Quantocracy

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Quantocracy’s Daily Wrap for 07/10/2016

This is a summary of links featured on Quantocracy on Sunday, 07/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Should system traders override their systems? [Better System Trader]

    What an eventful few weeks weve had since the last episode. The results of the Brexit decision took a lot of people by surprise and the markets reacted accordingly. What was interesting about this market event is that we all knew the date and time period when the Brexit votes would start rolling in, so we had a rough idea when we might see some type of market reaction, if the market reacted at

Quantocracy’s Daily Wrap for 07/09/2016

This is a summary of links featured on Quantocracy on Saturday, 07/09/2016. 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, 07/09 as voted by our readers: Backtesting Based on Multiple Signals Beware of Overfitting [Alpha Architect] Cloud-Based Automated Trading System with Machine Learning [Quant Insti] Alphas measurement problem [Flirting with Models] Can a simple Market Internals technique improve trading strategy results? [Better System Trader]

Best Links of the Last Two Weeks

The best quant mashup links for the two weeks ending Saturday, 07/09 as voted by our readers:

We also welcome one new blog making its first ever appearance on the mashup:

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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 07/07/2016

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

  • An Alternative Investment Strategy with Value and Momentum [Alpha Architect]

    Anyone who follows our website should be familiar with the extensive evidence behind our favorite stock selection strategies: Value Investing Momentum Investing The evidence suggests that high-conviction ( We document why high conviction is important for both value and momentum strategies here and here. We document how value and momentum work as a system here. But there is a potential problem: The
  • Visualizing Fixed Income ETFs with T-SNE [Quant Dare]

    In recent articles we were talking about PCA and ISOMAP, as techniques for dimensionality reduction. On this occasion, we put the focus on T-SNE, in relation with visualization and understanding of multidimensional datasets in a low dimension space, where the human eye can find patterns easily. T-SNE was developed in 2008 by Laurens van der Maaten and Geoffrey Hinton. It comprises of two main
  • Momentum Rotation 60 Day ROC System Metrics [DTR Trading]

    It's been a while since my last post. I had planned on writing this particular article about three months ago, but work got in the way of my writing and testing Over the next few weeks I will try to close out this series on momentum rotation using my 60 day ROC example written for AmiBroker. After I finish this series, I will get back to option strategy backtesting I thought it was

Quantocracy’s Daily Wrap for 07/06/2016

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

  • Advanced Algorithmic Trading and QSTrader – Second Update [Quant Start]

    This is a quick update post to let readers know that the pre-order release of Advanced Algorithmic Trading has had a new update, adding over 50 pages of material. This brings the current release up to 250 pages. To access the new content, customers simply need to follow the download link received in the original purchase email. If the download email has been misplaced then please email
  • Mini-Meucci : Applying The Checklist – Steps 10+ [Return and Risk]

    In this final leg of The Checklist tour we'll be looking at the Dynamic Allocation step and touch briefly on ex-post Performance Analysis. Dynamic Allocation Essentially this involves repeating the previous 9-steps on a periodic basis (e.g. a sequence of monthly allocations) according to a chosen allocation policy. Examples of dynamic allocations include systematic strategies (based on
  • Intro to Algorithmic Trading with Heikin-Ashi [Quantiacs]

    Algorithmic trading is a field thats generally quite daunting to beginners, forcing them to juggle learning advanced programming techniques and market mechanics. Throughout the process theres usually not a lot of guidance, and even less coding examples. Our goal is to demystify this process and take you from beginner to quant with a hands-on lesson. Well program our own technical
  • Trend Following vs Countertrend Trading Strategies [QuantLab.co.za]

    Introduction A blog series to contrast the key distinctions between trend following and countertrend strategies during building, testing and trading. In this post we examine the effects of data integrity and simulated trade sample size on backtested performance. Price Data Integrity One of the major obstacles for traders looking to research trend following models is data. Since trend following
  • Trend Following UP in June (Thanks Brexit) [Wisdom Trading]

    Brexit might have been globally thought of as bad news for the markets, but it was good for trend following. It marked a quick up movement in last months performance, quickly reversing the negative performance of the month to turn it back positive, after the 23rd June vote. The YTD performance is slightly back in positive territory. Below is the full State of Trend Following report as of last

Quantocracy’s Daily Wrap for 07/05/2016

This is a summary of links featured on Quantocracy on Tuesday, 07/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cloud-Based Automated Trading System with Machine Learning [Quant Insti]

    Maxime Fages Maximes career spanned across the strategic aspects of value and risk, with a particular focus on trading behaviors and market microstructure over the past few years. He embraced a quantitative angle in M&A, fund management or currently corporate strategy and has always been an avid open-source software user. Maxime holds a MBA from Insead and a MScEng from Ecole Nationale
  • Alpha’s measurement problem [Flirting with Models]

    Alpha is the holy grail of asset management: risk-free excess returns generated by investment skill. Alpha is one of the most commonly quoted summary statistics yet measuring alpha is surprisingly difficult. Without an understanding of measurement uncertainty, fit of our model, or even the risk factors utilized to calculate alpha, the statistic loses its applicability.
  • State of Trend Following in June [Au Tra Sy]

    The month of June started positive for the trend following index, before a V-shaped movement pre/post-Brexit, that ended the month in positive territory. The YTD figure is still in the red. Please check below for more details. Detailed Results The figures for the month are: June return: 2.94% YTD return: -1.96% Below is the chart displaying individual system results throughout June:

Quantocracy’s Daily Wrap for 07/03/2016

This is a summary of links featured on Quantocracy on Sunday, 07/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Human significance, economic significance and statistical significance [Eran Raviv]

    We are now collecting a lot of data. This is a good thing in general. But data collection and data storage capabilities have evolved fast. Much faster than statistical methods to go along with those voluminous numbers. We are still using good ole fashioned Fisherian statistics. Back then, when you had not too many observations, statistical significance actually meant something. It does not