Quantocracy

Quant Blog Mashup

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Quantocracy’s Daily Wrap for 01/07/2017

This is a summary of links featured on Quantocracy on Saturday, 01/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Advanced Time Series Plots in Python [Black Arbs]

    POST OUTLINE Motivation Get Data Default Plot with Recession Shading Add Chart Titles, Axis Labels, Fancy Legend, Horizontal Line Format X and Y Axis Tick Labels Change Font and Add Data Markers Add Annotations Add Logo/Watermarks MOTIVATION Since I started this blog a few years ago, one of my obsessions is creating good looking, informative plots/charts. I've spent an inordinate amount of

Quantocracy’s Daily Wrap for 01/06/2017

This is a summary of links featured on Quantocracy on Friday, 01/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beat the Market with Meucci and Markowitz [Propfolio Management]

    I am very excited to finally share some of my research exploring Meuccis (Meucci (2005)) portfolio optimization methods, and how the resulting portfolios compare to the use of historical data. For those unfamiliar with Attilio Meucci, he runs an annual Advanced Risk and Portfolio Managment Bootcamp in New York City every summer. The bootcamp attracts academics and professionals within the
  • Writing Puts, Or Just Pretending To [Throwing Good Money]

    Which color do you like better? Green or brown? Im partial to the green curve myself. That green curve comes from writing putssort of. Writing puts can be a lower volatility play that makes you money in choppy or flat markets, falls more softly in down markets, and seriously under-performs when the market goes on a tear upward. Whats a put and where do you put them? If youre an

Quantocracy’s Daily Wrap for 01/05/2017

This is a summary of links featured on Quantocracy on Thursday, 01/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Site News: Dabbling in Ads and Where All the Clicks Went in 2016 [Quantocracy]

    Two bits of site news: First, after 4+ years of running this site mostly gratis, Ive decided to dabble in adding advertisements, so expect to begin seeing the first baby steps with a handful of ads from Google. Ive tried to keep the ads as unobtrusive as possible and my hope is that your reading experience will be essentially unchanged. On to more fun news, heres an updated view of where

Site News: Dabbling in Ads and Where All the Clicks Went in 2016

Two bits of site news:

First, after 4+ years of running this site mostly gratis, I’ve decided to dabble in adding advertisements, so expect to begin seeing the first baby steps with a handful of ads from Google. I’ve tried to keep the ads as unobtrusive as possible and my hope is that your reading experience will be essentially unchanged.

On to more fun news, here’s an updated view of where the all the clicks on the mashup went in 2016. The numbers include any site that received at least 500 clicks, but ignore our Twitter, Stocktwits and daily RSS/Email.

I’ve labeled the top 10 recipients. The number in parenthesis represents the number of links we carried from that site, and the % represents the portion of all clicks that that site received.

Alpha Architect remains the top dog at 7.1% of clickthroughs (how they maintain their amazingly consistent publishing schedule I’ll never know).

We’re casting a wide net though. The top 10 recipients together only received about 35% of total clickthroughs. A whopping 117 sites received at least 500 clickthroughs for the year. I’m happy with that. The goal of Quantocracy is to improve not just the quality of work in the quantitative blogosphere (read more), but the quantity as well.

At no point in history has so much good work on these subjects been shared so freely. To all of the denizens of Quantocracy: a big mahalo, gracias, 謝謝 and thank you for helping this community to grow.

And finally, the 5 most clicked links of the year:

  1. Machine learning for financial prediction: experimentation with Aronson’s work – part 1 [Robot Wealth]
  2. Machine learning for financial prediction: experimentation with Aronson’s work – part 2 [Robot Wealth]
  3. Recommended Reading [Robot Wealth]
  4. A simple breakout trading rule (pysystemtrade) [Investment Idiocy]
  5. Get Rich Slowly [Financial Hacker]

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 01/04/2017

This is a summary of links featured on Quantocracy on Wednesday, 01/04/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend Following UP in December, Down in 2016 [Wisdom Trading]

    December 2016 Trend Following: UP +1.38% / 2016: -18.15% December closed 2016 on a slight positive note, avoiding six straight months of negative returns for our State of Trend Following index. An inflection point was felt in the markets towards the close of the year, but this was obviously not enough to offset what has been a strong under-performance in the second half of the year. The first half
  • R/Finance 2017: Call for Papers [Foss Trading]

