Source: Quantitative Trading: Is News Sentiment Still Adding Alpha?

Source: Enhancing Statistical Significance of Backtests by Dr. Ernest Chan – Quantopian Blog

GNUroot Debian

Source: Using R on Android · Tea & Stats

Source: A Better Way to Model the VIX – Six Figure Investing

Source: Risk Parity in Python | Quantdare

Source: Prophet – Time series prediction | Hardik Patel

Source: Analyzing Cryptocurrency Markets Using Python

Source: PyQuant News

Source: How to Run Trading Algorithms on Google Cloud Platform in 6 Easy Steps – Robot Wealth

Source: Hello World! – koppian adventures

Source: Wiley: The Complete Family Office Handbook: A Guide for Affluent Families and the Advisors Who Serve Them – Kirby Rosplock

Source: Quantitative Trading

Providing current and historical end of day (EOD), intraday data, and fundamentals that is accurate, affordable, and easy to use through APIs.

Source: Market Data – EOD data , intraday data, fundamental data

Source: Investment Idiocy: Playing with Docker – some initial results (pysystemtrade)

Describes a potential cointegration-based strategy based on the economics of aluminum smelting and natural gas usage, implemented in the QSTrader open source backtesting framework.

Source: Aluminum Smelting Cointegration Strategy in QSTrader – QuantStart

The next two blog posts will explore the basics of the statistical arbitrage strategies outlined in Ernest Chan’s book, Algorithmic Trading: Winning Strategies and Their Rationale. In the first pos…

Source: Cointegrated ETF Pairs Part I

Source: Placing your first Forex trade with Python – Jon.IO

The first step in building such a system is to define what mean reversion is. Mean reversion systems are looking for markets that are unusually high or low.

Source: Mean Reversion Trading System – Milton Financial Market Research Institute

The Data Library contains current benchmark returns and historical benchmark returns data, downloads and details.

Source: Kenneth R. French – Data Library

A New Regime Switching Trading Model. The strategy uses a social factor to switch between mean reversion and momentum trading environments. Over 5 years the strategy achieves impressive returns of …

Source: A Regime Switching Model: Momentum vs Mean Reversion – MKTSTK

Describes how to apply the Python-based Theano library to deep learning/statistical machine learning problems. In this article we consider logistic regression.

Source: Deep Learning with Theano – Part 1: Logistic Regression – QuantStart

Source: Most Useful Investment Blogs ~ Dual Momentum TM

Source: Jon.IO

Source: Universal behaviour in the stock market: Time dynamics of the electronic orderbook

Source: Predicting Volatility by Dr. Ernest Chan

Read news, tips, and the latest research about quantitative trading.

Source: Intro to Algorithmic Trading with Heikin-Ashi

Can a simple Market Internals technique actually improve trading strategy results? We test it out and share the results and code, see for yourself.

Source: Case study: Can a simple Market Internals technique actually improve trading strategy results? – Better System Trader

Source: Quantitative Trading: Things You Don’t Want to Know about ETFs and ETNs

How do you handle repetitive tasks? If you’re like most people, you work through a task in a variety of […]

Source: Want to Know the Secret to Inefficient Prices? Lazy Prices. – Alpha ArchitectAlpha Architect

Describes the time series analysis concept of cointegration, which can be exploited to develop mean reverting trading strategies

Source: Cointegrated Time Series Analysis for Mean Reversion Trading with R – QuantStart

Source: Forecasting the VIX to Improve VIX-Derivatives Trading – Blog

Backtesting strategies with R

Source: Backtesting Strategies with R

Finviz is an excellent free stock screener and lots of other free features but here’s a quick tip that I started using only recently to scan for trade setups.

Source: A Quick Tip When Using The Finviz Stock Screener – ProfitSquawk

Source: Quantitative Trading: Mean reversion, momentum, and volatility term structure

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable atten

Source: Classification-Based Financial Markets Prediction Using Deep Neural Networks by Matthew Francis Dixon, Diego Klabjan, Jin Hoon Bang :: SSRN

Source: Recurrent Learning (RL) Artificial Neural Networks (ANN) for trading using Python | Talaikis

This article details how to learn advanced undergraduate level mathematics solely from self-study via textbooks, lecture notes and videos found on the internet.

