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Quantocracy’s Daily Wrap for 02/09/2017

This is a summary of links featured on Quantocracy on Thursday, 02/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why TAA Has Been So Successful in Crises [Allocate Smartly]

    Most Tactical Asset Allocation (TAA) strategies have followed the same basic storyline. They keep pace with the market during the good times (like we find ourselves in right now), and shine during the bad times. To illustrate, the graphs below shows the average return of all of the TAA models that we track (orange) versus the 60/40 benchmark (grey) during the Dot-Com Bust of 2000-02 and Global
  • A Curious Intraday Pattern in Brazilian Stock Index Futures [Quantogo]

    Since the first article of this blog (Technical Analysis for intraday stocks trading? FORGET IT!), im pointing to the fact that there is a lot of cross correlation between stocks and between stocks and the future index. Thats not new to anyone and even those who are starting at the quantitative trading/analysis come to realize this on their own. But, is there some way we can explore that
  • Podcast: Strategy objectives, statistical significance and market behavior w/ @Alphatative [Chat With Traders]

    Returning to Chat With Traders for a second time is David Bushfirst on episode 23. David began as a discretionary trader, more than 20-years ago, but over time hes developed into a quant trader. And hes exceptionally good at what he does; Davids been the first place winner of two (real money) trading competitions in recent years. Last time David was on we spoke fairly extensively about

Quantocracy’s Daily Wrap for 02/05/2017

This is a summary of links featured on Quantocracy on Sunday, 02/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: The Leverage Space Trading Model by Ralph Vince [Amazon]

    The cornerstone of money management and portfolio optimization techniques has remained the same throughout history: maximize gains and minimize risk. Yet, asserts Ralph Vince, the widely accepted approaches of combining assets into a portfolio and determining their relative quantities are wrongand will cost you. They illuminate nothing, he says, aside from providing the illusion of safety
  • Podcast: Combining simple concepts to build robust strategies with Art Collins [Better System Trader]

    Im excited to be sharing this one with you today for a number of reasons. Firstly, Ive been trying to get this guest on the show for over a year now, in fact its been longer than that because we first got in touch in July 2015, so its been a long time in the making. But secondly, and more importantly, was for the trading ideas this guests has, particularly the content that he shared in

Quantocracy’s Daily Wrap for 02/04/2017

This is a summary of links featured on Quantocracy on Saturday, 02/04/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Back to Basics: Introduction to Algorithmic Trading [Robot Wealth]

    This is the first in a series of posts in which we will change gears slightly and take a look at some of the fundamentals of algorithmic trading. So far, Robot Wealth has focused on machine learning and quantitative trading research, but I had several conversations recently that motivated me to explore some of the fundamental questions around algorithmic trading. In the next few posts, we will
  • Value and Growth Stock Behavior During Market Declines [Quantpedia]

    Using data for five major stock market declines during the 1987-2008 period, this paper provides evidence that value stocks are generally less sensitive to major stock market declines than growth stocks, controlling for beta, firm size, and industry group. Further analysis using several hundred different significant market move events between 1980 and 2015 confirms the observation that value

Quantocracy’s Daily Wrap for 02/03/2017

This is a summary of links featured on Quantocracy on Friday, 02/03/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Your Complete Guide to Factor-Based Investing from @LarrySwedroe [Amazon]

    There are hundreds of exhibits in the investment factor zoo. Which ones are actually worth your time, and your money? Larry Swedroe and Andrew Berkin, co-authors of The Incredible Shrinking Alpha, bring you a thorough yet still jargon-free and accessible guide to applying one of todays most valuable quantitative, evidence-based approaches to outperforming the market: factor investing.
  • Factor Investing is More Art, and Less Science [Alpha Architect]

    Albert Einstein is reported to have said the following: The more I learn, the more I realize how much I dont know. I can relate. Having studied finance for a long time (PhD, professor, books, articles, etc.), I think I now know less about how the stock market works. In fact, I probably should have stopped studying finance after I read Ben Grahams Intelligent Investor, over 20 years ago. Life
  • Zero Lag Moving Average Filter | Trading Strategy [Oxford Capital]

    I. Trading Strategy Developer: John Ehlers and Ric Way. Source: Ehlers, J., Way, R. (2010). Zero Lag (well, almost). Concept: Trend following trading strategy based on moving average filters. Research Goal: To verify performance of the Zero Lag Moving Average (ZLMA). Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Zero Lag Moving Average (ZLMA) crosses over Exponential
  • Trend following starts 2017 with negative January [Wisdom Trading]

    January 2017 Trend Following: DOWN -2.84% If December bucked the trend of the last 6 months, January was a continuation of the downward direction seen in the second half of 2016. The index starts 2017 with a negative performance, in the context of global uncertainty, and keeps flirting with the maximum drawdown level. But you know what we have to say about that. Note that we added a section in
  • State of Trend Following in January: Down [Au Tra Sy]

