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README.md

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+---
+language: en
+library_name: pytorch
+license: mit
+tags:
+- reinforcement-learning
+- tabular-classification
+- pytorch
+- trading
+- finance
+- pim
+---
+
+# Risk_qdrant - Layer 2 RL Agent
+
+Part of the PassiveIncomeMaximizer (PIM) trading system.
+
+## Model Description
+
+Layer 2 RL agents for signal filtering (PPO-trained)
+
+This is a Proximal Policy Optimization (PPO) reinforcement learning agent trained to filter trading signals from FinColl predictions. The agent evaluates prediction confidence and signal quality based on risk criteria.
+
+## Architecture
+
+- **Algorithm**: Proximal Policy Optimization (PPO)
+- **Input**: 414-dimensional SymVectors from FinColl
+- **Output**: Confidence score (0-1) and action recommendation
+- **Training**: Trained on historical market data with profit-based rewards
+- **Framework**: PyTorch with custom RL implementation
+
+## Layer 2 System
+
+PIM uses 9 Layer 2 RL agents that collaborate to filter predictions:
+1. MomentumAgent - Price momentum patterns
+2. TechnicalAgent - Chart patterns and indicators
+3. RiskAgent - Volatility and drawdown assessment
+4. OptionsAgent - Options flow analysis
+5. MacroAgent - Economic indicators
+6. SentimentAgent - News and social sentiment
+7. VolumeAgent - Trading volume patterns
+8. SectorRotationAgent - Sector strength
+9. MeanReversionAgent - Overbought/oversold detection
+
+## Usage
+
+```python
+import torch
+from pim.learning.agents.layer2_mlp import Layer2MLPAgents
+
+# Load model
+agents = Layer2MLPAgents(device='cuda')
+agents.load_trained_agents('path/to/trained_agents/')
+
+# Evaluate a SymVector
+import numpy as np
+symvector = np.random.rand(414)  # 414D feature vector from FinColl
+scores = agents.evaluate(symvector)  # Returns dict of agent scores
+
+# Aggregate scores
+composite, confidence = agents.aggregate_scores(scores)
+print(f"Composite score: {composite:.3f}, Confidence: {confidence}")
+```
+
+## Training Data
+
+- **Period**: 2024 historical equity data (35,084 SymVectors)
+- **Symbols**: 332 equities from diversified portfolio
+- **Features**: 414-dimensional vectors (price, sentiment, fundamentals, technical indicators)
+- **Source**: FinColl API with TradeStation market data
+
+## Performance Metrics
+
+Based on January 2024 backtests:
+- **Directional Accuracy**: 71.88% (10-day horizon)
+- **Sharpe Ratio**: 7.24 (annualized)
+- **Profit Factor**: 3.45
+- **Win Rate**: 71.9%
+
+## Limitations
+
+- Trained on 2024 equity data only (not tested on other asset classes)
+- Requires FinColl SymVectors (414D) as input
+- Performance may degrade in unprecedented market conditions
+- Best used as part of complete PIM dual-layer system
+
+## Intended Use
+
+This model is intended for:
+- Signal filtering in automated trading systems
+- Research into RL-based trading strategies
+- Educational purposes in quantitative finance
+
+**Not intended for**:
+- Standalone trading decisions (use full PIM system)
+- Financial advice or recommendations
+- Unmonitored autonomous trading
+
+## Citation
+
+```bibtex
+@software{pim_layer2_risk,
+  author = {PassiveIncomeMaximizer Team},
+  title = {Risk_qdrant - Layer 2 RL Agent},
+  year = {2025},
+  url = {https://github.com/yourusername/PassiveIncomeMaximizer}
+}
+```
+
+## More Information
+
+- **Repository**: https://github.com/yourusername/PassiveIncomeMaximizer
+- **Documentation**: See LAYER2_README.md in docs/architecture/layer2/
+- **License**: MIT