Quant Beckman
@quantbeckman
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Quantitative Researcher & Dev. | Financial Data Scientist | Machine Learning Engineer | Mathematical Research | Algorithmic Trading Systems
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Joined July 2019
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For those interested in pursuing a career in
#quant
research, here's a guide to the essential skills and foundational knowledge required:
A) Programming language:
-Python
-C++/Rust/Java
-Bash
-SQL/NoSQL
B) Mathematics:
-Linear algebra
-Probability and statistics
-Multivariate
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Did you read "The Man Who Solved the Market" by Gregory Zuckerman hoping to uncover Jim Simons' hidden secrets?
Perhaps you tuned in to Jim discussing Markov Chains, Baum-Welch, and other algorithms in interviews from the early days...
2 facts:
-The first one is a book
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Some lessons learned from my
#quant
career:
-Better data before better models
-Edge before win rate
-Antifragility before robustness
-More data, more alpha
-More complexity, more overfitting
-May your training data is not enough
-May your probabilities are not uniform
-In
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19
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Some realities about portfolio construction and hedge funds:
A) Markowitz's mean-variance optimization:
-It continuous being the most common technique
-It introduces errors and provides misleading results
-High past returns and low
correlations don't persist over time
-There is
5
20
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Implied volatility is calculated by taking the market price of the option, entering it into the Black-Scholes formula, and back-solving for the value of the volatility. Here the drawbacks of doing in that way:
-Assumes constant values for the risk-free rate of return and
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Thorp’s first hedge fund, Princeton Newport Partners, never had a down year. Let's talk about "a man for all markets":
-He discovered an options pricing formula before the Black-Scholes model became public
-He started the first ever quant hedge fund
-He was the first to use
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Companies like Two Sigma, Citadel, AQR, Point72, and others have large numbers of individuals, teams, and diverse profiles. In contrast, small
#trading
firms and family offices typically find their ideal team composition to include the following members:
-A skilled data scientist
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Do you want to know why there is so much secrecy in
#trading
? It's the same reason why the education sector, at best, teaches how to get Beta returns. However, they will never teach you how to generate alpha. Today I'm going to tell you something that might upset more than a few
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Normally, any quant is more concerned with things like signal, identifying inefficiencies, parameter optimization, the new
#ML
model... and a long etc.
But there are other aspects of trading that are also crucial.
Sometimes I wonder what would happen if brokers, instead of
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Everybody knows about
#clustering
. This unsupervised
#ML
technique has many applications in
#trading
(eg. Group features before feature selection). Here a list with the most popular algorithms and the geometry (metric used):
...
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In algorithmic
#trading
a portfolio is a type of ensemble that combines the outputs from multiple systems. The question here is what type of outputs?
-Basically the PnLs generated from each system
-The target is:
•To reduce the total variance
•Better performance than
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10
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During a recent conversation with a fellow
#quant
, we delved into the potential drawbacks of employing a
#Markovian
model for analyzing a limit order book. Here is a summary of our discussion:
...
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The hedge fund industry performed poorly for the past decade, besides management and performance fees have been falling for a long time. You already know what it is, a vehicle that pools capital. Are you interested in creating your own?
First and foremost, there are several
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Many papers in financial
#ML
treat stock returns as i.i.d. data. A simple example is modeling stock prices, Pt, as a random walk:
Pt=Pt−1+ϵt
Where ϵt, the returns, are i.i.d. with mean 0 and variance σ^2 (not necessarily normal). Here some important issues about that:
-Time
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The strategy of Eigenportfolios derived from the principal components of a set of returns and it is based on linear algebra concepts:
-It is constructed by taking an eigenvector and using its components as weights for the assets in the portfolio
-The idea is that each
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There are seven easy steps to follow when creating a
#trading
system. If you're starting out in this field, take a look at the common structure that any algorithmic system should have:
A. Data Ingestion and processing:
-Connect to market data sources or APIs
-Receive and parse
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Peter Brown, chief executive of
#Renaissance
Technologies, after taking over in 2009 said:
"We discovered that many people were doing jobs that could be automated so Renaissance we set out on a massive campaign to automate back-office operations."
This could remove some of the
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Ask most quants or PMs from hedge funds about negatively skewed returns,
and you may be surprised at their aversion to such distribution characteristics.
Negatively skewed returns are typically generated from:
-Strategies that sell volatility
-Strategies that have
positive
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Nowadays "quant" is a very broad term in the industry and the categories have definitely shifted over the years. Especially since the game is different from 90's (more competition, stricter regulation, stronger AI, smarter people...).
