algorithmic trading

back to index

description: the use of automated algorithms to execute trading orders in financial markets

106 results

pages: 224 words: 13,238

Electronic and Algorithmic Trading Technology: The Complete Guide
by Kendall Kim
Published 31 May 2007

While many hedge funds use traditional value and growth-based investing strategy, many use more advanced quantitative strategies, and are most likely to use cutting-edge technology such as algorithmic trading. The Aite Group estimates that at the end of 2004, approximately 25% of total equities trading volume was driven by algorithmic trading (see Exhibit 3.9). Within this 25%, the sell side was composed of 13% followed by hedge fund volume, which stood at 10% of the total. Algorithmic trading volume initiated by traditional money managers was less than 3%. The popular use of algorithmic trading by hedge funds can also be attributable to the explosive growth in hedge funds within the last 15 years (see Exhibit 3.10). IT Spending in Algorithmic Trading Algorithmic trading services will continue to rise.

After years sitting on the sidelines, these institutions, also known as the buy side, have finally entered the algorithmic trading game. The latest advance in electronic tools allows users of algorithmic trading strategies to predefine rules regarding how an order should be executed. Traders must calibrate the algorithms to suit their portfolio strategy. Market Share Algorithmic Trading Service Providers Other 9% Agency Brokers 28% Bulge Bracket Firms 63% Exhibit 1.1 Source: Algorithmic Trading Hype or Reality, Aite Group 2005. 6 Electronic and Algorithmic Trading Technology Buy-side firms such as Putnam Investments, the mutual fund giant that manages about $200 billion in assets, have used algorithms for the past couple of years.

Some exchanges now regulate the use of electronic and algorithmic trading, preventing their systems from being overloaded or to avoid repeating the crash of 1987. On July 7, 2005, the London Stock Exchange asked for algorithmic trading to be suspended after the London bombings. We are still in the infancy of algorithmic trading. Its impact on the corporate world is still uncertain. Algorithmic trading is now predominantly used to trade large capitalization companies, by making it easier to buy and sell large blocks of stock. It is a less well-suited means to trade small-cap or illiquid securities. The growing use of algorithmic trading could potentially 14 Electronic and Algorithmic Trading Technology lead brokers to further ignore the small-cap universe.

Learn Algorithmic Trading
by Sebastien Donadio
Published 7 Nov 2019

What this book covers Chapter 1, Algorithmic Trading Fundamentals, explains what algorithmic trading is and how algorithmic trading is related to high frequency or low latency trading. We will discuss the evolution of algorithmic trading, from rule-based to AI. We will look at essential algorithmic trading concepts, asset classes, and instruments. You will learn how to set up your mind for algorithmic decisions. Chapter 2, Deciphering the Markets with Technical Analysis, covers some popular technical analysis methods and shows how to apply them to the analysis of market data. We will perform basic algorithmic trading using market trends, support, and resistance.

In addition, the electronic trading exchanges themselves impose regulations and laws, the violation of which can also incur severe penalties. There are many market participants or algorithmic trading strategy behaviors that are forbidden. Some incur a warning or an audit and some incur penalties. Insider trading reports are quite well known by people inside and outside of the algorithmic trading business. While insider trading doesn't really apply to algorithmic trading or high-frequency trading, we will introduce some of the common issues in algorithmic trading here. This list is nowhere near complete, but these are the top regulatory issues in algorithmic trading or high-frequency trading. Spoofing Spoofing typically refers to the practice of entering orders into the market that are not considered bonafide.

Basic concepts regarding the modern trading setup Market sectors Asset classes Basics of what a modern trading exchange looks like Understanding algorithmic trading concepts Exchange order book Exchange matching algorithm FIFO matching Pro-rata matching Limit order book Exchange market data protocols Market data feed handlers Order types IOC – Immediate Or Cancel GTD – Good Till Day Stop orders Exchange order entry protocols Order entry gateway Positions and profit and loss (PnL) management From intuition to algorithmic trading Why do we need to automate trading? Evolution of algorithmic trading – from rule-based to AI Components of an algorithmic trading system Market data subscription Limit order books Signals Signal aggregators Execution logic Position and PnL management Risk management Backtesting Why Python?

pages: 1,082 words: 87,792

Python for Algorithmic Trading: From Idea to Cloud Deployment
by Yves Hilpisch
Published 8 Dec 2020

