QuantTradeAI is built for coding agents — Claude Code, Codex, Cursor, and similar tools — to research trading strategies without repeatedly writing data, backtest, sweep, artifact, and deployment plumbing from scratch.
You give the research objective. The agent uses a generated workspace, config/project.yaml, and the quanttradeai CLI to run repeatable strategy experiments, compare artifacts, and recommend winners.
AI agents can write code, but quant research requires repeatable infrastructure.
Without structure, agents write one-off scripts, scatter outputs across directories, and produce runs that cannot be compared or built on. Every session starts fresh with no memory of what worked.
QuantTradeAI provides:
- A stable experiment environment — one project config drives data, features, research, and agents
- Standardized run artifacts — every run writes structured outputs agents can read directly
- A promotion model — backtest → paper → live, with live execution gated behind explicit human approval
| Without QuantTradeAI | With QuantTradeAI | |
|---|---|---|
| Setup | Agent writes custom fetch and backtest scripts each time | Agent reuses the quanttradeai CLI against one project config |
| Outputs | Scattered across ad-hoc directories | Every run writes to runs/ with standardized artifacts |
| Comparison | No way to compare strategy variants | Scoreboards and --compare are built in |
| Structure | Research and agent code in separate scripts | One config/project.yaml drives both |
| Safety | No gate before live execution | Backtest → paper → live, each requiring explicit promotion |
Note
Package publishing is being stabilized. Until PyPI is available, use the local development setup below.
pip install quanttradeai
quanttradeai init my-lab
cd my-labOpen my-lab in Claude Code, Cursor, Codex, or another coding agent and ask:
"Research RSI and SMA crossover strategies on AAPL and MSFT and find the best one."
The agent uses config/project.yaml and the quanttradeai CLI to run experiments, compare results on a scoreboard, and recommend a winner — without you writing any backtest code.
my-lab/
├── config/project.yaml # canonical project config — start here
├── AGENTS.md # tells coding agents how to use the CLI and YAML
├── CLAUDE.md # Claude Code-specific context and guidance
├── .claude/skills/quanttradeai-research/ # skill pack for Claude Code agents
└── .quanttradeai/workspace.yaml # workspace-level metadata and defaults
The coding agent reads these files to understand how to use the framework correctly — you do not need to explain the workflow to it each session.
- Create strategy labs — initialize multi-agent projects from templates (
rule,model,llm,hybrid) - Run parameter sweeps — expand YAML-defined grids into parallel backtest variants
- Compare scoreboards — rank runs by Sharpe ratio, PnL, or other metrics
- Inspect artifacts — read
summary.json.run_resultandscoreboard.jsonfrom any run - Promote backtests to paper — move winning runs forward through explicit gates
- Generate deployment bundles — emit local runners, Docker Compose, or Render worker configs
- Keep live trading gated — live mode requires human acknowledgement at every promotion step
Every run writes machine-readable artifacts. Agents read these to decide what to do next — no manual result-parsing required.
| Artifact | What it contains |
|---|---|
summary.json → run_result |
High-level outcome: winner, ranked candidates, failures |
scoreboard.json |
Ranked metrics across all sweep variants |
results.json |
Per-variant metrics for batch and sweep runs |
metrics.json |
Full metrics for a single run |
resolved_project_config.yaml |
Exact config used for the run — fully reproducible |
Experiments start in backtest. Agents cannot self-promote to live trading.
| Mode | Gate |
|---|---|
| Backtest | Always available — no credentials needed |
| Paper | Replay-backed simulation — requires explicit promote from a passing backtest |
| Live | Requires --acknowledge-live from a human, plus validated streaming and risk config |
Caution
Live mode and broker-backed execution are fully opt-in. QuantTradeAI does not guarantee profitability.
| Capability | Status |
|---|---|
| Strategy labs, sweeps, and research runs | Supported |
| Rule, model, LLM, and hybrid agents | Supported |
| Backtest, paper (replay-backed), and live modes | Supported |
| Scoreboards and run comparison | Supported |
| Research-to-agent promotion pipeline | Supported |
| Deployment bundles (local, Docker Compose, Render) | Supported |
For the full list, see the docs.
git clone https://github.com/AKKI0511/QuantTradeAI.git
cd QuantTradeAI
poetry install --with devInitialize a workspace and open it in your coding agent:
quanttradeai init my-lab
cd my-labDev commands:
make format # auto-format
make lint # run linters
make test # run test suiteGet started — Getting Started · Docs
Configure — Project YAML · Config Overview
Reference — API Docs · Roadmap · Contributing
MIT. See LICENSE.