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Quantocracy’s Daily Wrap for 03/08/2017

This is a summary of links featured on Quantocracy on Wednesday, 03/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PSA: Your NCAA March Madness Rules are Garbage. Do This Instead. [Invest Resolve]

    On the heels of last years fun and successful March Madness Bracket Challenge (WHERE SKILL PREVAILS!), we are happy to invite any and all to 2017s version. Feel free to read the post for this years rules, but bear in mind this years pool is limited to 250 entrants, so dont wait: Register here. As with most investing topics, our thinking on March Madness bracket rules continues
  • Interactive brokers native python API [Investment Idiocy]

    Until quite recently interactive brokers didn't offer a python API for their automated trading software. Instead you had to put up with various 3rd party solutions, one of which swigibpy I use myself. Swigibpy wrapped around the C++ implementation. I wrote a series of posts on how to use it, starting here. Although swigiby has been very good to me its always better to use official solutions
  • What is Deep Learning? [Quant Start]

    Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first principles. These days it is almost impossible to work in any technology-heavy field without hearing about
  • 66 DTE Iron Condor Results Summary [DTR Trading]

    This article reviews the backtest results of iron condors (IC) entered at 66 days to expiration (DTE). These tests covered 9 IC variations, with short strike deltas at four locations (8, 12, 16, 20), utilizing 12 exits. In all, there were 432 test runs (9 variations x 4 deltas x 12 exits). Each test run executed slightly less than 200 SPX IC trades between the January 2007 expiration and the
  • Machine Learning in Python for Finance: 2-Day Workshop in Warsaw, Poland [Quant at Risk]

    After wonderful and rewarding 2-day workshop devoted to Python for Algo-Trading on March 4-5, it is my pleasure to announce a new, upcoming, on demand 2-Day Workshop on Machine Learning in Python for Finance (May 20-21, 2017). Since Machine Learning is the latest hottest topic covering different fields we will understand its aspects in a wide range of possible applications. Click here to learn
  • Historic data from native IB python API [Investment Idiocy]

    This is the second in a series of posts on how to use the native python API for interactive brokers. This post is an update of the post I wrote here, which used the 3rd party API swigibpy. Okay so you have managed to run the time telling code in my last post. Now we will do something a bit more interesting, get some market prices. Arguably it is still not that interesting, and this stuff will
  • Firm-Specific Information and Momentum Investing [Alpha Architect]

    When it comes to momentum investing, everyone is always looking for a better way to implement a momentum-based stock selection strategy (the same goes for a value strategy). We highlight a few methods in our book, Quantitative Momentum, as well as on our blog. We recently came across a paper from 2006 that has an improvement on a baseline momentum investing strategy, titled Firm-specific

Quantocracy’s Daily Wrap for 03/06/2017

This is a summary of links featured on Quantocracy on Monday, 03/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading with Copulas [Jonathan Kinlay]

    In a previous post, Copulas in Risk Management, I covered in detail the theory and applications of copulas in the area of risk management, pointing out the potential benefits of the approach and how it could be used to improve estimates of Value-at-Risk by incorporating important empirical features of asset processes, such as asymmetric correlation and heavy tails. In this post I will take a very
  • Visualizing the Anxiety of Active Strategies [Flirting with Models]

    Prospect theory states that the pain of losses exceeds the pleasure of equivalent gains. An oft-quoted ratio for this pain-to-pleasure experience is 2-to-1. Evidence suggests a similar emotional experience is true for relative performance when investors compare their performance to common reference benchmarks. The anxiety of underperforming can cause investors to abandon approaches before they
  • The No-Short Return Premium [Quantpedia]

    Theory predicts that securities with greater limits to arbitrage are more subject to mispricing and thus should command a higher return premium. We test this prediction using the unique regulatory setting from the Hong Kong stock market, in which some stocks can be sold short and others cannot. We show that no-short stocks on average earn significantly higher returns than shortable stocks and the

Quantocracy’s Daily Wrap for 03/05/2017

This is a summary of links featured on Quantocracy on Sunday, 03/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Strategy Validation with Dave Bergstrom (@DBurgh) [Better System Trader]

    With the toolsets we have available to us today its really quite easy to create a trading strategy by just mining market data. As weve just heard in that opening bit of audio and also from previous podcast guests too, if you try enough combinations you can find something that appears to work purely by chance or by luck. The challenge however is trying to identify something that could be

Quantocracy’s Daily Wrap for 03/03/2017

This is a summary of links featured on Quantocracy on Friday, 03/03/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More Data or Fewer Predictors: Which is a Better Cure for Overfitting? [EP Chan]

    One of the perennial problems in building trading models is the spareness of data and the attendant danger of overfitting. Fortunately, there are systematic methods of dealing with both ends of the problem. These methods are well-known in machine learning, though most traditional machine learning applications have a lot more data than we traders are used to. (E.g. Google used 10 million YouTube
  • Evidence-Based Investing? Take that Alpha and Shove It. [Alpha Architect]

    Johnny Paycheck has a great country song centered around the following lyric: Take this job and shove itI aint working here no more Campell Harvey, in the 2017 AFA Presidential Address, elaborates an analogous comment on the current state of the financial economics field: Take this alpha and shove itI aint publishing this research no more Prof. Harvey is rightly concerned that
  • Using Time-Series Momentum to Intentionally Miss the Best Months. Yes, Really. [Invest Resolve]

