In Part 1 we looked at using TimescaleDB and SQLAlchemy to build a relational database model of the Sharadar equity dataset with a Python object model sitting on top. The initial cut of this project ran on my desktop and broke up each of the dataset loads into a simple script that I could run in

The purpose of seasonal adjustment is to remove seasonal and calendar effects from economic time series. It is a common procedure but also a complex one, with side effects. Seasonal adjustment has two essential stages. The first accounts for deterministic effects by means of regression and selects a

In the prior post I showed results for a new equities strategy which uses a combination of signals to create and risk manage a high-momentum portfolio. Further investigation revealed that I had neglected on a couple of fronts: failed to account for dividends (which are substantial) some data issues

The premise of using either time-series momentum or “trend-following” using moving averages is the same only the math differs very slightly (see Which Trend Is Your Friend? by AQR): using some fixed lookback you can time market cycles and capture more upside than downside and therefore improve

Japanese traders invented candle patterns in the 17th century. Some traders believe that those patterns are still valid. But alas, no one yet got rich with them. Still, trading book authors are all the time inventing new patterns, in hope to find one that is really superior to randomly entering

I’ve posted and updated the “Twas 3 Nights Before Christmas” study on the blog here several times since 2008. The study will kick in at the close today (12/22). This year I will again show the Nasdaq version of the study. While all the major indices have performed well during this period, the

This example investigates the performance of R in comparison to Julia language. Additionally shows how to easily call Julia inside R code. With that being said, we will load JuliaCall library that enables us to do so. Alternatively, there is also XRJulia library available. library(JuliaCall) It is

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Using daily adjusted close data from 20201118 to 20201218 for Dow 30 stocks, we compute correlation coefficients, apply a threshold of 0.8 to find similar stocks, and

Is there a better way than the Fed model to measure relative attractiveness of equities and bonds. In his October 2020 paper entitled “Towards a Better Fed Model”, Raymond Micaletti examines seven Fed Model alternatives, each comparing a 10-year forward annualized estimate of equity returns to

Suppose, during a nightly TV weather broadcast, that a reporter presented forecasts by persons, with no credentials in mathematical meteorology, who based their analysis on eyeballing a few charts and graphs. If anyone took such amateur forecasts seriously, when a severe storm was approaching,

I have been thinking about the recent institutional buying that has propelled the price of bitcoin to stratospheric levels; in particular considering how one might detect some of this interest early. Bitcoin and crypto in general is quite interesting in that at some level, due to the decentralized

In his famous 1981 paper, “The Relationship Between Return and Market Value of Common Stocks,” Rolf Banz found that small firms have higher risk-adjusted returns than large firms. This was one of the first major challenges to the capital asset pricing model (CAPM) and market efficiency in

A subscriber asked about a strategy that switches between an equal-weighted portfolio of Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM) when the S&P 500 Index is above its 200-day simple moving average (SMA200) and an equal-weighted portfolio of SPDR Gold Shares (GLD) and iShares 20+

One of the most appealing areas of Artificial Intelligence is Reinforcement Learning, for its applicability to a variety of areas. It can be applied to different kinds of problems, in the present article we will analyze an interesting one: Reinforcement Learning for trading strategies. Reinforcement

We’re returning to our portfolio discussion after detours into topics on the put-write index and non-linear correlations. We’ll be investigating alternative methods to analyze, quantify, and mitigate risk, including risk-constrained optimization, a topic that figures large in factor research.

The rise of intangibles has been increasingly used as an argument for the poor performance of the value factor However, this idea is not supported by data The type of market environment is marginally more useful for explaining the value factor performance INTRODUCTION Great storytelling may be the

I have written many times over the years about the bullish tendency of the market during opex week in December. I used to refer to it as “The Most Wonderful Week of the Year”. And it was…up until 2018. So below is an updated look at the stats and profit curve for owning SPX from the close of

Theory and plausibility suggest that precious metal prices benefit from inflation and negative real interest rates. This makes gold, silver, platinum, and palladium natural candidates for hedges against inflationary monetary policy. Long-term empirical evidence supports the inflation-precious metal

We would like to present our newest own-research about factor allocation and passive versus active strategies clash. However, respecting our blog format, this version is largely shortened and we invite you to read the full version. Introduction In the equity market, there are two types of investing:

The most common question I get is how do you determine that a strategy is no longer working. It is also the question that I don’t have a good answer for. I have written several posts about this: Trading the Equity Curve, How to turn off a strategy using historical volatility, Broken Strategy or

Investing is never easy, but some times are easier than others. Buying US government bonds at 10%+ yields when inflation was steadily decreasing in the 1980s was probably less worrying than buying them today at negative real yields. In today’s world, bonds may have largely lost their

Several years ago, I wrote about some experimentation I’d done with data mining for predictive features from financial data. The article has had several tens of thousands of views and nearly 100 comments. I think the popularity of the article lay in its demonstration of various tools and modeling

Survey data and asset prices can be combined to estimate high-frequency growth expectations. This is a specific form of nowcasting that implicitly captures all types of news on the economy, not just official data releases. Methods for estimation include the Kalman filter, MIDAS regression, and

As most of you know I have a regular(ish) gig talking on the Top Traders Unplugged systematic investor podcast, every month or so with Niels Kaastrup-Larsen and Moritz Seibert. Anyway on the most recent episode we got chatting about whether open or closed equity should matter when trading a

