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May 7, 2019 01:35
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L/S Equity algo
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import quantopian.algorithm as algo | |
import quantopian.optimize as opt | |
from quantopian.pipeline import Pipeline | |
from quantopian.pipeline.factors import SimpleMovingAverage | |
from quantopian.pipeline.filters import QTradableStocksUS | |
from quantopian.pipeline.experimental import risk_loading_pipeline | |
from quantopian.pipeline.data.psychsignal import stocktwits | |
from quantopian.pipeline.data import Fundamentals | |
from quantopian.pipeline.factors import CustomFactor | |
from quantopian.pipeline.data.builtin import USEquityPricing | |
# Constraint Parameters | |
MAX_GROSS_LEVERAGE = 1.0 | |
TOTAL_POSITIONS = 600 | |
# Here we define the maximum position size that can be held for any | |
# given stock. If you have a different idea of what these maximum | |
# sizes should be, feel free to change them. Keep in mind that the | |
# optimizer needs some leeway in order to operate. Namely, if your | |
# maximum is too small, the optimizer may be overly-constrained. | |
MAX_SHORT_POSITION_SIZE = 2.0 / TOTAL_POSITIONS | |
MAX_LONG_POSITION_SIZE = 2.0 / TOTAL_POSITIONS | |
def initialize(context): | |
""" | |
A core function called automatically once at the beginning of a backtest. | |
Use this function for initializing state or other bookkeeping. | |
Parameters | |
---------- | |
context : AlgorithmContext | |
An object that can be used to store state that you want to maintain in | |
your algorithm. context is automatically passed to initialize, | |
before_trading_start, handle_data, and any functions run via schedule_function. | |
context provides the portfolio attribute, which can be used to retrieve information | |
about current positions. | |
""" | |
algo.attach_pipeline(make_pipeline(), 'long_short_equity_template') | |
# Attach the pipeline for the risk model factors that we | |
# want to neutralize in the optimization step. The 'risk_factors' string is | |
# used to retrieve the output of the pipeline in before_trading_start below. | |
algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors') | |
# Schedule our rebalance function | |
algo.schedule_function(func=rebalance, | |
date_rule=algo.date_rules.week_start(), | |
time_rule=algo.time_rules.market_open(hours=0, minutes=30), | |
half_days=True) | |
# Record our portfolio variables at the end of day | |
algo.schedule_function(func=record_vars, | |
date_rule=algo.date_rules.every_day(), | |
time_rule=algo.time_rules.market_close(), | |
half_days=True) | |
class Momentum(CustomFactor): | |
#Here we define a basic momentum factor using a CustomFactor. We take | |
#the momentum from the past year up until the beginning of this month | |
#and penalize it by the momentum over this month. We are tempering a | |
#long-term trend with a short-term reversal in hopes that we get a | |
#better measure of momentum. | |
inputs = [USEquityPricing.close] | |
window_length = 252 | |
def compute(self, today, assets, out, prices): | |
out[:] = ((prices[-21] - prices[-252])/prices[-252] - | |
(prices[-1] - prices[-21])/prices[-21]) | |
def make_pipeline(): | |
""" | |
A function that creates and returns our pipeline. | |
We break this piece of logic out into its own function to make it easier to | |
test and modify in isolation. In particular, this function can be | |
copy/pasted into research and run by itself. | |
Returns | |
------- | |
pipe : Pipeline | |
Represents computation we would like to perform on the assets that make | |
it through the pipeline screen. | |
""" | |
# Factors | |
value = Fundamentals.ebit.latest / Fundamentals.enterprise_value.latest | |
quality = Fundamentals.roe.latest | |
sentiment_score = SimpleMovingAverage( | |
inputs=[stocktwits.bull_minus_bear], | |
window_length=3, | |
) | |
free_cash_to_assets = Fundamentals.free_cash_flow.latest | |
momentum = Momentum() | |
# end factors | |
universe = QTradableStocksUS() | |
