151 Trading Strategies by Zura Kakushadze & Juan Andrés Serur

151 Trading Strategies is a technically oriented survey of systematic trading methods across a broad range of financial markets and asset classes. Kakushadze and Serur define a trading strategy as a set of instructions for establishing specified asset holdings at predefined times, noting that strategies may pursue outright profit, hedging, risk transfer, arbitrage, or other portfolio objectives. The book therefore extends well beyond conventional directional trading and includes relative-value, market-neutral, hedging, volatility, factor, arbitrage, and machine-learning approaches.

The authors organize the material across options, equities, ETFs, fixed income, indexes, volatility, foreign exchange, commodities, futures, structured assets, convertibles, tax arbitrage, distressed assets, real estate, cash, cryptocurrencies, global macro, and infrastructure. Many strategies are presented mathematically, with formulas defining portfolio construction, payoffs, break-even points, maximum profits or losses, hedging relationships, factor exposures, or optimization procedures. The resulting framework is closer to a quantitative strategy reference than to a discretionary trading manual.

The stock chapter illustrates the breadth of the methodology through price and earnings momentum, value, low-volatility and implied-volatility factors, multifactor portfolios, residual momentum, pairs trading, several forms of mean reversion, moving-average systems, support and resistance, event-driven trading, machine learning, statistical arbitrage, market making, and alpha combinations. Similar treatment is applied to other markets, including volatility carry, FX carry, futures trend following, fixed-income relative value, convertible arbitrage, commodity factors, cryptocurrency models, and global macro strategies.

The authors also emphasize that financial markets are adaptive rather than static. Strategies that once worked can weaken or disappear as market structure, technology, competition, transaction costs, and participant behavior change. Accordingly, the book is positioned as a structured survey of strategies practitioners have considered rather than a claim that all 151 methods will remain profitable indefinitely.

An appendix extends the framework into implementation by providing R source code for illustrating out-of-sample backtesting. The authors explicitly distinguish legitimate historical testing from look-ahead bias and discuss expected returns, universe construction, risk models, portfolio optimization, trading costs, dollar neutrality, and performance measurement. The book itself does not provide empirical backtests for all strategies; its purpose is to describe the methods and direct readers toward the underlying literature.

✅ What You’ll Learn

• How trading strategies can be structured for directional profit, hedging, arbitrage, relative value, income, volatility exposure, and market neutrality.
• How common option structures are constructed and how their payoff, break-even, maximum-profit, and maximum-loss characteristics are calculated.
• How quantitative equity strategies use momentum, value, volatility, mean reversion, pairs relationships, moving averages, statistical arbitrage, and multifactor signals.
• How sector rotation, trend following, mean reversion, and leveraged-product techniques can be applied to ETFs.
• How fixed-income strategies use duration, convexity, yield-curve positioning, butterfly structures, carry, value, and spread arbitrage.
• How volatility, FX, commodity, and futures strategies incorporate carry, momentum, term structure, hedging pressure, relative value, and risk premia.
• How machine-learning approaches are applied to stocks and cryptocurrencies.
• How arbitrage concepts extend to indexes, convertibles, structured products, tax relationships, and other specialized markets.
• How global macro strategies can incorporate economic fundamentals, inflation, fixed income, and scheduled economic announcements.
• How out-of-sample backtesting, risk models, portfolio constraints, transaction costs, and optimization fit into quantitative strategy research.

💡 Key Benefits

• Provides a single structured reference covering a wide cross-section of systematic trading methods and asset classes.
• Connects trading concepts with mathematical definitions instead of relying solely on qualitative descriptions.
• Helps readers compare directional, market-neutral, arbitrage, factor, hedging, and volatility-based approaches within one framework.
• Provides extensive references for readers who want to investigate the empirical and academic literature behind individual strategies.
• Introduces practical quantitative-research issues including backtest design, look-ahead bias, portfolio optimization, risk modeling, and trading costs.
• Supports strategy research across both traditional markets and newer areas such as machine learning and cryptocurrencies.

👤 Who This Book Is For

• Intermediate to advanced traders with familiarity with financial markets, derivatives, portfolio concepts, and quantitative reasoning.
• Quantitative traders and researchers looking for a broad catalogue of systematic strategies across multiple asset classes.
• Finance students, academics, analysts, and practitioners seeking mathematically specified examples of trading and hedging methodologies.
• Algorithmic traders who want a reference spanning factor models, arbitrage, machine learning, portfolio optimization, and out-of-sample testing.

📚 Table of Contents

• Options
• Stocks
• Exchange-Traded Funds (ETFs)
• Fixed Income
• Indexes
• Volatility
• Foreign Exchange (FX)
• Commodities
• Futures
• Structured Assets
• Convertibles
• Tax Arbitrage
• Miscellaneous Assets
• Distressed Assets
• Real Estate
• Cash
• Cryptocurrencies
• Global Macro
• Infrastructure