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dystyle

ecmlpkdd2015-challenge

[2015, ECMLPKDD] 4th & 22nd place solution - Taxi trajectory prediction and trip time Prediction

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

econometrics

edgarscrape

Script that monitor's the SEC's Edgar RSS feed and alerts via email & text message when "insider buys" or large buys by officers in public companies occur.

eiten

Statistical and Algorithmic Investing Strategies for Everyone

elegantrl

Scalable and Elastic Deep Reinforcement Learning Using PyTorch. Please star. 🔥

elitequant

A list of online resources for quantitative modeling, trading, portfolio management

emcee

The Python ensemble sampling toolkit for affine-invariant MCMC

emissions-api

Web API to easily access satellite-based emission data

emotional-analysis-of-internet-news

“互联网新闻情感分析”赛题，是CCF大数据与计算智能大赛赛题之一。对新闻情绪进行分类，0代表正面情绪、1代表中性情绪、2代表负面情绪。

engine

Open Source Risk Engine

ensemble_amazon

Code to share different ensemble techniques with focus on meta-stacking , using data from Amazon.com - Employee Access Challenge kaggle competition

eq-stat-arb

equal-risk-pricing-with-deep-hedging

# Equal-risk-pricing-with-deep-hedging This notebook presents an example of the equal risk pricing framework with deep hedging from my paper Carbonneau, A. and Godin, F. (2020). Equal Risk Pricing of Derivatives with Deep Hedging.

event-driven-stock-prediction-using-deep-learning

A deep learning method for event driven stock market prediction. Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price movements

event-registry-python

Python package for API access to news articles and events in the Event Registry

example-hftish

Example Order Book Imbalance Algorithm

example-scalping

A working example algorithm for scalping strategy trading multiple stocks concurrently using python asyncio

example-strategies

Example trading strategies to get you started with Jesse

examples

Example deep learning projects that use wandb's features.

examples-1

Example strategies

exchanges-php

This is a virtual currency SDK that brings together multiple exchanges

factory_boss

Fake entire data schemas. Easily.

fannie-mae

fast-eacl

fb-analysis

Dashboard to explore your personal Facebook data dump

feature-selection

Features selector based on the self selected-algorithm, loss function and validation method

fibonacci-retracement

figasr

Lexicon-based Sentiment Analysis for Economic and Financial Applications in R