04. Strategy Implementation in Practice

AI For Trading C6 L5 A04 Strategy Implementation -In Practice V3.1

Strategy Evaluation and Testing in Model Predictions

Key Components:

  • Model Predictions: Importance of considering model predictions critically.
  • Rankings and Thresholds: Use these to filter out less reliable predictions, enhancing confidence before deployment.

Testing Strategies:

  • Experimentation Playground: Conduct thorough testing, especially through back testing and forward testing, before putting models into production.
    • Back Testing: Simulate trading using historical data to test strategies.
    • Forward Testing: Further validation in more current data scenarios.

Performance Metrics:

  • Benchmarking: Compare model strategies against passive benchmarks over similar securities.
  • Financial Measures: Analyze metrics like Compound Annual Growth Rate (CAGR) alongside trading considerations such as fees and slippage.

Decision Making:

  • Risk vs. Reward: Evaluate if the potential benefits outweigh the risks and effort when a strategy either lags behind or barely surpasses the benchmark.

Understanding and implementing rigorous testing and metrics evaluation is crucial for assessing the viability and potential success of predictive models.

In executing trades based on the predictions we get from a classification model, why would we want the prediction probabilities, instead of just the predicted classes?
SOLUTION: To evaluate the confidence level of our predictions, helping us decide when to take action.

We've trained a classification model for trading. How can we use a naive baseline score when deciding which predictions to execute trades on?
SOLUTION: To compare the model's confidence in its predictions against a simple rule-based approach to gauge its potential effectiveness.

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