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Quantocracy’s Daily Wrap for 06/12/2016

This is a summary of links featured on Quantocracy on Sunday, 06/12/2016. 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, 06/11 as voted by our readers: A Survey of Deep Learning Techniques Applied to Trading [Greg Harris] Diversification Will Always Disappoint [Flirting with Models] Capital correction (pysystemtrade) [Investment Idiocy] Will Bonds Deliver Crisis Alpha in the Next Crisis? [Alpha Architect] The Internal Bar Strength Indicator [Jonathan
  • System Parameter Permutation – a better alternative? [Better System Trader]

    When I wrote my Wagner Award winning paper "Know your System! Turning Data Mining from Bias to Benefit," I had two goals in mind: Introduce a new method to reasonably estimate the long-run expected performance of a trading system, and Provide a simple method for the average system trader to understand and employ the method. I've subsequently realized that the paper's focus
  • Strategy Evaluation with Dave Walton [Better System Trader]

    Today we're covering a topic which can really be a concern for traders of all levels, from beginner to pro, and that is the topic of strategy evaluation. Have you ever found that real-life performance does not match expected results? Or perhaps you have a strategy that is stuck in a drawdown and wondering if it's actually broken? I'm sure we've all heard of data mining bias,

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 06/11 as voted by our readers:

Big ups to Greg Harris for breaking into the list of 10 top ranked blogs at Quantocracy. I’m looking forward to big things in the future, and I highly recommend you follow Greg now on his blog and via Twitter.

We also welcome three blogs making their first ever appearance on the mashup.

And finally, Jacques added a number of excellent new books to our Machine Learning library:

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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 06/11/2016

This is a summary of links featured on Quantocracy on Saturday, 06/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PDF: To Win With Smart Beta Ask If the Price Is Right [Research Affiliates]

    This is the second of a series on the future of smart beta. In our first article in this seriesHow Can Smart Beta Go Horribly Wrong? 1we show, using U.S. data, that the relative valuation of a strategy (in comparison with its own historical norms) is correlated with the strategys subsequent return at a five-year horizon. The high past performance of many of the increasingly

Quantocracy’s Daily Wrap for 06/10/2016

This is a summary of links featured on Quantocracy on Friday, 06/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Fundamentals of Machine Learning for Predictive Data Analytics [Amazon]

    Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in
  • Mini-Meucci : Applying The Checklist – Steps 3-5 [Return and Risk]

    "In the future, instead of striving to be right at a high cost, it will be more appropriate to be flexible and plural at a lower cost. If you cannot accurately predict the future then you must flexibly be prepared to deal with various possible futures." Edward de Bono, author and thinker extraordinaire (born 1933) In this third leg of The Checklist tour, we will take 3 more steps,
  • State of Trend Following in May [Au Tra Sy]

    A strong down month in May for the state of trend following index, which solidifies the downtrend from the last two months and takes the YTD performance in the red, after the strong start to the year. Please check below for more details. Detailed Results The figures for the month are: May return: -6.17% YTD return: -4.52% Below is the chart displaying individual system results throughout May:

Quantocracy’s Daily Wrap for 06/09/2016

This is a summary of links featured on Quantocracy on Thursday, 06/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Markov Switching Regimes say bear or bullish? [Quant Dare]

    We continue with our last OBSSESION trying to capture an index trend but at the moment, not playing with future information. Markov Switching RegimesWe are going to introduce the Markov Switching Regimes (MSR) model which, as its name indicates, tries to capture when a regimen has changed to another one. This would be a change between opposite trends or it could consist in passing from being
  • Simple Machine Learning Model to Trade SPY (h/t AlgoTrading Reddit) [Alpha Plot]

    I have created a quantitative trading strategy that incorporates a simple machine learning model to trade the SPY as part of my ongoing research in quantitative trading. The focus here was not on creating a strategy with alpha but rather to develop a framework both in my mind and in code to develop more advanced models in the future. 1. Does SPY Exhibit Short-Term Mean Reversion or Momentum?
  • Trend Following carries on with downtrend in May [Wisdom Trading]

    May 2016 Trend Following: DOWN -7.37% / YTD: -1.71% This time, the negative performance for the index last month takes the Year-To-Date performance in the red, for the first time in 2016. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for May: Wisdom State of Trend Following – May 2016 And the 12-month chart: Wisdom State of Trend Following
  • Your best strategy in 2016 so far [Quant Investing]

