Backtesting Frameworks & Algo Trading Libraries

Nothing here needs to cost money: the engines with the most careful fill and cost modelling are free, and the real bill in this category is data. What separates them is not features but two things a feature list will not tell you — whether anyone is still fixing the code, and what the LICENSE file lets you build. The most-starred Python backtester in the listing has shipped nothing since April 2023, and two of the best-known engines are copyleft.

• Parameter sweeps and factor research — VectorBT, or VectorBT PRO if you need the maintained one.
• One strategy, one instrument, learning the ropes — Backtesting.py.
• The same code backtesting and trading live — NautilusTrader.
• A hosted engine with survivorship-bias-free data included — QuantConnect.
• Allocation and rebalancing rather than entries and exits — bt.
• No code at all — Composer.
• A packaged desktop application you own outright — AmiBroker.

Check the commit log before the feature list

This is the one category where the most useful fact about a product is not on its website. Almost everything here is open source, feature lists converge, and the question that actually decides your next two years is whether anyone is still fixing it. Three checks, in order:

The last commit on the default branch. Not the star count, which only goes up. Backtrader has well over 20,000 stars, is the name every thread recommends first, and has had nothing merged into master since April 2023. It is a museum piece with an excellent reputation. QSTrader last committed in June 2024; fastquant, a wrapper over Backtrader, has been dormant since 2023 and is broken on PyPI as installed.

The newest release, not the newest commit. A repository can accumulate typo fixes for a year without cutting a release, and what you install is the release. PyPI, NuGet and the GitHub releases tab all carry the date.

Which Python it claims. This is where a dormant project actually bites. An unmaintained package breaks the release that pandas or NumPy changes under it, and nobody merges the fix — so its dependency pins quietly become your dependency pins. The maintained projects here declare a current interpreter range and drop old ones; the abandoned ones declare nothing at all.

None of this means a dormant framework is unusable. Backtesting logic does not rot the way a broker adapter does, and a stable, well-documented engine you have read is worth more than a busy one you have not. It means the bugs are now yours, and you should decide that on purpose.

Vectorised and event-driven are a claim about what can be modelled

Vendors sell this as a performance choice. It is not: it decides which strategies the engine can express at all.

Vectorised means signals are computed over the entire price series as arrays, and positions are derived from those arrays afterwards. The result is genuinely fast — tens of thousands of parameter combinations in the time an event loop does one. The cost is that nothing in the simulation can depend on the state the simulation is in. Position size as a function of current equity, a stop that trails an open trade, an order that might not fill, two instruments whose orders interact: each of these is either bolted on through a callback, at which point most of the speed is gone, or approximated in a way that flatters the result.

Event-driven means the engine replays data one event at a time and your strategy sees only what existed at that instant. It is slower by orders of magnitude, and it is the only architecture in which the backtest and the live trading loop can be the same object. That, not realism in the abstract, is the reason to accept the speed penalty.

The practical rule: factor research and parameter sweeps want vectorised; anything you intend to run with money wants event-driven. Plenty of people use one of each, and that is a sane answer.

What the engine can and cannot model

Intrabar fills. Given a daily bar, the engine knows the open, high, low and close and nothing whatever about the order in which they occurred. If your stop and your target both sit inside one bar, which filled first is a guess the framework makes on your behalf — usually the pessimistic one, not always, and rarely on the front page of the documentation. A strategy that regularly puts both levels inside a single bar is measuring that assumption rather than an edge. Feed finer bars or stop designing around it.

Commissions and slippage. Find the defaults before you read a single equity curve. Zero slippage and a flat percentage commission are common defaults, and they flatter a high-turnover strategy out of all proportion. Whether the engine can express a per-share commission with a minimum, a fill priced off the spread, borrow cost on a short, or any market-impact function at all is a real difference between these products, and it lives in the API reference rather than the README.

