We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012) observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the

Previously on this blog, we wrote about a machine-learning-based parameter optimization technique we invented, called Conditional Parameter Optimization (CPO). It appeared to work well on optimizing the operating parameters of trading strategies, but increasingly, we found that its greatest power

Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in

Every trader knows that there are market regimes that are favorable to their strategies, and other regimes that are not. Some regimes are obvious, like bull vs bear markets, calm vs choppy markets, etc. These regimes affect many strategies and portfolios (unless they are market-neutral or

One major impediment to widespread adoption of machine learning (ML) in investment management is their black-box nature: how would you explain to an investor why the machine makes a certain prediction? What's the intuition behind a certain ML trading strategy? How would you explain a major

What is the probability of profit of your next trade? You would think every trader can answer this simple question. Say you look at your historical trades (live or backtest) and count the winners and losers, and come up with a percentage of winning trades, say 60%. Is the probability of profit of

The monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors alike. (When I was teaching a class at a well-known proprietary trading firm, the traders suddenly ran out of

Simulating returns using either the traditional closed-form equations or probabilistic models like Monte Carlo has been the standard practice to match them against empirical observations from stock, bond and other financial time-series data. (See Chan and Ng, 2017 and Lopez de Prado, 2018.) Some of

Nowadays it is nearly impossible to step into a quant trading conference without being bombarded with flyers from data vendors and panel discussions on news sentiment. Our team at QTS has made a vigorous effort in the past trying to extract value from such data, with indifferent results. But the

Many algorithmic traders justifiably worship the legends of our industry, people like Jim Simons, David Shaw, or Peter Muller, but there is one aspect of their greatness most traders have overlooked. They have built their businesses and vast wealth not just by sitting in front of their trading

In his famous book "Thinking, Fast and Slow", the Nobel laureate Daniel Kahneman described one common example of a behavioral finance bias: "You are offered a gamble on the toss of a [fair] coin. If the coin shows tails, you lose $100. If the coin shows heads, you win $110. Is this

Order flow is signed trade size, and it has long been known to be predictive of future price changes. (See Lyons, 2001, or Chan, 2017.) The problem, however, is that it is often quite difficult or expensive to obtain such data, whether historical or live. This is especially true for foreign exchange

As traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less. This article describes how one can take a small amount of capital and multiply it as much as 10 fold in one

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

Exploring alternative datasets to augment financial trading models is currently the hot trend among the quantitative community. With so much social media data out there, its place in financial models has become a popular research discussion. Surely the stock market’s performance influences the

I’ve long been interested in the behavior of corporate insiders and how their actions may impact their company’s stock. I had done some research on this in the past, albeit in a very low-tech way using mostly Excel. It’s a highly compelling subject, intuitively aligned with a company’s

I have been troubled by the following paradox in the past few years. If a stock's log returns (i.e. change in log price per unit time) follow a Gaussian distribution, and if its net returns (i.e. percent change in price per unit time) have mean m and standard distribution s, then many finance

One of the perennial problems in building trading models is the spareness of data and the attendant danger of overfitting. Fortunately, there are systematic methods of dealing with both ends of the problem. These methods are well-known in machine learning, though most traditional machine learning

Much has been written about the Post-Earnings Announcement Drift (PEAD) strategy (see, for example, my book), but less was written about pre-earnings announcement strategies. That changed recently with the publication of two papers. Just as with PEAD, these pre-announcement strategies do not make

I wrote in a previous article about why we should backtest even end-of-day (daily) strategies with intraday quote data. Otherwise, the performance of such strategies can be inflated. Here is another brilliant example that I came across recently. Consider the oil futures ETF USO and its evil twin,

Everybody know that volatility depends on the measurement frequency: the standard deviation of 5-minute returns is different from that of daily returns. To be precise, if z is the log price, then volatility, sampled at intervals of τ, is volatility(τ)=√(Var(z(t)-z(t-τ))) where Var means taking

Predicting volatility is a very old topic. Every finance student has been taught to use the GARCH model for that. But like most things we learned in school, we don't necessarily expect them to be useful in practice, or to work well out-of-sample. (When was the last time you need to use calculus

I have been a big fan of options trader and author Euan Sinclair for a long time. I have cited his highly readable and influential book Option Trading in my own work, and it is always within easy reach from my desk. His more recent book Volatility Trading is another must-read. I ran into him at the

Most time series techniques such as the ADF test for stationarity, Johansen test for cointegration, or ARIMA model for returns prediction, assume that our data points are collected at regular intervals. In traders' parlance, it assumes bar data with fixed bar length. It is easy to see that this

(This post is based on the talk of the same title I gave at Quantopian's NYC conference which commenced at 3.14.15 9:26:54. Do these numbers remind you of something?)A correct backtest of a trading strategy requires accurate historical data. This isn't controversial. Historical data that

In our drive to extract alphas from a variety of non-price data, we came across this old-fashioned source: Commitments of Traders (COT) on futures. This indicator is well-known to futures traders since 1923 (see www.cmegroup.com/education/files/COT_FBD_Update_2012-4-26.pdf), but there are often

By Yang GaoEstimize is an online-community utilizing 'wisdom of crowds' to offer intelligence about market. It contains a wide range of crowd-sourced estimates from over 4,500 buy-side, sell-side and individual analysts. Studies (from Deustche Bank and Rice University among others) show