Basic Python Operations for Exploring Stock Data and Indicators
Summary
The article demonstrates foundational analysis of stock price and volume data with Python and pandas. It describes loading OHLCV records, inspecting the date index, counting observations, finding the maximum closing price and its date, calculating daily percentage changes, and filtering days with gains above a stated threshold. It then adds a 20-day simple moving average of closing prices and a five-day rolling average of volume.
These operations show how to prepare a time series and create simple descriptive features that could support later strategy research. The examples use Apple data, but the article does not report a trading rule, backtest, or performance evidence. A moving average or filtered return by itself does not establish a profitable signal, and the document does not discuss data quality, transaction costs, or out-of-sample validation. Its value is as an introductory data-handling walkthrough rather than a tested trading strategy.
Key ideas
- OHLCV data can be loaded into a pandas time series for inspection and analysis.
- Basic summaries include counting observations and locating the maximum closing price.
- Daily percentage changes can be calculated and filtered by a chosen threshold.
- Rolling windows can produce a simple moving average and an average-volume feature.
- These calculations are exploratory tools and do not demonstrate strategy performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.