Test different portfolio settings to achieve the portfolio results you're looking for. Portfolio settings are best altered when you have already created a portfolio with strong quantile performance (Q1  Q5).
Portfolio Settings can be layered on top of an existing machine learning model to generate new portfolios for every given model. For example, with one model you could have a 130% long and 30% short portfolio setting in addition to a 100% long and 0% short portfolio setting. This will create two separate and distinct portfolios based off of the selections of the original model.
Changing portfolio settings comes at no additional credit cost. Experimenting with different portfolios allows you to see how your portfolio would perform using different trading and allocation strategies. Changing or adding a portfolio doesn’t stop existing live portfolios that work off the same model. You can change the portfolio settings of live models, though they will take some time to update (varies depending on number of stocks and duration of backtest).
SECURITIES BREAKDOWN
Description  
Allow multiple securities per company  Allows the machine to trade multiple securities (at the same time) for the same company. If it is ON and a company has listed PREF shares and COMMON shares both will show up as tradeable options for that company. If it is OFF then system will try to determine the "correct" security to trade based on volume, liquidity, and share type. 
Allow depositary receipts  Companies with tickers ending in .Y and .F will be disallowed from trading within the model with this setting off. Turn it on to allow trading in these securities. 
Price cleaning  Price Cleaning removes any very large price movements from the securities, specifically with a daily change greater than +500% or 83.33%. There are some legitimate securities with moves this large that will be impacted by turning this on, but the advantage is it prevents these securities from overwhelming the backtest. 
Allocate weight to benchmark  Enables part of the model weight to be allocated to benchmark. 
Benchmark Allocation: weight to allocate to benchmark  
Minimum Share Price: The minimum share price you want the portfolio to allow.  
Minimum share price  The minimum share price you want the portfolio to allow. 
Pairs Trading  Pairs Trading forces the portfolio to be created by going long / short an equal number of stocks. The portfolio will try to find an offsetting position for every long by finding securities that are both highly correlated and have a significant difference in star rating. 
Longs  
Percent to long  The machine will attempt to buy the top X% of stocks, subject to the minimum and maximum specified. This is on a per “portfolio” basis, so if you have sector neutral turned on it will be top X% per sector. 
Minimum number of longs  The minimum number of stocks to buy. This is on a per “portfolio” basis, so if you have sector neutral turned on it will be minimum per sector. 
Maximum number of longs  The maximum number of stocks to buy. This is on a per “portfolio” basis, so if you have sector neutral turned on it will be maximum per sector. 
Min. long position per stock  The minimum weighting of a long position within the portfolio. 
Max. long position per stock  The maximum weighting of a long position within the portfolio. 
Percent of portfolio long  The exact sum of all long position weightings in the portfolio. 
Shorts  
Percent to short  The machine will attempt to short the bottom X% of stocks, subject to the minimum and maximum specified. This is on a per “portfolio” basis, so if you have sector neutral turned on it will be bottom X% per sector 
Minimum number of shorts  The minimum number of stocks to short. This is on a per “portfolio” basis, so if you have sector neutral turned on it will be minimum per sector. 
Maximum number of shorts  The maximum number of stocks to short. This is on a per “portfolio” basis, so if you have sector neutral turned on it will be maximum per sector. 
Min. short position per stock  The minimum weighting of a short position within the portfolio. 
Max. short position per stock  The maximum weighting of a short position within the portfolio. 
Percent of portfolio short  The exact sum of all short position weightings in the portfolio. 
EVALUATION BREAKDOWN
Description  
Portfolio starting value  The value of the portfolio when it begins trading. If its backtest spans from 2005  2020, the starting value in 2005 will be whatever you set here. 
Compound returns  When toggled on, this will allow the portfolio to compound returns. 
Currency  The currency you would like your portfolio to be calculated in. Note that if the currency differs from its exchange (i.e. GBP on the S&P), the prior close foreign exchange rate will be used for calculations. 
TRADING BREAKDOWN
Trading  Description 
Price Type  Price to use for trading 
Trading cost  A cost that will be applied to every trade. 
Initial weighting  Alpha Weight: The initial weighting will be done such that higher ranked stocks have a higher weight. 
Equal Weight: The initial weighting will be done such that all stocks selected to be in the portfolio will have an equal weight.  
Market Cap Weight: The initial weighting will be done such that all stocks are weighted according to their previous day’s market capitalization.  
Rebalance period  When the machine rebalances the portfolio. You can choose a specific day if you prefer to execute trades on a specific day. 
Smooth periods  The number of periods over which to smooth the signals. See FAQ on portfolio optimization for more details. 
Smooth weighting type  Alpha Weight: The strength of the signal matters in the smoothing. For example, if a stock had a score of 5 stars in period A, 2 stars in period B, your 2 period smoothing would be 7 / 2 = 3.5. 
Equal Weight: Only the direction of the signal matters here. For example, if you were 5 stars in period A and 2 stars in period B, your 2 period smoothing would be 0 / 2 = 0 (+1 in period 1 and 1 in period 2)  
Enable stop loss  Turn on stop losses for the portfolio. This forces sales of securities that have exceeded the stop loss level. All trades will occur at the next trading time (i.e. next day OPEN for OPEN price models). 
Stop loss threshold  Will sell positions after they hit a threshold. For example, 10% / stop loss, when a position loses 10% the machine will sell a portion of the position (defined by stop loss percentage). 
Stop loss percentage to sell  The percentage of the position the machine will sell if the stop loss is triggered. 
Enable Stop Gain  Turn on stop gains for the portfolio. This forces sales of securities that have exceeded the stop gain level. All trades will occur at the next trading time (i.e. next day OPEN for OPEN price models). 
Stop gain threshold  Will sell positions after they hit a threshold. For example, 10% / stop gain, when a position gains 10% the machine will sell a portion of the position (defined by stop gain percentage). 
Stop gain percentage to sell  The percentage of the position the machine will sell if the stop gain is triggered. 
Allow multiple stop loss/gain between rebalance days  Allows the machine to repeatedly trigger stop losses within a rebalance period. For example, if you trade Mondays and the stock drops 15% per day during the week and the stop loss trigger is 10%, then every day will trigger the stop loss. If toggled off the stop loss can only be triggered once per stock per rebalance period. 