# We winsorize our factor values in order to lessen the impact of outliers | |
# For more information on winsorization, please see | |
# https://en.wikipedia.org/wiki/Winsorizing | |
value_winsorized = value.winsorize(min_percentile=0.05, max_percentile=0.95) | |
# quality_winsorized = quality.winsorize(min_percentile=0.05, max_percentile=0.95) | |
# sentiment_score_winsorized = sentiment_score.winsorize(min_percentile=0.05,max_percentile=0.95) | |
free_cash_to_assets_winsorized = free_cash_to_assets.winsorize(min_percentile=0.05, max_percentile=0.95) | |
momentum_winsorized = momentum.winsorize(min_percentile=0.05, max_percentile=0.95) | |
# Here we combine our winsorized factors, z-scoring them to equalize their influence | |
combined_factor = ( | |
# value_winsorized.zscore() + | |
# quality_winsorized.zscore() + | |
# sentiment_score_winsorized.zscore() + | |
free_cash_to_assets_winsorized.zscore() + | |
momentum_winsorized.zscore() | |
) | |
# Build Filters representing the top and bottom baskets of stocks by our | |
# combined ranking system. We'll use these as our tradeable universe each | |
# day. | |
longs = combined_factor.top(TOTAL_POSITIONS//2, mask=universe) | |
shorts = combined_factor.bottom(TOTAL_POSITIONS//2, mask=universe) | |
# The final output of our pipeline should only include | |
# the top/bottom 300 stocks by our criteria | |
long_short_screen = (longs | shorts) | |
# Create pipeline | |
pipe = Pipeline( | |
columns={ | |
'longs': longs, | |
'shorts': shorts, | |
'combined_factor': combined_factor | |
}, | |
screen=long_short_screen | |
) | |
return pipe | |
def before_trading_start(context, data): | |
""" | |
Optional core function called automatically before the open of each market day. | |
Parameters | |
---------- | |
context : AlgorithmContext | |
See description above. | |
data : BarData | |
An object that provides methods to get price and volume data, check | |
whether a security exists, and check the last time a security traded. | |
""" | |
# Call algo.pipeline_output to get the output | |
# Note: this is a dataframe where the index is the SIDs for all | |
# securities to pass my screen and the columns are the factors | |
# added to the pipeline object above | |
context.pipeline_data = algo.pipeline_output('long_short_equity_template') | |
# This dataframe will contain all of our risk loadings | |
context.risk_loadings = algo.pipeline_output('risk_factors') | |
def record_vars(context, data): | |
""" | |
A function scheduled to run every day at market close in order to record | |
strategy information. | |
Parameters | |
---------- | |
context : AlgorithmContext | |
See description above. | |
data : BarData | |
See description above. | |
""" | |
# Plot the number of positions over time. | |
algo.record(num_positions=len(context.portfolio.positions)) | |
# Called at the start of every month in order to rebalance | |
# the longs and shorts lists | |
def rebalance(context, data): | |
""" | |
A function scheduled to run once every Monday at 10AM ET in order to | |
rebalance the longs and shorts lists. | |
Parameters | |
---------- | |
context : AlgorithmContext | |
See description above. | |
data : BarData | |
See description above. | |
""" | |
# Retrieve pipeline output | |
pipeline_data = context.pipeline_data | |
risk_loadings = context.risk_loadings.dropna() | |
# 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_factor) | |
# Define the list of constraints | |
constraints = [] | |
# Constrain our maximum gross leverage | |
constraints.append(opt.MaxGrossExposure(MAX_GROSS_LEVERAGE)) | |
# Require our algorithm to remain dollar neutral | |
constraints.append(opt.DollarNeutral()) | |
# Add the RiskModelExposure constraint to make use of the | |
# default risk model constraints | |
neutralize_risk_factors = opt.experimental.RiskModelExposure( | |
risk_model_loadings=risk_loadings, | |
version=0 | |
) | |
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 algo.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. | |
algo.order_optimal_portfolio( | |
objective=objective, | |
constraints=constraints | |
) |
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