Last month we launched Meta Strategy, our own smart approach to combining the 40+ tactical asset allocation strategies tracked on our site. Each month, Meta selects 10 strategies and then trades their combined asset allocation. Members can follow Meta in near-real time. Each month’s 10 strategies

In this article we’ll look into a real options trading strategy, like the strategies that we code for clients. This one however is based on a system from a trading book. As mentioned before, options trading books often contain systems that really work – which can not be said about stock or forex

This article series provides an opportunity to move towards more interactive analysis. My plan is to integrate more Jupyter notebooks and Github repos into my research/publishing workflow. For datasets that are too big to share through github I will provide a download link both here and in the

Most researchers create factor portfolios by taking the top & bottom 30% of stocks, which results in large portfolios Portfolios can be reduced, but firm risks start influencing factor returns with too few stocks Most investors are likely better of buying factor products then building factor

Using historical data from the options market along with realized subsequent returns, we can translate risk-neutral probabilities into real-world probabilities. Market implied probabilities are risk-neutral probabilities derived from the derivatives market. They incorporate both the probability of

Short selling frictions cannot explain the persistence of seven prominent stock anomalies. Long-only investing is robust and profitable and can be further enhanced by using a synthetic short. Moreover, portfolios restricted to stocks that are easy to short sell continue to have large and significant

One of the tenets of modern portfolio theory is that you cannot generally beat the market after transaction costs. Yet academic researchers have shown that momentum consistently beats the market. Other factors besides momentum have also cast doubt on the efficacy of the efficient market hypothesis.

Last month I presented a detailed roadmap for the redevelopment of QSTrader, our open-source systematic trading simulation engine. Today I want to discuss our progress in the month since that article was published and what still remains to be completed prior to the initial 0.1.0 alpha release.

This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world

This is a simple reference article for readers that might wonder where I get/got my options data from. In this regard I would like to shout out the contributors to the pandas-datareader, without their efforts this process would be much more complex. Intuitive Explanation So this code consists of

Developer: Larry Williams (“All in one: Price, volume and open interest”); R. D. Donchian (Breakout Channels). Concept: Trading strategy based on price breakouts confirmed by POIV (Price, Open Interest, and Volume) filters. Research Question: Can combined filters improve price breakouts?

Assigning weights to portfolio assets is challenging when we have to consider multiple constraints. Asset allocation may be seen as a constraint satisfaction problem (CSP), and some algorithms allow us to define our own restrictions and look for an optimal weight distribution. In this post, we will

We present some empirical evidence for short volatility strategies and for the cyclical pattern of their P&L. The cyclical pattern of the short volatility strategies produces an alpha in good times but collapses to the beta in bad times. We introduce a factor model with risk-aversion to explain

Risk parity portfolios attempt to diversify across asset classes and strategies by risk contribution as opposed to dollar allocation. Implementing a risk parity strategy requires making a number of important construction decisions. A key question we have to answer is “How are we going to measure

Allocate Smartly tests and tracks asset allocation strategies sourced from books, academic papers and other publications. Most of the strategies that we test though never make it on to this site. There are a variety of reasons that might be, but often it’s simply because they’re not very good.

The year 2017 might be regarded as the year where cryptocurrencies became mainstream. Investment funds focused on cryptocurrencies were launched, the CBOE announced Bitcoin futures for the end of the year and some everyday expenses like booking flights at Expedia can be paid in Bitcoins.

I have long been frustrated by the lack of an "out of the box" solution for plotting OHLC candlestick charts natively in Octave, the closest solution I know being the highlow plot function from the financial package ( which does not yet implement a candle function ) over at Octave

By considering the same risk measure, ϱ, applied to two or more portfolios (credit loss distributions, profit-and-loss distributions, etc.) one desires to have a subadditivity property in place: ϱ(X1+X2)≤ϱ(X1)+ϱ(X2) i.e. meaning that two combined portfolios should never be more risky than the

Optimizing the parameters of a trading strategy via backtesting has one major problem: there are typically not enough historical trades to achieve statistical significance. Whatever optimal parameters one found are likely to suffer from data snooping bias, and there may be nothing optimal about them

On most mainstream finance websites, a good chunk of the stories discuss the FED and where interest rates are going. Intuitively, this makes sense: The FED is arguably an extremely influential component of U.S. economy. But how do markets respond to the FED? Is the response rational, irrational, or

One of the inherent challenges in designing strategies is the need to specify certain parameters. Volatility parameters tend to work fairly well regardless of lookback, but there are inherent trade-offs to using short-term versus longer-term volatility. The former is more responsive to current

A reader emailed me about testing a weekly mean reversion rotation strategy on S&P500 stocks. My first thought was, why had I not done this type of test before? The very first strategy that I worked on with Larry Connors was this type of strategy. The strategy I will be testing today is a

The current equity bull market has not been kind to non-U.S. allocations. At a recent conference I attended, the term ‘TINA: there is no alternative’ came up more than once in the context of allocating investor portfolios. It captures the collective sentiment that equities, despite a massive

Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. This is with the intention that ensembles will achieve better prediction accuracy than individual classifiers. In machine learning

This post will be about comparing strategies from the paper “Easy Volatility Investing”, along with a demonstration of R’s table.Drawdowns command. First off, before going further, while I think the execution assumptions found in EVI don’t lend the strategies well to actual live trading

