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 my_rebalance_opt(context, data):
"""
Our monthly rebalancing routine
"""
# First update the stock universe.
context.output = pipeline_output('investment_universe')
context.security_list = context.output.index
# Get data
hist_window = max(context.momentum_window,
context.momentum_window2) + context.exclude_days
hist = data.history(context.security_list, "close", hist_window, "1d")
data_end = -1 * context.exclude_days # exclude most recent data
momentum1_start = -1 * (context.momentum_window + context.exclude_days)
momentum_hist1 = hist[momentum1_start:data_end]
momentum2_start = -1 * (context.momentum_window2 + context.exclude_days)
momentum_hist2 = hist[momentum2_start:data_end]
# Calculate momentum scores for all stocks.
momentum_list = momentum_hist1.apply(slope) # Mom Window 1
momentum_list2 = momentum_hist2.apply(slope) # Mom Window 2
# Combine the lists and make average
momentum_concat = pd.concat((momentum_list, momentum_list2))
mom_by_row = momentum_concat.groupby(momentum_concat.index)
mom_means = mom_by_row.mean()
# Calculate inverse volatility, for position size.
inv_vola_table = hist.apply(inv_vola_calc)
alpha = (mom_means * inv_vola_table).dropna()
objective = opt.MaximizeAlpha(alpha)
constrain_gross_leverage = opt.MaxGrossLeverage(MAX_GROSS_LEVERAGE)
constrain_pos_size = opt.PositionConcentration.with_equal_bounds(
-MAX_SHORT_POSITION_SIZE,
MAX_LONG_POSITION_SIZE,
)
market_neutral = opt.DollarNeutral()
sector_neutral = opt.NetPartitionExposure.with_equal_bounds(
labels=context.output.sector,
min=-MAX_SHORT_SECTOR_SIZE,
max=MAX_LONG_SECTOR_SIZE,
)
order_optimal_portfolio(objective,
constraints=[
constrain_gross_leverage, constrain_pos_size,
market_neutral, sector_neutral
],
universe=context.security_list)
def rebalance(context, data):
### Optimize API
pipeline_data = context.pipeline_data
### Extract from pipeline any specific risk factors you want
# to neutralize that you have already calculated
risk_factor_exposures = pd.DataFrame(
{'market_beta': pipeline_data.market_beta.fillna(1.0)})
# We fill in any missing factor values with a market beta of 1.0.
# We do this rather than simply dropping the values because we have
# want to err on the side of caution. We don't want to exclude
# a security just because it's missing a calculated market beta,
# so we assume any missing values have full exposure to the market.
### Here we define our objective for the Optimize API. We have
# selected MaximizeAlpha because we believe our combined factor
# ranking to be proportional to expected returns. This routine
# will optimize the expected return of our algorithm, going
# long on the highest expected return and short on the lowest.
objective = opt.MaximizeAlpha(pipeline_data.combined_rank)
### Define the list of constraints
constraints = []
# Constrain our maximum gross leverage
constraints.append(opt.MaxGrossLeverage(MAX_GROSS_LEVERAGE))
# Require our algorithm to remain dollar neutral
constraints.append(opt.DollarNeutral())
# Add a sector neutrality constraint using the sector
# classifier that we included in pipeline
constraints.append(
opt.NetPartitionExposure.with_equal_bounds(
labels=pipeline_data.sector,
min=-MAX_SECTOR_EXPOSURE,
max=MAX_SECTOR_EXPOSURE,
))
# Take the risk factors that you extracted above and
# list your desired max/min exposures to them -
# Here we selection +/- 0.01 to remain near 0.
neutralize_risk_factors = opt.WeightedExposure(
loadings=risk_factor_exposures,
min_exposures={'market_beta': -MAX_BETA_EXPOSURE},
max_exposures={'market_beta': MAX_BETA_EXPOSURE})
constraints.append(neutralize_risk_factors)
# With this constraint we enforce that no position can make up
# greater than MAX_SHORT_POSITION_SIZE on the short side and
# no greater than MAX_LONG_POSITION_SIZE on the long side. This
# ensures that we do not overly concentrate our portfolio in
# one security or a small subset of securities.
constraints.append(
opt.PositionConcentration.with_equal_bounds(
min=-MAX_SHORT_POSITION_SIZE, max=MAX_LONG_POSITION_SIZE))
### Put together all the pieces we defined above by passing
# them into the order_optimal_portfolio function. This handles
# all of our ordering logic, assigning appropriate weights
# to the securities in our universe to maximize our alpha with
# respect to the given constraints.
order_optimal_portfolio(objective=objective, constraints=constraints)
def do_portfolio_construction(context, data):
