Step 1 — Database Exploration

Before writing a single line of analysis, a professional quant audits the data. This step answers:

• Which tickers are available?
• What is the date coverage?
• What are the price ranges?
• Are there any obvious data quality issues?

SQL Audit Query Pattern

SELECT
    ticker,
    COUNT(*)          AS trading_days,
    MIN(date)         AS start_date,
    MAX(date)         AS end_date,
    ROUND(MIN(close_price), 2) AS min_price,
    ROUND(MAX(close_price), 2) AS max_price,
    ROUND(AVG(close_price), 2) AS avg_price
FROM equity_prices
GROUP BY ticker
ORDER BY ticker;

Database Schema

equity_prices  (date, ticker, open_price, high_price, low_price, close_price, volume)
macro_indicators (date, indicator, value, release_date)
trade_ledger   (trade_id, ticker, direction, quantity, price, notional, desk)

💼 Analyst tip: At a hedge fund, the first question about any new dataset is always: "How many rows, which dates, and any gaps?" Getting this wrong wastes hours downstream.

Your Task

Run the audit query below. Verify you have 5 tickers, roughly 252 rows each (one trading year), and sensible price ranges.