frequency-merge

What problem does it solve?

Merging financial datasets with different time frequencies (e.g., daily prices with quarterly fundamentals) is complex and prone to lookahead bias. This skill automates the process, ensuring accurate, point-in-time data alignment, saving you from manual data manipulation errors.

Core Features & Use Cases

• Automated Data Alignment: Expertly combine daily, monthly, quarterly, and annual financial data from disparate sources.
• Bias Prevention: Utilizes robust forward-fill strategies (pd.merge_asof with direction='backward') to eliminate lookahead bias, ensuring reliable analysis.
• Use Case: Merge daily stock prices with quarterly company earnings reports to analyze valuation multiples, or combine monthly returns with annual financial statements, all without manual data manipulation.

Quick Start

Merge the daily stock prices from 'daily_prices.parquet' with the quarterly fundamentals from 'quarterly_fundamentals.parquet' for all tickers, ensuring no lookahead bias.