pipeline_data = context.pipeline_data
todays_universe = pipeline_data.index
# Objective
# ---------
# For our objective, we simply use our naive ranks as an alpha coefficient
# and try to maximize that alpha.
#
# This is a **very** naive model. Since our alphas are so widely spread out,
# we should expect to always allocate the maximum amount of long/short
# capital to assets with high/low ranks.
#
# A more sophisticated model would apply some re-scaling here to try to generate
# more meaningful predictions of future returns.
objective = opt.MaximizeAlpha(pipeline_data.alpha)
# Constraints
# -----------
# Constrain our gross leverage to 1.0 or less. This means that the absolute
# value of our long and short positions should not exceed the value of our
# portfolio.
constrain_gross_leverage = opt.MaxGrossLeverage(MAX_GROSS_LEVERAGE)
# Constrain individual position size to no more than a fixed percentage
# of our portfolio. Because our alphas are so widely distributed, we
# should expect to end up hitting this max for every stock in our universe.
constrain_pos_size = opt.PositionConcentration.with_equal_bounds(
-MAX_SHORT_POSITION_SIZE,
MAX_LONG_POSITION_SIZE,
)
# Constrain ourselves to allocate the same amount of capital to
# long and short positions.
market_neutral = opt.DollarNeutral()
# Constrain ourselve to have a net leverage of 0.0 in each sector.
sector_neutral = opt.NetPartitionExposure.with_equal_bounds(
labels=pipeline_data.sector,
min=-0.0001,
max=0.0001,
)
# Run the optimization. This will calculate new portfolio weights and
# manage moving our portfolio toward the target.
algo.order_optimal_portfolio(
objective=objective,
constraints=[
constrain_gross_leverage,
constrain_pos_size,
market_neutral,
sector_neutral,
],
universe=todays_universe,
)
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 my_rebalance(context, data):
"""
Execute orders according to our schedule_function() timing.
"""
risk_model_factors = context.risk_factors
risk_model_factors = risk_model_factors.join(context.predicted_probs,
how='right').dropna()
predictions = risk_model_factors.ML
# Filter out stocks that can not be traded
predictions = predictions.loc[data.can_trade(predictions.index)]
# Select top and bottom N stocks
predictions = pd.concat([
predictions.nlargest(N_STOCKS_TO_TRADE // 2),
predictions.nsmallest(N_STOCKS_TO_TRADE // 2)
])
todays_universe = predictions.index
predictions -= 0.5 # predictions are probabilities ranging from 0 to 1
# Setup Optimization Objective
objective = opt.MaximizeAlpha(predictions)
# Setup Optimization Constraints
constrain_gross_leverage = opt.MaxGrossLeverage(1.0)
constrain_pos_size = opt.PositionConcentration.with_equal_bounds(-.02, .02)
market_neutral = opt.DollarNeutral()
# TypeError: cannot do label indexing on <class 'pandas.indexes.base.Index'> with these indexers [nan] of <type 'float'>
sector_neutral = opt.NetPartitionExposure.with_equal_bounds(
labels=context.risk_factors.Sector.dropna(),
min=-0.0001,
max=0.0001,
)
# Run the optimization. This will calculate new portfolio weights and
# manage moving our portfolio toward the target.
order_optimal_portfolio(
objective=objective,
constraints=[
constrain_gross_leverage,
constrain_pos_size,
market_neutral,
sector_neutral,
],
universe=todays_universe,
)
def do_portfolio_construction(context, data):
pipeline_data = context.pipeline_data
todays_universe = pipeline_data.index
# Objective here was to maximise alpha which is
# our factor defined in the pipeline.
objective = opt.MaximizeAlpha(pipeline_data.alpha)
# Constrain our gross leverage to 1.0 or less.
# This means that the absolute value of our long and short positions
# should not exceed the value of our portfolio.
constrain_gross_leverage = opt.MaxGrossLeverage(MAX_GROSS_LEVERAGE)
# Constrain individual position size to no more than a fixed percentage
# of our portfolio.
constrain_pos_size = opt.PositionConcentration.with_equal_bounds(
-MAX_SHORT_POSITION_SIZE,
MAX_LONG_POSITION_SIZE,
)
# Constrain ourselves to allocate the same amount of capital to
# long and short positions. Not used in the simulations in this work.
market_neutral = opt.DollarNeutral()
# Constrain the maximum average exposure
# to individual sectors to -10% - 10%.
sector_neutral = opt.NetPartitionExposure.with_equal_bounds(
labels=pipeline_data.sector,
min=-0.10,
max=0.10,
)
# Run the optimization.
# This will calculate new portfolio weights and
# manage moving our portfolio toward the target.
algo.order_optimal_portfolio(
objective=objective,
constraints=[
constrain_gross_leverage,
constrain_pos_size,
# market_neutral, ---> not used in the study.
sector_neutral,
],
universe=todays_universe,
)
