alphaengine 0.2.0

Validated research tooling for investment strategies: deflation, overfitting detection, and honest trial counts.

AlphaEngine

Validated research tooling for investment strategies. Run a parameter search, get back the shape of the result and an honest read on whether it survives the number of things you tried.

pip install alphaengine

from alphaengine import sweep

r = sweep(backtest_fn, {"fast": [5, 10, 20], "slow": [50, 100, 200]}, data=prices)

r.surface()    # is the result a broad plateau or a single lucky configuration?
r.verdict()    # deflated for the 9 trials that were actually run
r.save()       # study.json, on your disk

Everything above runs offline, with no account and no key.

Try it without writing anything

The repo ships a runnable project module, so you can see the whole offline half work before deciding whether any of this is for you:

git clone https://github.com/quantOSC/alphaengine && cd alphaengine
pip install -e .
python -m examples.momentum

trials     9  (derived_from_grid)
verdict    marginal
surface    ridge
dsr        0.6352

That is a moving-average crossover on synthetic prices from a fixed seed — no download, no data licence, same numbers on every machine. It is a demonstration of the wiring, not a strategy. A crossover on a random walk has no edge, and the verdict says so rather than flattering it. That is the example working, not failing.

Writing your own backtest_fn

Two rules, both easy to get wrong the first time, and the reason the example above exists to copy:

Return a bare 1-D return series. Not a dict, not a stats object — the per-period returns themselves. sweep does np.asarray(list(raw)), so a dict of results iterates its keys and fails on the first string.

Return the same length for every combination. PBO splits the trial matrix into time blocks and compares configurations within each block, which only means anything if they line up in time. Ragged output is refused rather than truncated, because silently trimming produces a confident number over series that do not correspond. In practice: pick a warm-up long enough for the slowest window in your grid and start every configuration there.

WARMUP = 200   # covers the slowest `slow` in the grid

def backtest_fn(*, data, fast, slow):
    close = data["close"]
    return [
        (close[i + 1] - close[i]) / close[i] * (1 if sma(close, fast, i) > sma(close, slow, i) else 0)
        for i in range(WARMUP, len(close) - 1)
    ]

data is whatever you want it to be — a DataFrame, a dict of series, an array. The package never inspects it and it never leaves your machine.

What it does

Runs your parameter grid. sweep() calls your backtest function once per combination. It does not backtest anything itself, so the engine you already trust stays the engine you trust.

Counts the trials for you. The statistics that correct a Sharpe ratio for multiple testing need to know how many variants were tested. That number is almost never recorded, because nobody counts what they discarded. Running the grid makes it len(grid), so it never has to be asked for or asserted.

Refuses to flatter an unrecorded count. Since 0.2.0, omitting n_trials means not_recorded — not 1. The trial count comes back null, n_trials_source travels beside it, and a verdict of edge is unreachable without a recorded denominator. A deflated Sharpe is a ratio; deflating by a denominator nobody wrote down does not produce a weaker claim, it produces a claim about nothing.

Shows you the neighbourhood. The output is whether your result sits on a broad plateau or a knife edge, and where the robust region is centred. A single spike surrounded by failures is a result fitted to its own parameters.

Produces a portable study. A JSON artifact holding what was tried, what came back, and a content hash of the data it ran on. Readable in a text editor, diffable, and versioned so it still parses in two years.

Running a workflow from the terminal

The offline half above is complete on its own. A workflow adds a sequence — what to run, in what order, and what stops the run — and that sequence lives on a QuantOS workflow server, so this part needs an account.

alphaengine version                                        # no account needed
alphaengine workflows                                      # what your workspace offers
alphaengine run validate_study --project examples.momentum

The run narrates itself, because a loop you cannot watch is a loop you cannot trust. The server names an op, this machine executes it and hands back figures:

validate_study · <your workflow server>
  server →  compute.sweep
  local  ·  done
  server →  compute.deflated_sharpe
  local  ·  done
  server →  Stop: the surface is a ridge, not a plateau

--project names an ordinary module of yours exposing data and, if the workflow sweeps, backtest_fn. Authenticate with a key from the portal:

export QUANTOS_API_KEY=ae_live_...     # QUANTOS_API_URL for self-hosted or VPC

A stop exits 0. "This did not clear the bar" is the system working, not a broken build — a non-zero exit there would make every CI pipeline treat an honest refusal as a failure, which is exactly the pressure that gets honesty controls switched off. Only a run that could not execute a step exits non-zero.

