Portfolio Maximizer

Autonomous trading infrastructure · Licensed to regulated institutions

Our own audit deleted
104.2% of our reported profit.
We are leading with that.

The trading book showed +$642.51 at a profit factor of 2.91. Our forensic tooling traced the entire gain to a data defect on a single instrument, fenced those records, and restated the published result to −$27.17.

Nobody made us do that. No allocator asked. The system found it, flagged it, and the number on this page is the restated one. That capability — not the P&L — is what this round is buying.

Portfolio Maximizer is an autonomous trading engine: it decides, sizes and exits without a human in the loop, on a fixed schedule, under mechanical risk limits. We are not claiming a proven edge. We are claiming a running machine that can establish whether one exists — and that has already demonstrated it will report against its own interest.

The asset

Eleven months of engineering that most trading operations never build.

Most systematic strategies are a backtest in a notebook and a broker API key. What is expensive, slow, and rarely built is everything between: the ingestion and validation layer, the reconciliation of intended state against actual state, and above all the evidence layer that decides which records are admissible as proof of anything.

That layer is the reason this page reports −$27.17 rather than +$642.51. It is a database-enforced integrity apparatus that separates opening from closing legs, links every close to its originating entry, quarantines synthetic and diagnostic records, and fences records whose fills cannot be shown to be clean.

It is also the part a competitor cannot copy from a paper, and the part an allocator cannot verify by looking at a return series. It has to be built and it has to be trusted, which is a slower thing to earn.

All figures in this section counted directly from the repository and production database on 20 August 2026.

Counted, not estimated

  • 1,046 commits since 30 Sep 2025
  • 64,390 lines of core Python, nine packages
  • 119,439 lines of test code
  • 5,585 automated tests across 441 files
  • 251 documentation files
  • 46 versioned configuration files
  • 4 Docker targets, incl. GPU compose

Test code outweighs core application code 1.9 to 1. That ratio is the engineering claim, and it is the one that is fully verifiable today.

The evidence

The restatement, in full.

Between 23 January 2026 and 11 August 2026 the system closed 32 live round-trips on $25,000 of the founder's own capital. 9 of those closes are fenced. Here is what the book looks like before and after that fence, side by side.

Before the fence

All closed round-trips

Round-trips
32
Net P&L
+$642.51
Win rate
37.5%
Profit factor
2.906
Payoff ratio
4.843×

This is the number a less careful operation would have put on the front page. It is not wrong arithmetic — it is the correct sum over the wrong set of rows.

What we removed

9 closes on one instrument

Instrument
GC=F
Closes fenced
9
P&L removed
+$669.68
Share of gross profit
104.2%
Defect
stale-fill

The entry path could name a price bar already superseded at write time, subsidising the fill. Traced, quantified at +$608.67 of pure subsidy, fenced under a signed migration, and closed at the write path so it cannot recur.

What we publish

Admissible evidence cohort

Round-trips
23
Net P&L
−$27.17
Win rate
21.7%
Profit factor
0.831
Payoff ratio
2.992×

Essentially flat. On 21 observations this is neither evidence of an edge nor evidence against one — and saying so precisely is the whole point of the section below.

The one number that did survive: the asymmetry works

On the fenced-clean cohort the average win is $26.75 against an average loss of $8.94 — a payoff ratio of 2.99×. The mechanical design does exactly what it was built to do: losses are capped, winners run. That sets breakeven at a win rate of 25.0%, and the observed rate is 21.7% — short by 1.6 percentage points on 23 trades. The structure is in place; the hit rate is the open question.

What sample size would settle this?

This is the question the round funds, so we made it interactive rather than asserting an answer. Set a hypothesised true win rate and a sample size; the panel recomputes the 95% Wilson interval and an exact one-sided binomial test against the 25.0% breakeven, in your browser. The arithmetic is in assets/js/edge-model.js.

23.8%
21

Defaults are the live position: 23 closes at an observed 21.7%. Leaving the rate where it is and dragging the sample shows the interval tightening around a sub-breakeven value — that is the honest depiction, and we have not rigged the default to flatter us. Raising the rate to 35% shows the sample that would establish an edge if one exists: roughly 136 closes. At 40%, roughly 60. The payoff ratio is itself estimated from these 21 trades and carries its own error, so the intervals shown are, if anything, optimistic.

What is independently established

Model quality clears its gate: the lift check returns PASS with decision KEEP, demonstrated during the holding period. Signal-to-trade attribution passes — every close links to the forecast that opened it. Outcome coverage is 34/34.

What is not yet established

The overall production gate reads FAIL. Operational SLOs are in breach: cycle success 89.7% against a 95% target, and a 13-day run of aborted cycles. Both are disclosed here because both are real, and both are engineering work rather than open questions.

What we will not estimate

Sharpe ratio, maximum drawdown and return volatility need a longer series than 23 closes supports. Publishing them now would be false precision. They come from the Phase 1 record, computed on the admissible cohort, or not at all.

