jonahhill Goto Github PK

jbooktrader

A Java ATS for market-depth-based trading strategies

jonahhill

Config files for my GitHub profile.

json-simple

A simple Java toolkit for JSON. You can use json-simple to encode or decode JSON text.

json-streamer

A fast streaming JSON parser for Python that generates SAX-like events using yajl

jupyterlab_quantrocket_ibgui

keras-for-numerai

Participation in the Numerai competition with Keras

lean

Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#, VB, Java)

libtrading

Libtrading, an ultra low-latency trading connectivity library for C and C++.

machinejs

Automated machine learning- just give it a data file! Check out v2 of this project at ClimbsRocks/auto_ml

metalsmith-broken-link-checker

Metalsmith plugin to check for internal broken links

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

mlprojects-py

mongo-docker

Instant MongoDB Cluster Environment based on docker

moonchart

Performance tear sheets and backtest analysis for Moonshot

moonshot

Vectorized backtester and trading engine for QuantRocket

mop

Stock market tracker for hackers

novnc

VNC client using HTML5 (Web Sockets, Canvas) with encryption (wss://) support.

numer.ai

numerai

Numerai Contest

numerai-1

numerai-2

Numer.ai contest

numerai-3

Numerai Hedge Fund Market Prediction

numerai-4

model for prediction challenge at http://numer.ai

numerai-5

Code from my experiments on Numerai

numerai-nn-keras

Predict Numerai scores using neural networks.

numerai-scripts

My continuing work on the Numer.ai machine learning challenge.

numeraiexperiments

Experimental things to see if they do good on numerai.ai trading stuff , so me and Beyhan to earn bitcoins :))

numerapi

automatically download and upload data for the numer.ai machine learning competition

numerflow

Data workflows for the numer.ai machine learning competition

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)