Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 09/07/2017

This is a summary of links featured on Quantocracy on Thursday, 09/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Two Strategies you can start trading tomorrow – Time of Day effects in FX continued [Quant Journey]

    My latest post at http://quantsjourney.blogspot.co.uk/2017/09/time-of-day-effects-in-fx.html was on time of days effects in FX and I was claiming that you can actually make money with simple strategies depending on time of day. Below you will find 2 very simple strategies you can play with and make some money. Do not forget sending my 20%, I know I can trust you. I will test these strategies with
  • StockTwits Sentiment Analysis [EP Chan]

    Exploring alternative datasets to augment financial trading models is currently the hot trend among the quantitative community. With so much social media data out there, its place in financial models has become a popular research discussion. Surely the stock markets performance influences the reactions from the public but if the converse is true, that social media sentiment can be used to
  • Best Operating System For Quant Trading? [Quant Start]

    One question that I am asked frequently is which operating system to use for quantitative trading research and implementation. The short answer, as of the writing date of this article, is if you want to carry out any serious/mathematical quant trading research (machine learning/deep learning) you should make use of Ubuntu 16.04 LTS Linux, with a desktop version on a local research machine and the

Quantocracy’s Daily Wrap for 09/06/2017

This is a summary of links featured on Quantocracy on Wednesday, 09/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time of Day effects in FX [Quant Journey]

    Time of day is critical for trading, it is even possible building trading strategies solely depending on time of day (I will keep this for another post) I will be using the concept of quality and define a high quality market, from an intraday timing perspective, as a market when trading range and volume are high and spread is low. I assume this as a good time to trade as trading cost (spread) is
  • A Random Forest Test For Jumps in Stock Markets Using R [Top of The Bell Curve]

    In the previous article we looked at how one can use Neural Networks to detect jumps present in returns of a particular stock. In this blog post, we build on the thinking established in the previous article and use a Random Forest to detect jumps present in stock market returns. I have build an interactive web application which allows the user to select the share they want to test for jumps, and
  • R vs MATLAB – round 4 [Eran Raviv]

    This is another comparison between R and MATLAB (Python also in the mix this time). In previous rounds we discussed the differences in 3d visualization, differences in syntax and input-output differences. Today is about computational speed. Spoiler alert: MATLAB wins by a knockout. A genuinely fair speed comparison across different software can be tricky. Almost all operations can be coded in more
  • Foreseeing the future: a user s guide [Quant Dare]

    Everybody would like to see the future. If youre a portfolio manager, youd definitely love to see the future. Many posts here on QuantDare deal with the challenge of predicting the future (with Prophet, Random Forests, Lasso, etc). This time, we talk about something different: imagine we are able to predict the future exactly. Now what? How could we exploit this priceless information? As we
  • Modeling Expected Drawdown Risk [Capital Spectator]

    There are no silver bullets for profiling risk, but drawdowns properties arguably give this metric a leg up over most of the competition. The combination of an intuitive framework, simplicity, and sharp focus on how markets actually behave is a tough act to beat. Perhaps the strongest argument in favor of drawdown can be summed up by recognizing that peak-to-trough declines always resonate with
  • Broken Strategy or Market Change: Investigating Underperformance [Alvarez Quant Trading]

    I recently had someone email me about the performance of a strategy I created back in late 2005/early 2006 and traded for a few years. I remember the strategy being a daily mean reversion set up with an intraday pullback entry. I figured it probably had not done well over the last decade. I stopped trading in the middle of 2008 because I did not like how it was behaving. In the backtest it did

Quantocracy’s Daily Wrap for 09/05/2017

This is a summary of links featured on Quantocracy on Tuesday, 09/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Leverage Up When You re Down? [QuantStrat TradeR]

    This post will investigate the idea of reducing leverage when drawdowns are small, and increasing leverage as losses accumulate. Its based on the idea that whatever goes up must come down, and whatever comes down generally goes back up. I originally came across this idea from this blog post. So, first off, lets write an easy function that allows replication of this idea. Essentially, we have
  • Getting Started with Neural Networks for Algorithmic Trading [Robot Wealth]

    If youre interested in using artificial neural networks (ANNs) for algorithmic trading, but dont know where to start, then this article is for you. Normally if you want to learn about neural networks, you need to be reasonably well versed in matrix and vector operations the world of linear algebra. This article is different. Ive attempted to provide a starting point that doesnt
  • The Butterfly Effect in Retirement Planning [Flirting with Models]

    Summary The low current market outlook for stocks and bonds paints a gloomy picture for retirees under common retirement forecasting assumptions. However, assumptions such as net investment returns and retirement spending can have a large impact on forecasted retirement success, even for small changes in parameters. By boosting returns through a combination of broader asset class and strategy
  • State of Trend Following in August [Au Tra Sy]

