sklearn_svr.py 文件源码

def addDailyReturn(dataset):
    """
    Adding in daily return to create binary classifiers (Up or Down in relation to the previous day)
    """

    #will normalize labels
    le = preprocessing.LabelEncoder()

    #print "dataset['Adj_Close']\n", dataset['Adj_Close'][:5]

    #print "dataset['Adj_Close'].shift(-1)\n", dataset['Adj_Close'].shift(1)[:5]

    dataset['UpDown'] = (dataset['Adj_Close']-dataset['Adj_Close'].shift(1))/dataset['Adj_Close'].shift(1)
    #print dataset['UpDown'][240:]

    # will be denoted by 3 when transformed
    dataset.UpDown[dataset.UpDown > 0] = "sell"

    dataset.UpDown[dataset.UpDown == 0] = "hold"

    dataset.UpDown[dataset.UpDown < 0] = "buy"
    #print dataset['UpDown'][:10]
    dataset.UpDown = le.fit(dataset.UpDown).transform(dataset.UpDown)

    #print dataset['UpDown']