A professional system for cryptocurrency market analysis and algorithmic trading strategies, featuring a modern web interface for real-time market insights and historical performance analysis. This project combines advanced data processing, machine learning, and real-time monitoring to provide comprehensive cryptocurrency market analysis.
- Interactive dashboard with real-time cryptocurrency data
- Technical indicators visualization (RSI, MACD, Bollinger Bands)
- Multi-language support (English/Spanish)
- Customizable time periods for analysis
- Market trend identification and recommendations
- Direct integration with Binance API for real-time crypto data
- ETL pipeline for efficient data processing
- Real-time data extraction from multiple sources:
- Binance API for cryptocurrency data
- Yahoo Finance for additional market data
- Advanced data transformation and feature engineering
- Efficient data loading and storage
- Historical data analysis and backtesting
- Moving Average Crossover strategy
- Backtesting system with performance metrics
- Risk analysis and performance evaluation
- Advanced metrics calculation (Sharpe, Sortino, VaR, etc.)
- Strategy optimization and parameter tuning
- Real-time market monitoring
- Customizable price alerts
- Trend change notifications
- Volume spike detection
- Performance tracking and reporting
Our Telegram bot provides real-time market insights and automated trading signals. Here's what you can do with it:
-
Market Analysis
- Get real-time price updates
- Receive technical analysis reports
- View trend predictions
- Monitor volume and volatility
-
Trading Signals
- Automated buy/sell signals
- Price alerts
- Trend change notifications
- Volume spike alerts
-
Custom Commands
/start - Initialize the bot /help - Show available commands /analyze <symbol> - Get technical analysis /list - Show available cryptocurrencies /alert <symbol> - Set price alerts /trend <symbol> - Get trend analysis /volume <symbol> - Check volume analysis
Telegram bot interface showing real-time market analysis and commands
- Bitcoin (BTC)
- Ethereum (ETH)
- Binance Coin (BNB)
- Stellar (XLM)
- Ripple (XRP)
- Dogecoin (DOGE)
Main dashboard interface showing real-time market data and technical analysis
Detailed technical analysis with multiple indicators
Market trend analysis and recommendations
Multi-language support with English and Spanish interfaces
Cryptocurrency selection interface with real-time price updates
Here's an overview of the dashboard's functionalities through screenshots:
This image shows the main dashboard interface, including language and cryptocurrency selection, along with key real-time market metrics.
Visualize detailed technical analysis with indicators like SMA 20, SMA 50, Bollinger Bands, and MACD, essential for understanding market trends.
A combined view of the dynamic investor report, offering a summary of the period, recommendations, and an explanation of the market trend, along with the historical data table.
-
Data Processing Layer
- ETL pipeline for data extraction and transformation
- Real-time data processing
- Historical data management
- Feature engineering
-
Analysis Layer
- Technical analysis engine
- Strategy implementation
- Backtesting system
- Performance metrics calculation
-
Presentation Layer
- Streamlit web interface
- Telegram bot integration
- Real-time alerts system
- Data visualization
-
Monitoring Layer
- Prometheus metrics
- Performance monitoring
- System health checks
- Alert management
- Backend: Python 3.8+
- Web Framework: Streamlit
- Data Processing: Pandas, NumPy
- Data Sources:
- Binance API (primary source for crypto data)
- Yahoo Finance (supplementary market data)
- Machine Learning: Scikit-learn
- Visualization: Plotly
- Monitoring: Prometheus
- Containerization: Docker
- Orchestration: Docker Compose
- Workflow Management: Apache Airflow
- Python 3.8+
- Dependencies listed in
requirements.txt - Streamlit for web interface
- Binance API for cryptocurrency data
- YFinance for supplementary market data
- python-telegram-bot for Telegram integration
- Docker and Docker Compose (optional)
- Prometheus (optional)
- Clone the repository:
git clone https://github.com/RanuK12/algorithmic-trading-python.git
cd algorithmic-trading-python- Install dependencies:
pip install -r requirements.txt-
Configure API Keys:
- Create a Binance account and get your API keys
- Add the keys to your environment variables or
.envfile:BINANCE_API_KEY=your_api_key_here BINANCE_API_SECRET=your_api_secret_here
-
Configure Telegram Bot:
- Create a new bot using @BotFather
- Get your bot token
- Add the token to your environment variables or
