Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 06/29/2016

This is a summary of links featured on Quantocracy on Wednesday, 06/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deciphering Correlation Hedged Momentum [TrendXplorer]

    In a new SeekingAlpha contribution (pending approval) we combine PAAs protective multi-market breadth approach with a generalized momentum metric based on correlation hedged returns. The resulting model is called Generalized Protective Momentum (GPM). In this blogpost the correlation hedge is deciphered. The correlation hedge is a simplified version of Keller and Butlers EAA-formula (see
  • Pruitt, The Ultimate Algorithmic Trading System Toolbox [Reading the Markets]

    I am in the process of learning to code in Python and am, I must admit, no programming genius. So I was delighted to see that George Pruitt, best known for his book on TradeStations EasyLanguage (Building Winning Trading Systems with TradeStation) had written a new book that covered not only the TradeStation platform but also AmiBroker, Excel (with VBA), and Python. The Ultimate Algorithmic

Quantocracy’s Daily Wrap for 06/28/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting Based on Multiple Signals – Beware of Overfitting [Alpha Architect]

    One of the dangers of being a quantitative investor is that when you see patterns in historical data you might wrongly assume they will repeat. Put another way, you might believe an effect is driven by a genuine relationship, when in reality the results are spurious and the result of luck. We wrote here about "anomaly chasing" and the risks of data mining in backtests. A responsible
  • Loading Data with Pandas [Quintuitive]

    On at least a couple of occasions lately, I realized that I may need Python in the near future. While I have amassed some limited experience with the language over the years, I never spent the time to understand Pandas, its de-facto standard data-frame library. Where does one start? For me its usually with the data. Simple stuff, loading, wrangling, etc. Re-writing my little R6 helper class to

Quantocracy’s Daily Wrap for 06/27/2016

This is a summary of links featured on Quantocracy on Monday, 06/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility and measures of risk-adjusted return with Python [Quant Insti]

    In this post we see how to compute historical volatility in python, and the different measures of risk-adjusted return based on it. We have also provided the python codes for these measures which might be of help to the readers. Introduction Volatility measures the dispersion of returns for a given security. Volatility can be measured by the standard deviation of returns for a security over a
  • Stock Market Anomalies and Baseball Cards [Alpha Architect]

    I still have a Ken Griffey Jr. Rookie Card. To be honest, I dont even know where the thing is, but I hope it is it worth a ton of money at this point (although I doubt it). So disclaimer up front: I dabbled in baseball card trading back in the day. And for all of you out there who used to trade baseball cards, youll enjoy this recent research paper from Joey Engelberg, Linh Le, and Jared
  • 6 Reasons Why Your Fund Checklist is Hurting Performance [Flirting with Models]

    Summary Most advisors have a fund checklist or screen: a list of selection criteria they employ to help determine whether a fund is worthy of further evaluation. The vast majority of checklists we see employ a performance screen based on a 3- or 5-year period. We believe that employing such a performance screen not only misleads selection efforts, but also can be harmful to portfolio performance.
  • The Trouble with Alpha: Part I (h/t @AbnormalReturns) [Dynamic Beta]

    Investors equate alpha to outperformance. A high alpha fund presumably delivers substantial excess returns relative to its benchmark. True alpha is short-hand for manager skill. Statistically, alpha simply is the result of a linear regression between two return streams. The regression finds the straight line (ordinary least squares) that best fits the time series. Visually, beta is the slope
  • Consider Factors In Fixed Income [Larry Swedroe]

    Its been well-documented that, in equity investing, assets have earned premiums because they are exposed to the risks of a certain factor. Given that the literature provides us with a veritable factor zoo (there are more than 300), for investors to consider adding exposure to a factor, it should meet the following criteria: Persistent: It holds across long periods of time and various

Quantocracy’s Daily Wrap for 06/26/2016

This is a summary of links featured on Quantocracy on Sunday, 06/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Last Two Weeks [Quantocracy]

    The best quant mashup links for the two weeks ending Saturday, 06/25 as voted by our readers: Recommended Reading [Robot Wealth] Binary Options: Scam or Opportunity? [Financial Hacker] Some harmless data-mining: Testing individual words in EDGAR filings [Greg Harris] Simple Machine Learning Model to Trade SPY [Alpha Plot] Want to Know the Secret to Inefficient Prices? Lazy Prices. [Alpha
  • Momentum Anomaly and Baseball Cards [Quantpedia]