Constrain market cap  Force weighting to be near the target weights for a market cap weighted index of your entire stock universe. 
Maximum market cap variation  How far from the target market cap weighted index each stock can deviate (positive or negative). 
Market cap flex  How far from the target market cap weighted the stock can deviate relative to the existing weight (i.e. at 75% a 1% position cannot exceed 1.75%) 
Maximum ownership  The maximum percentage of a company the portoflio is allowed to own. For example, if a company had a market cap of $100MM and this was 5% the portfolio could not own more than $5MM of that company. 
Execution delay  This option adds the number of days selected to the trade execution. If you select T+1 and your rebalance period is set to Weekly (Monday), it will trade on Tuesday, and so on. 
Missing market cap  The amount in millions (MM) to replace any missing market capitalization information with. This will impact the maximum ownership setting. 
Blacklist  None: No blacklist usage. 
Company: Respect the blacklist for your organization.  
Model: Respect the blacklist created for this model only.  
Both: Respect the blacklist created for this model and your organization.  
Turnover Buffering 
Turnover Buffering is a technique used to reduce the turnover of a portfolio. The long / short number of stocks will still be respected, but the technique used will change. For example, if the portfolio was to be long a maximum of 50 stocks the stocks in the final portfolio would be: a) the stocks that are in the top 50 ranks on that day and were in the long portfolio last rebalance period b) the top N stocks (by rank) not already in the portfolio where N is the long buffer limit c) the top ranked stocks that were in the portfolio last rebalance period and are ranked in the top K% of stocks, where K is the long buffer limit. See FAQ on Optimization for further details. 
Short Buffer Limit: The turnover buffer will use this number to determine the hard cutoff to not include in the short portfolio. If set to 25% it will only allow the bottom 25% of stocks to appear in the portfolio, similar to Percent to Short (which should be lower than this number).  
Number of Short Stocks in Buffer: How many "new" shorts the turnover buffer will ideally allow per rebalance period.  
Long Buffer Limit: The turnover buffer will use this number to determine the hard cutoff to not include in the long portfolio. If set to 25% it will only allow the top 25% of stocks to appear in the portfolio, similar to Percent to Long (which should be lower than this number).  
Number of Long Stocks in Buffer:How many "new" longs the turnover buffer will ideally allow per rebalance period. 
OPTIMIZATION BREAKDOWN
Description  
Optimizer type  No Optimization: None 
Reduce Risk: Optimizer designed to reduce portfolio volatility. This tends to result in the best overall performance.  
Maximize Sharpe: Optimizer designed to maximize Sharpe using out of sample estimates for expected return. Estimates for expected return have an outsized impact on the performance of this optimizer  
Maximize Sharpe V2: Optimizer designed to maximize the portfolio's exposure to the underlying signal while simultaneously minimizing exposure to volatility.  
Maximize Alpha: Optimizer designed to maximize expected return based on outofsample estimates for expected return. Estimates for expected return have an outsized impact on the performance of this optimizer  
Min VaR  Minimize Value at Risk by looking back at the proposed portfolio over the last year and trying to minimize drawdowns.  
Max VaR Sharpe  Maximize Value at Risk Sharpe by looking back at the proposed portfolio over the last year and trying to maximize Sharpe by observed return for the portfolio vs observed volatility.  
Max VaR Sharpe V2: Maximize Value at Risk Sharpe by looking back at the proposed portfolio over the last year and trying to maximize Sharpe by maximizing exposure to signal for the portfolio vs historically observed volatility.  
Minimize Skew  Minimize the skew of the portfolio by trying to find a set of portfolio weightings that results in returns that are closer to the mean.  
Optimizer bounds  Tight: The optimizer will only allow the weightings of securities in your portfolio to stay tight (close) to their initial weighting. 
Loose: The optimizer will allow the weightings of securities in your portfolio to move a medium amount more from their initial weighting.  
Wide: The optimizer will allow almost any weight between the minimum and maximum weight per long or short position.  
Factor constraints  Allows you to select factors and restrict the number of standard deviations the solution is away from the mean (0). For example, Momentum = Lower Bound 0.5 and Upper Bound +0.5. The portfolio will not have Momentum exposures outside of those bounds (at rebalance dates). The order of factors is important. If you have 10 constraints and it cannot solve, it will attempt again to solve with the top 9 and so on. 
Dividend yield  If toggled on the optimizer will try to solve for a historical dividend yield that is: stock universe + LB < portfolio dividend yield < stock universe + UB. 
Bounds (relative to benchmark)  The LB (lower bound) and UB (upper bound) relative to the benchmark that the optimizer should target. i.e. if stock universe dividend yield is 1% and LB = 0.5% and UB = 1% then the target yield will be 1.5% < target yield < 2%. 
Tracking error  If toggled on the optimizer will try to solve for an expected tracking error less than the amount specified. This is done outofsample and the ex post tracking error will likely be higher. 
Maximum tracking error (vs Benchmark)  Amount of exante tracking error to constrain the optimizer by. 
Lower turnover  Specific to portfolio optimization only  it adds a second goal of keeping turnover low while trying to maximize the selected optimizer type. 
Beta neutral  If toggled on the optimizer will add or subtract units of the benchmark to get exante beta to be equal to the net exposure of the portfolio. 
Active risk  Set the portfolio optimization parameters to be relative to the score from the stock universe. i.e. if you set Momentum to 0.5 to 1.0 and the stock universe has a market cap weighed score of 0.2 then the optimizer will solve for 0.7 to 1.2. 
Use holding period  Set any covariance or other calculations within the optimizer to use a holding period return instead of daily return series 
COMBINATION BREAKDOWN
Description  
Combination method  Overlay: Takes a core model and overlays one or more models on top, using an intersection method. It works on a "delta" basis, where every stock pick in the models used to overlay the core model is scored from 1 to 1 (1 being the best score). You can alter the core model weights by a percentage of the delta based on that score. So a score of 0.50 will modify the core model score by 12.5% if the delta is 25%. 
Combination: Takes a core model and combines one or more models on top, using a union method. You set a weight for each model in your groupings of models (including the core model). The scores from each of the core models are converted from 1 to 1 and then multiplied by the weight. The scores for each stock on each day are then added together. 

