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Quantocracy’s Daily Wrap for 03/19/2017

This is a summary of links featured on Quantocracy on Sunday, 03/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Machine Learning with Kris Longmore of @Robot_Wealth [Better System Trader]

    Machine learning has seen a huge amount of growth over recent years with the increase in available data and processing power. Its an incredibly powerful toolset for uncovering patterns and relationships in data, however, these tools can be challenging to learn, apply correctly and are also open to abuse. Our guest for the episode, Kris Longmore from Robot Wealth, specializes in Machine
  • Back to Basics Part 2 How to Succeed at Algorithmic Trading [Robot Wealth]

    There is a lot of information about algorithmic and quantitative trading in the public domain today. The type of person who is attracted to the field naturally wants to synthesize as much of this information as possible when they are starting out. As a result, newcomers can easily be overwhelmed with analysis paralysis and wind up spending a lot of their valuable spare time working on
  • Visualising Intraday Market Correlation [Ryan Kennedy]

    I stumbled across a great post on MKTSTK about visualising volatility and correlations of multiple timeseries with streamgraphs, and it got me thinking about where else a streamgraph might be useful to visualise financial data. Rather than looking at an individual assets, I thought it might be interesting to explore the behaviour of the market at times throughout the day, and in turn see how this

Quantocracy’s Daily Wrap for 03/17/2017

This is a summary of links featured on Quantocracy on Friday, 03/17/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research Review | 17 March 2017 | Risk Factors [Capital Spectator]

    Contrarian Factor Timing is Deceptively Difficult Clifford S. Asness (AQR Capital Management), et al. March 7, 2017 The increasing popularity of factor investing has led to valuation concerns among some contrarian-minded investors, and fears of imminent mean-reversion and underperformance. In this paper, the authors find that despite their recent popularity the most common factors or styles,

Quantocracy’s Daily Wrap for 03/16/2017

This is a summary of links featured on Quantocracy on Thursday, 03/16/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simulating Correlated Random Walks for the S&P 500 [MKTSTK]

    Waaaaaay back in the day, I showed how to simulate correlated random walks using copulas. I was really thinking about the application to pairs trading back then which was fine, because one of the limitations was that the method could only simulate two random variables at a time. If you wanted to do some large universe like the S&P 500 you had to do everything pairwise, and then you
  • Puts as Protection [Timely Portfolio]

    Many asset management firms are happily enjoying record revenue and profits driven not by inorganic growth or skillful portfolio management but by a seemingly endless increase in US equity prices. These firms are effectively commodity producers entirely dependent on the price of an index over which the firm has no control. The options market presents an easy, cheap, and liquid form of protection
  • Analysis of Asymmetrical Moving Average for Buy/Sell Signals [Quantpedia]

    ost market participants are risk adverse and people tend to close their long positions once they perceive a formation of downturn in the market. Large sudden price drops can always be observed near the end of uptrends. On the other hand, people tend to have their own preferences in deciding the market entrance timings and large sudden price changes are relatively less commonly observed near the
  • Podcast: Trading the Mean Reversion Curve [Better System Trader]

    One of the challenges of Mean Reversion trading is deciding when to get into a trade. How far from the mean should we actually wait before we consider getting into a trade? In a trending environment where the dips are shallow, getting in closer to the mean can bring lots of trading opportunities which can perhaps translate into more profits, however when market conditions change that approach can

Quantocracy’s Daily Wrap for 03/15/2017

This is a summary of links featured on Quantocracy on Wednesday, 03/15/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • TAA Strategy Combining Risk Parity & Trend Following [Allocate Smartly]

    This is a test of a tactical asset allocation strategy from the excellent paper: The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation (1). The strategy combines two important tools: trend-following (to determine what assets to hold) and risk parity (to determine how much of each asset to hold), to produce one of the least volatile strategies that we track.
  • Simple ConnorsRSI Strategy on S&P500 Stocks [Alvarez Quant Trading]

    A frequently asked question is how I pick which variation from an optimization run to trade. This post will cover a ConnorsRSI strategy on S&P500 stocks. We will use a wide range on the parameters to give us lots choices to be used in the next post. I the next post, I will show how I take the results and narrow it down to one potential variation to trade. And then the final post, I will cover
  • Vix And Fed Rate Decision Announcments [Voodoo Markets]

    Since today is Fed day, i thought id take a look at how rate decisions have affected Vix. Vix data starts from early 90s so well have start from there. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 import quandl import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import datetime as dt from pandas.tseries.offsets import *

