Mark Best, Developer in Frome, United Kingdom
Mark is available for hire
Hire Mark

Mark Best

Verified Expert  in Engineering

Econometrics Developer

Location
Frome, United Kingdom
Toptal Member Since
October 25, 2017

Mark is truly engrossed in his work of studying people and their behaviors. For the past decade, he's worked on automated trading and time series forecasting—gaining hands-on experience with searching for patterns and opportunities in huge data sets. His statistics background enables him to work in the machine learning space building NLP models for various apps. Mark enjoys working with different data sets and extracting unusual insights from them.

Portfolio

Toptal
Python 3, Machine Learning, Portfolio Analytics, Pandas
Private Trading Company
Forecasting, C++, R, Python, Cryptocurrency, Cryptocurrency APIs, Data Science...
Deep Grey Research
Python 3, C++, Machine Learning, Trading, CME, Git, Data Science...

Experience

Availability

Part-time

Preferred Environment

Git, Eclipse, Windows, Linux

The most amazing...

...thing I've built is a self-tuning automated pricing system for government bond auctions. Clients receive a price based on market conditions and their behavior.

Work Experience

Consultant

2017 - PRESENT
Toptal
  • Built an HFT cryptocurrency trading platform for multiple different exchanges. The strategies were able to manage risk and orders with low latency even in very difficult market conditions.
  • Implemented Black Litterman research papers for portfolio optimization.
  • Built out a crypto backtesting framework on top of Backtrader to test strategies on Poloniex.
Technologies: Python 3, Machine Learning, Portfolio Analytics, Pandas

Quantitive Researcher | Trader

2014 - PRESENT
Private Trading Company
  • Researched and traded black and grey box crypto and forex trading strategies.
  • Built an algorithmic trading framework for evaluating and productizing strategies; the strategies use a range of statistical and machine learning techniques to find and exploit price inefficiencies.
  • Designed API libraries for data collection, cleaning, and processing of market data.
Technologies: Forecasting, C++, R, Python, Cryptocurrency, Cryptocurrency APIs, Data Science, Predictive Modeling, Quantitative Finance, Pandas, Statistics

Execution Quantitative Researcher

2020 - 2020
Deep Grey Research
  • Built HFT execution models for Eurodollar pro-rata books.
  • Implemented C++ versions of Python research code for production deployment.
  • Built a data framework for backtesting strategies on the Eurodollar market in CME.
Technologies: Python 3, C++, Machine Learning, Trading, CME, Git, Data Science, Predictive Modeling, Quantitative Finance, Pandas, Statistics

Quantitaive Programmer | Researcher

2016 - 2016
Lightstone
  • Built a framework for connecting and storing tick data from CME and Eurex in a custom high-performance database.
  • Programmed a high-performance multi-threaded trading simulator in C++ capable of processing up to 5 million messages per second on a single thread.
  • Designed and integrated Python research tools with the C++ simulator to test trading strategies.
  • Traded the finalized algorithms into a third-party trading platform for the live execution of the algorithm in Eurex.
Technologies: Agile Software Development, Forecasting, Boost.Python, C++, Python, Quantitative Finance, Statistics

Quantitative Analyst

2013 - 2014
Credit Suisse
  • Built a toolset for the analysis of client trading performance (trading analytics).
  • Delivered the results via a Tableau framework allowing for the easy distribution, modification, and extension of the analysis.
Technologies: Agile Software Development, Forecasting, Tableau, R, C++, Python, Quantitative Finance, Pandas, Statistics

Associate Director

2012 - 2012
Eladian Partners
  • Traded and researched an aggressive cross-asset, high-frequency strategy designed for global futures markets (cross-asset futures trading). The strategy used genetic programming to combine and calibrate various alphas. This also included research for a passive variant of the strategy for fixed income market making.
  • Traded and researched a statistical arbitrage and market-making strategy for government bond futures on CME and Eurex.
  • Managed the operations and risk of the strategy as well as built and calibrated a fully functional passive simulator to test improvements to the strategy.
Technologies: Agile Software Development, Git, C++, Python, Capital Markets

