Stock Algorithms are easy to write, but complex to make great

This is my journey so far. My project is focused on stock market investing and it has been an enlightening experience by shedding light on the complexities and multifaceted nature of the investment world. At this point, one of my key takeaways is the realization that investing in the stock market is not a one-size-fits-all endeavor. There exists so many strategies. Each of them has a unique approach and methodology. Some of them incorporate foundations of others, while some strategies flat out conflict with other strategies. To navigate through this maze, it’s critical to select a strategy that resonates with one’s investment philosophy. Then you need to rigorously test its viability. A powerful tool in this testing process is backtesting.

Backtesting is a concept where historical data is used to simulate the performance of a strategy or model to evaluate its effectiveness over a period. By applying historical data, investors can gain insights into how a strategy would have fared under past market conditions, providing a foundation for making more informed decisions moving forward.

Another critical aspect of investing that has come to the forefront is understanding volatility and risk. Volatility refers to the rate at which the price of an asset increases or decreases for a given set of returns, indicating the stability or instability of the asset. Risk, on the other hand, is the potential for losing some or all of the original investment. It is a measure of the uncertainty or variability of returns, and understanding these concepts is crucial for developing a robust investment strategy.

As part of this class, my endeavor to write an algorithmic approach to investing has been both challenging and rewarding. The goal is to create an algorithm that can not only outperform the returns of the S&P 500 but also do so with a frequency that maximizes profitability. However, the challenge lies in the algorithm’s trading frequency. It currently does not trade often enough, leading to periods where it underperforms due to missed opportunities. My team and I are in the throes of addressing this issue, though the solution remains elusive. Our focus is on exploring various modifications and enhancements to the algorithm, with the hope that further investigation and experimentation will lead to a breakthrough.