Pinned Repositories
ACV
package for optimal out-of-sample forecast evaluation and testing under stationarity
atp_betting
Automated-Fundamental-Analysis
Python program that rates stocks out of 100 based on valuation, profitability, growth, and price performance metrics, relative to the company's sector.
bayesian-football
Replication of 'Bayesian hierarchical model for the prediction of football results' using pymc
book_rating
Book recommendation model.
causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
covid19_dataset_cz
covid19_dataset_cz
Deep-Portfolio-Management
Source code for the blog post on the evolution of the asset allocation methods
nannyml
Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.
Tennis
Model for predicting WTA tennis matches outcome.
Tomkess's Repositories
Tomkess/ACV
package for optimal out-of-sample forecast evaluation and testing under stationarity
Tomkess/atp_betting
Tomkess/Automated-Fundamental-Analysis
Python program that rates stocks out of 100 based on valuation, profitability, growth, and price performance metrics, relative to the company's sector.
Tomkess/book_rating
Book recommendation model.
Tomkess/causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
Tomkess/covid19_dataset_cz
covid19_dataset_cz
Tomkess/Deep-Portfolio-Management
Source code for the blog post on the evolution of the asset allocation methods
Tomkess/Digital-DataScience-WebApp
Tomkess/DrMax-Group-Replenishment
Tomkess/nannyml
Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.
Tomkess/Stock
Stock Market Prediction Using Unsupervised Features
Tomkess/udacity_ds
The folder containing the exercises from udacity nanodegree program.
Tomkess/z_ds
Tomkess/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Strategy
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learn
Tomkess/duino-coin
ᕲ Duino-Coin is a coin that can be mined with almost everything, including Arduino boards.
Tomkess/FastTreeSHAP
Fast SHAP value computation for interpreting tree-based models
Tomkess/FinQBoost
Financial Portfolio Quintile Probability Forecaster #2 winner of M6 Financial Forecasting Competition
Tomkess/fortuna_scraper
Tomkess/investment-portfolio-optim
An investment portfolio of stocks was created using LSTM stock price prediction and optimized weights. The performance of this portfolio was better compared to an equally weighted portfolio and a market capitalization-weighted portfolio.
Tomkess/M6
Tomkess/MtMs_sinusoidal_task
Tomkess/NBA-Machine-Learning-Sports-Betting
NBA sports betting using machine learning
Tomkess/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Tomkess/QMiners-Hackathon
Hackathon - betting agent
Tomkess/qminers_hackatlon2020
Tomkess/Stock-Prediction-Portfolio-Optimization
A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WIP and I will keep on adding new ideas to this in future.
Tomkess/Stock-Screener-using-Technical-Analysis-and-Portfolio-Optimization-using-Efficient-Frontiers
The Goal of this project is to screen the individual stocks from the S&P 500 stock list and make an optimized portfolio of stocks with better returns, risk, Sharpe ratio and comparative diversity.
Tomkess/StockScreener-1
Simple S&P 500 screening with momentum and random forest algorithms.
Tomkess/Tennis-Betting-ML
Machine Learning model(specifically log-regression with stochastic gradient descent) for tennis matches prediction. Achieves accuracy of 66% on approx. 125000 matches
Tomkess/tennis-prediction-ml
Machine learning models to predict tennis results