Let's see the typical
#quant
profiles:
-Desk
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To make
#trading
systems run as efficiently as possible, you need an IT ecosystem that’s tuned for ultra low-latency communications. The options are:
-First option: A datacenter located in the exchange (Equinix LD4)
-Second option: Your own network switch engineered for high
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Where do you get the data for your models? As a
#quant
, you don't have many choices. The most popular ones are:
-Option 1: You get data from your broker
-Option 2: You get data from free sources
-Option 3: You pay for data
-Option 4: Your organization has data lakes
The most
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Yesterday, I was in a discussion with several members of the trading community about the lack of serious efforts to implement new rule extraction algorithms since 1995. Why is that? Even the current winner of the World Cup
#Trading
Championshipsrelies on this type of algorithm.
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Everyone is familiar with Marcos López de Prado's book, Advances in Financial Machine Learning, right!?
What you find on the internet are different opinions about it:
-Good reviews from academics and quants (probably because they feel part of that community and none of them want
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As a quant you are always immersed in the cutting-edge landscape of financial technology and data science. Your role involves leveraging mathematical models and algorithms, so in the realm of
#ML
and MLOps, one critical subset that garners significant attention is ModelOps:
-It
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The core principles of quantitative
#trading
revolve around utilizing mathematical and statistical models:
-The more structure you impose in the trading model, the fewer parameters you need
-The less parameters, the more efficiently your model cut through noise
...
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Where do you find alpha?
-Alpha is not in the data
-Alpha is not in the model
-Alpha is not in the execution
-Alpha is not in the technology
-Alpha is in relationships
Do you really know what you are mining? Some examples of relationships where one trader gets alpha but not the
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Perhaps this scenario rings a bell: someone shows you a backtest that's perfect in-sample, but once the system goes live, it starts losing money.
Have you considered Noise Test Optimization? This method is widely used by
#quants
to randomly add noise to historical price data:
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Formulaic alphas are mathematical expressions that transform raw financial data into quantitative signals:
-These alphas are constructed using a variety of mathematical operations and are designed to extract meaningful patterns and features
-They are defined as 𝑓(⋅), transforms
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Building
#trading
systems have two basic phases and need two essential roles in MLOps:
-Model development & data scientists
-Model deployment & data engineers
This subfield is based on:
MLOps = DataML + DevOps
Where we have the DataML loop for model development:
-Formulate
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Structured prediction in
#trading
requires searching over a combinatorial number of structures. The goal is to predict market micro-structured & trends rather than individual labels. However there are some issues:
...
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Many
#trading
practitioners may think that a backtest alone is sufficient for transitioning from development to live. Neglecting to assess the strategy from multiple perspectives can result in significant and rapid losses.
First a definition: A backtest is a historical
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Uncertainty pervades every stage of the
#ML
pipeline, including:
-Data preprocessing
-Feature engineering
-Model training
-Model prediction
-Model deployment
In data-driven ML methods, uncertainty arises from:
-The intrinsic ambiguity in the data
-Variations in sampling
-Flawed
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There is a bad habit among quants that can be defined with one word: Parameters... parameters, parameters, and more parameters:
-As the parameters increase, the
#trading
model becomes more and more complex (=overfitting)
-However, not having parameters doesn't help either
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Let's review the Avellaneda-Stoikov market-making algorithm:
-Stoikov addressed the inventory risk problem in
#MM
-It calculates a new reference price for creating buy and sell orders
-It considers the execution probability, price impact, and inventory risk to determine this
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I will try to remove
#HFT
hype.
Nowadays it is based on:
-The use of extremely high-speed infrastructure for generating, routing, and executing orders
-The use of individual data feeds from exchanges as well as co-located servers in order to minimize network and other types of
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When we talk about algorithmic
#trading
there are so many problems to solve. The most typical are related to:
-Market impact and liquidity
-Execution and latency
-Data quality and integrity
-Operational costs
-System reliability
-Portfolio management and turnover
-Quantitative
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Here a summary of the
#Markovitz
's curse. One of the biggest failures in
#portfolio
#optimization
.
#PMs
know about it, but who cares, right? Let's see the pitfalls:
-It computes multiple objective functions, weighted by coefficients where the choice of these weights influences
Some realities about portfolio construction and hedge funds:
A) Markowitz's mean-variance optimization:
-It continuous being the most common technique
-It introduces errors and provides misleading results
-High past returns and low
correlations don't persist over time
-There is
5
20
131
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9
35
Predicting confidence intervals & mean reversion
#trading
strategies are 2 faces of the same coin. Here the most popular methods and some considerations to take into account:
-Bayesian methods: No marginal validity, only scalable with approximate inference, no validation set
...
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9
35
In the initial stages of designing low-latency
#trading
systems, every quant faces similar questions:
-Is more efficient to process labels as integers, floats, or strings (1, 1.0, or '1')?
