Nostradamus
Unverified ML strategy on Forex by renatovotto. BotFinder score 18 out of 100.
Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.
Source: github
BotFinder analysis pending.
Nostradamus
Trading Tesla with Machine Learning and Sentiment Analysis 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 Scope 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 s
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)