Combine Portfolios: combines the postoptimized and constructed portfolios from 1 or more other portfolios. It is using the resultant portfolio weights as input signals – and not the machine learning signals from the models underlying the portfolios. 

Combine model  The models / portfolios that you are going to combine using the selected combination methods. 
Full Overlay: Completely overrides the "core model", effectively just using if for universe selection. 
BENCHMARK BREAKDOWN
Description  
Benchmark  Set one or more benchmarks and the weighting for each. 
Adjust benchmark for net exposure  If your portfolio has a net exposure greater or less than 100%, this will adjust the benchmark returns to match that net exposure. 
Equal Weight Benchmark 
Instead of using the defined benchmarks, set the benchmark to be an equal weighted (per rebalance period) version of the stock universe. 
Market Cap Benchmark 
Instead of using the defined benchmarks, set the benchmark to be a market cap weighted (per rebalance period) version of the stock universe. 
Sector neutral equal weight benchmark  Instead of using the defined benchmarks, set the benchmark to be a "sector neutral" equal weighed (per rebalance period) version of the stock universe. This takes the sector weighting and divides by the number of stocks in that sector, so each sector will have a different individual security weighting. 
Description  
Sector neutral  Will be constructed as a series of portfolios  each matching the market cap weighting of each of the stock universe sectors. This means that min. and max. number of stocks "per portfolio" becomes "per sector". 
Sector neutral spread  The sector neutral spread is how far away from the benchmark sector allocation the machine can deviate. This is adjusted for net exposure, i.e. if your net exposure is 0% the target sector allocations will be 0%. 
Name based sector weighting  Base sector weightings on number of names. For example, if there are 200 names in the index and financials has 20 companies, the weight of financials should be 10% (20/200). 
Define sector weights  Allow the user to define Sector Weights. If off it will use market cap sector weights on each rebalance day for any sector weighting decisions. If on it will use your defined weights. 
Individual sector weights  There are three different options: blank  continue to use market cap to determine this sectors weight. Zero (0)  completely remove this sector from the universe. Any number above zero, this will be the defined weight for this sector for the entire backtest. Please enter these values as per a 100% long universe, any adjustments (i.e. for a 65 / 35 portfolio) will be made automatically. 
SIGNALS BREAKDOWN
Description  
Turnover Optimization  Turnover optimization will reduce the turnover at the signal level, making that ranked list more stable. 
Turnover Importance: High importance means the algorithm will aim to keep turnover low, whereas low importance on turnover will let it vary more.  
Factor Neutralize Signal  Turn on an optimization process that will factor neutralize the signal according to the specifications you set. This is done prior to portfolio construction (unlike Optimization) and helps reduce bias in the model. A 0 score would be neutral, so default settings are LB = 0.25 and UB = +0.25. 
Factor Constraints: select the factors you would like the signal to be constrained by and their lower (LB) and upper (UB) bounds. 

