Basic Digital Asset Return, Volatility, and Sharpe Analysis in Python
Summary
This tutorial outlines a workflow for analyzing daily cryptocurrency market data with Python and a notebook. It retrieves historical OHLCV data for selected USD pairs on Coinbase Pro, narrows the universe to liquid assets, checks and plots the data, and calculates trading volume, daily returns, return distributions, cumulative performance, and Sharpe ratios. The example reports that XRP and Stellar had notable trading volume relative to larger assets, while Bitcoin and Ether accounted for most traded value; Dash led cumulative and risk-adjusted returns over the chosen sample.
The article presents exploratory analysis rather than a trading strategy or portfolio backtest. The findings depend on the historical period, exchange, asset selection, and near-zero risk-free-rate assumption described by the author. It does not show the underlying charts or numerical results in the extracted text, and the observed performance ranking should not be treated as a forecast. The workflow is a starting point for testing hypotheses, with portfolio construction deferred to a later installment.
Key ideas
- The workflow retrieves daily OHLCV data for selected USD-denominated digital asset pairs on one exchange.
- Liquidity rankings are used to limit the sample and reduce concerns about slippage and difficult exits.
- The analysis compares volume, daily returns, return distributions, cumulative returns, and Sharpe ratios.
- In the sample discussed, Dash ranked highest on cumulative and risk-adjusted returns.
- Historical rankings are sample-dependent and do not establish future performance or a complete portfolio strategy.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.