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Quantocracy’s Daily Wrap for 10/15/2015

This is a summary of links featured on Quantocracy on Thursday, 10/15/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Guns, Bombs and eSports: Applying Data and Portfolio Analytics to Counter-Strike Gambling [Kevin Pei]

    Since the publication of Bill James' seminal work, Baseball Abstract, and the rise to stardom for the Oakland A's, Sports Analytics – the application of statistics to competitive sports – has been (and still is) a prominent topic within the industry. Thus, it is only reasonable for practitioners to apply this movement to the new and upcoming playing field called eSports, which has gained
  • Intraday Strategy Backtesting in R Part 2 (Rule-based Strategies) [Portfolio Effect]

    In this post we take intraday backtesting with PortfolioEffectHFT package one step further by adding a simple signal-based rebalancing rule. Using this rule we will create two trading portfolios a high frequency strategy portfolio and a low frequency portfolio and compare them with each other in terms of their intraday risk and performance. Both strategies would employ a price moving average
  • I Hired a Contract Coder [Financial Hacker]

    Youre a trader with serious ambitions to use algorithmic methods. You already have an idea to be converted to an algorithm. Only problem: You do not know to read or write code. So you hire a contract coder. A guy whos paid for delivering a script that you can drop in your MT4, Ninja, TradeStation, or Zorro platform. Congratulations, now youre an algorithmic trader. Just start the script
  • Seasonality debunked (partially) [RRSP Strategy]

    Ive previously written about a bi-annual seasonality pattern in US equity markets: https://rrspstrategy.wordpress.com/2014/05/16/bi-annual-seasonality/ The quarterly average market (Mkt-RF) returns from 1950 to present are shown below (data from Ken Frenchs library). Quarters 1-4 are even years and 5-8 are odd years. seas-Q The table shows that mean returns of quarters 4-6 are greater than
  • Ben Graham Would be Proud: Fundamental Analysis Works [Alpha Architect]

    Here is an interesting working paper on the use of fundamental analysis in stock selection. The authors take a machine learning approach to building out statistical fair-value Ben Graham and David Dodd would be proud. Of course, this isnt surprising if youve read our treatise on systematic value investing. Fundamental Analysis Works Stock prices cannot be the outcome of a rational
  • ‘Javascript for Financial Analysts’ – Help Wanted [John Orford]

    The still-in-progress 'Javascript for Financial Analysts' book is now up on Leanpub. The goal of the book is to help financial analysts automate their daily tasks by using Javascript in the browser. Not only that, but do it elegantly. Giving people a viable alternative to Excel is a lofty goal, I could do with some help. If you want to pitch in, take a look at the Github repo or send me
  • SPX Straddle – 59 DTE – Results Summary [DTR Trading]

    Over the last five blog posts we looked at the backtest results for 4120 options straddles sold on the S&P 500 Index (SPX) at 59 days-to-expiration (DTE). Eight different loss approaches were tested on these straddles. On top of these eight loss approaches, tests were conducted with no profit taking, and profit taking at 10%, 25%, 35%, and 45% of the credit received. For background information

Quantocracy’s Daily Wrap for 10/14/2015

This is a summary of links featured on Quantocracy on Wednesday, 10/14/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Returns clustering with K-means algorithm [Quant Dare]

    Do you know how a fireman and the direcion of a financial time series are related? If your answer is no, youre reading the right post. Voronoi diagram Suppose that you are a worker in an emergency center in a city and your job is to tell the pilots of firefighter helicopters to take off. You receive an emergency call because there is a point of the city on fire and a helicopter is necessary to
  • Keep Skewness In Perspective [Larry Swedroe]

    Diego Amaya, Peter Christoffersen, Kris Jacobs and Aurelio Vasquez, authors of the new paper, Does Realized Skewness Predict the Cross-Section of Equity Returns?, examined higher moments of volatility, skewness and kurtosis to determine if they have provided incremental explanatory power in the cross section of stock returns. Before reviewing the authors findings, which appear in the
  • Apples and Oranges: A Random Portfolio Case Study [GestaltU]

