Financial Data Scraper

A comprehensive financial data scraping and analytics tool designed for collecting, processing, and analyzing forex and cryptocurrency market data. This tool provides real-time data collection, historical data analysis, and advanced financial analytics for trading and research purposes.

Features

Multi-Source Data Collection - Scrape data from multiple financial sources
Real-time Data Streaming - Live market data collection and processing
Historical Data Analysis - Comprehensive historical data analysis
Technical Indicators - Built-in technical analysis indicators
Data Visualization - Interactive charts and data visualization
API Integration - Integration with popular financial APIs
Data Storage - Efficient data storage and management
Automated Scheduling - Automated data collection scheduling

Data Sources

Forex Data

Forex.com - Real-time forex data and historical prices
OANDA - Professional forex data and analytics
FXCM - Forex market data and trading information
Yahoo Finance - Free forex data and market information
Alpha Vantage - Real-time and historical forex data

Cryptocurrency Data

CoinGecko - Comprehensive cryptocurrency data
CoinMarketCap - Cryptocurrency market data and rankings
Binance API - Real-time crypto trading data
Coinbase Pro - Professional crypto trading data
Kraken API - Cryptocurrency exchange data

Alternative Data

News Sources - Financial news and sentiment data
Social Media - Social media sentiment analysis
Economic Indicators - Economic data and indicators
Central Bank Data - Central bank announcements and data

Data Collection

Web Scraping

HTML Parsing - BeautifulSoup and Scrapy for web scraping
API Integration - RESTful API integration for data collection
Rate Limiting - Intelligent rate limiting and request management
Error Handling - Robust error handling and retry mechanisms
Data Validation - Data quality validation and cleaning

Real-time Streaming

WebSocket Connections - Real-time data streaming via WebSockets
Event-driven Architecture - Event-driven data processing
Data Buffering - Efficient data buffering and processing
Connection Management - Automatic connection management and recovery

Analytics Capabilities

Technical Analysis

Moving Averages - Simple, exponential, and weighted moving averages
Oscillators - RSI, MACD, Stochastic, and other oscillators
Trend Indicators - ADX, Parabolic SAR, and trend indicators
Volume Analysis - Volume-based indicators and analysis
Pattern Recognition - Chart pattern recognition and analysis

Statistical Analysis

Descriptive Statistics - Mean, variance, skewness, kurtosis
Correlation Analysis - Asset correlation and cointegration analysis
Volatility Analysis - Historical and implied volatility analysis
Risk Metrics - Value at Risk, Expected Shortfall, and risk metrics
Performance Analysis - Return analysis and performance metrics

Machine Learning

Time Series Forecasting - ARIMA, SARIMA, and ML-based forecasting
Anomaly Detection - Detection of market anomalies and outliers
Sentiment Analysis - News and social media sentiment analysis
Feature Engineering - Automated feature engineering for ML models
Model Training - Automated model training and validation

Tech Stack

Language: Python 3.8+
Web Scraping: BeautifulSoup, Scrapy, Selenium
Data Processing: Pandas, NumPy, SciPy
Database: PostgreSQL, MongoDB, InfluxDB
Visualization: Matplotlib, Plotly, Dash
Machine Learning: Scikit-learn, TensorFlow, PyTorch
APIs: FastAPI, Flask for data serving

Installation & Setup

Clone the repository:

git clone https://github.com/1cbyc/financial-data-scraper.git
cd financial-data-scraper

Install dependencies:
```
pip install -r requirements.txt
```
Set up database:
```
python setup_database.py
```

Configure data sources:

cp config.example.yaml config.yaml
# Edit configuration with your API keys and preferences

Start data collection:

python scraper.py --source forex --symbols EUR/USD,GBP/USD

Usage Examples

Basic Data Collection

from financial_scraper import FinancialScraper

# Initialize scraper
scraper = FinancialScraper(
    api_keys={
        'alpha_vantage': 'your_api_key',
        'oanda': 'your_api_key'
    }
)

# Collect forex data
forex_data = scraper.collect_forex_data(
    symbols=['EUR/USD', 'GBP/USD'],
    timeframe='1h',
    start_date='2024-01-01',
    end_date='2024-12-31'
)

# Collect crypto data
crypto_data = scraper.collect_crypto_data(
    symbols=['BTC/USD', 'ETH/USD'],
    timeframe='1d'
)

Real-time Data Streaming

from data_streamer import DataStreamer

# Initialize streamer
streamer = DataStreamer()

# Start real-time streaming
def on_data(data):
    print(f"Received: {data}")

streamer.start_streaming(
    symbols=['EUR/USD', 'BTC/USD'],
    callback=on_data
)

Technical Analysis

from technical_analysis import TechnicalAnalyzer

# Initialize analyzer
analyzer = TechnicalAnalyzer()

# Calculate technical indicators
indicators = analyzer.calculate_indicators(
    data=forex_data,
    indicators=['sma', 'ema', 'rsi', 'macd']
)

# Generate trading signals
signals = analyzer.generate_signals(indicators)

Data Storage and Management

Database Schema

-- Market data table
CREATE TABLE market_data (
    id SERIAL PRIMARY KEY,
    symbol VARCHAR(20) NOT NULL,
    timestamp TIMESTAMP NOT NULL,
    open DECIMAL(10,5),
    high DECIMAL(10,5),
    low DECIMAL(10,5),
    close DECIMAL(10,5),
    volume DECIMAL(15,2),
    source VARCHAR(50),
    created_at TIMESTAMP DEFAULT NOW()
);

-- Technical indicators table
CREATE TABLE technical_indicators (
    id SERIAL PRIMARY KEY,
    symbol VARCHAR(20) NOT NULL,
    timestamp TIMESTAMP NOT NULL,
    indicator_name VARCHAR(50) NOT NULL,
    value DECIMAL(10,5),
    parameters JSONB
);

Data Processing Pipeline

Data Collection - Collect data from multiple sources
Data Cleaning - Clean and validate collected data
Data Storage - Store data in appropriate databases
Data Processing - Calculate technical indicators and analytics
Data Serving - Serve processed data via APIs

Visualization and Reporting

Interactive Dashboards

Real-time Charts - Live market data visualization
Technical Analysis - Technical indicator charts
Performance Metrics - Performance and risk metrics
Correlation Analysis - Asset correlation visualization

Automated Reports

Daily Reports - Daily market summary and analysis
Weekly Reports - Weekly performance and trend analysis
Monthly Reports - Monthly comprehensive market analysis
Custom Reports - Customizable report generation

API Endpoints

Data Endpoints

GET /api/v1/data/{symbol} - Get historical data
GET /api/v1/real-time/{symbol} - Get real-time data
GET /api/v1/indicators/{symbol} - Get technical indicators
POST /api/v1/collect - Trigger data collection

Analytics Endpoints

GET /api/v1/analysis/{symbol} - Get market analysis
GET /api/v1/correlation - Get correlation analysis
GET /api/v1/volatility - Get volatility analysis
POST /api/v1/forecast - Generate price forecasts

Project Impact

This scraper has been used for:

Trading Systems - Data feed for algorithmic trading systems
Research - Academic and industry research
Risk Management - Market risk analysis and management
Portfolio Management - Portfolio analysis and optimization

Future Enhancements

Alternative Data - Integration with alternative data sources
AI/ML Integration - Advanced AI/ML capabilities
Cloud Deployment - Cloud-based data processing
Mobile App - Mobile data access and visualization
Real-time Alerts - Real-time market alerts and notifications