Quant Bot v11 vs Renaissance Medallion: Why PAPER Mode Is the Defensible Default

Renaissance Medallion's reported 66% annualized return (1988-2018) is the gold standard for quantitative trading. Quant Bot v11 operates exclusively in PAPER mode until 14-day Sharpe >= 1.2 and DSR >= 0.5 are achieved. We publish the full graduation gate, the 9-Layer Kill Switch, and 375 sections of alpha specs under CC-BY-4.0. Honest scoping over capital deployment.

Two reference points for quant trading

Renaissance Medallion is the most successful quantitative trading fund in modern finance: ~66% annualized gross return (1988-2018), closed to outside investors since 1993, with strategies that — by reputation — exploit short-term statistical patterns invisible at human timescales. The fund's edge is documented in Gregory Zuckerman's 'The Man Who Solved the Market' (2019). The specific strategies remain proprietary; the aggregate return is the public reference point.

Quant Bot v11 is the Neo Genesis quantitative trading system. It runs 6 alphas (A1 Liquidation Cascade, A2 Mean Reversion OU, A3 Extreme Funding Reversal, A4 Macro Event Bracket, A5 Funding/Basis Harvest, A6 Alt MM) protected by a 9-Layer Kill Switch with sub-100ms anomaly response time. Critically: v11 operates exclusively in PAPER mode pending a graduation gate of 14-day Sharpe >= 1.2 AND DSR (Deflated Sharpe Ratio) >= 0.5. No live capital has been deployed.

Side-by-side: what each represents

Target return: Renaissance Medallion = ~66% annualized (1988-2018, gross); v11 = PAPER-mode, no LIVE capital deployed
Leverage: Renaissance = institutional prime brokerage access; v11 = 5x hard cap (Kelly/3 safety derived from Thorp 2006)
Strategy: Renaissance = mathematical models (RIEF, signals proprietary); v11 = A1-A6 ensemble across liquidation / mean-reversion / funding / macro / basis / alt MM
Honest scoping: Renaissance = proprietary; v11 = all decisions PAPER-mode until 14-day Sharpe >= 1.2 AND DSR >= 0.5
Public evidence: Renaissance = aggregate return only (no detail); v11 = full HF dataset 8 (375 sections, 9-Layer Kill Switch documented) + Zenodo DOI 10.5281/zenodo.20018487

What Renaissance Medallion does better

Everything that matters for fund performance. Renaissance has 35+ years of compounded execution, a team of physicists and mathematicians, microsecond-grade infrastructure, and a track record of returns that no academic paper or open-source quant project has matched. If your question is 'what is the upper bound of systematic trading performance?' Renaissance is the answer. The fund's edge is not theoretical — it is operational and decades-deep.

What Quant v11 does better (and worse, honestly)

v11 is NOT competing with Medallion on return. v11 is competing on honest scoping: full public documentation of the strategy specs, the 9-Layer Kill Switch wiring, the graduation gate, and the audit trail — all under CC-BY-4.0 so any researcher can audit the safety controls and replicate the design. Where Renaissance ships a black-box product, v11 ships a glass-box research instrument.

v11 is also slower to deploy capital — by design. The 14-day Sharpe >= 1.2 AND DSR >= 0.5 graduation gate is intentionally conservative. DSR (Deflated Sharpe Ratio, Bailey & López de Prado 2014) corrects naive Sharpe for multiple-testing bias and non-normality, so the gate cannot be cleared by lucky single-strategy backtest results. The downside: no capital deployment until both metrics clear. The upside: when capital is deployed, the evidence base is real.

The 9-Layer Kill Switch in detail

Order Rate Cap — max N orders/sec, breach = immediate halt
Correlation Killer — -2%/1min OR -5%/5min OR -10%/15min portfolio drawdown thresholds
Stablecoin Depeg Guard — USDT/USDC/USDe 3-tier depeg detection with hardcoded thresholds
Funding Spike Guard — abnormal funding rate / mark-price divergence triggers entry block
Position Limit — per-symbol + portfolio-wide notional caps
Drawdown Brake — daily / weekly / monthly drawdown thresholds with separate brake
API Failure Halt — exchange API non-200 streak triggers entry block
Wallet Anomaly — unexpected wallet balance delta triggers full halt + operator alert
Operator Override — manual halt button accessible from any operator surface (Telegram, dashboard, CLI)

Why PAPER mode is the defensible default

Most quantitative trading failures come from deploying capital before the strategy is proven. The 2007 quant crisis (Khandani & Lo 2007) showed that even well-designed strategies can fail in correlated ways when capital is deployed too quickly. The 2010 Flash Crash showed that algorithmic strategies can amplify market dislocations. The 2012 Knight Capital incident ($440M loss in 45 minutes) showed that a single deployment mistake can be catastrophic.

