u/Must_Dragonfruit

The pitch in one line: a free REST API where your bot paper-trades against SPY on a public leaderboard, and the stats engine assumes you overfit.

Why it exists: I'm running a research program on whether a fine-tuned model can beat SPY — preregistered, with placebo controls, the whole apparatus. One model on a leaderboard proves nothing. The more independent strategies on the board, the sharper the null distribution gets. So the arena is free. Your models make the science better. That's the trade.

The data (the part I wish I'd had years ago)

• GET /api/v1/lake/eod/ENRNQ — yes, Enron. ~59,000 US symbols with adjusted daily history back to 2000, and ~35,000 of them are delisted. Most free data quietly deletes the bankruptcies, buyouts and delistings — that's why your backtest looks like genius and your live account doesn't.
• GET /api/v1/lake/members?asof=2015-06-30 — the S&P 500 as it stood that day. Every join and leave since 2000. Stop testing 2015 strategies on 2026's survivors.
• GET /api/v1/bars_bulk?symbols=… — daily OHLCV for 500 tickers in one call, no per-ticker rate limit. The backtest workhorse.
• Options chains with greeks, fundamentals, news + sentiment. Same key.

The leaderboard (where it gets opinionated)

• Create a model, set weights (or option legs), publish. It marks daily against SPY.
• GET /models/{id}/performance returns your equity curve, maxDD — and deflated Sharpe (Bailey & López de Prado) plus Holm-Bonferroni significance across every model on the board. Twenty-eight strategies tried means the bar rises for all twenty-eight. The board taxes itself for multiple testing. Your 2-week hot streak will not survive it, and that's the point.
• Open your model and anyone can pull the full strategy — positions, methodology, the reasoning changelog — and fork it onto their own board.
• Top public models get paper-traded on a real Alpaca paper book. P&L public.

For the bot people

The whole loop is ~15 lines of Python — create model, set positions, read performance. It's built to be driven by an agent: hand the key to Claude Code, say "build and maintain a momentum model," walk away. There's even POST /api/v1/escalate so an autonomous bot can email a human (me) when it's stuck. Rate-limited to 6/hour because bots get dramatic.

Honest limits

EOD/delayed data on the free tier. Pro is free for six months. Paper trading only — it's a lab, not a broker. Yet. It runs on one person's Azure bill, so rate limits exist. Data ships with provenance in every response; the survivorship-free history is EODHD-derived, licensed for research use through the API.

Docs + key: engo.capital/wiki

The delisted tickers are the feature. 35,000 dead companies is the only honest control group anyone has.

The pitch in one line: a free REST API where your bot paper-trades against SPY on a public leaderboard, and the stats engine assumes you overfit.

Why it exists: I'm running a research program on whether a fine-tuned model can beat SPY — preregistered, with placebo controls, the whole apparatus. One model on a leaderboard proves nothing. The more independent strategies on the board, the sharper the null distribution gets. So the arena is free. Your models make the science better. That's the trade.

The data (the part I wish I'd had years ago)

• GET /api/v1/lake/eod/ENRNQ — yes, Enron. ~59,000 US symbols with adjusted daily history back to 2000, and ~35,000 of them are delisted. Most free data quietly deletes the bankruptcies, buyouts and delistings — that's why your backtest looks like genius and your live account doesn't.
• GET /api/v1/lake/members?asof=2015-06-30 — the S&P 500 as it stood that day. Every join and leave since 2000. Stop testing 2015 strategies on 2026's survivors.
• GET /api/v1/bars_bulk?symbols=… — daily OHLCV for 500 tickers in one call, no per-ticker rate limit. The backtest workhorse.
• Options chains with greeks, fundamentals, news + sentiment. Same key.

The leaderboard (where it gets opinionated)

• Create a model, set weights (or option legs), publish. It marks daily against SPY.
• GET /models/{id}/performance returns your equity curve, maxDD — and deflated Sharpe (Bailey & López de Prado) plus Holm-Bonferroni significance across every model on the board. Twenty-eight strategies tried means the bar rises for all twenty-eight. The board taxes itself for multiple testing. Your 2-week hot streak will not survive it, and that's the point.
• Open your model and anyone can pull the full strategy — positions, methodology, the reasoning changelog — and fork it onto their own board.
• Top public models get paper-traded on a real Alpaca paper book. P&L public.

For the bot people

The whole loop is ~15 lines of Python — create model, set positions, read performance. It's built to be driven by an agent: hand the key to Claude Code, say "build and maintain a momentum model," walk away. There's even POST /api/v1/escalate so an autonomous bot can email a human (me) when it's stuck. Rate-limited to 6/hour because bots get dramatic.

Honest limits

EOD/delayed data on the free tier. Pro is free for six months. Paper trading only — it's a lab, not a broker. Yet. It runs on one person's Azure bill, so rate limits exist. Data ships with provenance in every response; the survivorship-free history is EODHD-derived, licensed for research use through the API.

Docs + key: engo.capital/wiki

The delisted tickers are the feature. 35,000 dead companies is the only honest control group anyone has.

my best algorithm has a 1.52 sharpe ratio and it's a daytrader.

daily-rebalanced FAST residual momentum. Beta-residualize each S&P-100 mega-cap vs SPY over 40d.

I tested several hundred different algorithms, 99% barely beat the SPY. I have an LLM researching more that get sent to a quant engine that are backtested to see if they pass the SPY gate. They are all duds.

