Predict Five-Minute Returns from Order-Book Data
Last updated: July 26, 2026
Quick Overview
Explain how you would predict five-minute returns using order-book data. Discuss the methodology and any machine learning techniques you would apply.
Tower
July 26, 20261
0
19 solved
Explain how you would predict five-minute returns using order-book data. Discuss the methodology and any machine learning techniques you would apply.
This question is typically asked during the technical screen of a data scientist interview at Tower to assess the candidate's understanding of time series prediction and their ability to work with financial data. The interviewer is evaluating the candidate's grasp of machine learning concepts, their familiarity with order-book data, and their ability to apply these concepts to real-world financial scenarios.
What the Interviewer Expects
- Explain the significance of order-book data in predicting returns.
- Discuss feature engineering techniques relevant to time series data.
- Outline a machine learning model suitable for the prediction task.
- Address potential challenges and how to mitigate them.
- Mention evaluation metrics for assessing model performance.
Key Topics to Cover
How to Approach This
- Understand the bias-variance trade-off. High training accuracy but low test accuracy signals overfitting.
- Choose evaluation metrics carefully based on the problem. Accuracy alone is often insufficient.
- Feature engineering is often more impactful than model selection.
- Know when to use tree-based models (tabular data) vs neural networks (unstructured data).
- Handle class imbalance with SMOTE, class weights, or appropriate loss functions.
Possible Follow-up Questions
- What features would you extract from the order-book data?
- How would you handle missing or noisy data in your dataset?
- Can you explain how you would evaluate the performance of your model?
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Explore ML Interview PrepSample Answer
Requirements
To predict five-minute returns from order-book data, we first need to understand the data structure and its relevance. Order-book data contains real-time information about buy and sell orders for a sp...
High-Level Design
The approach will involve several steps:
- Data Collection: Gather historical order-book data along with corresponding price movements.
- Feature Engineering: Extract relevant features suc...
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