Sector Neutral: Make the signal sector neutral, plus or minus the signal sector neutral spread. 

Industry Neutral: Make the signal industry neutral, plus or minus the signal sector neutral spread. 

Sector Neutral Spread: The amount by which the signal can deviate from sector or industry neutral. 

Dense Signals 
Add data to Dense Signals file 
Include Forward Returns: Include forward returns for securities at each rebalance date in the dense signals file 

Include Share Quantities: Include share quantities in the dense signals file 

Add RIC Code: Add RIC (Reuters Instrument Code) to the dense signals file 

Explain Model  Use the explain scores to drive stock rankings rather than using the rankings generated from ML 
Explain Model: Use explain scores as the input scores, instead of using the direct scores from ML. This only works for ML models where Rank 2 has already been generated.  
Method: "Winsorize: Clip outlier drivers' explain scores. Note: Winsorizing/Cutting is only used to generate the rankings from the explain scores. It will not change how the explain scores are displayed. 

Threshold: If the method is Winsorize, clip explain scores outside +/ {threshold} down to the threshold boundaries." "If the method is Cutoff, remove the top {threshold} and bottom {threshold} explain drivers from the model. 
ESG BREAKDOWN
Description  
Minimum environment percentile  Minimum Environment Percentile. The scores are from 0  95 and represent percentile scores within a sector and region. Very few stocks score a full 100% on any metric so we limit the maximum to 95. 
Minimum social percentile  Minimum Social Percentile. The scores are from 0  95 and represent percentile scores within a sector and region. Very few stocks score a full 100% on any metric so we limit the maximum to 95. 
Minimum governance percentile  Minimum Governance Percentile. The scores are from 0  95 and represent percentile scores within a sector and region. Very few stocks score a full 100% on any metric so we limit the maximum to 95. 