QuantaAlpha transforms how you discover quantitative alpha factors by combining LLM intelligence with evolutionary strategies. Just describe your research direction, and watch as factors are automatically mined, evolved, and validated through self-evolving trajectories.

💬 Research Direction → 🧩 Diversified Planning → 🔄 Trajectory → ✅ Validated Alpha Factors

Demo: Below is a short demo of the full flow from research direction to factor mining and backtesting UI.

git clone https://github.com/QuantaAlpha/QuantaAlpha.git
cd QuantaAlpha
conda create -n quantaalpha python=3.10
conda activate quantaalpha
# Install the package in development mode
SETUPTOOLS_SCM_PRETEND_VERSION=0.1.0 pip install -e .
# Install additional dependencies
pip install -r requirements.txt

cp configs/.env.example .env

Edit .env with your settings:

# === Required: Data Paths ===
QLIB_DATA_DIR=/path/to/your/qlib/cn_data # Qlib data directory
DATA_RESULTS_DIR=/path/to/your/results # Output directory
# === Required: LLM API ===
OPENAI_API_KEY=your-api-key
OPENAI_BASE_URL=https://your-llm-provider/v1 # e.g. DashScope, OpenAI
CHAT_MODEL=deepseek-v3 # or gpt-4, qwen-max, etc.
REASONING_MODEL=deepseek-v3

QuantaAlpha requires two types of data: Qlib market data (for backtesting) and pre-computed price-volume HDF5 files (for factor mining). We provide all of them on HuggingFace for convenience.

Dataset: https://huggingface.co/datasets/QuantaAlpha/qlib_csi300

Why provide HDF5 files? The system can auto-generate daily_pv.h5 from Qlib data on first run, but this process is very slow. Downloading pre-built HDF5 files saves significant time.

# Option A: Using huggingface-cli (recommended)
pip install huggingface_hub
huggingface-cli download QuantaAlpha/qlib_csi300 --repo-type dataset --local-dir ./hf_data
# Option B: Using wget
mkdir -p hf_data
wget -P hf_data https://huggingface.co/datasets/QuantaAlpha/qlib_csi300/resolve/main/cn_data.zip
wget -P hf_data https://huggingface.co/datasets/QuantaAlpha/qlib_csi300/resolve/main/daily_pv.h5
wget -P hf_data https://huggingface.co/datasets/QuantaAlpha/qlib_csi300/resolve/main/daily_pv_debug.h5

# 1. Extract Qlib data
unzip hf_data/cn_data.zip -d ./data/qlib
# 2. Place HDF5 files into the default data directories
mkdir -p git_ignore_folder/factor_implementation_source_data
mkdir -p git_ignore_folder/factor_implementation_source_data_debug
cp hf_data/daily_pv.h5 git_ignore_folder/factor_implementation_source_data/daily_pv.h5
cp hf_data/daily_pv_debug.h5 git_ignore_folder/factor_implementation_source_data_debug/daily_pv.h5

Note: daily_pv_debug.h5 must be renamed to daily_pv.h5 when placed in the debug directory.

# Point to the extracted Qlib data directory (must contain calendars/, features/, instruments/)
QLIB_DATA_DIR=./data/qlib/cn_data
# Output directory for experiment results
DATA_RESULTS_DIR=./data/results

The HDF5 data directories can also be customized via environment variables if you prefer a different location:

# Optional: override default HDF5 data paths
FACTOR_CoSTEER_DATA_FOLDER=/your/custom/path/factor_source_data
FACTOR_CoSTEER_DATA_FOLDER_DEBUG=/your/custom/path/factor_source_data_debug

./run.sh "<your input>"
# Example: Run with a research direction
./run.sh "Price-Volume Factor Mining"
# Example: Run with custom factor library suffix
./run.sh "Microstructure Factors" "exp_micro"

The experiment will automatically mine, evolve, and validate alpha factors, and save all discovered factors to all_factors_library*.json.

After mining, combine factors from the library for a full-period backtest:

# Backtest with custom factors only
python -m quantaalpha.backtest.run_backtest \
 -c configs/backtest.yaml \
 --factor-source custom \
 --factor-json all_factors_library.json
# Combine with Alpha158(20) baseline factors
python -m quantaalpha.backtest.run_backtest \
 -c configs/backtest.yaml \
 --factor-source combined \
 --factor-json all_factors_library.json
# Dry run (load factors only, skip backtest)
python -m quantaalpha.backtest.run_backtest \
 -c configs/backtest.yaml \
 --factor-source custom \
 --factor-json all_factors_library.json \
 --dry-run -v

Results are saved to the directory specified in configs/backtest.yaml (experiment.output_dir).

