Trading Algorithms

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

About Course

This course covers two of the seven trading strategies that work in emerging markets. The seven include strategies based on momentum, momentum crashes, price reversal, persistence of earnings, quality of earnings, underlying business growth, behavioral biases and textual analysis of business reports about the company. In the first part of the course, you will learn how to read an academic paper. What parts to pay attention to and what parts to skim through will be discussed here. For every strategy, first you will be introduced to the original research and then how to implement the strategy. The first strategy, Piotroski F -score will be discussed in detail. You will be taught how to calculate the F - Score and how to use this score in a strategy. This is followed by the next strategy, Post earnings announcement drift (PEAD).

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Course Introduction

Trading Algorithms Trading algorithms are essential for the financial markets. They provide the tools to make sure that the price of a security meet its expected value. In this course, we will learn the basic concepts and performance of algorithms and their trading costs. We will also cover the basics of strategy, signal and retrace. We will cover the basic structure of a trading strategy and the different types of orders. We will also cover the basic structure of a trading window and the different types of orders. We’ll also learn how to interpret the results and interpretation of trading algorithms. We’ll use an example case in which we take a position against a stock and the price of the stock changes. We’ll use the basic structure of an order to show how an order can change the market. We’ll also use the signal and retrace functions to illustrate the concepts and algorithms we’ll learn about. This is the third and last course in the Financial Cryptography specialization. The course takes learners on a journey through financial history, engineering technology, and computer science. Financial Cryptography allows students to learn about the mathematics behind modern trading and the trading costs of financial instruments.Cryptographic Methods Cryptographic Formulas Signals and Retraces Strategy Tracing Human Evolution This course examines the evolution of human populations over time and examines the human population growth and decline. Using the