    Trend Following started the year with the same flavour as it ended 2016: down. The index posted a negative performance in January but is still slightly up since the low in October last year. Please check below for more details. Detailed Results The figures for the month are: January return: -3.32% YTD return: -3.32% Below is the chart displaying individual system results throughout January:
  • Two Swing Trade Systems (Part 2) [Throwing Good Money]

    Yesterday I discussed two swing-trade systems that work pretty well in out-of-sample data. While each works differently, they overlap enough that you dont get any benefit from running them both at the same time. One great thing about these two systems is that theyre dead simple to manage. Trade at the open or the close, simple math, etc etc. I will repeat the caveat from yesterday: these

Quantocracy’s Daily Wrap for 02/02/2017

This is a summary of links featured on Quantocracy on Thursday, 02/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factors are Not Commodities [Investing Research]

    The narrative of Smart Beta products is that factors are becoming an investment commodity. Factors are not commodities, but unique expressions of investment themes. One Value strategy can be very different from another, and can lead to very different results. There are many places that factor portfolios can differ. The difficulty for asset allocators is in identifying how factor strategies differ
  • A Simple Machine Learning Model to Trade SPY [Signal Plot]

    I have created a quantitative trading strategy that incorporates a simple machine learning model to trade SPY as part of my ongoing research in quantitative trading. The focus here was not on creating a strategy with alpha but rather to develop a framework both in my mind and in code to develop more advanced models in the future. 1. Does SPY Exhibit Short-Term Mean Reversion or Momentum? Examining
  • Advanced Algorithmic Trading – Final Release [Quant Start]

    The QuantStart team are very happy to announce that the full version of Advanced Algorithmic Trading has now been released. This brings the total number of pages to 517. To access the full version customers simply need to follow the download link received in the original pre-order purchase email. If the download email has been misplaced then please email support@quantstart.com and the link will be
  • Factor Investing Book from @LarrySwedroe [Alpha Architect]

    Well, I was midway through a formal book review on Larry and Andrews new book, Your Complete Guide to Factor-Based Investing, when I noticed that the team over at GestaltU already wrote the review I was going to write great job and I encourage everyone to read it. larry factor book So we wont rehash what has already been said about Larry and Andrews book, instead, Ill bullet
  • Prototyping and backtesting trading strategies naively in python [No Noise Only Alpha]

    The fastest way to test the profitability of a trading model generating signals is to do a simple backtest (which means no hindsight biases i.e at least 1 period of timeframe lag from signal even if you timeframe is in milliseconds) using historical time series. actual returns = absolute return (no hindsight biases to signal) transaction cost spillage Spillage really matters when the trade

Quantocracy’s Daily Wrap for 02/01/2017

This is a summary of links featured on Quantocracy on Wednesday, 02/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in January [Allocate Smartly]

    This is a summary of the January performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in
  • How to Apply Machine Learning to Trading [Signal Plot]

    Recently, I have been interested in applying machine learning to trading. This post contains some of my thoughts regarding a framework for thinking about trading as a machine learning problem, treating trading as a classification or regression problem, and transforming the output of a machine learning model into a trading signal. 1. Introduction to Machine Learning Applications to Trading Machine
  • Creating a stock market sentiment Twitter bot with automated image processing [Troy Shu]

    One of the side projects I worked on in the past handful of months was Mr. Market Feels: a stock market sentiment Twitter bot that used automated image processing to extract and tweet the value of CNN Moneys Fear and Greed Index every day. Motivation There have been attempts to backtest the predictive power of the Fear and Greed Index when buying and selling the overall stock market index
  • Non-parametric Estimation [Quant Dare]

    How can we predict future returns of a series? Many series contain enough information in their own past data to predict the next value, but how much information is useable and which data points are the best for the prediction? Is it enough to use only the most recent data points? How much information can we extract from past data? Once we have answered all these questions we should think about

Quantocracy’s Daily Wrap for 01/30/2017

This is a summary of links featured on Quantocracy on Monday, 01/30/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Timing with Value [Flirting with Models]

    Cliff Asness, Antti Ilmanen, and Thomas Maloney of AQR are out with a new paper about market timing with value, titled: Market Timing: Sin a Little. Specifically, the paper explores whether the Shiller PE (also known as the cyclically adjusted P/E, or CAPE) can be effectively used to directionally time equity market exposure. Hot off the presses, we wanted to provide our take on the results. The
  • Should We Be Holding More Cash? [Flirting with Models]

    Modern portfolio theory provides a way for investors to identify the efficient frontier: the set of portfolios that maximize return per unit of risk. Taken to its logical conclusion, modern portfolio theory states that all investors should invest in the same global market portfolio and increase or decrease risk through the use of leverage or cash, respectively. In practice, investors appear to
  • Deep Learning for the Walk-Forward Loop [Quintuitive]