    This article was motivated by a provocative discussion with a thoughtful RIA. Lets call him Harry. Harry expressed some disappointment with the performance of Global Tactical Asset Allocation (GTAA) strategies over the past few years relative to some popular tactical U.S. sector rotation funds. Harrys definition of GTAA is any strategy that regularly alters its allocation across a wide
  • How to make proper equity simulations on a budget Part 1 Data [Following the Trend]

    Simulating an equity strategy is difficult. Much more so than simulating a futures strategy. Theres a lot more moving parts to care about. Much more complexity. All too often, I see articles and books that just skipped the difficult parts. Either they didnt understand it, or they hoped it wouldnt matter. It does. When I set out to write Stocks on the Move, I wanted to make sure that
  • Javascript for Financial Analysts Book – ‘Fold’ [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 4. ~ Up until now we have introduced a handful of new concepts which needed just two words of vocabulary – map and filter. Fold however, is a new piece of vocabulary and one of the most powerful concepts in computer science rolled into one! Similar to the idea of 'optionality' in quantitative finance, once you
  • Harnessing the power of machine learning for money making algo strategies w/ @BMouler [Chat With Traders]

    What youre about to hear is an interview with Bert Mouler hes a trader of futures and equities, and has been involved with markets since 07. But he does things a little different to most Hes an algorithmic trader who harnesses the power of machine learning to discover and develop profitable trading strategies. This is an area that hasnt been covered in previous episodes,

Quantocracy’s Daily Wrap for 10/13/2015

This is a summary of links featured on Quantocracy on Tuesday, 10/13/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • When process and performance disagree [Flirting with Models]

    Summary Due diligence often focuses on the three Ps: people, philosophy, and process. There is an important 4th P: performance. When a portfolio has a consistent process, performance can ebb and flow as the strategy goes in or out of favor. For example, value stocks have been out of favor for 8 years. If a strategy goes through an extended period of underperformance, we must ask whether the
  • Absolute Strength Momentum Investing Strategy [Alpha Architect]

    Here we highlight an interesting working paper titled Absolute Strength: Exploring Momentum in Stock Returns by Gulen and Petkova (2015). The abstract is the following: We document a new pattern in stock returns that we call absolute strength momentum. Stocks that have significantly increased in value in the recent past (absolute strength winners) continue to gain, and stocks that have
  • Multivariate volatility forecasting (3), Exponentially weighted model [Eran Raviv]

    Broadly speaking, complex models can achieve great predictive accuracy. Nonetheless, a winner in a kaggle competition is required only to attach a code for the replication of the winning result. She is not required to teach anyone the built-in elements of his model which gives the specific edge over other competitors. In a corporation settings your manager and his manager and so forth MUST feel
  • Giving up on Runge-Kutta Methods (for now?) [Dekalog Blog]

    Over the last few weeks I have been looking at using Runge-Kutta methods for the creation of features, but I have decided to give up on this for now simply because I think I have found a better way to accomplish what I want. I was alerted to this possible approach by this post over at http://dsp.stackexchange.com/ and following up on this I remembered that a few years ago I coded up John
  • Value Investing: The Pain Train has Arrived and it Sucks [Alpha Architect]

    A few months ago we highlighted a surprising result: cheap high-quality stocks were getting crushed by expensive junk stocks. The spread at the end of June was around 18%. Here is the chart from the old post (details on construction are in the original post): cheap hiqh quality versus expensive low quality stocks The results are hypothetical results and are NOT an indicator of future results and

Quantocracy’s Daily Wrap for 10/12/2015

This is a summary of links featured on Quantocracy on Monday, 10/12/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Get Started with R quantmod Package? [Quant Insti]

    The quantmod package for R is designed to assist the quantitative trader in the development, testing, and deployment of statistically based trading models. It is a rapid prototyping environment where enthusiasts can explore various technical indicators with minimum effort. It offers charting facilities that is not available elsewhere in R. Quantmod package makes modeling easier and analysis
  • More Factors Don t Always Help [Larry Swedroe]

