Machine learning and trading is a very interesting subject. It is also a subject where you can spend tons of time writing code and reading papers and then a kid can beat you while playing Mario Kart. In the nexts posts, we are going to talk about: Optimize entries and exits. This and only this could

My friend Ben from A Wealth of Common Sense poses the interesting question, How Should Alternative Investments Be Benchmarked? Please go read his post for a number of interesting thoughts on that topic. In this post, rather than rehash his arguments, I'll go a different direction and will try

The goal of this research is to find various set-ups and exit strategies that could be used for trading the opening range breakouts. The time frames we will be looking at are 10min, 15min and 30min opening range breakouts. We will focus our attention on the very liquid futures markets in particular

Low volatility funds are everywhere. The reasons for their proliferation are clear: Who wouldn’t want to own something with the label “low volatility” and Recent performance has been great. Open the AUM floodgates! But perhaps not all is well in low volatility land. A recent snippet by Josh

In this post I want to discuss ways to make use of signals from relevant market indices in your trading. These signals can add value regardless of whether you trade algorithmically or manually. The techniques described here are one of the most widely applicable in the quantitative analyst's

Last week I organised for Attilio Meucci to give a webinar to members of CFA Singapore and NUS Risk Management Institute on an Introduction to “The Checklist” – Ten Steps for Advanced Risk and Portfolio Management. It served the dual-purpose of a professional development talk and marketing of

I have spent the last few weeks trying to get my recursive sine wave formula for period calculations to work, but try as I might I can only get it to do so under ideal theoretical conditions. Once any significant noise, trend or combination thereof is introduced the calculations explode and give

It’s been about a year since we launched Quantocracy. Over that time, we’ve sent about 450,000 clickthroughs to sites in the quant community, and that doesn’t even include RSS, Twitter, StockTwits and Facebook. To all of the denizens of Quantocracy: a big mahalo, gracias, 謝謝 and thank you

The good thing about using open-source software is the community around it. There are very many R packages online, and recently CRAN package download logs were released. This means we can have a look at the number of downloads for each package, so to get a good feel for their relative popularity. I

The math of losses is not a secret: as losses grow, the return required to hit breakeven grows asymmetrically. At losses below -15%, this relationship is nearly linear; at higher levels, the gap grows dramatically. By reducing the “capture ratio” of an investment approach, we can exploit this

The best quant mashup links for the week ending Saturday, 05/14 as voted by our readers: Machine learning for financial prediction: experimentation with Aronson’s latest work – part 2 [Robot Wealth] Deep Learning with Theano – Part 1: Logistic Regression [Quant Start] How To Compute Turnover

The analysis presented below combines two separate frameworks that were previously outlined: Avoiding Bear Markets to Improve Risk-Adjusted Returns The Case for Put Writing / Further Improving PutWrite Performance The first post outlined how avoiding bear markets (by only holding equities when they

I have been trying to use dimensional analysis to understand asset-pricing problems. In many hard physical problems, it is possible to gain some insight about the functional form of the solution by examining the dimensions of the relevant input variables. In the canonical example of this brand of

Remember 52 Pick-Up? A dubious sibling, usually older, would ask if you wanted to play a game while holding a deck of cards. Emphatically, you agree, only to be showered with the deck of playing cards and told to pick them up. Wikipedia in its infinite wisdom defines 52 pick up as "a game of

Value investing is an investment philosophy that has been extensively discussed and examined at least since the days of Ben Graham, who popularized it as a discipline in the 20s and 30s. While there are some who are dismissive of its advantages as a long-term strategy, the historical evidence is

Over the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image recognition, natural language processing and speech recognition.

In exploratory data analysis, we often need to visualize our data in different formats, in order to gain more understanding about the numbers and the relationship between the parameters. One such wonderful and informative representation is the ‘Heatmap’, which is basically a colored image, the

This post will demonstrate how to take into account turnover when dealing with returns-based data using PerformanceAnalytics and the Return.Portfolio function in R. It will demonstrate this on a basic strategy on the nine sector SPDRs. So, first off, this is in response to a question posed by one

The state of trend following was negative last month, as it was in March. The index is now just above the zero-line for the year, back from nearly the +20% mark a month and a half ago. Please check below for more details. Detailed Results The figures for the month are: April return: -2.35% YTD

Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often

My first post on using machine learning for financial prediction took an in-depth look at various feature selection methods as a data pre-processing step in the quest to mine financial data for profitable patterns. I looked at various methods to identify predictive features including Maximal

Blogging is hard. Quant blogging is even harder. I sometimes hear from bloggers in our community that their motivation to blog has faded. Most begin writing with little expectation, just happy to take a break from crunching numbers to interact with actual humans. Sometimes that optimism wanes

We investigate the biases in the backtested performance of “alternative beta” strategies using a sample of 215 commercially promoted trading strategies across five asset classes. Our results lend support to the cautions in recent literature regarding backtest overfitting and lack of robustness

This blog post is available for download here. Summary When a manager outperforms, it implies that other investors have underperformed. In understanding an investment process, we believe it is critical to understand the source of this outperformance to determine whether it is sustainable or not. We

The increasing popularity of index investing forces us to ask the following questions: What necessary functions does active management perform in a financial system? What is the optimal amount of active management to have in such a system, to ensure that those functions are carried out? If the size

Almost at the very moment I published a post featuring some interesting research by Glabadanidis (“Market Timing With Moving Averages” (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425 – see Yes, You Can Time the Market. How it Works, And Why), several readers wrote