Course Tag

Trading Strategy Post-Earnings-Announcement Drift (PEAD) F1 Score Trading

Related Wiki Topic

Article Example
NASDAQ futures Investment in trading algorithms research (a mathematical rule set for futures trading entry, exit, and stop loss points often calculated and executed by computer) is phenomenal. Investment banking firm Goldman Sachs devotes more of its resources, tens of millions annually, to developing trading algorithms than it does on trade desk staffing. Trading algorithms may be as exotic as biology theorems like neural network applied to financial market trading by Gang Dong of Rutgers University, or completely based on current market time/price analysis.
Algorithmic trading In July 2007, Citigroup, which had already developed its own trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day. Citigroup had previously bought Lava Trading and OnTrade Inc.
Automated trading system The use of high-frequency trading (HFT) strategies has grown substantially over the past several years and drives a significant portion of activity on U.S. markets. Although many HFT strategies are legitimate, some are not and may be used for manipulative trading. Given the scale of the potential impact that these practices may have, the surveillance of abusive algorithms remains a high priority for regulators. FINRA has reminded firms using HFT strategies and other trading algorithms of their obligation to be vigilant when testing these strategies pre- and post-launch to ensure that the strategies do not result in abusive trading.
Automated trading system FINRA conducts surveillance to identify cross-market, cross-product manipulation of the price of underlying equity securities, typically through abusive trading algorithms, and strategies used to close out pre-existing option positions at favorable prices or establish new option positions at advantageous prices.
Swing trading Simpler rule-based trading approaches include Alexander Elder's strategy, which measures the behavior of an instrument's price trend using three different moving averages of closing prices. The instrument is only traded Long when the three averages are aligned in an upward direction, and only traded Short when the three averages are moving downward. Trading algorithms/systems may lose their profit potential when they obtain enough of a mass following to curtail their effectiveness: "Now it's an arms race. Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits," observes Andrew Lo, the Director of the Laboratory For Financial Engineering, for the Massachusetts Institute of Technology.
Quantopian Quantopian is a Boston-based company that aims to create a crowd-sourced hedge fund by letting freelance quantitative analysts develop, test, and use trading algorithms to buy and sell securities.
Bloomberg L.P. Bloomberg Tradebook is an electronic agency brokerage for equity, futures, options and foreign exchange trades. Its "buyside" services include access to trading algorithms, analytics and marketing insights, while its "sellside" services include connection to electronic trading networks and global trading capabilities. Bloomberg Tradebook was founded in 1996 as an affiliate of Bloomberg L.P.
Bloomberg Tradebook Bloomberg Tradebook, LLC., the agency broker of Bloomberg L.P., serves global investment advisors, money managers, hedge funds, proprietary desks and broker dealers, with access to global trading venues, proprietary trading algorithms, execution consulting services, pre-and-post trade analytics and independent research.
Day trading An estimated one third of stock trades in 2005 in United States were generated by automatic algorithms, or high-frequency trading. The increased use of algorithms and quantitative techniques has led to more competition and smaller profits.
Triangular arbitrage Researchers have shown a decrease in the incidence of triangular arbitrage opportunities from 2003 to 2005 for the Japanese yen and Swiss franc and have attributed the decrease to broader adoption of electronic trading platforms and trading algorithms during the same period. Such electronic systems have enabled traders to trade and react rapidly to price changes. The speed gained from these technologies improved trading efficiency and the correction of mispricings, allowing for less incidence of triangular arbitrage opportunities.
Knight Capital Group The Knight Capital Group was an American global financial services firm engaging in market making, electronic execution, and institutional sales and trading. With its high-frequency trading algorithms Knight was the largest trader in U.S. equities, with a market share of 17.3% on NYSE and 16.9% on NASDAQ. The company agreed to be acquired by Getco LLC in December 2012 after an August 2012 trading error lost $460 million. The merger was completed in July 2013, forming KCG Holdings.
Complex event processing Recent improvements in CEP technologies have made it more affordable, helping smaller firms to create trading algorithms of their own and compete with larger firms. CEP has evolved from an emerging technology to an essential platform of many capital markets. The technology's most consistent growth has been in banking, serving fraud detection, online banking, and multichannel marketing initiatives.
Smart order routing Here are some US statistics from 2006-2007: "Smart order routing capabilities for options are anonymous and easy to use, and optimizes execution quality with each transaction". "In a study conducted earlier this year in conjunction with Financial Insights, BAS found that about 5% of all equity orders were executed using trading algorithms, with this number expected to increase to 20% by 2007”.
High-frequency trading High-frequency trading is quantitative trading that is characterized by short portfolio holding periods All portfolio-allocation decisions are made by computerized quantitative models. The success of high-frequency trading strategies is largely driven by their ability to simultaneously process large volumes of information, something ordinary human traders cannot do. Specific algorithms are closely guarded by their owners. Many practical algorithms are in fact quite simple arbitrages which could previously have been performed at lower frequency—competition tends to occur through who can execute them the fastest rather than who can create new breakthrough algorithms.
Proprietary trading Because of recent financial regulations like the Volcker Rule in particular, most major banks have spun off their prop trading desks or shut them down altogether. However, prop trading is not gone. It is carried out at specialized prop trading firms and hedge funds. Some notable prop trading firms are T3 Trading Group, LLC, Quantlab Financial, LLC, Virtu Financial, Tower Research Capital LLC, Mako Global Derivatives, Optiver, TransMarket Group, Trillium Trading, DRW, and First New York Securities. The prop trading done at these firms is usually highly technology-driven, utilizing complex quantitative models and algorithms.
Freedom of Information Act (United States) The Dodd–Frank Wall Street Reform and Consumer Protection Act, signed into law in July 2010, included provisions in section 929I that shielded the Securities and Exchange Commission (SEC) from requests under the Freedom of Information Act. The provisions were initially motivated out of concern that the FOIA would hinder SEC investigations that involved trade secrets of financial companies, including "watch lists" they gathered about other companies, trading records of investment managers, and "trading algorithms" used by investment firms.
Electronic trading By 2011 investment firms on both the buy side and sell side were increasing their spending on technology for electronic trading. With the result that many floor traders and brokers were removed from the trading process. Traders also increasingly started to rely on algorithms to analyze market conditions and then execute their orders automatically.
Algorithmic trading As noted above, high-frequency trading (HFT) is a form of algorithmic trading characterized by high turnover and high order-to-trade ratios. Although there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, specialized order types, co-location, very short-term investment horizons, and high cancellation rates for orders.
High-frequency trading High-frequency trading comprises many different types of algorithms. Various studies reported that certain types of market-making high-frequency trading reduces volatility and does not pose a systemic risk, and lowers transaction costs for retail investors, without impacting long term investors. Other studies, summarized in Aldridge, Krawciw, 2017 find that high-frequency trading strategies known as "aggressive" erode liquidity and cause volatility.
UNX UNX was founded in 1998 by Oscar Olmedo, Poul Moller and Randy Abernethy to provide direct market access trading systems to institutional clients. In 2000, the company launched Basket Trading Center, a web based single stock and portfolio trading platform that provided clients access to North American execution venues. That year, UNX also launched its smart routing and tactical order routing engine. In 2002, UNX acquired Embarcadero Securities and launched the Metabook Execution Management System. In 2009, UNX introduced the broker-neutral Catalyst platform and began the process of integrating trading algorithms of major broker-dealers into the UNX Marketplace.