Data Cleaning for Trading

Lesson Content

Data cleaning is the unsung hero of quantitative trading, especially in the Indian market where data quality issues are prevalent. In NSE and BSE datasets, common problems include missing values in the price series, incorrect timestamps due to market holidays, and corporate action adjustments not properly reflected. The first step involves identifying and handling missing data points—whether through forward filling, interpolation, or more sophisticated methods like Kalman filtering depending on the data's nature. Next, timestamps must be standardized to account for Indian market holidays and trading hours, ensuring your backtests accurately reflect market conditions. Corporate actions like stock splits, dividends, and bonus issues require special attention; failing to adjust prices can lead to spurious backtest results. Finally, outlier detection is crucial—abnormal price movements due to data entry errors must be identified and either corrected or excluded. In the Indian context, this is particularly important for small-cap stocks with lower liquidity where price ticks can be erratic.