A single-instrument Python backtester — one OHLC series, one strategy, no live trading.
Open-source stock market tools
What each licence actually allows — MIT, Apache, AGPL, GPL — and why open-source code almost never comes with open data.
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Open source solves the software, not the data
Every tool on this page ships source you can read, fork and run. None of them ships the market data, and that is the constraint that shapes the whole list.
Exactly one market-data API in the catalogue is open source — yfinance — and it is a client, not a source: it reads endpoints Yahoo publishes for its own website. The code is Apache-2.0; the numbers that come back carry no licence to redistribute at all, which its own maintainer says plainly. Everywhere else the pattern holds: the engine is open, the feed is bought.
The licence is the field to read first
MIT, Apache-2.0, BSD — do what you like, keep the notice. That is 41 of the 54 open-source cards on 8 October 2026 — 19 MIT, 16 Apache-2.0, six BSD or NCSA. Most of the agent and MCP layer sits here, along with Lightweight Charts and Plotly.js.
AGPL-3.0 — the condition triggers on network use. Run a modified copy as a service other people reach and you owe them the source. Six cards carry it, Ghostfolio, SEC EDGAR MCP and Backtesting.py among them; OpenBB left it for Apache-2.0 with its V5 release in September 2026. Running one for yourself or your household never trips it; building a product on top of one is the case to check.
GPL-3.0, LGPL-3.0 — Backtrader and Lumibot are GPL, which reaches into anything you link them with. NautilusTrader is LGPL, which does not, provided you link rather than modify.
Split licences wear the label without covering the whole product. TimescaleDB is Apache-2.0 at the core, but compression, continuous aggregates, retention policies and gapfill are under the source-available Timescale License — which is to say the features you would install it for. InfluxDB 3 is MIT or Apache-2.0 for Core only; Enterprise is commercial and its source is not public.
Where the open tools cluster
Four categories are mostly open source: analysis libraries, 12 of 13 cards; financial MCP servers, 3 of 3; AI research agents and copilots, 4 of 6; and backtesting frameworks, 15 of 24. In each the product is the code.
Charting platforms and screeners and news and sentiment APIs have no open-source card at all; fundamental research has one and market data APIs two, and each of those is open code over somebody else's prices. There the product is a licence to show you the numbers, and a licence cannot be forked.
The interesting middle is charting libraries, four open and five proprietary, and portfolio trackers, where three open trackers exist because the job — what you hold and what it is worth — is one you may not want on a vendor's server at all.
All 54 of them
Showing 54 of 54
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Backtesting Frameworks & Algo Trading Libraries15
An event-driven Python backtester with 122 indicators, frozen since April 2023.
Python backtesting for allocation and rebalancing rules, not entries and exits.
A one-call wrapper over Backtrader, dormant since 2023 and broken on PyPI.
Financial LLM adapters, instruction datasets and training notebooks, all MIT.
Gym-style market environments and deep-RL agents for trading research, MIT-licensed.
FinRL's successor, carrying one portfolio-weight vector from stock selection to Alpaca.
The community continuation of ib_insync — same API, new maintainers, TWS still required.
Open-weights transformer pre-trained on OHLCV bars. The training corpus is not released.
QuantConnect's engine without the cloud — your server, your data, your broker login.
One strategy class for backtest and live, plus a built-in LLM agent runtime.
Rust core, Python API, one strategy that backtests and trades live unchanged.
Microsoft's ML factor-research pipeline. The data its CLI downloads stops in late 2020.
An institutional-shaped Python backtester whose last commit to master was June 2024.
The maintained fork of Quantopian's Zipline. Bring your own data — it ships almost none.
Market Analysis & Portfolio Optimization Libraries12
GARCH and the rest of the volatility-model family, plus the tests you need around them.
The maintained fork of Quantopian's empyrical — risk statistics, no plots.
Sessions, minutes and holidays for 69 exchanges, keyed by ISO-10383 code.
Performance stats, drawdowns and portfolio weights from a DataFrame of prices.
Exchange schedules as pandas DataFrames, with CME, CBOE options and bond-market calendars.
The DataFrame indicator library whose repo is gone and whose last release is a year old.
Prices in, weights out — efficient frontier, Black-Litterman and HRP.
The open-source derivatives pricing library banks actually use, reachable from Python.
HTML tearsheets and about eighty risk metrics from one pandas series of returns.