Look-ahead. Every framework has a convention for whether a signal computed on a bar may trade on that same bar, and they differ: trade at the close of the signal bar, at the next open, or at the next bar's close. A series shifted by one is the most common way a backtest lies, and it is invisible in the results — the curve just looks good.

Survivorship bias is yours, not the engine's. No framework in this listing fixes it and none claims to. Free equity data contains the tickers that are still listed; every company that went bankrupt, was acquired or was delisted is simply absent, so any strategy that scans a universe is being tested on the survivors. Correcting it needs a delisted-securities archive and point-in-time index membership, which is a paid data purchase in every case. It is the most expensive line in a serious backtest and the one no feature comparison mentions.

The LICENSE file, not the badge

The licences here span the full range, and the differences are the kind a legal team asks about before a commercial deployment, not afterwards.

Permissive — MIT and Apache-2.0 — puts no condition on what you build. Zipline-reloaded is Apache-2.0, and so is QuantConnect's LEAN engine, the open-sourced core of a commercial cloud platform. bt and QSTrader are MIT.

GPL-3.0 and LGPL-3.0 part company on linking. LGPL lets proprietary code use the library; GPL does not, once you distribute the result. The two best-known engines here sit one on each side of that line: Backtrader is GPL-3.0-or-later and NautilusTrader is LGPL-3.0-only.

AGPL-3.0 extends copyleft across the network: offering the library's functionality as a hosted service counts as distribution. Backtesting.py is AGPL-3.0. Your strategies remain yours; a SaaS built on top of the library is a different conversation.

Source-available, sold as open source. Apache-2.0 with a Commons Clause is not an OSI licence — it withdraws the right to sell a product or service whose value derives substantially from the software, which is precisely what a data or signals business would be doing. VectorBT is licensed this way, while its actively developed successor VectorBT PRO is closed and paid. StockSharp publishes its source on a public repository and is described everywhere as open source; its LICENSE is a proprietary EULA.

Open the file. It takes thirty seconds and it is the contract.

Four jobs behind one word

Backtesting is the base job: replay history, apply costs, produce a return series and a set of statistics. Judge these on the fill and cost model, on what data formats they ingest, and on how long a sweep takes.

Live trading is a different product that happens to share a repository. It is settled by venue and broker adapters, and by whether the backtest and the live path are literally the same code. Ask what the framework does on a reconnect, a partial fill, or a restart while a position is open — a backtester has never once had to answer any of those questions, which is why the engines that take live trading seriously look heavyweight next to the ones that do not.

Research is the notebook job, and the question is not "does this strategy work" but "is there any information in this signal". It wants fast sweeps, factor and alpha plumbing, and reporting you can read; it does not care about order routing at all. This is where vectorised engines are not a compromise but the correct tool.

No-code builds the strategy in a UI and runs it in the vendor's cloud. The trade is complete and worth stating plainly: no dependencies, no data wrangling, no infrastructure — and exactly the data, the universe and the fill assumptions the vendor chose, none of which you can inspect or change. A no-code backtest is a claim made by the vendor rather than a measurement you made.

Ranking a Rust execution engine against a drag-and-drop cloud backtester produces nothing. Decide which of the four you are here for, then compare only inside it.

What any of this costs

Most of this category is free, and where money changes hands it is rarely for the engine. Cloud platforms charge for a seat plus compute, assembled per account rather than priced as a tier, and bundle the survivorship-bias-free data that would otherwise be your largest bill. Desktop products are still sold outright — AmiBroker's editions run $299, $379 and $499 as one-time licences with a couple of years of updates, and Wealth-Lab is $49.95 a month, $399.95 a year or $1,595 for a lifetime licence. A no-code platform will typically let you build and backtest for nothing and charge only to automate the trading, on the order of $384 a year. Open-core .NET platforms follow the same shape from the other direction: a free tier that genuinely runs live, with paid editions starting near $995 for the first year.

The exception that swallows all of it is data. Budget for the delisted archive before you budget for the software.