Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 09/22/2017

This is a summary of links featured on Quantocracy on Friday, 09/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Timing Investigation: Interest Rates, Value Spreads, and Factor Premiums [Alpha Architect]

    Now that the Federal Reserve has begun the process of raising interest rates, and has announced their intention to begin to unwind their policy of quantitative easing (reducing the amount of bonds in their portfolio, either by selling holdings or allowing holdings to mature), investors may be concerned about the impact of rising interest rates on factor premiums. Wei Dai, senior researcher at
  • Downloading Historical Data Using Oanda’s API and R [Dekalog Blog]

    It has been about 5 months since my last blog post and in this time I have been working away from home, been on summer holiday and spent some time mucking about on boats, so I have not been able to devote as much time to my blog as I would have liked. However, that has now changed, and this blog post is about obtaining historical data. Many moons ago I used to download free, EOD data from

Quantocracy’s Daily Wrap for 09/21/2017

This is a summary of links featured on Quantocracy on Thursday, 09/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Option Chain Extraction For NSE Stocks Using Python [Quant Insti]

    We are back again with another post on Python. Our last post, Basic Operations on Stock data using Python was well received and we are glad to see the number of likes & shares for the post on various quant trading and Python forums. Keep them coming! Financial market data is a very critical element of a trading system. Be it historical or live data, you need data for various purposes
  • Trinity Portfolio (Lite) from @MebFaber [Allocate Smartly]

    This is a test of the Trinity Portfolio from Meb Faber and Cambria Investments, so named for the three key elements of the strategy: (1) a globally diversified mix of assets, (2) a tilt towards the value and momentum factors, and (3) exposure to momentum and trend-following. Weve titled our test Trinity Lite because weve made some not insignificant changes to Fabers original model
  • Seven Habits of Highly Ineffective Quants [CXO Advisory]

    Why dont machines rule the financial world? In his September 2017 presentation entitled The 7 Reasons Most Machine Learning Funds Fail, Marcos Lopez de Prado explores causes of the high failure rate of quantitative finance firms, particularly those employing machine learning. He then outlines fixes for those failure modes. Based on more than two decades of experience, he concludes that:

Quantocracy’s Daily Wrap for 09/20/2017

This is a summary of links featured on Quantocracy on Wednesday, 09/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SVM Trend Strategy on Nikkei 225 Mini Futures [Golden Compass]

    Motivation Support Vector Machines (SVM) are among the most popular Supervised Learning techniques for classification and regression, due to their ease in usage to find non-linear patterns. They work by separating data by finding an optimal threshold known as a decision boundary or hyperplane, to classify observations. When new data is presented to the SVM, it can distinguish which side of the
  • Evidence Based Investing is Dead. Long Live Evidence Based Investing! Part 1 [Invest Resolve]

    Michael Edesses article, The Trend that is Ruining Finance Research makes the case that financial research is flawed. In this two-part article series, we will examine the points that Michael raises in some detail. We find his arguments have some merit. Importantly however, his article fails to undermine the value of finance research in general. Rather, his points serve to highlight that
  • ETF Sector Trading: The effect of daily, weekly and monthly timeframes [Alvarez Quant Trading]

    I recently gave a presentation on Sector trading using the 200-day moving average at the Northwest Traders and Technical Analysts. Some questions asked were: What if we only trade this monthly? What if we used weekly bars to trade only weekly? Wat if we used weekly bars to trade monthly? The reason for these questions was to reduce the frequency of having to check signals and the total number of

Quantocracy’s Daily Wrap for 09/18/2017

This is a summary of links featured on Quantocracy on Monday, 09/18/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Lie of Averages [Flirting with Models]

    Averages are often used to summarize data: but sometimes fitting for the average means fitting nothing at all. Expected returns are a meaningful input to portfolio construction, but are unlikely to be the returns actually realized. Reality rarely looks average. The world is dynamic and forecasts can change. Not only should we expect that things will not be average, but we should expect that our
  • Factor Allocation 101: Equal vs Volatility-Weighted [Factor Research]

    Equal-weight and volatility-weighted allocations are two common factor allocation frameworks Risk-return ratios are not higher with volatility-weighted allocations Different reasons can explain the superiority of equal-weight allocations INTRODUCTION In July we published a research report Factors & Volatility-Based Risk Management were we analysed Value, Size and Momentum based on
  • The Weakest Week (Updated) [Quantifiable Edges]

    From a seasonality standpoint, there isnt a more reliable time of the year to have a selloff than this upcoming week. In the past I have referred to is as The Weakest Week. Since 1961 the week following the 3rd Friday in September has produced the most bearish results of any week. Below is a graphic to show how this upcoming week has played out over time. 2017-09-17 image1 As you can see
  • Research Review | Portfolio Management [Capital Spectator]

    Asset Allocation in a Low Yield Environment John Huss (AQR Capital Mgt.), et al. August 17, 2017 The year 2016 saw bond yields fall to unprecedented low levels in major developed markets, with nominal yields on 10-year German and Japanese government bonds even turning negative. While yields have risen off their lows in 2017, we are still in a very low rate environment. Does this demand exceptional
  • Why Machine Learning Funds Fail [Quantpedia]

