strategy6_scaling+.py 文件源码

def create_model(context, data):
    # Get the relevant daily prices
    recent_prices = data.history(context.assets, 'price',context.history_range, '1d')

    context.ma_50 =recent_prices.values[-50:].mean()     
    context.ma_200 = recent_prices.values[-200:].mean() 
    #print context.ma_50
    #print context.ma_200
    time_lags = pd.DataFrame(index=recent_prices.index)
    time_lags['price']=recent_prices.values
    time_lags['returns']=(time_lags['price'].pct_change()).fillna(0.0001)
    time_lags['lag1'] = (time_lags['returns'].shift(1)).fillna(0.0001)
    time_lags['lag2'] = (time_lags['returns'].shift(2)).fillna(0.0001)
    time_lags['direction'] = np.sign(time_lags['returns'])


    X = time_lags[['returns','lag2']] # Independent, or input variables
    Y = time_lags['direction'] # Dependent, or output variable
    X_scaled = preprocessing.scale(X)
    context.model.fit(X_scaled, Y) # Generate our model