This post goes into an in depth analysis of the commitment of traders report and its usefulness for predicting price movements. The CFTC collects data on the daily positions of large participants in the commodity markets. The data is aggregated in the weekly COT report which is published every

In a previous article the monthly rebalance feature of the open-source backtesting library QSTrader was demonstrated on a simplistic equities/bonds ETF mix portfolio. In this article new streamlined code will be presented to allow straightforward modification of the portfolio weightings. In

The benefit of including commodities in individual investor portfolios is often up for debate, focusing on aspects such as expected returns, volatility, access, and diversification. While we think that commodities can add value if the risks are understood, many passive ETFs that offer commodity

We talked about the defacto standard for message communication in our previous article on FIX protocol. “The Financial Information Exchange (FIX) Protocol is a message standard developed to facilitate the electronic exchange of information related to securities transactions. It is intended for use

Financial research has uncovered many relationships between investment factors and stock returns. For investors, an important question is whether the publication of this research can impact the future size of factor premiums. Asking this question is crucial on two fronts. First, if anomalies are the

This post explores applying NEAT to trading the S&P. The learned strategy significantly out performs buying and holding both in and out of sample. Features: A key part of any machine learning problem is defining the features and ensuring that they’re normalised in some fashion. The features

Would it not be great to have the models for Protective Asset Allocation (PAA) and Global Protective Momentum (GPM) in Excel, so you can run your own backtests without AmiBroker? And not being limited to a pre-defined universe? Actually, now you can. Based on a foundation by InvestExel, Denis

The performance of trend following strategies can be ascribed to the difference between long-term and short-term realized variance. We revisit this general result and show that it holds for various definitions of trend strategies. This explains the positive convexity of the aggregate performance of

With more than 70% of the trading volumes in the US markets being automated, the rise of the algorithms seem more inevitable than ever before. The mechanical jobs are shifting to computers and only those who can tame the machines can rule the trade markets. Equipping oneself with the skills of

Eugene Fama’s Efficient Market Hypothesis argues that because stock prices follow a “random walk,” future price behavior cannot be predicted. In his seminal paper, “Random Walks in Stock Market Prices,” he explains the relationship between prices and fundamentals: If the random-walk theory

I. Trading Strategy Developer: John Ehlers and Ric Way. Source: Ehlers, J., Way, R. (2010). Zero Lag (Well, Almost). Concept: Trend following trading strategy based on moving average filters. Research Goal: To verify performance of the Zero Lag Moving Average Filter. Specification: Table 1. Results:

This is a test of Dick Stoken’s Active Combined Asset (ACA) strategy from his book Survival of the Fittest for Investors. This tactical asset allocation strategy uses price channel breakouts to choose between pairs of opposing risk and defensive asset classes. Results from 1988, net of transaction

Equal weighting asset allocation doesn’t look encouraging as a portfolio-design tool, based on last week’s preliminary analysis. Can we salvage equal weighting by expanding the opportunity set with a more granular mix of funds? In a word, no. As we’ll see, equal weighting across asset classes

The debate for the best way to build a multi-factor portfolio – mixed or integrated – rages on. Last week we explored whether the argument held that integrated portfolios are more capital efficient than mixed portfolios in realized return data for several multi-factor ETFs. This week we explore

I have been looking forward to Wes Gray and Jack Vogel's new book, Quantitative Momentum. It is the only book besides my own Dual Momentum that relies on academic research to develop systematic momentum strategies. My book uses a macro approach of applying momentum to indices and asset classes.

From a seasonal standpoint option expiration week is often a pretty good week for the market. October is one of those months where it has been especially good over the years. The study below examines performance during October op-ex week. 2016-10-17 image1 I decided to exclude 2008 because action

Many institutional global asset managers are constrained by the need to invest in long-only strategies with zero or minimal leverage. This means that their strategies are often highly correlated to "the market" (usually the S&P500 index). While it is difficult to minimise this

Algorithmic Trading in Indian Markets using Python We have told you why Python is one of the preferred languages to do algo trading in this article. We have also told you how algorithmic trading in India. Since we are gearing up for our webinar on Trading in Indian Markets using Python (Not

Why is it that some traders can create trading strategies that perform well in real-time trading while other strategies fall apart? How do some traders keep their trading strategies fresh and adaptive to market conditions while other strategies just stop working altogether? Robert Pardo, president

Since we’ve released our new book, Quantitative Momentum, we’ve received a handful of basic questions related to momentum–specifically as it relates to stock selection. At this point, the so-called “momentum effect” has occupied academic researchers for several decades. Researchers have

In my previous post, I started the implementation of WPF program entry point and the View ViewModel interaction basics. The goal of this “Client chapter” is to get a client application which connects to the basic server application I covered earlier. This post continues the basic infrastructure

Unlike any other business, algorithmic trading has the advantage of being independent of marketing, sales, customers and all those things that need the ‘pretty people’ to make it run. Also, you get almost instant feedback on how good you are in your business. For anyone who is numerically

Pairs trading is a venerable trading strategy. There is agreement that it worked fine in the far past. But it is less clear if it still profitable today. In this working paper the universe of eligible pairs is defined by the holdings of a given ETF. It is shown that the stocks must be from ETFs

A search for all equity ETF's available to U.S. investors in Bloomberg leads to a list of 969 candidates, a surprisingly large number of options for a relatively new investment vehicle. Given that most focus on large capitalization stocks here in the U.S. (not all, but most), this means that

This post will be an in-depth review of Alpha Architect’s Quantitative Momentum book. Overall, in my opinion, the book is terrific for those that are practitioners in fund management in the individual equity space, and still contains ideas worth thinking about outside of that space. However, the