Install this into the venv you do research in — not with pipx or uv tool install. The compute steps execute in-process against your own DataFrames, so tool isolation, which is normally the right way to install a CLI, is the one thing that cannot work here. You cannot have isolation and in-process data access, and your data not moving is the point.

No LLM dependency and no key field. AgentDriver takes a callable, so you bring your own model by passing a function. There is nowhere in this tool to put a model key, ours or yours, and that is how "runs under your own account" is satisfied structurally rather than promised.

What is in it

Module	Contents
alphaengine.core	deflated Sharpe, PSR, PBO via CSCV, CPCV, minimum track record length, performance and risk statistics
alphaengine.sweep	the grid runner and the sensitivity surface
alphaengine.study	the study artifact and its schema
alphaengine.client	the workflow client and the step executor
alphaengine.cli	the alphaengine terminal entry point

Two runtime dependencies, numpy and scipy, both already present in a typical research environment. import alphaengine makes no network call and needs no account. Factor decomposition and cointegration testing need statsmodels and are available as pip install 'alphaengine[factors]'.

Getting a study to somebody else

save() writes to your disk and needs no account. When the work has to reach the PM who will act on it, report() sends the study — and only the study.

import os
from alphaengine import Study, sweep

os.environ["QUANTOS_API_KEY"] = "ae_live_..."   # created in the portal

r = sweep(backtest_fn, grid, data=prices)
r.save()                                        # yours, on your disk, always

Study.from_sweep(r, label="momentum, 9 configs").report()

What crosses is an explicit allowlist: the trial count and how it was obtained, a content hash of the data, the verdict, the shape of the neighbourhood, the performance figures. Your returns, your prices and your parameter grid stay on the machine, and a guard keyed on length rather than field name refuses to send anything series-shaped whatever it is called.

Reporting is the only part of this package that touches a network, so it is the only part that is not imported until you call it. import alphaengine still makes no network call.

Where this sits in QuantOS

AlphaEngine is the open research layer of the QuantOS platform. It is the piece that runs on your machine, against your data, and it is complete on its own: everything above works offline and forever, at no cost.

The QuantOS platform builds on it. Studies produced here can be persisted to a firm's record, referenced when an idea becomes a position, and assembled into the reports that go to an investment committee or an allocator. The library computes; the platform remembers and reports. The two halves are separated so that the part touching your data has no reason to phone home.

The methods

Everything in core comes from the published literature. Nothing here is a proprietary formula, which is deliberate: a referee whose reasoning you cannot inspect is not a referee.

Deflated Sharpe Ratio, Probabilistic Sharpe Ratio, minimum track record length Bailey, D. H., and López de Prado, M. (2012). "The Sharpe Ratio Efficient Frontier." Journal of Risk 15(2), 3 to 44. Bailey, D. H., and López de Prado, M. (2014). "The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality." Journal of Portfolio Management 40(5), 94 to 107.

Probability of Backtest Overfitting via CSCV Bailey, D. H., Borwein, J., López de Prado, M., and Zhu, Q. J. (2017). "The Probability of Backtest Overfitting." Journal of Computational Finance 20(4), 39 to 69.

Combinatorial purged cross-validation López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley, chapters 7 and 12.

Multiple testing in asset pricing Harvey, C. R., Liu, Y., and Zhu, H. (2016). "... and the Cross-Section of Expected Returns." Review of Financial Studies 29(1), 5 to 68. Harvey, C. R., and Liu, Y. (2015). "Backtesting." Journal of Portfolio Management 42(1), 13 to 28.

Downside deviation Sortino, F. A., and Price, L. N. (1994). "Performance Measurement in a Downside Risk Framework." Journal of Investing 3(3), 59 to 64.

Factor regression standard errors (in the factors extra) Newey, W. K., and West, K. D. (1987). "A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix." Econometrica 55(3), 703 to 708.

Unit root testing for cointegration (in the factors extra) Dickey, D. A., and Fuller, W. A. (1979). "Distribution of the Estimators for Autoregressive Time Series with a Unit Root." Journal of the American Statistical Association 74(366), 427 to 431.

Reproducibility

The values these functions return are treated as a public contract. A study written today has to reproduce in two years, so a change to a computed value is a breaking change requiring a version bump even when the signature is unchanged. While the leading digit is 0 the minor position carries that rule — 0.1 → 0.2 is what a changed figure costs — so every 0.2.x release produces identical numbers. CI fails if a pinned value moves.

Development

python -m venv .venv && .venv/bin/pip install -e '.[factors]' pytest ruff
.venv/bin/python -m pytest -q
.venv/bin/python -m ruff check .

Licence

Apache-2.0. See LICENSE.