-What about the features? {1,0,1}, {1.0,0.0,1.0}, 101, '101', etc ?
-May a mix of numbers
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Today is about one simple approach that you can find in any
#ML
and econometric arsenal: Penalized regression**
When running a regression, especially one with many predictors, the results have:
-A tendency to overfit the data
-Reduce out-of-sample predictive properties
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#Trading
model drift can be categorized into several broad categories:
-Concept drift:
a) Posterior class probability P(Y|X)
b) Conditional covariates: P(X|Y)
-Data drift: covariates P(X)
-Label drift: prior probabilities P(Y)
To deal with them you need to understand
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Ask any quant or discretionary trader; they are all focused on how to set up a strategy, how to test it, what data to use, on which assets, risk management... and there is one point that always gets left out, the hierarchy of the strategies and their degree of formalization to
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Let's say we have a bunch of market features represented as a feature vector x = [x1, x2, ... , xd]. Often, many of these features don't add much value in telling apart different classes.
For example, we might have d = 100 features, but we might be able to classify X just about
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A fellow told me once: "You might run out of money, but never out of memory". Though said in jest, there's truth to it:
-Every
#quant
needs to build models that are efficient in memory usage
-Effective memory management is crucial for achieving low latency
-Speed is directly
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The first line of defense against uncertainty is diversification. It is a risk management strategy used to reduce your chances of experiencing large losses and risk exposure:
-Market risk
-Liquidity risk
-Model risk
-Data risk
-Systematic risk
-...
The literature is quite
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It is quite common to find quants overusing ensemble methods, regardless of whether they are junior or senior. However, it is much rarer to see them designing multi-agent
#trading
systems based on cooperation. So let's talk about it, because it's on everyone's lips.
Multi-agent
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#Trading
systems are distributed systems, that means they work by sending and receiving information between independent units (data vendors, brokers, platforms...). This fact has created two interconnected paradigms:
-Message centric --> Order routing
-Data centric --> Modeling
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May one of the hottest topics in
#trading
is eFPGA or embedded FPGA:
-It embeds one or more FPGAs in the form of IP into chips such as ASIC, ASSP, or SoC
-This IP can be licensed for use, and its use is similar to other IPs used in semiconductor design
In other words, an eFPGA
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I read this sentence today: "
#Trading
is less about knowledge and more about being attentive; to the market, to the price, and to oneself..." and I couldn't help but remember the book Fooled by Randomness by Nassim Taleb.
Here are some of my favorite quotes that make this
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Every
#quant
wants to know how risky their system is and what is the chance to lose a part of the account or the entire balance. This situation is called the risk of ruin and it is a well known problem in probability theory.
Many of them talk about maximum drawdown, as if there
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The value formula in the
#trading
business is based on:
(Trackrecord • current ROI)
V = -----------------------------
(Speed • Risk)
Let's talk about the denominator:
-Speed = Faster
-Risk = Riskless
The best way to face this problem is based on a
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You might want to hire for your
#trading
business roles like:
-Data Engineers
-Data Scientists
-Data Architects
That will mean creating a "talent pipeline". But what is a data architect? And how does he fit in this field?
-A data architect is an IT professional role responsible
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Price transformations as features -> Price as labels or targets -> Feed your
#ML
model and voila = Loses
Why? The "only" reason is noise.
In ML, "noise" is defined as:
-Random or unpredictable variations in data
-Disruptions in identifying target patterns or relationships
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Yesterday, I was talking with a fellow about how absurd it is to learn
#trading
in courses. There are interesting topics with extremely high utility but whose application is intended for other contexts:
-In general terms, trading books are no better
-It makes more sense to read a
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The volatility of a portfolio that is compound with multiple independent
#trading
systems can be influenced by various factors. Here are some of them and the reason why you may experience a wide range of non-linear volatility conditions:
-Correlation among systems
-Asset
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Monte Carlo method can be used for
#trading
simulation. The popular applications are:
-For the robustness test
-To add disturbances while keeping the structural shape
-To create specific scenarios and regimes
-To avoid historical data
-To optimize parameters based on noise
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Dichotomic pattern mining is a specific area within the field of data mining that focuses on identifying patterns in data that can be split into two distinct labels {-1,1}:
-This method is particularly useful to extract alpha from events
-The goal is to identify relationships
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Why are traders making more money than others? Because of these four reasons:
-More predictive factors
-More effective models
-Faster execution
-Lower costs
Let's focus on the second aspect. So the question is: Why are models better than others?
Better learning --> Better loss
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Economic data stinks, period. Especially time series data. They’re not like the toy datasets you find in other sectors:
-If your data isn’t I.I.D., forget about modeling it with
#ML
-If it’s not interchangeable, forget about modeling it with ML
Using features based on technical
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