Quantocracy

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Quantocracy’s Daily Wrap for 04/02/2016

This is a summary of links featured on Quantocracy on Saturday, 04/02/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Evolving Neural Networks through Augmenting Topologies Part 2 of 4 [Gekko Quant]

    This part of the tutorial on using NEAT algorithm explains how genomes are crossed over in a meaningful way maintaining their topological information and how speciation (group genomes into species) can be used to protect weak genomes with new topological information from prematurely being eradicated from the gene pool before their weight space can be optimised. The first part of this tutorial can

Quantocracy’s Daily Wrap for 04/01/2016

This is a summary of links featured on Quantocracy on Friday, 04/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bayesian Linear Regression Models with PyMC3 [Quant Start]

    To date on QuantStart we have introduced Bayesian statistics, inferred a binomial proportion analytically with conjugate priors and have described the basics of Markov Chain Monte Carlo via the Metropolis algorithm. In this article we are going to introduce regression modelling in the Bayesian framework and carry out inference using the PyMC3 MCMC library. We will begin by recapping the classical,
  • Bold, Confident & WRONG: Why You Should Ignore Expert Forecasts [GestaltU]

    If you read the paper, watch the news, and listen to investment experts you are doing it all wrong. There are no market wizards; the emperors have no clothes; most people are swimming naked. The following paragraphs offer abundant and incontrovertible evidence condemning expert judgment for the great sham it really is. We also offer some practical ways to cope with the terrifying reality

Quantocracy’s Daily Wrap for 03/31/2016

This is a summary of links featured on Quantocracy on Thursday, 03/31/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added to “Beginner Math” Category: Introduction to Linear Algebra [Amazon]

    Gilbert Strang's textbooks have changed the entire approach to learning linear algebra — away from abstract vector spaces to specific examples of the four fundamental subspaces: the column space and nullspace of A and A'. Introduction to Linear Algebra, Fourth Edition includes challenge problems to complement the review problems that have been highly praised in previous editions. The
  • Build Better Strategies! Part 4: Machine Learning [Financial Hacker]

    Deep Blue was the first computer that won a chess championship, in 1996. It took 20 more years until another computer program, AlphaGo, could defeat the best human Go player. Deep Blue was a model based system with a fixed chess library and hardwired chess rules. AlphaGo is a data-mining system, a deep neural network trained with thousands of Go games. Not only improved hardware, but also a
  • Autoregressive model in S&P 500 and Euro Stoxx 50 [Quant Dare]

    In this post we are talking about autoregressive models and their application to a financial world. This model follows the idea that the next value of the serie is related with the p previous values. Definition of p-order autoregressive model An autoregressive model or AR is a type of modelling that explains predicted variables as a linear combination of the last p observed values plus a constant
  • The Dynamic Duo Of Risk Factors: Part II [Capital Spectator]

    Last weeks post on analyzing US equity value and momentum risk premia ended with a question: How much, if any, improvement should we expect by adding a dynamic system for managing exposure to these risk factors vs. a buy-and-hold strategy? What follows is a preliminary effort in searching for an answer. As a preview, the results are mixed, but this may be an artifact of a) focusing on value and
  • Parallel Tempering and Adaptive Learning Rates in Restricted Boltzmann Machine Learning [Dekalog Blog]

    It has been a while since my last post and in the intervening time I have been busy working on the code of my previous few posts. During the course of this I have noticed that there are some further improvements to be made in terms of robustness etc. inspired by this Master's thesis, Improved Learning Algorithms for Restricted Boltzmann Machines, by KyungHyun Cho. Using the Deepmat Toolbox
  • How to Value Nadex Bull Spreads? [MKTSTK]

    Exotic options have always been a hobby of mine. One of the curious things about Dodd-Frank was it started to push swap trading onto exchanges. As such, a cottage industry of exchange traded exotics (in the US they're technically swaps) has popped up over the last few years. The biggest of these markets by volume seems to be Nadex so I recently became a member and started playing around with
  • Benchmarking Commodity CTAs [Quantpedia]

    While much is known about the financialization of commodities, less is known about how to profitably invest in commodities. Existing studies of Commodity Trading Advisors (CTAs) do not adequately address this question because only 19% of CTAs invest solely in commodities, despite their name. We compare a novel four-factor asset pricing model to existing benchmarks used to evaluate CTAs. Only our

Quantocracy’s Daily Wrap for 03/29/2016

This is a summary of links featured on Quantocracy on Tuesday, 03/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Monte Carlo Simulation function for your back-test results in R [Open Source Quant]

    In this post on bettersystemtrader.com, Andrew Swanscott interviews Kevin Davey from KJ Trading Systems who discusses why looking at your back-test historical equity curve alone might not give you a true sense of a strategys risk profile. Kevin Davey also writes on the topic here for futuresmag.com.So i wrote a Monte Carlo-type simulation function (in R) to see graphically how my back-test
  • Trading the index with seasonal strategies [ENNlightenment]

    I recently listened to an interesting interview at Better System Trader with Jay Kaeppel on Seasonality, a topic which I hadnt done much backtesting on previously. Jay outlined 3 rules for constructing a seasonal trading strategy on the stock index: – Stay long the last 4 days and first 3 days of the month (S_EOM) – Stay long the middle of the month, business days 9, 10, 11 (S_MOM) – Stay long

Quantocracy’s Daily Wrap for 03/28/2016

This is a summary of links featured on Quantocracy on Monday, 03/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning and Its Application in Forex Markets [Quant Insti]