Sebastopol: O’Reilly. 1 Note that computers can only generate pseudorandom numbers as approximations for truly random numbers. Index A absolute maximum drawdown, Case Study AdaBoost algorithm, Vectorized Backtesting addition (+) operator, Data Types adjusted return appraisal ratio, Algorithmic Trading algorithmic trading (generally)advantages of, Algorithmic Trading basics, Algorithmic Trading-Algorithmic Trading strategies, Trading Strategies-Conclusions alpha seeking strategies, Trading Strategies alpha, defined, Algorithmic Trading anonymous functions, Python Idioms API key, for data sets, Working with Open Data Sources-Working with Open Data Sources Apple, Inc.intraday stock prices, Getting into the Basics reading stock price data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON retrieving historical unstructured data about, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data app_key, for Eikon Data API, Eikon Data API AQR Capital Management, pandas and the DataFrame Class arithmetic operations, Data Types array programming, Making Use of Vectorization(see also vectorization) automated trading operations, Automating Trading Operations-Strategy Monitoringcapital management, Capital Management-Kelly Criterion for Stocks and Indices configuring Oanda account, Configuring Oanda Account hardware setup, Setting Up the Hardware infrastructure and deployment, Infrastructure and Deployment logging and monitoring, Logging and Monitoring-Logging and Monitoring ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object online algorithm, Online Algorithm-Online Algorithm Python environment setup, Setting Up the Python Environment Python scripts for, Python Script-Strategy Monitoring real-time monitoring, Real-Time Monitoring running code, Running the Code uploading code, Uploading the Code visual step-by-step overview, Visual Step-by-Step Overview-Real-Time Monitoring B backtestingbased on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach Python scripts for classification algorithm backtesting, Classification Algorithm Backtesting Class Python scripts for linear regression backtesting class, Linear Regression Backtesting Class vectorized (see vectorized backtesting) BacktestLongShort class, Long-Short Backtesting Class, Long-Short Backtesting Class bar charts, matplotlib bar plots (see Plotly; streaming bar plot) base class, for event-based backtesting, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class Bash script, Building a Ubuntu and Python Docker Imagefor Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up for Python/Jupyter Lab installation, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Bitcoin, pandas and the DataFrame Class, Working with Open Data Sources Boolean operationsNumPy, Boolean Operations pandas, Boolean Operations C callback functions, Retrieving Streaming Data capital managementautomated trading operations and, Capital Management-Kelly Criterion for Stocks and Indices Kelly criterion for stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Kelly criterion in binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting Carter, Graydon, FX Trading with FXCM CFD (contracts for difference)algorithmic trading risks, Logging and Monitoring defined, CFD Trading with Oanda risks of losses, Long-Short Backtesting Class risks of trading on margin, FX Trading with FXCM trading with Oanda, CFD Trading with Oanda-Python Script(see also Oanda) classification problemsmachine learning for, A Simple Classification Problem-A Simple Classification Problem neural networks for, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited Python scripts for vectorized backtesting, Classification Algorithm Backtesting Class .close_all() method, Placing Orders cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Upinstallation script for Python and Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Jupyter Notebook configuration file, Jupyter Notebook Configuration File RSA public/private keys, RSA Public and Private Keys script to orchestrate Droplet set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up Cocteau, Jean, Building Classes for Event-Based Backtesting comma separated value (CSV) files (see CSV files) condaas package manager, Conda as a Package Manager-Basic Operations with Conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager basic operations, Basic Operations with Conda-Basic Operations with Conda installing Miniconda, Installing Miniconda-Installing Miniconda conda remove, Basic Operations with Conda configparser module, The Oanda API containers (see Docker containers) contracts for difference (see CFD) control structures, Control Structures CPython, Python for Finance, Python Infrastructure .create_market_buy_order() method, Placing Orders .create_order() method, Placing Market Orders-Placing Market Orders cross-sectional momentum strategies, Strategies Based on Momentum CSV filesinput-output operations, Input-Output Operations-Input-Output Operations reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python .cummax() method, Case Study currency pairs, Logging and Monitoring(see also EUR/USD exchange rate) algorithmic trading risks, Logging and Monitoring D data science stack, Python, NumPy, matplotlib, pandas data snooping, Data Snooping and Overfitting data storageSQLite3 for, Storing Data with SQLite3-Storing Data with SQLite3 storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects TsTables package for, Using TsTables-Using TsTables data structures, Data Structures-Data Structures DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class, Reading from a CSV File with pandas, DataFrame Class-DataFrame Class DataFrame objectscreating, Vectorization with pandas storing, Storing DataFrame Objects-Storing DataFrame Objects dataism, Preface DatetimeIndex() constructor, Plotting with pandas decision tree classification algorithm, Vectorized Backtesting deep learningadding features to analysis, Adding Different Types of Features-Adding Different Types of Features classification problem, The Simple Classification Problem Revisited-The Simple Classification Problem Revisited deep neural networks for predicting market direction, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features market movement prediction, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features trading strategies and, Machine and Deep Learning deep neural networks, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features delta hedging, Algorithmic Trading dense neural network (DNN), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction dictionary (dict) objects, Reading from a CSV File with Python, Data Structures DigitalOceancloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up droplet setup, Setting Up the Hardware DNN (dense neural network), The Simple Classification Problem Revisited, Using Deep Neural Networks to Predict Market Direction Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Imagebuilding a Ubuntu and Python Docker image, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image defined, Docker Images and Containers Docker images versus, Docker Images and Containers Docker imagesdefined, Docker Images and Containers Docker containers versus, Docker Images and Containers Dockerfile, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image Domingos, Pedro, Automating Trading Operations Droplet, Using Cloud Instancescosts, Infrastructure and Deployment script to orchestrate set-up, Script to Orchestrate the Droplet Set Up-Script to Orchestrate the Droplet Set Up dynamic hedging, Algorithmic Trading E efficient market hypothesis, Predicting Market Movements with Machine Learning Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Dataretrieving historical structured data, Retrieving Historical Structured Data-Retrieving Historical Structured Data retrieving historical unstructured data, Retrieving Historical Unstructured Data-Retrieving Historical Unstructured Data Euler discretization, Python Versus Pseudo-Code EUR/USD exchange ratebacktesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars evaluation of regression-based strategy, Generalizing the Approach factoring in leverage/margin, Factoring In Leverage and Margin-Factoring In Leverage and Margin gross performance versus deep learning-based strategy, Using Deep Neural Networks to Predict Market Direction-Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features-Adding Different Types of Features historical ask close prices, Retrieving Historical Data-Retrieving Historical Data historical candles data for, Retrieving Candles Data historical tick data for, Retrieving Tick Data implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time logistic regression-based strategies, Generalizing the Approach placing orders, Placing Orders-Placing Orders predicting, Predicting Index Levels-Predicting Index Levels predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels retrieving streaming data for, Retrieving Streaming Data retrieving trading account information, Retrieving Account Information-Retrieving Account Information SMA calculation, Getting into the Basics-Generalizing the Approach vectorized backtesting of ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy event-based backtesting, Building Classes for Event-Based Backtesting-Long-Short Backtesting Classadvantages, Building Classes for Event-Based Backtesting base class, Backtesting Base Class-Backtesting Base Class, Backtesting Base Class building classes for, Building Classes for Event-Based Backtesting-Long-Short Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class Python scripts for, Backtesting Base Class-Long-Short Backtesting Class Excelexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON F featuresadding different types, Adding Different Types of Features-Adding Different Types of Features lags and, Using Logistic Regression to Predict Market Direction financial data, working with, Working with Financial Data-Python Scriptsdata set for examples, The Data Set Eikon Data API, Eikon Data API-Retrieving Historical Unstructured Data exporting to Excel/JSON, Exporting to Excel and JSON open data sources, Working with Open Data Sources-Working with Open Data Sources reading data from different sources, Reading Financial Data From Different Sources-Reading from Excel and JSON reading data from Excel/JSON, Reading from Excel and JSON reading from a CSV file with pandas, Reading from a CSV File with pandas reading from a CSV file with Python, Reading from a CSV File with Python-Reading from a CSV File with Python storing data efficiently, Storing Financial Data Efficiently-Storing Data with SQLite3 .flatten() method, matplotlib foreign exchange trading (see FX trading; FXCM) future returns, predicting, Predicting Future Returns-Predicting Future Returns FX trading, FX Trading with FXCM-References and Further Resources(see