# Backtesting.py A single-instrument Python backtester — one OHLC series, one strategy, no live trading. *https://stockmarketstack.com/tools/backtesting-py · Backtesting Frameworks & Algo Trading Libraries* ## Also covered on - [Backtesting.py on CryptoMarkets.tools](https://cryptomarkets.tools/tools/backtesting-py.md) — why one OHLC series and no intrabar path hurts more on a 1m crypto series ## Facts ### At a glance | Field | Value | | --- | --- | | Vendor | Zach Lûster | | Category | Backtesting Frameworks & Algo Trading Libraries | | Job | backtesting | | Website | https://kernc.github.io/backtesting.py/ | | Pricing model | open-source | | Free tier | true | | Open source | true | | Licence | AGPL-3.0-or-later | | Self-hosted | true | | Tested hands-on | false | | Last updated | 2026-09-14 | ### Coverage | Field | Value | | --- | --- | | Asset classes | stocks, futures, forex, crypto | | Markets | global | | Works outside the US | true | | Data latency | none | | Platforms | library | | AI features | none | ### Interfaces | Field | Value | | --- | --- | | API | false | | Webhooks | false | | Scripting | Python | | Python | true | | Spreadsheet add-in | false | | MCP server | false | | Export | none | ### Capabilities Yes: backtesting No: charting, screening, scanning, automation, live_trading, paper_trading, portfolio_tracking, broker_import, tax_reporting, alerts, news, options_analysis *Verified: pricing 2026-09-13; capabilities 2026-09-14; coverage 2026-09-14.* ## What it is A Python library of about 3,900 lines — 1,800 of them the engine itself — that takes one OHLC(V) DataFrame and one `Strategy` subclass, walks the bars, and returns a pandas Series of statistics — return, CAGR, Sharpe, Sortino, Calmar, max drawdown, win rate, profit factor, SQN — plus the trade list and an interactive Bokeh chart of the equity curve and fills. Two classes and about a dozen keyword arguments is the whole surface, and that is the point. The engine is event-driven per bar. Market orders fill at the next bar's open unless you set `trade_on_close=True`; limit, stop and stop-limit orders fill when the bar's range touches them. `spread`, `commission` (a rate, a `(fixed, relative)` tuple, or a callable on size and price), `margin`, `hedging` and `exclusive_orders` are the entire cost and position model. Indicators are declared in `init()` and computed once across the whole series — `self.I()` refuses anything that is not an array as long as the data — so an indicator never sees the state the simulation is in, and anything that depends on an open position is written by hand in `next()`. The run also does not begin until every declared indicator is non-NaN, which quietly costs a 200-bar moving average the first 200 bars of the sample. ## Pricing Free, with nothing to buy — no pro build, no hosting, no data bundle, just a GitHub Sponsors link. The cost is the licence: AGPL-3.0-or-later is stronger copyleft than most Python quant libraries carry, and it is the thing to check before this goes near a product. ## Data & coverage Ships no market data beyond three sample sets used in the documentation. You supply the DataFrame — `Open`, `High`, `Low`, `Close`, optionally `Volume`, on a datetime or plain range index, extra columns readable from the strategy. Any instrument, any market, any bar size works, because the engine never learns what the instrument is. ## Integrations Indicator-library-agnostic: `self.I()` wraps any callable returning an array, so TA-Lib, pandas-ta or your own NumPy function work unchanged. Optimisation is grid search or the SAMBO model-based optimiser, run across processes, with a heatmap of the parameter space. NumPy, pandas and Bokeh; Python 3.9 and up. ## Limitations - **No live trading and no paper trading.** Not a broker interface nobody implemented — there is no interface. - **One instrument per backtest.** No portfolio-level sizing, no rebalancing, no shared cash across symbols. - **Bar resolution caps the realism.** When a contingent stop-loss or take-profit would fire in the same bar its parent stop or limit order became a trade, the library warns that it cannot assert the intra-candle path, defers to the next matching bar, and calls the resulting trade "somewhat dubious" in its own message. - **Whole units only**, unless you wrap the run in `FractionalBacktest`. - **Volume is decoration.** The column is optional, filled with NaN when absent, and the broker never reads it: an order fills in full at the price the bar touched whatever its size. No partial fills, no volume cap, no market impact. - **No borrow cost, no financing, no dividends.** Beyond the spread and the commission, none of the three appears anywhere in the engine. - **An order the account cannot afford is cancelled, not reduced.** Short of margin, the broker drops it with a `UserWarning`; a relative size (`0 < size < 1`) is floored to whole units of the margin available, and when that floors to zero the order simply goes away. On the default `cash=10_000` the library also warns at construction if any close exceeds the account. - **No options and no multi-leg positions.** - **Slow release cadence.** 0.6.5 in July 2025, 0.6.6 in July 2026. ## Alternatives [VectorBT](https://stockmarketstack.com/tools/vectorbt) for parameter sweeps at a scale this will not reach, [bt](https://stockmarketstack.com/tools/bt) for portfolio-level strategy composition, [NautilusTrader](https://stockmarketstack.com/tools/nautilus-trader) when the strategy has to run live without a rewrite. [Backtrader](https://stockmarketstack.com/tools/backtrader) is the other name everyone offers; Backtesting.py's own alternatives list files it under "Obsolete / Unmaintained". ## FAQ ### Can Backtesting.py trade live? No. There is no broker connectivity of any kind in the library — no order routing, no paper-trading endpoint, no scheduler. A run ends with a pandas Series of statistics and an HTML plot, and getting from there to a live order is code you write yourself against a broker API. ### Can it backtest a portfolio of several instruments? No. A Backtest takes exactly one OHLC(V) DataFrame. The MultiBacktest wrapper added in 0.6.3 runs the same strategy over a list of datasets in parallel to compare the results per ticker; each run has its own cash and its own equity curve, so there is no cross-asset position sizing, no shared capital and no rebalancing. ### Is Backtesting.py still maintained? Yes, but slowly. The repository is not archived, the last commit to master is 5 August 2026, and 0.6.6 shipped on 22 July 2026 — a year after 0.6.5 in July 2025, and almost entirely bug fixes. 46 issues and 37 pull requests are open. The GitHub releases tab is empty and always has been — the project tags a version and pushes it to PyPI without ever cutting a GitHub release — so an empty releases page here is not the sign of death it usually is. ### Does the AGPL licence matter for my strategy code? It depends on what you do with it. Running backtests privately triggers nothing. Distributing a product built on the library, or offering one over a network, is where AGPL-3.0 asks for the corresponding source of your version — which is why commercial shops often reach for an MIT- or Apache-licensed engine instead. ## Also worth comparing - [Backtrader](https://stockmarketstack.com/tools/backtrader.md) — An event-driven Python backtester with 122 indicators, frozen since April 2023. - [bt](https://stockmarketstack.com/tools/bt.md) — Python backtesting for allocation and rebalancing rules, not entries and exits. - [fastquant](https://stockmarketstack.com/tools/fastquant.md) — A one-call wrapper over Backtrader, dormant since 2023 and broken on PyPI. - [Zipline-reloaded](https://stockmarketstack.com/tools/zipline-reloaded.md) — The maintained fork of Quantopian's Zipline. Bring your own data — it ships almost none. - [AmiBroker](https://stockmarketstack.com/tools/amibroker.md) — Windows portfolio backtester scripted in AFL, sold as a perpetual licence. - [Blueshift](https://stockmarketstack.com/tools/blueshift.md) — Free hosted Python backtesting with bundled minute data and broker execution.