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My notes and solutions to homework of The Analytics Edge course on edX
Categorization of stock market reactions to corporate earnings releases for liquid US publicly traded stocks
A repository to explore the concepts of applied econometrics in the context of financial time-series.
AtsPy: Automated Time Series Models in Python (by @firmai)
Examples on how to use the alpha vantage library
A short term stock trading algorithm built in Quantopian based on post earnings announcement drift and sentiment analysis
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
Course material for Business Analytics in Practice - IS833
USF MSDS 501 Computation for Analytics Course
Coursera's Machine Learning by Andrew Ng
Course Description In this course, we will introduce a number of financial analytic techniques. You will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant. Course Goals and Objectives Upon completion of this course, you should be able to: Understand the forecasting process. Evaluate a forecast. Describe time series data. Perform moving average analysis. Perform exponential smoothing. Develop a Holt-Winters model. Develop an ARIMA model. Understand how to create a portfolio of assets. Understand a basic trading algorithm.
Python package for data.world
Coursera's Econometrics Methods and Applications using python
Tools & Notebooks for FinanceTrading ML
Portfolio of investment and finance-related data science projects
A program for financial portfolio management, analysis and optimisation.
An R package with Python support for multi-step-ahead forecasting with machine learning and deep learning algorithms
Simple Python module for downloading fundamental financial data from financials.morningstar.com.
Making it easy to backtest and create trading bots
Machine Learning in Asset Management (by @firmai)
Series of talks/workshops aimed at guiding newcomers through Python basics, Data Analysis and Machine Learning.
Following the Machine Learning for Finance course on Udacity.
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
This repo is part of code implementation for "SENN: Stock Ensemble-based Neural Network"
📦 R package for data and supplemental functions for OpenIntro resources
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.