Sold by: MphasisÂ
Deployed on AWS
Quantum simulated annealing based financial portfolio asset optimization aimed to maximize return and minimize risk.
Overview
Quantum Simulated annealing is used to perform financial portfolio asset allocation optimization. The optimization aims at achieving maximum return possible with minimum risk. Quantum based annealing helps in better exploration of energy landscape because of quantum tunneling phenomenon, resulting in optimal portfolio selection in a shorter time. The solution selects optimal stocks from a given list of stocks and provides asset allocation in the selected stocks. The solution takes in input, rate of return per stock as well as correlation between each pair of stocks to perform optimization.
Highlights
- Financial services companies are constantly attempting to understand the financial markets to deliver best possible returns to their customers. Using advanced quantum-based annealing for financial portfolio asset allocation gives a leverage on search space exploration for better solution, with a quicker response time. This helps decision makers to arrive at better recommendations of financial portfolio asset allocation as well as faster turn around time to adopt new strategies for asset management.
- This is a software-based approach for quantum annealing for financial portfolio asset allocation. Optimal selection of hyper parameters for quantum simulated annealing helps in achieving quantum tunneling while exploring the energy landscape. Quantum tunneling helps in sudden shift from high energy state to lower energy state, without traversing the energy path. This results in faster turnaround time.
- Need customized Quantum Computing solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Financing for AWS Marketplace purchases
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.2xlarge instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $40.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the version 1.3 of this algorithm.
Additional details
Inputs
- Summary
Input:
- Supported content type: application/zip
- Keep the input files in a folder, zip the folder and provide the zipped folder as input to this algorithm.
- The Folder should contain two files: "cov_matrix.csv" and "rate_of_return.csv".
- The "cov_matrix.csv" contains the covariance values between stocks.
- In the "cov_matrix.csv", the first column and first row contains the company names.
- The "rate_of_return.csv" file should have two columns: "company" and "rate_of_return".
- The sample input and sample output can be found at this Marketplace Listing's page.
- Please keep the order of companies same in both the files.
Output:
Instructions for score interpretation:
- Content type: text/csv
- Two columns: 'company' and 'budget_districution'
- Column 'budget_distribution' contains fraction of budget invested in the respective company
- The last two rows show Portfolio expected return and Portfolio expected risk respectively.
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'application/zip' --region us-east-2 output.csvSubstitute the following parameters:
- "model-name" - name of the inference endpoint where the model is deployed
- file_name - input zip file name
- application/zip - type of the given input
- output.csv - filename where the inference results are written to
Resources:
- Input MIME type
- text/csv, text/plain, application/zip
See Input Summary
See Input Summary
Resources
Vendor resources
Support
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