# Predix

Python Platform PyTorch Optuna

Pandas LightGBM Qlib llama.cpp

AI-powered Quantitative Trading Agent for EUR/USD Forex

Installation β€’ Quick Start β€’ Configuration β€’ Features

CI Status Security Scan Coverage License Conventional Commits Ruff Stars Forks Issues Last Commit

--- ## πŸ–₯️ CLI Dashboard ```bash rdagent predix ``` ![Predix CLI Welcome Screen](docs/cli-welcome-screen.png) *The Predix CLI shows system status, available commands, and quick start guide.* --- ## Overview **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: - πŸ“Š **Data Analysis** – Automatically analyzes market patterns and microstructure - πŸ’‘ **Strategy Discovery** – Proposes novel trading factors and signals - 🧠 **Model Evolution** – Iteratively improves predictive models - πŸ“ˆ **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. ## Acknowledgments 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](https://github.com/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](https://github.com/TauricResearch/TradingAgents)** (Apache 2.0 License) - Inspiration for our multi-agent debate system, reflection mechanism, and memory management modules. - **[ai-hedge-fund](https://github.com/virattt/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. --- ## Installation ### System Requirements | Component | Minimum | Recommended | |-----------|---------|-------------| | **GPU VRAM** | 8 GB | 16 GB (RTX 4080 / 5060 Ti) | | **RAM** | 16 GB | 32 GB | | **Storage** | 20 GB | 50 GB (models + data) | | **OS** | Linux (Ubuntu 22.04+) | Linux | | **CUDA** | 12.0+ | 12.4+ | > Local LLMs require a CUDA-capable GPU. The default model (Qwen3.6-35B Q3) uses ~13.6 GB VRAM. CPU-only inference is possible but very slow (not recommended for production use). ### Prerequisites - **Conda** (Miniconda or Anaconda) β€” required for environment management - **Docker** β€” required for sandboxed factor/model code execution (`docker run hello-world` to verify) - **llama.cpp** β€” for local LLM inference (see [llama.cpp build guide](https://github.com/ggml-org/llama.cpp)) - **Linux** β€” officially supported; macOS/Windows may work with adjustments ### Quick Install ```bash # Clone repository git clone https://github.com/TPTBusiness/Predix cd Predix # Create and activate conda environment conda create -n predix python=3.10 -y conda activate predix # Install in editable mode pip install -e . # Verify Docker is accessible docker run --rm hello-world ``` > **Important:** Predix requires a conda environment to manage dependencies properly. > Using plain Python or other environment managers may cause conflicts. --- ## Data Setup Predix requires **1-minute EUR/USD OHLCV data** in HDF5 format. This is a hard prerequisite β€” the system cannot run without it. ### Step 1: Get the data Download 1-minute EUR/USD data (2020–present) from any of these free sources: | Source | Cost | Notes | |--------|------|-------| | **[Dukascopy](https://www.dukascopy.com/swiss/english/marketfeed/historical/)** | Free | Best quality free EUR/USD tick data | | **[OANDA API](https://developer.oanda.com/)** | Free (demo) | Requires API key, programmatic access | | **[TrueFX](https://truefx.com/)** | Free | Institutional-quality tick data | | **[Kaggle](https://www.kaggle.com/datasets?search=EURUSD+1min)** | Free | Search "EURUSD 1 minute" | | **MetaTrader 5** | Free | Export via `copy_rates_range()` | ### Step 2: Convert to HDF5 ```python import pandas as pd df = pd.read_csv('eurusd_1min.csv', parse_dates=['datetime']) df = df.rename(columns={'open': '$open', 'close': '$close', 'high': '$high', 'low': '$low', 'volume': '$volume'}) df['instrument'] = 'EURUSD' df = df.set_index(['datetime', 'instrument']) for col in ['$open', '$close', '$high', '$low', '$volume']: df[col] = df[col].astype('float32') import os os.makedirs('git_ignore_folder/factor_implementation_source_data', exist_ok=True) df.to_hdf('git_ignore_folder/factor_implementation_source_data/intraday_pv.h5', key='data', mode='w') ``` ### Required HDF5 format | Field | Type | Description | |-------|------|-------------| | **Index** | MultiIndex `(datetime, instrument)` | Timestamp + currency pair | | **`$open`** | float32 | Open price | | **`$close`** | float32 | Close price | | **`$high`** | float32 | High price | | **`$low`** | float32 | Low price | | **`$volume`** | float32 | Tick volume | **Save location:** `git_ignore_folder/factor_implementation_source_data/intraday_pv.h5` --- ## Configuration ### Environment Setup Create a `.env` file in the project