01
At Closing
Your builder seeds a $30,000 account in our AI-powered quant hedge fund — a tax-neutral purchase-price adjustment, not a discount that disappears the moment you sign.
Falsification, in the open
Five strategies survived eighty-two pre-registered attempts to kill them.
The survivors are audited daily, verified forward on unseen data, and published with descriptive grades. Everything that failed is retired in public — with the record kept.
Live · Alpha Leagues
Season 0 opens Tuesday, July 14.
Season 0 · soft launch · Jul 14 → Jul 21, rules sealed before the season (H-ALPHA-LEAGUES-SEASON-1 + reframe addendum).
A fantasy league where the players are real, audited trading strategies. The rules are sealed and pre-registered, and anyone can recompute the standings from the published data files. Season 0 is our live shakedown — the promoted Season 1 opens with a fresh pre-registration, new features, and the full player catalog. Season 0 results stay on the permanent record.
01
Pick a division
Five to choose from, each with its own roster size and salary cap.
02
Draft under the cap
The cap makes you choose — the best three don't fit.
03
Save your team
It gets a SHA-256 seal and your score accrues from your first session.
Alpha Leagues is a record-only fantasy game over real, audited trading strategies — game mechanics, not investment advice.
The verified book
What survived, on the record.
Descriptive Charter grade cards, read at runtime from the sealed-ledger bake. Not a ranking, not allocation advice — every line is certified-not-deployed and tracked record-only, warts-and-all.
| Strategy | Grade | Status |
|---|---|---|
| Loading the verified book from the sealed ledgers… | ||
34 strategies tested, retired, and displayed with cause — that's why the active roster means something. Hall of Retired →
The Quant Club
A new asset class — built into the home.
A members-only AI trading lounge and hedge fund ecosystem inside master-planned communities. Residents access live AI agents, strategy sessions, and community alpha discussions. Dedicated garage infrastructure powers the fund 24/7.
This is more than an amenity — it's a new asset class.
How it works
From closing day to compounding.
Four steps. The first happens before you move in. The last continues for as long as you own the home.
02
The Lounge
Access your community's members-only trading floor: live AI strategy sessions, market intelligence, and community alpha discussions in a Starbucks-meets-Wall-Street setting.
03
AI Infrastructure
Garage-based compute clusters run our agent swarms 24/7 — institutional-grade execution, residentially delivered. Each community is its own micro-fund operations hub.
04
Compound Freedom
Watch your wealth grow while you live elevated. Real AI alpha that can help cover your mortgage — turning a home from a holding into a performance asset.
For Builders
Stop competing on price. Start competing on performance.
Deep discounts hurt your margin and reset the comp. A $30,000 seed account hands buyers a story that doesn't depreciate — and gives you recurring revenue from the fund itself.
- Replace deep discounts with a high-value $30k seed account
- Faster inventory velocity + higher closing rates
- New recurring revenue from the 2/20 hedge fund share — zero upfront cost
- Powerful differentiation in a buyer-saturated market
- Turnkey amenity: lounge build-out + AI infrastructure managed end-to-end
For Residents
Your home should compound wealth, not just hold value.
A discount evaporates the moment you sign. A $30,000 seed account in a working AI hedge fund keeps working — for the rest of the time you own the home.
- $30,000 performance seed account funded by your builder at closing
- On-site luxury trading lounge — community alpha, in person
- Real AI agents that can help pay your mortgage, not theory
- A community of like-minded homeowners building wealth together
- Future RWA tokenization for liquidity when you want it
The AI Edge
Institutional-grade AI. Residential delivery.
The math that runs sovereign funds, packaged for the community garage. Discipline first, returns second.
01
Falsification first
Every strategy faces a pre-registered, sealed test designed to kill it. Most die — and stay on display in the Hall of Retired. What survives is the residue, not the pitch.
02
Risk-first design
Measured against decades of regime data — not last quarter. Cost tiers are frozen before the test runs, beta-in-disguise is screened out, and lucky best-of-N sweeps are deflated away.
03
Institutional discipline
A measured, sealed, run-forward process applied to a residential-scale product — SHA-256 pre-registration, open-source verification, public corrections when we find our own defects.
04
Garage-grade infra
Dedicated on-prem compute per community. Sovereign, low-latency, scalable across every Quant Club site.
How we research
Eighty-two sealed tests. Five passes. Everything else on display.
Every candidate strategy faces a pre-registered falsification test — sealed before it runs, one attempt, frozen cost tiers. Most are refuted, and the refutations stay public. That is why the survivors mean something.
01
SOURCE
claims
Books, papers, championship strategies, practitioner claims — anything testable.
02
PRE-REGISTER
the test
The full test is sealed (SHA-256 + OpenTimestamps) before it runs. One attempt.
03
TEST
to destruction
Walk-forward, permutation nulls, matched-random controls, beta screens, deflation.
04
PUBLISH
the verdict
Refuted stays refuted — and stays on display. Passes get a descriptive grade card.
05
TRACK
forward, record-only
Survivors are watched on unseen data — no capital, no orders, warts-and-all.