PAPER mode addresses all three failure modes. The graduation gate (14-day Sharpe >= 1.2 AND DSR >= 0.5) requires sustained performance, not a backtest snapshot. The 9-Layer Kill Switch addresses runtime amplification risks. The Owner Sovereignty enforcement addresses deployment-mistake risk by requiring explicit human approval for any blast_radius >= 3 action. Renaissance has the team and infrastructure to deploy capital safely without these gates. A 1-person operation does not — and admitting that is the honest scoping choice.

What this means for systematic traders

Publishing strategy specs under CC-BY-4.0 does not give away the edge. Execution and risk management are the edge.
DSR is the right gate metric — naive Sharpe is gameable
9-Layer defense in depth catches what single-gate systems miss
PAPER mode + graduation gate is the responsible default for new strategies
Honest scoping > marketing for long-term credibility

Frequently asked

Is Quant Bot v11 making money?

No. v11 operates exclusively in PAPER mode. Capital deployment is gated by 14-day Sharpe >= 1.2 AND DSR >= 0.5 (Deflated Sharpe Ratio per Bailey & Lopez de Prado 2014). The honest scoping default is: no capital deployment until both metrics clear, even if individual alphas show backtest gains. The Sharpe alone is not sufficient because of multiple-testing bias.

Why publish the strategy specs publicly?

The edge in quantitative trading is in execution and risk management, not in strategy ideas. The 6 alphas (A1-A6) are well-known categories in the literature; the operational evidence that lets you run them safely is the differentiator. Publishing the specs under CC-BY-4.0 invites reviewer scrutiny of the safety controls, which strengthens the audit position. Renaissance has team and decades-deep infrastructure; a 1-person operation does not, and publishing is part of how trust gets built.

What is the Deflated Sharpe Ratio?

DSR (Bailey & Lopez de Prado 2014) corrects naive Sharpe ratio for the number of trials used to select the strategy, the variance of those trials, and the skewness/kurtosis of returns. A naive Sharpe of 2.0 might correspond to a DSR of 0.5 after correction. The DSR threshold of 0.5 means the strategy is unlikely to be a multiple-testing artifact. Together with 14-day Sharpe >= 1.2, the gate addresses both small-sample and selection bias risks.

How is v11 different from running a Renaissance-style fund?

v11 is not a fund. It is a research instrument that ships under CC-BY-4.0 with full operational evidence. The closest analogy is a published reproducibility paper for quant strategies. Anyone can audit the 9-Layer Kill Switch, the alpha specs, and the graduation gate. Renaissance ships returns; v11 ships methodology and safety controls.

What happens when the graduation gate clears?

When 14-day Sharpe >= 1.2 AND DSR >= 0.5 are both achieved on a specific alpha, that alpha graduates from PAPER mode to a small live deployment. The deployment size is capped at 5% of allocated capital, monitored against the 9-Layer Kill Switch, and continues to be evaluated against the same gates. Failure to maintain the gates triggers automatic rollback to PAPER mode.

Can I cite Quant v11 in academic work?

Yes. Cite as: Heo, Yesol (2026). Quant v11 Ensemble: 6-Alpha Specs with 9-Layer Kill Switch. Neo Genesis Research. https://neogenesis.app + Zenodo DOI 10.5281/zenodo.20018487. The HF dataset includes 375 sections × 9 columns from 19 source files (alpha specs, expert reports, RISK_KILLSWITCH wiring, external validation). The BibTeX template is at /llms-full.txt under section 'BibTeX templates'.

References

Sora Orchestrator vs OpenAI Agents SDK: Owner Sovereignty and Multi-Provider Failover — OpenAI Agents SDK ships a single-vendor sandbox with tool-call confirmation. Sora runs across Gemini, Claude, Local LLM, and Ollama with Owner Sovereignty Article 0 and a 9-Layer Kill Switch. We compare audit surface, blast-radius classification, and failover paths.
Open-Source Research at Neo Genesis: NeurIPS, Datasets, Zenodo DOIs — Why every research output ships under CC-BY-4.0 to Hugging Face + Zenodo, and the rule that distinguishes open research from closed product code at Neo Genesis.
How We Run 11 Products with One Person — Operational architecture: how one operator and one autonomous AI system run eleven live products simultaneously.
V-Score Quality Gating: Rejecting AI Content That Falls Below 184.5 — How Neo Genesis blocks 30%+ of AI-generated drafts before they ship: V-Score formula, six-factor breakdown, and the 184.5 hard threshold that protects every published post.

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