also EUR/USD exchange rate) FXCMFX trading, FX Trading with FXCM-References and Further Resources getting started, Getting Started placing orders, Placing Orders-Placing Orders retrieving account information, Account Information retrieving candles data, Retrieving Candles Data-Retrieving Candles Data retrieving data, Retrieving Data-Retrieving Candles Data retrieving historical data, Retrieving Historical Data-Retrieving Historical Data retrieving streaming data, Retrieving Streaming Data retrieving tick data, Retrieving Tick Data-Retrieving Tick Data working with the API, Working with the API-Account Information fxcmpy wrapper packagecallback functions, Retrieving Streaming Data installing, Getting Started tick data retrieval, Retrieving Tick Data fxTrade, CFD Trading with Oanda G GDX (VanEck Vectors Gold Miners ETF)logistic regression-based strategies, Generalizing the Approach mean-reversion strategies, Getting into the Basics-Generalizing the Approach regression-based strategies, Generalizing the Approach generate_sample_data(), Storing Financial Data Efficiently .get_account_summary() method, Retrieving Account Information .get_candles() method, Retrieving Historical Data .get_data() method, Backtesting Base Class, Retrieving Tick Data .get_date_price() method, Backtesting Base Class .get_instruments() method, Looking Up Instruments Available for Trading .get_last_price() method, Retrieving Streaming Data .get_raw_data() method, Retrieving Tick Data get_timeseries() function, Retrieving Historical Structured Data .get_transactions() method, Retrieving Account Information GLD (SPDR Gold Shares)logistic regression-based strategies, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction mean-reversion strategies, Getting into the Basics-Generalizing the Approach gold pricemean-reversion strategies, Getting into the Basics-Getting into the Basics momentum strategy and, Getting into the Basics-Getting into the Basics, Generalizing the Approach-Generalizing the Approach Goldman Sachs, Python and Algorithmic Trading, Algorithmic Trading .go_long() method, Long-Short Backtesting Class H half Kelly criterion, Optimal Leverage Harari, Yuval Noah, Preface HDF5 binary storage library, Using TsTables-Using TsTables HDFStore wrapper, Storing DataFrame Objects-Storing DataFrame Objects high frequency trading (HFQ), Algorithmic Trading histograms, matplotlib hit ratio, defined, Vectorized Backtesting I if-elif-else control structure, Python Idioms in-sample fitting, Generalizing the Approach index levels, predicting, Predicting Index Levels-Predicting Index Levels infrastructure (see Python infrastructure) installation script, Python/Jupyter Lab, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab Intel Math Kernel Library, Basic Operations with Conda iterations, Control Structures J JSONexporting financial data to, Exporting to Excel and JSON reading financial data from, Reading from Excel and JSON Jupyter Labinstallation script for, Installation Script for Python and Jupyter Lab-Installation Script for Python and Jupyter Lab RSA public/private keys for, RSA Public and Private Keys tools included, Using Cloud Instances Jupyter Notebook, Jupyter Notebook Configuration File K Kelly criterionin binomial setting, Kelly Criterion in Binomial Setting-Kelly Criterion in Binomial Setting optimal leverage, Optimal Leverage-Optimal Leverage stocks and indices, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices Keras, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction, Adding Different Types of Features key-value stores, Data Structures keys, public/private, RSA Public and Private Keys L lags, The Basic Idea for Price Prediction, Using Logistic Regression to Predict Market Direction lambda functions, Python Idioms LaTeX, Python Versus Pseudo-Code leveraged trading, risks of, Factoring In Leverage and Margin, FX Trading with FXCM, Optimal Leverage linear regressiongeneralizing the approach, Generalizing the Approach market movement prediction, Using Linear Regression for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction review of, A Quick Review of Linear Regression scikit-learn and, Linear Regression with scikit-learn vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy, Linear Regression Backtesting Class list comprehension, Python Idioms list constructor, Data Structures list objects, Reading from a CSV File with Python, Data Structures, Regular ndarray Object logging, of automated trading operations, Logging and Monitoring-Logging and Monitoring logistic regressiongeneralizing the approach, Generalizing the Approach-Generalizing the Approach market direction prediction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction Python script for vectorized backtesting, Classification Algorithm Backtesting Class long-only backtesting class, Long-Only Backtesting Class-Long-Only Backtesting Class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class-Long-Short Backtesting Class, Long-Short Backtesting Class longest drawdown period, Risk Analysis M machine learningclassification problem, A Simple Classification Problem-A Simple Classification Problem linear regression with scikit-learn, Linear Regression with scikit-learn market movement prediction, Using Machine Learning for Market Movement Prediction-Generalizing the Approach ML-based trading strategy, ML-Based Trading Strategy-Persisting the Model Object Python scripts, Linear Regression Backtesting Class trading strategies and, Machine and Deep Learning using logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction macro hedge funds, algorithmic trading and, Algorithmic Trading __main__ method, Backtesting Base Class margin trading, FX Trading with FXCM market direction prediction, Predicting Future Market Direction market movement predictiondeep learning for, Using Deep Learning for Market Movement Prediction-Adding Different Types of Features deep neural networks for, Using Deep Neural Networks to Predict Market Direction-Adding Different Types of Features linear regression for, Using Linear Regression for Market Movement Prediction-Generalizing the Approach linear regression with scikit-learn, Linear Regression with scikit-learn logistic regression to predict market direction, Using Logistic Regression to Predict Market Direction-Using Logistic Regression to Predict Market Direction machine learning for, Using Machine Learning for Market Movement Prediction-Generalizing the Approach predicting future market direction, Predicting Future Market Direction predicting future returns, Predicting Future Returns-Predicting Future Returns predicting index levels, Predicting Index Levels-Predicting Index Levels price prediction based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction vectorized backtesting of regression-based strategy, Vectorized Backtesting of Regression-Based Strategy market orders, placing, Placing Market Orders-Placing Market Orders math module, Data Types mathematical functions, Data Types matplotlib, matplotlib-matplotlib, Plotting with pandas-Plotting with pandas maximum drawdown, Risk Analysis, Case Study McKinney, Wes, pandas and the DataFrame Class mean-reversion strategies, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approachbasics, Getting into the Basics-Generalizing the Approach generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Miniconda, Installing Miniconda-Installing Miniconda mkl (Intel Math Kernel Library), Basic Operations with Conda ML-based strategies, ML-Based Trading Strategy-Persisting the Model Objectoptimal leverage, Optimal Leverage-Optimal Leverage persisting the model object, Persisting the Model Object Python script for, Automated Trading Strategy risk analysis, Risk Analysis-Risk Analysis vectorized backtesting, Vectorized Backtesting-Vectorized Backtesting MLPClassifier, The Simple Classification Problem Revisited MLTrader class, Online Algorithm-Online Algorithm momentum strategies, Momentumbacktesting on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars basics, Getting into the Basics-Getting into the Basics generalizing the approach, Generalizing the Approach Python code with a class for vectorized backtesting, Momentum Backtesting Class Python script for custom streaming class, Python Script Python script for momentum online algorithm, Momentum Online Algorithm vectorized backtesting of, Strategies Based on Momentum-Generalizing the Approach MomentumTrader class, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time MomVectorBacktester class, Generalizing the Approach monitoringautomated trading operations, Logging and Monitoring-Logging and Monitoring, Real-Time Monitoring Python scripts for strategy monitoring, Strategy Monitoring Monte Carlo simulationsample tick data server, Sample Tick Data Server time series data based on, Python Scripts motives, for trading, Algorithmic Trading MRVectorBacktester class, Generalizing the Approach multi-layer perceptron, The Simple Classification Problem Revisited Musashi, Miyamoto, Python Infrastructure N natural language processing (NLP), Retrieving Historical Unstructured Data ndarray class, Vectorization with NumPy-Vectorization with NumPy ndarray objects, NumPy and Vectorization, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functionscreating, ndarray Creation linear regression and, A Quick Review of Linear Regression regular, Regular ndarray Object nested structures, Data Structures NLP (natural language processing), Retrieving Historical Unstructured Data np.arange(), ndarray Creation numbers, data typing of, Data Types numerical operations, pandas, Numerical Operations NumPy, NumPy and Vectorization-NumPy and Vectorization, NumPy-Random NumbersBoolean operations, Boolean Operations ndarray creation, ndarray Creation ndarray methods, ndarray Methods and NumPy Functions-ndarray Methods and NumPy Functions random numbers, Random Numbers regular ndarray object, Regular ndarray Object universal functions, ndarray Methods and NumPy Functions vectorization, Vectorization with NumPy-Vectorization with NumPy vectorized operations, Vectorized Operations numpy.random sub-package, Random Numbers NYSE Arca Gold Miners Index, Getting into the Basics O Oandaaccount configuration, Configuring Oanda Account account setup, Setting Up an Account API access, The Oanda API-The Oanda API backtesting momentum strategy on minute bars, Backtesting a Momentum Strategy on Minute Bars-Backtesting a Momentum Strategy on Minute Bars CFD trading, CFD Trading with Oanda-Python Script factoring in leverage/margin with historical data, Factoring In Leverage and Margin-Factoring In Leverage and Margin implementing trading strategies in real time, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time looking up instruments available for trading, Looking Up Instruments Available for Trading placing market orders, Placing Market Orders-Placing