    Slightly positive month for the State of Trend Following index but still negative Year-To-Date performance, in the double digits. Please check below for more details. Detailed Results The figures for the month are: August return: 0.79% YTD return: -11.1% Below is the chart displaying individual system results throughout August: StateTF August And in tabular format: System August Return YTD Return
  • Volume Filters (Part 1) | Trading Strategy (Entry & Exit) [Oxford Capital]

    Volume Filters: Part 1 | Trading Strategy (Entry & Exit) I. Trading Strategy Developer: R. D. Edwards, J. Magee (Volume Filters); R. D. Donchian (Price Breakout Channels). Concept: Trading strategy based on price breakouts confirmed by volume filters (i.e. volume breakouts). Research Question: Can volume filters improve price breakouts? Specification: Table 1. Results: Figure 1-2. Trade Setup:
  • Want to Work for Alpha Architect? We’re Hiring! [Alpha Architect]

    Our firm is growing rapidly and were looking to hire new teammates (one initially, possibly another down the road). If you are passionate about investor education and helping us deliver affordable alpha, please reach out! We just posted a new job for an execution trader/researcher role. Jack and Yang discussing a research project Please pass around to those who would be interested. The job
  • Trend Following Down in August [Wisdom Trading]

    August 2017 Trend Following: DOWN -1.61% / YTD: -16.60% August was only slightly negative thanks to a late recovery from the mid-month level, where the index was down by over 5%. The YTD performance is still strongly in the red. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for August: Wisdom State of Trend Following – August 2017 And the

Quantocracy’s Daily Wrap for 09/04/2017

This is a summary of links featured on Quantocracy on Monday, 09/04/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Smart Portfolios: A post about a book, NN Taleb, and two conferences [Investment Idiocy]

    September 18th is the official publishing date of my second book, "Smart Portfolios: A practical guide to building and maintaining intelligent investment portfolios (Harriman House, 2017)". This blog post will give you some more information about the book, and more importantly help you decide if it's worth buying. I'll also let you know about a couple of forthcoming conferences
  • Value + Quality or High Quality Value Stocks? [Factor Research]

    SUMMARY Investors can either combine single-factors into a portfolio or sort stocks for several factor characteristics Double-sorting seems to work better for Value & Quality than for Value & Momentum The combination portfolios show the highest risk-return profiles, albeit at lower returns INTRODUCTION London recently hosted the World Championships in Athletics where sportsmen competed in
  • Profit Margins, Bayes Theorem, and the Dangers of Overconfidence [Philosophical Economics]

    Its the fall of 2011. Investors are caught up in fears of another 2008-style financial crisis, this time arising out of schisms in the Eurozone. The S&P 500 is trading at 1200, the same price it traded at in 1998, roughly 13 years earlier, despite the fact that its earnings today are almost three times as high as they were back then. The indexs trailing price-to-earnings (P/E) ratio sits
  • A Look At Historical Post-Labor Day SPX Performance [Quantifiable Edges]

    Way back in 2009 I showed a study that suggested Labor Day week performance has been somewhat dependent on whether the market has rallied over the 20 trading days leading up to it. I decided to take a new look at that study today. Below are updated results of post-Labor Day action when the previous 20 days have seen gains versus losses. 2017-09-04-1 This shows a poor performance record when there

Quantocracy’s Daily Wrap for 09/02/2017

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

  • Tactical Asset Allocation in August [Allocate Smartly]

    This is a summary of the recent performance of a wide range 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. Learn more about what we do or let AllocateSmartly help you follow these strategies in
  • Improving Your Sharpe Ratio by Adding Additional Strategies [Geodesic Edge]

    Identifying and building a portfolio of uncorrelated trading strategies is the main aim of many quantitative hedge funds. Given that one would like to add a new strategy to an existing set of strategies, what is the marginal gain the can be expected over the status quo? In addition, how can one optimize the Sharpe ratio of this new set of strategies by allocating capital between different

Quantocracy’s Daily Wrap for 09/01/2017

This is a summary of links featured on Quantocracy on Friday, 09/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Federal Regulations and Stock Market Returns [CXO Advisory]

    Do changes in the U.S. federal regulatory burden predict U.S. stock market returns? To check, we consider two measures of the regulatory burden: Annual number of pages in the Federal Register (FR) during 1936-2016 in which all newly proposed rules are published along with final rules, executive orders, and other agency noticesprovides a sense of the flow of new regulations issued
  • Trend-Following with Valeriy Zakamulin: Trading the S&P 500 Index (Part 7) [Alpha Architect]

    The Standard and Poors (S&P) 500 index is a value-weighted stock index based on the market capitalizations of 500 large companies in the US. This index was introduced in 1957 and intended to be a representative sample of leading companies in leading industries within the US economy. Stocks in the index are chosen for market size, liquidity, and industry group representation. The S&P 500

Quantocracy’s Daily Wrap for 08/29/2017

This is a summary of links featured on Quantocracy on Tuesday, 08/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation Cointegration [Jonathan Kinlay]

