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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

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.

Quantocracy’s Daily Wrap for 09/30/2015

This is a summary of links featured on Quantocracy on Wednesday, 09/30/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting with HoltWinters Exponential Smoothing [Quant Insti]

    I recently enrolled in the QuantInsti Executive Program in Algorithmic Trading and one of the areas in quantitative finance that interests me greatly is the analysis of financial time series. During the course we will take on a massive project to build our own trading strategy with the help of a mentor, and in attempt to familiarize myself with the work, I built this simple strategy using the
  • Stop Losses and Profit Targets. Plus Happy Birthday Excel! [Alvarez Quant Trading]

    In the post, Maximum Loss Stops: Do you really need them?, we looked at how maximum loss stops changed the results of a mean reversion strategy. At the end of the post I asked the readers to vote for what to try next. Let us see how these are ideas turn out. The rules from the original post Setup Close greater than 100-day moving average Close less than the 5-day moving average 3 lower lows Member
  • Timing / Stock Pick Duality Strategy [John Orford]

    Months ago I came up with an idea. How about building a portfolio where choosing positions and a holding period is interchangeable? Weird right? Let me explain. Academics assume day over day returns have little to do with each other and that markets are more or less efficient. So increasing your holding period results in more risk but it tails off exponentially (at the rate of the square root of
  • Risk is Still Not a Mathematical Concept [Factor Wave]

    I wrote last week that many different measures of risk can be used to demonstrate that low risk stocks outperform high risk stocks, and illustrated this by sorting stocks according to the Hurst exponent. Today we are going to offer another example of this by using entropy as the measure of risk. The original concept of entropy was the amount of disorder in a thermodynamic system. However this was
  • A Few Notes on Systematic Trading [CXO Advisory]

    Robert Carver introduces his 2015 book, Systematic Trading: A Unique New Method for Designing Trading and Investing Systems, by stating that: I dont believe there is any magic system that will automatically make you huge profits, and you should be wary of anyone who says otherwise, especially if they want to sell it to you. Instead, success in systematic trading is mostly down to avoiding
  • Python code for the two trading rules in “Systematic Trading” [Investment Idiocy]

    This is a brief post aimed at those who have already bought a copy of "Systematic Trading" (by the way thanks!) As you've probably noticed I've included excel sheets here explaining the two trading rules I describe in the book – exponentially weighted moving average crossover ("EWMAC") and Carry ("Carry"). Since I also have the python code for these rules, I

Quantocracy’s Daily Wrap for 09/29/2015

This is a summary of links featured on Quantocracy on Tuesday, 09/29/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Combining volatility, momentum, and trend in asset allocation [Alpha Architect]

    The Efficient Market Hypothesis (EMH) has been widely called into question in the investment literature, through two main anomalies: timing and low-volatility anomalies. In this paper, we aim to combine the predictive power of timing and low-volatility strategies to deliver better risk-adjusted portfolio performance. We adopt a two-step approach for a constant dataset composed of 18 country MSCI

Quantocracy’s Daily Wrap for 09/28/2015

This is a summary of links featured on Quantocracy on Monday, 09/28/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 09/26/2015

This is a summary of links featured on Quantocracy on Saturday, 09/26/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Empirical Finance: Meeting Fiduciary Standards Through Skepticism, Not Cynicism [GestaltU]

    Michael Edesses is out with a scathing article lambasting the field of empirical finance. He draws inspiration from Harvey, Liu and Zhus (HLZ) recent article, entitled and the Cross Section of Expected Returns, but extends HLZs conclusions to an absurd limit. In this article, we discuss why we embrace the framework of healthy skepticism described by HLZ, but in the context of a more

Quantocracy’s Daily Wrap for 09/25/2015

This is a summary of links featured on Quantocracy on Friday, 09/25/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A story of poor statistical intuition [Investment Idiocy]

    In my last post I had a bit of a controversial pop at a brilliant and successful billionaire hedge fund manager; Jim Simons. In continuing my futile quest to raise the level of debate in the quantitative investment community I thought I'd have a go at another clever and very wealthy guy, Cliff Asness, founder of giant fund AQR. Cliff Asness. There is nothing wrong with his statistical
  • The S&P 500 Death Cross Time to Panic? [iMarketSignals]

    At the end of August 2015 the 50-day moving average of the S&P500 crossed its 200-day moving average to the downside the 33rd occurrence of a Death Cross since 1950. The performance of the S&P500 was investigated for periods ranging from one year before to two years after a Death Cross. During the last 65 years there were ten recessions. A Death Cross preceded six recessions and
  • Runge-Kutta Methods [Dekalog Blog]

    As stated in my previous post I have been focusing on getting some meaningful features as possible inputs to my machine learning based trading system, and one of the possible ideas that has caught my attention is using Runge-Kutta methods to project ( otherwise known as "guessing" ) future price evolution. I have used this sort of approach before in the construction of my perfect
  • Correlation and Cointegration [Quant Dare]

    I want a strategy that is able to choose the assets that makes it look like an index Yt. -Then take the ones most correlated to it. -Ok, but look: CC1 The Xt and the Xt+c series have exactly the same correlation with Yt -I prefer Xt+c!! -Yes, but I am trying to be very similar to Yt and Xt+c have a large deviation. -Cointegration? -If two or more series are individually integrated (in the time

Quantocracy’s Daily Wrap for 09/23/2015

This is a summary of links featured on Quantocracy on Wednesday, 09/23/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sorry Jim, trend following probably still works (though not the fast stuff) [Investment Idiocy]