Market Orders Python script for custom streaming class, Python Script retrieving account information, Retrieving Account Information-Retrieving Account Information retrieving historical data, Retrieving Historical Data-Factoring In Leverage and Margin working with streaming data, Working with Streaming Data Oanda v20 RESTful API, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting offline algorithmdefined, Signal Generation in Real Time transformation to online algorithm, Online Algorithm OLS (ordinary least squares) regression, matplotlib online algorithmautomated trading operations, Online Algorithm-Online Algorithm defined, Signal Generation in Real Time Python script for momentum online algorithm, Momentum Online Algorithm signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time transformation of offline algorithm to, Online Algorithm .on_success() method, Implementing Trading Strategies in Real Time, Online Algorithm open data sources, Working with Open Data Sources-Working with Open Data Sources ordinary least squares (OLS) regression, matplotlib out-of-sample evaluation, Generalizing the Approach overfitting, Data Snooping and Overfitting P package manager, conda as, Conda as a Package Manager-Basic Operations with Conda pandas, pandas and the DataFrame Class-pandas and the DataFrame Class, pandas-Input-Output OperationsBoolean operations, Boolean Operations case study, Case Study-Case Study data selection, Data Selection-Data Selection DataFrame class, DataFrame Class-DataFrame Class exporting financial data to Excel/JSON, Exporting to Excel and JSON input-output operations, Input-Output Operations-Input-Output Operations numerical operations, Numerical Operations plotting, Plotting with pandas-Plotting with pandas reading financial data from Excel/JSON, Reading from Excel and JSON reading from a CSV file, Reading from a CSV File with pandas storing DataFrame objects, Storing DataFrame Objects-Storing DataFrame Objects vectorization, Vectorization with pandas-Vectorization with pandas password protection, for Jupyter lab, Jupyter Notebook Configuration File .place_buy_order() method, Backtesting Base Class .place_sell_order() method, Backtesting Base Class Plotlybasics, The Basics multiple real-time streams for, Three Real-Time Streams multiple sub-plots for streams, Three Sub-Plots for Three Streams streaming data as bars, Streaming Data as Bars visualization of streaming data, Visualizing Streaming Data with Plotly-Streaming Data as Bars plotting, with pandas, Plotting with pandas-Plotting with pandas .plot_data() method, Backtesting Base Class polyfit()/polyval() convenience functions, matplotlib price prediction, based on time series data, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction .print_balance() method, Backtesting Base Class .print_net_wealth() method, Backtesting Base Class .print_transactions() method, Retrieving Account Information pseudo-code, Python versus, Python Versus Pseudo-Code publisher-subscriber (PUB-SUB) pattern, Working with Real-Time Data and Sockets Python (generally)advantages of, Python for Algorithmic Trading basics, Python and Algorithmic Trading-References and Further Resources control structures, Control Structures data structures, Data Structures-Data Structures data types, Data Types-Data Types deployment difficulties, Python Infrastructure idioms, Python Idioms-Python Idioms NumPy and vectorization, NumPy and Vectorization-NumPy and Vectorization obstacles to adoption in financial industry, Python for Finance origins, Python for Finance pandas and DataFrame class, pandas and the DataFrame Class-pandas and the DataFrame Class pseudo-code versus, Python Versus Pseudo-Code reading from a CSV file, Reading from a CSV File with Python-Reading from a CSV File with Python Python infrastructure, Python Infrastructure-References and Further Resourcesconda as package manager, Conda as a Package Manager-Basic Operations with Conda conda as virtual environment manager, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager Docker containers, Using Docker Containers-Building a Ubuntu and Python Docker Image using cloud instances, Using Cloud Instances-Script to Orchestrate the Droplet Set Up Python scriptsautomated trading operations, Running the Code, Python Script-Strategy Monitoring backtesting base class, Backtesting Base Class custom streaming class that trades a momentum strategy, Python Script linear regression backtesting class, Linear Regression Backtesting Class long-only backtesting class, Long-Only Backtesting Class long-short backtesting class, Long-Short Backtesting Class real-time data handling, Python Scripts-Sample Data Server for Bar Plot sample time series data set, Python Scripts strategy monitoring, Strategy Monitoring uploading for automated trading operations, Uploading the Code vectorized backtesting, Python Scripts-Mean Reversion Backtesting Class Q Quandlpremium data sets, Working with Open Data Sources working with open data sources, Working with Open Data Sources-Working with Open Data Sources R random numbers, Random Numbers random walk hypothesis, Predicting Index Levels range (iterator object), Control Structures read_csv() function, Reading from a CSV File with pandas real-time data, Working with Real-Time Data and Sockets-Sample Data Server for Bar PlotPython script for handling, Python Scripts-Sample Data Server for Bar Plot signal generation in real time, Signal Generation in Real Time-Signal Generation in Real Time tick data client for, Connecting a Simple Tick Data Client tick data server for, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server visualizing streaming data with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars real-time monitoring, Real-Time Monitoring Refinitiv, Eikon Data API relative maximum drawdown, Case Study returns, predicting future, Predicting Future Returns-Predicting Future Returns risk analysis, for ML-based trading strategy, Risk Analysis-Risk Analysis RSA public/private keys, RSA Public and Private Keys .run_mean_reversion_strategy() method, Long-Only Backtesting Class, Long-Short Backtesting Class .run_simulation() method, Kelly Criterion in Binomial Setting S S&P 500, Algorithmic Trading-Algorithmic Tradinglogistic regression-based strategies and, Generalizing the Approach momentum strategies, Getting into the Basics passive long position in, Kelly Criterion for Stocks and Indices-Kelly Criterion for Stocks and Indices scatter objects, Three Real-Time Streams scientific stack, NumPy and Vectorization, Python, NumPy, matplotlib, pandas scikit-learn, Linear Regression with scikit-learn ScikitBacktester class, Generalizing the Approach-Generalizing the Approach SciPy package project, NumPy and Vectorization seaborn library, matplotlib-matplotlib simple moving averages (SMAs), pandas and the DataFrame Class, Simple Moving Averagestrading strategies based on, Strategies Based on Simple Moving Averages-Generalizing the Approach visualization with price ticks, Three Real-Time Streams .simulate_value() method, Running a Simple Tick Data Server Singer, Paul, CFD Trading with Oanda sockets, real-time data and, Working with Real-Time Data and Sockets-Sample Data Server for Bar Plot sorting list objects, Data Structures SQLite3, Storing Data with SQLite3-Storing Data with SQLite3 SSL certificate, RSA Public and Private Keys storage (see data storage) streaming bar plots, Streaming Data as Bars, Sample Data Server for Bar Plot streaming dataOanda and, Working with Streaming Data visualization with Plotly, Visualizing Streaming Data with Plotly-Streaming Data as Bars string objects (str), Data Types-Data Types Swiss Franc event, CFD Trading with Oanda systematic macro hedge funds, Algorithmic Trading T TensorFlow, Using Deep Learning for Market Movement Prediction, Using Deep Neural Networks to Predict Market Direction Thomas, Rob, Working with Financial Data Thorp, Edward, Capital Management tick data client, Connecting a Simple Tick Data Client tick data server, Running a Simple Tick Data Server-Running a Simple Tick Data Server, Sample Tick Data Server time series data setspandas and vectorization, Vectorization with pandas price prediction based on, The Basic Idea for Price Prediction-The Basic Idea for Price Prediction Python script for generating sample set, Python Scripts SQLite3 for storage of, Storing Data with SQLite3-Storing Data with SQLite3 TsTables for storing, Using TsTables-Using TsTables time series momentum strategies, Strategies Based on Momentum(see also momentum strategies) .to_hdf() method, Storing DataFrame Objects tpqoa wrapper package, The Oanda API, Working with Streaming Data trading platforms, factors influencing choice of, CFD Trading with Oanda trading strategies, Trading Strategies-Conclusions(see also specific strategies) implementing in real time with Oanda, Implementing Trading Strategies in Real Time-Implementing Trading Strategies in Real Time machine learning/deep learning, Machine and Deep Learning mean-reversion, NumPy and Vectorization momentum, Momentum simple moving averages, Simple Moving Averages trading, motives for, Algorithmic Trading transaction costs, Long-Only Backtesting Class, Vectorized Backtesting TsTables package, Using TsTables-Using TsTables tuple objects, Data Structures U Ubuntu, Building a Ubuntu and Python Docker Image-Building a Ubuntu and Python Docker Image universal functions, NumPy, ndarray Methods and NumPy Functions V v20 wrapper package, The Oanda API, ML-Based Trading Strategy-Persisting the Model Object, Vectorized Backtesting value-at-risk (VAR), Risk Analysis-Risk Analysis vectorization, NumPy and Vectorization, Strategies Based on Mean Reversion-Generalizing the Approach vectorized backtestingdata snooping and overfitting, Data Snooping and Overfitting-Conclusions ML-based trading strategy, Vectorized Backtesting-Vectorized Backtesting momentum-based trading strategies, Strategies Based on Momentum-Generalizing the Approach potential shortcomings, Building Classes for Event-Based Backtesting Python code with a class for vectorized backtesting of mean-reversion trading strategies, Momentum Backtesting Class Python scripts for, Python Scripts-Mean Reversion Backtesting Class, Linear Regression Backtesting Class regression-based strategy, Vectorized Backtesting of Regression-Based Strategy trading strategies based on simple moving averages, Strategies Based on Simple Moving Averages-Generalizing the Approach vectorization with NumPy, Vectorization with NumPy-Vectorization with NumPy vectorization with pandas, Vectorization with pandas-Vectorization with pandas vectorized operations, Vectorized Operations virtual environment management, Conda as a Virtual Environment Manager-Conda as a Virtual Environment Manager W while loops, Control Structures Z ZeroMQ, Working with Real-Time Data and Sockets About the Author Dr.

