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Quantocracy’s Daily Wrap for 03/10/2017

This is a summary of links featured on Quantocracy on Friday, 03/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Streaming market data from native python IB API [Investment Idiocy]

    This the third in a series of posts on using the native python API for interactive brokers. You should read the first, and the second, before this one. It is an updated version of this older post, which used a third party API (swigibpy) which wraps around the C++ API. I've changed the code, but not the poor attempts at humour. In my last post we looked at getting a single snapshot of
  • Index Mapping For ETF Proxies [TrendXplorer]

    In order to present results as realistic as possible in our PAA-paper, we constructed long-term end-of-month data series for popular ETF proxies, like SPY, GLD and TLT (see paper appendix on SSRN). All data series start December 1969. For the pre-inception history, the proxies are derived from suitable indices. As part of a complete revision of the long-term data set, the construction process is
  • A Visual Quantitative Analysis of RSI using Tradestation and Excel [Beyond Backtesting]

    The traditional way to treat the RSI is to treat low RSI levels as good buying opportunities while treating high RSI levels as selling opportunities. However, we seek to gain fresh insight into the nature of RSI, with an eye toward discovering possible momentum return, by exploring the RSI using a visual quantitative approach. Exporting And Visualizing The Data We are interested in the next day
  • FX Carry Risk Mitigation Papers [Quantpedia]

    We analyze the worst currency carry loss episodes in recent decades, including causes, attribution by currency, timing, and the duration of carry drawdowns. To explore the determinants of the length of carry losses, a model of carry drawdown duration is estimated. We find evidence that drawdown duration varies systematically with expected return from the carry trade at the onset of the drawdown,
  • Python for Algo and Crypto-Currency Trading: 2-Day Workshop in London (July 8-9) [Quant at Risk]

    Within our unique 2-Day Intensive Workshop in London, UK on Python for Algorithmic and Crypto-Currency Trading we dive into most recent and hot topics in algo-trading. We will cover and analyse a well explored world of classical assets (stocks, FX currencies) extended by trading techniques aimed at crypto-currencies (inter alia, the bitcoin). Click here to find out more and register for this

Quantocracy’s Daily Wrap for 03/09/2017

This is a summary of links featured on Quantocracy on Thursday, 03/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting Stock Returns using ARIMA model [Quant Insti]

    Prediction is very difficult, especially about the future. Many of you must have come across this famous quote by Neils Bohr, a Danish physicist. Prediction is the theme of this blog post. In this post, we will cover the popular ARIMA forecasting model to predict returns on a stock and demonstrate a step-by-step process of ARIMA modeling using R programming. What is a forecasting model in
  • Playing with Prophet on Financial Time Series [Quant Dare]

    Two weeks ago, Facebook launched Prophet, an amazing forecasting tool available in Python and R. Heres a bit of info from the Facebook research website: Forecasting is a data science task that is central to many activities within an organization. For instance, large organizations like Facebook must engage in capacity planning to efficiently allocate scarce resources and goal setting in order
  • What hand traders can learn from system traders, and vice versa w/ @AdamHGrimes [Chat With Traders]

    Adam Grimes has been a trader for more than 20-years, hes traded all major asset classes, across various timeframes. Hes traded independently, with a prop firm, and hes run other trading businesses also. The main focus of this episode is to explore some of the things which discretionary traders can adapt from quantitative traders, and vice versameaning, what things can quants take from

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

Check Out Our Awesome New Book Library

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 Learning

The General Programming and Market History categories are brand new, and a number of categories have been entirely reworked, particularly General Quant Finance, Python and Financial Math.

I’m very excited to have the subject matter expert in each category doing the curation. Who’s better than Rob on hedge funds, Mike on Python, Ilya on R, or Kris on machine learning? Exactly. A big mahalo gentlemen for your contribution to the Quantocracy community.

[check out the new book library now]

Why the change?

Unfortunately, Jacques Joubert was unable to continue curating the books section due to compliance issues with his new firm. Jacques has made a number of important contributions to this community over the years, and did a yeoman job with the very difficult task of trying to curate such a broad array of books all by himself.

Jacques asked me to pass along this note. Your presence will be sorely missed sir.

I wanted to say a special thank you to Mike for letting me take part in curating the books list on Quantocracy. I have learned a lot whilst putting it together and made many friends along way. Going forward the books list will be curated by several members of our community, many from the “A-team” and I look forward to reading their recommendations.

P.S. For those of you who are left curious, I decided that I would like to lower my overall online presence, and free up some more time to dedicate to my career objectives.

Best Regards
Jacques Joubert

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.