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The Amazing Efficacy of Cluster-based Feature Selection

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 drawdown? This lack of "interpretability" is not just a problem for financial ML, it is a prevalent issue in applying ML to any domain. If you don’t understand the [...]

What is the probability of profit of your next trade? (Introducing PredictNow.Ai)

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 your next trade 0.6? This might be a good initial estimate, but it is also a completely useless number. Let me explain. [...]

Why does our Tail Reaper program work in times of market turmoil?

I generally don't like to write about our investment programs here, since the good folks at the National Futures Association would then have to review my blog posts during their regular audits/examinations of our CPO/CTA. But given the extraordinary market condition we are experiencing, our kind cap intro broker urged me to do so. Hopefully there is enough financial insights here to benefit those who do not wish to invest with us. As the name [...]

US nonfarm employment prediction using RIWI Corp. alternative data

Introduction 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 the classroom to their desks on a Friday morning just before 8:30am EST.) Naturally, there were many efforts in the past trying to predict this number, [...]

Experiments with GANs for Simulating Returns (Guest post)

By Akshay Nautiyal, Quantinsti   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 the stylised facts of return distributions are as follows: The tails of an empirical return distribution are always thick, indicating lucky gains and enormous losses are [...]

Is News Sentiment Still Adding Alpha?

By Ernest Chan and Roger Hunter   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 central quandary of testing pre-processed alternative data is this: is the null result due to the lack of alpha [...]

The most overlooked aspect of algorithmic trading

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 screens or scribbling complicated equations all day long, but by collaborating and managing other talented traders and researchers. If you read the recent interview of Simons, or the book by Lopez [...]

Loss aversion is not a behavioral bias

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 gamble attractive? Would you accept it?" (I have modified the numbers to be more realistic in a financial market setting, but otherwise it [...]

FX Order Flow as a Predictor

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 transactions which occur over-the-counter. Recognizing the profit potential of such data, most FX market operators guard them as their crown jewels, never to be revealed [...]

A novel capital booster: Sports Arbitrage

By Stephen Hope   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 year by taking advantage of large market inefficiencies (leading to arbitrage opportunities) in the sports asset [...]