ml-finance-python

long_short_equity_template.py

(7834B)

 """This algorithm demonstrates the concept of long-short equity.
 It uses two fundamental factors to rank equities in our universe.
 It then longs the top of the ranking and shorts the bottom.
 For information on long-short equity strategies, please see the corresponding
 lecture on our lectures page:
 
 https://www.quantopian.com/lectures
 
 WARNING: These factors were selected because they worked in the past over the specific time period we choose.
 We do not anticipate them working in the future. In practice finding your own factors is the hardest
 part of developing any long-short equity strategy. This algorithm is meant to serve as a framework for testing your own ranking factors.
 
 This algorithm was developed as part of
 Quantopian's Lecture Series. Please direct any
 questions, feedback, or corrections to max@quantopian.com
 """
 
 import numpy as np
 import pandas as pd
 import quantopian.algorithm as algo
 import quantopian.optimize as opt
 from quantopian.pipeline import Pipeline
 from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage 
 
 from quantopian.pipeline.filters import QTradableStocksUS
 from quantopian.pipeline.experimental import risk_loading_pipeline
 
 from quantopian.pipeline.data.builtin import USEquityPricing
 from quantopian.pipeline.data.zacks import broker_ratings
 from quantopian.pipeline.data.psychsignal import stocktwits
 
 # Constraint Parameters
 MAX_GROSS_LEVERAGE = 1.0
 NUM_LONG_POSITIONS = 300
 NUM_SHORT_POSITIONS = 300
 
 # 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*1.0/(NUM_LONG_POSITIONS+NUM_SHORT_POSITIONS)
 MAX_LONG_POSITION_SIZE = 2.0*1.0/(NUM_LONG_POSITIONS+NUM_SHORT_POSITIONS)
 
 def make_pipeline():
     """
     Create and return 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.
     """
     
     # The factors we create here are based on broker recommendations data and a moving
     # average of sentiment data
     diff = (
         broker_ratings.rating_cnt_strong_buys.latest+broker_ratings.rating_cnt_mod_buys.latest -
         (broker_ratings.rating_cnt_strong_sells.latest+broker_ratings.rating_cnt_mod_sells.latest)
     )
     # Here we temper the diff between recommended buys and sells with a ratio of what
     # percentage of brokers actually rated a given security
     rat = broker_ratings.rating_cnt_with.latest/ \
         (broker_ratings.rating_cnt_with.latest+broker_ratings.rating_cnt_without.latest)
     alpha_signal = diff*rat
     
     sentiment_score = SimpleMovingAverage(
         inputs=[stocktwits.bull_minus_bear],
         window_length=3,
     )
 
     universe = QTradableStocksUS()
 
     # Construct a Factor representing the rank of each asset by our value
     # quality metrics. We aggregate them together here using simple addition
     # after zscore-ing them
     combined_factor = (
         alpha_signal.zscore() + sentiment_score.zscore()
     )
 
     # Build Filters representing the top and bottom NUM_POSITIONS stocks by our combined ranking system.
     # We'll use these as our tradeable universe each day.
     longs = combined_factor.top(NUM_LONG_POSITIONS, mask=universe)
     shorts = combined_factor.bottom(NUM_SHORT_POSITIONS, 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 initialize(context):
     # Here we set our slippage and commisions. Set slippage 
     # and commission to zero to evaulate the signal-generating
     # ability of the algorithm independent of these additional
     # costs.
     set_commission(commission.PerShare(cost=0.0, min_trade_cost=0))
     set_slippage(slippage.VolumeShareSlippage(volume_limit=1, price_impact=0))
     context.spy = sid(8554)
 
     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
     algo.attach_pipeline(risk_loading_pipeline(), 'risk_factors')
 
     # Schedule my rebalance function
     schedule_function(func=rebalance,
                       date_rule=date_rules.every_day(),
                       time_rule=time_rules.market_open(hours=0,minutes=30),
                       half_days=True)
     # record my portfolio variables at the end of day
     schedule_function(func=recording_statements,
                       date_rule=date_rules.every_day(),
                       time_rule=time_rules.market_close(),
                       half_days=True)
 
 
 def before_trading_start(context, data):
     # 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 recording_statements(context, data):
     # Plot the number of positions over time.
     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):
     ### Optimize API
     pipeline_data = context.pipeline_data
     
     risk_loadings = context.risk_loadings
 
     ### 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
     )
     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
     )