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1
Intro
2
Postgres aggregate functions
3
Examples
4
Twitter Stock
5
Time Bucketing
6
Time Bucket Gap Fill
7
Time Bucket Gap Fill Example
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Continuous Aggregates
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Hourly Bars
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Daily Bars
11
Daily Bars Queries
12
Window Functions
13
Top Gainers
14
Last 5 Minutes of Trading
15
Close of the Day
16
Bullish Engulfing
17
Refreshing Continuous Aggregates
Description:
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

Part Time Larry
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