All Projects → QuantConnect → Research

QuantConnect / Research

Licence: apache-2.0
Open sourced research notebooks by the QuantConnect team.

Projects that are alternatives of or similar to Research

Deep Algotrading
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading
Stars: ✭ 173 (-1.7%)
Mutual labels:  jupyter-notebook
Recommendation
Recommendation System using ML and DL
Stars: ✭ 174 (-1.14%)
Mutual labels:  jupyter-notebook
Analytical Tutorials
Tutorials for writing analytical scripts
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Clinicalbert
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Itwmm
In The Wild 3D Morphable Models
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Python Machine Learning Book 3rd Edition
The "Python Machine Learning (3rd edition)" book code repository
Stars: ✭ 2,883 (+1538.07%)
Mutual labels:  jupyter-notebook
Prml
Python implementations (on jupyter notebook) of algorithms described in the book "PRML"
Stars: ✭ 174 (-1.14%)
Mutual labels:  jupyter-notebook
Gluon Fashionai Attributes
Stars: ✭ 176 (+0%)
Mutual labels:  jupyter-notebook
Muffin Cupcake
classifying muffin and cupcake recipes using support vector machines
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Astropy Tutorials
Tutorials for the Astropy Project
Stars: ✭ 174 (-1.14%)
Mutual labels:  jupyter-notebook
Home Credit Default Risk
2nd Place Solution 💰🥈
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Web Database Analytics
Web scrapping and related analytics using Python tools
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Deep Learning With Keras Notebooks
Jupyter notebooks for using & learning Keras
Stars: ✭ 2,077 (+1080.11%)
Mutual labels:  jupyter-notebook
Captcha
Breaking captchas using torch
Stars: ✭ 174 (-1.14%)
Mutual labels:  jupyter-notebook
Attentionn
All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Kipoi
Kipoi's model zoo API
Stars: ✭ 174 (-1.14%)
Mutual labels:  jupyter-notebook
Keras Tutorials
Keras-Tutorials
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook
Reinforcement learning ai video games
Code for each week's short video of Siraj Raval Course on Reinforcement Learning "AI for Video Games"
Stars: ✭ 176 (+0%)
Mutual labels:  jupyter-notebook
Tech
Documentation of all collective action from tech workers.
Stars: ✭ 176 (+0%)
Mutual labels:  jupyter-notebook
Intro Spacy Nlp
An introduction to using spaCy for NLP and machine learning
Stars: ✭ 175 (-0.57%)
Mutual labels:  jupyter-notebook

alt tag

This repository is a collection of research notebooks and tutorials using the QuantConnect LEAN platform. Research covers a range of topics from tutorial focused demonstrations to topical analysis of modern movements in the financial markets.

Topical Events

Idea Streams PodCast

Research 2 Production Notebook Series

Analysis Examples

  • Fudamental Factor Analysis: This research applies MorningStar fundamental data to demonstrate how to select the effective factors for long/short strategies.

  • Kalman Filter Based Pairs Trading: This research demonstrates the basic principle of pairs trading and introduces the concepts of cointegration and Kalman Filter for pairs trading.

  • Mean-Variance Portfolio Optimization: This research demonstrates the mean-variance approach to asset allocation in modern portfolio theory and shows how to find the efficient frontier.

  • EMA Cross Strategy Based on VXX: This research demonstrates how to build a simple EMA cross strategy with Python and how to get the performance statistics of the strategy.

  • Pairs Trading Strategy Based on Cointegration: This research goes through the development process step-by-step of a pairs trading strategy and shows how to backtest the strategy.

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].