Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems

Abstract

The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using neural networks is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined. Simulated market scenarios are filtered based on the determined ranges of allowable values. A range of filtered simulated market factor values for each market factor is determined. Updated market factor interaction-interface mechanisms are generated.

What is claimed is: 
     
         1 . A machine learning predefined scenario constructing apparatus, comprising:
 a memory;   a component collection in the memory;   a processor disposed in communication with the memory and configured to issue a plurality of processor-executable instructions from the component collection, the processor-executable instructions structured as:
 obtain, via at least one processor, a user selection of a set of simulated market scenarios via a simulation selection interaction-interface mechanism, the set of simulated market scenarios generated using a set of deep learning neural networks, each simulated market scenario in the set of simulated market scenarios structured to comprise a set of simulated market factor values corresponding to a set of market factors; 
 determine, via at least one processor, a range of unfiltered simulated market factor values for each market factor from the set of market factors, the range of unfiltered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of simulated market scenarios for the respective market factor; 
 generate, via at least one processor, a set of market factor interaction-interface mechanisms, each market factor interaction-interface mechanism in the set of market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of unfiltered simulated market factor values for the respective market factor; 
 obtain, via at least one processor, a user modification to a range of allowable values of a market factor from the set of market factors via the market factor interaction-interface mechanism associated with the modified market factor; 
 update, via at least one processor, a set of customized market factors from the set of market factors based on the user modification; 
 determine, via at least one processor, a range of allowable values for each customized market factor from the set of customized market factors; 
 filter, via at least one processor, the set of simulated market scenarios based on the determined ranges of allowable values for the set of customized market factors to determine a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each customized market factors from the set of customized market factors; 
 determine, via at least one processor, a range of filtered simulated market factor values for each market factor from the set of market factors, the range of filtered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of filtered simulated market scenarios for the respective market factor; and 
 generate, via at least one processor, an updated set of market factor interaction-interface mechanisms, each updated market factor interaction-interface mechanism in the set of updated market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of filtered simulated market factor values for the respective market factor. 
   
     
     
         2 . The apparatus of  claim 1 , further, comprising:
 the instructions to generate the set of simulated market scenarios using the set of deep learning neural networks are structured to comprise instructions to:
 determine, via at least one processor, a set of historical market scenarios and a set of time period buckets; 
 determine, via at least one processor, for each time period bucket from the set of time period buckets, a subset of historical market scenarios, from the set of historical market scenarios, associated with the respective time period bucket; 
 train, via at least one processor, for each time period bucket from the set of time period buckets, a deep learning neural network, from the set of deep learning neural networks, using the subset of historical market scenarios associated with the respective time period bucket; 
 determine, via at least one processor, for each time period bucket from the set of time period buckets, a number of simulated market scenarios to generate using the trained deep learning neural network associated with the respective time period bucket; and 
 generate, via at least one processor, for each time period bucket from the set of time period buckets, the determined number of simulated market scenarios for the respective time period bucket, using the trained deep learning neural network associated with the respective time period bucket. 
   
     
     
         3 . The apparatus of  claim 2 , further, comprising:
 the instructions to determine the set of historical market scenarios are structured to comprise instructions to:
 determine, via at least one processor, a historical data set, a rolling window period length, and the set of market factors; 
 determine, via at least one processor, a set of rolling window periods using the historical data set and the rolling window period length; and 
 calculate, via at least one processor, for each market factor from the set of market factors, for each rolling window period from the set of rolling window periods, a change to the respective market factor during the respective rolling window period,
 each historical market scenario from the set of historical market scenarios structured to comprise calculated changes to the set of market factors during a rolling window period. 
 
   
     
     
         4 . The apparatus of  claim 3 , further, comprising:
 the instructions to calculate a change to a market factor during a rolling window period are structured to comprise instructions to:
 determine, via at least one processor, the delta between values of the market factor at a beginning time point and an ending time point of the rolling window period. 
   
     
     
         5 . The apparatus of  claim 4 , further, comprising:
 the processor-executable instructions structured as:
 determine, via at least one processor, that historical data for the market factor during the rolling window period is unavailable for a time point; and 
 impute, via at least one processor, the unavailable historical data for the time point using a machine learning method. 
   
     
     
         6 . The apparatus of  claim 3 , further, comprising:
 the rolling window period length is structured to be equal to the time period length.   
     
     
         7 . The apparatus of  claim 2 , further, comprising:
 the set of time period buckets is structured to have an equal fixed length for each time period bucket.   
     
     
         8 . The apparatus of  claim 2 , further, comprising:
 the set of time period buckets is structured to have a variable length for each time period bucket, the variable length for each time period bucket determined by judging the overall goodness of fit between the set of simulated market scenarios and the set of historical market scenarios.   
     
