All Projects → jmartinezheras → Reproduce Stock Market Direction Random Forests

jmartinezheras / Reproduce Stock Market Direction Random Forests

Licence: apache-2.0
Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Projects that are alternatives of or similar to Reproduce Stock Market Direction Random Forests

Learning Deep Learning
Paper reading notes on Deep Learning and Machine Learning
Stars: ✭ 388 (+479.1%)
Mutual labels:  jupyter-notebook, paper
Labnotebook
LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
Stars: ✭ 526 (+685.07%)
Mutual labels:  jupyter-notebook, reproducibility
User Machine Learning Tutorial
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Stars: ✭ 393 (+486.57%)
Mutual labels:  jupyter-notebook, random-forest
Machine Learning With Python
Python code for common Machine Learning Algorithms
Stars: ✭ 3,334 (+4876.12%)
Mutual labels:  jupyter-notebook, random-forest
H2o 3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Stars: ✭ 5,656 (+8341.79%)
Mutual labels:  jupyter-notebook, random-forest
2018 Machinelearning Lectures Esa
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Stars: ✭ 280 (+317.91%)
Mutual labels:  jupyter-notebook, random-forest
Hyperparameter Optimization Of Machine Learning Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Stars: ✭ 516 (+670.15%)
Mutual labels:  jupyter-notebook, random-forest
Research Paper Notes
Notes and Summaries on ML-related Research Papers (with optional implementations)
Stars: ✭ 218 (+225.37%)
Mutual labels:  jupyter-notebook, paper
Dnc Tensorflow
A TensorFlow implementation of DeepMind's Differential Neural Computers (DNC)
Stars: ✭ 587 (+776.12%)
Mutual labels:  jupyter-notebook, paper
Srflow
Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch
Stars: ✭ 537 (+701.49%)
Mutual labels:  jupyter-notebook, paper
Enet Real Time Semantic Segmentation
ENet - A Neural Net Architecture for real time Semantic Segmentation
Stars: ✭ 238 (+255.22%)
Mutual labels:  jupyter-notebook, paper
Deep Embedded Memory Networks
https://arxiv.org/abs/1707.00836
Stars: ✭ 19 (-71.64%)
Mutual labels:  jupyter-notebook, paper
Jupyterwith
declarative and reproducible Jupyter environments - powered by Nix
Stars: ✭ 235 (+250.75%)
Mutual labels:  jupyter-notebook, reproducibility
Action Recognition Visual Attention
Action recognition using soft attention based deep recurrent neural networks
Stars: ✭ 350 (+422.39%)
Mutual labels:  jupyter-notebook, paper
Triplet Attention
Official PyTorch Implementation for "Rotate to Attend: Convolutional Triplet Attention Module." [WACV 2021]
Stars: ✭ 222 (+231.34%)
Mutual labels:  jupyter-notebook, paper
Pytorch classification
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
Stars: ✭ 395 (+489.55%)
Mutual labels:  jupyter-notebook, random-forest
Infiniteboost
InfiniteBoost: building infinite ensembles with gradient descent
Stars: ✭ 180 (+168.66%)
Mutual labels:  jupyter-notebook, random-forest
Dragan
A stable algorithm for GAN training
Stars: ✭ 189 (+182.09%)
Mutual labels:  jupyter-notebook, paper
Cvpr 2019 Paper Statistics
Statistics and Visualization of acceptance rate, main keyword of CVPR 2019 accepted papers for the main Computer Vision conference (CVPR)
Stars: ✭ 527 (+686.57%)
Mutual labels:  jupyter-notebook, paper
Computer Vision Action
computer vision learning, include python machine learning action; computer vision based on deep learning ;coursera deeplearning.ai and other cv learning materials collect ...
Stars: ✭ 19 (-71.64%)
Mutual labels:  jupyter-notebook, paper

reproduce-stock-market-direction-random-forests

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Khaidem, Luckyson, Snehanshu Saha, and Sudeepa Roy Dey. "Predicting the direction of stock market prices using random forest." arXiv preprint arXiv:1605.00003 (2016). paper

This is my attemp to reproduce this paper. In my way I found that the results I got are much worse than those from the authors and I wonder if the authors accidentaly had a data leakage issue.

Please, let me know if you notice any mistake in the analysis / code or if you feel there is something I misunderstood.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].