Sample interview questions: How do you handle transactional data and trade records in your quantitative models?
Sample answer:
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Data Cleaning and Preparation:
- Validate and verify the integrity of data by performing data quality checks for missing values, duplicate entries, outliers, and data consistency.
- Preprocess transactional data by converting dates, formatting numbers, and mapping values to consistent formats to ensure uniformity.
- Conduct data imputation to fill missing values using statistical methods (e.g., mean, median, or last observation carried forward) or machine learning techniques.
- Handle duplicate entries by identifying and removing them to prevent double counting or incorrect calculations.
- Transform data into the appropriate format required by quantitative models, such as time-series, panel data, or cross-sectional data.
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Data Aggregation and Summarization:
- Aggregate transactional data at various levels (e.g., daily, weekly, or monthly) to match the desired time frame for analysis.
- Summarize trade records by calculating metrics such as total volume, average price, open interest, and value traded.
- Group and segment data based on specific criteria, such as asset class, market sector, or geographic region, to facilitate analysis.
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Feature Engineering:
- Extract relevant features from transactional data that are informative for quantitative models.
- Transform raw features into more meaningful and actionable insights using statistical techniques (e.g., logarithmic transformations, differencing, or moving averages).
- Create synthetic features by combining or modifying existing features to capture complex relationships and enhance model performance.
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Time Series Analysis:
- Apply time series analysis techniques to identify trends, seasonality, and patterns in transactional data.
- Develop time series models (e.g., ARIMA, GARCH, or exponential smoothing) to fore… Read full answer
Source: https://hireabo.com/job/1_2_44/Quantitative%20Developer