"Trading System - Low Level System Design"

1. Create a Well-Defined Folder Structure for S3

The idea is to organize the data into a hierarchy that is easy to query and retrieve later. The folder structure should follow a pattern that makes it clear where the data for each stock symbol is and which date it corresponds to.

For example, you can structure it like this:

s3://your-bucket/intraday/{stock_symbol}/{date}/data.csv
s3://your-bucket/daily_ohlcv/{date}/{stock_symbol}/ohlcv.csv

Explanation:

• intraday/{stock_symbol}/{date}/data.csv: This stores intraday data for each stock symbol, organized by date.
• daily_ohlcv/{date}/{stock_symbol}/ohlcv.csv: This stores daily OHLCV data, organized by date first (to optimize querying for a specific date range), and then by stock symbol.

2. Create a Script to Organize Data

You need a script that:

• Scrapes the intraday data and stores it in a structured format on S3.
• Organizes data in folders as per the above structure.
• The script should handle:
  • Folder creation: It checks if a folder for that stock symbol and date exists. If not, it creates the folder.
  • Data upload: After scraping, the script uploads the data (in CSV or JSON format) to the appropriate S3 folder.

3. Automating the Process

• Scheduling: Use a scheduling mechanism (like cron or Airflow) to run the script at defined intervals.
  • For example, you can run the intraday data scraper every 15 minutes, hourly, or daily based on your needs.

Script Example (in Go or Python):

Here’s a simplified example using Python and the boto3 library to upload data to S3:

import boto3
import os
from datetime import datetime
 
# Initialize S3 client
s3_client = boto3.client('s3')
 
def upload_data_to_s3(stock_symbol, date, data, bucket_name):
    # Define the folder structure
    folder = f'intraday/{stock_symbol}/{date}/'
    s3_key = f'{folder}data.csv'
    
    # Upload data to S3
    s3_client.put_object(Body=data, Bucket=bucket_name, Key=s3_key)
    print(f"Data uploaded to {s3_key}")
 
# Example data
stock_symbol = 'AAPL'
date = datetime.now().strftime('%Y-%m-%d')
data = 'timestamp,price\n2024-11-17T12:00:00,150.00'  # This would be your scraped data
bucket_name = 'your-bucket-name'
 
upload_data_to_s3(stock_symbol, date, data, bucket_name)

This script uploads the scraped data for each stock symbol into the appropriate folder (intraday/{stock_symbol}/{date}/data.csv).

4. Benefits of This Structure

• Efficiency: S3 folders are hierarchical and efficient when using AWS tools for querying (e.g., AWS Athena or Redshift Spectrum).
• Scalability: Storing data by stock symbol and date helps scale the data as you grow your dataset with more equities and over time.
• Easy Data Retrieval: You can easily query or load data by specifying the stock symbol and date range.

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• This pipeline is Build and ready on production.