     
         9 . The apparatus of  claim 2 , further, comprising:
 the instructions to train a deep learning neural network for a time period bucket using the associated subset of historical market scenarios are structured to comprise instructions to:
 select, via at least one processor, a historical market scenario from the associated subset of historical market scenarios; and 
 train, via at least one processor, the deep learning neural network for the time period bucket on the selected historical market scenario using a variational autoencoder. 
   
     
     
         10 . The apparatus of  claim 9 , further, comprising:
 the deep learning neural network for the time period bucket is trained to generate a set of Gaussian mixture latent variables.   
     
     
         11 . The apparatus of  claim 10 , further, comprising:
 the instructions to generate simulated market scenarios for the time period bucket, using the trained deep learning neural network associated with the time period bucket, are structured to comprise instructions to:
 generate, via at least one processor, a set of random values for the set of Gaussian mixture latent variables; and 
 generate a simulated market scenario, from the simulated market scenarios for the time period bucket, from the generated set of random values using a neural network decoder of the trained deep learning neural network associated with the time period bucket. 
   
     
     
         12 . The apparatus of  claim 1 , further, comprising:
 the simulation selection interaction-interface mechanism is structured to comprise a pricing date selection interaction-interface mechanism and a simulation model selection interaction-interface mechanism.   
     
     
         13 . The apparatus of  claim 1 , further, comprising:
 the range of unfiltered simulated market factor values for a market factor structured to include an average simulated market factor value in the set of simulated market scenarios for the market factor; and   the range of filtered simulated market factor values for the market factor structured to include an average value in the set of filtered simulated market scenarios for the market factor.   
     
     
         14 . The apparatus of  claim 1 , further, comprising:
 each market factor interaction-interface mechanism in the set of market factor interaction-interface mechanisms is structured to include a slider interaction-interface mechanism to affect user modification to a range of allowable values of a market factor associated with the respective market factor interaction-interface mechanism.   
     
     
         15 . The apparatus of  claim 1 , further, comprising:
 the instructions to update the set of customized market factors based on the user modification are structured to comprise instructions to add the modified market factor to the set of customized market factors.   
     
     
         16 . The apparatus of  claim 1 , further, comprising:
 the instructions to update the set of customized market factors based on the user modification are structured to comprise instructions to remove the modified market factor from the set of customized market factors.   
     
     
         17 . The apparatus of  claim 1 , further, comprising:
 the processor-executable instructions structured as:
 generate, via at least one processor, a predefined scenario datastructure that includes a simulation identifier associated with the set of simulated market scenarios, identifiers of customized market factors from the set of customized market factors, and the ranges of allowable values for the set of customized market factors. 
   
     
     
         18 . The apparatus of  claim 1 , further, comprising:
 the processor-executable instructions structured as:
 generate, via at least one processor, a predefined scenario datastructure that includes a simulation identifier associated with the set of simulated market scenarios, and identifiers of filtered simulated market scenarios from the set of filtered simulated market scenarios. 
   
     
     
         19 . The apparatus of  claim 1 , further, comprising:
 the processor-executable instructions structured as:
 generate, via at least one processor, a set of factor group filter interaction-interface mechanisms, each factor group filter interaction-interface mechanism in the set of factor group filter interaction-interface mechanisms structured to be associated with a subset of market factor interaction-interface mechanisms from the set of market factor interaction-interface mechanisms. 
   
     
     
         20 . The apparatus of  claim 19 , further, comprising:
 the processor-executable instructions structured as:
 obtain, via at least one processor, a user selection of a factor group filter interaction-interface mechanism in the set of factor group filter interaction-interface mechanisms; and 
 generate, via at least one processor, a filtered set of market factor interaction-interface mechanisms, each filtered market factor interaction-interface mechanism in the set of filtered market factor interaction-interface mechanisms structured to be associated with the selected factor group filter interaction-interface mechanism. 
   
     
     
         21 . The apparatus of  claim 1 , further, comprising:
 the set of simulated market scenarios is further generated using a set of multi-variate mixture datastructures.   
     
     
         22 . The apparatus of  claim 1 , further, comprising:
 the processor-executable instructions structured as:
 calculate, via at least one processor, a set of expected returns for a set of securities, each expected return in the set of expected returns configured as calculated for a security during a simulated market scenario in the set of simulated market scenarios using:
 the respective security's conditional Beta during the respective simulated market scenario, determined using a set of decision tree ensembles, trained to estimate conditional Beta of the respective security, based on a first subset of the set of simulated market factor values, and 
 the respective security's conditional default probability during the respective simulated market scenario, determined using a set of decision tree ensembles, trained to estimate conditional default probability of the respective security, based on a second subset of the set of simulated market factor values. 
 