Quantitative Researcher

2009 - 2011
Citigroup
  • Developed a pricing model for the European government bond business. The model worked by risk factor decomposition, forecasting, and recomposition to generate far better prices than models used pre-2008.
  • Designed and implemented a probabilistic market making spread model to optimize the P&L, market risk, and balance sheet usage of a government request for a quote (RFQ) bond business. The model allows the probability of trading to be implied from the market given a set of attributes.
  • Researched and traded a high-frequency market making strategy for bond futures on Eurex (German bond future market making). The research included the development and calibration of a backtesting environment as well as deriving and testing trading signals.
  • Built the internal matching engine for the City Velocity IRS business. This is more complex than a futures matching engine since the consistency of spread and butterfly books also needed to be ensured.
Technologies: Spring, R, Java, Quantitative Finance, Capital Markets

Master of Science Candidate in Quantitative Finance

2008 - 2009
Cass Business School
  • Studied econometrics; built studies of statistical modeling, prediction, and forecasting.
  • Studied computational statistics: the application of computer option pricing models, Monte Carlo simulations, and other numerical methods.
Technologies: LaTeX, R, MATLAB

Quantitative Programmer

2006 - 2008
Deutsche Bank
  • Implemented components within EMMA (electronic market making algorithm) to analyze recent client positions to forecast market movements and build positions passively via market making.
  • Designed and built trading components for vanilla IRS, curve spreads and butterflies on LiquidityHub, TradeWeb, and ReutersSwap electronic platforms.
  • Refactored and improved the programming frameworks for distribution of bond prices to third-party platforms such as Bloomberg and Reuters.
Technologies: Java, Capital Markets

Performance Analyst

2005 - 2006
Deutsche Bank
  • Managed a team of consultants to improve usability and performance of the Paragon Credit Risk system.
Technologies: Java, Capital Markets

NLP Processor

The code I developed is associated with a study that predicts the likelihood of successfully funding a Kickstarter project. The study specifically analyzes the project descriptions and aims to identify whether keywords significantly impact funding outcomes. Interestingly, the analysis revealed that specific keywords like "Arduino" or "craft beer" are considerably more popular among funded projects compared to their respective base categories.

The code extracts keywords from project descriptions and determines which keywords appear disproportionately more frequently in either the "funded" or "not funded" category. The probability of such disproportional occurrence, evaluated using a Bernoulli distribution, serves as the information ratio for each keyword. A higher information ratio indicates the keyword's greater usefulness in predicting funding success.

Low Latency Messaging Platform for Crypto Currency Trading

https://markrbest.github.io/hft-and-rust/
Rust is an interesting language and one that I think will improve the algorithmic trading space. Most high-frequency trading platforms are low latency asynchronous messaging systems. Rust's type safety and fearless concurrency make building high-performance, highly concurrent, multi-threaded messaging systems without much pain in alternative languages such as C++. Also, given the lack of run time and garbage collector, the system's latency is not only low but doesn't suffer from latency spikes caused by the GC. This was a great project for Rust and one that has really made me fall in love with it.

Paradigms

Data Science, Test-driven Development (TDD), Agile Software Development

Other

Machine Learning, Trading, Forecasting, Simulation Engines, Finance, Predictive Modeling, Quantitative Finance, Econometrics, Communication, Statistics, Capital Markets, Boost.Python, Computer Science, CME, Cryptocurrency, Cryptocurrency APIs, Portfolio Analytics, Algorithmic Trading, Backtesting Trading Strategies, Natural Language Processing (NLP)

Languages

Python, SQL, Python 3, Java, C++, R, Rust

Libraries/APIs

TensorFlow, Pandas

Tools

PyCharm, Eclipse IDE, Git, Subversion (SVN), Excel 2007, Jupyter, MATLAB, LaTeX, Tableau

Platforms

Linux, Eclipse, Windows, Oracle

Storage

MySQL, Kdb+, MongoDB

Frameworks

Flask, Spring

2008 - 2009

Master of Science Degree in Quantitative Finance

Cass Business School - London, UK

2001 - 2004

Bachelor of Science Degree in Computer Science

Warwick University - Warwick, UK

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring