Benchmarks are indispensable for investment analytics. The challenge is picking a relevant one. The stakes are high because the wrong benchmark can be worse than none at all. The good news is that the potential for error can be dramatically reduced by choosing a set of random benchmarks that are

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

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

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

In this piece I examine various way in which an investor can think about their active market timing decisions, often labeled with the innocuous term “rebalancing.” Rebalancing a portfolio is the finance version of “eat your vegetables” — the advice is taken as gospel, but very few people

Folks from Quantopian did a new independent analysis of a strategy we have in our database. An article is written by Jeremy Muhia and is focused on Momentum and Reversal Combined with Volatility Effect in Stocks (Strategy #155): https://www.quantopian.com/posts/do-momentum-and-reversals-coexist

This article reviews the backtest results for iron condors (IC) entered at 73 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

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

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

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,

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

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

Machine learning has seen a huge amount of growth over recent years with the increase in available data and processing power. It’s 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

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

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

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

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

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

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

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

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

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