Python Data Science Handbook: Essential Tools for Working with Data. Sebastopol: O’Reilly. Background information about algorithmic trading can be found, for instance, in these books: Chan, Ernest. 2009. Quantitative Trading: How to Build Your Own Algorithmic Trading Business. Hoboken et al: John Wiley & Sons. Chan, Ernest. 2013. Algorithmic Trading: Winning Strategies and Their Rationale. Hoboken et al: John Wiley & Sons. Kissel, Robert. 2013. The Science of Algorithmic Trading and Portfolio Management. Amsterdam et al: Elsevier/Academic Press. Narang, Rishi. 2013. Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading.

Conclusions Python is already a force in finance in general and is on its way to becoming a major force in algorithmic trading. There are a number of good reasons to use Python for algorithmic trading, among them the powerful ecosystem of packages that allows for efficient data analysis or the handling of modern APIs. There are also a number of good reasons to learn Python for algorithmic trading, chief among them the fact that some of the biggest buy- and sell-side institutions make heavy use of Python in their trading operations and constantly look for seasoned Python professionals. This book focuses on applying Python to the different disciplines in algorithmic trading, like backtesting trading strategies or interacting with online trading platforms.

High-Frequency Trading
by David Easley , Marcos López de Prado and Maureen O'Hara
Published 28 Sep 2013

