def initialize(context):
""" Define algorithm"""
print "Initialize..."
global TRAINING_STOCK, BACKTEST_STOCK
context.security = None #becomes symbol(BACKTEST_STOCK)
context.benchmark = symbol('SPY')
context.training_data = loadTrainingData(TRAINING_TIME, TRAINING_STOCK)
context.training_data_length = len(context.training_data) - 2
context.normalized_data = Manager.normalize(context.training_data) # will have to redo every time step
target = Manager.getTargets(context.normalized_data)
context.training_data = context.training_data[:-2] # delete last data entry, because it won't be used
context.normalized_data = context.normalized_data[:-2] # delete last data entry, because it won't be used
#print target
#plt.figure("Training Data")
#for i in range(len(context.normalized_data[0])):
# plt.plot([x[i] for x in context.normalized_data])
#plt.legend(['open', 'high', 'low', 'close', 'volume', 'price'], loc='upper left')
#plt.show()
print "Train..."
#print len(context.training_data), len(context.normalized_data), len(target)
context.strategy = STRATEGY_CLASS([context.normalized_data], [target], num_epochs=EPOCHS)
print "Capital Base: " + str(context.portfolio.cash)
def handle_data(context, data):
#print "Cash: $" + str(context.portfolio.cash), "Data: ", str(len(context.training_data))
#assert context.portfolio.cash > 0.0, "ERROR: negative context.portfolio.cash"
assert len(context.training_data) == context.training_data_length; "ERROR: "
context.security = symbol(BACKTEST_STOCK)
# data stored as (open, high, low, close, volume, price)
if IS_NORMALIZE:
feed_data = ([
data[context.security].open - data[context.security].open,
data[context.security].high - data[context.security].open,
data[context.security].low - data[context.security].open,
data[context.security].close - data[context.security].open
#data[context.security].volume,
#data[context.security].close,
])
else:
feed_data = ([
data[context.security].open,
data[context.security].high,
data[context.security].low,
data[context.security].close
#data[context.security].volume,
#data[context.security].close,
])
#keep track of history.
context.training_data.pop(0)
context.training_data.append(feed_data)
context.normalized_data = Manager.normalize(context.training_data) # will have to redo every time step
#print len(context.training_data), len(context.normalized_data), len(context.normalized_data[0])
prediction = context.strategy.predict(context.training_data)[-1]
print "Value: $%.2f Cash: $%.2f Predict: %.5f" % (context.portfolio.portfolio_value, context.portfolio.cash, prediction[0])
# Do nothing if there are open orders:
if has_orders(context, data):
print('has open orders - doing nothing!')
# Put entire position in
elif prediction > 0.5:
order_target_percent(context.security, .98)
# Take entire position out
else:
order_target_percent(context.security, 0)
#order_target_percent(context.security, -.99)
record(BENCH=data[context.security].price)
record(SPY=data[context.benchmark].price)
