Free 300k‑ticker finance DB
Quant Science highlighted a free GitHub Finance Database that contains about 300,000 tickers for Python‑based quantitative analysis, which can accelerate data‑engineering and strategy prototyping. Large, curated ticker lists remove an early friction point for backtesting and cross‑sectional experiments, although users still need to vet survivorship and corporate actions. Public datasets like this are useful building blocks for reproducible quant projects and classroom labs. (x.com)
Most beginner quant projects die before the first backtest, because someone spends the first weekend just trying to build a clean list of tradable symbols. A free GitHub project called FinanceDatabase tries to skip that step with more than 300,000 financial tickers in one place. (github.com) A ticker is the market’s shorthand label for an asset, the way “AAPL” points to Apple stock or “SPY” points to a large exchange-traded fund. If your list of labels is messy, every screen, ranking model, and backtest built on top of it gets messy too. (github.com) The repository says it includes equities, exchange-traded funds, mutual funds, indices, currencies, cryptocurrencies, and money markets. It also organizes many of them by country, exchange, sector, industry, and category, which is the kind of metadata people usually end up stitching together by hand. (github.com) That sounds boring until you try a cross-sectional strategy, which is just a model that compares many securities against each other on the same date. A momentum test across 2,000 stocks fails fast if half the symbols are delisted, duplicated, or mapped to the wrong exchange. (github.com) FinanceDatabase is not pitched as a live price feed or a fundamentals terminal. The project’s own readme says the goal is not to provide up-to-date fundamentals or stock data, but a broad catalog of symbols that other tools can query. (github.com) That distinction matters because a symbol catalog solves the “what exists?” problem, not the “what happened today?” problem. You can use it to find Japanese industrial stocks or United States exchange-traded funds, then pull prices and financial statements from somewhere else. (github.com) One third-party guide breaks the database into roughly 155,705 equities, 36,727 exchange-traded funds, 57,816 funds, 86,353 indices, 2,590 currencies, 3,624 cryptocurrencies, and 1,384 money-market entries. Those counts can change over time, but they show why people notice the project: it is much bigger than the usual “S and P 500 tickers.csv” starter file. (algotrading101.com) The catch is that a giant ticker list does not magically make research clean. If you test a strategy only on symbols that still exist today, you can accidentally erase the graveyard of bankrupt, merged, and delisted names and make old results look better than they were. (github.com) Corporate actions create a second trap. A stock split, ticker change, merger, or fund closure can break joins across datasets unless your price history, identifier history, and security master all agree on what the asset was called on each date. (github.com) That is why public symbol databases are best used like scaffolding, not like a finished building. They speed up classroom labs, prototype screens, and reproducible notebooks in Python, but serious live trading still needs audited data pipelines behind the scenes. (github.com)