    The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms

Quantocracy’s Daily Wrap for 09/14/2017

This is a summary of links featured on Quantocracy on Thursday, 09/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book from Rob Carver (@InvestingIdiocy): Smart Portfolios

    Smart Portfolios is about building and maintaining smart investment portfolios. At its heart are the three key questions every investor needs to answer: 1. What to invest in. 2. How much to invest. 3. When to make changes to a portfolio. Author Robert Carver addresses these three areas by providing a single integrated approach to portfolio management. He shows how to follow a step-by-step process
  • What Happens When You Data Mine 2 Million Fundamental Quant Strategies [Alpha Architect]

    As we have mentioned before, here, here and here, there is overwhelming evidence that the number of stock anomalies in the universe is much lower than originally thought. Most of the previous research papers attempt to filter out past anomalies in the literature (generally over 300+) by applying more stringent standards, such as higher p-values or more advanced statistical tests. A working paper
  • Podcast: Using creative thought and automation to bypass human flaws with @BMouler [Chat With Traders]

    It was exactly 100-episodes ago when I first had Bert Mouler on the podcast. This week, Im joined by him again for a second interview Bert is an algorithmic trader with a serious focus on machine learning. His trading decisions are driven purely by data, and he goes to great lengths to remove human bias and flaws through the use of automation. While listening, I encourage you to keep an open

Quantocracy’s Daily Wrap for 09/12/2017

This is a summary of links featured on Quantocracy on Tuesday, 09/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • There is Value in the Value Factor [Factor Research]

    Equity factors can be valued using fundamental metrics Value and Size are cheap while Low Volatility and Growth are expensive Likely more meaningful for medium- to long-term than short-term investors INTRODUCTION The term Factor Investing reached an all-time high this year according to Google Trends, which is mirrored by an abundance of smart beta and risk premia products being issued by
  • Dynamic Asset Allocation for Practitioners Part 4: Momentum Weighting [Invest Resolve]

    In the first three articles of our Dynamic Asset Allocation for Practitioners series (article 1, article 2, article 3), we focused on the first half of the total process. We specified a universe of global asset classes and sorted it on relative strength with 21 different raw and risk-adjusted momentum indicators, each subjected to a battery of robustness testing 250,000 tests in total. We now
  • High Frequency Trading II: Limit Order Book [Quant Start]

    In this article series Imanol Prez, a PhD researcher in Mathematics at Oxford University, and an expert guest contributor to QuantStart continues the discussion of high-frequency trading via the introduction of the limit order book. As we saw in the in the first article of the series, the objective of electronic markets is to match participants that are willing to sell an asset with participants
  • Support Academic Research by Filling Out The Financial Analysts Survey [Alpha Architect]

    Prof. Richard Price, an old friend, co-author, and Alpha Architect advisory board member, is working on some cool new co-authored research that requires audience participation! Dr. Price, alongside Professors Dipankar Ghosh, and Atul Rai, are conducting research to better understand what factors are used by professional financial analysts to assess a firm for investment purposes when the firm

Quantocracy’s Daily Wrap for 09/09/2017

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

  • Exploring Our Scraped Options Data Bid-Ask Spreads (Part-2) [Black Arbs]

    Notes on Part-2 The Data Bid-Ask Spread Analysis How Do Aggregate Bid-Ask Spreads Vary with Days To Expiration? How Do Bid-Ask Spreads Vary with Volume? How Do Bid-Ask Spreads Vary with Volatility? Summary Conclusions Notes on Part-2 Some astute readers in the comments noted that analysis based on the absolute difference in bid-ask price is not robust when considering the price of the underlying
  • Trend-Following with Valeriy Zakamulin: Trading in Various Financial Markets – Part 8 [Alpha Architect]

    In our final blog post, that finishes the trend-following series, we briefly review the results of the forward-tests of the profitability of various trend following rules in different financial markets: stocks, bonds, currencies, and commodities. The results of these tests allow us to better understand the properties of the trend following strategies, their advantages, and their disadvantages.

Quantocracy’s Daily Wrap for 09/08/2017

This is a summary of links featured on Quantocracy on Friday, 09/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Combine Commodity Style Strategies [Quantpedia]

    This paper develops a portfolio allocation framework to study the benefits of style integration and to compare the effectiveness of alternative integration methods in commodity markets. The framework is flexible enough to be applicable to any asset class for either long-short, long- or short-only styles. We study the nave equal-weighted integration and sophisticated integrations where the style
  • Night Terrors [Highly Evolved Vol]

    Following on from my recent posts about trading volatility over weekends, Im now going to briefly look at trading options overnight. Option traders have always complained when they were too long options overnight, expecting to usually lose money on overnight longs. This doesnt make sense in a pure Black-Scholes-Merton world. In that world the time decay (theta) will be balanced by the
  • Free Friday #20 Time Windows [Build Alpha]

    There has been a recent popularity regarding time windows and it is one I completely agree with! There are certain structural changes that happen throughout the 24 hour session and as a trader it is important to take note of these when designing a system or strategy (or just placing trades). For example, how is my strategy's performance when Asia closes? How about when the US opens? There are

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