root: ```bash # 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 ``` ### LLM Server (llama.cpp) ```bash ~/llama.cpp/build/bin/llama-server \ --model ~/models/qwen3.6/Qwen3.6-35B-A3B-UD-Q3_K_XL.gguf \ --n-gpu-layers 24 \ --no-mmap \ --port 8081 \ --ctx-size 240000 \ --parallel 2 \ --batch-size 512 --ubatch-size 512 \ --host 0.0.0.0 \ -ctk q4_0 -ctv q4_0 \ --reasoning off ``` > **Important flags:** > - `--ctx-size 240000 --parallel 2` β€” allocates **2 slots Γ— 120,000 tokens each**. `fin_quant` prompts can reach 80k+ tokens with full factor history; a smaller slot causes silent overflow and empty responses. > - `--reasoning off` β€” **critical**: completely disables Qwen3 chain-of-thought. `--reasoning-budget 0` is not sufficient and produces empty JSON responses. > - `--n-gpu-layers 24` β€” 4 fewer than maximum on RTX 5060 Ti (16 GB), freeing ~500 MB VRAM for the larger KV cache. > - `-ctk q4_0 -ctv q4_0` β€” quantises the KV cache to 4-bit, reducing VRAM from ~5 GB to ~1.3 GB at 240k context. ### Data Configuration Edit [`data_config.yaml`](data_config.yaml) to customize walk-forward splits: ```yaml instrument: EURUSD frequency: 1min data_path: ~/.qlib/qlib_data/eurusd_1min_data 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: spread_bps: 1.5 target_arr: 9.62 max_drawdown: 20 ``` --- ## Quick Start ### Prerequisites checklist ```bash # 1. Docker running? docker run --rm hello-world # 2. Data in place? ls git_ignore_folder/factor_implementation_source_data/intraday_pv.h5 # 3. LLM server running? curl http://localhost:8081/health ``` ### 1. Run Trading Loop ```bash conda activate predix rdagent fin_quant # or with explicit options: rdagent fin_quant --loop-n 5 --step-n 2 ``` ### 2. Monitor Results ```bash # Web dashboard rdagent server_ui --port 19899 --log-dir git_ignore_folder/RD-Agent_workspace/ # then open http://127.0.0.1:19899 # Best strategies so far python predix.py best ``` ### 3. Run Continuously ```bash while true; do rdagent fin_quant sleep 5 done ``` --- ## CLI Commands ### Factor & Strategy Loop | Command | Description | |---------|-------------| | `rdagent fin_quant` | Start autonomous factor + model evolution loop | | `rdagent fin_quant --loop-n 5` | Run exactly 5 evolution loops | | `rdagent fin_quant --with-dashboard` | Start with web dashboard | | `rdagent fin_quant --cli-dashboard` | Start with CLI Rich dashboard | | `rdagent fin_factor` | Factor-only evolution | | `rdagent fin_model` | Model-only evolution | ### Strategy Reports | Command | Description | |---------|-------------| | `python predix.py best` | Show top strategies by composite score | | `python predix.py best -n 20 -m sharpe` | Top 20 by Sharpe ratio | | `python predix.py best --show NAME` | Full metadata for one strategy | | `python predix_gen_strategies_real_bt.py` | Generate 10 strategies with LLM + real backtest | | `python predix_gen_strategies_real_bt.py 20` | Generate 20 strategies | ### Kronos Foundation Model | Command | Description | |---------|-------------| | `python predix.py kronos-factor` | Generate Kronos predicted-return factor (daily stride, ~15 min GPU) | | `python predix.py kronos-factor --pred 30` | 30-bar prediction horizon | | `python predix.py kronos-factor --device cpu` | CPU inference (slower) | | `python predix.py kronos-eval` | Evaluate Kronos IC / hit rate vs LightGBM baseline | | `python predix.py kronos-eval --pred 96` | Daily horizon evaluation | ### Factor Evaluation | Command | Description | |---------|-------------| | `python predix.py evaluate --all` | Evaluate all generated factors | | `python predix.py top -n 20` | Show top 20 factors by IC | | `python predix.py portfolio-simple` | Simple portfolio optimization | ### Parallel Execution | Command | Description | |---------|-------------| | `python predix_parallel.py --runs 5 --api-keys 1 -m openrouter` | Run 5 parallel factor evolutions | | `python predix_parallel.py --runs 20 --api-keys 2 -m openrouter` | Run 20 runs with 2 API keys | ### Monitoring & Debug | Command | Description | |---------|-------------| | `rdagent server_ui --port 19899 --log-dir ` | Start web dashboard | | `rdagent health_check` | Validate environment setup | | `python predix_batch_backtest.py` | Batch backtest multiple factors | | `python predix_rebacktest_strategies.py` | Re-backtest existing strategies | --- ## Features ### πŸ”„ Iterative Factor Evolution 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 ### πŸ›‘οΈ Trading Protection System Automatic risk management to prevent excessive losses: - **Max