uhasan1

002_MachineLearning_eBook

Advanced-machine-learning

AIT_Trading_Algorithms

Alpha Factors and Trading Algorithms on Quantopian created by Philip Kiely, Richard Greenbaum, Rudolph Hernandez, and Alex Foster for our Data Mining final project.

awesome-algorithmic-trading

A curated list of awesome algorithmic trading frameworks, libraries, software and resources

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

EliteQuant_Python

Python quantitative trading and investment platform; Python3 based multi-threading, concurrent high-frequency trading platform that provides consistent backtest and live trading solutions. It follows modern design patterns such as event-driven, server/client architect, and loosely-coupled robust distributed system. It follows the same structure and performance metrix as other EliteQuant product line, which makes it easier to share with traders using other languages.

gnidart

Reference for Financial Trading

Machine-Learning-and-Reinforcement-Learning-in-Finance

Machine Learning and Reinforcement Learning in Finance New York University Tandon School of Engineering

Quantum-Machine-Learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web [krishnakumarsekar/awesome-quantum-machine-learning]

Quick-Ref-Cheat-Sheets

Quick-Ref-Cheat-Sheets

uhasan1/ml_scraps

Scraps of random machine learning code

uhasan1/parallel_ml_tutorial

Tutorial on scikit-learn and IPython for parallel machine learning

uhasan1/develop

generic project files

uhasan1/DeepLearning-NLP

Introduction to Deep Learning for Natural Language Processing

uhasan1/FinancialInstrument

mirror of FinancialInstrument on R-Forge; an infrastructure for managing metadata of financial instruments

uhasan1/ML-Tools

Variety of machine learning algorithms written in python

uhasan1/002_MachineLearning_eBook

uhasan1/quantandfinancial

Automatically exported from code.google.com/p/quantandfinancial

uhasan1/representation-learning

Unsupervised Deep Learning and Representation Learning Tutorial

uhasan1/curator

A stock curation tool following Joel Greenblatt's Magic Formula Investing technique

uhasan1/awesome-fintech

A curated list of amazingly awesome financial libraries, resources and shiny things.

uhasan1/minibook-2nd-data

Datasets used in the IPython Minibook, 2nd edition (2015)

uhasan1/multifactor-models

A Survey of Multi-Factor Models

uhasan1/thesis_ml_fm

The usage of Machine Learning Algorithms for analysing financial fundamental values to examine the performance over time to predict high return on publicly traded companies (Stocks). The performance will be measured on the return after taxes and inflation and compared with the Index. This thesis will examine discriminative machine learning methods such a Support Vector Machine (SVM), Linear Regression and Neural Networks to find winner stocks for future investments. Most professional investors combine the human expertise with computational techniques to filter different stock exchanges to separate winners- from losers stocks and make final investment judgements. This thesis shows how to create such a filter system and find the winner stocks for professional investors or retail investors by comparing different metrices and increase the future performance.

uhasan1/machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

uhasan1/QLExtension

A quantitative library based on QuantLib

uhasan1/machine-learning-module

the best machine learning tutorials on the web