Quantocracy

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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

Quantocracy’s Daily Wrap for 07/01/2016

This is a summary of links featured on Quantocracy on Friday, 07/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Video: Factor Models for Traders by EP Chan (h/t Quant News)

    Factor models are not just for long-term investors. They can help traders find out why their strategies are suffering. This talk highlights the difference between factor and "alpha" models, and what short-term factors traders can use.
  • Podcast with Wes Gray of Alpha Architect (h/t Abnormal Returns) [Big Picture]

    This week on our Masters in Business podcast, we speak with Wes Gray, former Captain in U.S. Marines, and founder of Alpha Architect. He studied economics at Wharton, graduated with honors before getting his MBA and PhD at University of Chicago. Instead of heading to Wall Street like so many MBAs, however, he joined the Marines, went to Iraq, where he embedded with the Iraqi Army as a U.S. Marines
  • Quantified News Analytics: Profitability vs Pitfalls [Quant Insti]

    As sources and volumes of news have grown, so has the techniques to gather, extract, aggregate and categorise them. Important news can result in large positive or negative returns. However, owing to many news sources, we need to ask a fundamental question: Is news analytics profitable in every situation or are there some pitfalls that needs to be avoided? Information flow in News Analytics news
  • Taxonomy of CTAs [Quantpedia]

    Recently a range of alternative risk premia products have been developed promising investors hedge fund/CTA like returns with higher liquidity, transparency and relatively low fees. The attractiveness of these products rests on the assumption that they can deliver similar returns. Using a novel reporting bias free sample of 3,419 CTA funds as a testing ground, our results suggest this assumption

Quantocracy’s Daily Wrap for 06/30/2016

This is a summary of links featured on Quantocracy on Thursday, 06/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can a simple Market Internals technique actually improve trading strategy results? [Better System Trader]

    In my 10+ years full-time trading career, I have found very few tools and tactics that would get my attention so deeply as Market Internals. In 2014, I spent about 6 months in a row with this unique traders tool, exploring its possibilities every single day, searching for new and creative implementation ideas for my own automated trading systems (ATSs). With a real obsession with this concept,
  • The Case for Momentum in Expensive Markets [EconomPic]

    Charlie Bilello, one of my favorite follows on Twitter, analyzed the relationship between market valuation and future returns (over various time horizons) in a recent post Valuation, Timing, and a Range of Outcomes. The post contained some very insightful tables, such as the one below, where he shows that valuations matter… if you pay less for stocks, you will generally be provided with higher
  • Questioning Everything You Knew about Asset Allocation [Alpha Architect]

    Is a 100% stock allocation crazy? As long as one addresses their needs for liquidity (as to avoid extracting capital from the markets at bad times) and can tolerate the market price volatility, a 100% or near-100% allocation to equities is not as outlandish as one might suspect. Focusing on fundamentals and valuations instead of market prices should alleviate much of the unnecessary concern with

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