Use deep learning, genetic programming and other methods to predict stock and market movements

Overview

StockPredictions

Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements.

Both successful and unsuccessful experiments will be posted. This section is things that are currently being explored. Completed projects will be wrapped up and moved to another repository to keep things simple.

The main goal of this project is to learn more about time series analysis and prediction. The stock market just happens to have lots of complicated time series and available data

The first evolving neural net does the best job of predicting daily changes. It's impressive. That'll be my first go to tool

The NASDAQ Evolved Network is a good simple example that should be easy to apply to any index

Data sources:

http://finance.yahoo.com/

https://fred.stlouisfed.org/

https://stooq.com

Data and the cleaning programs:

https://github.com/timestocome/StockMarketData

Recommended Reading:

http://www.e-m-h.org/Fama70.pdf Efficient Market Hypothesis

http://faculty.chicagobooth.edu/workshops/finance/pdf/Shleiferbff.pdf Bubbles for FAMA

http://www.unofficialgoogledatascience.com/2017/04/our-quest-for-robust-time-series.html How Google does series predictions

http://www.econ.ucla.edu/workingpapers/wp239.pdf Let's Take the Con Out of Economics

https://www.manning.com/books/machine-learning-with-tensorflow Meap Machine Learning with TensorFlow

https://www.amazon.com/gp/product/B01AFXZ2F4/ Everybody Lies, Big Data, New Data, and What the Internet can tell us about who we really are

https://www.amazon.com/gp/product/B06XDWV2Z2 The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets

https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series Finding anomalies in time series

https://www.wired.com/2009/02/wp-quant/ Wired: The Formula that Killed Wall St

http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6419.2007.00519.x/abstract What do we know about the profitability of technical analysis

https://eng.uber.com/neural-networks/ Engineering extreme event forecasting at Uber with RNNs

http://lib.ugent.be/fulltxt/RUG01/001/315/567/RUG01-001315567_2010_0001_AC.pdf An empirical analysis of algorithmic trading on financial markets

http://www.radio.goldseek.com/bachelier-thesis-theory-of-speculation-en.pdf The Theory of Speculation, L. Bachelier

http://dl.acm.org/citation.cfm?id=1541882 Anomaly Detection: A Survey 2009 ACM

http://www.mrao.cam.ac.uk/~mph/Technical_Analysis.pdf Technical Analysis

https://is.muni.cz/th/422802/fi_b/bakalarka_final.pdf Prediction of Financial Markets Using Deep Learning ( see: https://github.com/timestocome/FullyConnectedForwardFeedNets for an example fully connected deep learning network )

http://www.doc.ic.ac.uk/teaching/distinguished-projects/2015/j.cumming.pdf An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain

On my reading list:

http://socserv.mcmaster.ca/racine/ECO0301.pdf Nonparametric Econometrics: A Primer

http://natureofcode.com/ The Nature of Code

http://www.penguinrandomhouse.com/books/314049/scale-by-geoffrey-west/9781594205583/ Scale: The universal laws of growth...

https://en.wikipedia.org/wiki/The_Drunkard%27s_Walk The Drunkard's Walk

Useful Websites:

http://www.nber.org/ The National Bureau of Economic Research

https://fred.stlouisfed.org/ FRED, Federal Reserve Bank of St Louis

http://www.zerohedge.com/ ZeroHedge, mostly noise, occasionally something useful appears

Cool tools:

https://facebookincubator.github.io/prophet/docs/quick_start.html Facebook Prophet - Python and R time series prediction library

https://research.google.com/pubs/pub41854.html Inferring causal impact using bayesian structural time series models ( Google has an R package http://google.github.io/CausalImpact/ to go with this paper )

https://gbeced.github.io/pyalgotrade/ Python Algorithmic Trading Library

http://pybrain.org/ PyBrain Machine Learning Library

https://github.com/CodeReclaimers/neat-python Python NEAT Library for evolving neural networks

Podcasts:

http://www.podcastchart.com/podcasts/berkshire-hathaway-2017-annual-shareholders-meeting/episodes/berkshire-hathaway-vice-chairman-charlie-munger-speaks-with-yahoo-finance-editor-in-chief-andy-serwer 2017 Berkshire Hathaway Shareholder's Meeting