Drawdown Protection** - Pauses trading when drawdown exceeds threshold (default: 15%) - **Cooldown Period** - Enforces mandatory rest period after significant losses (default: 4h after 5% loss) - **Stoploss Guard** - Detects clusters of stoplosses and blocks trading (default: max 5 per day) - **Low Performance Filter** - Filters out consistently underperforming factors (Sharpe < 0.5, Win Rate < 40%) ### 🧠 Model Architecture Search Automatically explores and refines predictive models: - Linear baselines (LightGBM, XGBoost) - Deep learning (LSTM, Transformer, Temporal CNN) - Ensemble methods ### πŸ“š Knowledge Base Built-in knowledge accumulation across loops: - Successful factors are archived - Failed attempts inform future proposals - Cross-loop learning improves robustness ### πŸ–₯️ Interactive UI Real-time dashboard for monitoring: - Factor performance metrics - Model architecture evolution - Cumulative returns and drawdowns - Code diffs and implementation history ### πŸ€– Kronos Foundation Model Integration Predix integrates [Kronos-mini](https://github.com/shiyu-coder/Kronos) β€” a 4.1M parameter OHLCV foundation model pretrained on 12+ billion K-lines from 45 global exchanges (AAAI 2026, MIT): - **Option A β€” Alpha Factor**: Rolling daily inference generates a `KronosPredReturn` factor. Every 96 bars (one trading day), Kronos predicts the next day's return from the previous 512 bars of EUR/USD OHLCV data. The factor is forward-filled to 1-min frequency and plugs directly into Predix's factor evaluation pipeline. - **Option B β€” Model Evaluation**: Kronos runs alongside LightGBM as a standalone predictor. IC (Information Coefficient), IC IR, and directional hit rate are computed over the full dataset for direct comparison with LightGBM-generated models. ```bash # One-time setup git clone https://github.com/shiyu-coder/Kronos ~/Kronos # Generate factor (Option A) β€” saves to results/factors/ python predix.py kronos-factor # Evaluate as model (Option B) β€” prints IC vs LightGBM reference python predix.py kronos-eval ``` ### πŸ”’ Security & Quality Automated quality assurance: - **60 Integration Tests** β€” all features tested automatically on every commit - **Bandit Security Scanner** β€” pre-commit security checks - **Weekly Dependency Audit** β€” automated vulnerability scan via GitHub Actions --- ## Project Structure ``` predix/ β”œβ”€β”€ rdagent/ # Core agent framework β”‚ β”œβ”€β”€ app/ # CLI and scenario apps β”‚ β”œβ”€β”€ components/ # Reusable agent components β”‚ β”‚ β”œβ”€β”€ backtesting/ # Backtest engine & protections β”‚ β”‚ β”‚ β”œβ”€β”€ backtest_engine.py β”‚ β”‚ β”‚ β”œβ”€β”€ vbt_backtest.py # Unified backtest engine β”‚ β”‚ β”‚ β”œβ”€β”€ results_db.py β”‚ β”‚ β”‚ └── protections/ # Trading protection system β”‚ β”‚ └── coder/ # Factor & model coding (CoSTEER + Optuna) β”‚ β”œβ”€β”€ core/ # Core abstractions β”‚ β”œβ”€β”€ scenarios/ # Domain-specific scenarios β”‚ └── utils/ # Utilities β”œβ”€β”€ test/ # Test suite (134 tests) β”‚ └── backtesting/ # Backtest unit tests β”œβ”€β”€ web/ # Web UI frontend β”œβ”€β”€ data_config.yaml # Walk-forward split configuration β”œβ”€β”€ pyproject.toml # Project metadata └── requirements.txt # Dependencies ``` --- ## Requirements Core dependencies (see [`requirements.txt`](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` --- ## License This project is licensed under the **MIT License** – see the [`LICENSE`](LICENSE) file for details. ### Attribution Requirements If you use this code or concepts in your project, you **must**: 1. Include the MIT License text 2. Keep the copyright notice: "Copyright (c) 2025 Predix Team" 3. Provide attribution to the original project See [`ATTRIBUTION.md`](ATTRIBUTION.md) for detailed guidelines and examples. --- ## Contributing Contributions are welcome! Please: 1. Fork the repository 2. Create a feature branch (`git checkout -b feat/my-feature`) 3. Commit using [Conventional Commits](https://www.conventionalcommits.org/) (`git commit -m 'feat: add my feature'`) 4. Push to the branch (`git push origin feat/my-feature`) 5. Open a Pull Request with a conventional commit title For major changes, please open an issue first to discuss your approach. --- ## Citation If you use Predix in your research, please cite the underlying framework: ```bibtex @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} } ``` --- ## Support - **Issues**: [GitHub Issues](https://github.com/TPTBusiness/Predix/issues) --- ## Disclaimer 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.