Quantocracy’s Daily Wrap for 03/14/2017

This is a summary of links featured on Quantocracy on Tuesday, 03/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • They Can’t All Be That Smart [Investing Research]

    Smart Beta is a label applied broadly to all factor-based investment strategies. In a recent WSJ article on Smart Beta, Yves Choueifaty, the CEO of Tobam, said There's a huge range of possibilities in the smart-beta world, and they can't all be that smart. This paper separates the factor investing landscape, gives a to framework to analyze the edge of various approaches and lets you
  • Dual Momentum with Stock Selection [Alpha Architect]

    Gary Antonacci may not be happy to learn that his "Dual Momentum" label has been pirated by a team of academics (Huang, Zhang, and Zhou)(1)(2) in a new paper that explores the combination of price and fundamental momentum stock-picking strategies. The authors also investigate the common rebuttal that transaction costs destroy stock momentum strategies. The authors perform a variety of

Quantocracy’s Daily Wrap for 03/12/2017

This is a summary of links featured on Quantocracy on Sunday, 03/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding K-Means Clustering [Eran Raviv]

    Google K-means clustering, and you usually you find ugly explanations and math-heavy sensational formulas*. It is my opinion that you can only understand those explanations if you dont need them; meaning you are already familiar with the topic. Therefore, this is a more gentle introduction to K-means clustering. Here you will find out what K-Means Clustering, an algorithm, actually does.
  • AAII Sentiment At New Spx 21 Week Highs [Voodoo Markets]

    Nothing quantitative here, just taking a look at how the AAII setiment has been when Spx is making new 21 week rolling highs. The recent AAII setiment has turned siginificantly negative even as Spx is plowing up and wanted to see when has that happened in the past.

Quantocracy’s Daily Wrap for 03/10/2017

This is a summary of links featured on Quantocracy on Friday, 03/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Streaming market data from native python IB API [Investment Idiocy]

    This the third in a series of posts on using the native python API for interactive brokers. You should read the first, and the second, before this one. It is an updated version of this older post, which used a third party API (swigibpy) which wraps around the C++ API. I've changed the code, but not the poor attempts at humour. In my last post we looked at getting a single snapshot of
  • Index Mapping For ETF Proxies [TrendXplorer]

    In order to present results as realistic as possible in our PAA-paper, we constructed long-term end-of-month data series for popular ETF proxies, like SPY, GLD and TLT (see paper appendix on SSRN). All data series start December 1969. For the pre-inception history, the proxies are derived from suitable indices. As part of a complete revision of the long-term data set, the construction process is
  • A Visual Quantitative Analysis of RSI using Tradestation and Excel [Beyond Backtesting]

    The traditional way to treat the RSI is to treat low RSI levels as good buying opportunities while treating high RSI levels as selling opportunities. However, we seek to gain fresh insight into the nature of RSI, with an eye toward discovering possible momentum return, by exploring the RSI using a visual quantitative approach. Exporting And Visualizing The Data We are interested in the next day
  • FX Carry Risk Mitigation Papers [Quantpedia]

    We analyze the worst currency carry loss episodes in recent decades, including causes, attribution by currency, timing, and the duration of carry drawdowns. To explore the determinants of the length of carry losses, a model of carry drawdown duration is estimated. We find evidence that drawdown duration varies systematically with expected return from the carry trade at the onset of the drawdown,
  • Python for Algo and Crypto-Currency Trading: 2-Day Workshop in London (July 8-9) [Quant at Risk]

    Within our unique 2-Day Intensive Workshop in London, UK on Python for Algorithmic and Crypto-Currency Trading we dive into most recent and hot topics in algo-trading. We will cover and analyse a well explored world of classical assets (stocks, FX currencies) extended by trading techniques aimed at crypto-currencies (inter alia, the bitcoin). Click here to find out more and register for this

Quantocracy’s Daily Wrap for 03/09/2017

This is a summary of links featured on Quantocracy on Thursday, 03/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Forecasting Stock Returns using ARIMA model [Quant Insti]

    Prediction is very difficult, especially about the future. Many of you must have come across this famous quote by Neils Bohr, a Danish physicist. Prediction is the theme of this blog post. In this post, we will cover the popular ARIMA forecasting model to predict returns on a stock and demonstrate a step-by-step process of ARIMA modeling using R programming. What is a forecasting model in
  • Playing with Prophet on Financial Time Series [Quant Dare]

