leanpeek 0.1.0

Lean backtest results, at a glance.

What is leanpeek

leanpeek reads a QuantConnect LEAN backtest result JSON and turns it into one human sentence — plus a terminal sparkline of the equity curve and drawdown, so you can see the shape of the result before opening any file. If you want the details, it writes a small report (summary.csv, report.md) and a chart (equity curve + drawdown, with an optional ticker close overlay).

It was born as a bootcamp mini-project: the class ran a Samsung Electronics Buy-and-Hold backtest on quantconnect/lean and the assignment was "turn the results into something you'd actually use." leanpeek is that something — a tiny, dependency-light CLI that makes the engine's output legible instead of a 215 KB JSON file.

Quick start

pip install -e .          # or: pip install leanpeek (once published)

# Point it at a directory containing SamsungBuyAndHold.json (+ optional samsung.csv)
leanpeek -r sample

[local] 2024-01-01 ~ 2025-01-01: 1,000,000 -> 973,600 (net -2.640%), max drawdown 3.800%, Sharpe -3.919 | 1 order(s) (first: 2024-01-03 Buy 1 @ 79,600)
    equity  ▆▆▅▅▅▅▅▅▅▅▅▅▅▆▇▇▇▇▆▆▆▇▆▆▆▅▆▆▇▇▇███▇▇▆▆▆▆▆▅▄▃▃▃▃▂▂▂▂▂▁▁▂▂  (min 970,300 / max 1,008,200)
  drawdown  █▇▇▇▇▇▇▇▇▇▇▇▇▇████▇▆▆▇▇▇▆▆▆▇▇▇▇███▇▇▆▅▆▆▅▅▄▃▃▃▃▂▂▂▂▂▁▁▂▂  (min -3.8 / max 0.0)
reports -> leanpeek-out

(That exact output is pinned by tests/test_sample.py.) Add --banner for an ASCII-art wordmark, --ascii-plain if your terminal can't render block glyphs, and --spark-width N to control the sparkline length.

leanpeek writes three files into leanpeek-out/ by default (override with -o):

$ leanpeek -r sample
[local] 2024-01-01 ~ 2025-01-01: 1,000,000 -> 973,600 (net -2.640%), max drawdown 3.800%, Sharpe -3.919 | 1 order(s) (first: 2024-01-03 Buy 1 @ 79,600)
    equity  ▆▆▅▅▅▅▅▅▅▅▅▅▅▆▇▇▇▇▆▆▆▇▆▆▆▅▆▆▇▇▇███▇▇▆▆▆▆▆▅▄▃▃▃▃▂▂▂▂▂▁▁▂▂  (min 970,300 / max 1,008,200)
  drawdown  █▇▇▇▇▇▇▇▇▇▇▇▇▇████▇▆▆▇▇▇▆▆▆▇▇▇▇███▇▇▆▅▆▆▅▅▄▃▃▃▃▂▂▂▂▂▁▁▂▂  (min -3.8 / max 0.0)
reports -> leanpeek-out

$ leanpeek -r sample --banner
 _                                  _
| | ___  __ _ _ __  _ __   ___  ___| | __
| |/ _ \/ _` | '_ \| '_ \ / _ \/ _ \ |/ /
| |  __/ (_| | | | | |_) |  __/  __/   <
|_|\___|\__,_|_| |_| .__/ \___|\___|_|\_\
                   |_|

[local] 2024-01-01 ~ 2025-01-01: 1,000,000 -> 973,600 (net -2.640%), max drawdown 3.800%, Sharpe -3.919 | 1 order(s) (first: 2024-01-03 Buy 1 @ 79,600)
    equity  ▆▆▅▅▅▅▅▅▅▅▅▅▅▆▇▇▇▇▆▆▆▇▆▆▆▅▆▆▇▇▇███▇▇▆▆▆▆▆▅▄▃▃▃▃▂▂▂▂▂▁▁▂▂  (min 970,300 / max 1,008,200)
  drawdown  █▇▇▇▇▇▇▇▇▇▇▇▇▇████▇▆▆▇▇▇▆▆▆▇▇▇▇███▇▇▆▅▆▆▅▅▄▃▃▃▃▂▂▂▂▂▁▁▂▂  (min -3.8 / max 0.0)
reports -> leanpeek-out

You can also run it as a module: python -m leanpeek -r sample.

What it reads

A LEAN result JSON stores:

• equity curve under charts.Strategy Equity.series.Equity.values ([timestamp, open, high, low, close] rows),
• orders as a dict keyed by order id,
• metrics under statistics / runtimeStatistics.

leanpeek parses exactly that shape — no QuantConnect account, no data provider, no network. Point it at any LEAN -results folder.

Verification

Every number in this README is regenerated from the committed run in sample/ and pinned by tests/test_sample.py. CI runs ruff + pytest on Python 3.10 and 3.12 — no network, no Docker. If a future LEAN version changes the result format, the tests will notice before the README drifts.

Limitations

• This is an educational example, not investment advice.
• The bundled sample strategy does not model KRX fees, dividends, trading calendar, or FX (as noted in the LEAN sample strategy), so treat the numbers as engine/format verification, not realistic performance.

Acknowledgements

• The sample run was produced with the official quantconnect/lean Docker image and the QuantConnect LEAN engine.

License

MIT © 2026 Noah TaeHwan. See LICENSE.