They find very strong evidence that computers do not trade with each other as much as predicted, concluding that the strategies used by algorithmic traders are more correlated and less diverse than those used by human traders. Next, they investigate the effect that both algorithmic trading activity and the correlation between algorithmic trading strategies have on the occurrence of triangular arbitrage opportunities. They indicate that algorithmic trading activity is found to reduce the number of triangular arbitrage opportunities, as the algorithmic traders quickly respond to the posted quotes by non-algorithmic traders and profit from any potential arbitrage. Furthermore, a higher degree of correlation between algorithmic trading strategies reduces the number of arbitrage opportunities. There is evidence that an increase in trading activity where computers are posting quotes decreases the number of triangular arbitrage opportunities.

Algorithmic traders make prices more efficient by posting quotes that reflect new information. 75 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 76 — #96 i i HIGH-FREQUENCY TRADING Chaboud et al also investigate the effect algorithmic traders on the degree of autocorrelation in high-frequency currency returns: they estimate the autocorrelation of high-frequency, five-second returns over five-minute intervals. Similar to the evolution of arbitrage opportunities in the market, the introduction and growth of algorithmic trading coincides with a reduction in the absolute value of autocorrelation. On average, algorithmic trading participation reduces the degree of autocorrelation in high-frequency currency returns by posting quotes that reflect new information more quickly. Finally, Chaboud et al report highly correlated algorithmic trading behaviour in response to an increase in absolute value of the autocorrelation in high-frequency currency returns; this supports the concern that high-frequency traders have very similar strategies, which may hinder the price discovery process (Jarrow and Protter 2011).

Saar, 2012, Low-Latency Trading, Johnson School Research Paper Series no. 35-2010, December. Hendershott, T., and R. Riordan, 2011, “Algorithmic Trading and Information”, Technical Report, June. Hendershott, T., C. M. Jones and A. J. Menkveld, 2011, “Does Algorithmic Trading Improve Liquidity?” The Journal of Finance 66(1), pp. 1–33. Jarrow, R., and P. Protter, 2011, “A Dysfunctional Role of High Frequency Trading in Electronic Markets”, Technical Report, Cornell University Working Paper, June. Johnson, B., 2010, Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies. London: 4Myeloma Press. Kirilenko, A.

pages: 400 words: 121,988

Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets
by Donald MacKenzie
Published 24 May 2021

In the case of shares, as sketched in chapter 3, the “paperwork crisis” of the late 1960s created a substantially broader incentive for members of Congress to become involved, and the reforms that resulted were much less closely shaped by stock exchanges. One consequence of those different interactions with the political system is the big difference, discussed in previous chapters, between the signals (patterns of data that inform how algorithms trade) available to HFT algorithms trading shares and those trading futures. With most leading US shares traded on multiple exchanges, the fastest possible information on what is happening on those other exchanges is vital to HFT. Many financial futures, in contrast, are traded only on the Chicago Mercantile Exchange, making the flow of information to it from other datacenters often a little less important; more typically, it is the flow of data from the CME that is crucial.

See, for example, the 2012 review (primarily based on this early literature) by the UK’s Foresight Programme, which painted a broadly positive picture of what it referred to as computer-based trading as having reduced transaction costs and improved efficiency and liquidity—albeit with what the review cautioned was perhaps “greater potential for periodic illiquidity”—and with “no direct evidence” that HFT increased market volatility (UK Government Office for Science 2012: 11–12). Some of the underlying studies (such as the widely cited Brogaard 2010) indeed suggested that the presence of HFT can actually reduce volatility.2 More recent research on algorithmic trading in financial economics both differentiates HFT more clearly from other forms of algorithmic trading and focuses more strongly on the central divide within HFT between market-making and liquidity-taking strategies. A useful 2016 review by Albert Menkveld of the evolving literature (to which readers can turn if they wish to explore this literature in more detail) finds that “HFT market-making reduces transaction cost[s],” but also suggests that “HFTs are able to predict” and profit from the flow of what are often called the child orders that are generated by the execution algorithms that break up large orders from institutional investors into small parts.

In advertising, a pixel is a tiny transparent component of an ad that is copied into a user’s computer, smartphone, etc., when that device loads the ad, and which then transfers information back to the advertiser and/or a data-gathering firm. Appendix: A Note on the Literature on HFT 1. For a critique of Flash Boys by a former high-frequency trader, see Kovac (2014). 2. A good example of an early study—examining algorithmic trading as a whole, not HFT specifically—is Hendershott, Jones, and Menkveld (2011), who use electronic-message traffic on the New York Stock Exchange as a proxy for levels of algorithmic trading. They circumvent the problem of the endogeneity of traders’ decisions to trade algorithmically rather than manually by examining the impact of a series of essentially exogenous events: the phasing in, by the NYSE, in 2003, in a series of steps planned well in advance, of automatic dissemination of changes to the prices of the best bid and offer in the NYSE’s order book for a given stock.

pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems
by Irene Aldridge
Published 1 Dec 2009

The proportion of buy-side traders using algorithms in their trading increased from 9 percent in 2008 to 26 percent in 2009, with algorithms at least partially managing over 40 percent of the 18 HIGH-FREQUENCY TRADING Ease of use 7% Speed 11% Other 3% Cost 20% Customization 4% Reduced market impact 13% Execution consistency 6% Anonymity 22% Trader productivity 14% FIGURE 2.6 Reasons for using algorithms in trading. Source: The TRADE Annual Algorithmic Survey. total order flow, according to the 2009 Annual Algorithmic Trading Survey conducted by the TRADE Group. In addition to the previously mentioned factors related to adoption of algorithmic trading, such as productivity and accuracy of traders, the buy-side managers also reported their use of the algorithms to be driven by the anonymity of execution that the algorithmic trading permits. Stealth execution allows large investors to hide their trading intentions from other market participants, thus deflecting the possibilities of order poaching and increasing overall profitability.

From the original rudimentary order processing to the current state-of-the-art all-inclusive trading systems, high-frequency trading has evolved into a billion-dollar industry. To ensure optimal execution of systematic trading, algorithms were designed to mimic established execution strategies of traditional traders. To this day, the term “algorithmic trading” usually refers to the systematic execution process—that is, the optimization of buy-and-sell decisions once these buy-and-sell decisions were made by another part of the systematic trading process or by a human portfolio manager. Algorithmic trading may determine how to process an order given current market conditions: whether to execute the order aggressively (on a price close to the market price) or passively (on a limit price far removed from the current market price), in one trade or split into several smaller “packets.”

