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quant-2

Quant finance scripts

quant-3

Quantitative Analysis Research see more at https://teddykoker.com

quant-quest-3

Quant Quest 3

quant-trading

Python quantitative trading strategies including MACD, Pair Trading, Heikin-Ashi, London Breakout, Awesome, Dual Thrust, Parabolic SAR, Bollinger Bands, RSI, Pattern Recognition, CTA, Monte Carlo, Options Straddle

quant_finance

Repository for teachings on Quant Finance

quantdsl

Quant DSL

quantecon.py

A community based Python library for quantitative economics

quantecon_nyu_2016

Quantitative Economics

quantfin

quant finance in pure haskell

quantitative

quantitative - Quantitative finance back testing library

quantitative-finance-papers-using-deep-learning

quantmod

Quantitative Financial Modelling Framework

quantpp

quant++: A C++ quantitative trading framework.

quantpy

A framework for quantitative finance In python.

quantropy

The financial pipeline for the data-driven investor to research, develop and deploy robust strategies.

quantsoftwaretoolkit

QuantSoftwareToolkit

quantstack-talks

Slides for quantstack talks

quantstats

Portfolio analytics for quants, written in Python

quantstrat

quepy

A python framework to transform natural language questions to queries in a database query language.

queries

PostgreSQL database access simplified

questdb

An open source SQL database designed to process time series data, faster

queuelib

Collection of persistent (disk-based) and non-persistent (memory-based) queues for Python

quickfixj

The official QuickFIX/J project repository

quickref

a CLI shortcut for recording and searching short note snippets

r

openMpp R package for OpenM++ project

r-causal

R Wrapper for Tetrad Library

r-flame

R Implementation of FLAME and other algorithms in the AME framework

r-in-finance

This course, taught by Prof.Jerzy in NYU, applies the R programming language to momentum trading, statistical arbitrage (pairs trading), and other active portfolio management strategies. The course implements volatility and price forecasting models, asset pricing and factor models, and portfolio optimization. The course applies machine learning techniques, such as backtesting (cross-validation) and parameter regularization (shrinkage).

r-inla