[See Description] Accessing Fundamental company Data - Programming for Finance with Python - Part4 | Summary and Q&A

TL;DR

This tutorial discusses the inclusion of fundamental data in algorithmic trading and highlights the differences between fundamental data and quantitative data.

Key Insights

• ❓ Most algorithmic traders rely on quantitative data derived from historical prices, which is inherently biased.
• ❓ Fundamental data provides valuable insights into a company's value and growth potential.
• 👻 Quantopian allows access to a vast database of fundamental metrics for incorporation into trading strategies.
• 🦔 Incorporating fundamental data can give traders an edge over those who rely solely on quantitative data.
• ❓ Obtaining fundamental data programmatically has been challenging, but it can be done with platforms like Quantopian.
• 🎰 Fundamental data can be used in machine learning algorithms to improve trading strategies.
• 🥳 Price-to-book ratio and price-to-earnings ratio are two fundamental metrics commonly used in trading strategies.

Transcript

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Questions & Answers

Q: How is fundamental data different from quantitative data in algorithmic trading?

Fundamental data pertains directly to a company's value, such as price-to-book ratio or historical growth, while quantitative data is derived from historical prices and patterns.

Q: Why is it important to incorporate fundamental data in trading strategies?

Fundamental data provides a more accurate measure of a company's value, allowing traders to make informed decisions based on the company's financial health and growth potential.

Q: How can fundamental data be obtained programmatically?

Platforms like Quantopian provide access to a database of fundamental metrics, like Morning Star, which can be queried using SQL Alchemy queries.

Q: Can fundamental data be used in machine learning algorithms?

Yes, fundamental data can be used to train classifiers and incorporate machine learning techniques into trading strategies.

Summary & Key Takeaways

• Most algorithmic traders rely on quantitative data derived from historical prices, but fundamental data, which directly relates to a company's value, is a more accurate measure.

• Obtaining fundamental data programmatically has been challenging, but platforms like Quantopian allow access to a vast database of fundamental metrics.

• By incorporating fundamental data into trading strategies, traders can gain an edge over others who rely solely on quantitative data.