    Professors Eugene Fama and Kenneth French have a new paper, Incremental Variables and the Investment Opportunity Set, that provides some important insights for investors considering funds designed to supply exposure to multiple factors, or styles, of investing. In their study, they note: Much asset pricing research is a search for variables that improve understanding of the cross section
  • Daily Academic Alpha: Momentum Investing and Asset Allocation [Alpha Architect]

    The results in this paper wont surprise most who are regular readers, but the paper below does a nice job explaining things in a simple way. For more advanced asset allocation methods that use momentum one can check out past blogs on the subject here, here , and here. Momentum Investing & Asset Allocation This paper highlights the use of a new strategic approach within a quantitative
  • Happy Columbus Day Again? [Quantifiable Edges]

    While the stock market is open on Monday, banks, schools, government offices, and the bond market are closed. In past years with the bond market closed, the stock market has done quite well on Columbus Day. Of course the most famous Columbus Day rally was in 2008 when the market gained over 11% after having crashed the week before. A few times here on the blog (most recently 10/13/13) I showed

Quantocracy’s Daily Wrap for 10/10/2015

This is a summary of links featured on Quantocracy on Saturday, 10/10/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Zweig Breadth Thrust as a case study in quantitative analysis [Humble Student of the Markets]

    Academic financial quantitative analysis began in earnest in the 1970's as a response to the Efficient Market Hypothesis (EMH). EMH proponent believed that you can't beat the market with stock picking because everything about a stock is already known by the market. As a test of EMH, researchers began to scour the CRSP tapes of stock prices and found "anomalies". They found that
  • All Trading is Quant Trading [MKTSTK]

    Quant trading is a redundant term: all trading is quant trading. Whether your an arbitrageur or a technician, fund manager or high frequency trader, you are basing your trading on the quantitative analysis of the market, you just might not realize it. A lot of times there seems to be an artificial divide between the realms of quant trading, technical analysis, and fundamental analysis. Ultimately

Quantocracy’s Daily Wrap for 10/09/2015

This is a summary of links featured on Quantocracy on Friday, 10/09/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Scalping Irrational? [Financial Hacker]

    Clients often ask for strategies that trade on very short time frames. Some are possibly inspired by I just made $2000 in 5 minutes stories on trader forums. Others have heard of High Frequency Trading and concluded that the higher the frequency, the better must be the trading. Zorro developers had been pestered for years until they finally implemented tick-based price histories and
  • Volatility Stat-Arb Shenanigans [QuantStrat TradeR]

    This post deals with an impossible-to-implement statistical arbitrage strategy using VXX and XIV. The strategy is simple: if the average daily return of VXX and XIV was positive, short both of them at the close. This strategy makes two assumptions of varying dubiousness: that one can observe the close and act on the close, and that one can short VXX and XIV. So, recently, I decided to play
  • Is research in finance and economics reproducible? [Mathematical Investor]

    Reproducibility in scientific research In the past year or two, the reproducibility of research results in finance and economics has come under serious question. If it is any comfort, similar difficulties have emerged in numerous other scientific fields. In 2011, a team of Bayer researchers attempted to reproduce a set of key published pharmaceutical studies. They were only able to validate 11 out
  • Cumulative market gains are zero across even years [RRSP Strategy]

    Mkt-RF returns in even years sum to zero over the last 50+ years (data from Ken Frenchs library). This could be a spurious result although the stats suggest otherwise. odd-even-years Is this result statistically significant? Applying Students t-test gives a statistic of 2.3, i.e. mean returns of even versus odd years are different at the 5% significance level.
  • Simple Tests of Sy Harding s Seasonal Timing Strategy [CXO Advisory]

    Several readers have inquired over the years about the performance of Sy Hardings Street Smart Report Online (now unavailable due to Mr. Hardings death), which included the Seasonal Timing Strategy. This strategy combines the markets best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).

