Downloading Market Data and Building Plotly Candlestick Subplots
Summary
This tutorial shows how to retrieve daily price data from AlphaVantage, convert nested JSON or CSV responses into Pandas DataFrames, and prepare several ETFs for charting. It explains that API responses may default to a limited history, describes sorting and filtering dates, and demonstrates adding a moving average before arranging five ETF candlestick charts in a Plotly subplot. The examples focus on data handling and visualization rather than testing a trading signal.
The article also introduces AlphaVantage’s forex and cryptocurrency endpoints and shows how to retrieve delisted-company listings. That discussion connects historical universe membership to survivorship bias in equity backtests: testing only current index constituents can overstate past performance. Data access limits, changing premium classifications, and variable history coverage affect reproducibility. The article cautions that free data services are appropriate for learning and prototyping, while their terms may rule out live trading use; it does not evaluate data quality or provide a strategy performance study.
Key ideas
- Nested API responses can be reshaped, transposed, and date-sorted into time-indexed DataFrames.
- Filtering downloaded history by date supports focused analysis while retaining the full dataset.
- Plotly subplots can display multiple ETF candlestick charts together, with a larger panel for a broad-market fund.
- Delisted listings help researchers reconstruct historical equity universes and reduce survivorship bias.
- Data availability, request limits, premium classifications, and provider terms constrain practical use.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.