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['daily_returns']=time_lags['price'].pct_change()
time_lags['multiple_day_returns'] = time_lags['price'].pct_change(3)
time_lags['rolling_mean'] = time_lags['daily_returns'].rolling(window = 4,center=False).mean()
time_lags['time_lagged'] = time_lags['price']-time_lags['price'].shift(-2)
X = time_lags[['price','daily_returns','multiple_day_returns','rolling_mean']].dropna()
time_lags['updown'] = time_lags['daily_returns']
time_lags.updown[time_lags['daily_returns']>=0]='up'
time_lags.updown[time_lags['daily_returns']<0]='down'
le = preprocessing.LabelEncoder()
time_lags['encoding']=le.fit(time_lags['updown']).transform(time_lags['updown'])
# X = time_lags[['lag1','lag2']] # Independent, or input variables
# Y = time_lags['direction'] # Dependent, or output variable
context.model.fit(X,time_lags['encoding'][4:]) # Generate our model
random-forest-daily-returns.py 文件源码
python
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