Large multinational banks often use a variety of instruments in order to protect themselves from risk. One of the most common ones is options. These are contracts which allow the buyer the right but not the obligation to exchange the stock at a specified price on a specific date. These are used typically to manage risk and ensure that the bank is not overexposed to market risk in any specific sector.
The type of tool used for hedging risk is dependent primarily on the state of the market. The client we were working with wanted to use reinforcement learning – the same type of learning that was used in game engines such as Alpha Go. They wanted an algorithm developed that could detect the “state” of the market and prompt traders to act accordingly based on historical data.
A large number of variables were taken into consideration for this problem such as the stock price, momentum, earnings releases and 10-K reports, this data was collated and aggregated into a standardised format. At the industry level, these companies were grouped and statistics were calculated such as the average volatility of a stock in that industry. Integration was then done with a live Bloomberg price feed to give traders an indication of the state of the market – bullish or bearish for a given stock based on this information.
An automated hybrid solution built on top of Python was created to allow the client to anticipate unusual movements of the market would allow them to better provide their services and manage the risk associated with it. Interactive graphical tools were built in Java Swing which integrated with these Python services in order for the floor traders to better assess the best course of action to take in a specific scenario.
DataSpartan delivered both research results for this case and an interactive visualisation of maker movements to their requirements. This tool allows them to better anticipate shock and explain tendencies. The current tool is integrated with on the dashboards of options traders as a customisation widget – documentation was also provided to the client in-house development team in case they wanted to expand it further.