The engine

Extreme Value Theory decides when the asymmetry is available.

The strategy does not try to predict direction more often than not. It tries to be paid more when right than it gives back when wrong. Expected value is then a two-parameter question — win rate p and payoff ratio b:

E[R] = p·b − (1 − p)

Breakeven falls at p* = 1/(1+b). At the observed b = 2.992 that is 25.0% — the system can be wrong three times in four and remain solvent, provided b holds. Across the clean cohort, it has.

A barbell only pays if large upside moves actually occur, so the entry gate is a distributional test rather than a view. Fitting a Generalised Pareto Distribution to threshold exceedances yields a shape parameter ξ; when ξ > 0.25 the upper tail is heavy enough that outsized moves are plausible rather than assumed. The same family underpins flood modelling and catastrophe reinsurance, where the question is identical: how far out does the tail reach?

Position size follows a Hill-adaptive Kelly fraction, scaled well below full Kelly because parameter uncertainty at this sample size makes full Kelly reckless.

Four-model ensemble, weighted by regime

  • GARCH Time-varying volatility, used to calibrate stops and targets.
  • SAMoSSA Spectral level forecasting; carries the dominant ensemble weight in optimised regimes.
  • SARIMAX State-space baseline supplying a signal orthogonal to the spectral models.
  • MSSA-RL Reinforcement layer optimising direction-weighted return.

Weights are assigned from each model's measured track record in the currently detected regime, not fixed in advance. The ensemble's advantage over its best single member is the quantity the lift gate tests — and currently passes.

  1. 01 Ingest Nightly OHLCV collection, four-stage validation, Parquet storage.
  2. 02 Screen Tail index computed per instrument. Entry gated on ξ > 0.25.
  3. 03 Forecast Four models produce independent signals, combined by regime weight.
  4. 04 Size Hill-adaptive Kelly fraction sets capital at risk per position.
  5. 05 Prove Fill freshness enforced at write time; every close linked, audited, admissible or fenced.

The autonomy

Unattended means measured, not asserted.

The engine wakes on a fixed schedule, ingests, forecasts, sizes, places and exits without a human in the loop. The honest way to describe that is not "runs 24/7" — it is to publish the availability, including the days it did not run. Here is the last 14 trading days, counted from the cycle ledger.

65% Availability Credits correct pre-cycle refusals — the safety chain working
65% Clean cycle rate Ran and completed with no fault of any kind
82 Cycles run Of 110 scheduled slots
28 Never fired The defect class we are repairing, not a market decision

This is a breach of our own SLO, and we are stating it first

Our governance sets a 95% cycle-success target. We are below it. One day in the window — 2026-08-13 — the engine did not fire at all, and on another every cycle that started failed. Availability is a scheduler and host-uptime problem, not a strategy problem, and it is fully inside our control to fix. We publish it because an allocator who discovers a 63% figure themselves will reasonably ask what else was rounded up.

Refusals are not failures

Some cycles correctly declined to trade — stale data, a failed integrity pre-check, a regime gate. Those are counted separately and credited to availability. An engine that refuses on bad input is behaving exactly as designed; conflating that with downtime would flatter neither number.

The gap we found in our own alerting

One refusal in the window generated no delivered alert. The engine did the right thing and nobody was told. Detection without notification is an unmonitored system wearing the costume of a monitored one, and closing that class is ahead of any feature work.

Why the denominator is trustworthy

The expected-slot count is derived from the registered scheduler triggers, not assumed. It previously fell back to a hardcoded guess under one invocation path; that was found and fixed, and the report now names its source on every run so a silent fallback cannot recur.

The market

What a Nigerian allocator actually gives up.

Any pitch to Nigerian capital that quotes 28% inflation and a 31% hurdle is working from stale data, and a Lagos allocator will know it in the first minute. Headline CPI printed 15.91% in June 2026, down from 15.93% in May, after the CPI rebasing.

The binding constraint is not inflation. It is opportunity cost. The CBN held the Monetary Policy Rate at 26.5% on 21 July 2026, its third hold of the year. Naira sovereign instruments therefore pay a high nominal yield — roughly 10.6 points of real return — at sovereign risk. That is the genuine alternative, and it is a demanding one.

We will also state the part that cuts against us. The naira has appreciated about 4.3% against the dollar so far in 2026 on the back of CBN FX reform. Dollar exposure has been a drag this year, not a windfall. Any pitch built on "the naira always falls" is describing 2023, not today.

So we do not claim to beat naira T-bills, and this is not a substitute for them. The honest case is narrower and, we think, more durable: an uncorrelated, USD-denominated, systematic satellite position against a book that is otherwise concentrated in a single sovereign and a single currency — sized as a satellite, not a core.

