The Algorithmic Trading Handbook: Crypto & Forex Trading Strategies With Trading Algorithms

Algorithmic trading, simplified.
The Algorithmic Trading Handbook breaks down the complex world of automated investing into clear, actionable insights. This essential guide provides a complete overview of algorithmic systems, trading strategies, and the latest technologies shaping modern financial markets.

What You’ll Learn:

  • The fundamentals of algorithmic trading and why it’s transforming markets

  • Key components of a trading algorithm — from entry signals to execution

  • How machine learning trading and data-driven models optimize performance

  • The role of automated trading bots in retail and institutional settings

  • Emerging trends in alternative investments and financial technology

  • How to evaluate or build your own algorithmic trading software

  • Learning about forex trading strategies

Who This Is For:

  • Traders and investors exploring automation for the first time

  • Professionals curious about the power of forex bots, crypto bots, and quantitative strategies

  • Anyone seeking a clear, structured introduction to machine learning in investing

  • Individuals comparing autonomous trading platforms for performance and risk control

Why It Matters:

Algorithmic trading isn’t just for hedge funds anymore. With the right knowledge and tools, anyone can leverage automation to trade faster, smarter, and more consistently — and this handbook shows you how.

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Your starting point for mastering modern, automated investing.

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AI Quantitative
Researcher

Bingham Zhou

Bingham Zhou, CFA, has over 15 years of experience as a quantitative researcher. His expertise spans systematic equity strategies, CTA trend-following, and interest rate proprietary trading in both U.S. and Asian markets. He holds advanced degrees from MIT, Carnegie Mellon, and Yale.

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Quant–Investment Strategist
Greg doscher

Greg Doscher was a CFO for many years who built out many quantitative strategies and investment tools to manage and enhance risk adjusted returns in the company’s pension plan. Prior to joining Nurp, he consolidated his skills in coding and discretionary trading to develop a comprehensive and fully automated algorithmic trading system deployed across 200+ futures markets and cryptocurrencies that encompassed all of the trading strategies he had honed over the last 22 years in finance

Quant–Investment Strategist
Marcin Borratynski

Marcin was Head of Quant IT at the USD 4bn+ CERN Pension Fund, where he spent nearly a decade building quantitative asset allocation systems and implementing algorithmic investment strategies for a multi-asset institutional portfolio.Before joining Nurp Marcin was also Senior Quant Strategist at Evooq, a Swiss-based fund managing four strategies across equities, gold, and equity derivatives.Marcin holds a degree in Computer Science an MBA from the University of Geneva and the Certificate in Quantitative Finance (CQF).

Product Manager

Abhayjit Anand

Abhay has worked with Nurp since 2022. As a Product Strategist, he focuses on building, refining, and commercializing algorithmic trading strategies. He brings seven years of experience in financial trading – combining macro research, technical analysis, quantitative strategy development, and market psychology. Alongside his work at Nurp, Abhay also serves as an Investment Analyst at Orca Capital. Before entering financial markets professionally, he spent eight years at IBM, including three years in the AI & data division as a Delivery Lead managing complex implementation projects.