    The buy-and-hold crowd, including many mutual fund companies and a large cross-section of vocal pundits, like to talk about how missing the N best days/months in the market causes a serious impairment to long-term investor returns. What they fail to mention is that, because stock market volatility clusters during periods of market crisis, the best daily and monthly stock market returns are

Quantocracy’s Daily Wrap for 03/02/2017

This is a summary of links featured on Quantocracy on Thursday, 03/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Check Out Our Awesome New Book Library [Quantocracy]

    Check out our awesome new book library curated by four of the top rated authors in our community: Investment Idiocy (Rob Carver): General Quantitative Finance, Market History, Hedge Funds, General Programming Quant Start (Michael Halls-Moore): Python, C++, Financial Math, Quant Jobs & Interviews QuantStrat TradeR (Ilya Kipnis): R Programming Robot Wealth (Kris Longmore): Quant Trading, Machine
  • The Downside Of Momentum [Larry Swedroe]

    Momentum has been found to be a persistent and pervasive factor in the returns not only of equities, but in other asset classes (including bonds, commodities and currencies). With equities (compared to the market, value, size, profitability and quality factors), during the period 1927 through 2015, momentum has earned both the highest premium (9.6%) and the highest Sharpe ratio (0.61). However,
  • Prices Transformation Cheat Sheet [Quant Dare]

    In this entry, we discover the secrets behind prices transformation in financial series. Do you use price series in things such as technical analysis visualisation? Do you use return series in things such as volatility calculations? Do you use equity series in things such as comparing products with prices on different scales? If you answered yes to at least two of these questions, look at

Quantocracy’s Daily Wrap for 03/01/2017

This is a summary of links featured on Quantocracy on Wednesday, 03/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in February [Allocate Smartly]

    This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in
  • Active Managers Should Love Passive Investing – It Makes Them Better! [Alpha Architect]

    In a recent letter to its investors, Crispin Odey commented as follows:(1) Money managers specializing in picking stocks and bonds are being driven out by mindless passive investing. Odey is a London based hedge fund manager, whose flagship fund lost almost 50% in 2016.(2) Photo courtesy of Wes. All complaints can be directed towards him. Photo courtesy of Wes. All complaints can be directed

Quantocracy’s Daily Wrap for 02/27/2017

This is a summary of links featured on Quantocracy on Monday, 02/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Misattributing Bad Behavior [Flirting with Models]

    The behavior gap is the difference between the returns on an investment and the returns that an investor realizes in that investment. Behavioral biases ingrained in human nature, such as anchoring, hindsight, and overconfidence drive emotional decisions that can lead to a behavior gap, but quantitative assessments of investor underperformance is often misleading, especially on an aggregated basis.

Quantocracy’s Daily Wrap for 02/26/2017

This is a summary of links featured on Quantocracy on Sunday, 02/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Hidden Markov Models with Python Networkx and Sklearn [Black Arbs]

    Who is Andrey Markov? What is the Markov Property? What is a Markov Model? What makes a Markov Model Hidden? A Hidden Markov Model for Regime Detection Conclusion References Who is Andrey Markov? Markov was a Russian mathematician best known for his work on stochastic processes. The focus of his early work was number theory but after 1900 he focused on probability theory, so much so that he taught

Quantocracy’s Daily Wrap for 02/23/2017

This is a summary of links featured on Quantocracy on Thursday, 02/23/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Potential Return-Free Risk of Bonds [EconomPic]

    I've read too many posts / articles that outline why a rise in rates is good for long-term bond investors (as that would allow reinvestment at higher rates). While this can be true depending on the duration of bonds owned and/or for nominal returns over an extended period of time, it is certainly not true over shorter periods of time and absolutely not true for an investor in most real return
  • How Short Positions Affect Factor Investing? [Quantpedia]

    The performances of factor investing rely heavily on short sales, not only for building the initial long-short strategy, but also for regularly rebalancing the positions. Since short selling is subject to both legal restrictions and substantial costs, this paper examines how severely restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between
  • Dual Momentum Analysis [Quant Dare]

    Why dual momentum? Because strategies based on highest relative momentum show great results in the long run, but can experience deep falls and have little participation in the posterior rebounds after large market falls. To sidestep these drawbacks, here it is laid out a strategy based on Gary Antonaccis studies about Dual Momentum and Absolute Momentum, with the difference that, while he used

Quantocracy’s Daily Wrap for 02/22/2017

This is a summary of links featured on Quantocracy on Wednesday, 02/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Zoo or Unicorn Ranch? [Dual Momentum]

    According to Morningstar, as of June 2016, the assets in smart beta exchange traded products totaled $490 billion. BlackRock forecasts smart beta using size, value, quality, momentum, and low-volatility will reach $1 trillion by 2020 and $2.4 trillion by 2025. This annual growth rate of 19% is double the growth rate of the entire ETF market. Are factors the cure-all for our investment needs? Or
  • Explaining the Low Risk Effect with @LarrySwedroe [Alpha Architect]

    As my co-author, Andrew Berkin, and I(1) explain in our new book, Your Complete Guide to Factor-Based Investing,(2) one of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50
  • Country ETF Rotation Reader s Suggestions [Alvarez Quant Trading]

    My last post on Country ETF Rotation generated several ideas of what to test to improve the results. See the original post for the list ETFs being traded. One important test I left out from the original post was a baseline case. An idea applied to all the tests was trading more ETFS. For all tests, I will be showing results of trading (2,5,8) ETFs in the spreadsheet. Testing is from 1/1/2007 to