Source: How to Learn Advanced Mathematics Without Heading to University – Part 1 – QuantStart

Introduction In his famous book – Think and Grow Rich, Napolean Hill narrates story of Darby, who after digging for a gold vein for a few years walks away from it when he was three feet away from it! Now, I don’t know whether the story is true or false. But, I surely know of

Source: Cheatsheet – Python & R codes for common Machine Learning Algorithms

Xlwings is another free and open source package allowing communication between Excel and Python.  It now incorporates ExcelPython, and is included in the Anaconda Python package, so will support my…

Source: Use Matplotlib from Excel with xlwings | Newton Excel Bach, not (just) an Excel Blog

Do Volatility Futures Provide Useful Information for Future S&P500 Performance? Volatility or VIX Futures are based on the S&P500 index and are calculated from the implied volatility of dif…

Source: Volatility Futures and S&P500 Performance

Source: Fast and simple vectorized backtesting using Python, pandas | Talaikis

Source: Scrapy at a glance — Scrapy 1.0.5 documentation

This article focuses on statistical arbitrage, coded in R. It is a combination of EPAT class notes and author’s source code.

Source: EPAT Final Project by Jacques Joubert

Given the downturn in the energy sector and the persistent economic weakness abroad, investors have become increasingly focused on the possibility of a U.S. recession.  In this piece, I’m goi…

Source: In Search of the Perfect Recession Indicator | PHILOSOPHICAL ECONOMICS

Blog about algorithmic trading with new methods and new indicators

Source: Build Better Strategies! Part 2: Model-Based Systems – The Financial Hacker

Source: 15 years of forex and CFDs tick data to MongoDB using Python. Part Two | Talaikis

Source: The Financial Data Finder A – G

Data is ubiquitous — but sometimes it can be hard to see the forest for the trees, as it were. Many companies of various sizes believe they have to collect their own data to see benefits from big data analytics, but it’s simply not true. There are hundreds (if not thousands) […]

Source: Big Data: 35 Brilliant And Free Data Sources For 2016 – Forbes

Source: Welcome to the VSTOXX Advanced Services Tutorials based on Python — VSTOXX Advanced Services 2.0 (August 2014) documentation

Source: ts_dx_analytics slides

This book introduces the software package R for econometrics. It is designed to be highly compatible with Jeffrey Wooldridge’s ‘Introductory Econometrics’.

Source: Using R for Introductory Econometrics

Securities master is an organisation-wide database that stores fundamental, pricing and transactional data for a variety of financial instruments.

Source: Securities Master System Explained

Source: Architecture -I- – algorythmn’trader

Summary of algorithmic trading system requirements including functional, non-functional, access, and integration requirements

Source: Algorithmic trading system requirements

Factor modelling is everywhere these days. I wrote about smart beta here. It is good to quantify performance drivers but the usual caveats apply to quantitative studies utilizing backward looking d…

Source: Fama French Multifactor Model in Python | Largecaptrader

The standard multivariate test of Gibbons et al. (1989) commonly used in studies comparing the performance of asset pricing models requires the number of stocks

Source: Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test by Shafiqur Rahman, Matthew J. Schneider, Gary Antonacci :: SSRN

Revisiting the issue of return predictability, we show there is substantial predictive power in combining forecasting variables. We apply correlation screening

Source: A Practitioner’s Defense of Return Predictability by Blair Hull, Xiao Qiao :: SSRN

Source: Random portfolios: correlation clustering | Predictive Alpha

Source: Monte Carlo simulation for information advantage testing using Python | Talaikis

We study the profitability of optimal mean reversion trading strategies in the US equity market. Different from regular pair trading practice, we apply maximum

Source: On the Profitability of Optimal Mean Reversion Trading Strategies by Peng Huang, Tianxiang Wang :: SSRN

Source: Quantitative Trading Strategy Using R: A Step by Step Guide

Suppose that you had the magical ability to foresee turns in the business cycle before they happened.  As an investor, what would you do with that ability?  Presumably, you would use it to time the…

Source: Growth and Trend: A Simple, Powerful Technique for Timing the Stock Market | PHILOSOPHICAL ECONOMICS

Source: New Data Sources for R

Algorithmic Trading has been at the centre-stage of the trading world for a few years now. Here we’ve explained whole automated trading system architecture.

Source: Automated Trading System Architecture Explained

Source: Import for multiple financial data series from Quandl to MySQL database using Python | Talaikis

In the first post in this series, I explored mean reversion of individual financial time series using techniques such as the Augmented Dickey-Fuller test, the

Source: Exploring mean reversion and cointegration: part 2 | Robot Wealth

trading-backtest – A stock backtesting engine written in modern Java. And a pairs trading (cointegration) strategy implementation using a bayesian kalman filter model.

