Mastering the Market Insights — Building the Ultimate Trading Data Pipeline
In the realm of trading, a robust data strategy involves tapping into a mix of traditional and alternative data sources. Traditional market data, such as stock prices and historical trends, provides a foundation, while alternative sources like social media sentiments and economic indicators offer unique insights.
This series of articles will guide you through the process of gathering data from various sources, including traditional financial exchanges, external APIs, news feeds and more.
Employing a standardized ETL (Extract, Transform, Load) process, predominantly utilizing Pandas and Apache Spark, we’ll cover the following key steps:
- Extract (data ingestion). Retrieving information of different sources (data ingestion).
- Transform (validation, cleaning and transformation). The data will undergo a cleaning and validation process to ensure accuracy and reliability.
- Load (storage). Once validated, the data will be stored in a centralized data “lake”, utilizing mainly a database, but also other storage formats (Parquet, …).
The final architecture of our trading data pipeline will be the shown below:
We’ll leave the trading data analysis for another article series.
- Part 1: Implementing a data pipeline for populating indices composition. A pipeline will be crafted to extract the composition of various indices (SP&500, …) from a market provider and store this information in our database.
- Part 2: Implementing a data pipeline for populating stocks details. This article will delve into creating a pipeline to extract and store detailed information about individual stocks (sector, industry, …).
- Part 3: Implementing a data pipeline for daily historical data.
- Part 4: Executing daily historical data pipeline in a cluster
- Part 5: Orchestratrion of pipelines. How to orchestrate and automate all steps and data pipelines.
- Part 6: Populating factor and indicators in files. This article delves in creating Parquet files that will be used for data analysis.
- Bonus: Weekly data aggregation. This article delves in continuous aggregates from daily data.
- Bonus: Automate the computation of max and min historical values. This article delves in trade-offs on how to compute data in tables, on-demand (SQL, Dataframes, …) or other formats.
Welcome to this thrilling journey into the heart of market data architecture!
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*Note that this article does not provide personal investment advice and I am not a qualified licensed investment advisor. All information found here is for entertainment or educational purposes only and should not be construed as personal investment advice.
