When I chose my capstone project, I wanted to choose a project that would challenge me. Luckily, my first choice was chosen which was Algorithmic Stock Market Trading Strategies and was paired with a great team. My breakthrough would be expecting this project to be challenging but not expecting it to test my skill set beyond anticipated. Building a program that assists in executing trades efficiently and objectively than traditional manual trading methods requires a lot of thought and sufficient planning. Learning about algorithmic trading can be challenging for several reasons: complexity of financial markets, programming proficiency, continuous learning, and backtesting and optimization.
The financial market is very intricate and dynamic where it is influenced by many factors such was economic indicators, geopolitical events and market sentiments. It is important to understand these complexities as it is crucial when developing effective trading strategies. A lot of coding is involved in algorithmic trading as we are trying to automate the process of trading so being proficient in a programming language is important in order to implement strategies and interact with trading platforms and APIs. The financial market is always evolving where trading strategies in the past may not perform up to par in the future so it’s important to be continuously learning recent market trends, new technologies and changes in regulations to adjust our strategies accordingly. Lastly, before utilizing an algorithm, it must go through extensive backtesting and optimization to assess the performance of the algorithm using historical data. It requires extensive knowledge of limitations relating to backtesting and the ability to optimize it to real-world conditions. All in all, learning about algorithmic trading can be very rewarding and seeing how successful quantitive developers are gives me motivation to possibility strive for a role in fintech.
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