Quantocracy

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Quantocracy’s Daily Wrap for 11/13/2016

This is a summary of links featured on Quantocracy on Sunday, 11/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: A Practical Guide To Quantitative Finance Interviews [Amazon]

    This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews. The book covers a variety of topics that you are likely to encounter in quantitative interviews:
  • Podcast: Mean Reversion strategies with @QuantLabInfo [Better System Trader]

    The performance profile of Mean Reversion is extremely desirable to a lot of traders. Mean reversion trading strategies can produce high win rates and a smooth equity curve, however there are risks, which can result in giving back a large portion of profits, or of your trading account, some times in a very short period of time. So what can you do to build mean reversion strategies that produce
  • Diversification For The Long Term [Larry Swedroe]

    The table below, taken from the newly released book I co-authored with Andrew Berkin, Your Complete Guide to Factor-Based Investing, shows the annual premium and Sharpe ratio for the equity factors of market beta, size, value, momentum, profitability and quality. It also shows the odds that each premium will produce a negative return over various time horizons. There are two important

Quantocracy’s Daily Wrap for 11/11/2016

This is a summary of links featured on Quantocracy on Friday, 11/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pandas tutorial : Convert tick by tick data to OHLC data [Quant Insti]

    In this post, we will explore a feature of Python pandas package. We usually find queries about converting tick-by-tick data into OHLC (Open, High, Low and Close) frequently. This can be accomplished with minimal effort using pandas package. The OHLC data is used for performing technical analysis of price movement over a unit of time (1 day, 1 hour etc.). We have already seen How OHLC data is used

Quantocracy’s Daily Wrap for 11/10/2016

This is a summary of links featured on Quantocracy on Thursday, 11/10/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Algorithmic Trading (Part 2): Pairs Trading and Statistical Arbitrage [Keith Selover]

    This post will address what pairs trading is, how you can test for a pairs trading opportunity, and how to implement a pairs trading strategy. For information on the libraries Ive used and how I structured my trading methods, I recommend starting with my previous post on the subject. Pairs Trading is a Statistical Arbitrage strategy. In the strategy, a trader trades two stocks that tend to
  • TAA portfolios: Antonacci s Composite Dual Momentum [Investing For A Living]

    One of the TAA strategies that I have often been asked about is Antonaccis Composite Dual Momentum (ACDM from now on). I never got around to tracking or writing about it but now the the folks at Allocate Smartly have it covered. In this post Ill highlight the key details of the strategy and its results using the recent blog post from Allocate Smartly. The ACDM strategy basically applies
  • 100 Years of dow jones returns [Voodoo Markets]

    A quick look at annual returns over the 100+ years of daily percent change (close to close) data that we have on dow jones 1 2 3 4 5 6 7 import matplotlib.pyplot as plt import pandas as pd import numpy as np import datetime dj = local_csv("DjiaHist.csv", date_column = "Date", use_date_column_as_index = True) dia = get_pricing("DIA", start_date =
  • Five points of caution for dividend investors [Factor Investor]

    At a time when demand for income generating assets is at an all-time high, the yields on income generating assets are at, or near, all-time lows. While the headlines often speak to the number of Baby Boomers entering retirement, the more important statistic is actually the amount of wealth entering retirement. According to the U.S. Census Bureau, of the 125 million households in the U.S., 32% fall

Quantocracy’s Daily Wrap for 11/08/2016

This is a summary of links featured on Quantocracy on Tuesday, 11/08/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Preliminary Tests of Currency Strength Indicator [Dekalog Blog]

    Since my last post on the currency strength indicator I have been conducting a series of basic randomisation tests to see if the indicator has better than random predictive ability. The first test was a random permutation test, as described in Aronson's Evidence Based Technical Analysis book, the code for which I have previously posted on my Data Snooping Tests Github page. These results were
  • Over-Rebalancing [Meb Faber]

    Research Affiliates has been churning out some great content lately. In their recent piece titled Timing Smart Beta Strategies? Of Course! Buy Low, Sell High! they examine some value based factor rotation strategies. Namely, they examined rotating among the factors that had the worst 1,3,5,and 10 year trailing performance. Not surprisingly it worked well. So I went and re-ran a similar

Quantocracy’s Daily Wrap for 11/07/2016

This is a summary of links featured on Quantocracy on Monday, 11/07/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Quantitative Momentum from @AlphaArchitect [Amazon]

    Quantitative Momentum brings momentum investing out of Wall Street and into the hands of individual investors. In his last book, Quantitative Value, author Wes Gray brought systematic value strategy from the hedge funds to the masses; in this book, he does the same for momentum investing, the system that has been shown to beat the market and regularly enriches the coffers of Wall Street's
  • Outperforming by Underperforming [Flirting with Models]

