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Quantocracy’s Daily Wrap for 11/24/2017

This is a summary of links featured on Quantocracy on Friday, 11/24/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • From Potential to Proven: Why AI is Taking Off in the Finance World [Robot Wealth]

    This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world behind to join an ex-clients proprietary trading firm. I thought Id jot down a few thoughts

Quantocracy’s Daily Wrap for 11/22/2017

This is a summary of links featured on Quantocracy on Wednesday, 11/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Get Free Intraday Options Data With Pandas-DataReader [Black Arbs]

    This is a simple reference article for readers that might wonder where I get/got my options data from. In this regard I would like to shout out the contributors to the pandas-datareader, without their efforts this process would be much more complex. Intuitive Explanation So this code consists of three components. The first is the actual script that wraps the pandas-datareader functions and
  • Volume Filters (Part 3) | Trading Strategy (Entry & Exit) [Oxford Capital]

    Developer: Larry Williams (All in one: Price, volume and open interest); R. D. Donchian (Breakout Channels). Concept: Trading strategy based on price breakouts confirmed by POIV (Price, Open Interest, and Volume) filters. Research Question: Can combined filters improve price breakouts? Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Entry Setup: High[i] >
  • A Few Tips for Volatility Trading [Quantpedia]

    We present some empirical evidence for short volatility strategies and for the cyclical pattern of their P&L. The cyclical pattern of the short volatility strategies produces an alpha in good times but collapses to the beta in bad times. We introduce a factor model with risk-aversion to explain the risk-premium of short volatility strategies as a compensation to bear losses in bad market
  • Asset allocation with constraints using Backtracking [Quant Dare]

    Assigning weights to portfolio assets is challenging when we have to consider multiple constraints. Asset allocation may be seen as a constraint satisfaction problem (CSP), and some algorithms allow us to define our own restrictions and look for an optimal weight distribution. In this post, we will show how to define a CSP for your portfolio and how to use the Backtracking algorithm to obtain an

Quantocracy’s Daily Wrap for 11/20/2017

This is a summary of links featured on Quantocracy on Monday, 11/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Risk Parity: How Much Data Should We Use When Estimating Volatilities and Correlations? [Flirting with Models]

    Risk parity portfolios attempt to diversify across asset classes and strategies by risk contribution as opposed to dollar allocation. Implementing a risk parity strategy requires making a number of important construction decisions. A key question we have to answer is How are we going to measure risk? One approach is to use historical data to estimate risk. When using this approach, we have
  • Sector Rotation with Fama-French Alphas [Allocate Smartly]

    Allocate Smartly tests and tracks asset allocation strategies sourced from books, academic papers and other publications. Most of the strategies that we test though never make it on to this site. There are a variety of reasons that might be, but often its simply because theyre not very good. Usually we just let those strategies slip gently into the good night, but this one took a lot of work
  • Candlestick Plotting Function for Octave [Dekalog Blog]

    I have long been frustrated by the lack of an "out of the box" solution for plotting OHLC candlestick charts natively in Octave, the closest solution I know being the highlow plot function from the financial package ( which does not yet implement a candle function ) over at Octave Sourceforge. This being the case, I decided to write my own candlestick plotting functions, the codes for
  • Quant Strategies in the Cryptocurrency Space [Factor Research]

    The year 2017 might be regarded as the year where cryptocurrencies became mainstream. Investment funds focused on cryptocurrencies were launched, the CBOE announced Bitcoin futures for the end of the year and some everyday expenses like booking flights at Expedia can be paid in Bitcoins. Institutional investors have been cautious entering the space, but are slowly getting more active given that

Quantocracy’s Daily Wrap for 11/19/2017

This is a summary of links featured on Quantocracy on Sunday, 11/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Transforming a QP Problem to an SOCP Problem [Numerical Method]