.envfile:TELEGRAM_BOT_TOKEN=your_bot_token_here
-
Run the dashboard:
python -m streamlit run main.py- Start the Telegram bot (in a separate terminal):
python telegram_bot.pydocker-compose up -dThe dashboard provides several key features:
- Language Selection: Choose between English and Spanish interfaces
- Cryptocurrency Selection: Select from supported cryptocurrencies
- Time Period Selection: Choose analysis period (1d to 1y)
- Technical Indicators:
- RSI (Relative Strength Index)
- MACD (Moving Average Convergence Divergence)
- Bollinger Bands
- Moving Averages (20 and 50 periods)
-
Start the Bot
- Send
/startto initialize the bot - Use
/helpto see available commands
- Send
-
Market Analysis
- Use
/analyze BTCto get technical analysis for Bitcoin - Receive technical analysis reports
- View trend predictions
- Monitor volume and volatility
- Use
-
Price Alerts
- Set price alerts with
/alert BTC 50000 - Get notified when price targets are reached
- Receive trend change notifications
- Set price alerts with
-
Real-time Updates
- Get instant market updates
- Receive trading signals
- Monitor portfolio performance
Here's an example of the analysis output for Bitcoin:
Trading Analysis - BTC
==================================================
1. Overall Performance:
- Total Return: -27.87%
- Annualized Return: -18.32%
- Sharpe Ratio: -0.37
- Sortino Ratio: -0.50
2. Trading Activity:
- Number of Trades: 378
- Success Rate: 49.21%
- Profit Factor: 0.94
- Average Gain/Loss Ratio: 0.97
3. Risk Analysis:
- Annual Volatility: 39.22%
- Maximum Drawdown: -55.93%
- Calmar Ratio: -0.33
- VaR (95%) : -3.98%
- Expected Shortfall (95%) : -5.74%
- Total Return: Overall percentage gain/loss
- Positive: Strategy is profitable
- Negative: Strategy is losing money
- Annualized Return: Yearly equivalent return
-
20%: Excellent
- 10-20%: Good
- < 10%: Poor
-
- Sharpe Ratio: Risk-adjusted returns
-
1: Good
-
2: Very Good
-
3: Excellent
-
- Sortino Ratio: Downside risk-adjusted returns
-
1: Good
-
2: Very Good
-
3: Excellent
-
- Number of Trades: Total executed trades
- High: > 100 trades/month
- Medium: 50-100 trades/month
- Low: < 50 trades/month
- Success Rate: Percentage of profitable trades
-
50%: Good
-
60%: Very Good
-
70%: Excellent
-
- Profit Factor: Gross profit to gross loss ratio
-
1: Profitable
-
1.5: Good
-
2: Excellent
-
- Average Gain/Loss Ratio: Profit per winning trade vs loss per losing trade
-
1: Good
-
1.5: Very Good
-
2: Excellent
-
- Annual Volatility: Standard deviation of returns
- < 20%: Low risk
- 20-40%: Medium risk
-
40%: High risk
- Maximum Drawdown: Largest peak-to-trough decline
- < 20%: Low risk
- 20-40%: Medium risk
-
40%: High risk
- VaR (95%): Maximum expected loss with 95% confidence
- < 5%: Low risk
- 5-10%: Medium risk
-
10%: High risk
- Expected Shortfall: Average of losses beyond VaR
- < 7%: Low risk
- 7-15%: Medium risk
-
15%: High risk
- Calmar Ratio: Annualized return to maximum drawdown
-
1: Good
-
2: Very Good
-
3: Excellent
-
- Volume Analysis
- Volume trend
- Volume vs price correlation
- Volume spikes detection
- Trend Analysis
- Moving average crossovers
- Support/resistance levels
- Trend strength indicators
- Momentum Indicators
- RSI overbought/oversold levels
- MACD signal crossovers
- Stochastic oscillator signals
├── main.py # Main Streamlit application
├── telegram_bot.py # Telegram bot implementation
├── src/
│ ├── data/
│ │ ├── __init__.py
│ │ └── exchange.py # Market data handling
│ ├── strategies/
│ │ ├── base.py # Base strategy class
│ │ └── moving_average.py
│ ├── backtest/
│ │ ├── runner.py # Backtesting system
│ │ └── __main__.py
│ ├── analysis/
│ │ └── technical.py # Technical analysis
│ └── utils/
│ └── helpers.py # Utility functions
├── notebooks/
│ └── analysis/ # Jupyter notebooks for analysis
├── docs/
│ └── images/ # Documentation images
├── tests/ # Test suite
├── monitoring/
│ └── prometheus/ # Prometheus setup
├── airflow/ # Airflow DAGs
│ └── dags/ # Workflow definitions
└── requirements.txt # Project dependencies
We welcome contributions! Here's how you can help:
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Follow PEP 8 style guide
- Write unit tests for new features
- Update documentation as needed
- Use meaningful commit messages
This project is licensed under the MIT License - see the LICENSE file for details.
RanuK12 - @RanuK12
Project Link: https://github.com/RanuK12/crypto-analysis-dashboard
- Yahoo Finance for market data
- Streamlit for the web framework
- Plotly for interactive visualizations
- Python-Telegram-Bot for bot integration
- All contributors and supporters of the project
MIT — © 2026 Ranuk IT Solutions | ranuk.dev