Continuous Trading

Insights into the alpha pipeline and effective machine learning

Introduction

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Often you will see researchers working with X period trade bars and developing strategies that rebalance their portfolio at a given interval (commonly that of their data interval).

This is great for keeping your logic simple and reducing the need for large data processing/storage on the research side, but it leaves money on the table.

It also makes us easier to front-run and leads to us missing out on potential short-term seasonality gains in execution. Excessive turnover also poses a problem when we keep the logic this simple.

Index

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1. Introduction

2. A Simple & Dirty Example

3. When Should We Exit?

4. Continuous Trading:

   1. Data

   2. Features

   3. Forecasts

   4. Portfolio Optimization [Code Here]

   5. Execution

5. Avoiding Frontrunners

6. Final Remarks