Quantitative Equity Research Report

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(trade_date)   AS start_date,
    MAX(trade_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    (id, ticker, trade_date, open_price, high_price, low_price, close_price, volume)
macro_indicators (id, release_date, indicator_name, value, prior_value)
trade_ledger     (trade_id, ticker, trade_date, side, quantity, price, 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 520 rows each (about two trading years, 2022-01-03 to 2023-12-29), and sensible price ranges.