What's your best performance?

you can now bring your API key and test our stock database for free and make any models that you want. You can also test your own models.

building out the data pipeline for my algo bots:

so far i have:

[ ] **T2 — Prices feeder ():** `data-prices` job pulls 10yr daily bars for the universe → `prices/`

[ ] **T3 — → fundamentals + quotes** into the bucket (gateway-side push). **Unlock Studio "cheap (value)".**

[ ] **T4 — → quotes + account NAV/positions** into the bucket (gateway-side push).

[ ] **T5 — News feeder (RSS → bucket):**→ `news/` + `newsidx`, ticker-tagged; **Then add the Studio "good news" signal** (recency/sentiment per ticker).

- [ ] **T6 — Freshness/status:** a `data-status` view (last update per dataset + coverage) so gaps are visible.

- [ ] **T7 — Options-chain archive (tick-stream parity):** daily EOD snapshot of QQQ/SPY/IWM/DIA (+ more) chains with OI/IV/greeks → `options/` + a `/api/v1/options/history` endpoint. Reuse `arena-quotes` () + `options.py` (BS greeks). Our history starts now (theirs is 2019→). Add live-intraday later.

- [ ] **T8 — Futures tick + L2 capture (tick-stream parity, FORWARD-only):** stream NQ (etc.) trades + Level-2

what am i missing?

Day-trading sleeve: daily fast residual momentum (20d residual return, beta-neutralized, top-12, daily). GATE_PASS in backtest (OOS Sharpe 1.52, +20.6pct excess) but regime-dependent. Paper-trading forward toward the publish gate.

WEIGHTS / HOLDINGS
*

2 rebalances · equity $102,054.47

I call it the Strait of Houzz. Blew up today with a 7% return out of the gate.

Basically, all the stuff we bombed up in the ME has to be rebuilt.

prompt is below or you can play with it on my site:

You are an autonomous quant building a paper-trading model

GOAL: replicate the strategy "Reconstruction Materials" as my own model, then let it forward-paper trade vs SPY.

THESIS / REASONING:

thematic reconstruction supply-chain basket (long aggregates/reserves + cement/SCM + steel + timber substitutes + equipment + EPC, short SPY). ILLUSTRATIVE backtest from 2026-06-01 — the theme was defined today (hindsight), NOT a live track; forward paper accrues from today. A narrative tilt, not a measured edge.

Forward return +6.73%, weekly 1.47.

METHODOLOGY (rule / universe / cadence / params):

House candidate · FW3 flywheel · stage reject. Backtest: CAGR 149.0, Sharpe 4.77, maxDD -1.67, deflated-SR-excess 0.0. Holdings/weights = the universe + target weights below.

TARGET WEIGHTS (ticker: weight, +long / −short, gross ≤ 1.0):

SPY: -1.0000

VMC: 0.0450

MLM: 0.0450

EXP: 0.0450

SUM: 0.0450

CRH: 0.0450

CX: 0.0450

NUE: 0.0450

STLD: 0.0450

MT: 0.0450

GGB: 0.0450

RS: 0.0450

WFG: 0.0450

WY: 0.0450

CAT: 0.0450

DE: 0.0450

TEX: 0.0450

PWR: 0.0450

ACM: 0.0450

J: 0.0450

FLR: 0.0450

OC: 0.0450

BLDR: 0.0450

STEPS:

TO MAKE IT MINE (not a straight copy): change the weights, universe, or cadence above before step 2. A

different book produces a different number — that promotes it from a badged replica to its own ranked variant.

I fine-tuned a foundation model trained on 45GB of personal data and then tested it against my own trades over a 15 year period and against the S&P.

Then I gave it all the tools of a quant fund, The system has the tools of a quant fund, factor models, HRP optimization, deflated Sharpe, probability-of-backtest-overfitting, macro regime overlays, etc..

But the intuitive leap baked by the personal data supersedes the financial data...

Then I put the model on its own vm and gave it API access and its own trading account with a small stake ($10k).

So far, I'm beating the S&P by about 15%. Thought it was interesting and wanted to share with you all. Let me know if you have any questions.

Also, do you have any ideas on what else I can do to make it more effective?

I've fed it all the Edgar market data I could scrape. Do you have any other suggestions?

Hi,

I made a site for the different AI traders I am testing out... basically I've built several personalities for my fine-tuned model.

you can see it here... let me know if you have any other ideas

I fine-tuned it with a 45GB corpus. Running on its own VM with IBKR gateway.

https://preview.redd.it/b8vbetdsd88h1.png?width=1170&format=png&auto=webp&s=5e4e2f82b087f183f937ffc007aa7dc9398c2fec

I fine-tuned a foundation model trained on 45GB of personal data and then tested it against my own trades over a 15 year period and against the S&P.

Then I gave it all the tools of a quant fund, The system has the tools of a quant fund, factor models, HRP optimization, deflated Sharpe, probability-of-backtest-overfitting, macro regime overlays, etc..

But the intuitive leap baked by the personal data supersedes the financial data...

Then I put the model on its own vm and gave it API access and its own trading account with a small stake ($10k).

So far, I'm beating the S&P by about 5%. Thought it was interesting and wanted to share with you all. Let me know if you have any questions.

Also, do you have any ideas on what else I can do to make it more effective?

I've fed it all the Edgar market data I could scrape. Do you have any other suggestions?