These are some of the questions that popular forums get inundated with from aspiring novice algorithmic traders around the world. A good starting point for a wannabe trader would be to pick up a good book, immerse oneself, and absorb all that the book has to offer. This post details down the core

This is a test of a tactical bond strategy from Paul Novell of Investing for a Living. The model rotates among a broad basket of bond asset classes based on rules similar to Gary Antonacci’s Dual Momentum. Results from 1970, net of fees, follow. Read more about our backtests or let AllocateSmartly

In the last post, I compared three systems that traded the same instrument (SPY) in different ways, and also compared the combination of the three systems. Combining those systems reduced risk, which allowed us to increase our position size (either through more cash or using leverage). We could then

Opex week in January is one that the market has seen some struggles over the last 18 years. Below is the list of January op-ex weeks from 1999 – 2016 with their full week performance results. There have been 7 years in which January op-ex week occurred in conjunction with Martin Luther King Day.

This article looks at iron condors (IC) entered at 45 days to expiration (DTE). The introduction to this series, here, describes the different variations of SPX iron condors (IC) and exits that were tested. As mentioned in the 38 DTE IC results summary post, these tests covered 9 IC variations, with

In this paper we consider the question of how to improve the efficacy of strategies designed to capture factor premiums in equity markets and, in particular, from the value, quality, low risk and momentum factors. We consider a number of portfolio construction approaches designed to capture factor

Ben Carlson at Ritholtz Asset Management reminds us that backtesting offers no shortcuts to investment nirvana. As he correctly points out, there are numerous shortcomings in the art/science of reconstructing the historical results of an investment strategy. But it’s also true that backtesting, if

Last year was not a good year for trend following, with many commenting that the performance for the strategy has been declining over the last few years. We decided to look at the performance of the Wisdom State of Trend Following index on a long timeframe, to let the results speak over the long

Last week’s commentary highlighted why we believe traditionally built portfolios may face return headwinds going forward. Traditionally built stock/bond allocations also exhibit extremely high risk concentrations. Non-traditional exposures, now available as low-cost ETFs, can help introduce

In previous articles the concept of cointegration was considered. It was shown how cointegrated pairs of equities or ETFs could lead to profitable mean-reverting trading opportunities. Two specific tests were outlined–the Cointegrated Augmented Dickey-Fuller (CADF) test and the Johansen

The next two blog posts will explore the basics of the statistical arbitrage strategies outlined in Ernest Chan’s book, Algorithmic Trading: Winning Strategies and Their Rationale. In the first post we will construct mean reverting time series data from cointegrated ETF pairs. The two pairs we

January is here again and market commentators are already telling stories about the so-called January Effect. Some articles (examples here and here) are saying the effect is an illusion, while others are claiming the effect can help you make some profits (examples here and here). Before we dig into

Skewness is a statistical measure of how returns behave in the tails of a probability distribution. Wikipedia has a more robust definition of skewness with some good visuals here. If an investment (e.g., stocks) has negative skewness this means that the extreme returns are more likely to be negative

Bloomberg Gadfly's Lisa Abramowicz (follow her on twitter here) outlined in a recent piece The Credit Boom that Just Won't Die the insatiable demand for investment grade credit. Last month, bankers and investors told Bloomberg's Claire Boston that they expected U.S. investment-grade

Asset returns based on low frequency prices (e.g. end-of-day quotes) are still dominating modern portfolio analysis. To make portfolio metrics more relevant intraday and improve the precision of estimates, new data frequency needs to be explored. In this presentation we demonstrate how using high

The guest for this episode is Jack Vogel from Alpha Architect, a quantitative asset management and consulting firm. Jack has published a number of papers on SSRN and also co-authored a couple of books including “Quantitative Momentum: a practitioners guide to building a momentum-based stock

Existing research has documented cross-sectional seasonality of stock returns – the periodic outperformance of certain stocks relative to others during the same calendar month, weekday, or pre-holiday periods. A model based on the differential sensitivity of stocks to investor mood explains these

POST OUTLINE Motivation Get Data Default Plot with Recession Shading Add Chart Titles, Axis Labels, Fancy Legend, Horizontal Line Format X and Y Axis Tick Labels Change Font and Add Data Markers Add Annotations Add Logo/Watermarks MOTIVATION Since I started this blog a few years ago, one of my

I am very excited to finally share some of my research exploring Meucci’s (Meucci (2005)) portfolio optimization methods, and how the resulting portfolios compare to the use of historical data. For those unfamiliar with Attilio Meucci, he runs an annual Advanced Risk and Portfolio Managment

Which color do you like better? Green or brown? I’m partial to the green curve myself. That green curve comes from writing puts…sort of. Writing puts can be a lower volatility play that makes you money in choppy or flat markets, falls more softly in down markets, and seriously under-performs

Two bits of site news: First, after 4+ years of running this site mostly gratis, I’ve decided to dabble in adding advertisements, so expect to begin seeing the first baby steps with a handful of ads from Google. I’ve tried to keep the ads as unobtrusive as possible and my hope is that your

After reviewing the 2016 performance of trend-following (-18.15%), its unclear why anyone would mention the word “trend following” in a public forum. But we’ll give it a whirl anyway… The comedian Victor Borge once famously observed, “Santa Claus has the right idea – visit people only

My last post on using PercentRank to measure mean reversion proved very popular. A reader looked at the trades and wondered if it would be best to exit after five days because the average trade with longer holds was a loser. I am surprised I have not covered this topic before. Background Early in

Happy new year to all readers! With best wishes for your trading in the coming twelve months, which — I’m sure you’ll agree — will prove interesting from several perspectives. We start the year by looking back at the performance of trend following over the year just passed. Unsurprisingly