    Two weeks ago, Facebook launched Prophet, an amazing forecasting tool available in Python and R. Heres a bit of info from the Facebook research website: Forecasting is a data science task that is central to many activities within an organization. For instance, large organizations like Facebook must engage in capacity planning to efficiently allocate scarce resources and goal setting in order
  • What hand traders can learn from system traders, and vice versa w/ @AdamHGrimes [Chat With Traders]

    Adam Grimes has been a trader for more than 20-years, hes traded all major asset classes, across various timeframes. Hes traded independently, with a prop firm, and hes run other trading businesses also. The main focus of this episode is to explore some of the things which discretionary traders can adapt from quantitative traders, and vice versameaning, what things can quants take from

Quantocracy’s Daily Wrap for 03/08/2017

This is a summary of links featured on Quantocracy on Wednesday, 03/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PSA: Your NCAA March Madness Rules are Garbage. Do This Instead. [Invest Resolve]

    On the heels of last years fun and successful March Madness Bracket Challenge (WHERE SKILL PREVAILS!), we are happy to invite any and all to 2017s version. Feel free to read the post for this years rules, but bear in mind this years pool is limited to 250 entrants, so dont wait: Register here. As with most investing topics, our thinking on March Madness bracket rules continues
  • Interactive brokers native python API [Investment Idiocy]

    Until quite recently interactive brokers didn't offer a python API for their automated trading software. Instead you had to put up with various 3rd party solutions, one of which swigibpy I use myself. Swigibpy wrapped around the C++ implementation. I wrote a series of posts on how to use it, starting here. Although swigiby has been very good to me its always better to use official solutions
  • What is Deep Learning? [Quant Start]

    Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first principles. These days it is almost impossible to work in any technology-heavy field without hearing about
  • 66 DTE Iron Condor Results Summary [DTR Trading]

    This article reviews the backtest results of iron condors (IC) entered at 66 days to expiration (DTE). These tests covered 9 IC variations, with short strike deltas at four locations (8, 12, 16, 20), utilizing 12 exits. In all, there were 432 test runs (9 variations x 4 deltas x 12 exits). Each test run executed slightly less than 200 SPX IC trades between the January 2007 expiration and the
  • Machine Learning in Python for Finance: 2-Day Workshop in Warsaw, Poland [Quant at Risk]

    After wonderful and rewarding 2-day workshop devoted to Python for Algo-Trading on March 4-5, it is my pleasure to announce a new, upcoming, on demand 2-Day Workshop on Machine Learning in Python for Finance (May 20-21, 2017). Since Machine Learning is the latest hottest topic covering different fields we will understand its aspects in a wide range of possible applications. Click here to learn
  • Historic data from native IB python API [Investment Idiocy]

    This is the second in a series of posts on how to use the native python API for interactive brokers. This post is an update of the post I wrote here, which used the 3rd party API swigibpy. Okay so you have managed to run the time telling code in my last post. Now we will do something a bit more interesting, get some market prices. Arguably it is still not that interesting, and this stuff will
  • Firm-Specific Information and Momentum Investing [Alpha Architect]

    When it comes to momentum investing, everyone is always looking for a better way to implement a momentum-based stock selection strategy (the same goes for a value strategy). We highlight a few methods in our book, Quantitative Momentum, as well as on our blog. We recently came across a paper from 2006 that has an improvement on a baseline momentum investing strategy, titled Firm-specific

Quantocracy’s Daily Wrap for 03/06/2017

This is a summary of links featured on Quantocracy on Monday, 03/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pairs Trading with Copulas [Jonathan Kinlay]

    In a previous post, Copulas in Risk Management, I covered in detail the theory and applications of copulas in the area of risk management, pointing out the potential benefits of the approach and how it could be used to improve estimates of Value-at-Risk by incorporating important empirical features of asset processes, such as asymmetric correlation and heavy tails. In this post I will take a very
  • Visualizing the Anxiety of Active Strategies [Flirting with Models]

    Prospect theory states that the pain of losses exceeds the pleasure of equivalent gains. An oft-quoted ratio for this pain-to-pleasure experience is 2-to-1. Evidence suggests a similar emotional experience is true for relative performance when investors compare their performance to common reference benchmarks. The anxiety of underperforming can cause investors to abandon approaches before they
  • The No-Short Return Premium [Quantpedia]

    Theory predicts that securities with greater limits to arbitrage are more subject to mispricing and thus should command a higher return premium. We test this prediction using the unique regulatory setting from the Hong Kong stock market, in which some stocks can be sold short and others cannot. We show that no-short stocks on average earn significantly higher returns than shortable stocks and the