Combining Fundamental, Macroeconomic, News, and Social Data for Trading
Summary
The article surveys data sources that can supplement price and volume analysis. It covers company ratios and financial statements, macroeconomic indicators, earnings dates, financial news, tweets, and sentiment scores. It names services and Python libraries for retrieving or processing these inputs, including Yahoo Finance, DBnomics, News API, Tweepy, and VADER sentiment analysis. Its main workflow is to collect fundamental and economic context alongside news or social posts, then score the text to inform potential trading signals.
Examples include retrieving unemployment and CPI series, finding earnings dates, and gathering Bitcoin headlines. The article presents these as data access illustrations rather than evidence of strategy performance: it supplies no tested signals, returns, or comparison of providers. Data availability, coverage, and cost vary; the authors note that some sentiment services are paid and recommend checking the accuracy and suitability of the information. The material is a starting point for alternate-data research, not a validated trading method.
Key ideas
- Fundamental screening can use ratios and company financial statements.
- Macroeconomic series such as unemployment and consumer prices add economic context.
- Earnings calendars, financial news, and social posts can be gathered from separate providers.
- Text sentiment can be derived with a lexicon-based tool or obtained from a specialized service.
- The article explains data collection but does not validate trading signals or report performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.