OPTIMIZING-STOCK-TRADING-STRATEGY-WITH-K-MEANS-CLUSTERING
Unverified ML strategy on Indices by Amey-Thakur. BotFinder score 18 out of 100.
Big Data Analytics [BDA] Mini Project
Source: github
BotFinder analysis pending.
OPTIMIZING-STOCK-TRADING-STRATEGY-WITH-K-MEANS-CLUSTERING
Optimizing Stock Trading Strategy with K-Means Clustering An analytical project utilizing unsupervised machine learning to cluster stocks based on their volatility and returns, identifying latent market patterns and optimizing diversified trading strategies. Source Code · Technical Specification · Video Demo · Live Demo --- Authors · Overview · Features · Structure · Quick Start · Usage Guidelines · License · About · Acknowledgments --- Authors Terna Engineering College | Computer Engineering | Batch of 2022 | Amey Thakur | Hasan Rizvi | Mega Satish | | :---: | :---: | :---: | [!IMPORTANT] 🤝🏻 Special Acknowledgement Special thanks to Hasan Rizvi and Mega Satish for their meaningful contributions, guidance, and support that helped shape this work. --- Overview This project investigates the application of K-Means Clustering on financial market data. By categorizing stocks into distinct clusters based on their historical price movements, the system provides
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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)