Special care should be taken, however, to distinguish high-frequency trading from electronic trading, algorithmic trading, and systematic trading. Figure 2.5 illustrates a schematic difference between high-frequency, systematic, and traditional long-term investing styles. Electronic trading refers to the ability to transmit the orders electronically as opposed to telephone, mail, or in person. Since most orders in today’s financial markets are transmitted via computer networks, the term electronic trading is rapidly becoming obsolete. Algorithmic trading is more complex than electronic trading and can refer to a variety of algorithms spanning order-execution processes as well as high-frequency portfolio allocation decisions.

Quantitative Trading: How to Build Your Own Algorithmic Trading Business
by Ernie Chan
Published 17 Nov 2008

Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike.” J AC K E T D ES I G N : PAU L M c C A RT H Y J AC K E T A RT: © D O N R E LY E A —STEVE HALPERN, founder, HCC Capital, LLC How to Build Your Own Algorithmic Trading Business trader and consultant who advises clients on how Quantitative Trading or sophisticated theories. Instead, he highlights the Wiley Trading B y some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with Quantitative Trading limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game?

Ernie successfully distills a large amount of detailed and difficult subject matter down to a very clear and comprehensive resource for novice and pro alike.” J AC K E T D ES I G N : PAU L M c C A RT H Y J AC K E T A RT: © D O N R E LY E A —STEVE HALPERN, founder, HCC Capital, LLC How to Build Your Own Algorithmic Trading Business trader and consultant who advises clients on how Quantitative Trading or sophisticated theories. Instead, he highlights the Wiley Trading B y some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with Quantitative Trading limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game?

latest news, ideas, and trends in quantitative —PETER BORISH, Chairman and CEO, Computer Trading Corporation trading, you’re welcome to visit Dr. Chan’s blog, epchan.blogspot.com, as well as his premium “Dr. Ernest Chan provides an optimal framework for strategy development, back-testing, risk management, content Web site, epchan.com/subscriptions, programming knowledge, and real-time system implementation to develop and run an algorithmic trading which you’ll have free access to with purchase of business step by step in Quantitative Trading.” this book. —YASER ANWAR, trader As an independent trader, you’re free from the con- “Quantitative systematic trading is a challenging field that has always been shrouded in mystery, straints found in today’s institutional environment— seemingly too difficult to master by all but an elite few.

pages: 257 words: 13,443

Statistical Arbitrage: Algorithmic Trading Insights and Techniques
by Andrew Pole
Published 14 Sep 2007

Statistical Arbitrage Algorithmic Trading Insights and Techniques ANDREW POLE John Wiley & Sons, Inc. Statistical Arbitrage Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia. Wiley is globally committed to developing and marketing print and electronic products and services for our customers’ professional and personal knowledge and understanding. The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors.

The Wiley Finance series contains books written specifically for finance and investment professionals as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e-commerce, risk management, financial engineering, valuation, and financial instrument analysis, as well as much more. For a list of available titles, visit our Web site at www.Wiley Finance.com. Statistical Arbitrage Algorithmic Trading Insights and Techniques ANDREW POLE John Wiley & Sons, Inc. c 2007 by Andrew Pole. All rights reserved. Copyright  Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. Wiley Bicentennial logo: Richard J. Pacifico. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the Web at www.copyright.com.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. For more information about Wiley products, visit our Web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Pole, Andrew, 1961– Statistical arbitrage : algorithmic trading insights and techniques / Andrew Pole. p. cm. — (Wiley finance series) Includes bibliographical references and index. ISBN 978-0-470-13844-1 (cloth) 1. Pairs trading. 2. Arbitrage---Mathematical models. 3. Speculation-Mathematical models. I. Title. HG4661.P65 2007 332.64’5 — dc22 2007026257 ISBN 978-0-470-13844-1 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 To Eliza and Marina Contents Preface xiii Foreword xix Acknowledgments CHAPTER 1 Monte Carlo or Bust Beginning Whither?

pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets
by David J. Leinweber
Published 31 Dec 2008

Orders exceeding the size limits for automation were routed to specialists and market makers. Algorithm Wars 67 This was algorithmic trading without algorithms, an early form of direct market access. The first user interfaces were for one stock at a time, electronic versions of simple, single paper buy and sell slips. This became tedious, and soon execution capabilities for a list of names followed. Everyone was happy to be able to produce and screen these lists using their new Lotus 1-2-3 spreadsheets, which totaled everything up nicely to avoid costly errors. We were only a step away from algorithmic trading. Programmers at the order origination end grew more capable and confident in their abilities to generate and monitor an ever larger number of small orders.

The next step was breaking those orders down into pieces small enough to execute electronically, and spreading them out in time. Innovative systems like ITG’s QuantEx, discussed in Chapter 7, allowed traders without large software staffs to use and define analytics and rules to control electronic trading. This began to look like what we consider to be algorithmic trading today. The big news in algorithmic trading in the late 1980s was that you could do it at all. The first algo strategies were based on simple rules, like “send this order out in 10 equal waves, spaced equally in time from open to close.” But these were predictable and easy to game by manipulating the price on a thin name with a limit order placed just before the arrival of the next wave, getting bagged in classic “Spy vs.

Its first investment was in Integrated Analytics Corporation (IAC), which Dale Prouty and I founded to deliver the specialized and less filling expert system environment needed for financial applications. Years later we published a paper, “A Little Artificial Intelligence Goes a Long Way on Wall Street” on the details; an updated version appears in Chapter 7. We called all this “electronic order working” back then, since we didn’t know it was algorithmic trading. How Do You Keep the Rats from Eating the Wires? Shortly after we started the company, a colleague from the AI group at Arthur D. Little, the venerable Cambridge consulting firm, asked me to fill Intr oduction xxxi in for him at the last minute at a technology session at a finance conference being held in Los Angeles; his dog was sick.

pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World
by Christopher Steiner
Published 29 Aug 2012

“This is it, it’s all right here,” Peterffy said, pointing at an IBM computer squatting next to the sole Nasdaq terminal in the room. “We do it all from this.” A tangle of wires ran between the Nasdaq machine and the IBM, which hosted code that dictated what, when, and how much to trade. The Nasdaq employee didn’t realize it, but he had walked in on the first fully automated algorithmic trading system in the world. Peterffy’s setup didn’t merely suggest what to trade, as other systems had in the past. It didn’t simply pump out trades that humans would later carry out. The computer, by way of a surreptitious hack into the trading terminal, made all of the decisions and executed all of the trades.

