[See Description] Quantopian Fetcher - Python for Finance with Zipline and Quantopian 9

TL;DR

This tutorial explores how to access and use data outside of Quandl to backtest trading algorithms, specifically using the SentDex API for sentiment analysis.

Transcript

what is going on everybody welcome to the 9th Python for Finance tutorial video in this video we're gonna be talking about expanding the boundaries we're actually going outside of the quanto peon boundaries so up until this point we have only covered things that are included and are a part of quanto peon as far as whether we're calculating a moving... Read More

Key Insights

• 👻 Quandl's fetcher allows users to access CSV files hosted on external URLs and use the data for analysis.
• 👻 The SentDex API provides sentiment analysis for various companies, allowing users to gauge market sentiment and potentially trade based on it.
• 📡 Backtesting algorithms using sentiment signals can provide insights into the effectiveness of using sentiment analysis for trading.
• 👤 Users can also use their own signals instead of SentDex signals by following the specified format.
• 🤬 It is important to consider data updates and symbol lookup dates when using external data sources in backtesting algorithms.
• 🍵 The tutorial emphasizes the need to properly handle data fetching and preprocessing for successful analysis.

Questions & Answers

Q: Can we use data outside of Quandl in Python for Finance?

Yes, we can use the fetcher in Quandl to access CSV files hosted on external URLs and store them in a Panda's data frame.

Q: What is the SentDex API used for?

The SentDex API provides sentiment analysis for various companies, allowing users to analyze the sentiment of a company and potentially trade based on it.

Q: How can we backtest a trading algorithm using sentiment analysis?

By fetching sentiment signals from the SentDex API and using them as inputs, we can backtest a trading algorithm based on the sentiment analysis of different companies.

Q: Can we use our own signals instead of the SentDex signals for backtesting?

Yes, the tutorial shows the format required for the signals, and users can use their own signals by following the same format.

Summary & Key Takeaways

• The tutorial discusses the limitations of using data only within Quandl and explores the need to access external data sources.

• It introduces the SentDex API and explains how it provides sentiment analysis for various companies.

• The tutorial demonstrates how to use the fetcher in Quandl to access CSV files from external URLs and store them in a Panda's data frame.

• It covers the format of the syntax API and provides an example of using sentiment signals for backtesting a trading algorithm.