    In the last post we covered Machine learning (ML) concept in brief. In this post we explain some more ML terms, and then frame rules for a forex strategy using the SVM algorithm in R. To use ML in trading, we start with historical data (stock price/forex data) and add indicators to build a model in R/Python/Java. We then select the right Machine learning algorithm to make the predictions. First,
  • Glamour Can Distract Investors [Larry Swedroe]

    Theres very strong historical evidence to support the existence of a value premium in equity markets. While theres no dispute over the existence of the value premium (value stocks have provided an annual average return 5% higher than growth stocks over the long term), there is much debate over the cause of the difference in returns. In one camp are financial economists who argue that the
  • The Internal Bar Strength Indicator [System Trader Success]

    The internal bar strength or (IBS) is an oscillating indicator which measures the relative position of the close price with respect to the low to high range for the same period. The calculation for Internal Bar Strength is as follows IBS = (Close Low) / (High Low) * 100; For example, on 13/01/2016 the QQQ etf had a high price of $106.23, a low price of $101.74 and a close price of

Quantocracy’s Daily Wrap for 03/27/2016

This is a summary of links featured on Quantocracy on Sunday, 03/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Week [Quantocracy]

    These are the best quant mashup links for the week ending Saturday, 03/26 as voted by our readers: FX: multivariate stochastic volatility part 2 [Predictive Alpha] Predicting Stock Market ReturnsLose the Normal and Switch to Laplace [Six Figure Investing] Momentum for Buy-and-Hold Investors [Dual Momentum] Support Vector Machines classifier combining mean reversion and momentum indicators

Quantocracy’s Daily Wrap for 03/24/2016

This is a summary of links featured on Quantocracy on Thursday, 03/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • On the 60/40 portfolio mix [Eran Raviv]

    Not sure why is that, but traditionally we consider 60% stocks and 40% bonds to be a good portfolio mix. One which strikes decent balance between risk and return. I dont want to blubber here about the notion of risk. However, I do note that I feel uncomfortable interchanging risk with volatility as we most often do. I am not unhappy with volatility, I am unhappy with realized loss, that is
  • On Backtesting: An All-New Chapter from our Adaptive Asset Allocation Book [GestaltU]

    If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times – and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category. We're pretty psyched about that. In our book, we spent a great deal of time summarizing the research
  • The Comprehensive Guide to Stock Price Calculation [Quandl]

    Adjusted stock prices are the foundation for time-series analysis of equity markets. Good analysts insist on properly-adjusted stock data. But the best analysts understand the adjustment process from first principles. This is Quandl's guide to the creation and maintenance of accurate adjusted historical stock prices. Introduction Adjustment Principles 1.Cash Dividends 2.Stock Dividends
  • Markov Chain Monte Carlo for Bayesian Inference – The Metropolis Algorithm [Quant Start]

    In previous discussions of Bayesian Inference we introduced Bayesian Statistics and considered how to infer a binomial proportion using the concept of conjugate priors. We discussed the fact that not all models can make use of conjugate priors and thus calculation of the posterior distribution would need to be approximated numerically. In this article we introduce the main family of algorithms,
  • Have benchmarks made us bad active investors? [Alpha Architect]

    Obsession with short-term performance against market cap benchmarks preordains the dysfunctionality of asset markets. The problems start when trustees hire fund managers to outperform benchmark indexes subject to limits on annual divergence Benchmarking causes, first, the inversion of the relationship between risk and return so that high volatile securities and asset classes offer lower returns
  • Responding to Your Comments on Our Adaptive Asset Allocation Book [SkewU]

    If you've been a regular reader of our blog, you already know that we recently published our first book Adaptive Asset Allocation: Dynamic Portfolios to Profit in Good Times – and Bad. As of this writing, it still stands as the #1 new release in Amazon's Business Finance category. We're pretty psyched about that. This has been – and continues to be – a very interesting experience.
  • The Dynamic Duo Of Risk Factors: Part I [Capital Spectator]

    The value and momentum factors have earned high praise in recent years as complementary sources of risk premia for designing and managing equity portfolios. AQRs widely cited paper Value and Momentum Everywhere a few years back helped popularize the idea, pointing to applications in equities and beyond. Theres no shortage of support from the wider world of investment management.
  • A Few Notes On Adaptive Asset Allocation [CXO Advisory]

    In the introductory text for Part I of their 2016 book, Adaptive Asset Allocation: Dynamic Global Porfolios to Profit in Good Times and Bad, Adam Butler, Michael Philbrick and Rodrigo Gordillo state: we have come to stand for something square and real, a true Iron Law of Wealth Management: We would rather lose half our clients during a raging bull market than half of our clients money
  • Smart Beta Strategies in Australia [Quantpedia]

    "Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart is smart beta investing can be empirically examined by testing the performance of those factors
  • [Academic Paper] Optimal Delta Hedging for Options [@Quantivity]

    The practitioner Black-Scholes delta for hedging options is a delta calculated from the Black-Scholes-Merton model (or one of its extensions) with the volatility parameter set equal to the implied volatility. As has been pointed out by a number of researchers, this delta does not minimize the variance of changes in the value of a traders position. This is because there is a non-zero
  • Slides from Investing in Smart Beta Conference [Flirting with Models]

    Justin spoke at the Investing in Smart Beta conference this week in Fort Lauderdale, FL. He spoke alongside Research Affiliates in a session titled "The Smart Beta Checklist: Choosing The Best Strategy & Risk/Return Profile For Your Portfolio." Here's a quick description: Depending on market conditions and clients objectives, an array of disparate smart beta strategies can