Reference rates, verified August 2026

CBN monetary policy rate held 21 Jul 2026
26.50%
Nigeria headline CPI Jun 2026, NBS
15.91%
Implied real naira yield
≈10.6%
Naira vs USD YTD 2026
+4.33%

We are deliberately not plotting a strategy return against these. On 21 admissible closes any annualised figure we drew would be an artefact of the sample, and putting a bar next to a 26.5% sovereign yield would imply a comparison the evidence does not support yet.

The business

The software is the product. We are a vendor, not a manager.

A seed investment here is not an allocation to a strategy — if it were, the 26.5% sovereign yield would win today and we would say so. It is equity in infrastructure whose primary revenue path is licensing, not performance fees.

Nigerian asset managers, pension fund administrators and family offices run large books with very little systematic infrastructure. What they lack is not a view on the market; it is exactly what has been built here — validated data ingestion, regime-aware forecasting, mechanical risk sizing, and an audit layer that can survive a regulator asking how a number was produced.

That last capability is the wedge. The restatement documented above is the demonstration: a system that fences its own profit when the fills are not clean is precisely what a compliance function needs and cannot buy off the shelf in this market.

We are a software vendor and nothing else. We do not hold client money, do not execute on a client's behalf, and do not take a share of any licensee's trading result. Licence fees are flat and recurring, priced on the software. The licensed institution holds the accounts, executes, and carries the regulatory obligation. That boundary is deliberate and we will not cross it.

The proprietary book continues in parallel, on the founder's own capital, because it is the reference implementation and the source of the evidence. It is not the business model.

Revenue paths, in priority order

1 Licensing the signal and audit layer to Nigerian asset managers and PFAs. Recurring, capital-light, not contingent on strategy returns.
2 Infrastructure-as-a-service — the ingestion, validation and integrity stack sold independently of the forecasting models.
3 Enterprise licensing and support retainers — multi-desk deployments, calibration and integration support. Flat, recurring, and priced on the software, never on the licensee’s trading outcome.

Ordering matters. A business whose only revenue requires the edge to be proven is a bet on one uncertain event. This one earns from the infrastructure while the statistical question resolves.

How access is granted

The source is not public and not open source. The repository is private, and access is granted per licensee against an issued token — not by adding accounts to an organisation and hoping the list stays current.

  1. 01 Request Write to the founder. Evaluation access and commercial terms are agreed in writing before anything is issued.
  2. 02 Licence A named licence agreement is executed. Tokens are issued to a person or entity, never shared or transferable.
  3. 03 Token A scoped, time-bound token is issued directly by the founder. It is the only route to the private repository.
  4. 04 Rotate Tokens expire and are reissued on a fixed cycle. A lapsed licence simply does not receive the next one.

Rotation is doing two jobs. Commercially, it is how a licence stays a licence rather than a one-time handover of the asset. Operationally, it means revocation is the default state rather than an action someone has to remember to take — an access list that is never pruned is the same class of defect as a monitor that never fires, and this project has already paid for that lesson once.

The roadmap

Milestones defined by statistical thresholds, not calendar dates.

Complete

Machine and microscope

Engine live on own capital since January. Integrity layer built, and proven by catching a defect worth 102% of reported profit.

Months 1–3

Operational hardening

Clear the SLO breach: cycle success to 95%+, eliminate multi-day abort runs. Close the production gate. Unglamorous and prerequisite.

Months 3–9

Statistical adequacy

Accumulate to 60–136 admissible closes — the range that resolves the edge question at 80% power. Publish Sharpe and drawdown, or publish the null.

Months 6–12

First licence

Package the audit and ingestion layer for external deployment. One paying Nigerian institutional customer is the milestone, not a revenue target.

Year 2+

Institutional licensing

First licensed deployments inside regulated brokers and investment houses, who hold the accounts and carry the regulatory obligation.

The founder

Linda Bestman Enock

Project founder. Portfolio Maximizer is built and operated by an in-house quantitative development team, with working expertise in time series modelling, GARCH, Extreme Value Theory and portfolio optimisation.

The system has run on founder capital since inception. Every trade in the record above was taken with the founder's own funds, and every defect described on this page was found and disclosed internally rather than by a counterparty or an allocator.

Team composition, technical leadership and detailed biographies are provided during diligence under NDA.

How to verify any of this

Repository access

Granted on request under NDA. Commit history, test suite and the full migration record are inspectable.

Raw trade records

The database, both cohorts, and the exclusion flags that separate them. The restatement can be reproduced end to end.

The defect file

The diagnostic that found the stale-fill subsidy, its quantification, and the write-path guard that closed it.

Full memorandum, audit reports and raw trade records available under NDA.

Linda Bestman Enock · Project Founder

ezekwu77@gmail.com · lindaenock123@gmail.com

Repository access is by issued token only — request one directly, and see How access is granted. Tokens are licensed, scoped to a named party, and rotated on a fixed cycle.

Telephone provided on request. Diligence questions on methodology are welcome and will be answered with the underlying data. If you find something we have overstated, we want to know — that is the same process that produced the restatement above.