Sample interview questions: How do you handle high-frequency market data in your quantitative models?
Sample answer:
Data Management Techniques for High-Frequency Market Data in Quantitative Models:
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Real-time streaming infrastructure: Establish a robust and scalable infrastructure to ingest and process high-frequency data in real-time, utilizing technologies such as Kafka or Apache Flink.
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Data aggregation and downsampling: Perform real-time aggregation of tick data to higher time intervals (e.g., minute bars) to reduce data volume and computational load, while retaining key signal information.
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Data filtering and cleaning: Implement data filtering and cleaning pipelines to remove outliers, noise, and invalid data points that could bias model estimates.
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Time-series databases: Utilize time-series databases like InfluxDB or TimescaleDB to store and query large volumes of high-frequency data, providing fast and efficient access for model training and inference.
Model-Specific Considerations:
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Tick-based models: For models that rely on individual tick data (e.g., high-frequency trading strategies), develop algorithms that can efficiently handle the massive volume and temporal nature of tick data.
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Event-based models: Create models that are triggered b… Read full answer
Source: https://hireabo.com/job/1_2_44/Quantitative%20Developer