Lesson 10.8: Final Project - Design a Trading Strategy
This is the culmination of your entire journey. Your mission is to act as a quantitative researcher at a hedge fund and propose a new, novel trading strategy that combines concepts from at least three different modules you have studied. This is not a coding challenge; it is a test of your ability to think like a quant, synthesize complex ideas, and communicate them effectively.
Part 1: The Assignment
You will write a 2-3 page strategy proposal document. This document should be structured like a professional quant research paper and must include the following sections:
- Hypothesis: A clear, testable statement of the market inefficiency you believe you have found.
- Data Sources: A description of the traditional and/or alternative data you will use.
- Methodology: A detailed, step-by-step description of your signal generation process. This is the heart of the project.
- Backtesting and Validation: A plan for how you would rigorously test this strategy, including how you would avoid common pitfalls like look-ahead bias and data snooping.
- Risk Factors: A discussion of the primary risks to the strategy and why it might fail.
Part 2: Strategy Ideas (To Get You Started)
The goal is creativity. Combine concepts in interesting ways. Here are a few examples to spark your imagination:
Hypothesis: The speed of mean reversion in a cointegrated pair of stocks increases following a "sentiment shock."
Modules Used: Time Series (Cointegration), NLP (Sentiment Analysis), Linear Models (VECM).
Hypothesis: An XGBoost model can better predict short-term returns if it is fed features from a GARCH model (e.g., the forecasted volatility) and is trained only during predicted low-volatility regimes.
Modules Used: Time Series (GARCH), Ensembles (XGBoost), Linear Models.
Hypothesis: The momentum of "statistical factors" derived from PCA of a stock universe is a better predictor of returns than the momentum of individual stocks.
Modules Used: Unsupervised Learning (PCA), Time Series (ARIMA on factor scores), Linear Models.
Hypothesis: An LSTM can learn the non-linear, dynamic relationship of a cointegrated spread more effectively than a simple Ornstein-Uhlenbeck (AR(1)) process, leading to better entry/exit signals.
Modules Used: Time Series (Cointegration), Deep Learning (LSTM), Linear Models.
Conclusion: You are a Quant
Completing this project marks the end of your formal training in this curriculum. You have acquired the language, the tools, and the critical mindset of a modern quantitative analyst and data scientist.
The journey of learning never ends. The market is a constantly evolving puzzle. But you now have a powerful and robust toolkit to begin tackling that puzzle on your own terms.
Good luck.