Python Jesse-optuna Resources

Kaggle Competition: Forest Cover Type Prediction In this project we predict the forest cover type (the predominant kind of tree cover) using the carto

Indicator divergence library This module aims to help to find bullish/bearish divergences (regular or hidden) between two indicators using argrelextre

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.

numerai_NN_example Numerai tournament example scripts using pytorch NN, lightGBM and optuna https://numer.ai/tournament Performance of my model based

Jesse Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies. Why Jesse? In short, Jesse is

Jesse optuna Only works with the dashboard version / branch of jesse. The config.yml should be self-explainatory. Installation # install from git pip

AutoXGB XGBoost + Optuna: no brainer auto train xgboost directly from CSV files auto tune xgboost using optuna auto serve best xgboot model using fast

HPOflow - Sphinx DOC Tools for Optuna, MLflow and the integration of both. Detailed documentation with examples can be found here: Sphinx DOC Table of

mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do

easyopt is a super simple yet super powerful optuna-based Hyperparameters Optimization Framework that requires no coding.

An AutoML Library made with Optuna and PyTorch Lightning Installation Recommended pip install -U gradsflow From source pip install git+https://github.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can