    A Quadratic Programming problem (QP) in the form of \begin{aligned} & \underset{x}{\text{minimize}} & & \frac{1}{2} x^T H x + p^T x \\ & \text{subject to} \\ & & & A_{eq} x = b_{eq} \\ & & & A x \geq b \end{aligned} where H \in \Re^{n \times n}, A \in \Re^{m \times n}, p, x \in \Re^n, b \in \Re^m, can be transformed to a Second-Order Cone Programming (SOCP)
  • Recalibrating Expected Shortfall to Match Value-at-Risk for Discrete Distributions [Quant at Risk]

    By considering the same risk measure, , applied to two or more portfolios (credit loss distributions, profit-and-loss distributions, etc.) one desires to have a subadditivity property in place: (X1+X2)(X1)+(X2) i.e. meaning that two combined portfolios should never be more risky than the sum of the risk of two portfolios separately. Unfortunately, the Value-at-Risk risk measure does not

Quantocracy’s Daily Wrap for 11/17/2017

This is a summary of links featured on Quantocracy on Friday, 11/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Optimizing trading strategies without overfitting [EP Chan]

    Optimizing the parameters of a trading strategy via backtesting has one major problem: there are typically not enough historical trades to achieve statistical significance. Whatever optimal parameters one found are likely to suffer from data snooping bias, and there may be nothing optimal about them in the out-of-sample period. That's why parameter optimization of trading strategies often
  • Monetary Momentum [Alpha Architect]

    On most mainstream finance websites, a good chunk of the stories discuss the FED and where interest rates are going. Intuitively, this makes sense: The FED is arguably an extremely influential component of U.S. economy. But how do markets respond to the FED? Is the response rational, irrational, or something in between? This is a good question, and one discussed in a working paper which we examine

Quantocracy’s Daily Wrap for 11/16/2017

This is a summary of links featured on Quantocracy on Thursday, 11/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Adaptive Volatility [CSS Analytics]

    One of the inherent challenges in designing strategies is the need to specify certain parameters. Volatility parameters tend to work fairly well regardless of lookback, but there are inherent trade-offs to using short-term versus longer-term volatility. The former is more responsive to current market conditions while the latter is more stable. One approach is to use a range of lookbacks which

Quantocracy’s Daily Wrap for 11/15/2017

This is a summary of links featured on Quantocracy on Wednesday, 11/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Weekly Mean Reversion Rotation Strategy on S&P500 Stocks [Alvarez Quant Trading]

    A reader emailed me about testing a weekly mean reversion rotation strategy on S&P500 stocks. My first thought was, why had I not done this type of test before? The very first strategy that I worked on with Larry Connors was this type of strategy. The strategy I will be testing today is a simpler version and different universe but how well will it hold up? Basic Rules Testing period is from

Quantocracy’s Daily Wrap for 11/14/2017

This is a summary of links featured on Quantocracy on Tuesday, 11/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Comparing Some Strategies from Easy Volatility Investing, and the Table.Drawdowns Command [QuantStrat TradeR]

    This post will be about comparing strategies from the paper Easy Volatility Investing, along with a demonstration of Rs table.Drawdowns command. First off, before going further, while I think the execution assumptions found in EVI dont lend the strategies well to actual live trading (although their risk/reward tradeoffs also leave a lot of room for improvement), I think these
  • Ensemble Methods for E-Mini S&P 500 Futures Long/Short Strategy [Golden Compass]

    Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. This is with the intention that ensembles will achieve better prediction accuracy than individual classifiers. In machine learning research, most research papers focus on evaluating the performance of single algorithms. In recent years,
  • Better Small Cap Premium [Quantpedia]

    We find that when measured in terms of dollar-turnover, and once beta-neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of 5.1, the Cold-Minus-Hot (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to market-cap based SMB, CMH portfolios are much less anti-correlated to the Low-Vol
  • How to Balance Short and Long term Goals in Asset Allocation [Alpha Architect]

    Peng Wang and Jon Spinney A version of this paper can be found here Want to read our summaries of academic finance papers? Check out our Academic Research Insight category. What are the research questions? Investors following a purely quantitative approach to asset allocation are often left with unintuitive portfolios with high turnover. On the other hand, those who pursue ad-hoc approaches face
  • Investing Outside the U.S. – Purgatory for Pessimists [Factor Investor]

