Add backtest metrics module (Sharpe, max drawdown, inventory-time-weighted PnL)

[Pattermesh]

What

Adds a reusable backtest-metrics module to the backtest engine with unit tests against known numeric vectors.

New: src/backtest/metrics.py

Pure, side-effect-free functions (numpy only):

• sharpe_ratio(returns, risk_free_rate=0.0, periods_per_year=1.0) — mean(excess)/std(excess) with optional annualisation; returns 0.0 for <2 points or zero volatility.
• max_drawdown(equity_curve) — largest running-peak-to-trough drop, in curve units; 0.0 for monotonic or trivial curves.
• inventory_time_weighted_pnl(pnl_curve, inventory_curve) — sum of abs(inventory_carried) * pnl_increment per step, surfacing whether PnL is compensation for inventory risk.

Tests: tests/unit/backtest/test_metrics.py

17 tests with hand-computed expected values, e.g. drawdown of [100,120,90,110,80,130] == 40, ITW-PnL of pnl=[0,10,5,20], inv=[2,-3,4,1] == 65. Covers risk-free subtraction, sqrt-periods annualisation, short-inventory magnitude, length-mismatch ValueError, and edge/empty cases.

Refactor: src/backtest/engine.py

BacktestEngine.run now consumes the shared sharpe_ratio / max_drawdown instead of inline duplicates. Behaviour preserved (same mean/std Sharpe, same running-peak drawdown).

Test output

$ PYTHONPATH=. python3 -m pytest tests/unit/backtest/test_metrics.py -q
17 passed in 0.56s

$ PYTHONPATH=. python3 -m pytest -q
144 passed, 2 skipped, 703 warnings

CI (.github/workflows/ci.yml) already exists and runs python -m pytest tests/ -v, which auto-discovers the new tests/unit/backtest/ directory — no workflow change needed.

🤖 Generated with Claude Code

[abhicris]

Welcome to kcolbchain, @Pattermesh — glad you're here. 🌱

Here's what happens from this PR:

1. Our automated review looks for obvious issues (tests, secrets, size) within a couple of hours.
2. If it's clean and CI passes, we merge without back-and-forth.
3. If we need changes, we'll leave a specific comment — not a generic nit. Push another commit and we re-review.

While you wait:

• Run the repo's tests locally (see the repo README.md).
• Keep the PR scoped to one concern — bigger PRs land slower.
• Don't commit tokens or .env contents.

What happens after your first merge

• You're listed on https://kcolbchain.com/contributors/ (opt-out anytime via data/contributors-optout.txt).
• At 2+ merged PRs you're a Regular; at 5+ you're a Fellow — Fellows are the pool we invite first for paid research, partner-org gigs, and grant-funded work. Details: https://kcolbchain.com/docs/org-contribution-policy.html
• Looking for what to pick up next? https://kcolbchain.com/invitations/ lists open help-wanted / good-first-issue across every kcolbchain repo.

Thanks for writing the code. We're building this to last.