    The ninth annual R/Finance conference for applied finance using R will be held on May 19 and 20, 2017 in Chicago, IL, USA at the University of Illinois at Chicago. The conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing, market microstructure, and econometrics. All will be discussed within the context of using R as a
  • A Modern, Behavior-Aware Asset Allocation [Flirting with Models]

    Happy New Year! To kick off the year, we want to share a white paper we penned mid-December containing our views on building a modern strategic asset allocation. The white paper covers: Why we believe tailwinds from the last 30 years are turning into headwinds for traditionally allocated stock-bond portfolios. Why the normative optimal portfolio may not be the optimal achievable portfolio and the
  • Using Trend-Following Rules to Enhance Factor Performance [Alpha Architect]

    After reviewing the 2016 performance of trend-following (-18.15%), its unclear why anyone would mention the word trend following in a public forum. But well give it a whirl anyway The comedian Victor Borge once famously observed, Santa Claus has the right idea visit people only once a year. In studying investment markets, many have taken a similar approach, preferring a
  • The Bayesian Information Criterion [Alex Chinco]

    Imagine that were trying to predict the cross-section of expected returns, and weve got a sneaking suspicion that x might be a good predictor. So, we regress todays returns on x to see if our hunch is right, \begin{align*} r_{n,t} = \hat{\mu}_{\text{OLS}} + \hat{\beta}_{\text{OLS}} \cdot x_{n,t-1} + \hat{\epsilon}_{n,t}. \end{align*} The logic is straightforward. If x explains enough of
  • N-Day exits with Mean Reversion [Alvarez Quant Trading]

    My last post on using PercentRank to measure mean reversion proved very popular. A reader looked at the trades and wondered if it would be best to exit after five days because the average trade with longer holds was a loser. I am surprised I have not covered this topic before. Background Early in while working for Larry Connors, I had done a mean reversion test. I was looking at the trades and
  • State of Trend Following in December [Au Tra Sy]

    Happy new year to all readers! With best wishes for your trading in the coming twelve months, which Im sure youll agree will prove interesting from several perspectives. We start the year by looking back at the performance of trend following over the year just passed. Unsurprisingly the State of Trend Following posted a loss for 2016. There was a long downtrend in the performance of
  • Are you Ready to Witness Finance Research on Steroids? [Alpha Architect]

    The 2017 American Finance Association conference is kicking off later this week in Chicago. If you havent been before check it out. The conference is the biggest meeting of top-tier academic researchers on the planet. You can review all the research being presented at the following link. Some of the more exciting sessions that Im reviewing: Behavioral Finance I Behavioral Finance II
  • 38 DTE Iron Condor Results Summary – Part 2 [DTR Trading]

    In the last post, 38 DTE Iron Condor Results Summary, I showed the backtest results from 97,416 iron condor (IC) trades. All of those test results were based on weekly expiration data at 38 days to expiration (DTE). In this post, we'll look at a few key metrics and how those metrics differ between weekly data and monthly data. The charts below are organized similar to those in the prior post.

Quantocracy’s Daily Wrap for 01/03/2017

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

  • Demystifying the Hurst Exponent Part 2 [Robot Wealth]

    What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? Thats the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including some Python code for its calculation, check out our previous post. Even if you
  • Tactical Asset Allocation in December [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
  • Testing Momentum s Robustness [Sharpe Returns]

    Happy new year! I have noticed that my quantitative posts get the most readership and discussion. So this year, Ill be posting a lot more research and will start the year off by exploring momentums robustness. There are two good ways to test the robustness of a rules-based trading strategy: The test of time – how does the strategy behave in different market regimes? Parameter sensitivity
  • Are Commodities Still a Good Portfolio Diversifier? [Dual Momentum]

    Overfitting the data is a serious problem when constructing financial models. One way to guard against this is to have lots of data. This helps you determine if your results are robust by seeing how they hold up over different time periods. But this assumes the underlying market dynamics remain stable over time. That is not always the case. Gogi Gerwal gives a good example of how you may be misled
  • Wakey, Wakey: Trends in Active Fund Pre-Fee Excess Returns [Basis Pointing]

