When dealing with missing bars in stocks data, there are several approaches you can take to handle them:
- Drop the missing bars: If the missing bars are sporadic and do not affect the overall analysis significantly, you can choose to drop them from the dataset. This approach is suitable when the missing bars are a small fraction of the entire dataset.
- Interpolate the missing bars: You can fill in the missing bars using interpolation techniques. Interpolation estimates the missing values based on the existing data points before and after the missing bar. Linear interpolation, cubic spline interpolation, or other sophisticated methods can be used depending on the nature of the data. However, be aware that interpolation may introduce biases and affect statistical properties.
- Use backfill or forward-fill methods: Backfill fills the missing bars with the last available bar, while forward-fill uses the next available bar to fill the gaps. This approach assumes that there is little variation between consecutive bars and can be useful in cases where the time intervals between bars are relatively short.
- Imputation based on other features: If you have additional features or data points that are not missing, you can employ regression or machine learning techniques to predict the missing bars based on the available information. This method is more complex but could provide better estimates depending on the quality and relevance of the additional data.
- Consider the context and impact: Assess the importance of the missing bars in your analysis. If the missing data represents a critical period or event, it may be advisable to seek alternative sources of data to fill the gaps or adjust the analysis to minimize the impact of the missing bars.
Remember that the chosen method of handling missing bars should be based on an understanding of the data, the context, and the objectives of your analysis.