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Quantocracy’s Daily Wrap for 06/03/2016

This is a summary of links featured on Quantocracy on Friday, 06/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Python Machine Learning [Amazon]

    Leverage Pythons most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask and answer tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python
  • Relative Strength Index (RSI) Model [Oxford Capital]

    I. Trading Strategy Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (The RSI Momentum Oscillator). Source: (i) Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets; (ii) Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research. Concept: The long equity trading system based on the
  • Factor Attribution of Jim Cramer’s Mad Money Charitable Trust [Quantpedia]

    This study analyzes the complete historical performance of Jim Cramers Action Alerts PLUS portfolio from 2001 to 2016 which includes many of the stock recommendations made on Cramers TV show Mad Money. Both since inception of the portfolio and since the start of Mad Money in 2005 (when it was converted into a charitable trust), Cramers portfolio has underperformed the S&P
  • Do European stocks follow the US on a daily basis? [UK Stock Market Almanac]

    Do European stocks follow the lead of the US market from the previous day? In other words if, say, the US market is down one day are European stocks more likely to fall in their trading session the following day? To test this the following chart plots the daily returns of the S&P 500 Index against the corresponding daily return of the EuroSTOXX 50 Index for the following day. Europe v US
  • Bubble Investing: Learning from History [Alpha Architect]

    We just wrote a piece for Forbes on financial bubbles in the lab. Punchline: investors initially underreact to fundamentals, then they overreact, and eventually prices correct. But how common are crashes? Ben has some interesting thoughts, but the results are limited to the US market. Now, one of my favorite academic authors Prof. Bill Goetzmann has a new paper that speaks to understand
  • Webinar: Feature Selection with Machine Learning [Quant Insti]

    Feature Selection is the automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on. Feature selection methods aid you in your mission to create an accurate predictive model. They help you by choosing features that will give you as good or better accuracy whilst requiring less data. The methods can
  • The Internal Bar Strength Indicator [Jonathan Kinlay]

    Internal Bar Strength (IBS) is an idea that has been around for some time. IBS is based on the position of the days close in relation to the days range: it takes a value of 0 if the closing price is the lowest price of the day, and 1 if the closing price is the highest price of the day. More formally: IBS = (Close Low) / (High Low) The IBS effect may be related to intraday

Quantocracy’s Daily Wrap for 06/02/2016

This is a summary of links featured on Quantocracy on Thursday, 06/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cointegrated Time Series Analysis for Mean Reversion Trading with R [Quant Start]

    A while back we considered a trading model based on the application of the ARIMA and GARCH time series models to daily S&P500 data. We mentioned in that article as well as other previous time series analysis articles that we would eventually be considering mean reverting trading strategies and how to construct them. In this article I want to discuss a topic called cointegration, which is a
  • A Factor Investor s Perspective of the Economic Cycle [Factor Investor]

    Debates abound on the relative importance of the economic cycle to investment success. Peter Lynch famously said, "If you spend more than 13 minutes analyzing economic and market forecasts, you've wasted 10 minutes. On the flip side, macro investment houses have constructed intricate frameworks to understand the economic machine. The challenge with economic data is that it is

Quantocracy’s Daily Wrap for 05/31/2016

This is a summary of links featured on Quantocracy on Tuesday, 05/31/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diversification Will Always Disappoint [Flirting with Models]

    Summary For its ability to reduce risk without necessarily sacrificing potential reward, diversification is known as the only free lunch on Wall Street. Diversification provides investors with the important ability to invest in the face of uncertainty. When viewed for its pieces instead of as a whole, a well-diversified portfolio will likely always contain an element that disappoints. This has
  • mini-Meucci : Applying The Checklist – Step 1 [Return and Risk]

    Introduction In this mini-Meucci series of posts we'll put the 10 steps of The Checklist into practice by constructing a low volatility portfolio in Python. This toy/basic example will be a short tourist trip, highlighting key attractions that you can then explore further… Of course, these posts should be read in conjunction with the latest slides (I'll be referring to the slides
  • A Few Little Links [Factor Wave]

    I'm currently working on three things: a VIX option trading strategy, a piece about how factors relate to earnings announcements and a Kelly criterion type thing for options. But none is particularly close to being done. So I thought i would post a few links to articles that I found interesting. I'm not (just) doing this to put up a blog post. I think both of these are good reads. First,

Quantocracy’s Daily Wrap for 05/30/2016

This is a summary of links featured on Quantocracy on Monday, 05/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Survey of Deep Learning Techniques Applied to Trading [Greg Harris]

