Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 03/27/2017

This is a summary of links featured on Quantocracy on Monday, 03/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • All About Factors & Smart Beta [Flirting with Models]

    This week's commentary is a long-form presentation all about factor investing and smart beta. We cover four topics. In the first section, we explore the basics of factors: what are they and where do they come from? The second topic explores why implementation details matter and why long-only factor investing can be significantly different than long/short academic research. We then explore the

Quantocracy’s Daily Wrap for 03/24/2017

This is a summary of links featured on Quantocracy on Friday, 03/24/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Getting position and accounting data out of IB native python API [Investment Idiocy]

    This is the final post. Not the final post of the blog; which may be good news or bad. But the final post in my short series on using the new native python API for interactive brokers. Having got some prices and submitted some orders we want to know whether we made any money or not; and what positions we have. Although the code is rather trivial, interpreting the results requires some
  • Is There a Less Expensive Hedge Than a Protective Put? [Relative Value Arbitrage]

    The spot VIX index finished last Friday at 11.28, a relatively low number, while the SKEW index was making a new high. The SKEW index is a good proxy for the cost of insurance and right now it appears to be expensive. A high reading of SKEW means investors are buying out of the money puts for protection. CBOE SKEW index as at close of March 17, 2017 With the cost of insurance so high, is there a

Quantocracy’s Daily Wrap for 03/21/2017

This is a summary of links featured on Quantocracy on Tuesday, 03/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intro to Expectation-Maximization, K-Means, Gaussian Mixture Models with Python, Sklearn [Black Arbs]

    Post Outline Part 1 Recap Part 2 Goals Jupyter (IPython) Notebook References part 1 recap In part 1 of this series we got a feel for Markov Models, Hidden Markov Models, and their applications. We went through the process of using a hidden Markov model to solve a toy problem involving a pet dog. We concluded the article by going through a high level quant finance application of Gaussian mixture
  • Cryptocurrency Time-Series for N-CryptoAsset Portfolio Analysis in Python [Quant at Risk]

    Welcome to a brand new era of financial assets the crypto-assets. The impossible became possible. Yes, now you can trade cryptocurrencies: money that have been created in a virtual world with a physical impact onto our everyday cash-in-the-bank reality. The grande picture is still enigmatic for majority of us. Not too many have even heard of cryptocurrencies different than bitcoin (BTC).

Quantocracy’s Daily Wrap for 03/20/2017

This is a summary of links featured on Quantocracy on Monday, 03/20/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Placing orders in the native python IB API [Investment Idiocy]

    This the fourth in a series of posts on using the native python API for interactive brokers. You should read the first, second, and third, 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 otherwise the post is pretty similar. We are nearly at the end of our journey of simplistic
  • Diversification in Multi-Factor Portfolios [Flirting with Models]

    The debate rages on over the application of valuation in factor-timing methods. Regardless, diversification remains a prudent recommendation. How to diversify multi-factor portfolios, however, remains up for debate. The ActiveBeta team at Goldman Sachs finds new evidence that composite diversification approaches can offer a higher information ratio than integrated approaches due to interaction

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.