Quantocracy’s Daily Wrap for 10/07/2015

This is a summary of links featured on Quantocracy on Wednesday, 10/07/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R [Quant Start]

    In this article I want to show you how to apply all of the knowledge gained in the previous time series analysis posts to a trading strategy on the S&P500 US stock market index. We will see that by combining the ARIMA and GARCH models we can significantly outperform a "Buy-and-Hold" approach over the long term. Strategy Overview The idea of the strategy is relatively simple but if
  • How to look up a Stock s Short Interest with Python [MKTSTK]

    Today I was trying to investigate short interest in the Energy sector: the group as a whole has rallied hard over the last few days and I suspect a short covering rally is at play, so some testing is in order. Much to my dismay, my searches didnt return an easy way to do this in Python. Lots of websites offer the data but quants want to consume the data programmatically Luckily it was super
  • Optimization of Equity Momentum [Quantpedia]

    Standard mean-variance optimized momentum outperforms the traditional equally weighted momentum strategy if the expected return vector used reflects momentum's top and bottom only characteristic. This top and bottom only characteristic is the phenomenon that only the stocks in the top decile of momentum's ranking outperform and that only stocks in the bottom decile underperform, while

Quantocracy’s Daily Wrap for 10/06/2015

This is a summary of links featured on Quantocracy on Tuesday, 10/06/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • VIX Trading Strategies in September [Volatility Made Simple]

    Weve tested 24 simple strategies for trading VIX ETPs on this blog (separate and unrelated to our own strategy). And while I cant speak for all traders, based on all of my readings both academic and in the blogosphere, the strategies weve tested are broadly representative of how the vast majority of traders are timing these products. Below Ive shown the September results of all 24
  • How to be a Quant [Turing Finance]

    Since writing about my experience writing the CFA Level I exam in June, I have received many emails from people interested in finding out how to become a quant. To some extent, this post will answer that question. That said, this post is actually not about how to become a quant, it is about how to be a quant in whatever sector of the financial services industry you already work in. Being is quant
  • An Example of a Trading Strategy Coded in C++ [Quant Insti]

    Any trading strategy can be broken down into a set of events and the reaction to those events. The reactions can get infinitely complex and varying but essentially strategy writing is quite simply put exactly that. The kind of events and their frequency would depend on the markets and the instruments on which this strategy would be working on however, broadly speaking most markets would have
  • A Review of DIY Financial Advisor from @AlphaArchitect [QuantStrat TradeR]

    This post will review the DIY Financial Advisor book, which I thought was a very solid read, and especially pertinent to those who are more beginners at investing (especially systematic investing). While it isnt exactly perfect, its about as excellent a primer on investing as one will find out there that is accessible to the lay-person, in my opinion. Okay, so, official announcement: I am
  • An Example of a Trading Strategy Coded in R [Quant Insti]

    Back-testing of a trading strategy can be implemented in four stages. Getting the historical data Formulate the trading strategy and specify the rules Execute the strategy on the historical data Evaluate performance metrics In this post, we will back-test our trading strategy in R. The quantmod package has made it really easy to pull historical data from Yahoo Finance. The one line code below
  • A little demonstration of portfolio optimisation [Investment Idiocy]

    I've had a request for the code used to do the optimisations in chapter 4 of my book "Systematic Trading" (the 'one-period' and 'bootstrapping' methods; there isn't much point in including code to the 'handcrafted' method as it's supposed to avoid programming). Although this post will make more sense if you've read the book, it can also
  • Test for Jumps using Neural Networks [Top of the Bell Curve]

    Modelling of financial markets is usually undertaken using stochastic process. Stochastic processes are collection of random variables indexed, for our purposes, by time. Examples of stochastic processes used in finance include GBM, OU, Heston Model and Jump Diffusion processes. For a more mathematically detailed explanation of Stochastic processes, diffusion and jump diffusion models, read this
  • Using Random Portfolios To Test Asset Allocation Strategies [Capital Spectator]