    The current equity bull market has not been kind to non-U.S. allocations. At a recent conference I attended, the term TINA: there is no alternative came up more than once in the context of allocating investor portfolios. It captures the collective sentiment that equities, despite a massive bull run and rising valuations, are one of few viable asset classes to park capital. Expected returns
  • Can asset bubbles be mathematically quantified before they burst? [Alpha Architect]

    The subject of asset bubbles and market crashes has fascinated me for more than 20 years. As an options market maker for Susquehanna International Group (SIG), extreme price movements were a daily source of concern. I sat next to Jeff Yass for years and watched him manage option positions in thousands of different stocks. Almost daily he would be celebrating a big win in a stock that had an
  • Hedge Fund Factor Exposure and Alternatives [Factor Research]

    Equity hedge fund returns have been disappointing over the last 14 years An exposure analysis shows no structural factor exposure, but frequent factor rotation Multi-factor long-short products are an interesting alternative, depending on the fee level INTRODUCTION Hedge fund assets reached an all-time high in 2017 with $3.3 trillion under management. Although returns were muted in recent years,

Quantocracy’s Daily Wrap for 11/13/2017

This is a summary of links featured on Quantocracy on Monday, 11/13/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Case Against Overweighting International Equity [Flirting with Models]

    Weve read a number of outlooks and commentaries lately from firms arguing for investors to take a tactical tilt away from U.S. equities and towards International equities. The logic behind this tilt is largely driven by relative valuations: international equities appear significantly cheaper than U.S. equities based on most valuation metrics. In this commentary, we discuss why such a
  • Podcast: Building Mean Reversion trading strategies with @AlvarezQuant – Part 3 [Better System Trader]

    And were back for the final episode in this 3-part series on building Mean Reversion strategies with Cesar Alvarez from Alvarez Quant Trading. In the 1st episode we discussed the goal of Mean Reversion trading, how to select a trading universe, a number of effective techniques to measuring Mean Reversion and how to combine indicators to identify better quality trades. In the 2nd episode we
  • Market returns in odd and even weeks [UK Stock Market Almanac]

    A couple of years ago the Almanac wrote about a strange characteristic of the UK equity market which was the difference in performance in odd and even weeks. The original article is here (see the original article for the definition of odd/even weeks etc.) To recap briefly, the FTSE 100 Index saw much stronger returns in odd weeks than even weeks. Lets see whats happened recently and if this
  • Matrix Iterations for Adaptive Asset Allocation [TrendXplorer]

    Adaptive Asset Allocation (AAA) is based on the Nobel Prize winning portfolio theory of Markowitz (1952) AAA combines assets momentum, volatilities, and cross-correlations for building diversified investment portfolios In a tactical application AAA exploits momentum for crash detection and results in consistent returns at mitigated risk levels Actually, their encounter was coincidental. The

Quantocracy’s Daily Wrap for 11/10/2017

This is a summary of links featured on Quantocracy on Friday, 11/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Get Funding For Your Trading Strategy [Quant Insti]

    So, its been some time since youve been thinking of making more money out of your successful trading strategy. And why should you not? After all, youve worked hard for it and there is only a small % of people who are successful in this business. The idea is to add more funds to your trading strategy and get more profits. How To Get Funding For Your Trading Strategy?Click To Tweet So how
  • Factor Investors Beware: Positive SMB May Not Mean You Own Small-Caps [Alpha Architect]

    Regression analysis is used all the time to assess how a portfolio loads on certain factors. The most common factor loadings examined are the market, size, value, and momentum factors. This can be an informative exercise, and there are nice tools online, such as portfolio visualizer, which allow investors to examine factor loadings on funds. Note: We have an article (with an excel file),
  • Research Review | 10 November 2017 | Factor Strategies [Capital Spectator]

    Investing in a Multi-Asset Multi-Factor World Alexandar Cherkezov (Invesco), et al. August 31, 2017 In this article, we advance the use of factor investing across multiple asset classes. It turns out that style factors well established in the equity domain such as value, momentum or quality do extend to other asset classes as well. Even more so, multi-asset multi-factors significantly