   
     
     
         23 . A machine learning predefined scenario constructing processor-readable, non-transient medium, comprising processor-executable instructions structured as:
 obtain, via at least one processor, a user selection of a set of simulated market scenarios via a simulation selection interaction-interface mechanism, the set of simulated market scenarios generated using a set of deep learning neural networks, each simulated market scenario in the set of simulated market scenarios structured to comprise a set of simulated market factor values corresponding to a set of market factors;   determine, via at least one processor, a range of unfiltered simulated market factor values for each market factor from the set of market factors, the range of unfiltered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of simulated market scenarios for the respective market factor;   generate, via at least one processor, a set of market factor interaction-interface mechanisms, each market factor interaction-interface mechanism in the set of market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of unfiltered simulated market factor values for the respective market factor;   obtain, via at least one processor, a user modification to a range of allowable values of a market factor from the set of market factors via the market factor interaction-interface mechanism associated with the modified market factor;   update, via at least one processor, a set of customized market factors from the set of market factors based on the user modification;   determine, via at least one processor, a range of allowable values for each customized market factor from the set of customized market factors;   filter, via at least one processor, the set of simulated market scenarios based on the determined ranges of allowable values for the set of customized market factors to determine a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each customized market factors from the set of customized market factors;   determine, via at least one processor, a range of filtered simulated market factor values for each market factor from the set of market factors, the range of filtered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of filtered simulated market scenarios for the respective market factor; and   generate, via at least one processor, an updated set of market factor interaction-interface mechanisms, each updated market factor interaction-interface mechanism in the set of updated market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of filtered simulated market factor values for the respective market factor.   
     
     
         24 . A machine learning predefined scenario constructing processor-implemented system, comprising:
 means to process processor-executable instructions;   means to issue processor-issuable instructions from a processor-executable component collection via the means to process processor-executable instructions, the processor-issuable instructions structured as:
 obtain, via at least one processor, a user selection of a set of simulated market scenarios via a simulation selection interaction-interface mechanism, the set of simulated market scenarios generated using a set of deep learning neural networks, each simulated market scenario in the set of simulated market scenarios structured to comprise a set of simulated market factor values corresponding to a set of market factors; 
 determine, via at least one processor, a range of unfiltered simulated market factor values for each market factor from the set of market factors, the range of unfiltered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of simulated market scenarios for the respective market factor; 
 generate, via at least one processor, a set of market factor interaction-interface mechanisms, each market factor interaction-interface mechanism in the set of market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of unfiltered simulated market factor values for the respective market factor; 
 obtain, via at least one processor, a user modification to a range of allowable values of a market factor from the set of market factors via the market factor interaction-interface mechanism associated with the modified market factor; 
 update, via at least one processor, a set of customized market factors from the set of market factors based on the user modification; 
 determine, via at least one processor, a range of allowable values for each customized market factor from the set of customized market factors; 
 filter, via at least one processor, the set of simulated market scenarios based on the determined ranges of allowable values for the set of customized market factors to determine a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each customized market factors from the set of customized market factors; 
 determine, via at least one processor, a range of filtered simulated market factor values for each market factor from the set of market factors, the range of filtered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of filtered simulated market scenarios for the respective market factor; and 
 generate, via at least one processor, an updated set of market factor interaction-interface mechanisms, each updated market factor interaction-interface mechanism in the set of updated market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of filtered simulated market factor values for the respective market factor. 
   
     
     
         25 . A machine learning predefined scenario constructing processor-implemented process, comprising executing processor-executable instructions to:
 obtain, via at least one processor, a user selection of a set of simulated market scenarios via a simulation selection interaction-interface mechanism, the set of simulated market scenarios generated using a set of deep learning neural networks, each simulated market scenario in the set of simulated market scenarios structured to comprise a set of simulated market factor values corresponding to a set of market factors;   determine, via at least one processor, a range of unfiltered simulated market factor values for each market factor from the set of market factors, the range of unfiltered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of simulated market scenarios for the respective market factor;   generate, via at least one processor, a set of market factor interaction-interface mechanisms, each market factor interaction-interface mechanism in the set of market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of unfiltered simulated market factor values for the respective market factor;   obtain, via at least one processor, a user modification to a range of allowable values of a market factor from the set of market factors via the market factor interaction-interface mechanism associated with the modified market factor;   update, via at least one processor, a set of customized market factors from the set of market factors based on the user modification;   determine, via at least one processor, a range of allowable values for each customized market factor from the set of customized market factors;   filter, via at least one processor, the set of simulated market scenarios based on the determined ranges of allowable values for the set of customized market factors to determine a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each customized market factors from the set of customized market factors;   determine, via at least one processor, a range of filtered simulated market factor values for each market factor from the set of market factors, the range of filtered simulated market factor values for a market factor structured to include a minimum simulated market factor value and a maximum simulated market factor value in the set of filtered simulated market scenarios for the respective market factor; and   generate, via at least one processor, an updated set of market factor interaction-interface mechanisms, each updated market factor interaction-interface mechanism in the set of updated market factor interaction-interface mechanisms structured to be associated with a market factor from the set of market factors and structured to display the range of filtered simulated market factor values for the respective market factor.