The subject of asset bubbles and market crashes has fascinated me for more than 20 years. As an options market maker for Susquehanna International Group (“SIG”), extreme price movements were a daily source of concern. I sat next to Jeff Yass for years and watched him manage option positions in

Equity hedge fund returns have been disappointing over the last 14 years An exposure analysis shows no structural factor exposure, but frequent factor rotation Multi-factor long-short products are an interesting alternative, depending on the fee level INTRODUCTION Hedge fund assets reached an

Peng Wang and Jon Spinney A version of this paper can be found here Want to read our summaries of academic finance papers? Check out our Academic Research Insight category. What are the research questions? Investors following a purely quantitative approach to asset allocation are often left with

We find that when measured in terms of dollar-turnover, and once beta-neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of 5.1, the “Cold-Minus-Hot” (CMH) anomaly is certainly not less significant than other well-known factors such as Value or

We’ve read a number of outlooks and commentaries lately from firms arguing for investors to take a tactical tilt away from U.S. equities and towards International equities. The logic behind this tilt is largely driven by relative valuations: international equities appear significantly cheaper than

And we’re back for the final episode in this 3-part series on building Mean Reversion strategies with Cesar Alvarez from Alvarez Quant Trading. In the 1st episode we discussed the goal of Mean Reversion trading, how to select a trading universe, a number of effective techniques to measuring Mean

Adaptive Asset Allocation (AAA) is based on the Nobel Prize winning portfolio theory of Markowitz (1952) AAA combines asset’s momentum, volatilities, and cross-correlations for building diversified investment portfolios In a tactical application AAA exploits momentum for crash detection and

So, it’s been some time since you’ve been thinking of making more money out of your successful trading strategy. And why should you not? After all, you’ve worked hard for it and there is only a small % of people who are successful in this business. The idea is to add more funds to your trading

Regression analysis is used all the time to assess how a portfolio “loads” on certain factors. The most common factor loadings examined are the market, size, value, and momentum factors. This can be an informative exercise, and there are nice tools online, such as portfolio visualizer, which

Investing in a Multi-Asset Multi-Factor World Alexandar Cherkezov (Invesco), et al. August 31, 2017 In this article, we advance the use of factor investing across multiple asset classes. It turns out that style factors well established in the equity domain – such as value, momentum or quality –

Why are overnight periods riskier? For one, you can’t use stops to limit your risk. But more importantly, the distribution of overnight returns has far more extreme negative returns than the intraday or close-to-close periods. Let’s take a look at some stats on close-to-open, open-to-close, and

Once we are familiar with the theory surrounding Risk Parity, it’s time to put the strategy into practice and try out the algorithm for ourselves. We discover how it works, analyse the strategy and create our own portfolios. Thanks to the posts written by T.Fuertes and mplanaslasa we already know

This paper presents a new dataset for the annual risk-free rate in both nominal and real terms going back to the 13th century. On this basis, we establish for the first time a long-term comparative investigation of ‘bond bull markets’. It is shown that the global risk-free rate in July 2016

Last month saw a strong upwards performance from the State of Trend Following index, single-handedly reversing half the negative performance for the year, which still stands close to double-digit territory. Please check below for more details. Detailed Results The figures for the month are: October

We track a wide range of tactical asset allocation strategies in near real-time (41 and counting), which members can combine into their own custom portfolios. We provide members with a wealth of data to understand how each strategy fits into a coherent trading plan, but we don’t tell members the

In previous installments of replicating indexes I profiled the style-analysis methodology and presented an example using a hedge fund index. Now let’s turn to a strategy of replicating the S&P 500 Index with a handful of stocks that are considered socially responsible investments (SRI).

When you come in contact with cryptocurrencies, e.g. Bitcoin (BTC), you quickly realise that there is no single price of BTC at any given moment. The reason is that Bitcoin is traded on different markets. It can be worth more on Coinbase exchange and less on Kraken exchange. In particular, the

The differences between correlation and cointegration can often be confusing. While there are some helpful explanations online, I wasn’t satisfied with the visual examples. When looking at a plot of an actual pair of symbols where the correlation and cointegration test results differ, it can be

Changes in earnings are comprised of the expected earnings number plus any seasonal component of earnings. If the seasonal component is expected then it should not affect prices in an efficient market. However, unusual returns have been documented surrounding earnings announcements at the seasonal

Long/short portfolios are helpful tools for quantifying the value-add of portfolio changes, especially for active strategies. In the context of fees, we can isolate the implicit fee of the manager’s active decisions (active share) relative to a benchmark and ask ourselves whether we think that

Integrated Value, Growth & Quality portfolios generated attractive returns year-to-date 2017 Sorting stocks on several characteristics results in relatively smooth performance Mitigates the issue of factor timing, but not of factor selection INTRODUCTION Year-to-date 2017 is shaping up as a

Auquan recently concluded another version of QuantQuest, and this time, we had a lot of people attempt Machine Learning with our problems. It was good learning for both us and them (hopefully!). This post is inspired by our observations of some common caveats and pitfalls during the competition when

October 2017 Trend Following: UP +7.12% / YTD: -15.39% Below is the full State of Trend Following report as of last month, which saw our trend following index post a strong positive performance. Performance is hypothetical. Chart for October: Wisdom State of Trend Following - October 2017 And the

Often in financial markets, as in our daily life, we imitate the decisions of predecessors, instead of analysing available information and making our own decisions. This decision imitation could lead to collective hysteria, and investment calls may be influenced by these panicked situations. Imagine