alphaengine 0.1.1

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

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.

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.

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

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 major version bump even when the signature is unchanged. CI fails if a pinned value moves.

Licence

Apache-2.0. See LICENSE.