Installation • Quick Start • Configuration • Features
Predix is an autonomous AI agent for quantitative trading strategies in the EUR/USD forex market. Built on a multi-agent framework, Predix automates the full research and development cycle:
- 📊 Data Analysis – Automatically analyzes market patterns and microstructure
- 💡 Strategy Discovery – Proposes novel trading factors and signals
- 🧠 Model Evolution – Iteratively improves predictive models
- 📈 Backtesting – Validates strategies on historical 1-minute data
Predix is optimized for 1-minute EUR/USD FX data (2020–2026) and uses Qlib as the underlying backtesting engine.
This project draws inspiration from various open-source projects in the AI trading and multi-agent systems space. We thank all the authors for their innovative work that helped shape our understanding of these patterns.
Special thanks to:
-
Microsoft RD-Agent (MIT License) - Foundation for our autonomous R&D agent framework. We extend our gratitude to the RD-Agent team for their excellent foundational work.
-
TradingAgents (Apache 2.0 License) - Inspiration for our multi-agent debate system, reflection mechanism, and memory management modules.
-
ai-hedge-fund - Inspiration for macro analysis (Stanley Druckenmiller agent), risk management concepts, and market regime detection.
All code in Predix is originally written and implemented independently. Predix extends these frameworks with EUR/USD forex-specific features, 1-minute backtesting capabilities, comprehensive risk management, and trading dashboards.
- Python 3.10 or 3.11
- Docker (required for sandboxed code execution)
- Linux (officially supported; macOS/Windows may work with adjustments)
# Clone repository
git clone https://github.com/TPTBusiness/Predix
cd predix
# Create conda environment
conda create -n predix python=3.10
conda activate predix
# Install in editable mode
pip install -e .[test,lint]- Create
.envfile:
# Local LLM (llama.cpp)
OPENAI_API_KEY=local
OPENAI_API_BASE=http://localhost:8081/v1
CHAT_MODEL=qwen3.5-35b
# Embedding (Ollama)
LITELLM_PROXY_API_KEY=local
LITELLM_PROXY_API_BASE=http://localhost:11434/v1
EMBEDDING_MODEL=nomic-embed-text
# Paths
QLIB_DATA_DIR=~/.qlib/qlib_data/eurusd_1min_data- Start LLM server (llama.cpp):
~/llama.cpp/build/bin/llama-server \
--model ~/models/qwen3.5/Qwen3.5-35B-A3B-Q3_K_M.gguf \
--n-gpu-layers 36 \
--ctx-size 80000 \
--port 8081# Activate conda environment
conda activate predix
# Start EURUSD trading loop
rdagent fin_quant
# With options
rdagent fin_quant --loop-n 5 --step-n 2# Start the UI dashboard
rdagent server_ui --port 19899 --log-dir git_ignore_folder/RD-Agent_workspace/
# Or open in browser
# http://127.0.0.1:19899To run the trading loop continuously with auto-restart:
# Simple loop
while true; do
rdagent fin_quant
sleep 5
done# Start the UI dashboard
rdagent ui --port 19899 --log-dir log/ --data-scienceThen open http://127.0.0.1:19899 in your browser.
Edit data_config.yaml to customize:
instrument: EURUSD
frequency: 1min
data_path: ~/.qlib/qlib_data/eurusd_1min_data
# Walk-forward split
train_start: "2022-03-14"
train_end: "2024-06-30"
valid_start: "2024-07-01"
valid_end: "2024-12-31"
test_start: "2025-01-01"
test_end: "2026-03-20"
# Market context for LLM prompts
market_context:
spread_bps: 1.5
target_arr: 9.62 # Target annual return (%)
max_drawdown: 20 # Max drawdown (%)| Variable | Description | Example |
|---|---|---|
CHAT_MODEL |
LLM for reasoning | gpt-4o, deepseek-chat |
EMBEDDING_MODEL |
Embedding model | text-embedding-3-small |
OPENAI_API_KEY |
API key for OpenAI | sk-... |
DEEPSEEK_API_KEY |
API key for DeepSeek | sk-... |
DS_LOCAL_DATA_PATH |
Local data directory | ./data |
Predix continuously proposes, implements, and validates new alpha factors:
- Learns from backtest feedback
- Avoids overfitting through walk-forward validation
- Discovers non-obvious patterns in order flow, volatility, and session dynamics
Automatically explores and refines predictive models:
- Linear baselines (LightGBM, XGBoost)
- Deep learning (LSTM, Transformer, Temporal CNN)
- Ensemble methods
Built-in knowledge accumulation across loops:
- Successful factors are archived
- Failed attempts inform future proposals
- Cross-loop learning improves robustness
Real-time dashboard for monitoring:
- Factor performance metrics
- Model architecture evolution
- Cumulative returns and drawdowns
- Code diffs and implementation history
predix/
├── rdagent/ # Core agent framework
│ ├── app/ # CLI and scenario apps
│ ├── components/ # Reusable agent components
│ ├── core/ # Core abstractions
│ ├── scenarios/ # Domain-specific scenarios
│ └── utils/ # Utilities
├── constraints/ # Constraint definitions
├── docs/ # Documentation
├── web/ # Web UI frontend
├── data_config.yaml # Data configuration
├── pyproject.toml # Project metadata
└── requirements.txt # Dependencies
Predix uses 1-minute EUR/USD data. To prepare your dataset:
# Run the data setup script (if provided)
./setup_predix_eurusd.sh
# Or manually place data in:
# ~/.qlib/qlib_data/eurusd_1min_data/Expected data columns: $open, $close, $high, $low, $volume
| Command | Description |
|---|---|
rdagent fin_quant |
Full factor & model co-evolution |
rdagent fin_factor |
Factor-only evolution |
rdagent fin_model |
Model-only evolution |
rdagent fin_factor_report --report-folder=<path> |
Extract factors from financial reports |
rdagent general_model <paper-url> |
Extract model from research paper |
rdagent data_science --competition <name> |
Kaggle/data science competition mode |
rdagent ui --port 19899 --log-dir <path> |
Start monitoring dashboard |
rdagent health_check |
Validate environment setup |
Core dependencies (see requirements.txt for full list):
- LLM:
openai,litellm - Data:
pandas,numpy,pyarrow - ML:
scikit-learn,lightgbm,xgboost - Backtesting:
qlib(via Docker) - UI:
streamlit,plotly,flask
This project is licensed under the MIT License – see the LICENSE file for details.
If you use this code or concepts in your project, you must:
- Include the MIT License text
- Keep the copyright notice: "Copyright (c) 2025 Predix Team"
- Provide attribution to the original project
See ATTRIBUTION.md for detailed guidelines and examples.
Contributions are welcome! Please:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
For major changes, please open an issue first to discuss your approach.
If you use Predix in your research, please cite the underlying framework:
@misc{yang2025rdagentllmagentframeworkautonomous,
title={R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science},
author={Yang, Xu and Yang, Xiao and Fang, Shikai and Zhang, Yifei and Wang, Jian and Xian, Bowen and Li, Qizheng and Li, Jingyuan and Xu, Minrui and Li, Yuante and others},
year={2025},
eprint={2505.14738},
archivePrefix={arXiv},
primaryClass={cs.AI}
}- Issues: GitHub Issues
Predix is provided "as is" for research and educational purposes only. It is not intended for:
- Live trading or financial advice
- Production use without thorough testing
- Replacement of qualified financial professionals
Users assume all liability and should comply with applicable laws and regulations in their jurisdiction. Past performance does not guarantee future results.