Explore data analysis techniques for detecting price and volume patterns in stock market data using SQL and TimescaleDB. Learn to leverage PostgreSQL aggregate functions and TimescaleDB-specific features like Continuous Aggregates. Discover how to identify common price patterns such as bullish engulfing and three bar breakouts. Dive into time bucketing, gap filling, and window functions to analyze Twitter stock data. Master the creation of hourly and daily bars, query top gainers, examine the last 5 minutes of trading, and analyze closing prices. Gain insights on refreshing continuous aggregates to keep your data up-to-date for effective stock market analysis.
Detecting Price and Volume Patterns with SQL and TimescaleDB
Overview
Syllabus
Intro
Postgres aggregate functions
Examples
Twitter Stock
Time Bucketing
Time Bucket Gap Fill
Time Bucket Gap Fill Example
Continuous Aggregates
Hourly Bars
Daily Bars
Daily Bars Queries
Window Functions
Top Gainers
Last 5 Minutes of Trading
Close of the Day
Bullish Engulfing
Refreshing Continuous Aggregates
Taught by
Part Time Larry