The ninth annual R/Finance conference for applied finance using R will be held on May 19 and 20, 2017 in Chicago, IL, USA at the University of Illinois at Chicago. The conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing,

Happy New Year! To kick off the year, we want to share a white paper we penned mid-December containing our views on building a modern strategic asset allocation. The white paper covers: Why we believe tailwinds from the last 30 years are turning into headwinds for traditionally allocated stock-bond

December 2016 Trend Following: UP +1.38% / 2016: -18.15% December closed 2016 on a slight positive note, avoiding six straight months of negative returns for our State of Trend Following index. An inflection point was felt in the markets towards the close of the year, but this was obviously not

The 2017 American Finance Association conference is kicking off later this week in Chicago. If you haven’t been before — check it out. The conference is the biggest meeting of top-tier academic researchers on the planet. You can review all the research being presented at the following link. Some

Imagine that we’re trying to predict the cross-section of expected returns, and we’ve got a sneaking suspicion that x might be a good predictor. So, we regress today’s returns on x to see if our hunch is right, \begin{align*} r_{n,t} = \hat{\mu}_{\text{OLS}} + \hat{\beta}_{\text{OLS}} \cdot

In the last post, 38 DTE Iron Condor Results Summary, I showed the backtest results from 97,416 iron condor (IC) trades. All of those test results were based on weekly expiration data at 38 days to expiration (DTE). In this post, we'll look at a few key metrics and how those metrics differ

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 posting, I compared the prices of US active mutual fund to estimates of future pre-fee excess returns. In summary, I found that the annual expenses of most active funds met or exceeded a generous estimate of their potential before-fee excess returns. That is, many funds look like

Happy new year! I have noticed that my quantitative posts get the most readership and discussion. So this year, I’ll be posting a lot more research and will start the year off by exploring momentum’s robustness. There are two good ways to test the robustness of a rules-based trading strategy:

Overfitting the data is a serious problem when constructing financial models. One way to guard against this is to have lots of data. This helps you determine if your results are robust by seeing how they hold up over different time periods. But this assumes the underlying market dynamics remain

Statistical Arbitrage , A.K.A StatArb is a pair trading strategy that invloves buying and selling a pair of stocks based on a underlying correlation between them. This correlation usually exist in a given sector or competitors, for example Pepsi (PEP) and Coca-Cola (KO) is a pretty popular pair. The

Happy new year! It’s that time again, when everyone with a blog does a wrap up of the previous year. Here’s my look-back. Many of you follow along with the “+/-30% per quarter wider-market breadth indicator”. Which is too much of a mouthful, so I’ve humbly named it after myself instead. I

This code tries to show how to use genetic algorithms to create a simple trading strategy. It is intended as a proof of concept, rather than trying to provide a ready-to-use strategy. The historical data used is the following: SP500.dat. It is the closing prices of S&P 500 from 2006/12/18 to

This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity

A recent post on Bloomberg website entitled Rising VIX Paints Doubt on S&P 500 Rally pointed out an interesting observation: While the S&P 500 Index rose to an all-time high for a second day, the advance was accompanied by a gain in an options-derived gauge of trader stress that usually

Happy New Year, one day early. Here’s wishing 2017 is successful for you in whichever way you define success. Aren’t calendars wonderful? A couple of days ago, up pops a reminder on my calendar to revisit a post I did a year ago. At the very beginning of 2016, I wrote a post on whether yearly

As we approach the end of 2016, the Shiller CAPE 10 stands at about 28, a level rarely exceeded (with the exception of in the late-1990s technology-driven bull market). Such heights cause many investors to worry about what current valuations may mean for future expected returns. I’ll try to

This is a test of the “Golden Butterfly”, the homegrown buy & hold strategy from PortfolioCharts.com. PortfolioCharts is to buy & hold what AllocateSmartly is to tactical asset allocation, an independent and unbiased catalog of strategy performance, so when they put their stamp of

Quantopian & Quantpedia Trading Strategy Series continues ... Now with a 4th article, again written by Matthew Lee, focused on Cross-Sectional Equity Mean Reversion (Strategy #13):

Another year. Looking at my google analytics reports I can’t help but wonder how is it that I am so bad in predicting which posts would catch audience attention. Anyhow, top three for 2016 are: On the 60/40 portfolio mix The case for Regime-Switching GARCH Most popular machine learning R packages

On behalf of the entire Newfound Research team, we would like to wish you and yours a happy holiday season. We treat this weekly research commentary as a sacred part of our investment process. We continue to be honored and humbled by the vast and growing number of readers it reaches, a sign of the

As 2016 nears its finish line, here we are with the list of recommended reading on our blog with the top-rated blog posts, as voted by you! Enjoy the last few days doing what you love most! Read on. System Architecture of Algorithmic Trading This one is straight out of a lecture in the curriculum of

Ever wondered if you can design a profitable trading strategy by trading volatility ETFs ? Well, yes you can. Those ETFs are highly ineffective vehicles on a long term investment horizon. However short term strategies have shown to be a rewarding way to trade these ETFs. Before we move onto strategy

There are instances where I "borrow" an idea from someone (actually... most of my posts were at a minimum inspired by someone else). In this case, I am stealing the initial concept from Ryan Detrick who posted the following chart of annual U.S. stock returns going back ~200 years as there

If you couldn’t tell from our recent monster commodity futures post, we’ve been thinking a lot about futures recently. The futures research area is relatively “fresh,” and a lot more exciting than hacking through equity stock selection research where we already understand the basic answer

When talking about quantitative trading, there are a large number of indicators and operators we can use as a buy/sell rule. But apart from deciding what indicator we will follow, the most important part would be setting the correct parameters. So, one method we can use to find adequate parameters