    In a previous post I looked at ways of modeling the relationship between the CBOE VIX Index and the Year 1 and Year 2 CBOE Correlation Indices: The question was put to me whether the VIX and correlation indices might be cointegrated. Lets begin by looking at the pattern of correlation between the three indices: VIX-Correlation1 VIX-Correlation2 VIX-Correlation3 If you recall from my previous
  • Academic Research Insights: Global Equities and Overreaction [Alpha Architect]

    What are the research questions? Is there a consistent and reliable long term overreaction pattern in global equity markets? In US equity markets, buying long term losers and selling long term winners (also called long term price reversal) is a well-documented anomaly. Does it also exist in global equity markets? Do known risk characteristics explain all or part of the excess returns associated
  • Iron Condor Results Summary – Part 4 – Top Performers By Metric [DTR Trading]

    In this article we will look at a subset of the 3024 iron condor strategy variations that were tested between January 2007 and September 2016. Specifically, we will look at the 1512 iron condor strategy variations that used both stop losses and profit targets. Out of these 1512 variations we will look at the top performers in terms of the following metrics: P&L / Trade (total return) Largest
  • Statistical Arbitrage Using Pair Trading In The Mexican Stock Market [Quant Insti]

    There are very few algo trading firms/strategies that are operating in the Mexican stock exchange. I believe this should provide great opportunities as there is little competition. Contrary to a more developed market, arbitrage opportunities arent readily realized which suggests there might be opportunities for those looking and able to take advantage of them. This is the main motivation for

Quantocracy’s Daily Wrap for 08/28/2017

This is a summary of links featured on Quantocracy on Monday, 08/28/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Statistical Arbitrage on a Cross-border Soybean Crush Spread [Golden Compass]

    Pairs trading is one of the simplest forms of statistical arbitrage which involves exploiting relative mispricings between two similar assets. It operates based on the assumption of the law of one price; that anomalies among securities valuation will occur in the short run but in the long run, will be corrected by market efficiency. In academic literature, studies such as such as Bogomolov (2010)
  • An Interactive Dynamic Delta Hedging Example in R [Top of The Bell Curve]

    Delta hedging is a technique used by trades to reduce the directional risk of a position. This delta hedging strategy results in the reduction of the variability of the profit and loss (pnl) of the position. A position that is delta hedged is said to be delta neutral. In this blog we will look at delta hedging European options under the Black and Scholes framework. A European option is an option
  • Smart Beta and Factor Correlations to the S&P 500 [Factor Research]

    SUMMARY Most smart beta products exhibit correlations of > 0.9 to the S&P 500 Factors show correlations of zero on average However, factor correlations are highly volatile across the market cycle INTRODUCTION In our recent research note Smart Beta vs Factors in Portfolio Construction we analysed the impact of including Value & Growth smart beta ETFs in an equity-centric portfolio

Quantocracy’s Daily Wrap for 08/25/2017

This is a summary of links featured on Quantocracy on Friday, 08/25/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Definitive Guide To Momentum Investing and Trading [Signal Plot]

    During my review of several quantitative trading books and papers, I kept on seeing information on two classes of trading strategies: mean reversion and momentum. I thought the things I read explained mean reversion quite clearly, but I wasnt entirely clear on how to implement momentum investing and trading strategies, so I decided to research it more thoroughly. This post focuses on what I
  • Trend-Following with Valeriy Zakamulin: Testing Profitability of Trading Rules (Part 6) [Alpha Architect]

    The difficulty in testing the profitability of trend-following rules stems from the fact that the procedure of testing involves either a single- or multi-variable optimization. Specifically, any trading rule considered in Part 3 has at least one parameter that can take many possible values. For example, in the Moving Average Crossover rule, MAC(s,l), there are two parameters: the size of the
  • Theta and Weekends Again [Highly Evolved Vol]

    Last week we stated that market makers don't fully account for weekend decay in equity options. Today we show specific results. Christopher Jones and Joshua Shemesh studied this issue and presented the findings in a paper that they presented to the 2010 American Finance Association meeting. They looked at the returns of long option portfolios on U.S. equities from 1996 to 2007 and found the
  • Podcast: Building entries without curvefitting [Better System Trader]

    You may have noticed over the past few weeks of Thursday Trading Thoughts that weve been following a theme. In episode 113 we heard about a test Kevin Davey calls the Monkey test, which can be used to measure the effectiveness of entries and exits. Then in episode 114 we reviewed a technique that Dave Bergstrom shared to measure the decay of a trading edge so that we can determine

Quantocracy’s Daily Wrap for 08/24/2017

This is a summary of links featured on Quantocracy on Thursday, 08/24/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Luck and Portfolio Tranching [Allocate Smartly]

    In this post we discuss portfolio tranching (i.e. dividing a portfolio into overlapping slices of the same underlying strategy) to minimize timing luck. This is an under discussed but important topic in tactical asset allocation. For more smart thoughts on portfolio tranching, see this excellent piece from Newfound Research. For our test case, well use a TAA strategy particularly