    I am sure you also don't run after the most recent best performing investment strategy. I stopped doing this, a long time ago, after I (quite a few times) discovered I was the last to jump on the strategy just as it stopped working. But I suspect you also find it interesting to see what has worked well so far this year. That is why I decided to take a look at what investment strategy would

Quantocracy’s Daily Wrap for 06/08/2016

This is a summary of links featured on Quantocracy on Wednesday, 06/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Capital correction (pysystemtrade) [Investment Idiocy]

    This post is about how should you adjust the trading capital you have at risk given the profitability (or not) of your trading account. I'm posting this for three reasons. Firstly it's a pretty important topic. I address, in some detail, how to set your risk target for a given amount of trading capital in chapter 9 of my book. I only briefly discuss what you should do thereafter, once
  • Random Asset Allocation in the ASX200 [Ryan Kennedy]

    To paraphrase the old adage; "a monkey throwing darts will outperform most fund managers". I have seen this concept explored several times in relation to the SP500, but I was interested to see if it had any relevance to the ASX200. Our monkey with darts will be a random number generator, selecting 10 stocks to buy from the XJO in equal weight. We test with $100,000 of capital. Benchmark
  • Trend Model via Difference Between Long and Short-Term Variance [Quantpedia]

    We relate the performance of trend following strategy to the difference between a long-term and a short-term variance. We show that this result is rather general, and holds for various definitions of the trend. We use this result to explain the positive convexity property of CTA performance and show that it is a much stronger effect than initially thought. This result also enable us to highlight

Quantocracy’s Daily Wrap for 06/07/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Will Bonds Deliver Crisis Alpha in the Next Crisis? [Alpha Architect]

    Bonds are often viewed as being great diversifiers due to the perception that they perform well during tough times for stocks. Historically this has been a true statement. But will it continue? Our answer: unclear. Most investors use correlation to measure the diversification benefit an investment might provide an existing portfolio. However, this article uses a slightly different approach to

Quantocracy’s Daily Wrap for 06/06/2016

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

  • New Book Added: Data Science from Scratch with Python [Amazon]

    Data science libraries, frameworks, modules, and toolkits are great for doing data science, but theyre also a good way to dive into the discipline without actually understanding data science. In this book, youll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills,
  • Need for Speed: High Frequency Economic News Trading [Justinas Brazys]

    Markets are efficient if new information is incorporate instantly and essentially without any trading, i.e. price jumps to the correct level that represents all available information at the time. If you believe markets are indeed perfectly efficient, there seems to be no point in using news as a source of alpha in trading. However it is unlikely that information can be incorporated instantaneously
  • Tactical Trend-Following: Core or Alternative? [Flirting with Models]

    Answering whether a strategy should be a core holding or an alternative holding often has less to do with the investment strategy itself and more to do with an investors understanding of how that strategy will perform. Asset classes and strategies that investors are comfortable with, and have a strong understanding of why and when they will perform a certain way, are strong contenders for core
  • Summer, Winter and the Volatility Premium [Factor Wave]

    A member of our slack channel recently asked if there was an equivalent of "sell in May" for volatility trading. Does the volatility premium, the difference between implied volatility and the subsequent realized volatility, differ during summer and winter months? To test this idea for the S&P 500, I calculated the difference between the VIX and the realized volatility over the next

Quantocracy’s Daily Wrap for 06/05/2016

This is a summary of links featured on Quantocracy on Sunday, 06/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mini-Meucci : Applying The Checklist – Step 2 [Return and Risk]

    "Guessing before proving! Need I remind you that it is so that all important discoveries have been made? Henri Poincar, French mathematician (1854-1912) In this second leg of The Checklist tour, Estimation, we are going to make some educated guesses about the true unknown distribution of the invariants. But first… Recap and a bit more on Quest for Invariance The quest for invariance is
  • Computation of the Loss Distribution not only for Operational Risk Managers [Quant at Risk]

    In the Operational Risk Management, given a number/type of risks or/and business line combinations, the quest is all about providing the risk management board with an estimation of the losses the bank (or any other financial institution, hedge-fund, etc.) can suffer from. If you think for a second, the spectrum of things that might go wrong is wide, e.g. the failure of a computer system, an