    In a recent posting, I compared the prices of US active mutual fund to estimates of future pre-fee excess returns. In summary, I found that the annual expenses of most active funds met or exceeded a generous estimate of their potential before-fee excess returns. That is, many funds look like theyre priced to fail. What I didnt include in that posting, though, was detail on how I derived

Quantocracy’s Daily Wrap for 01/02/2017

This is a summary of links featured on Quantocracy on Monday, 01/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Statistical Arbitrage: Finding Correlated Stock Pairs (h/t Algotrading Reddit) [Above Index]

    Statistical Arbitrage , A.K.A StatArb is a pair trading strategy that invloves buying and selling a pair of stocks based on a underlying correlation between them. This correlation usually exist in a given sector or competitors, for example Pepsi (PEP) and Coca-Cola (KO) is a pretty popular pair. The logic behind the strategy is that pair stocks tend to follow one another, so when they fall outta
  • “Matt s Breadth Indicator” Update [Throwing Good Money]

    Happy new year! Its that time again, when everyone with a blog does a wrap up of the previous year. Heres my look-back. Many of you follow along with the +/-30% per quarter wider-market breadth indicator. Which is too much of a mouthful, so Ive humbly named it after myself instead. I wanted to provide an update since Ive been tracking it for awhile. The premise is this: breadth

Quantocracy’s Daily Wrap for 01/01/2017

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

  • Genetic algorithm for trading in Cpp [Imanol Perez]

    This code tries to show how to use genetic algorithms to create a simple trading strategy. It is intended as a proof of concept, rather than trying to provide a ready-to-use strategy. The historical data used is the following: SP500.dat. It is the closing prices of S&P 500 from 2006/12/18 to 2016/06/15, so almost 10 years. Genetic algorithms Genetic algorithms simulate the evolution of a

Quantocracy’s Daily Wrap for 12/31/2016

This is a summary of links featured on Quantocracy on Saturday, 12/31/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Relationship Between the VIX and SP500 Revisited [Relative Value Arbitrage]

    A recent post on Bloomberg website entitled Rising VIX Paints Doubt on S&P 500 Rally pointed out an interesting observation: While the S&P 500 Index rose to an all-time high for a second day, the advance was accompanied by a gain in an options-derived gauge of trader stress that usually moves in the opposite direction The article refers to a well-known phenomenon that under normal market
  • Over 300 quant links from #QuantLinkADay [Cuemacro]

    Ive been tweeting regularly over the past few years, usually around quant finance, coding and also a bit on burgers. Last December I decided to regularly start tweeting a quant link every day, for which I used the imaginative hashtag #QuantLinkADay (yes, that hashtag took a lot of thought!), to flag interesting quant papers (generally focusing on newly published material), quant tutorials,
  • Backtesting the Implied Volatility Long/Short Strategy [Black Arbs]

    This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT The shape of the volatility smirk has significant cross-sectional
  • Predictive Nature Of Valuations [Larry Swedroe]

    As we approach the end of 2016, the Shiller CAPE 10 stands at about 28, a level rarely exceeded (with the exception of in the late-1990s technology-driven bull market). Such heights cause many investors to worry about what current valuations may mean for future expected returns. Ill try to provide some insights by reviewing the literature, which demonstrates a link between current valuations
  • Divide By 20: One Year later [Throwing Good Money]

    Happy New Year, one day early. Heres wishing 2017 is successful for you in whichever way you define success. Arent calendars wonderful? A couple of days ago, up pops a reminder on my calendar to revisit a post I did a year ago. At the very beginning of 2016, I wrote a post on whether yearly performance was mean-reverting, and found some interesting things. You might want to go back and take

Quantocracy’s Daily Wrap for 12/30/2016

This is a summary of links featured on Quantocracy on Friday, 12/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PortfolioCharts’ Golden Butterfly [Allocate Smartly]

    This is a test of the Golden Butterfly, the homegrown buy & hold strategy from PortfolioCharts.com. PortfolioCharts is to buy & hold what AllocateSmartly is to tactical asset allocation, an independent and unbiased catalog of strategy performance, so when they put their stamp of approval on a portfolio, it deserves consideration. This strategy is similar in concept to Brownes