Since today is Fed day, i thought id take a look at how rate decisions have affected Vix. Vix data starts from early 90’s so we’ll 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

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

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

Sector rotation is a popular investment strategy whereby managers actively reallocate capital from one investment sector to another based upon changing market conditions. There are many ways to run sector rotation strategy, including: business cycle indicators, macroeconomic indicators, value-based,

I’ve noted a number of times that Op-ex week in general is pretty bullish. March, April, October, and December it has been especially so. S&P 500 options began trading in mid-1983. The table below is one I have showed in March each of the last several years. It goes back to 1984 and shows

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 don’t need them; meaning you are already familiar with the topic. Therefore, this is a more gentle introduction

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.

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

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

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

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

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

“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

Two weeks ago, Facebook launched Prophet, an amazing forecasting tool available in Python and R. Here’s 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

Adam Grimes has been a trader for more than 20-years, he’s traded all major asset classes, across various timeframes. He’s traded independently, with a prop firm, and he’s run other trading businesses also. The main focus of this episode is to explore some of the things which discretionary

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

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,

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

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

On the heels of last year’s fun and successful March Madness Bracket Challenge (“WHERE SKILL PREVAILS!”), we are happy to invite any and all to 2017’s version. Feel free to read the post for this year’s rules, but bear in mind this year’s pool is limited to 250 entrants, so don’t wait:

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

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

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

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

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

With the toolsets we have available to us today it’s really quite easy to create a trading strategy by just mining market data. As we’ve just heard in that opening bit of audio and also from previous podcast guests too, if you try enough combinations you can find something that appears to work

One of the perennial problems in building trading models is the spareness of data and the attendant danger of overfitting. Fortunately, there are systematic methods of dealing with both ends of the problem. These methods are well-known in machine learning, though most traditional machine learning

The buy-and-hold crowd, including many mutual fund companies and a large cross-section of vocal pundits, like to talk about how missing the N best days/months in the market causes a serious impairment to long-term investor returns. What they fail to mention is that, because stock market volatility

Johnny Paycheck has a great country song centered around the following lyric: Take this job and shove it…I ain’t working here no more… Campell Harvey, in the 2017 AFA Presidential Address, elaborates an analogous comment on the current state of the financial economics field: Take this alpha

Check out our awesome new book library curated by four of the top rated authors in our community: Investment Idiocy (Rob Carver): General Quantitative Finance, Market History, Hedge Funds, General Programming Quant Start (Michael Halls-Moore): Python, C++, Financial Math, Quant Jobs & Interviews

In this entry, we discover the secrets behind prices transformation in financial series. Do you use price series in things such as technical analysis visualisation? Do you use return series in things such as volatility calculations? Do you use equity series in things such as comparing products with

Momentum has been found to be a persistent and pervasive factor in the returns not only of equities, but in other asset classes (including bonds, commodities and currencies). With equities (compared to the market, value, size, profitability and quality factors), during the period 1927 through 2015,

This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we don’t (yet) include every published TAA model, these strategies are broadly representative of the

In a recent letter to its investors, Crispin Odey commented as follows:(1) Money managers specializing in picking stocks and bonds are being driven out by mindless passive investing. Odey is a London based hedge fund manager, whose flagship fund lost almost 50% in 2016.(2) Photo courtesy of Wes. All

The behavior gap is the difference between the returns on an investment and the returns that an investor realizes in that investment. Behavioral biases ingrained in human nature, such as anchoring, hindsight, and overconfidence drive emotional decisions that can lead to a behavior gap, but

Who is Andrey Markov? What is the Markov Property? What is a Markov Model? What makes a Markov Model Hidden? A Hidden Markov Model for Regime Detection Conclusion References Who is Andrey Markov? Markov was a Russian mathematician best known for his work on stochastic processes. The focus of his

I've read too many posts / articles that outline why a rise in rates is good for long-term bond investors (as that would allow reinvestment at higher rates). While this can be true depending on the duration of bonds owned and/or for nominal returns over an extended period of time, it is

The performances of factor investing rely heavily on short sales, not only for building the initial long-short strategy, but also for regularly rebalancing the positions. Since short selling is subject to both legal restrictions and substantial costs, this paper examines how severely restrictions on

Why dual momentum? Because strategies based on highest relative momentum show great results in the long run, but can experience deep falls and have little participation in the posterior rebounds after large market falls. To sidestep these drawbacks, here it is laid out a strategy based on Gary

According to Morningstar, as of June 2016, the assets in smart beta exchange traded products totaled $490 billion. BlackRock forecasts smart beta using size, value, quality, momentum, and low-volatility will reach $1 trillion by 2020 and $2.4 trillion by 2025. This annual growth rate of 19% is

My last post on Country ETF Rotation generated several ideas of what to test to improve the results. See the original post for the list ETFs being traded. One important test I left out from the original post was a baseline case. An idea applied to all the tests was trading more ETFS. For all tests,

As my co-author, Andrew Berkin, and I(1) explain in our new book, “Your Complete Guide to Factor-Based Investing,”(2) one of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relation between risk and return.

Trend-following strategies – such as managed futures and tactical equity – have historically provided crisis alpha against sustained drawdowns. For short-horizon events (e.g. single day, week, or month events), the effectiveness of these approaches in managing risk is largely based on the luck

In the previous article on Hidden Markov Models it was shown how their application to index returns data could be used as a mechanism for discovering latent "market regimes". The returns of the S&P500 were analysed using the R statistical programming environment. It was seen that

We’ve added a major new feature to our members area: historical allocation analysis. Every strategy that we track now includes a brand new subpage, which is updated daily and devoted to helping members better understand how each asset class has contributed to the strategy’s performance. In this

Limited data is the financial modeler’s biggest challenge. Making assumptions about risk is tough enough under the best of circumstances. All too often it’s even tougher when the historical record is thin. There are several ways to manage this challenge, including bootstrapping, aka resampling

Spx is on a low volatility streak, taking a look at how long the streaks usually last and how the current streak relates to past instances. Also looking at Spx returns once the spell breaks – as do probably most others, i expected volatility to pick up, that does not seem to be the case. Bill Luby

Our epic piece on factors from a few weeks ago is still ringing in our own ears: Are factors even real? Or just data-mining? The conclusion: who knows. We need more data. And more data we can find. To include a recent master’s thesis on nordic country equities, which looks at Size, value,

Over the last twenty five years significant advances have been made in the theory of asset processes and there now exist a variety of mathematical models, many of them computationally tractable, that provide a reasonable representation of their defining characteristics. While the Geometric Brownian

A few words about outliers In statistics, outliers are as thorny topic as it gets. Is it legitimate to treat the observations seen during global financial crisis as outliers? or are those simply a feature of the system, and as such are integral part of a very fat tail distribution? I recently read a