Tradytics - Founder at Tradytics

Tradytics

Founder at Tradytics

Preston, England, Pakistan

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Summary

🤩

Rockstar

Tradytics is the founder of Tradytics and a data-focused software engineer with five years’ experience building AI-driven trading tools from Preston, England. He develops machine-learning systems for finance, notably contributing core anomaly detection and data-loading work to surpriver to spot big-moving stocks before they move. He also architects statistical and algorithmic strategies in eiten, implementing eigen-portfolio methods, genetic algorithms, and random matrix theory for noise-filtered covariance estimation. Hands-on across the stack, he improves model accuracy through feature engineering, volatility filters, and robust backtesting pipelines. Comfortable shipping research into production, he blends quantitative finance knowledge with practical engineering to make sophisticated strategies accessible. A subtle strength is his emphasis on reproducible command-line tooling and data workflows that enable rigorous future testing.

5 years of coding experience

Github Skills (30)

trading-algorithms10

anomaly-detection10

python10

trading10

scikit10

statistics10

pandas10

machine-learning10

tradingview10

algotrading10

optimisation10

scikit-learn10

algorithmic-trading10

optimization10

data-analysis10

Programming languages (1)

Python

Github contributions (4)

tradytics/eiten

Sep 2020 - Sep 2020

Statistical and Algorithmic Investing Strategies for Everyone

Role in this project:

Data Scientist

Contributions:62 commits, 4 PRs, 18 pushes in 14 days

Contributions summary:Tradytics implemented and modified core components of the eiten project, focusing on algorithmic trading strategies and portfolio management. They worked on data loading, return calculations, and the application of various strategies, including eigen portfolio and genetic algorithm approaches. The user also integrated random matrix theory for noise filtering in the covariance matrix, demonstrating a focus on statistical analysis and financial modeling. Furthermore, they incorporated command-line arguments for flexible configuration and added backtesting and future testing capabilities.

eigenvaluesinvestment-portfolioalgorithmic-tradingstatisticspython

tradytics/surpriver

Aug 2020 - Sep 2020

Find big moving stocks before they move using machine learning and anomaly detection

Role in this project:

ML Engineer

Contributions:54 commits, 8 PRs, 23 pushes in 22 days

Contributions summary:Tradytics primarily contributed to the `detection_engine.py` and `data_loader.py` files, which involve anomaly detection for stock analysis. Their contributions include implementing argument parsing, adding a volatility filter, fixing bugs, and adjusting the data loading process. The user also made changes to the feature generation process and refactored code related to future data handling, which suggests a focus on improving the model's accuracy and functionality.

stock-marketanomalypythondata-sciencestock-analysis

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