I’m going to start this post by saying that it makes no sense for anyone to pay management fees to get a return stream that is highly correlated to any existing asset class. Unfortunately, many actively managed funds fall in this category. There’s two reasons for this. One, you can replicate

So you’re a trend-follower. Great. But here is a question: What do you invest in when your rules suggest “risk off?” Many investors suggest low duration cash or t-bills. Seems reasonable. But is it optimal? Perhaps we should invest in longer duration risk-off assets like 10-yr bonds? We

Yesterday’s post on equal weighting for asset allocation motivated a reader to point out that equal weighting’s tendency to outperform in equity portfolios is due to frequent rebalancing events. A passively managed market-cap-weighted portfolio, by contrast, is allowed to drift, with weights

When I worked at Bridgewater Associates, I regularly came across marketing materials about the theory behind risk parity and evidence that risk parity portfolios perform better than traditional portfolios on a risk-adjusted basis. The theory and evidence was quite convincing. The story goes that Ray

Last Tuesday I flew out to New York City, USA to give a talk at the Quantopian NYC Meetup and moderate a panel on "Programming Wars" at the Trading Show New York 2016. Both events were extremely interesting and I met a lot of great people. I want to write a brief summary of the trip as it

My previous post explained a bit the overall concept of this client development series. The essence of MVVM pattern was discussed, as well the base line of the project structure was created. Now I pick it up and extend it by some implementations step by step until we get a running version of the

The above chart was generated in Python. It's the result of backtesting a basic algorithmic trading strategy that makes use of the Relative Strength Index (RSI). In this tutorial I'll walk through implementing and graphing a simple strategy. The tutorial should provide a framework that

Equal weighting has an encouraging record as a design choice for earning a moderately higher premium in the stock market compared with the conventional weighting system of holding shares in proportion to their market-capitalization weight. A leading real-world example: the Guggenheim S&P 500

Another down month for the State of Trend Following report, taking the year in the red a bit further for 2016. Please check below for more details. Detailed Results The figures for the month are: September return: -1.64% YTD return: -7.07% Below is the chart displaying individual system results

The dot-com bubble of the late 90s was a wild time in the stock market. Internet stocks were trading through the roof, tech IPOs were a practically daily experience, and people quit their jobs to make millions day trading. And why not? Even a day trading chimp could make money in a market that went

I will be in Texas next week giving presentations. Click the links below for more details. I hope to see some readers there. October 17, 2016 Austin Market Technicians Association For more information see https://www.mta.org/event-registration/austin-chapter-meeting-featuring-cesar-alvarez/ October

Since the publication in 1992 of Eugene Fama and Kenneth French’s paper “The Cross-Section of Expected Stock Returns,” the traditional way to think about diversification has been to view portfolios as a collection of asset classes. However, we now have a nontraditional way to think about

The debate for the best way to build a multi-factor portfolio – mixed or integrated – rages on. FTSE Russell published a video supporting their choice of an integrated approach, arguing that by using the same dollar to target multiple factors at once, their portfolio makes more efficient use of

During the past few decades, newly discovered stock anomalies have been embarrassing existing factor models, such as the Fama-French 3-factor. As many readers know, each long or short “leg” of these popular long/short factor portfolios is generally constructed by ranking stocks on one specific

Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends [1] The predictive modeling in trading is a modeling process wherein we predict the

We examine a new method for identifying close economic substitutes in the context of relative value arbitrage. We show that close economic substitutes correspond to a special case of cointegration whereby individual prices have approximately the same exposure to a common nonstationary factor. A

A number of readers have asked us to test the two aggressive versions of Meb Faber’s GTAA strategy from his seminal paper: A Quantitative Approach to Tactical Asset Allocation. I can’t understate the importance that Faber’s work has had in popularizing the idea of TAA with the general

Ilya Kipnis at QuantStrat TradeR reminds us that the Hidden Markov Model (HMM), which can be a powerful tool for detecting regime change in markets and macro, has its limitations and pitfalls. In particular, Kipnis reports that HMM’s value as a prediction tool for the stock market is dubious.

A second successive strong down month for the State of Trend Following index, bringing the Year-To-Date performance further in the red as we enter the last quarter of the year. The current drawdown is getting closer to the max Drawdown (24% vs. 32%) which, as we highlighted last month, can be a good

One of the biggest problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicted a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50 years the most

This post will be about attempting to use the Depmix package for online state prediction. While the depmix package performs admirably when it comes to describing the states of the past, when used for one-step-ahead prediction, under the assumption that tomorrow's state will be identical to

The long wait is over. Our newest book–Quantitative Momentum–is finally here. After 2 years of research review, results replication, reverse engineering, internal idea generation, writing, editing, and final publication, we have a final product. We think the book will help fulfill our firm

In this short five minute video I will answer the following questions: Have you used HV10/HV100 ratio? Have you found any value in it? When trading multiple strategies, how do you decide what percentage to allocate to each. What do you think about asset allocation ETF strategies, like Ray Dalio’s

Smart Beta funds are hot. According to ETF.com, more than half of the 150 funds launched in 2016 implemented smart beta strategies. For the year to June 30, 2016, ETFGI’s most recent data show that assets in smart beta funds have a five-year annual compound growth rate of 31.3 percent. And, low

This market timing model compares the performance of two different types of stock groups over time and provides signals when to invest or not to invest in the stock market. When the performance of the Hi-Beta stocks becomes lower than, or equal to Lo-Beta stocks the model exits the stock market and