📈 Bitcoin Strategy Backtester

A professional Python-based financial chart generator and data analysis tool for cryptocurrency and stock market visualization. Generate publication-ready charts with technical indicators, volume analysis, and intelligent data caching.

📋 Table of Contents

• Overview
• Features
• Installation
• Quick Start
• Usage
• Chart Types & Indicators
• Technical Architecture
• Examples
• Development
• Contributing
• License

🚀 Overview

The Bitcoin Strategy Backtester is a powerful financial chart generation and data analysis tool built for traders, analysts, and developers. It provides professional-grade charting capabilities with real-time data fetching, intelligent caching, and comprehensive technical analysis features.

What it does:

• Generates professional financial charts for any Yahoo Finance symbol (BTC-USD, AAPL, TSLA, etc.)
• Provides multiple chart styles: candlestick, OHLC, line, and area charts
• Calculates and displays technical indicators with proper statistical validity
• Offers interactive CLI interface for rapid chart generation
• Manages data efficiently with SQLite caching and automatic expiry

Target Users:

• Financial analysts creating presentations and reports
• Cryptocurrency traders analyzing market trends
• Python developers building financial applications
• Researchers studying market behavior and technical analysis

✨ Features

📊 Professional Chart Generation

• Multiple Chart Styles: Candlestick, OHLC bars, line charts, and area charts
• Comprehensive Timeframes: 5D, 1M, 3M, 6M, YTD, 1Y, 5Y, and MAX (all available data)
• Technical Indicators: Moving averages (MA50, MA200) with proper lookback calculations
• Volume Analysis: Subplot and overlay volume visualization with up/down coloring
• Scaling Options: Linear and logarithmic price scales

🔄 Intelligent Data Management

• Real-time Data: Fetches live market data from Yahoo Finance
• SQLite Caching: Local database storage for improved performance
• Cache Expiry System: Automatic data refresh with configurable expiry (1-24 hours)
• Gap Filling: Smart handling of weekend and holiday data gaps
• Data Validation: Comprehensive OHLCV data quality checks

🖥️ Interactive CLI Interface

• Command-line Interface: Simple, powerful chart generation commands
• Interactive Mode: Step-by-step chart creation with guided prompts
• Cache Management: Built-in commands for cache status, refresh, and cleanup
• Chart History: Automatic saving and organization of generated charts
• Export Options: High-resolution PNG output for presentations

🏗️ Professional Architecture

• Modular Design: Clean separation of data, visualization, and interface layers
• Extensible Framework: Easy integration of new indicators and chart types
• Memory Efficient: Optimized data handling for large datasets
• Error Handling: Robust error recovery and user feedback
• Test Coverage: Comprehensive test suite with pytest

🛠 Installation

Prerequisites

• Python 3.11 or higher
• pip package manager
• Git for cloning the repository

Setup Instructions

1. Clone the repository

   git clone https://github.com/XaJason/bitcoin-strategy-backtester.git
   cd bitcoin-strategy-backtester

2. Create virtual environment

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install --upgrade pip
   pip install -r requirements.txt

4. Verify installation

   python chart_explorer.py --help

🚀 Quick Start

Generate Your First Chart

# Bitcoin 1-year candlestick chart with moving averages
python chart_explorer.py BTC-USD --timeframe 1Y --style candlestick --indicators BOTH --volume subplot

# Apple stock 6-month chart with MA50
python chart_explorer.py AAPL --timeframe 6M --style candlestick --indicators MA50

# Tesla 5-year line chart with logarithmic scale  
python chart_explorer.py TSLA --timeframe 5Y --style line --scale log

Interactive Mode

python chart_explorer.py
# Follow the prompts to create custom charts

Cache Management

# Check cache status
python chart_explorer.py --cache-status

# Refresh all cached data
python chart_explorer.py --cache-refresh

# View cache statistics
python chart_explorer.py --cache-info

📈 Chart Types & Indicators

Supported Chart Styles

• Candlestick: Professional OHLC candlestick charts with customizable colors
• OHLC: Traditional open-high-low-close bar charts
• Line: Clean line charts using closing prices
• Area: Filled area charts for trend visualization

Technical Indicators (Currently Implemented)

• MA50: 50-period Simple Moving Average
• MA200: 200-period Simple Moving Average
• BOTH: Display both MA50 and MA200 simultaneously
• Proper Lookback: Indicators calculated with sufficient historical data for statistical validity

