Orthrus: AI-Powered Algorithmic Trading Engine

An advanced algorithmic trading platform that combines DistilRoBERTa transformer models with sophisticated technical analysis to generate data-driven investment insights. Features comprehensive backtesting, adaptive risk management, and market regime detection for systematic trading strategies.

🎯 What Makes Orthrus Special

🤖 Hybrid AI Sentiment Analysis (Hence the Name): Combines VADER + DistilRoBERTa for superior financial news interpretation
📈 Adaptive Market Regime Detection: Automatically adjusts strategy based on BULL/BEAR/SIDEWAYS market conditions
🎯 Comprehensive Testing Framework: Extensive backtesting across multiple market scenarios and timeframes
🛡️ Advanced Risk Management: Dynamic stop-loss, take-profit, and volatility protection systems
⚡ Production-Ready: Professional-grade backtesting system with fair performance comparisons

🚀 Features

Dual Trading Strategies

VALUE Strategy: Rolling window Final_Buy_Score analysis for identifying statistically oversold opportunities
MOMENTUM Strategy: MACD/RSI combination with Golden Cross/Death Cross detection for swing trading and trend following

Revolutionary Hybrid Sentiment Analysis

Hybrid AI System: Combines VADER's nuanced scoring (-1 to +1) with DistilRoBERTa's financial accuracy
Enhanced Financial Lexicon: 40+ custom financial terms (crash: -3.0, soar: +3.0, beat: +2.5, etc.)
Smart Disagreement Resolution: When models disagree, uses confidence-weighted correction
Source Reliability Weighting: Four-tier categorization system (Primary, Institutional, Aggregators, Entertainment)
Real-Time News: Alpaca News API integration with intelligent filtering

Advanced Technical Analysis Algorithms

Rolling Window Analysis: 3-5 day rolling window with aggressive recency weighting (decay_rate=0.5)
B(t) Calculation: Individual buy scores at each time period with exponential decay
Final_Buy_Score: Weighted average of recent B(t) values for stable signals
MACD: Moving Average Convergence Divergence with bullish crossover detection
RSI: Relative Strength Index for momentum assessment
Golden Cross/Death Cross: SMA(50) vs SMA(200) major trend signals
Buy/Sell Signal Visualization: Green/red triangles on price charts
Adaptive Risk Management: Dynamic thresholds, stop-loss, take-profit, and trailing stops

Interactive Visualization

Real-Time Charts: Combined sentiment and price data visualization with trading signals
Dual Interface: Interactive mode (input.py) and headless CLI mode (headless.py)
Signal Markers: Visual buy/sell indicators on matplotlib charts
Source Breakdown: Article count by reliability tier
Multiple Timeframes: 1D, 5D, 1M, 6M analysis periods with strategy-specific restrictions
Performance Optimized: Memory-efficient processing with intelligent caching

Concrete Trading Recommendations

BUY/HOLD/SELL Signals: Clear actionable recommendations with confidence percentages
Market Regime Detection: Automatic BULL/BEAR/SIDEWAYS market identification
Adaptive Thresholds: Dynamic buy/sell thresholds that adjust to market conditions
Risk Management: Regime-specific stop-loss and take-profit recommendations
Backtester-Proven Logic: Uses the same algorithms validated in historical testing

Comprehensive Backtesting System

Historical Analysis: Test strategies against years of Alpaca market data
Realistic Simulation: Day-by-day execution with no look-ahead bias
Advanced Risk Management: Adaptive stop-loss, take-profit, and trailing stops based on market regime
Market Regime Detection: Automatic adaptation to BULL/BEAR/SIDEWAYS markets with volatility tolerance
Performance Analytics: Fair buy-and-hold comparison (always starts Day 1) with alpha calculation
Bull Market Optimization: Volatility tolerance system for maximum bull market participation
CLI Interface: Streamlined command-line backtesting with comprehensive batch testing

📊 Technical Specifications

Shared Logic Architecture

The system uses a unified logic module (src/logic.py) that ensures consistency between the main program and backtester:

Single Source of Truth: All trading algorithms are centralized in one module
Automatic Synchronization: Changes to logic automatically apply to both systems
Proven Algorithms: Logic is validated through extensive backtesting before deployment
Maintainable Code: No duplication of complex trading algorithms

Time-Series Algorithm Architecture

The system uses a sophisticated rolling window approach:

B(t) Calculation: Individual buy scores at each time period

B(t) = Weighted average of sentiment scores at time t
Recency weighting: exp(-decay_rate × age_in_days)

Final_Buy_Score Calculation: Rolling window analysis

Final_Buy_Score(t) = Σ(B(i) × weight(i)) for i in [t-window, t]
Window size: 3-5 days with aggressive decay (0.5)

Market Regime Adaptation:
- BULL Market: Wider stop-loss (-40%), higher take-profit (up to 600%), larger positions (up to 3.6x)
- STRONG_BULL: Maximum aggressiveness with bull market duration scaling
- BEAR Market: Tighter stop-loss (-6%), lower take-profit (+12%), smaller positions (0.6x)
- SIDEWAYS: Standard parameters with dynamic threshold adjustment
- Volatility Override: Extreme volatility protection with bull market tolerance

