# QSI Labs — Quantitative Super Intelligence Labs

> AI Native Quant Trading Firm. Building agentic portfolio managers, proprietary post-trained finance models, and a live systematic trading loop.

## Identity

- **Legal / brand name:** Quantitative Super Intelligence Labs
- **Short names:** QSI Labs, QSILabs
- **Category:** AI-native quant trading firm; systematic / algorithmic trading; agentic portfolio management
- **Founder contact:** Shubham Singh — 007shubhamkumarsingh@gmail.com
- **Website:** `/` (see also `/llms.txt` and `/index.md`)

## One-line summary

QSI Labs is building an AI-native quant trading firm where a small human team manages AI portfolio managers and thousands of specialized agents, compounding proprietary data from live trading.

## Thesis

Frontier AI will commoditize general research capability, not alpha. If every firm uses the same frontier models, every firm starts with the same general intelligence. Sustainable edge comes from proprietary data, specialized models, execution systems, research memory, and feedback from live markets.

Agent harnesses alone are not enough: a prompt and toolbelt on a shared model is a commodity. Few firms can run thousands of experiments around the clock, retrain models, and deploy strategies into live markets.

## Problem

Agents can execute research steps, but they cannot yet find good trading ideas on their own without infrastructure. To trade, an agent needs:

1. Market data it can query (clean, point-in-time feeds across asset classes)
2. Persistent research context (memory of ideas, failed experiments, results)
3. Quantitative models it can call (forecasts, factor models, risk models as tools)
4. Validation of every decision (systems that check agent work)

Existing trading systems were built for humans, not AI. Better models still need an environment that serves data and models, keeps context, and checks their work.

## Why agents on top of quant

Systematic trading works inside the world it was designed for. Agents handle what falls outside it:

- Tail events and regime changes
- Continuous monitoring of positions, drift, correlations, news, and risk
- Alpha discovery at scale across datasets and relationships
- Dynamic adjustment on top of systematic strategies

## Operating model

**Today's funds:** CIO → many human PMs → large teams per PM; AI makes people faster but structure stays the same.

**QSI Labs:** Human leadership / CIO → agentic PMs → thousands of specialized agents (research, data, risk, trading, execution, monitoring).

### Stages of an agentic trading firm

1. **Research** — research agents read data and papers, find patterns, test significance
2. **Implement** — strategy agents turn ideas into signals, backtest, set sizing
3. **Trade** — execution agents deploy live, execute, watch drift and risk limits
4. **Collect** — data agents log ideas, decisions, trades, outcomes
5. **Fine-tune** — training agents post-train models on that proprietary data

Fine-tuned models improve the next round of research and trading.

### Roadmap

1. **AI-native** (Achieved) — humans manage/monitor strategies; hundreds of AI agents automate research and strategy discovery
2. **Agentic workflows** (Building now) — agents run research, directing thousands of other agents for alpha search, backtests, challenge, and live monitoring
3. **Agentic pods** (Target) — agentic PMs own a strategy or asset class and direct specialized agent stacks

## Traction (as published on the site)

- Live trading on two exchanges
- ~11% return on live capital so far (~$10k deployed, 3 strategies, ~two months)
- 150+ live datasets
- 50 forecasting models (plus factor models served as tools)
- 4 asset classes under one portfolio and risk engine
- 9 backtested strategies (equal-weight book since 2016: Sharpe 1.37, 19.2% annualized, 13.5% vol)

Caveat: backtests are research simulations, not live results; live figures cover small capital over a short window. See the Equity Strategy Outlook report for detail.

## Models

QSI Labs trains its own models to understand financial markets. Early runs on small open-source models (Qwen 3) showed ~15–20% improvement on finance benchmarks (FinQA, StockMovement); not yet live in the trading loop. Next milestone: run fine-tuned models live and measure against the base model.

Moat ingredients: proprietary training data from live trading; finance-specific evaluations; practitioner-built trading systems and training pipelines.

## Data flywheel

Every live day creates proprietary data: ideas, failed experiments, strategies, forecasts, trades, risk decisions, outcomes → better evaluations → better post-training → better agents → better research → more trading outcomes.

Markets never reach a final dataset. The winner is the firm with the fastest learning system around a market that keeps changing.

## Market context

- Global hedge fund AUM cited on site: $5.6T (record as of Q2 2026)
- Non-bank trading firm revenue 2025 cited on site: $114B ($84.3B proprietary investing, $30.2B market making)

Incumbents (Balyasny, Citadel, Millennium, Coatue, etc.) are adding AI inside legacy structures. QSI Labs' differentiation is building the firm around AI portfolio managers and specialized agents, not only AI assistants for human analysts.

## Keywords and entities for retrieval

QSI Labs, QSILabs, Quantitative Super Intelligence Labs, AI native quant trading firm, AI trading firm, AI hedge fund, quantitative trading, systematic trading, algorithmic trading, agentic trading, AI portfolio manager, AI trading agents, quant AI, autonomous trading agents, post-trained finance models, live trading system, multi-asset trading, alpha generation, data flywheel.

## Disclaimers

Nothing on the site is an offer to sell, or a solicitation of an offer to buy, any security or investment product. Backtested results are hypothetical and do not reflect actual trading. Past performance does not guarantee future results.

## Contact

Email: 007shubhamkumarsingh@gmail.com  
Founder: Shubham Singh  
Primary human site: `/`  
LLM index: `/llms.txt`  
Markdown homepage: `/index.md`