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Quantocracy’s Daily Wrap for 04/05/2016

This is a summary of links featured on Quantocracy on Tuesday, 04/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Detecting Human Fear in Electronic Trading: Emotional Quantum Entanglement [Quant at Risk]

    This post presents an appealing proof for the progressing domination of algorithmic trading over human trading. By analysing the US stock market between 1960 and 1990, we estimate a human engagement (human factor) in live trading decisions taken after 2000. We find a clear distinction between 2000-2002 dot-era trading behaviour and 2007-2008 crisis. By the use of peculiar data samples, we
  • Taleb: “Problems and Inverse Problems” Follow-Up [Blue Event Horizon]

    In my previous post I published a bunch of R Scripts that will enable a reader of Taleb's "Silent Risk", Chapter 3, Section 3.2 "Problems and Inverse Problems" to play with the ideas he presents. I thought I should discuss one of the results those scripts produce that does not jive with Taleb's. I know from writing blog posts that it is incredibly difficult to be
  • Bounceback portfolio 2016 [UK Stock Market Almanac]

    The Bounceback Portfolio invests in the 10 worst performing FTSE 350 stocks of the previous year and holds them for the 3-month period, January-March. Performance in 2016 The following table lists the ten worst performing FTSE 350 stocks in 2015. These ten stocks form the 2016 Bounceback Portfolio. Company TIDM 2015 2016 (Jan-Mar) Anglo American AAL -75.1 84.4 Glencore GLEN -69.7 73.9 KAZ Minerals

Quantocracy’s Daily Wrap for 04/04/2016

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

  • Strategy Development with Perry Kaufman [Better System Trader]

    Im sure we all want to create trading strategies that perform better and last for longer but there are a number of issues we need to look out for when developing robust trading strategies, some are well-known and some perhaps arent. In this episode well be talking with Perry Kaufman about strategy development and more specifically some of the issues that can catch us out when creating
  • The case for Regime-Switching GARCH [Eran Raviv]

    GARCH models are very responsive in the sense that they allow the fit of the model to adjust rather quickly with incoming observations. However, this adjustment depends on the parameters of the model, and those may not be constant. Parameters estimation of a GARCH process is not as quick as those of say, simple regression, especially for a multivariate case. Because of that, I think, the
  • Snake Oil and Low Volatility Investing [Factor Investor]

    It is estimated that 180,000 Chinese immigrated to the United States in the latter half of the 19th century; many of them worked on the Transcontinental Railroad. Deeply routed in Chinese culture, the immigrants brought with them various medicinal remedies for common ailments. It was believed that the oil of Chinese water snakes was effective in treating inflammation and arthritis. Given the harsh
  • Taleb: “Silent Risk”, Chapter 3, Section 3.2 “Problems and Inverse Problems” [Blue Event Horizon]

    ection 3.2 in Chapter 3 of "Silent Risk", a draft of a book by Nassim Nicholas Taleb defines the "inverse problem" as follows: Definition 3.4 (The inverse problem). There are many more degrees of freedom (hence probability of making a mistake) when one goes from a model to the real world than when one goes from the real world to the model. He thens brings the problem into
  • Yahoo Data and Momentum Rotation – Analysis of 2015 Data [DTR Trading]

    I've taken a bit of a break from posting options strategy research, but before I dive back in I'm going to revisit some material I posted on Momentum Rotation systems last year. If you're new to my blog you may have missed my posts related to rotation system results and data. For the last several years, I have been trading monthly Momentum Rotation strategies across six accounts.
  • Trend Following Down in March [Wisdom Trading]

    March 2016 Trend Following: DOWN -2.84% / YTD: +6.86% The index gave back some of its gains from the beginning of the year, last month. The performance is still positive Year-To-Date and over the last 12 months. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for March: Wisdom State of Trend Following – March 2016 And the 12-month chart:

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