Complete code and webinar recording - https://blog.quantinsti.com/algo-trading-epat-projects-8-february-2022/ ----------------------------------------- An interactive program to train a Random Forest Classifier to predict Tesla daily prices using technical indicators and sentiment scores of Twitter posts, backtesting the trading strategy and producing performance metrics. The project leverages techniques, paradigms and data structures such as: - Functional and Object-Oriented Programming - Machine Learning - Sentiment Analysis - Concurrency and Parallel Processing - Direct Acyclic Graph (D.A.G.) - Data Pipeline - Idempotence The intention behind this project was to implement the end-to-end workflow of the backtesting of an Algorithmic Trading strategy in a program with a sleek interface, and with a level of automation such that the user is able to tailor the details of the strategy and the output of the program by entering a minimal amount of data, partly even in an interactive way. This should make the program reusable, meaning that it's easy to carry out the backtesting of the trading strategy on a different asset. Furthermore, the modularity of the software design should facilitate changes to adapt the program to different requirements (i.e. different data or ML models). ----------------------------------------- About the Presenter: Renato Votto (A Data analyst from the United Kingdom) Renato genuinely enjoys and thrives on developing software for automation, finding algorithmic solutions for complex problems and optimising programs. He possesses a quantitative edge and is proficient in Algorithmic Trading, Quant Development, Machine Learning, Data Engineering, Python, C++, and SQL. Renato has a background in Energy Engineering and is an alumnus of the Executive Programme in Algorithmic Trading (EPAT). Before enrolling in EPAT, he had also completed Machine Learning and Data Engineering studies. Currently working as an analyst at the Office For Gas and Electricity Markets (OFGEM), he is mainly involved in the development of software and tools for quantitative analysis and systematic identification of market manipulation. He is a Cryptocurrencies enthusiast, an avid rock and roll listener, loves to code, a mediocre guitar strummer, and when he is outdoors you might find him taking weird photographs. ----------------------------------------- Link to our Blog: https://blog.quantinsti.com/ Like us on Facebook @ https://www.facebook.com/quantinsti/ Follow us on Twitter @ https://twitter.com/QuantInsti Follow us on LinkedIn @ https://www.linkedin.com/school/quantinsti/ Follow us on Instagram @ https://www.instagram.com/quantinstian/ E-mail us @ sales@quantinsti.com -----------------------------------------

1. Trading Tesla with Machine Learning and
Sentiment Analysis
Renato Votto
Data Analyst
Ofgem, Market Intelligence and Oversight
London, U.K.

2. Motivations
• Consolidating knowledge (mixing old and new skills)
• Expanding skill set
• Exploring power of Machine Learning and NLP in Trading
• Building reusable framework and program

3. Steps
• Defining the problem
• Designing program structure
• Implementing functional and object oriented paradigms
• Bringing improvements and new features
• Testing usability
• Reviewing correctness of calculations and results

4. Overview Running the Program

5. Overview Running the Program

6. Overview Running the Program

7. Overview Strategy Performance

8. Overview Strategy Performance

9. Overview Strategy Performance

10. Challenges
• Complex program logic because of interactive design
• Ad-hoc solutions for some performance metrics computation
• Time, ability to focus and work/life balance

11. Overcoming Challenges
• Drawing schemes and diagrams on whiteboard
• Writing pseudocode when stuck
• Taking long breaks, doing something else, going for a walk
• Web searching your showstopper, studying what others did

12. Achievements
• Consolidated and expanded quantitative finance knowledge
• Expanded and improved programming skills
• Experienced the potential of ML in quantitative trading
• Gained confidence in handling end-to-end projects

13. Next Steps
• Recycling program for other projects (different assets, crypto)
• Sharing and expanding knowledge with other enthusiasts
• Researching new strategies and different ML models
• Implementing live trading strategy

14. Get in Touch
• LinkedIn: https://www.linkedin.com/in/renatovotto/
• Email: renatovotto@gmail.com
• Blog: https://quantifrutti.com/
• GitHub: https://github.com/renatovotto