Hybrid Sentiment Analysis System

The revolutionary hybrid approach combines two complementary AI models:

VADER (Valence Aware Dictionary and sEntiment Reasoner):
- Provides nuanced scoring from -1.0 to +1.0
- Enhanced with custom financial lexicon
- Fast processing for real-time analysis
DistilRoBERTa Financial Model:
- Fine-tuned for financial news classification (mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis)
- High accuracy for positive/negative/neutral detection
- Neutral dampening logic (if Neutral > 0.6, multiply score by 0.2)

Hybrid Logic:

If models agree: Use VADER's nuanced score
If models disagree: Apply confidence-weighted correction
Formula: abs(vader_score) × ai_confidence × 0.7

Financial Algorithms Used

Rolling Window Final_Buy_Score: 3-5 day window with exponential decay
MACD: 12-day EMA - 26-day EMA with 9-day signal line
RSI: 14-day momentum oscillator
Golden Cross: SMA(50) > SMA(200) (+2.5 momentum points)
Death Cross: SMA(50) < SMA(200) (-2.5 momentum points)
Hybrid Sentiment: VADER + DistilRoBERTa with financial lexicon
Adaptive Risk Management: Market regime detection with volatility tolerance
Bull Market Duration Scaling: Progressive aggressiveness in sustained bull markets
Momentum Reversal Detection: Dynamic regime updates based on price/sentiment divergence

Source Reliability Tiers

PRIMARY (Weight: 2.0): SEC filings, Company IR
INSTITUTIONAL (Weight: 1.6-1.8): Bloomberg, WSJ, Reuters
AGGREGATORS (Weight: 1.0-1.5): Yahoo Finance, MarketWatch, Benzinga
ENTERTAINMENT (Weight: 0.3-0.8): Motley Fool, Reddit, Social Media

🔒 Security & API Keys

IMPORTANT: This project requires Alpaca API keys for market data access.

Getting API Keys (Free)

Sign up at Alpaca Markets
Navigate to Paper Trading Dashboard
Generate API keys (paper trading is free)
Copy .env.example to .env and add your keys

Security Best Practices

✅ API keys are loaded via environment variables (secure)
✅ .env file is in .gitignore (not committed to repo)
✅ Uses paper trading endpoint (no real money at risk)
⚠️ Never commit real API keys to version control

🛠 Installation

Clone the repository

git clone https://github.com/yourusername/orthrus.git
cd orthrus

Create virtual environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```

Set up API keys (create .env file)

# Copy the example file
cp .env.example .env

# Edit .env with your actual Alpaca API keys
# Get free keys at: https://app.alpaca.markets/paper/dashboard/overview
ALPACA_API_KEY=your_actual_key_here
ALPACA_SECRET_KEY=your_actual_secret_here

🎯 Usage

Real-Time Analysis

Interactive Mode (Recommended)

Run the program: python src/main/input.py
Enter a stock ticker (e.g., TSLA, AAPL, MSFT)
Select strategy first (VALUE or MOMENTUM)
Choose timeframe based on strategy restrictions
View results and interactive charts with trading signals

Headless Mode (CLI/Automation)

Command-line analysis: python src/main/headless.py <ticker> <strategy> <period> [--save]

Examples:

# Quick analysis without GUI (run from project root)
python src/main/headless.py TSLA m 30

# Save chart to file
python src/main/headless.py AAPL v 90 --save

Perfect for: Automation, scripting, server environments, or when you don't want GUI popups

Historical Backtesting

Quick Start

# Basic backtest
python backtester/run_backtest.py TSLA m 30

# With custom capital and visualization
python backtester/run_backtest.py AAPL v 180 25000 --plot --save

Command Structure

python backtester/run_backtest.py <ticker> <strategy> <period> [cash] [--plot] [--save]

Parameters:

ticker: Stock symbol (TSLA, AAPL, NVDA, MSFT, etc.)
strategy: v (VALUE) or m (MOMENTUM)
period: Days (30, 90, 180) OR date range (2023-01-20 2023-07-20)
cash: Starting capital (default: $10,000)
--plot: Show performance charts
--save: Save results to JSON file

Advanced Testing

# Random batch testing (new flexible approach)
python backtester/batch_backtest.py           # Run 10 random backtests
python backtester/batch_backtest.py 25        # Run 25 random backtests  
python backtester/batch_backtest.py 50 --seed 123  # Reproducible results

# View all usage examples and tips
python backtester/examples.py

Strategy-Specific Timeframes

VALUE Strategy: 1M, 6M, YTD, MAX (long-term analysis)
MOMENTUM Strategy: 1D, 5D, 1M, 6M (short to medium-term signals)

Output Interpretation

Market Sentiment: 0-10 scale based on hybrid AI sentiment analysis
Market Analysis: 0-10 based on chosen strategy algorithms
Final Buy Score: Combined score with rolling window analysis
Trading Signal: 🟢 BUY / 🟡 HOLD / 🔴 SELL with confidence percentage
Market Regime: BULL / BEAR / SIDEWAYS detection for adaptive thresholds
Adaptive Thresholds: Dynamic buy/sell thresholds based on market conditions
Risk Management: Regime-specific stop-loss and take-profit recommendations
Top Headlines: Best/worst sentiment-scored news with confidence scores

📈 Strategy Details

VALUE Strategy

Objective: Identify statistically oversold stocks using rolling window analysis
Algorithm: Final_Buy_Score with 5-day rolling window and recency weighting
Timeframes: 1M, 6M, YTD, MAX
Best For: Long-term value investing, contrarian plays
Risk Management: Conservative thresholds with capital preservation focus

MOMENTUM Strategy

Objective: Capture trend continuation, breakouts, and major trend changes
Signals:
- MACD bullish crossover + RSI not overbought
- Golden Cross (SMA50 > SMA200) = major bullish signal
- Death Cross (SMA50 < SMA200) = major bearish signal
Timeframes: 1D, 5D, 1M, 6M
Best For: Swing trading, trend following, momentum plays
Risk Management: Dynamic thresholds with aggressive profit-taking

📈 Backtesting System

Comprehensive Strategy Testing

Location: backtester/ folder
Capability: Test strategies against historical Alpaca data with realistic simulation
Features:
- Real Alpaca market data integration (11+ years of data back to 2014)
- Day-by-day simulation with no look-ahead bias
- Pre-calculated B(t) and Final_Buy_Score for maximum efficiency
- Advanced adaptive risk management with market regime detection
- Bull market volatility tolerance system for enhanced participation
- Fair buy-and-hold comparison methodology
- Comprehensive testing framework across multiple market scenarios
- Performance analysis with detailed categorized results
- Visual performance charts and P&L tracking

Quick Reference

Basic Command Structure

python backtester/run_backtest.py <ticker> <strategy> <period> [cash] [--plot] [--save]

Parameters

ticker: Stock symbol (TSLA, AAPL, NVDA, MSFT, etc.)
strategy: m (momentum) or v (value)
period: Days (30, 90, 180) OR date range (2023-01-20 2023-07-20)
cash: Starting capital (default: $10,000)
--plot: Show performance charts
--save: Save results to JSON file

Usage Examples

# Basic momentum test (30 days)
python backtester/run_backtest.py TSLA m 30

# Value strategy with custom capital
python backtester/run_backtest.py AAPL v 180 25000

# Historical date range with charts and save
python backtester/run_backtest.py NVDA m 2023-01-20 2023-07-20 --plot --save

# High capital test with full analysis
python backtester/run_backtest.py MSFT m 90 50000 --plot --save

# Quick comparison (momentum vs value)
python backtester/run_backtest.py AAPL m 30 && python backtester/run_backtest.py AAPL v 30

# Comprehensive testing across multiple scenarios
python backtester/batch_backtest.py

# View all usage examples and tips
python backtester/examples.py

Pro Tips

Historical Dates: Use dates 15+ days old to avoid API rate limits
Momentum Strategy: Works best with 30-90 day periods for swing trading
Value Strategy: Optimal with 90-180 day periods for long-term analysis
Validation: Test multiple tickers to validate strategy robustness
Market Conditions: Test across different market regimes (bull/bear/sideways)
Capital Testing: Try different starting amounts to test scalability
Random Testing: Use batch_backtest.py for diverse, unbiased testing across sectors and time periods

Performance Optimization

Efficient Data Processing: All sentiment analysis done once at startup
Pre-calculated Scores: B(t) and Final_Buy_Score calculated in advance
Fast Simulation: Lookup-based trading decisions for maximum speed
Memory Management: Optimized data structures and garbage collection

⚠️ Disclaimer

This tool is for educational and research purposes only. Do not rely solely on SigmaStocks for investment decisions. Always conduct your own research and consider consulting with financial professionals before making investment choices.

🔧 Requirements

Python 3.8+
Internet connection for real-time data
Alpaca API keys for news and historical data
Optional: CUDA-compatible GPU for faster sentiment analysis
See requirements.txt for complete dependency list

📦 Dependencies

Core Analysis

pandas>=2.0.0 - Data manipulation and analysis
numpy>=1.24.0 - Numerical computing
yfinance>=0.2.0 - Financial data retrieval

Hybrid Sentiment Analysis

vaderSentiment>=3.3.2 - Nuanced sentiment scoring
transformers>=4.30.0 - DistilRoBERTa model
torch>=2.0.0 - PyTorch for neural networks
tokenizers>=0.13.0 - Text tokenization

News & Visualization

alpaca-py>=0.7.0 - Professional news data
matplotlib>=3.7.0 - Chart visualization
tqdm>=4.65.0 - Progress bars

Backtesting

pytz>=2023.3 - Timezone handling for historical data
concurrent.futures - Parallel processing for efficiency

📝 License

This project is licensed under the GNU GPLv3 License - see the LICENSE file for details.

Built with Python, Hybrid AI (VADER + DistilRoBERTa), Advanced Risk Management, and Comprehensive Backtesting

Name		Name	Last commit message	Last commit date
Latest commit History 50 Commits
backtester		backtester
src		src
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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aneesh223/orthrusquant

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