I modified the Long-Short Equity template in the Lecture Series to use the new risk model to bring it up to speed for contest submission. Here I'm using a combination of sentiment and broker ratings to rank securities in the QTradableStocksUS. Definitely feel free to swap out the factors that I used for factors of your own design.

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Cumulative performance:

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Custom data:

Total Returns

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Sharpe

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Sortino

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Benchmark Returns

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Returns | 1 Month | 3 Month | 6 Month | 12 Month |

Alpha | 1 Month | 3 Month | 6 Month | 12 Month |

Beta | 1 Month | 3 Month | 6 Month | 12 Month |

Sharpe | 1 Month | 3 Month | 6 Month | 12 Month |

Sortino | 1 Month | 3 Month | 6 Month | 12 Month |

Volatility | 1 Month | 3 Month | 6 Month | 12 Month |

Max Drawdown | 1 Month | 3 Month | 6 Month | 12 Month |

"""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 [email protected] """ 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_free 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_free.rating_cnt_strong_buys.latest+broker_ratings_free.rating_cnt_mod_buys.latest - (broker_ratings_free.rating_cnt_strong_sells.latest+broker_ratings_free.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_free.rating_cnt_with.latest/ \ (broker_ratings_free.rating_cnt_with.latest+broker_ratings_free.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 )