Twenty-six convex risk measures, four objectives, one cvxpy-backed optimiser.
Portfolio optimisation as scikit-learn estimators — fit, predict, cross-validate.
The C indicator library everything else wraps — and pip now ships the C part with it.
Tick Data Storage & Time-Series Databases6
Real-time OLAP cluster built for high-concurrency dashboards, not for research joins.
Columnar OLAP database that keeps years of ticks on disk cheaply and scans them fast.
In-process analytical SQL over Parquet tick files, with no server to run.
Line-protocol time-series database rewritten on Arrow, DataFusion and Parquet.
Open-source time-series SQL built for tick data — ASOF JOIN, SAMPLE BY, LATEST ON.
Hypertables, columnar compression and continuous aggregates bolted onto plain Postgres.
JavaScript Charting Libraries for Finance4
Apache-2.0 general-purpose charting with a candlestick series and nothing else financial.
Apache-2.0 canvas candlesticks with 27 indicators built in and zero dependencies.
The chart and nothing else — six series types on canvas, attribution required.
Candlestick and OHLC traces in an MIT library the notebook already has installed.
AI Research Agents & Copilots4
LLM investor personas score a ticker, deterministic code sizes the book, nothing trades.
Open-source agents that turn a ticker and your own API keys into an analyst report.
Analyst, bull, bear, risk and portfolio-manager agents argue over one ticker.
A local finance research agent with 74 MCP tools, nine backtest engines and 14 brokers.
Stock Portfolio Trackers3
Self-hosted open source portfolio tracking, or a hosted account for USD 48 a year.
Free open-source desktop tracker that reads your bank's PDF statements.
Local-first portfolio and net-worth tracker for desktop, mobile and your own Docker host.
Financial MCP Servers3
Thirty-seven stock-analysis tools for any MCP client, on Yahoo data and no key.
SEC filings and XBRL financials as MCP tools, under AGPL or a paid commercial licence.
Screeners, technical analysis and backtests for any MCP client. No TradingView account.
Stock Market Data APIs2
Trading Journal Software1
The open-source trade journal you run in Docker, or rent hosted for $4.99 a month.
Fundamental Data & Stock Research Platforms1
Equibles
ClaimedFinancial research by browser, REST API and hosted MCP, with an AGPL self-hosted core
Insider, 13F & Congressional Trade Trackers1
Every 13F since 2014, free and account-free, from a Rails app you can run yourself.
Retirement & Portfolio Planning Calculators1
Free, MIT-licensed claiming calculator — every claiming month, priced by present value.
Macro & Economic Data APIs1
Macro series from 93 statistics offices and central banks behind one open API.
FAQ
Does open source mean free?
The software, yes. The data, almost never. QuantConnect publishes its [LEAN](/tools/lean) engine under Apache-2.0 and charges a subscription for the hosted platform and the data that runs on it; Ghostfolio is AGPL and still needs a market-data provider behind it — Yahoo Finance and CoinGecko among the supported sources, which cost nothing, and a Premium key for its professional provider. Read the licence for what you may do with the code and the provider's terms for what you may do with the numbers — they are two different questions with two different answers.
Can I use AGPL code inside a commercial product?
You can sell it, and you can modify it. What the AGPL adds over the GPL is that running a modified copy as a network service counts as distribution, so the people using that service can ask you for the source. For a desktop app, a personal server or internal use that condition never fires. For a SaaS product built on someone else's AGPL code it is the clause to take to a lawyer before you write the first line.
What is the difference between open source and self-hosted?
Open source is about the licence on the code; self-hosted is about where the process runs. Every open-source tool in the catalogue can also be self-hosted, but the reverse fails for 13 cards. Four are tick databases (kdb+, OneTick, DolphinDB and ArcticDB), five are proprietary charting libraries, Highcharts Stock and SciChart.js among them, and four are backtesters — QuantRocket, VectorBT PRO, and VectorBT and PyBroker under the Commons Clause.
Which parts of the market are actually open source?
The parts where the value is in the code rather than in a licence to redistribute somebody else's prices. Analysis libraries, the MCP-and-agent layer and most backtesting frameworks are open source; charting platforms, screeners, news APIs and data vendors are almost entirely not. That split is not about culture — a screener's product is the feed it is licensed to show you, and a feed cannot be forked.