    Last month I tested random rebalancing strategies based on dates and found that searching for optimal points through time to reset asset allocation may not be terribly productive after all. Lets continue to probe this line of analysis by reviewing the results of randomly changing asset weights for testing rebalancing strategies. Ill use the same 11-fund portfolio thats globally
  • State of Trend Following in September: Still on the Rise [Wisdom Trading]

    September 2015: Trend Following UP +4.64% YTD: +13.98% Third month in a row of the index producing a positive return. The YTD and 12-month figures well in the black (over 10% and 35% respectively) show that trend following has been a good strategy to invest or trade in these past market conditions. Below is the full State of Trend Following report as of last month. Performance is
  • Research Review | 6 Oct 2015 | Portfolio Risk Management [Capital Spectator]

    How Do Investors Measure Risk? Jonathan Berk and Jules H. Van Binsbergen October 1, 2015 We infer which risk model investors use by looking at their capital allocation decisions. We find that investors adjust for risk using the beta of the Capital Asset Pricing Model (CAPM). Extensions to the CAPM perform poorly, implying that they do not help explain how investors measure risk. A Smart
  • Using Machine Learning to Select Your Indicators for a Trading Strategy [Inovance]

    Selecting the indicators to use is one of the most important and difficult aspects of building a successful strategy. Not only are there thousands of different indicators, but most indicators have numerous settings which amounts to virtually limitless indicator combinations. Clearly testing every combination is not possible, so many traders are left on their own selecting somewhat random inputs to
  • State of Trend Following in September: It was a Good Summer [Au Tra Sy]

    The index had a strong September, continuing the Summer uptrend started in mid-July. This has now resulted in the YTD performance returning to positive territory, after the Spring slump took the index from the Winter highs to negative performance. Lets see what Autumn (or Fall depending on where you live) has in store Please check below for more details. Detailed Results
  • Book Review: DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth [Scott’s Investments]

    Readers of Scotts Investments know I am a proponent of do-it-yourself investing solutions. I am also a big fan of Alpha Architect, so I was excited when I was asked to review a book which combines the best of both worlds, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth (DIY). Authors Wes Gray, Jack Vogel, and David Foulke, all of Alpha Architect, split DIY into two

Best Links of the Week

The best quant mashup links for the week ending Saturday, 10/03 as voted by our readers:

We also welcome two blogs making their first ever appearance on the mashup this week:

* * *

My fellow quant traders, ask not what Quantocracy can do for you, ask what you can do for Quantocracy. Vote for your favorite links on our quant mashup to encourage bloggers to write quality content. We do our part by providing this site without annoying advertising. All we ask is that you take a moment to participate in the process.

If you haven’t done so already, register to vote. Once registered, you can choose to remain logged in indefinitely, making voting as simple and painless as possible.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 10/02/2015

This is a summary of links featured on Quantocracy on Friday, 10/02/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Normal Drawdowns as a Timing Signal [EconomPic]

    The below analysis was purely an accident. I was actually looking into periods the U.S. stock market "suffered" a 10% drawdown for the absolute opposite reason; to show that a buy and hold investor should likely ignore these regularly occurring events. How regular? The always interesting Ryan Detrick points out: I looked at every calendar year since 1960 and looked at various correction
  • Volatility Reconcialiation [John Orford]

    Yesterday I wrote up a post, and immediately after, was sure I got something wrong. This is the offending chart. Volatility increases linearly as we add more positions to the equally weighted portfolio. What I failed to mention is that each position was weighted by 100% of the portfolio. So two positions meant the portfolio was leveraged 2x and so on. Stupid right? Well, not necessarily. What was
  • Relative Strength and Dividend Investing [Systematic Relative Strength]

    The portfolio manager of a large, active dividend fund was recently interviewed by Morningstar. (What Active Management Can Bring To Dividend Investing http://www.morningstar.com/cover/videocenter.aspx?id=716392). The portfolio manager argues that simply looking for stocks with high dividend yields is insufficient because so many of those very high yielding stocks go through dividend cuts.