
        return x[0] > x[1]

data_df.loc[:, "T_CCI"] = pd.concat(
    [data_df.X_CCI, data_df.X_CCI.shift(1)], axis=1
).apply(cci_trend, axis=1)

Momentum

動量 > 0 ，看多
動量 <= 0，看空

data_df.loc[:, "X_MOM"] = ta.mom(close=data_df.Close, length=10)

data_df.loc[:, "T_MOM"] = data_df.X_MOM > 0

A/D oscillator與MACD論文的公式很怪暫時先不考慮

結果

使用原始指標準確率：

使用趨勢指標準確率：

看起來是有增長，趨勢化資料明顯減輕了Overfitting狀況，明天考慮修改些指標的參數看是否會有所提升。