Financial Data Crawler and Structured Storage in SQL Database
This project, titled “Financial Data Crawler and Structured Storage in SQL Database,” aims to extract, clean, and store financial statistics from a financial website into a structured SQL database. The software is optimized with strategies to prevent blocking and enhance its performance. Below are the key stages of the project:
- Data Extraction
The core focus is extracting financial data from a financial website using a combination of web scraping tools, including:
- Scrapy: The primary framework for collecting data and managing HTTP requests.
- BeautifulSoup: Used for parsing HTML and extracting precise data.
- Selenium: Handles dynamic websites requiring JavaScript.
These tools are used in combination to collect data from various pages and complex web structures.
- Block Prevention Strategies
To avoid being blocked by target servers, several strategies were employed:
- Rate Limiting: Controls the number of requests to prevent overloading the server.
- Proxy and IP Rotation: Prevents requests from being blocked by frequently changing IP addresses.
- User-Agent Spoofing: Simulates requests from different browsers.
- Randomized Delays: Mimics user behavior to avoid detection by security systems.
- Data Cleaning
Extracted data often contains noise or irrelevant information. Data preprocessing involves:
- Invalid Data Removal: Filters out non-financial and irrelevant information.
- Formatting and Normalization: Standardizes dates, numbers, and currency formats.
- Resolving Data Inconsistencies: Ensures consistency across all collected data.
- Structured Storage in SQL Database
After cleaning and organizing the data, it is stored in a SQL database with a structured format:
- Financial Data Organization by Company and Time Period: Each company has its financial records stored across different time periods.
- Relational Structure for Complex Queries: The tables are designed relationally with foreign keys for faster and more efficient data access.
SQLAlchemy is used to manage the database and ORM connections between models.
- Crawler Optimization
The crawler was optimized to efficiently handle large volumes of data:
- Concurrency Management: Using asynchronous capabilities in Scrapy, requests and data processing are executed simultaneously to improve speed.
- Request Caching: Prevents redundant requests, increasing efficiency.
- Data Compression and Temporary Storage: Before final database storage, data is compressed and stored temporarily for optimal performance.
Technologies and Tools Used:
- Scrapy: Core crawling framework for request management.
- BeautifulSoup: For more precise HTML data extraction.
- Selenium: To handle dynamic websites with JavaScript.
- SQLAlchemy: Manages the database and ORM.
- Pandas & NumPy: For financial data analysis and statistics.
- Proxies & Rate Limiting: To prevent server blocks.
This project offers businesses and financial analysts accurate and rapid access to financial statistics, enabling them to make better decisions regarding financial analysis and investments.