    If you want long-term outperformance, you must be able to stomach short-term underperformance. As William Bernstein said, The most important investment ability is an emotional discipline. Investing is a team sport that requires this discipline from both the investment manager (to stick to his investment process) and the end user (to stick with the manager). Setting realistic expectations,
  • State of Trend Following in October [Au Tra Sy]

    The results from last months trend following index were only slightly negative, which is quite surprising as most of other indices were sharply down, including The Wisdom Trading State of Trend Following report, which I write as a version 2 of this report. The principles for the index are the same Goes to show that portfolio selection can still have a big impact on the short-term
  • Your best strategy in 2016 up till Q3 [Quant Investing]

    I wanted to send you this article shortly after the end of the third quarter 2016 but, like a lot of things, it slipped my mind. What has worked in 2016 value is not dead I will get right to the point about what strategy would have given you the best return so far in 2016. Here is a short summary: Price to book worked VERY well +34.6% (who would have thought that) Quality did not work Momentum
  • Testing A Euro Currency Futures Scalping Strategy, Part 5 [System Trader Success]

    Its been a couple of years since I reviewed this potential trading idea of a Euro currency futures scalping strategy. Over the series of articles, which are listed below, Ive been combing filter to demonstrate how I add different filters to a system based on market conditions. Testing A Euro Currency Futures Scalping Strategy Testing A Euro Currency Futures Scalping Strategy, Part 2 Testing
  • Python Data Visualization using Bokeh for Algo Traders and Quants [Quant Insti]

    A picture is worth a thousand words or said a wise woman a hundred years ago. True to every word of the idiom, the beauty of visualization lies in how clearly it might convey multiple messages. Visualization of data is one of the key functions of a data scientist and decoding the visual messages is of primary importance to the algo trader. The patterns (both hidden and the obvious) are of utmost

Quantocracy’s Daily Wrap for 11/05/2016

This is a summary of links featured on Quantocracy on Saturday, 11/05/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: 150 Most Frequently Asked Questions on Quant Interviews [Amazon]

    Topics: Mathematics, calculus, differential equations, Covariance and correlation matrices. Linear algebra, Financial instruments: options, bonds, swaps, forwards, futures, C++, algorithms, data structures, Monte Carlo simulations. Numerical methods, Probability. Stochastic calculus, Brainteasers The use of quantitative methods and programming skills in all areas of finance, from trading to risk
  • October brings another down month to Trend Following [Wisdom Trading]

    Election year is shaping up to be a bad year for trend following. October saw the State of Trend Following index post another successive down month. The current drawdown is still within the limits of the max value from the historical back-test run, but the Year-To-Date performance is now well into double-digit territory. It will be interesting to see if this bad patch is correlated with the
  • Bottom-Up Works Best With Multiple Factors [Larry Swedroe]

    CAPM was the first formal asset pricing model. Market beta was its sole factor. With the 1992 publication of their paper, The Cross-Section of Expected Stock Returns, Eugene Fama and Kenneth French introduced a new-and-improved three-factor model, adding size and value to market beta as factors that not only provided premiums, but helped further explain the differences in returns of
  • Research Review | 4 Nov 2016 | Risk Factors & Return Premia [Capital Spectator]

    Measuring Factor Exposures: Uses and Abuses Ronen Israel and Adrienne Ross (AQR Capital Management) September 19, 2016 A growing number of investors have come to view their portfolios (especially equity portfolios) as a collection of exposures to risk factors. The most prevalent and widely harvested of these risk factors is the market (equity risk premium); but there are also others, such as value
  • Principal Component Analysis [Quant Dare]

    Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of a data set, finding the causes of variability and sorting them by importance. >How? If you have a set of observations (features, measurements, etc.) that can be projected on a plane (X, Y) such as: DataSet representation You can display the previous graph from X* and Y* axes, which remain orthogonal. New axes

Quantocracy’s Daily Wrap for 11/04/2016

This is a summary of links featured on Quantocracy on Friday, 11/04/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Financial Signal Processing and Machine Learning [Amazon]

    The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the
  • Antonacci’s Composite Dual Momentum [Allocate Smartly]

    This is a test of Gary Antonaccis Composite Dual Momentum strategy from his seminal paper: Risk Premia Harvesting Through Dual Momentum. The model uses Antonaccis unique approach to measuring momentum, which considers both absolute (aka time-series) and relative (aka cross-sectional) momentum, to trade a much larger basket of asset classes than his more well-known GEM strategy. Results
  • Podcast: How to think about strategies like a quant w/ Derek Wong [Chat With Traders]

    On this episode, I have our very first guest from China; Derek Wonghe is the Director of Systematic Trading and Options at a private fund in Shanghai. Initially though, Derek got his start in the agricultural pits at the CBOT, then following on from this, hes worked at various quant shops in Chicago, South Korea, and now days, mainland China. After discussing Dereks backstory, we talk;