This is the code repository for the Unveiling the Quantum Leap in Financial Modeling
workshop at the Quantum Innovation Summit
, Dubai.
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 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
This section is to prepare data once we have downloaded the data by executing the retrieve_data.py
file.
Derivative pricing is one of those canonical examples in finance modeling. It tries to set the price for an option at a time
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.
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.
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.
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.
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.