    In the previous posts in these series (here, here and here) I used conventional machine learning to forecast the trading opportunities. Lately however I have been trying to move more and more towards deep learning. My first attempt was to extend the walk-forward loop to support neural networks, the building blocks of deep learning. To experiment with a neural network, I could have simply used the
  • The Definitive Guide to Shorting Leveraged ETFs [Signal Plot]

    This post documents some of my research in creating a trading strategy centered around shorting leveraged exchange-traded funds (ETFs). I present the following thought experiment to motivate readers: Suppose an underlying instrument increases by 25% on day 1 and decreases by 20% on day 2. The return of the underlying instrument is (1 + 0.25) * (1 0.20) 1 = 0%. Now suppose I construct a

Quantocracy’s Daily Wrap for 01/26/2017

This is a summary of links featured on Quantocracy on Thursday, 01/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Options Trading, Part 1 [Financial Hacker]

    Despite the many interesting features of options, private traders rarely take advantage of them (Im talking here of serious options, not binary options). Maybe options are unpopular due to their reputation of being complex. Or due to their lack of support by most trading software tools. Or due to the price tags of the few tools that support them, and the historical data that you need. Whatever
  • Country ETF Rotation [Alvarez Quant Trading]

    My recent research has been focused on finding strategies that are not highly correlated with the S&P500 index. One of my most popular posts is ETF Sector Rotation. The idea for this post is to apply those concepts to a list of country ETFs. Would this produce decent returns that were not highly correlated to the S&P500 index? I would like to see the correlation under .50. What about
  • Density Confidence Interval [Eran Raviv]

    Density estimation belongs with the literature of non-parametric statistics. Using simple bootstrapping techniques we can obtain confidence intervals (CI) for the whole density curve. Here is a quick and easy way to obtain CIs for different risk measures (VaR, expected shortfall) and using what follows, you can answer all kind of relevant questions. Density Confidence Interval To get to the
  • Common Factor Structure in a Cross-Section of Stocks [Quantpedia]

    We seek to describe the broad cross-section of average stock returns. We follow the APT literature and estimate the common factor structure among a large cross-section containing 278 decile portfolios (associated with 28 market anomalies). Our statistical model contains seven common factors (with an economic meaning) and prices well both the original portfolio returns and an efficient combination

Quantocracy’s Daily Wrap for 01/25/2017

This is a summary of links featured on Quantocracy on Wednesday, 01/25/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Keuning & Keller’s Generalized Protective Momentum [Allocate Smartly]

    This is a test of the Generalized Protective Momentum (GPM) strategy from JW Keuning and Wouter Keller. The strategy builds off of the authors popular Protective Asset Allocation (PAA) model that we discussed last month. Results for the GPM strategy from 1989, net of transaction costs, follow. Read more about our backtests or let AllocateSmartly help you follow this strategy in near real-time.
  • Analyzing Portfolios With Risk-Factor Profiles [Capital Spectator]

    Most investment portfolios are a collection of risk factors, such as exposure to credit and equity risk. Monitoring and managing these factors is critical. The standard approach is reviewing portfolios through a plain-vanilla asset allocation lens 60% stocks, 30% bonds, 10% cash, for instance. But the standard methodology is a blunt instrument. For a clearer view of whats driving your

Quantocracy’s Daily Wrap for 01/22/2017

This is a summary of links featured on Quantocracy on Sunday, 01/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sentiment Analysis Trading Strategy via Sentdex Data in QSTrader [Quant Start]

    In addition to the "usual" tricks of statistical arbitrage, trend-following and fundamental analysis, many quant shops (and retail quants!) engage in natural language processing (NLP) techniques to build systematic strategies. Such techniques fall under the banner of Sentiment Analysis. In this article a group of quantitative trading strategies will be developed that utilise a set of
  • Risk Management with @InvestingIdiocy [Better System Trader]

    Risk Management Its not as sexy as the latest hot indicator Or the undiscovered penny stock poised for an explosive move Or the trading guru who appeared out of nowhere and is now promising to share the secrets to making million dollar profits overnight But there are a whole host of risks that have the potential to destroy trading accounts in just seconds, so its an
  • PutWrite vs. BuyWrite Index Differences [Quantpedia]

    The CBOE PutWrite Index has outperformed the BuyWrite Index by approximately 1.1 percent per year between 1986 and 2015. That is pretty impressive. But troubling. Yes troubling because the theory of put-call parity tells us that such outperformance should be almost impossible via a compelling no-arbitrage restriction. This paper explains the mystery of this outperformance, which has
  • Machine Learning: An Introduction to Decision Trees [Quant Insti]

    A decision tree is one of the widely used algorithms for building classification or regression models in data mining and machine learning. A decision tree is so named because the output resulting from it is the form of a tree structure. Visualizing a sample dataset and decision tree structure Consider a sample stock dataset as shown in the table below. The dataset comprises of Open, High, Low,