Source: lukstei/trading-backtest · GitHub

Source: Augmented Dickey Fuller (ADF) Test for a Pairs Trading Strategy

Blog about algorithmic trading with new methods and new indicators

Source: Build Better Strategies! Part 2: Model-Based Systems – The Financial Hacker

Discusses how to create dynamical linear state space models and apply the Kalman Filter to predict or smooth the values in a time series

Source: State Space Models and the Kalman Filter – QuantStart

Source: Computational Statistics in Python — Computational Statistics in Python 0.1 documentation

Rank stocks according to return on equity and EV/EBITDA using free data from yahoo finance.

Source: A Poor Man’s Magic Formula – Relative Value – Quantitative Investing Down Under

Doing quantitative research implies a lot of data crunching and one needs clean and reliable data to achieve this. What is really needed is clean data that is easily accessible (even without an int…

Source: Maintaining a database of price files in R « The R Trader

A list of 25 websites where you can find lots of interesting quantitative trading strategies, system research, and quant trading ideas.

Source: 25 Places To Find Quantitative Trading Strategies

This series of posts is inspired by several chapters from Ernie Chan’s highly recommended book Algorithmic Trading. The book follows Ernie’s first contribution,

Source: Exploring mean reversion and cointegration with Zorro and R: part 1 | Robot Wealth

Source: Fetching options data from NASDAQ website with Python | Quant Corner

A trading strategy based on the ARIMA+GARCH model applied to the S&P500 stock index

Source: ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R – QuantStart

Contribute to quant_at development by creating an account on GitHub.

Source: burakbayramli/quant_at · GitHub

In this course you will discover the power of Functional Programming, using the OCaml language to write concise, efficient and elegant programs.

Source: Introduction to functional programming in OCaml | FUN – France Université Numérique

Source: Lectures-Quantopian

Source: The Kalman Filter and Pairs Trading | MKTSTK

MultCloud(https://www.multcloud.com/), a FREE and easy-to-use web app, supports for Managing Files and Transferring Files across Cloud Drives. Free provide 10TB traffic for data transmission.

I’ve received an unsolicited email today from Pedro Marcus, from DataOnFocus. While usually I don’t even open them due to the volume that I get each day, this…

Source: 27 free data mining books – Data Science Central

Appyling a Hurst Exponent strategy to the Jakarta Stock Exchange index has been very profitable

Source: Fractal Strategy Applied to Indonesian Index

Discover new modules and libraries you can use in your projects.

Source: Libraries – The Open Source Discovery Service

We review frequently used Python backtesting libraries like Zipline & PyAlgoTrade and examine them in terms of flexibility, ease of use and scalability.

Source: Python Backtesting Libraries For Quant Trading Strategies

I recently answered a question on Quora.com about how to earn $10,000 a month on a $500,000 investment (You can find it here) and I opened my big mouth and invited people to contact me directly about details on how make it happen. I figured a few people would read it and want to talk shop… I didn’t realize, however, that it would go viral.Since then, I’ve been bombarded with more requests for resources on “increasing deal flow” than I could ever hope to respond to individually, so this post is dedicated to pointing investors who want to get involved in private deals in the right

Source: Deal Flow for Little Guys: Top 10 Resources for Small Private Investments | Matthew Duckworth | LinkedIn

Source: VOLATILITY FIGHTER: Simple Chaos Indicator

Source: Investment Idiocy: Systems building – accounting

Source: Investment Idiocy: Systems building – execution

Source: R financial time series tips everyone should know about « The R Trader

Source: Data Science: How Do We Get Started? – Part Three | Pindrop Security

Source: Quantitative Trading

The Python Quants, Quant Platform, Python, Quantitative Finance, Big Data, Wiley Finance, Yves Hilpisch, Derivatives Analytics, Python Training, Finance Training, Derivatives Training

Source: Derivatives Analytics with Python (Wiley Finance) – Data Analysis, Models, Simulation, Calibration and Hedging

Source: Creating Algorithmic Trading Portfolios with Quantopian (PART II) | Kevin Pei

Source: What is social data? | MKTSTK

-*+There are a lot of “winning” strategies for bull markets floating around. “Buy the pullbacks” is certainly one of them. Does this sound simple enough to implement to you? While I am no Sheldon Cooper (although I have a favorite couch seat), I still like to live in a somewhat well defined world, a world […]

Source: A Better ZigZag – Quintuitive

We divide hedging methods between single-period and multi-period. After reviewing some well-known hedging algorithms, two new procedures are introduced, called

Source: Advances in Cointegration and Subset Correlation Hedging Methods by Marcos Lopez de Prado, David Leinweber :: SSRN