151 Trading Strategies

We provide detailed descriptions, including over 550 mathematical formulas, for over 150 trading strategies across a host of asset classes (and trading styles). This includes stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility (as an asset class), real estate, distressed assets, cash, cryptocurrencies, miscellany (such as weather, energy, inflation), global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms (such as artificial neural networks, Bayes, k-nearest neighbors). We also give: source code for illustrating out-of-sample backtesting with explanatory notes; around 2,000 bibliographic references; and over 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical. This is the complete version of the book.

151 Trading Strategies

Features trading strategies for a variety of asset classes and trading styles including stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage

The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.

Zura Kakushadze received his Ph.D. in theoretical physics from Cornell University, USA at 23, was a Postdoctoral Fellow at Harvard University, USA and an Assistant Professor at C.N. Yang Institute for Theoretical Physics at Stony Brook University, USA. He received an Alfred P. Sloan Foundation Fellowship in 2001. After expanding into quantitative finance, he was a Director at RBC Capital Markets, Managing Director at WorldQuant, Executive Vice President and substantial shareholder at Revere Data (now part of FactSet), and Adjunct Professor at the University of Connecticut, USA. Currently he is the President and CEO of Quantigic Solutions and a Full Professor at Free University of Tbilisi, Georgia. He has over 17 years of hands-on experience in quantitative trading and finance, 130+ publications in physics, finance, cancer research and other fields, 3,400+ citations and h-index 30+, 130,000+ downloads on SSRN, and over a quarter million followers on LinkedIn.

Juan Andrés Serur holds a Master's Degree in Finance from the University of CEMA, Argentina. With more than 6 years of experience in trading in the stock market, he currently works as a quantitative analyst and strategist in an Argentine quantitative asset management firm and as a financial consultant for large corporations. In addition, he serves as the Academic Secretary of the Master of Finance Program at the University of CEMA, where he teaches undergraduate and postgraduate computational finance courses as an Assistant Professor. In 2016 he won the First Prize in an Argentine Capital Markets Simulation Challenge for Universities and Professional Institutions.

Both of the above plots can be reproduced in the attached research notebook. During backtesting, this adjustment is done during trading by setting up a QuoteBarConsolidator for each security in our universe. On each new consolidated QuoteBar, we update the trailing window of L1 data, then calculate the latest spread adjustment values.

The lack of performance for this arbitrage strategy is mostly attributed to the fees it incurs while trading. This is common for an intraday arbitrage strategy, but we discuss ways to reduces these fees in the conclusion of this tutorial. After removing the costs of commissions, crossing the spread, and slippage, the strategy outperforms the SPY over the entire backtesting period. Without these costs, the strategy generates a 1.09 Share ratio while the SPY generates a 0.732 Sharpe ratio. See the backtest performance without fees below.

The intraday arbitrage strategy we built and tested throughout this tutorial underperforms the SPY benchmark in terms of Sharpe ratio when including trading costs. Without these costs, we found the strategy outperforms the SPY in terms of Sharpe ratio. In our implementation, we specified the Alpha model to initiate trading when atleast a 0.02% profit threshold is reached for 3 seconds. Both of these parameters are set lower than the strategy examined in Marshall et al (2010) for demonstration purposes. Increasing the profit threshold will lead to more profitable, but fewer, trades that may overcome the cost of trading. We leave this area of study for future research. Additional areas of future research include increasing the resolution of data from second to tick and incorportating an execution model that utilizes limit orders to reduce fees.

In the model, agents are allowed to employ the strategies used by the following five types of investors: contrarians, random traders, momentum traders, fundamentalists and exit strategy holders. Specifically, the authors start with the investigation of the dynamics of a tax free benchmark market; then the patterns of market behaviors and the behaviors of various types of investors are discussed with different levels of STTs in markets with mild and high fluctuations.

Introduction to the nature and functions of securities markets and financial instruments. The formulation of investment goals and policies, trading strategies, and portfolio management. Coverage of security analysis and valuation, evaluating portfolio performance, diversification, alternative investments. Prerequisite: FNCE 121 or 121S. (5 units)

This course uses real-life investments as a comprehensive means of exploring all phases of the real estate investment life cycle, from identifying potential transactions, examining various valuation methods, conducting due diligence, determining financing alternatives, implementing property management strategies, and analyzing disposition timing. Students will explore various types of real estate investments: multi-family, office, hotel, and ground-up development. This course does not satisfy an upper-division elective for finance majors and is not included when calculating the finance major GPA. Prerequisite: FNCE 121 or 121S, and FNCE 118. (5 unit)

Exploration of the ethical dimension of financial markets. Each week focuses on a different job function (investment manager, research analyst, trader, fund manager, corporate controller, bank officer, etc.) and explores the intersection between legal responsibility and ethical action. Topics include fiduciary responsibility, insider trading, moral hazard, agency, predatory lending, and financial market regulations concerning disclosure, price manipulation, suitability, etc. Current news items and regulatory activity will be incorporated in weekly discussions. Prerequisites: FNCE 121 or 121S, and FNCE 124. (5 units)

Provides an introduction to fixed income by covering the valuation and application of a range of fixed-income securities and credit derivatives. The main objective is to provide a foundation in the basic concepts and mathematics of these securities and their applications, holistically as it pertains to a means to (1) formulate investment strategies, (2) immunize investment portfolios, and (3) hedge attendant risks. Students will be able to describe basic features, pricing, and mathematics (duration and convexity) of fixed-income securities, model risk and return in fixed-income securities, describe basic similarities and differences in the deaures and bond mathematics for plain-vanilla versus more complex fixed-income securities, and describe and price basic credit derivatives and structured credit products. Prerequisites: FNCE 121 or 121S, and FNCE 124. (5 units)

Covers topics that are directly relevant to entrepreneurs, defined broadly to include all early employees in addition to founders, who are evaluating, communicating, and implementing new business opportunities. This course focuses on the start-up phase with an emphasis on venture-backed companies. The three main sections of the course are: Types of Businesses (primarily lecture and project-based), Financial Models (primarily project-based), and Investment Terms (primarily lecture-based). Types of Businesses covers the three types of entrepreneurs: lifestyle entrepreneurs, wealth-building entrepreneurs, and innovating entrepreneurs, along economic foundations that distinguish the three types of entrepreneurship. Financial Models covers the creation and uses of financial projection: revenue, costs, and profits/losses. Investment Terms covers the way investments in startup companies are generally structured. In all three sections, we will discuss the human biases that often distort entrepreneurial efforts, along with strategies to recognize and avoid the more costly. Prerequisites: FNCE 121 or 121S, and FNCE 124. (5 units) 041b061a72