Foundations of Crypto LFT and Data Handling

Define momentum in financial markets from a quantitative perspective, contrasting it with mean-reversion.

Calculate logarithmic and arithmetic returns for crypto assets and explain the implications of their statistical properties.

Fetch historical OHLCV data for multiple crypto assets using REST APIs and handle exchange-specific rate limits and pagination.

Design a data storage schema (e.g., Parquet, TimescaleDB) optimized for efficient time-series queries in a backtesting context.

Analyze stationarity of crypto asset returns using statistical tests (e.g., ADF, KPSS) and interpret the results for strategy selection.

Implement robust methods to detect and handle crypto-specific data anomalies, such as exchange downtime, flash crashes, and missing values.