trainall.py 文件源码

def train():
    os.chdir(dname)
    for selected_stock in onlyfiles:
        df = pd.read_csv(os.path.join('data_files',selected_stock))
        #preprocessing the data
        df = df[['Adj. Open',  'Adj. High',  'Adj. Low',  'Adj. Close', 'Adj. Volume']]
        #measure of volatility
        df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Low'] * 100.0
        df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0
        df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
        forecast_col = 'Adj. Close'
        df.fillna(value=-99999, inplace=True)
        forecast_out = int(math.ceil(0.01 * len(df)))
        df['label'] = df[forecast_col].shift(-forecast_out)

        X = np.array(df.drop(['label'],1))
        X = preprocessing.scale(X)
        X_lately = X[-forecast_out:]
        X = X[:-forecast_out]

        df.dropna(inplace=True)
        y = np.array(df['label'])
        X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)

        svr = SVR()
        pickle.dump(svr,open(join(dname+'/models/svr_unfit/', selected_stock+'svr.sav'),'wb'))
        svr.fit(X_train, y_train)

        lr = LinearRegression()
        pickle.dump(lr,open(join(dname+'/models/lr_unfit/', selected_stock+'lr.sav'),'wb'))
        lr.fit(X_train, y_train)

        mlp = MLPRegressor()
        pickle.dump(mlp,open(join(dname+'/models/mlp_unfit/', selected_stock+'mlp.sav'),'wb'))
        mlp.fit(X_train, y_train)

        pickle.dump(svr,open(join(dname+'/models/svr_fit/', selected_stock+'svr.sav'),'wb'))
        pickle.dump(lr,open(join(dname+'/models/lr_fit/', selected_stock+'lr.sav'),'wb'))
        pickle.dump(mlp,open(join(dname+'/models/mlp_fit/', selected_stock+'mlp.sav'),'wb'))

        print(selected_stock+" - trained")