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pim-layer2-rl-agents-Risk_qdrant

Reinforcement Learning
PyTorch
English
tabular-classification
trading
finance
pim
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metadata 
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 

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 

@software{pim_layer2_risk,
  author = {PassiveIncomeMaximizer Team},
  title = {Risk_qdrant - Layer 2 RL Agent},
  year = {2025},
  url = {https://github.com/yourusername/PassiveIncomeMaximizer}
}

More Information