Alan Clement is a Certified Financial Technician, full time independent trader, quantitative trading systems designer and private investment consultant. He is also a councillor with the Australian Technical Analysts Association and contributes to the technical analysis articles for Fairfax press. In

First draft of 'JavaScript for Financial Analysts' Chapter 5. ~ The most difficult thing to do in computer science is naming things. It has made many a programmer go insane. Exhibit A - Android's code names. Cupcake Donut Eclair Froyo Gingerbread Honeycomb Ice Cream Sandwich Jelly

Now for those of you who know me as a blogger might find this post a little unorthodox to my traditional style of writing, however in the spirit of evolution, inspired by a friend of mine Stuart Reid (TuringFinance.com), I will be following some of the tips suggested in the following blog post.

Warren Buffett make it clear why frequent trading damages one’s wealth: “Wall Street makes its money on activity. You make your money on inactivity.” (source) But is activity always a bad thing? Implicit in Buffett’s quote is an assumption that frictional costs outweigh any benefits of

Three bits of site news for both readers and webmasters: For readers: Our new “filter mashup” feature For webmasters: Our policy on voting for your own link and vote padding For webmasters: Quantocracy badge For readers: Our new “filter mashup” feature Each site on our blogroll in the

I’ve previously written about a bi-annual seasonality pattern in US equity markets: https://rrspstrategy.wordpress.com/2014/05/16/bi-annual-seasonality/ The quarterly average market (Mkt-RF) returns from 1950 to present are shown below (data from Ken French’s library). Quarters 1-4 are even

You’re a trader with serious ambitions to use algorithmic methods. You already have an idea to be converted to an algorithm. Only problem: You do not know to read or write code. So you hire a contract coder. A guy who’s paid for delivering a script that you can drop in your MT4, Ninja,

Here is an interesting working paper on the use of fundamental analysis in stock selection. The authors take a “machine learning” approach to building out statistical fair-value Ben Graham and David Dodd would be proud. Of course, this isn’t surprising if you’ve read our treatise on

Over the last five blog posts we looked at the backtest results for 4120 options straddles sold on the S&P 500 Index (SPX) at 59 days-to-expiration (DTE). Eight different loss approaches were tested on these straddles. On top of these eight loss approaches, tests were conducted with no profit

In this post we take intraday backtesting with PortfolioEffectHFT package one step further by adding a simple signal-based rebalancing rule. Using this rule we will create two trading portfolios – a high frequency strategy portfolio and a low frequency portfolio and compare them with each other in

The still-in-progress 'Javascript for Financial Analysts' book is now up on Leanpub. The goal of the book is to help financial analysts automate their daily tasks by using Javascript in the browser. Not only that, but do it elegantly. Giving people a viable alternative to Excel is a lofty

Since the publication of Bill James' seminal work, Baseball Abstract, and the rise to stardom for the Oakland A's, Sports Analytics - the application of statistics to competitive sports - has been (and still is) a prominent topic within the industry. Thus, it is only reasonable for

This article was motivated by a provocative discussion with a thoughtful RIA. Let’s call him Harry. Harry expressed some disappointment with the performance of Global Tactical Asset Allocation (GTAA) strategies over the past few years relative to some popular tactical U.S. sector rotation funds.

Simulating an equity strategy is difficult. Much more so than simulating a futures strategy. There’s a lot more moving parts to care about. Much more complexity. All too often, I see articles and books that just skipped the difficult parts. Either they didn’t understand it, or they hoped it

First draft of 'JavaScript for Financial Analysts' Chapter 4. ~ Up until now we have introduced a handful of new concepts which needed just two words of vocabulary - map and filter. Fold however, is a new piece of vocabulary and one of the most powerful concepts in computer science rolled

Diego Amaya, Peter Christoffersen, Kris Jacobs and Aurelio Vasquez, authors of the new paper, “Does Realized Skewness Predict the Cross-Section of Equity Returns?”, examined higher moments of volatility, skewness and kurtosis to determine if they have provided incremental explanatory power in

Here we highlight an interesting working paper titled “Absolute Strength: Exploring Momentum in Stock Returns” by Gulen and Petkova (2015). The abstract is the following: We document a new pattern in stock returns that we call absolute strength momentum. Stocks that have significantly increased

Summary Due diligence often focuses on the three Ps: people, philosophy, and process. There is an important 4th P: performance. When a portfolio has a consistent process, performance can ebb and flow as the strategy goes in or out of favor. For example, value stocks have been out of favor for 8

A few months ago we highlighted a surprising result: cheap high-quality stocks were getting crushed by expensive junk stocks. The spread at the end of June was around 18%. Here is the chart from the old post (details on construction are in the original post): cheap hiqh quality versus expensive low

Broadly speaking, complex models can achieve great predictive accuracy. Nonetheless, a winner in a kaggle competition is required only to attach a code for the replication of the winning result. She is not required to teach anyone the built-in elements of his model which gives the specific edge over

Over the last few weeks I have been looking at using Runge-Kutta methods for the creation of features, but I have decided to give up on this for now simply because I think I have found a better way to accomplish what I want. I was alerted to this possible approach by this post over at

The results in this paper won’t surprise most who are regular readers, but the paper below does a nice job explaining things in a simple way. For more advanced asset allocation methods that use momentum one can check out past blogs on the subject here, here , and here. Momentum Investing &

“The quantmod package for R is designed to assist the quantitative trader in the development, testing, and deployment of statistically based trading models.” It is a rapid prototyping environment where enthusiasts can explore various technical indicators with minimum effort. It offers charting

Professors Eugene Fama and Kenneth French have a new paper, “Incremental Variables and the Investment Opportunity Set,” that provides some important insights for investors considering funds designed to supply exposure to multiple factors, or styles, of investing. In their study, they note:

