QuantForge — an AI-driven, polyglot quant research environment

A self-directed summer project: an open-source quant research pipeline (data → strategies → backtest → portfolio optimization → risk/perf analytics), driven by an AI research agent over MCP tools, with a snazzy interactive UI and a cloud-hosted, budget-capped demo.

This is an independent, open-source project. It uses only public data and open-source / personal tooling. It is not affiliated with or built on any employer's internal systems.

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Why this exists

• Build something real and portfolio-worthy — recognizable quant workflows, implemented with rigor (no look-ahead/survivorship bias, transaction costs, out-of-sample evaluation), in code the author can defend line-by-line.
• Show the architecture, not just a toy — one Engine/Strategy interface, multiple compute backends, an AI agent that drives the whole thing through tools.

Architecture at a glance

data ingestion → strategies → backtest engine → portfolio optimization → risk/perf analytics → UI → cloud
                                       ▲                                          │
                                       └── AI research agent (propose→backtest→read→refine) ◀── MCP tools

Polyglot by design. Every stage exchanges data via an Arrow/Parquet interchange contract, so each piece can use the best tool for the job:

Stage	Tool	Status
Backtest engine	Python (custom vectorized)	core
Analytics / risk	R (tidyquant, PerformanceAnalytics, PortfolioAnalytics)	core
Visual workflow engine	KNIME	stretch
(future) comparison engine	—	future

The compute engine sits behind a single interface (src/quantforge/engine/base.py), so a new engine can drop in later without touching the rest of the app. See docs/architecture.md.

The AI layer

• MCP server exposes the pipeline as tools: load_data, run_backtest, optimize_portfolio, get_metrics.
• Research agent proposes a strategy, backtests it, reads its own metrics, and iterates — with mandatory overfitting guardrails (train / validation / untouched holdout, iteration cap, budget cap). See src/quantforge/ai/.
• Natural-language interface turns "backtest momentum on tech, 2015–2020, 10bps costs" into tool calls and explains the result in plain English.

Two distinct AI uses: the Claude API is a metered, budget-capped runtime feature of this app. Claude Code (a coding assistant) is a separate development tool used to build the repo.

Safety & cost control (read before deploying publicly)

A public URL that can trigger paid API calls will get hit by bots. This repo treats that as a hard requirement — see src/quantforge/ai/guardrails.py and docs/architecture.md:

• Global server-side budget ledger (daily + total caps); graceful fallback to cached runs.
• Independent AWS Budgets alarm at the infra layer.
• Expensive AI paths gated (passcode); open traffic gets cached scenarios only.
• No LLM-generated code is executed on the public server — public mode is parameter-only.

Quickstart

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env            # add ANTHROPIC_API_KEY, set AI_BUDGET_USD
pytest                          # correctness + rigor + safety tests
streamlit run app/streamlit_app.py

R analytics layer:

Rscript analytics_r/tearsheet.R   # reads the Parquet hand-off, emits a tearsheet

Repo layout

See docs/architecture.md for the full map. Start here: docs/PROJECT_BRIEF.md (what to build) and docs/TEN_WEEK_PLAN.md (the week-by-week checklist).

Status

Scaffold. Modules are stubs with docstrings + TODOs — the implementation is the project.