This short article investigates the rebalancing premium that investors may expect from risk parity portfolios¹. It is offered as an appendix to the paper, “Risk Parity: Methods and Measures of Success”. We define rebalancing premium as the difference between the compound return on a portfolio,

Robert Novy-Marx’s 2013 paper “ The Other Side of Value: The Gross Profitability Premium” not only provided investors with new insights into the cross-section of stock returns but also helped further explain some of Warren Buffett’s superior performance. Novy-Marx built upon a 2006 paper,

What happens after stocks suffer large one-day losses? This post finds that the proverbial "dead cat bounce" occurs overnight and is followed by continued losses the next day. Targeting international markets, I explore a trading strategy that aims to profit from the losses that follow a

There are no “right” answers when it comes to financial markets. There are generally trade-offs to all decisions. For example, stocking picking can be incredible and crush every other investment approach; but stock picking can also be horrible. Similiarily, 100% systematic investing,

I have been sharing examples of simple real-time trading research on my Twitter account. I do this kind of thing a lot in the training program of our trading group – and I’m sharing in the hope that it might also help a wider audience. Here’s a piece of analysis I did recently on a really

Decision Trees are one of the best known supervised classification methods. As explained in previous posts, “A decision tree is a way of representing knowledge obtained in the inductive learning process. The space is split using a set of conditions, and the resulting structure is the tree“ A

Below are some more out of sample plots for the Temporal Clustering solutions of the EUR_USD forex pair for the week just gone. The details of how these solutions are derived is explained in my previous post, Temporal Clustering on Real Prices. First is Tuesday's solution where the major (blue

As equity trading moves to less regulated markets and off of exchanges across the world, mutual fund families have increasingly taken advantage of this opportunity to reallocate trades. Fund families are able to offset opposite trades of their affiliated funds within an internal market referred to

I have a couple of equities strategies that I will start trading shortly, and I want to understand the risk from all angles. Towards this end I try to utilize both market signals and exogenous unstructured data to minimize surprise and maximize selection or prediction efficiency. In thinking about

A major problem of indicator-based strategies is that most indicators produce more or less noisy output, resulting in false signals. The faster the indicator reacts on market situations, the noisier is it usually. In the S&C December issue, John Ehlers proposed a de-noising technology based on

A while ago I posted an article titled “INVESTMENT PORTFOLIO OPTIMISATION WITH PYTHON – REVISITED” which dealt with the process of calculating the optimal asset weightings for a portfolio according to the classic Markowitz “mean-variance” approach. With this method we aim to maximise our

In the past have been a HFT market maker for FX and other traditional instruments, however have not investigated exchange-based market making in Crypto. As I have proprietary signals applicable for Crypto, thought it would be worthwhile to investigate the difficulty of market making on crypto

I’m a big fan of Ernie Chan’s quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them.

Given alternative data, machine learning, and AI advances, systematic should beat discretionary investing However, the performance of systematic and discretionary equity market neutral hedge funds has largely been the same since 2009 Both were also correlated to the stock market, offered low

Having now had time to run the code shown in my previous post, Temporal Clustering, part 3, in this post I want to show the results on real prices. Firstly, I have written two functions in Octave to identify market turning points and each function takes as input an n_bar argument which determines

Finance literature is abundant with theories. As academics, we like to think these theories foster behaviors and choices by investors, which in turn translate into asset prices. To test these theories, scholars typically try to infer the validity of these assumptions by examining outcomes. The

In “Stock Market Valuation and the 2020’s in R” I investigated whether the CAPE ratio could forecast the future trajectory of earnings and/or stock returns over the period 1980-2019. From this study, we made a couple of observations: The CAPE ratio cannot be used to forecast future earnings

The time around Thanksgiving has shown some strong tendencies over the years – both bullish and bearish. I have discussed them a number of times over the years. In the updated table below I show SPX performance results based on the day of the week around Thanksgiving. The bottom row is the Monday

There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures markets over plausible lookback windows, under consideration of a leverage constraint, and uses estimated assets under management as a scale factor.

What is survivorship bias, and why should you care about it? This post explains how survivorship bias can trick you into drawing faulty conclusions from your research, and what you need to know to avoid being tricked. What is survivorship bias? Equities datasets are said to have survivorship bias if

Running a marathon is similar to being a value investor, especially in recent years where it seems more like an ultra-marathon. Both activities are painful experiences that require the ability to suffer and persist through physically and emotionally straining times. Moreover, value investors always

If you regularly read this blog then you know I am not one to jump on the “AI Bandwagon”, being quickly weary of anyone flashing the “It’s Artificial Intelligence” joker card. Don’t get me wrong, I understand it is a sexy term I, but to me it always feels a bit like a sales pitch. If the

The S&P index committee recently announced that Tesla, already one of the biggest stocks listed in the country, would be included in the S&P 500. Here’s the press release: Due to TSLA’s size, it was widely expected to have entered the S&P 500 index much earlier – but S&P has

After many years there are many evidences that the low volatility anomaly works in stock markets. We have also mentioned this topic a long time ago to analysis the costs of it. This anomaly says stocks with less price variability deliver higher returns, contrary to everyone’s belief, which expects

Here I share a refreshing idea from the paper “Asymmetric correlations of equity portfolios” which was published in the Journal of financial Economics, a top tier journal in this field. The question is how much the observed conditional correlation on the downside (say) differs from the

There are many ways to find stocks with similar behavior based on how one defines similarity and the data used. In this article we use a 12 period channel where, for each period, we have (current adjusted close price – minimum value)/(maximum value – minimum value). Maximum and minimum values