Week 0: Intro
Xander S -
Welcome to my senior project! My name is Xander, and I’m a stock market enthusiast. Ever since the age of ten, I’ve actively traded public equities in both paper and live accounts, trying to figure out what strategies maximized my returns.
As I learned to code, my curiosity shifted toward algorithmic trading—the use of automated systems to buy and sell stocks. Over the past few decades, algorithmic trading has exploded in popularity, now accounting for 60% to 73% of US equity trading volume. Major financial firms like Citadel and Goldman Sachs have made billions using proprietary trading algorithms. However, little standardized, comprehensive research in the area exists. That’s where my project comes in.
My project, Timing the Market: Evaluating Mean Reversion and Momentum Algorithmic Trading Strategies, tests a wide range of technical trading strategies on public equities across varying market capitalizations, time frames, and market conditions.
I’m comparing two broad categories of trading strategies:
- Mean Reversion: The idea that stock prices tend to return to their historical average after extreme movements.
- Momentum Trading: The idea that stocks that are rising tend to keep rising, while falling stocks continue their decline.
My research involves testing a wide range of individual strategies that use technical indicators within these categories. The goal is to evaluate how well each strategy performs under different combinations of the following conditions:
- Stock Size: Micro-cap, small-cap, mid-cap, and large-cap, and mega-cap stocks
- Time Frames: Intraday, short term, medium term, and long term trading
- Market Conditions: Bull (rising), bear (falling), and sideways (low volatility)
I’ll backtest strategies using real historical data and analyze the results with regression analysis and performance metrics such as the Sharpe Ratio. I hope to determine which trading strategies are most and least effective under these varying conditions, and if there’s time, incorporate machine learning to further optimize the best performing strategy-condition pairs and implement them in live trading scenarios.
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