82
sealed hypothesis tests
5
clean Charter passes
34
retired & on display
0/2/5
bps cost tiers, frozen
Sealed before it runs · one attempt · refutations stay public
Methodology
How we screen every candidate — starting with the data.
Verification starts with the data. Before a candidate is screened, its bars pass a build-time integrity gate and are content-hashed into the seal — because a single bad column can fake an edge no statistic would catch. Then the statistical stages: walk-forward and CPCV ask whether the edge is consistent over time; bar and action permutation ask whether it's real — not survivor bias, not market drift; bootstrap CI asks whether we can distinguish it from zero. Clear the data gate and every stage, and you're deployable. Fail one and you stay in the lab.
00Data layer
Data Integrity
Is the data itself trustworthy — before we test anything?
A backtest is only as honest as its data: one mislabeled column or a single leaked future bar can manufacture an edge that was never there. So before a candidate is ever screened, its bars run through a Medallion pipeline (Bronze → Silver → Gold) built in dbt, where a build-time gate enforces OHLC sanity (high ≥ open/close/low, low ≤ the rest, no zero or negative prices), monotonic timestamps, and no look-ahead. Any violation fails the build — the Gold layer is never produced. The clean dataset is then content-hashed and that SHA is bound into the sealed pre-registration, so the exact data behind every result is provable forever.
Pass rule
Build-time OHLC + look-ahead gate passes AND dataset SHA-256 bound to the seal — else the result is labelled DATA_INVALID / DATA_UNVERIFIED and never scored.
What failure looks like
“A 1-minute file with high < low on a stretch of bars would sail through a naive backtest and print a gorgeous Sharpe. Here the build fails on the bad rows and the candidate never reaches a statistical gate.”
01Gate
Walk-Forward
Does the agent's edge hold across time?
We chop the out-of-sample window into 4 non-overlapping segments. Each segment is backtested independently. We check both the median Sharpe across segments AND the consistency (% of segments with positive Sharpe).
Pass rule
Median Sharpe ≥ 0.5 AND consistency ≥ 60%
What failure looks like
“Big winner in 2023, big loser in 2024 → fails consistency. Edge that requires one specific regime isn't deployment-grade.”
02Gate
CPCV
Is the edge robust to data ordering?
Combinatorial Purged Cross-Validation — 10 partitions, 2-at-a-time held out, 45 folds total. Purges adjacent bars to prevent leak. Tests whether removing any combination of segments breaks the strategy.
Pass rule
Min Sharpe > -0.5 AND IQR < 1.0 across the 45 folds
What failure looks like
“Strategy works on a continuous timeline but blows up when you remove one specific 6-month chunk → catastrophic regime dependency.”
03Gate
Bar Permutation
Is the Sharpe better than random bar order?
We shuffle the BAR ORDER 200 times (block-permutation, preserves autocorrelation). For each shuffle, the agent runs again and we record its Sharpe. The actual Sharpe must be in the top tail of the distribution.
Pass rule
p-value < 0.05 (actual Sharpe at or above the 95th percentile of shuffled Sharpes)
What failure looks like
“A 'lucky' agent that scored high on the real data but matches the distribution of randomly-shuffled-data Sharpes → no real edge, just survivor bias.”
04Gate
Action Permutation
Does the agent's TIMING actually matter?
We keep the bars, but shuffle the agent's POSITION sequence 200 times. If the shuffled-actions Sharpe matches the real Sharpe, the agent isn't making good decisions — it's riding market drift (buy-and-hold in disguise).
Pass rule
p-value < 0.05 (actual Sharpe > 95th percentile of shuffled-action Sharpes)
What failure looks like
“An agent that's always long when the market is up → shuffling its actions still produces a similar Sharpe. The 'strategy' is just market beta with extra steps.”
05Confidence check
Bootstrap CI
Is the Sharpe statistically distinguishable from zero?
Block-bootstrap the trade returns 500 times to build a 95% confidence interval around the actual Sharpe. We require the LOWER bound of that CI to be strictly positive.
Pass rule
Lower 95% CI bound > 0
What failure looks like
“Sharpe of +1.2 looks great, but with a 95% CI of [-0.4, +2.8] we can't rule out that the true Sharpe is zero or negative.”
Why a data gate, then five stages? Each one catches a different failure mode the others miss — and it starts at the data, because the cleanest statistics in the world can't rescue a contaminated dataset. A single-gate filter would let through agents that look great on one axis but fail on another. The gate count next to every candidate is the only number that matters for deployment readiness.
Our Story
From dream home to performance asset.
For most families, buying a home is the largest financial decision they ever make — and the moment that decision closes, the discounts evaporate. The walk-in pantry stays. The wealth curve doesn't.
Lifestyle Alpha was built to change that math. By embedding a working AI hedge fund into the home itself — funded by the builder at closing — the dream home becomes a wealth-compounding asset on day one. The lounge makes it visible; the agents make it real.
We bring institutional discipline: every strategy is tested to destruction under a sealed, pre-registered Charter before a dollar is ever considered, and the survivors are tracked forward in the open — record-only, warts-and-all. This isn't a course or a community Slack. It's an amenity built on research you can independently verify.
Founder note.
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