From Python to Rust

Backtesting code often grows up with its strategy. It starts as a few lines in a notebook, and it can end up as a service with no Python in it at all. This three-part tutorial takes one volatility-squeeze breakout along that path. The data and the rules never change, so the only difference between the versions is how they are written.

✅ Learn how to write the same strategy four times, from short research code down to a standalone program that runs without Python. All four are then checked against each other, order for order.

✅ Some rules cannot be prepared in advance, because they depend on what the strategy just did. Learn where a rule like that belongs, and see it produce the one result in this tutorial that could not have been worked out before the simulation ran.

✅ Finally, learn how to carry one portfolio forward as new bars arrive, instead of rerunning the whole backtest each time, and how a Rust process picks up where it left off after a restart 🔄

Topics

Static simulation

Learn how to backtest the same strategy with high-level VBT, Numba, PyO3 Rust, and native Rust

Dynamic simulation

Learn how to implement stateful portfolio callbacks and stream the same strategy in native Rust

Live simulation

Learn how to continue a path-dependent simulation as new bars arrive and preserve it across restarts