Volume Analysis

• Subplot: Volume displayed in separate chart below price data
• Overlay: Volume overlaid on price chart with transparency
• Up/Down Coloring: Green for up days, red for down days

Supported Symbols

Any Yahoo Finance symbol including:

• Cryptocurrencies: BTC-USD, ETH-USD, ADA-USD, DOT-USD
• US Stocks: AAPL, TSLA, GOOGL, MSFT, AMZN
• Indices: ^GSPC (S&P 500), ^DJI (Dow Jones), ^IXIC (NASDAQ)
• Forex: EURUSD=X, GBPUSD=X, USDJPY=X

🏗️ Technical Architecture

Core Components

bitcoin-strategy-backtester/
├── chart_explorer.py          # Main CLI interface
├── src/
│   ├── data/
│   │   ├── database.py        # SQLite OHLCV database
│   │   ├── cache_manager.py   # Intelligent caching system
│   │   └── fetcher.py         # Yahoo Finance data fetcher
│   ├── visualization/
│   │   ├── chart_engine.py    # Core plotting engine
│   │   ├── indicators.py      # Technical indicator calculations
│   │   ├── styles.py          # Chart styling and themes
│   │   └── formatters.py      # Axis formatting and labels
│   └── utils/
│       └── config.py          # Configuration management
├── examples/                  # Sample charts and demos
├── tests/                     # Comprehensive test suite
└── tools/                     # Database utilities

Technology Stack

• Data Processing: pandas, numpy for efficient data manipulation
• Visualization: matplotlib, mplfinance for professional chart rendering
• Data Source: yfinance for real-time market data
• Database: SQLite for local data caching
• Testing: pytest for comprehensive test coverage
• CLI: argparse for command-line interface

Performance Characteristics

• Data Fetching: ~1-2 seconds for real-time data
• Chart Generation: ~2-3 seconds for complex charts with indicators
• Cache Hit: <0.5 seconds for cached data retrieval
• Memory Usage: ~50-200MB depending on data timeframe
• Storage: ~1-5MB per cached symbol dataset

📸 Examples

Command Examples with Outputs

# Professional Bitcoin analysis chart
python chart_explorer.py BTC-USD --timeframe 1Y --style candlestick --indicators BOTH --volume subplot
# Output: chart_BTC-USD_1Y_candlestick_[timestamp].png

# Apple stock quarterly analysis
python chart_explorer.py AAPL --timeframe 3M --style candlestick --indicators MA50 --volume overlay
# Output: chart_AAPL_3M_candlestick_[timestamp].png

# Tesla long-term trend analysis
python chart_explorer.py TSLA --timeframe 5Y --style line --scale log --indicators MA200
# Output: chart_TSLA_5Y_line_[timestamp].png

Available Chart Examples

The examples/charts/ directory contains sample outputs including:

• Multi-timeframe analysis (5D to MAX)
• Various chart styles (candlestick, line, area, OHLC)
• Technical indicator combinations
• Volume visualization options
• Linear vs logarithmic scaling examples

Use Cases

• Portfolio Presentations: Generate professional charts for client meetings
• Technical Analysis: Analyze trends with proper moving average calculations
• Market Research: Compare multiple symbols across different timeframes
• Educational Content: Create charts for trading education and tutorials

🔧 Development

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=src --cov-report=html

# Run specific test modules
pytest tests/test_chart_engine.py -v

Code Quality

# Format code
black src/ tests/

# Check code style
flake8 src/ tests/

Adding New Features

1. New Indicators: Add methods to TechnicalIndicators class in indicators.py
2. Chart Styles: Extend chart plotting methods in chart_engine.py
3. Data Sources: Implement new fetcher classes following the YahooFetcher pattern

🤝 Contributing

This project welcomes contributions! Current areas for enhancement:

Contribution Guidelines

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-indicator)
3. Add tests for new functionality
4. Ensure all tests pass (pytest)
5. Submit a pull request

Development Setup

# Install development dependencies
pip install -r requirements.txt
pip install black flake8 pytest-cov

# Run pre-commit checks
python -m pytest
python -m black --check src/ tests/

📄 License

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

---

Built with ❤️ for the financial analysis community

Generate professional charts • Analyze market trends • Make data-driven decisions