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Quantocracy’s Daily Wrap for 10/19/2015

This is a summary of links featured on Quantocracy on Monday, 10/19/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Did the CME Data change impact your strategies? [System Trader Success]

    Effective September 21, 2015 the CME changed the closing time for futures that closed at 5:15 PM ET from 5:15 to 5:00 PM. The CME did this because the volume from 5:00 PM to 5:15 PM just wasnt that significant. My initial thought was no big deal, my strategies hardly ever traded during that time anyhow. Turns out, I was wrong. It could be a HUGE deal, depending on your strategies.
  • Changing Notions of Risk Management in Automated Trading [Quant Insti]

    Algorithmic trading risks can be categorized into the following: Access Consistency Quality Algorithm Technology Scalability There are 2 places where Risk Management is handled Within the application We need to ensure that wrong parameters are not set by the trader. It should not allow a trader to set grossly incorrect values nor any fat-finger errors. Before generating an order in the

Quantocracy’s Daily Wrap for 10/18/2015

This is a summary of links featured on Quantocracy on Sunday, 10/18/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Week [Quantocracy]

    The best quant mashup links for the week ending Saturday, 10/17 as voted by our readers: Returns clustering with K-means algorithm [Quant Dare] Absolute Strength Momentum Investing Strategy [Alpha Architect] Guns, Bombs and eSports: Applying Data and Portfolio Analytics to Counter-Strike Gambling [Kevin Pei] I Hired a Contract Coder [Financial Hacker] Volatility Stat-Arb Shenanigans [QuantStrat
  • Interview with Alan Clement [Better System Trader]

    Alan Clement is a Certified Financial Technician, full time independent trader, quantitative trading systems designer and private investment consultant. He is also a councillor with the Australian Technical Analysts Association and contributes to the technical analysis articles for Fairfax press. In this episode we talk about Rotational trading systems, the impact of stops on results and
  • Multi-Factor Investing [Dual Momentum]

    Multi-factor investing that combines value, momentum, quality/profitability, or low volatility factors is todays hot new investment approach. There has been an explosion of multi-factor ETFs recently with nine of the fourteen existing U.S. multi-factor funds coming to market this year, and five of them showing up within the past 60 days. Multi-factor funds may be a good thing, since single
  • Sovereign High Yield Bond Strategy [Meb Faber]

    There is ample research that shows that sorting government bonds on yield works great. The outperformance has been very consistent over the years at about 2% a year. The Dimson, Marsh, Staunton crew examined this in their 2012 GIRY issue, and graphic is below. These are returns from going long high yielding and short low yielding countries, but one issue with that strategy is that the drawdowns

Quantocracy’s Daily Wrap for 10/16/2015

This is a summary of links featured on Quantocracy on Friday, 10/16/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Site News [Quantocracy]

    Three bits of site news for both readers and webmasters: For readers: Our new filter mashup feature For webmasters: Our policy on voting for your own link and vote padding For webmasters: Quantocracy badge For readers: Our new filter mashup feature Each site on our blogroll in the sidebar to the right now includes the following three icons (if you dont see a sidebar to the right,
  • Backtesting Long Short Moving Average Crossover Strategy in Excel [Quant Insti]

    Now for those of you who know me as a blogger might find this post a little unorthodox to my traditional style of writing, however in the spirit of evolution, inspired by a friend of mine Stuart Reid (TuringFinance.com), I will be following some of the tips suggested in the following blog post. Being a student in the EPAT program I was excited to learn the methodology that others make use of when
  • Surprise…Trading More is Profitable for Active Funds! [Alpha Architect]

    Warren Buffett make it clear why frequent trading damages ones wealth: Wall Street makes its money on activity. You make your money on inactivity. (source) But is activity always a bad thing? Implicit in Buffetts quote is an assumption that frictional costs outweigh any benefits of enhanced returns due to increased activity. Surely this is true for retail investors with high

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