def rebalance(context, data):
# Calculate slopes for each futures
prediction = calc_slopes(context, data)
# Get target weights to futures contracts based on slopes
target_weights = get_target_weights(context, data, prediction)
# Exposure is noted for logging and record() plotting
context.exposure = {}
text = ''
for contract in target_weights:
context.exposure[contract.root_symbol] = target_weights[contract]
if target_weights[contract] != 0:
text += "\n%+3.1f%% \t%s \t(%s)" % (target_weights[contract] * 100,
contract.symbol,
contract.asset_name)
if text == '':
text = '\nNo positions to take'
log.info('Target position of today:' + text)
# Rebalance portfolio using optimaize API
order_optimal_portfolio(opt.TargetPortfolioWeights(target_weights),
constraints=[
opt.MaxGrossLeverage(context.maxleverage),
],
universe=target_weights)
def rebalance_pairs(context, data):
# equilibrium(平衡) から現在のスプレッドが離れているか計算
zscore = calc_spread_zscore(context, data)
# weightを計算
target_weights = get_target_weight(context, data, zscore)
if target_weights:
# TargetPortfolioWeights https://www.evernote.com/Home.action#b=177d468e-bf46-408c-8f29-97655b654be5&st=p&x=TargetPortfolioWeights&n=b9413a7e-db9e-4a8b-81c7-cb3929d925f2
order_optimal_portfolio(opt.TargetPortfolioWeights(target_weights),
constraints=[])
def allocate(context, data):
# Set objective to match target weights as closely as possible, given constraints
objective = opt.TargetPortfolioWeights(context.target_weights)
# Define constraints
constraints = []
constraints.append(opt.MaxGrossLeverage(MAX_GROSS_LEVERAGE))
algo.order_optimal_portfolio(objective=objective,
constraints=constraints,
universe=context.stocks)
def rebalance_pairs(context, data):
# Calculate how far away the current spread is from its equilibrium
zscore = calc_spread_zscore(context, data)
# Get target weights to rebalance portfolio
target_weights = get_target_weights(context, data, zscore)
if target_weights:
# If we have target weights, rebalance portfolio
order_optimal_portfolio(opt.TargetPortfolioWeights(target_weights),
constraints=[])
def rebalance(context, data):
context.short1 = data.current(context.continues[0][0], 'contract')
context.short2 = data.current(context.continues[1][0], 'contract')
context.long1 = data.current(context.continues[1][1], 'contract')
targets = {
context.short1: -0.33,
context.short2: -0.33,
context.long1: 0.33
}
for x in range(len(targets)):
order_optimal_portfolio(objective=opt.TargetPortfolioWeights(targets),
constraints=[])
def check_signal(context, data):
if context.counter != 0: #We only want to check after the first day
sentiment = context.sentiment
scores = {} #Holds the scores for the stock to invest in
for i in range(len(context.names)):
name = context.names[i]
symbol = context.symbols[i]
s = sentiment[name]
if s > 7:
scores[symbol] = s
weights = get_weights(scores)
print weights
objective = opt.TargetPortfolioWeights(weights)
order_optimal_portfolio(objective, [])
context.sentiment = data[
"Sentiment"] #Pull in todays sentiment for use on the next day
context.counter += 1