    Sorry Jim, trend following probably still works (though not the fast stuff) Sometimes really smart people still get things wrong. Even really smart people, who have been very successful, and are thus very rich. Jim Simons. Very Rich. Very Smart (www.pbs.org) During a recent TED talk Jim Simons said: Trend-following would have been great in the 60s, and it was sort of OK in the 70s. By the
  • ‘Javascript for Financial Analysts’ Chapter 3 – First Draft [John Orford]

    The first chapter ended with code which included a map and a filter which we will dive back into now with a less applied more intuitive example. Open up the JavaScript console and paste or type in the following code, [0,1,2,3,4,5,6,7,8,9] .filter( function(j){ return j%2===1; } ); This code filters the array of numbers and returns an array of odd numbers. > [1,3,5,7,9] One feature of filter and
  • Out-of-sample testing of Sell in May market timing rule [Quantitative Investor]

    In one of the previous posts I considered market timing with moving averages on 9 quite different equity indices that were chosen in other post, and came to the conclusion that the rule is the viable alternative to the standard B&H, allowing to avoid large drawdowns in one cases and even improve performance in others (e.g. it is bad idea to passively invest in Nikkei index, but even with
  • Momentum trends with Andreas @Clenow [Automated Trader]

    Andreas Clenow is CIO of Zurich-based ACIES Asset Management ($300+ million AuM), and author of 'Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies'. Why would he give up the super secret sauce in a tell-all? Automated Trader finds out. CTAsHedge Funds Andreas Clenow, CIO, ACIES Asset Management Andreas Clenow, CIO, ACIES Asset Management "People think that
  • Using a Random Forest and Hidden Markov Model to Improve Trade Performance [Inovance]

    Machine learning is a powerful tool for not only coming up with new strategies (like we do in TRAIDE) but also for improving your existing strategies. In this article, well cover adjusting your position size using a random forest algorithm and turning your strategy on an off using a Hidden Markov Model. You can copy and paste the R code to try it yourself on your own strategies. This article

Quantocracy’s Daily Wrap for 09/21/2015

This is a summary of links featured on Quantocracy on Monday, 09/21/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Active Share Debate: AQR versus the Academics [Alpha Architect]

    There is an interesting discussion in the geeky world of academic finance literature between the intellectual muscle at AQR and academia. aqr versus the academics on active share The discussion revolves around the following question: Does Active Share matter? This is an important topic for active ETFs and Mutual Funds in the marketplace. The original paper on this measure was written by
  • Risk Management for Automated Trading I : Lack of it [Quant Insti]

    Impact of Proliferation of Automated Trading Systems and Technology on Financial Markets With the advent of automated trading everything has become computerized. Risk management takes a whole new level in this technologically fast paced world. The trends in day-to-day trading have been changing. This change has led to many automated trading failures where risk was not managed in a sound manner.
  • Correlation and correlation structure [Eran Raviv]

    This post is about copulas and heavy tails. In a previous post we discussed the concept of correlation structure. The aim is to characterize the correlation across the distribution. Prior to the global financial crisis many investors were under the impression that they were diversified, and they were, for how things looked there and then. Alas, when things went south, correlation in the new
  • Forecasting interest rates [Econbrowser]

    There was lots of action in financial markets last week, with much of the attention focused on the U.S. Federal Reserve. The interest rate on a 10-year U.S. Treasury bond edged up 10 basis points early in the week in anticipation that the Fed might finally raise its target for the short-term interest rate. But it shed all that and more after the Fed announced it was standing pat for now. Price of

Quantocracy’s Daily Wrap for 09/20/2015

This is a summary of links featured on Quantocracy on Sunday, 09/20/2015. 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, 09/19 as voted by our readers: Hacking the Random Walk Hypothesis [Turing Finance] Getting Started: Building a Fully Automated Trading System [Quants Portal] Interview with Euan Sinclair [EP Chan] Interview with Dr Ernest Chan [Factor Wave] Probability Investing [Price Acti
  • Getting Started with Javascript – First Draft [John Orford]

    First draft of 'Javascript for Financial Analysts' Chapter 1. ~ Much of our coding time is spent in an interactive environment, colloquially called the 'REPL', 'Read-Eval-Print Loop' or console. The REPL reads input, evaluates it according to our code and prints it. Javascript's REPLs, can be found in any browser, by pressing Ctrl+Shif
  • When is a Backtest Too Good to be True? Part Two [Quintuitive]

    In the previous post, I went through a simple exercise which, to me, clearly demonsrtates that 60% out of sample guess rate (on daily basis) for S&P 500 will generate ridiculous returns. From the feedback I got, it seemed that my example was somewhat unconvincing. Lets dig a bit further then. Lets add Sharpe ratio and maximum drawdown to the CAGR and compute all three for
  • Will Yesterday s Shooting Star Make Bears Wishes Come True? [Dana Lyons]

    Like its bullish counterpart, the hammer, this bearish reversal pattern has been inconsistent in its forecasting abilities, except under certain conditions. Weve covered the hammer candlestick chart pattern on a couple occasions over the past few years, most extensively in this October 2014 post. The pattern, which involves a significant selloff from the open followed b
  • SPX Straddle – 38 DTE – Manage Profits at 25% [DTR Trading]

    In this post we look at the backtest results of selling a one-lot, at-the-money (ATM) straddle on the S&P 500 Index (SPX), initiated at 38 days-to-expiration (DTE). In this third post of five on 38 DTE straddles, we look at trades that use the same loss exits as shown in the first post, and in addition, take profits at 25% of the credit received. The results displayed in this post rep