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zkarbie/
pim-layer2-rl-agents-Technical_qdrant

Name: pim-layer2-rl-agents-Technical_qdrant
Availability: InStock

pytorch

reinforcement-learning

tabular-classification

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Technical_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 technical 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:

MomentumAgent - Price momentum patterns
TechnicalAgent - Chart patterns and indicators
RiskAgent - Volatility and drawdown assessment
OptionsAgent - Options flow analysis
MacroAgent - Economic indicators
SentimentAgent - News and social sentiment
VolumeAgent - Trading volume patterns
SectorRotationAgent - Sector strength
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_technical,
  author = {PassiveIncomeMaximizer Team},
  title = {Technical_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

zkarbie/pim-layer2-rl-agents-Technical_qdrant