The best quant mashup links for the two weeks ending Saturday, 05/07 as voted by our readers: Get Rich Slowly [Financial Hacker] Backtesting Strategies with R (h/t Algotrading Reddit) [Tim Trice] Quantopian Paper About In vs Out-of-Sample Performance of Trading Algorithms [Quantpedia] The Impact of

I. Trading Strategy Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (RSI Momentum Oscillator). Source: (i) Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets; (ii) Wilder, J. W. (1978). New Concepts in Technical

In the last article in the series we looked at the foundational courses that are often taken in a four-year undergraduate mathematics course. We saw that the major courses were Linear Algebra, Ordinary Differential Equations, Real Analysis and Probability. In the "second year" of our

What does it mean to be the “best” research? For me, this means the most influential in changing my view on the world. So the below list of “best” research represents the research that 1) changed my view of the world 2) helped sharpen my thinking. For context, I’ve been reading source

A big guest on the podcast this week—a man who for many won’t need any introduction, he is; Eric Scott Hunsader. Eric started out as an algorithmic trader in the early 80’s, soon after became a self-taught programmer, and since then he’s written many software applications for financial data.

A reader sent me a link to presentation, Robert Frey – 180 years of Market Drawdowns, about drawdowns and the time that a strategy is underwater. I highly suggest you watch it. I wanted to perform my own analysis on how often a buy and hold strategy on the S&P500 index is making new equity

In recent conversations with current and potential clients, we have seen an uptick in the use of indexed annuities as a tool for risk management. For the uninitiated, Fidelity succinctly described an indexed annuity in a recent blog post: "An indexed annuity is a contract issued and guaranteed

Vanguard founder John Bogle gave a powerful speech last month at the Q Group’s Spring Seminar that lays out the case (again) for favoring basic indexing and shunning complexity in matters of portfolio design and management. As Morningstar’s John Rekenthaler points out, Bogle wields the weapon of

When automated trading strategies are developed and evaluated using backtests on historical pricing data, there exists a tendency to overfit to the past. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of

The prior piece received a much stronger reaction than I expected. The topic is complicated, with ideas that are difficult to adequately express in words, so I’m going use this piece as a follow-up. I’m going to look at the specific example of the Tech Bubble, which is a clear case in which the

This book is designed to not only produce statistics on many of the most common technical patterns in the stock market, but to show actual trades in such scenarios. Test a strategy; reject if results are not promising Apply a range of parameters to strategies for optimization Attempt to kill any

Most trading systems are of the get-rich-quick type. They exploit temporary market inefficiencies and aim for annual returns in the 100% area. They require maintenance, supervision, and regular adaption to market conditions. Their expiration is often accompanied by large losses. But what if you’ve

Another down month for the index, with a slight loss. Two mild down months after two strong up months keep the index in positive territory for 2016. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for April: WSTF-201604-Index And the 12-month

Especially in economics/econometrics, modellers do not believe their models reflect reality as it is. No, the yield curve does NOT follow a three factor Nelson-Siegel model, the relation between a stock and its underlying factors is NOT linear, and volatility does NOT follow a Garch(1,1) process,

U.S. equity index funds have grown dramatically in recent decades, from a negligible $500MM in assets in the early 1980s to a staggering $4T today. The consensus view in the investment community is that this growth is unsustainable. Indexing, after all, is a form of free-riding, and a market can

Because I have been an investment professional for more than 40 years, I sometimes get asked my opinion about the markets. These questions usually come from those who invest without a systematic approach toward investing. Here are some typical questions and answers: Question: How much do you think

Investors love to chase after the “next big thing,” as investment strategies and styles come in and go out of vogue. The latest object of investor infatuation may be a revitalized interest in merger arbitrage. Consider this bloomberg headline: “Hedge Fund Investors Have Fallen in Love with

It is known that data mining algorithms are not perfect and they can fail under certain conditions. K-Means is an example of that triviality but there is a good alternative, K-Medoids. In a previous post, “Machine Learning: A Brief Breakdown” we already mentioned that K-Means is the cluster

In an earlier post I was looking at distance measures for clustering. In a still earlier post I had referred to analyzing hedge fund regulatory data using clustering to try to put the funds into groups by inferred strategy. I had to solve a problem with clustering that has being bothering me for a

The concept of diversification is based on the concept that a trader can reduce his risk exposure by entering several positions at the same time. The success of a traders portfolio is therefore based on reducing risk rather than maximizing returns. A trader should be able to withstand a string of

A few weeks back i wrote a post including the source code for a Monte Carlo simulation function in R. The idea was to randomly sample daily returns produced by a backtest and build a confidence interval distribution of the middle 50% and 90% of returns. Since then Brian Peterson got in touch with me

Conventional momentum strategies rely on 12 months of past returns for portfolio formation. Novy-Marx (2012) shows that the intermediate return momentum strategy formed using only twelve to seven months of returns prior to portfolio formation significantly outperforms the recent return momentum

Summary Many tactically risk-managed strategies use trend following to manage the risk of severe drawdowns, but in sideways markets, like those experienced in 2011 and 2015, trend following ends up lagging the market by buying high and selling low. As with insurance policies or static allocations to

This paper documents a new empirical fact. Long-short anomaly returns are strongly related to the day of the week. Anomalies for which the speculative leg is the short (long) leg experience the highest (lowest) strategy returns on Monday. The exact opposite pattern is observed on Fridays. The