ari5123_assignment
Unverified ML strategy on Crypto by achmand. BotFinder score 18 out of 100.
Rule-based Algorithmic Trading using a Genetic Algorithm and Machine Learning Signals for the Cryptocurrency Market.
Source: github
BotFinder analysis pending.
ari5123_assignment
Genetic Algorithm and Machine Learning Signals for the Cryptocurrency Market This is my final project for the ‘Intelligent Algorithmic Trading Assignment’ for the ARI5123 study unit. The final results for this project is best viewed using nbviewer, click on the following link to view results Jupyter Notebook. Resources for this project; algo module [Holds different trading strategies including AI/ML strategies] datareader module [Reads data from Binance, also have abstractions to be able to add other data sources] Jupyter Notebook/Results [Notebook with visualisations and results] Datasets [Datasets collected] Anaconda Environment Research Paper Setup Environment Python version 3.7.3 Running under: Ubuntu 18.04.1 LTS IDE/Text Editor: Visual Code An anaconda environment file (environment.yml) is supplied to be able to run the Jupyter Notebook. If you don’t have anaconda installed on your system, follow this tutorial to install anaconda on Ubuntu 18.04. Once anaconda is set up on your system execute the following commands to create an new environment from the supplied yaml file. Scope
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Drawdown profile
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Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
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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)