    Deep learning has been getting a lot of attention lately with breakthroughs in image classification and speech recognition. However, its application to finance doesn't yet seem to be commonplace. This survey covers what I've found so far that is relevant to systematic trading. Please tell me if you know of some research I've missed. Acronyms: DBN = Deep Belief Network LSTM = Long
  • Build Technical Indicators in Python [Quant Insti]

    Technical Indicator is essentially a mathematical representation based on data sets such as price (high, low, open, close, etc.) or volume of a security to forecast price trends. There are several kinds of technical indicators that are used to analyse and detect the direction of movement of the price. Traders use them to study the short-term price movement, since they do not prove very useful for
  • A Long Term Look At Memorial Week Seasonality [Quantifiable Edges]

    The week of Memorial Day has shown some interesting seasonal tendencies over the years. And for a long time it exhibited consistent bullishness. But it has faltered greatly the last several years. The chart below examines SPX performance from the Friday before Memorial Day to the Friday after it. 2016-05-30 image1 There was no substantial edge apparent throughout the 70s, but starting in 1983

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 05/28 as voted by our readers:

And in case you missed it, the latest from Quantocracy:

* * *

Votes by Clickthroughs

[click graph to enlarge]

Your votes matter to the quant community.

The graph to the right shows the average number of clickthroughs a link receives from our website (excluding RSS, Twitter and Stocktwits), broken out by the number of votes cast by our readers.

A core goal of Quantocracy is to have a positive impact on our corner of the financial world by rewarding the best work, and encouraging the best minds to keep writing.

As the graph makes clear, the citizens of Quantocracy are doing just that (way to go guys). Links with 11 or more votes receive nearly 6-times as many clickthroughs as a link with no votes (wow).

If you haven’t done so already, we invite you to register to vote and be a part of the effort. Your votes matter to the quant community.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 05/28/2016

This is a summary of links featured on Quantocracy on Saturday, 05/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 05/27/2016

This is a summary of links featured on Quantocracy on Friday, 05/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploring Extreme Asset Returns [Quant Dare]

    Tail or extreme assets returns have been extensively studied. In his amazing paper: Empirical properties of assets returns: stylized facts and statistical issues, Rama Cont provides a framework on statistical analysis of price variations in various types of financial markets. He presents Heavy tails in asset returns as a stylized fact, i.e., statistical properties common across a wide

Quantocracy’s Daily Wrap for 05/26/2016

This is a summary of links featured on Quantocracy on Thursday, 05/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Some Impressions from R Finance 2016 [Revolutions]

    R / Finance 2016 lived up to expectations and provided the quality networking and learning experience that longtime participants have come to value. Eight years is a long time for a conference to keep its sparkle and pizzazz. But, the conference organizers and the UIC have managed to create a vibe that keeps people coming back. The fact that invited keynote speakers (e.g. Bernhard Pfaff 2012,
  • Updated Dual Momentum Test [Scott’s Investments]

    I frequently get asked for updated tests on various strategies. Using Portfolio123 I ran a backtest on a Dual Momentum strategy from 1/1/2007 5/25/2016. The strategy is updated on Scotts Investments monthly, the most recent update is here. The strategy invests equally in one ETF from each of four baskets of ETFs/cash: Equities VTI, EFA, or Cash Credit Risk CIU, HYG, or Cash Real

Quantocracy’s Daily Wrap for 05/25/2016

This is a summary of links featured on Quantocracy on Wednesday, 05/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting the VIX to Improve VIX-Derivatives Trading [Quantpedia]

    Konstantinidi et. al. state in their broad survey of Volatility-Index forecasting: "The question whether the dynamics of implied volatility indices can be predicted has received little attention". The overall result of this and the quoted papers is: The VIX is too a very limited extend (R2 is typically 0.01) predictable, but the effect is economically not significant. This paper confirms
  • ConnorsRSI Analysis [Alvarez Quant Trading]

    A couple posts ago, I did the RSI Analysis. This post will focus on ConnorsRSI which I created while working for Larry Connors. When creating the indicator, the focus was on short-term mean-reversion results. We will look at that here but also how does it handle longer-term holds. Since I did not test this when I originally did the work, I was looking forward to seeing the results. ConnorsRSI

Quantocracy’s Daily Wrap for 05/24/2016

This is a summary of links featured on Quantocracy on Tuesday, 05/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Candid Discussion with an Algorithmic Trader [Quant Insti]

    The role of Algorithm in a persons life is too substantial to be ignored. From a simple coffee-making machine to the music system in his car, from elevators to search engine like Google, all are governed by a set of logical instructions Algorithms or Algos, which enable them to respond to a persons specific requirement. With the advent of internet, Algorithmic potential has been