    We show that the market for baseball cards exhibits anomalies that are analogous to those that have been documented in financial markets, namely, momentum, price drift in the direction of past fundamental performance, and IPO under performance. Momentum profits are higher among active players than retired players, and among newer sets than older sets. Regarding IPO under performance, we find that

Quantocracy’s Daily Wrap for 06/23/2016

This is a summary of links featured on Quantocracy on Thursday, 06/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Maybe the Exits are More Important… [Throwing Good Money]

    As traders, we spend a lot of time thinking about our entries into a trade. What stock, commodity or currency to choose, when is the best timing, etc. But what if the entries dont matter? What if trading is all about the exits? Ok, thats a really simple-minded statement, but Im a little simple-minded, so stick with me and lets see where this goes. I wanted to see what it would look
  • Hierarchical clustering, using it to invest [Quant Dare]

    Machine Learning world is quite big. In this blog you can find different posts in which the authors explain different machine learning techniques. One of them is clustering and here is another method: Hierarchical Clustering, in particular the Wards method. You can find some examples in Reproducing the S&P500 by clustering by fuzzyperson, Returns clustering with K-Means
  • Monthly and Yearly Decay Rates for Long Volatility Funds [Six Figure Investing]

    While its certain that short-term volatility exchange traded products (ETPs) like VXX, TVIX, and UVXY are doomed to march towards zero, their decay rates are not consistent. Things like bear markets and big corrections can cause big upward swings. On the downside, the term structure of VIX futures and the volatility of volatility can significantly impact decay rates in monthly and yearly time

Quantocracy’s Daily Wrap for 06/21/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Recommended Reading [Robot Wealth]

    If theres one thing Ive done a lot of over the last few years, reading would be it. Ive devoted a great deal of time to devouring any material that I thought might give me an edge in my trading textbooks, academic papers, blog articles, training courses, lecture notes, conference presentationsanything and everything I could get my hands on. I was browsing the folder called
  • Manage Your Luck [Systematic Relative Strength]

    There is a lot more luck involved in investing than people think. Im not saying there isnt skill involved in investing or that there arent ways to outperform the market over time. Even if you have a process that can be shown to outperform the market over long time periods, there can be a great deal of variation in returns from year to year. A well designed investment model can certainly

Quantocracy’s Daily Wrap for 06/20/2016

This is a summary of links featured on Quantocracy on Monday, 06/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Digging Deeper into Adaptive Asset Allocation [Alpha Architect]

    In some ways, investing is simple. After all, we all want the same things. High returns. Low volatility. Small max drawdowns. Unfortunately, its very difficultif not impossibleto have your cake and eat it too. There are always tradeoffs among these desires that have to be managed by investors. Want low volatility? Be willing to accept lower returns. Want to maximize returns? You may have
  • Johansen Test for Cointegrating Time Series Analysis in R [Quant Start]

    In the previous article on the Cointegrated Augmented Dickey Fuller (CADF) test we noted that one of the biggest drawbacks of the test was that it was only capable of being applied to two separate time series. However, we can clearly imagine a set of three or more financial assets that might share an underlying cointegrated relationship. A trivial example would be three separate share classes on
  • Mini-Meucci : Appplying The Checklist – Steps 8-9 [Return and Risk]

    "Predicting rain doesn't count. Building arks does." Warren Buffett, The Oracle of Omaha (born 1930) In this penultimate leg of the tour we'll be visiting 2 more attractions along Via Meucci, Construction and Execution. Construction Portfolio Construction is another yuge! topic. In a nutshell, the overall goal is to find optimal holdings that maximize Satisfaction, subject to
  • Beginner’s Guide to Automated Trading with Python [Quant Insti]

    Python has emerged as one of the most popular language to code in Algorithmic Trading, owing to its ease of installation, free usage, easy structure, and availability of variety of modules. Globally, Algo Traders and researchers in Quant are extensively using Python for prototyping, backtesting, building their proprietary risk and order management system as well as in optimisation of testing
  • Is Internal Bar Strength A Random Walk? The Case of Exxon-Mobil [Jonathan Kinlay]

    For those who prefer a little more rigor in their quantitative research, I can offer more a somewhat more substantive statistical argument in favor of the IBS indication discussed in my previous post. Specifically, we can show quite convincingly that the IBS process is stationary, a highly desirable property much sought-after in, for example, the construction of statistical arbitrage strategies.