Unveiling the Quantum Leap in Financial Modeling

This is the code repository for the Unveiling the Quantum Leap in Financial Modeling workshop at the Quantum Innovation Summit, Dubai.

Python environment

Make sure to create a local environment and install the listed requirements before running the code.

pip install -r requirements.txt

Also, additional connectivity to DWave, IBM and cloud providers (Azure, AWS) will require accounts in those services to fully reproduce existing bits of code.

Data retrieval

Data shown under the data folder is the one used by all the examples. It was obtained using Binance's API and the script named retrive_data.py using the following command

python retrieve_data.py --day "2024-27-02"

That command looks back for 30 days and retrieves data from 5 assets by default. More information on how to use this functionality can be obtained by the command

python retrieve_data.py --help

Section 0: Data Preparation

This section is to prepare data once we have downloaded the data by executing the retrieve_data.py file.

Section 1: Derivatives Valuation

Derivative pricing is one of those canonical examples in finance modeling. It tries to set the price for an option at a time $t$ in the future. In this section, we will explore how to create those predictions by using both classical and quantum computing resources.

Section 2: Portfolio Optimization

Portfolio optimization is another example where a subset from all the available STOCKs should be selected so that the selection meets budget and risk willingness constraints while maximizing the expected revenue from the asset selection.

Section 3: Credit Risk Analytics

This section performs a risk analysis in UK households using classical and quantum Machine Learning models to develop models for risk analysis and assess the financial stability of a given case. Here, we explore several techniques such as SVM and NN so a working knowledge of those will help the reader catch up when the Quantum version is presented.

Section 4: On Cloud connectivity and resource estimation

This section revolves around how to connect to major Cloud providers and their Quantum Computing offerings. There must exist a resource estimation before sending the circuits to the cloud-hosted resource given these can be rather expensive devices to use.

Section 5: On Simulators and Emulators

We must understand the fundamental differences between the two and the role noise plays when emulating our circuits in a realistic setup. Restrictions given by the physical chip need to be taken into consideration when transpiling the theoretical circuit.

Section 6: On handling noise

In this section, we will be covering some interesting facts regarding current NISQ devices. One needs to be aware of the complications that will be faced when working on actual devices and how to deal with their imperfections. It is just a brief reference to some of the problems and potential techniques to alleviate those.