Predix

AI-powered Quantitative Trading Agent for EUR/USD Forex

Installation • Quick Start • Configuration • Features

🖥️ CLI Dashboard

rdagent predix

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:

📊 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.

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 (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.

Installation

Prerequisites

Conda (Miniconda or Anaconda) - Required for environment management
Docker (required for sandboxed code execution)
Linux (officially supported; macOS/Windows may work with adjustments)

Quick Install

# 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 .

Important: Predix requires a conda environment to manage dependencies properly. Using plain Python or other environment managers may cause conflicts.

Configuration

Create .env file:

# 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

Quick Start

1. Run Trading Loop

# Activate conda environment
conda activate predix

# Start EURUSD trading loop
rdagent fin_quant

# With options
rdagent fin_quant --loop-n 5 --step-n 2

2. Monitor Results

# Start the UI dashboard
rdagent server_ui --port 19899 --log-dir git_ignore_folder/RD-Agent_workspace/

# Or open in browser
# http://127.0.0.1:19899

3. Loop Continuously

To run the trading loop continuously with auto-restart:

# Simple loop
while true; do
    rdagent fin_quant
    sleep 5
done

CLI Commands

Trading Loop

Command	Description
`rdagent fin_quant`	Start factor evolution loop
`rdagent fin_quant --loop-n 5`	Run 5 evolution loops
`rdagent fin_quant --with-dashboard`	Start with web dashboard
`rdagent fin_quant --cli-dashboard`	Start with CLI Rich dashboard

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

AI Strategy Generation (with REAL OHLCV Backtest)

Command	Description
`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
`python predix_gen_strategies_real_bt.py 5`	Generate 5 strategies (faster)

Strategy Reports

Command	Description
`python predix_strategy_report.py`	Generate reports for ALL strategies
`python predix_strategy_report.py results/strategies_new/123_MyStrategy.json`	Report for single strategy

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

Other Utilities

Command	Description
`python predix_batch_backtest.py`	Batch backtest multiple factors
`python predix_parallel.py`	Parallel factor evolution
`python predix_rebacktest_strategies.py`	Re-backtest existing strategies
`python debug_backtest.py`	Debug backtest alignment & IC

Environment Options

Env Variable	Description	Example
`OPENROUTER_API_KEY`	OpenRouter API key	`sk-or-v1-...`
`OPENAI_API_KEY`	Alternative: OpenAI key	`sk-...`
`CHAT_MODEL`	LLM model	`openrouter/qwen/qwen3.6-plus:free`
`OPENROUTER_MODEL`	Specific OpenRouter model	`openrouter/qwen/qwen3.6-plus:free`
`NO_COLOR`	Disable ANSI colors	`1`

Configuration

# Start the UI dashboard
rdagent ui --port 19899 --log-dir log/ --data-science

Then open http://127.0.0.1:19899 in your browser.

Configuration

Data Configuration

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 (%)

Environment Variables

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`

Features

🔄 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

🛡️ 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

📚 Knowledge Base

Built-in knowledge accumulation across loops:

Successful factors are archived
Failed attempts inform future proposals
Cross-loop learning improves robustness

🖥️ Interactive UI

Real-time dashboard for monitoring:

Factor performance metrics
Model architecture evolution
Cumulative returns and drawdowns
Code diffs and implementation history

🔒 Security & Quality

Automated quality assurance:

60 Integration Tests - All features tested automatically
Bandit Security Scanner - Pre-commit security checks
Pre-commit Hooks - Tests run before EVERY commit

Project Structure

predix/
├── rdagent/                 # Core agent framework
│   ├── app/                 # CLI and scenario apps
│   ├── components/          # Reusable agent components
│   │   ├── backtesting/     # Backtest engine & protections
│   │   │   ├── backtest_engine.py
│   │   │   ├── results_db.py
│   │   │   ├── risk_management.py
│   │   │   └── protections/ # Trading protection system (NEW)
│   │   │       ├── base.py
│   │   │       ├── max_drawdown.py
│   │   │       ├── cooldown.py
│   │   │       ├── stoploss_guard.py
│   │   │       ├── low_performance.py
│   │   │       └── protection_manager.py
│   │   ├── coder/           # Factor & model coding
│   │   └── loader.py        # Prompt & model loaders
│   ├── core/                # Core abstractions
│   ├── scenarios/           # Domain-specific scenarios
│   └── utils/               # Utilities
├── test/                    # Test suite
│   ├── integration/         # Integration tests (60 tests)
│   │   └── test_all_features.py
│   └── backtesting/         # Unit tests
│       └── test_protections.py
├── constraints/             # Constraint definitions
├── docs/                    # Documentation
├── web/                     # Web UI frontend
├── data_config.yaml         # Data configuration
├── pyproject.toml           # Project metadata
└── requirements.txt         # Dependencies

Data Setup

Predix requires 1-minute EUR/USD OHLCV data in HDF5 format.

Required Format

The data file must be saved as intraday_pv.h5 with the following structure:

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

Where to Get Data

Source	Cost	Notes
Dukascopy	Free	Best free EUR/USD tick data
OANDA API	Free (demo)	Requires API key
TrueFX	Free	Institutional-quality data
Kaggle	Free	Search "EURUSD 1 minute"
MetaTrader 5	Free	Export via `copy_rates_range()`

Quick CSV Conversion

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')
df.to_hdf('intraday_pv.h5', key='data', mode='w')

Expected data columns: $open, $close, $high, $low, $volume

CLI Commands

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 rl_trading --mode train --algorithm PPO`	Train RL trading agent
`rdagent rl_trading --mode backtest --model-path <path>`	Backtest with trained RL model
`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

RL Trading Examples

# Train new RL agent with PPO
rdagent rl_trading --mode train --algorithm PPO --total-timesteps 100000

# Backtest with trained model
rdagent rl_trading --mode backtest --model-path models/rl_trader.zip

# Disable trading protections (not recommended)
rdagent rl_trading --mode backtest --no-with-protections

# Get help
rdagent rl_trading --help

Note: RL Trading works without stable-baselines3 (uses simple fallback strategy). For full RL features, install: pip install -r requirements/rl.txt

Requirements

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

License

This project is licensed under the MIT License – see the LICENSE file for details.

Attribution Requirements

If you use this code or concepts in your project, you must:

Include the MIT License text
Provide attribution to the original project

See ATTRIBUTION.md for detailed guidelines and examples.

Contributing

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.

Citation

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}
}

Support

Issues: GitHub 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.

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Predix

AI-powered Quantitative Trading Agent for EUR/USD Forex

🖥️ CLI Dashboard

Overview

Acknowledgments

Installation

Prerequisites

Quick Install

Configuration

Quick Start

1. Run Trading Loop

2. Monitor Results

3. Loop Continuously

CLI Commands

Trading Loop

Parallel Execution

AI Strategy Generation (with REAL OHLCV Backtest)

Strategy Reports

Factor Evaluation

Other Utilities

Environment Options

Configuration

Configuration

Data Configuration

Environment Variables

Features

🔄 Iterative Factor Evolution

🛡️ Trading Protection System

🧠 Model Architecture Search

📚 Knowledge Base

🖥️ Interactive UI

🔒 Security & Quality

Project Structure

Data Setup

Required Format

Where to Get Data

Quick CSV Conversion

CLI Commands

RL Trading Examples

Requirements

License

Attribution Requirements

Contributing

Citation

Support

Disclaimer