672c873571
- ml-dqn/dqn.rs: `apply_accumulated_gradients` is a scaffolding method whose real optimizer step lives in the fused CUDA trainer. The `grads` map was already being dropped silently; reword the comment to describe that split explicitly (incidental: see trainer path for the live gradient application). - ml-features/mbp10_loader.rs: strip the "TODO optimize with binary search" parenthetical from the docstring. Linear search over the sorted snapshot slice is the intended behaviour for current call sites. - ml-hyperopt/optimizer.rs: `optimize_two_phase` short-circuits after Phase A because `DQNTrainer` is not `Clone`. Describe that limit and point callers at `optimize_parallel` (which requires `M: Clone`) rather than a hypothetical Phase B. - ml-checkpoint/signer.rs: `fetch_key_from_vault` is currently an env-var resolver. Reword to say so plainly — no Vault client is wired into this crate, production uses K8s secrets injected as env. - backtesting/dbn_replay.rs: `DbnReplayEngine::from_bytes` remains an Err stub because `DbnParser` is gated behind the `databento` feature which this crate does not enable. Replace the pseudocode block with a declarative comment. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
backtesting
Strategy backtesting engine for simulating trading strategies against historical market data.
Key Types
Backtester— main backtesting engineBacktestConfig— simulation configuration (time range, instruments, slippage, commissions)BacktestResults— performance metrics (Sharpe, max drawdown, alpha, beta, Sortino)
Features
- Historical data replay from Parquet files (ticks, order book snapshots, candles)
- Configurable slippage models (fixed, percentage, volume-based)
- Commission modeling (fixed, percentage, per-contract)
- Pluggable strategy interface
Usage
use backtesting::{Backtester, BacktestConfig};
let config = BacktestConfig { /* ... */ };
let results = backtester.run(&strategy)?;