Trading Strategy Engine
Money loves silence.
Trading Strategy Engine is a high-performance backtesting and trading engine for C++ and Python, delivered as a licensed binary library with examples and documentation. A model can be written with AI in minutes now; the harness around it did not get cheaper — position accounting, exposure at the instant of the fill, the order refused before it is sent. That is the part we ship, and it runs on your own premises, so the model, the data and orders logic never leave them.
What the engine does
Being right about the money
A backtest that is wrong still prints a profit, and nothing on the surface tells you which one you are looking at. That is why the invariants underneath are not only implemented but tested one by one, each with a test that fails the moment it breaks. Correctness you cannot see from the outside has to be demonstrated.
Your data and logic stay private
Fully on-premise. Strategy logic and order flow never leave your perimeter. You decide which AI to connect — external or local — while data and logic stay inside your infrastructure.
High-performance C++ engine
The industry standard for professional trading, not a retail stack. Large datasets, complex multi-currency portfolios, and detailed behavioural statistics for every backtest and live run.
Your strategy stays yours
The decision module is separated from the order-routing pipeline. A strategy plugs in as an opaque callable; its logic is never exposed to the engine.
One codebase, backtest to paper
A single codebase for backtesting and paper trading. Minimal friction to take a new idea to market — the shortest practical time-to-market.
Every candidate measured the same way
The same statistics for a two-line crossover as for a deep learning model, and the per-period features a selection model ranks them with. That comparability is what begins to matter once candidates are generated by the thousand.
ML and AI ready
The same robot scaffold serves a century-old technical indicator and a neural network alike — only the data processor changes. Plug in GBM/XGBoost or call out to an external AI service.
Save months and six figures
Building an equivalent in-house is months of work and a six-figure budget before it ever sees a live test. Architecture follows Peterson's Developing & Backtesting Systematic Trading Strategies.
What the engine does not do
No charting. You take the data the library records and visualize it in the tools you already use.
Supported platforms
- Windows (x64)
- Ubuntu 22.04 LTS (other Linux on request)
Read the API overview Browse robot examples See the measured numbers