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from sklearn.qda import QDA
#from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing
import numpy as np
import pandas as pd
def initialize(context):
context.assets = sid(8554) # Trade SPY
context.model = QDA()
context.lookback = 5 # Look back
context.history_range = 200
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))
# Trade at the start of every day
schedule_function(trade, date_rules.every_day(), time_rules.market_open(minutes=1))
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
def trade(context, data):
if context.model: # Check if our model is generated
# Get recent prices
recent_prices = data.history(context.assets,'price',context.lookback, '1d')
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()
prediction = context.model.predict(X)
if prediction == 1 and context.ma_50 > context.ma_200:
order_target_percent(context.assets, 1.0)
elif prediction == 2 and context.ma_50 < context.ma_200:
order_target_percent(context.assets, -1.0)
else:
pass
def handle_data(context, data):
pass
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