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Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Table of Contents (18) Chapters Close

Preface 1. Acquiring Financial Data FREE CHAPTER 2. Data Preprocessing 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Deploying the technical analysis app

In the previous recipe, we have created a full-fledged web app for technical analysis, which we can easily run and use locally. However, that is not always the goal, as we might want to access the app from anywhere or share it with our friends or colleagues. That is why the next step would be to deploy the app to the cloud.

In this recipe, we show how to deploy the app using Streamlit’s (the company) services.

Getting Ready

To deploy the app to Streamlit Cloud, we need to create an account there (https://forms.streamlit.io/community-sign-up). You will also need a GitHub account to host the code of the app.

How to do it…

Execute the following steps to deploy the Streamlit app to the cloud:

  1. Host the codebase of the app in a public repository on GitHub:
Figure 5.16: The codebase of the app hosted in a public GitHub repository

In this step, remember to host the entire codebase of the app, which can naturally be spread out over multiple...

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