PRAMAANA
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Independent quantitative research · since March 2026

Every call is onthe record beforethe market answers.

The model, the size of the edge, the cost of trading it — written down first, then graded by the tape a day later. The rules that decide what may touch capital were frozen in writing before any result existed, and they do not move afterwards.

Open callssealed 04:11 UTC·settled against the tape 24h later
  1. BTCshort83,215.30+0.13% from seal
  2. ETHlong2,669.19+0.12% from seal
  3. SOLlong117.810+0.08% from seal
  4. XRPshort1.488+0.14% from seal
  5. LTClong67.980+0.07% from seal
the market answers in--:--:--

Six gates. Set before the data.

A strategy passes every one or it never trades. Most do not — and the ones that fail are written down with the measurement that killed them.

every candidate entersfew reach the aperture

  1. 01Rationalename who is on the other side of the trade
  2. 02Costsspread, slippage, fees, taxes — no midpoint fills
  3. 03Chancemust beat random entries at identical turnover
  4. 04Indexmust beat buy-and-hold on bootstrap confidence
  5. 05Recencywhatever worked must still work post-2010
  6. 06DeflationSharpe penalised for every attempt ever made

Models

Per-asset gradient-boosted classifiers — eighty-three engineered features, trained across three hundred ninety thousand walk-forward samples — call the next twenty-four hours, every hour, priced at real venue costs. A day later the market settles each call against a fixed barrier. Nothing is backfilled; nothing is quietly withdrawn.

1000 calls settled · hourly · out-of-sample by construction

Funding

A direction-neutral engine watches what the derivatives market pays to hold a hedged position — hourly, across two venues. It deploys only above a rate fixed in advance, and holds cash, deliberately, until the market pays it.

The full ledger is inside.

Two hundred numbered entries since March, every verdict with the measurement behind it, hourly calls with their settlements, and the paper documenting the stack — federated learning with differential privacy and ZK-verified inference.

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