📘 Need help? Check our comprehensive User Guide for advanced configuration, experiment reproduction, and detailed usage examples.

QuantaAlpha provides a web-based dashboard where you can complete the entire workflow through a visual interface — no command line needed.

conda activate quantaalpha
cd frontend-v2
bash start.sh
# Visit http://localhost:3000

• ⚙️ Settings: Configure LLM API, data paths, and experiment parameters directly in the UI
• ⛏️ Factor Mining: Start experiments with natural language input and monitor progress in real-time
• 📚 Factor Library: Browse, search, and filter all discovered factors with quality classifications
• 📈 Independent Backtest: Select a factor library and run full-period backtests with visual results

We welcome all forms of contributions to make QuantaAlpha better! Here's how you can get involved:

• 🐛 Bug Reports: Found a bug? Open an issue to help us fix it.
• 💡 Feature Requests: Have a great idea? Start a discussion to suggest new features.
• 📝 Docs & Tutorials: Improve documentation, add usage examples, or write tutorials.
• 🔧 Code Contributions: Submit PRs for bug fixes, performance improvements, or new functionality.
• 🧬 New Factors: Share high-quality factors discovered in your own runs to benefit the community.

Special thanks to:

• Qlib - Quantitative investment platform by Microsoft
• RD-Agent - An automated R&D framework by Microsoft (NeurIPS 2025)
• AlphaAgent - Multi-agent alpha factor mining framework (KDD 2025)

• QuantaAlpha was founded in April 2025 by a team of professors, postdocs, PhDs, and master's students from Tsinghua University, Peking University, CAS, CMU, HKUST, and more.

🌟 Our mission is to explore the "quantum" of intelligence and pioneer the "alpha" frontier of agent research — from CodeAgents to self-evolving intelligence, and further to financial and cross-domain specialized agents, we are committed to redefining the boundaries of AI.

✨ In 2026, we will continue to produce high-quality research in the following directions:

• CodeAgent: End-to-end autonomous execution of real-world tasks

• DeepResearch: Deep reasoning and retrieval-augmented intelligence

• Agentic Reasoning / Agentic RL: Agent-based reasoning and reinforcement learning

• Self-evolution and collaborative learning: Evolution and coordination of multi-agent systems

📢 We welcome students and researchers interested in these directions to join us!

🔗 Team Homepage: QuantaAlpha

📧 Email: quantaalpha.ai@gmail.com

Initiated by Professor Liwen Zhang from Shanghai University of Finance and Economics (SUFE), AIFin Lab is deeply rooted in the interdisciplinary fields of AI + Finance, Statistics, and Data Science. The team brings together cutting-edge scholars from top institutions such as SUFE, FDU, SEU, CMU, and CUHK. We are dedicated to building a comprehensive "full-link" system covering data, models, benchmarks, and intelligent prompting.

📢 We are actively looking for talented students (UG/Master/PhD) and researchers worldwide who are passionate about AI Agent security and financial intelligence to join AIFin Lab!

📧 Email: aifinlab.sufe@gmail.com (please CC to zhang.liwen@shufe.edu.cn)

We look forward to hearing from you!

If you find QuantaAlpha useful in your research, please cite our work:

@misc{han2026quantaalphaevolutionaryframeworkllmdriven,
 title={QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining}, 
 author={Jun Han and Shuo Zhang and Wei Li and Zhi Yang and Yifan Dong and Tu Hu and Jialuo Yuan and Xiaomin Yu and Yumo Zhu and Fangqi Lou and Xin Guo and Zhaowei Liu and Tianyi Jiang and Ruichuan An and Jingping Liu and Biao Wu and Rongze Chen and Kunyi Wang and Yifan Wang and Sen Hu and Xinbing Kong and Liwen Zhang and Ronghao Chen and Huacan Wang},
 year={2026},
 eprint={2602.07085},
 archivePrefix={arXiv},
 primaryClass={q-fin.ST},
 url={https://arxiv.org/abs/2602.07085}, 
}

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