OpenBacktesting: Python backtesting framework¶
Write a Python strategy, run it on historical OHLC data, and inspect trades and performance. This manual is for people using the framework.
Run your first backtest → — a complete script using bundled data, with no data download or application integration required.
Choose your next task¶
| I want to… | Read |
|---|---|
| Load market data or set asset rules | Data, timeframes, and instruments |
| Define entry and exit logic | Strategy |
| Set cash, fees, and execution timing | Backtest |
| Place, cancel, protect, or close trades | Orders, Position, and Trade · Account and events |
| Calculate or compose indicators | Indicator usage · Full catalog |
| Make strategy parameters configurable | Inputs |
| Read return, equity, and trade details | Results · Portfolio export |
| Compare parameter values | Best-run optimization · Saved experiments |
| Add visuals or colors | Plotting declarations · Color |
| Reuse signal/trailing strategies or resample data | Helpers |
| Run a strategy file with declared defaults | Decorator and runtime |
How the pieces fit¶
- Prepare a pandas DataFrame containing one asset's bars.
- Define a
Strategy: register indicators ininit(), read the current bar and place orders innext(). - Construct
Backtest(data, MyStrategy, ...)and call.run(...). - Read statistics and execution tables, or export a Portfolio report.
Order tracks a request, Trade tracks one filled lot, and Position summarizes open exposure. An order normally executes at the next bar's open. Indicator values can be invalid during initial history; your strategy checks them before acting.
Tutorials and scope¶
First backtest · Trading rules · Indicator recipes · Runnable examples
The engine models one asset and one settlement currency per run. It exports plotting data but includes no chart renderer or live-trading connection. See supported boundaries before interpreting results.