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analytics-edge

My notes and solutions to homework of The Analytics Edge course on edX

anomalies-in-earnings-release-reactions

Categorization of stock market reactions to corporate earnings releases for liquid US publicly traded stocks

applied-econometric-time-series

A repository to explore the concepts of applied econometrics in the context of financial time-series.

atspy

AtsPy: Automated Time Series Models in Python (by @firmai)

av_example

Examples on how to use the alpha vantage library

beat-the-market

A short term stock trading algorithm built in Quantopian based on post earnings announcement drift and sentiment analysis

bulbea

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

business-analytics

Course material for Business Analytics in Practice - IS833

computation_course

USF MSDS 501 Computation for Analytics Course

coursera-machine-learning

Coursera's Machine Learning by Andrew Ng

coursera-uiuc-applying-data-analytics-in-finance

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.

data.world-py

Python package for data.world

econometrics

Coursera's Econometrics Methods and Applications using python

edhec_ai

edhec_bba_19_20

fin-algo

Tools & Notebooks for FinanceTrading ML

financial-data-analytics

Portfolio of investment and finance-related data science projects

finquant

A program for financial portfolio management, analysis and optimisation.

forecastml

An R package with Python support for multi-step-ahead forecasting with machine learning and deep learning algorithms

good-morning

Simple Python module for downloading fundamental financial data from financials.morningstar.com.

inferencial-statistics

lumibot

Making it easy to backtest and create trading bots

machine-learning-asset-management

Machine Learning in Asset Management (by @firmai)

machine-learning-for-dummies

Series of talks/workshops aimed at guiding newcomers through Python basics, Data Analysis and Machine Learning.

machine_learning

machine_learning_for_trading

Following the Machine Learning for Finance course on Udacity.

ml_from_edhec

mlfinlab

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

nlp_stacking_ensemble

This repo is part of code implementation for "SENN: Stock Ensemble-based Neural Network"

openintro

📦 R package for data and supplemental functions for OpenIntro resources