Three commands. Same input. Same output, byte-for-byte. We don't ask you to trust the Sharpe number โ we hand you the recipe.
Most trading-SaaS backtests are black boxes. Sharpe ratios get cited; methodology stays hidden; numbers can't be independently verified. We do the opposite.
The 5-year backtest behind the Performance Expectations card is a walk-forward run against pinned Alpaca bars with deterministic seeding. Every commit produces byte-identical output for the same input. You can clone the repo, run one command, and verify the published numbers โ no API keys, no calls home, no proprietary data feed.
If a competitor won't show their methodology this clearly, that's a signal worth thinking about.
All three are run from the repo root with Python 3.12+ and the dev dependencies installed. No broker keys required โ the backtest reads pinned bars, not live data.
# Clone the repo (private at launch โ request access via founders@tradingarbor.com) git clone https://github.com/pataskad/automated-trading-assistant cd automated-trading-assistant # Install dev dependencies (includes pandas, numpy, scipy) pip install -r requirements.txt
The committed snapshot at backtest/data/pinned_bars/
is what every shipped number was computed against. Set the
env var to point at it.
# Use the committed snapshot (deterministic; matches published numbers) export PINNED_BARS_PATH=$(pwd)/backtest/data/pinned_bars/snapshot.json export PYTHONHASHSEED=0 # Belt-and-suspenders for sort-order determinism
# Per-year breakdown: returns, Sharpe, drawdowns, trade counts python -m backtest.per_year_return_report_v2 --years 2021,2022,2023,2024,2025 # Verification battery: ablations + cross-checks before any param change ships python -m backtest.verify_swing_growth_stacked # Compare to live signal log (catches drift between backtest + production) python -m scripts.parity_check
Output is reproducible to the byte. If you re-run the same command on the same commit, the results match exactly. That's the whole point.
The shipped numbers from the committed hardened run
(2026-06 re-validation). Regenerate with
python -m backtest.bake_honest_performance against the
pinned 10-year bars and you should match these to the rounding
shown.
| Metric ยท deployed engine, $25K, minimum (1%) risk | Backtest (hardened) | Live-expected |
|---|---|---|
| Avg / year (compounded, injection-adjusted) | +12% | +6% |
| Avg / year (no injection) | +15% | โ |
| Worst year of 10 | โ14% | โ21% |
| Max drawdown (injected mean) | ~28% | worse |
| Sharpe (yearly mean/ฯ) | 0.87 | 0.61 |
Numbers reflect the committed
dashboard_data/honest_performance.json, which the
dashboard's Performance card reads directly. "Hardened" means:
compounded dollars (never summed percentages), every stop fill
repriced through overnight gaps, and a random 2%-per-trade total
loss injected to proxy blow-ups the still-listed universe can't
show. An earlier methodology summed per-trade percentages and
reported five-year averages near +182%/yr with a positive worst
year; the 2026-06 re-validation measured that accounting as the
artifact it was and retired it. Full trail:
backtest/REPLAY_RESEARCH.md.
So with the hardened backtest at +12%/yr (injection-adjusted, Sharpe 0.87, worst year โ14%), our planning number for live trading is +6%/yr at Sharpe ~0.61 with a worst year near โ21%. That's the number we plan around โ not a headline. These figures describe the engine at its deployed minimum-risk setting; higher-risk configurations exist in the research but do not surface here until they clear live fill-parity and paper validation gates.
Other things this backtest doesn't account for that live trading will: regimes outside 2016โ2026 (no dot-com or 2008-style secular bear exists in the data); broker outages; your specific account's fee structure; tax drag on short-term capital gains; the psychological cost of seeing a โ20%+ drawdown on a real account. Trade paper for at least 2-3 weeks before going live; the engines run identically against paper and live brokers, so you're stress-testing your own tolerance, not just the algorithm.
For someone doing real due diligence on the methodology โ investors, prospects with quant backgrounds, auditors โ the full breakdown is in our documentation.
It covers: