An agent that helps you predict the next day's stock data.
A production-ready stock analysis and prediction system that combines technical analysis, sentiment analysis, and AI-powered forecasting using OpenAI models.
- 📊 Comprehensive Technical Analysis: 6+ technical indicators (RSI, MACD, Moving Averages, OBV, Support/Resistance, Candlestick Patterns)
- 📰 Sentiment Analysis: News sentiment analysis using keyword-based scoring
- 🤖 AI-Powered Predictions: OpenAI GPT-4 powered next-day price forecasting
- ⚡ Async & Concurrent: High-performance async architecture with concurrent processing
- 🔧 Production Ready: FAANG-level software engineering practices with SOLID principles
- 📈 Batch Analysis: Analyze multiple stocks simultaneously
- 💾 Caching & Storage: Built-in result caching and file storage
- 🛠️ Extensible: Modular design with factory patterns and dependency injection
# Clone the repository
git clone https://github.com/RyoK3N/Buck_V1
cd Buck_V1
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
export OPENAI_API_KEY="your-openai-api-key"
export INDIAN_API_KEY="your-indian-api-key" # Optional for news data
# Or configure keys once using Python (creates a .env file)
python - <<'EOF'
from agent_scripts import set_api_keys
set_api_keys("your-openai-api-key", "your-indian-api-key")
EOFimport asyncio
from agent_scripts import create_agent
async def analyze_stock_example():
# Create the agent
agent = create_agent(
openai_api_key="your-openai-api-key",
indian_api_key="your-indian-api-key", # Optional
model="gpt-4o"
)
# Analyze a stock
async with agent:
results = await agent.analyze_and_predict(
symbol="BHEL.NS",
start_date="2024-01-01",
end_date="2024-01-10",
interval="1h"
)
# Display forecast
forecast = results['forecast']
print(f"Next-day forecast for BHEL.NS:")
print(f"Close: ₹{forecast['close']:.2f}")
print(f"Confidence: {forecast['confidence']:.2f}")
print(f"Reasoning: {forecast['reasoning']}")
# Run the example
asyncio.run(analyze_stock_example())# Analyze a single stock
python -m agent_scripts.cli analyze BHEL.NS --start 2025-06-18 --end 2024-06-20
# Run demo
python -m agent_scripts.cli demo --symbol BHEL.NS
# Use custom parameters
python -m agent_scripts.cli analyze BHEL.NS \
--start 2024-01-01 \
--end 2024-01-10 \
--interval 30m \
--model gpt-4o \
--api-key your-openai-keyThe main orchestrator class that coordinates data collection, analysis, and prediction.
from agent_scripts import BuckFactory
# Create with default settings
agent = BuckFactory.create_default_agent()
# Create with custom configuration
agent = BuckFactory.create_production_agent(
openai_api_key="your-key",
indian_api_key="your-news-key",
model="gpt-4o"
)analyze_and_predict(): Complete analysis workflow for a single stockbatch_analyze(): Analyze multiple stocks concurrentlyget_cached_analysis(): Retrieve cached analysis resultsclear_cache(): Clear all cached data
{
"symbol": "BHEL.NS",
"timestamp": "2024-01-15T10:30:00",
"data_info": {
"start_date": "2024-01-01",
"end_date": "2024-01-10",
"interval": "1h",
"data_points": 168,
"news_available": True
},
"analysis_results": [...], # Technical and sentiment analysis
"forecast": {
"date": "2024-01-11",
"open": 245.30,
"high": 248.75,
"low": 242.10,
"close": 247.80,
"confidence": 0.75,
"reasoning": "Technical indicators show bullish momentum..."
},
"metadata": {
"agent_version": "1.0.0",
"model_used": "gpt-4o",
"analysis_confidence": 0.82,
"prediction_confidence": 0.75
}
}The system follows SOLID principles with a clean, modular architecture:
market_forecaster/
├── interfaces.py # Abstract interfaces and protocols
├── stock_agent.py # Main orchestrator
├── data_providers.py # Data collection (Yahoo Finance, News API)
├── tools.py # Technical analysis tools
├── analyzers.py # Analysis coordinators
├── predictors.py # AI-powered prediction
├── config.py # Configuration management
└── cli.py # Command-line interface
- Data Providers: Fetch stock data (Yahoo Finance) and news data (Indian API)
- Analysis Tools: Individual technical indicators (RSI, MACD, etc.)
- Analyzers: Coordinate multiple tools for comprehensive analysis
- Predictors: Generate AI-powered forecasts using OpenAI
- Stock Agent: Main orchestrator that ties everything together
| Indicator | Purpose | Signals |
|---|---|---|
| RSI | Overbought/Oversold conditions | Buy < 30, Sell > 70 |
| MACD | Trend momentum and direction | Buy when MACD > Signal |
| Moving Averages | Trend identification | Buy when price > MA |
| OBV | Volume flow analysis | Rising OBV = Buying pressure |
| Support/Resistance | Key price levels | Buy near support, sell near resistance |
| Candlestick Patterns | Reversal signals | 19 classical patterns detected |
Create a .env file in the project root to store these variables. The
.gitignore file already excludes .env so your secrets remain private.
You can generate this file automatically using set_api_keys:
from agent_scripts import set_api_keys
set_api_keys("your-openai-key", "your-indian-key")# Required
OPENAI_API_KEY=your-openai-api-key
# Optional
INDIAN_API_KEY=your-indian-api-key
CHAT_MODEL=gpt-4o
TEMPERATURE=0.1
MAX_COMPLETION_TOKENS=500
NEWS_ITEMS=10
LOG_LEVEL=INFO
OUTPUT_DIR=outputfrom agent_scripts.interfaces import BuckConfig
from agent_scripts import BuckFactory
config = BuckConfig(
openai_api_key="your-key",
chat_model="gpt-4o",
temperature=0.1,
max_tokens=500,
news_items=10,
log_level="INFO"
)
agent = BuckFactory.create_custom_agent(config)- Async Architecture: Non-blocking I/O for better performance
- Concurrent Processing: Analyze multiple stocks simultaneously
- Caching: Built-in result caching to avoid redundant API calls
- Error Handling: Comprehensive error handling with graceful degradation
- Rate Limiting: Built-in concurrency controls to respect API limits
# Install development dependencies
pip install -r requirements.txt
# Run tests
pytest tests/ -v
# Run with coverage
pytest tests/ --cov=market_forecaster --cov-report=html🔍 Analyzing BHEL.NS from 2024-01-01 to 2024-01-10...
📈 Analysis Results for BHEL.NS
==================================================
Data Period: 2024-01-01 to 2024-01-10 (1h)
Data Points: 168
News Available: Yes
Model Used: gpt-4o
🔮 Next-Day Forecast (2024-01-11)
------------------------------
Open: ₹245.30
High: ₹248.75
Low: ₹242.10
Close: ₹247.80
Confidence: 0.75
Reasoning: Technical indicators show bullish momentum with RSI at 65, MACD positive crossover, and price above key moving averages. News sentiment is neutral with no major negative factors.
📊 Confidence Scores
--------------------
Analysis: 0.82
Prediction: 0.75
💾 Results saved to output directory
# Production environment variables
export OPENAI_API_KEY="prod-openai-key"
export INDIAN_API_KEY="prod-indian-key"
export LOG_LEVEL="INFO"
export CHAT_MODEL="gpt-4o"The buck_visualizer folder contains helper scripts for interactive Plotly charts. Each script downloads fresh data via data_provider_viz.py and then displays an interactive figure with helpful explanations. Example:
python buck_visualizer/price_ma_plot.py BHEL.NS 2024-01-01 2024-01-31Available scripts:
- price_ma_plot.py
- candlestick_volume_plot.py
- macd_plot.py
- bollinger_plot.py
- rsi_plot.py
- volatility_plot.py
- returns_histogram.py
- news_overlay_plot.py
These scripts require the optional plotly dependency. Install it via:
pip install -r requirements.txt- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the Apache License - see the LICENSE file for details.
** Now lets start making some Bucks 💰💰💰 **