Skip to content

Quantitative Trading Strategies

Please note the full code of each strategy is for paid Subscribers

Quantitative trading strategies utilize mathematical models to identify trading opportunities. Here are some of the top strategies used by quantitative traders:

  • Statistical Arbitrage:

  • Exploits price discrepancies between related assets based on historical correlation and mean reversion.

  • Momentum Investing:

  • Follows the principle that assets that have performed well in the recent past will continue to perform well in the near future.

  • Mean Reversion:

  • Based on the theory that prices and returns eventually move back towards the mean or average.

  • Machine Learning Techniques:

  • Uses algorithms such as neural networks and decision trees to predict market movements based on large data sets.

  • High-Frequency Trading (HFT):

  • Involves making thousands of trades within seconds, exploiting small price gaps and trading volume.

  • Factor Investing:

  • Focuses on capturing risk premiums in the markets through exposure to certain risk factors like size, value, and momentum.

  • Event-Driven Strategies:

  • Seeks to profit from stocks or securities that are influenced by events such as mergers, acquisitions, or significant corporate announcements.

Further Reading

  • For more in-depth analysis and case studies on quantitative trading strategies, refer to our Documentation or visit our Blog.