QuantVault

Install

Python 3.10+. No cloud account. Optional Parquet support via an extra.

pip install QuantVault

# from source
pip install -e ".[dev]"

# optional parquet export
pip install "QuantVault[export]"

How to use

QuantVault does not replace your backtest engine. You run research as usual - then register results in the ledger.

1. Init a ledger in your research folder.
   quant-vault init creates ./.quantvault/ledger.db.
2. Record an experiment when you launch (or finish) a run - strategy, params, tags, parent id.
3. Analyze with equity / returns / trades JSON. Metrics and chart artifacts are stored locally.
4. Validate research quality: quant-vault validate <id>.
5. Optional Monte Carlo for distribution / fan charts: quant-vault montecarlo <id> --file run.json.
6. Compare, lineage, export, dashboard whenever you need answers - all offline.

Python API

Same core as the CLI. Open a ledger, record runs, attach analysis and reproducibility metadata.

from quantvault import Ledger

with Ledger.open() as ledger:
    exp = ledger.record(
        "mean_reversion",
        parameters={"lookback": 20, "threshold": 1.5},
        tags=["pilot"],
    )

    # ... run your backtest ...

    ledger.analyze(
        exp.id,
        equity=equity_curve,
        trades=[{"pnl": 12.5, "notional": 10_000}],
        benchmark_returns=bench,
        cost_bps=5,
        slippage_bps=2,
    )
    ledger.validate(exp.id)
    ledger.run_monte_carlo(exp.id, returns, n_sims=500, seed=7)
    ledger.attach_repro(exp.id, seed=7, packages=["numpy"])

    print(ledger.compare(exp.id, other_id))
    print(ledger.list(strategy="mean_reversion", tag="pilot"))

Full CLI reference

Professional command surface. Global flag: --root PATH to choose the ledger directory.

Experiment management

Analysis and validation

Data and reproducibility

Portfolio, live, export, dashboard

Analyze / Monte Carlo input file

{
  "equity": [100, 101.2, 100.8, 102.5],
  "returns": [0.012, -0.004, 0.017],
  "trades": [{"pnl": 15.0, "notional": 10000}],
  "benchmark_returns": [0.001, 0.0, 0.002]
}

Everything QuantVault can do

Built as a thin local layer around your existing research workflow.

Local dashboard

quant-vault dashboard
# http://127.0.0.1:8787/
# /experiment/<id>
# /strategy/<name>

# synthetic end-to-end demo
python examples/demo_everything.py

Architecture

One local core. Three interfaces. Every substantive feature is available from Python and the CLI.

                 QUANTVAULT
                      |
                +-----+-----+
                | Local Core |  experiments · analytics · validation
                |            |  reproducibility · storage · research
                +-----+-----+
          +-----------+-----------+
          |           |           |
     Python API      CLI    Local Dashboard
          +-----------+-----------+
                      |
               Local Storage
            SQLite · files · artifacts

Privacy

This documentation site and the public GitHub repo contain the package only - never your strategies, fills, or market data.