While the stock market is open on Monday, banks, schools, government offices, and the bond market are closed. In past years with the bond market closed, the stock market has done quite well on Columbus Day. Of course the most famous Columbus Day rally was in 2008 when the market gained over 11%

Abstract: Sorting stocks based on value and momentum factors historically has led to outperformance over the broad US stock market. However, any long-only strategy is subject to similar volatility and drawdowns as the S&P 500. Drawdowns of 50%, or even 60-90% make implementation of a stock

This post looks at the results of adding additional value & quality factors to the previous momentum model mentioned here. Instead of purchasing stocks on momentum alone; I added a combined value score which aggregates stock rankings per valuation multiples such ROIC, EV/EBITDA, EV/FCF and so

The best quant mashup links for the week ending Saturday, 10/10 as voted by our readers: ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R [Quant Start] More On The Perils Of Statistical Hypothesis Testing – Part I [Price Action Lab] How to be a Quant [Turing Finance] Is

David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering. He is author of “Evidence Based Technical Analysis” and his most recent book “Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments” is an

Academic financial quantitative analysis began in earnest in the 1970's as a response to the Efficient Market Hypothesis (EMH). EMH proponent believed that you can't beat the market with stock picking because everything about a stock is already known by the market. As a test of EMH,

Quant trading is a redundant term: all trading is quant trading. Whether your an arbitrageur or a technician, fund manager or high frequency trader, you are basing your trading on the quantitative analysis of the market, you just might not realize it. A lot of times there seems to be an artificial

Reproducibility in scientific research In the past year or two, the reproducibility of research results in finance and economics has come under serious question. If it is any comfort, similar difficulties have emerged in numerous other scientific fields. In 2011, a team of Bayer researchers

Mkt-RF returns in ‘even years’ sum to zero over the last 50+ years (data from Ken French’s library). This could be a spurious result although the stats suggest otherwise. odd-even-years Is this result statistically significant? Applying Student’s t-test gives a statistic of 2.3, i.e. mean

Clients often ask for strategies that trade on very short time frames. Some are possibly inspired by “I just made $2000 in 5 minutes” stories on trader forums. Others have heard of High Frequency Trading and concluded that the higher the frequency, the better must be the trading. Zorro

This post deals with an impossible-to-implement statistical arbitrage strategy using VXX and XIV. The strategy is simple: if the average daily return of VXX and XIV was positive, short both of them at the close. This strategy makes two assumptions of varying dubiousness: that one can “observe the

Several readers have inquired over the years about the performance of Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death), which included the Seasonal Timing Strategy. This strategy combines “the market’s best average calendar entry [October 16] and exit

Early October: somewhere the leaves are turning brilliant colors, temperatures are cooling down and that back to school feeling is in the air. And for more people than ever before, it is going to seem to be a good time to commit to really learning R. I have some suggestions for R courses below, but

Yesterday Quantopian rolled out an update which added a few very nice features. All you really need to know is that you can now design your own custom filters and the 500 stock universe limit has been lifted. The following model uses these new additions to rank and buy 50 stocks according to their

The VIX and VXO have dropped sharply over the last 7 days. These are measures of options premium. When they are falling it means premium is declining, and options traders are less fearful. The study below looks for times when the VXO becomes stretched more than 20% below its 10-day moving average. I

In this article I want to show you how to apply all of the knowledge gained in the previous time series analysis posts to a trading strategy on the S&P500 US stock market index. We will see that by combining the ARIMA and GARCH models we can significantly outperform a "Buy-and-Hold"

Today I was trying to investigate short interest in the Energy sector: the group as a whole has rallied hard over the last few days and I suspect a short covering rally is at play, so some testing is in order. Much to my dismay, my searches didn’t return an easy way to do this in Python. Lots of

Standard mean-variance optimized momentum outperforms the traditional equally weighted momentum strategy if the expected return vector used reflects momentum's top and bottom only characteristic. This top and bottom only characteristic is the phenomenon that only the stocks in the top decile of

Since writing about my experience writing the CFA Level I exam in June, I have received many emails from people interested in finding out how to become a quant. To some extent, this post will answer that question. That said, this post is actually not about how to become a quant, it is about how to

Modelling of financial markets is usually undertaken using stochastic process. Stochastic processes are collection of random variables indexed, for our purposes, by time. Examples of stochastic processes used in finance include GBM, OU, Heston Model and Jump Diffusion processes. For a more

How Do Investors Measure Risk? Jonathan Berk and Jules H. Van Binsbergen October 1, 2015 We infer which risk model investors use by looking at their capital allocation decisions. We find that investors adjust for risk using the beta of the Capital Asset Pricing Model (CAPM). Extensions to the CAPM

This post will review the DIY Financial Advisor book, which I thought was a very solid read, and especially pertinent to those who are more beginners at investing (especially systematic investing). While it isn’t exactly perfect, it’s about as excellent a primer on investing as one will find out

Back-testing of a trading strategy can be implemented in four stages. Getting the historical data Formulate the trading strategy and specify the rules Execute the strategy on the historical data Evaluate performance metrics In this post, we will back-test our trading strategy in R. The quantmod

Selecting the indicators to use is one of the most important and difficult aspects of building a successful strategy. Not only are there thousands of different indicators, but most indicators have numerous settings which amounts to virtually limitless indicator combinations. Clearly testing every

Last month I tested random rebalancing strategies based on dates and found that searching for optimal points through time to reset asset allocation may not be terribly productive after all. Let’s continue to probe this line of analysis by reviewing the results of randomly changing asset weights

I've had a request for the code used to do the optimisations in chapter 4 of my book "Systematic Trading" (the 'one-period' and 'bootstrapping' methods; there isn't much point in including code to the 'handcrafted' method as it's supposed to