Owner
Linda MacPhee-Cobb
Physicist, Computer Scientist Interests: AI, Machine Learning, Signal Processing, Sensors, Robotics, Evolutionary Algorithms and Hardware
Linda MacPhee-Cobb
An Efficient Implementation of Analytic Mesh Algorithm for 3D Iso-surface Extraction from Neural Networks

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and top

Karbo 45 Dec 21, 2022
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usa

Marcel R. 349 Aug 06, 2022
Yoloxkeypointsegment - An anchor-free version of YOLO, with a simpler design but better performance

Introduction 关键点版本:已完成 全景分割版本:已完成 实例分割版本:已完成 YOLOX is an anchor-free version of

23 Oct 20, 2022
Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters"

Manga Character Screentone Synthesis Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters" presented in IEEE ISM 2

Tsubota 2 Nov 20, 2021
This repo is official PyTorch implementation of MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices(CVPRW 2021).

Github Code of "MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices" Introduction This repo is official PyTorch implementatio

Choi Sang Bum 203 Jan 05, 2023
Implements the training, testing and editing tools for "Pluralistic Image Completion"

Pluralistic Image Completion ArXiv | Project Page | Online Demo | Video(demo) This repository implements the training, testing and editing tools for "

Chuanxia Zheng 615 Dec 08, 2022
The best solution of the Weather Prediction track in the Yandex Shifts challenge

yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re

Ivan Yu. Bondarenko 15 Dec 18, 2022
PyTorch and GPyTorch implementation of the paper "Conditioning Sparse Variational Gaussian Processes for Online Decision-making."

Conditioning Sparse Variational Gaussian Processes for Online Decision-making This repository contains a PyTorch and GPyTorch implementation of the pa

Wesley Maddox 16 Dec 08, 2022
Semi-supervised Implicit Scene Completion from Sparse LiDAR

Semi-supervised Implicit Scene Completion from Sparse LiDAR Paper Created by Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou and YA-QIN ZH

114 Nov 30, 2022
Let's Git - Versionsverwaltung & Open Source Hausaufgabe

Let's Git - Versionsverwaltung & Open Source Hausaufgabe Herzlich Willkommen zu dieser Hausaufgabe für unseren MOOC: Let's Git! Wir hoffen, dass Du vi

1 Dec 13, 2021
Plugin adapted from Ultralytics to bring YOLOv5 into Napari

napari-yolov5 Plugin adapted from Ultralytics to bring YOLOv5 into Napari. Training and detection can be done using the GUI. Training dataset must be

2 May 05, 2022
DeceFL: A Principled Decentralized Federated Learning Framework

DeceFL: A Principled Decentralized Federated Learning Framework This repository comprises codes that reproduce experiments in Ye, et al (2021), which

Huazhong Artificial Intelligence Lab (HAIL) 10 May 31, 2022
Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling"

Unseen Object Amodal Instance Segmentation (UOAIS) Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee This

GIST-AILAB 92 Dec 13, 2022
Hough Transform and Hough Line Transform Using OpenCV

Hough transform is a feature extraction method for detecting simple shapes such as circles, lines, etc in an image. Hough Transform and Hough Line Transform is implemented in OpenCV with two methods;

Happy N. Monday 3 Feb 15, 2022
Riemann Noise Injection With PyTorch

Riemann Noise Injection - PyTorch A module for modeling GAN noise injection based on Riemann geometry, as described in Ruili Feng, Deli Zhao, and Zhen

2 May 27, 2022
Official Implementation of "Learning Disentangled Behavior Embeddings"

DBE: Disentangled-Behavior-Embedding Official implementation of Learning Disentangled Behavior Embeddings (NeurIPS 2021). Environment requirement The

Mishne Lab 12 Sep 28, 2022
🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗

🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗 This year's first semester Club Info challenge will put you at the head of a car racing

ClubINFO INGI (UCLouvain) 6 Dec 10, 2021
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

Anshuman Dalai 3 Jan 24, 2022
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V

Changlu Guo 151 Dec 28, 2022
Extremely simple and fast extreme multi-class and multi-label classifiers.

napkinXC napkinXC is an extremely simple and fast library for extreme multi-class and multi-label classification, that focus of implementing various m

Marek Wydmuch 43 Nov 14, 2022