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AutoTrader: Multi-Agent Trading System

Copyright (C) 2024-2025 Chris R. (iAmGiG) | Licensed under AGPL-3.0 | See NOTICE

Powered by AgentEdge - AI-assisted trading with human oversight

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Disclaimer

THIS SOFTWARE IS FOR EDUCATIONAL AND RESEARCH PURPOSES ONLY.

• Not Financial Advice: This system does not provide financial, investment, or trading advice
• Use at Your Own Risk: Trading involves substantial risk of loss
• No Warranties: Provided "as-is" without guarantees of accuracy or profitability
• Past Performance: Walk-forward validated results do not guarantee future performance

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Overview

AutoTrader is a research-driven trading platform featuring a multi-agent AI architecture powered by Microsoft AutoGen. The system implements walk-forward validated strategies with rigorous statistical testing and human oversight for paper/live trading via Alpaca Markets.

Core Philosophy: Validated momentum + regime filtering + human decision making > curve-fit technical indicators

Key Research Findings (Jan 2026)

Recent rigorous validation experiments with corrected methodology:

• GEX Regime Filtering: Research contradiction identified - MACD+RSI and academic TSMOM behave differently in GEX regimes. Phase 3B on hold pending resolution. See project status.
• Transaction Cost Robustness: MACD+RSI shows 44% pass rate vs TSMOM's 19% despite 10x higher turnover (#519)
• MACD Parameter Stability: No robustly profitable MACD configs found out-of-sample. Best OOS Sharpe: -0.223 (#518)
• Simulation Fidelity Gap: Path-dependent backtesting engine created to address "wick risk" (#528)

Strategy Validation Results

| Issue | Strategy | Finding | Status | |-------|----------|---------|--------| | #516 | TSMOM+GEX Hybrid | Median improvement: -2.9% (worse than baseline) | CLOSED | | #518 | MACD Stability | OOS Sharpe: -0.223 (least unprofitable) | CLOSED | | #519 | Transaction Costs | MACD+RSI: 44% pass rate, TSMOM: 19% | CLOSED |

Methodology: Walk-forward validation, turnover-proportional transaction costs, look-ahead bias protection, median reporting for outlier robustness.

Note: Past performance does not guarantee future results. Research is ongoing with focus on simulation fidelity improvements.

Production Status

| Component | Status | |-----------|--------| | VoterAgent | Production Ready | | CLI Trade Assistant | Complete | | Alpaca Integration | Operational | | Position Management | Complete | | Trading Cycle | Complete | | SQLite Cache | Complete (90%+ hit rate) | | LLM Intent Routing | Complete | | ScannerAgent | In Development | | RiskAgent | In Development | | ExecutorAgent | In Development |

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Core Agents

VoterAgent (Production Ready)

Location: src/autogen_agents/voter_agent.py

• MACD Signal Generation: Optimized 13/34/8 Fibonacci parameters
• RSI Momentum Analysis: 14-period with 30/70 thresholds
• Consensus Voting: Strong signals when both indicators agree
• Validated Performance: 0.856 Sharpe ratio over 2024-2025

LLM-Based Intent Classification

• Natural Language Commands: GPT-4o-mini parses trade requests
• Context-Aware Routing: Distinguishes "any open orders?" from ticker "ANY"
• Scalable Design: No hardcoded keyword patterns for ticker disambiguation

Agents in Development

• ScannerAgent: Market opportunity identification
• RiskAgent: Position sizing and risk management
• ExecutorAgent: Trade execution via Alpaca API
• TradingOrchestrator: Multi-agent coordination

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Installation

# Python 3.12+ required
conda create -n AutoTrader python=3.12
conda activate AutoTrader
pip install -e .

Configuration

Create config/config.yaml with API credentials:

POLYGON_IO: "your_key"           # Required: Market data
ALPHA_VANTAGE_KEY: "your_key"    # Required: Fallback data
ALPACA_PAPER_API_KEY: "your_key" # Required: Paper trading
ALPACA_PAPER_SECRET: "your_key"  # Required: Paper trading
ALPACA_ENDPOINT: "https://paper-api.alpaca.markets/v2"

# Optional - VoterAgent uses pure math, no LLM required
OPEN_AI_KEY: "sk-..."

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Quick Start

Interactive CLI

python main.py

# Example session:
> buy 10 AAPL              # Execute trade
> show portfolio           # View positions
> check my alerts          # Position alerts
> /schedule                # Scheduler management
> /help                    # All commands

Daemon Mode

python main.py --daemon

# Runs twice daily:
# - Morning: 9:20 AM ET (reconciliation)
# - Evening: 3:50 PM ET (review)

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CLI Commands

Trading

| Command | Description | |---------|-------------| | buy SYMBOL QTY | Place buy order | | sell SYMBOL QTY | Place sell order | | cancel ORDER_ID | Cancel order |

Information

| Command | Description | |---------|-------------| | show portfolio | Portfolio summary | | show positions | Open positions with P&L | | show orders | Order history | | show account | Account details |

Configuration

| Command | Description | |---------|-------------| | show timeframe | Current timeframe | | set timeframe 1d | Change timeframe | | show config-file | View YAML config | | show watchlist | Scanner symbols |

Workflow

| Command | Description | |---------|-------------| | morning-routine | Morning scan and analysis | | evening-summary | End-of-day report | | monitor | Watch positions for exits | | forward-test start NAME | Start validation test |

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Project Structure

AutoTrader-AgentEdge/
├── src/
│   ├── autogen_agents/     # AI agents (AutoGen framework)
│   │   ├── voter_agent.py  # Production MACD+RSI voting
│   │   ├── base_agent.py   # Base agent class
│   │   └── ...             # Other agents (in development)
│   ├── trading/            # Trading infrastructure
│   │   ├── broker/         # Alpaca integration
│   │   ├── orders/         # Order management
│   │   ├── positions/      # Position tracking
│   │   └── scheduling/     # Daily routines
│   ├── data_sources/       # Market data
│   └── cli/                # CLI tools
├── config/                 # API credentials (local only)
├── config_defaults/        # Default YAML configs
├── docs/                   # Documentation
├── reports/                # Trading reports
└── tests/                  # Test suite

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Documentation

| Topic | Location | |-------|----------| | Architecture | docs/02_architecture/ | | Development | docs/04_development/ | | Design Decisions | docs/05_decisions/ | | Features | docs/06_features/ | | Testing | docs/07_testing/ |

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Development

Testing

# Unit tests
python -m pytest tests/ -v

# Code quality
ruff check src/
black --check src/

# VoterAgent validation
python -c "from src.autogen_agents.voter_agent import VoterAgent; print('OK')"

Contributing

1. Pick an issue from GitHub Issues
2. Create a feature branch
3. Make changes following docs/05_decisions/
4. Submit PR to development branch

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License

AGPL-3.0 - See LICENSE file for details.