Some trading houses kept open phone lines between New York and Chicago so that clerks could bark prices back and forth and pounce on large pricing discrepancies. Peterffy’s automated system allowed his traders to harvest not only large mispricings but also smaller ones—and they almost always got to them before others. Peterffy had created the first algorithmic trading operation working from coast to coast. All trading activity from the handhelds was radioed to waiting terminals Peterffy had installed at each exchange. The computers there would then wire the data across the leased phone lines straight to Timber Hill’s offices in the World Trade Center, where it would be received by a large master algorithm called simply the Correlator, which ran phalanxes of code to dissect markets and pinpoint their weaknesses, while dispatching Timber Hill traders in each city to hammer them.

A student expecting an A on such a problem will produce an algorithm that never loses a game (but often plays to a draw). The algorithms used by a high-frequency trader or a speech recognition program work the same way. They’re fed inputs—perhaps the movements of different stock indices, currency rate fluctuations, and oil prices—with which they produce an output: say, buy GE stock. Algorithmic trading is nothing more than relying on an algorithm for the answers of when and what to buy and sell. Building an algorithm with many variables is more difficult than building one to play tic-tac-toe, but the idea is identical. The word algorithm comes from Abu Abdullah Muhammad ibn Musa Al-Khwarizmi, a Persian mathematician from the ninth century who produced the first known book of algebra, Al-Kitab al-Mukhtasar fi Hisab al-Jabr wa l-Muqabala (The Compendious Book on Calculation by Completion and Balancing).

pages: 318 words: 87,570

Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio
by Sal Arnuk and Joseph Saluzzi
Published 21 May 2012

His opinions are sought by leaders, regulators, market participants, and the media and are presented via white papers and Themis’ widely read blog. He is a frequent speaker at industry conferences, such as Trader Forum, Waters, National Organization of Investment Professionals (NOIP), and Fusion IQ’s Big Picture, on issues involving market access, algorithmic trading, and other sell- and buy-side concerns. He also provides expert commentary for media outlets such as the Associated Press, BBC Radio, Bloomberg TV and Radio, BNN, CNBC, Fox Business, NPR, Barron’s, The New York Times, The Wall Street Journal, USA Today, Time, Los Angeles Times, Bloomberg News, Pensions & Investments, and Advanced Trading.

The primary purpose of the stock exchanges has devolved to catering to a class of highly profitable market participants called high frequency traders, or HFTs, who are interested only in hyper-short term trading, investors be damned. The stock exchanges give these HFTs perks and advantages to help them be as profitable as possible, even if doing so adversely affects you, the investors, because HFT firms are the exchanges’ biggest customers. These HFTs use high-powered computers to automatically and algorithmically trade in and out of securities in speeds measured in microseconds (millionths of a second). Although there are few HFTs relative to the number of investors in the marketplace, the following is generally estimated in the industry: • HFTs account for 50–75% of the volume traded on the exchanges each day and a substantial portion of the stock exchanges’ profits

Because their algorithmic models price securities with such an emphasis on nearby prices and robust uninterrupted pricing data flow, when that data displays discrepancies, they withdraw their “liquidity provision” and shut down. The Joint CFTC-SEC Advisory Committee, set up to study and report findings on the events of May 6, 2010, summed it nicely: “In the present environment, where high frequency and algorithmic trading predominate and where exchange competition has essentially eliminated rule-based market maker obligations...even in the absence of extraordinary market events, limit order books can quickly empty and prices can crash.”1 Another concern is the market’s instrument makeup. In 2010, Exchange Traded Products (ETP), including its biggest category, exchange traded funds, or ETFs, reached an asset under management (AUM) level of $1.3 trillion.2 Only ten years prior, ETP AUM totaled a mere $66 billion.3 This represents nearly a 19-fold increase.

pages: 272 words: 76,154

How Boards Work: And How They Can Work Better in a Chaotic World
by Dambisa Moyo
Published 3 May 2021

The algorithm yields a binary buy-sell decision and is unable to consider the logic behind the board’s action. Thus, algorithmic trading feeds into the long-standing concern that the boards of these “dividend aristocrats” are, in effect, hamstrung. They are forced to make expedient and suboptimal decisions—such as paying a dividend instead of making a long-term strategic investment—for fear of provoking a harmful response from automated investors. As algorithmic trading continues to grow and to become more sophisticated, the role of the board becomes more difficult. Globally, regulation is helping preserve some transparency in this trading space, but the opacity of algorithmic trading will no doubt remain a challenge to most boardrooms.

More and more, the trades being made in the financial markets are being executed between machines, with few human decisions involved. Using preprogrammed, algorithmic trading instructions, automated trading enables the purchase or sale of stock orders that are too large to fill all at once—transactions that are sometimes in the billions of dollars. This sort of trading has become extremely common. In 2006, a third of all EU and US stock trades were driven by automatic programs. At the London Stock Exchange, over 40 percent of all orders were entered by algorithmic traders that year. According to a 2019 report by Coherent Market Insights, computer-led or algorithmic trading now accounts for 60 to 73 percent of all US equity trading.

According to a 2019 report by Coherent Market Insights, computer-led or algorithmic trading now accounts for 60 to 73 percent of all US equity trading. Algorithmic trading is widely used by large institutional investors—such as pension funds, mutual funds, investment banks, hedge funds, and insurance companies—that need to execute sizable orders in the financial markets. Through algorithmic trading, these institutions can minimize the cost, market impact, and risk inherent in the execution of particularly large trades. Computers have the advantage over humans of being able to rapidly trade based on large amounts of information. They can react faster to the temporary mispricing of stock and are able to compare stock prices from different markets simultaneously.

Day One Trader: A Liffe Story
by John Sussex
Published 16 Aug 2009

The sanitised, soulless world of electronic trading is a far cry from the dealing pits where traders with strong personalities used a loud voice and brute strength to get an edge. The rise of algorithmic trading and ‘black box’ technology – in which computerised mathematical algorithms are used to power dealing strategies independent of the human hand – is increasingly making the role of the professional trader obsolete. TABB Group, a financial technology consultancy firm, predicted that by 2009 algorithmic trading will account for half of all equity trading in the US. These computer programmes execute the same types of trades that dealers did on the floor – but at a much faster pace.

Of course banks are in a technology arms race to produce platforms which can process ever larger amounts of trades in a shorter space of time. None are going to risk losing their edge in electronic markets by scaling back their dependence on cutting edge trading technology. But the impact of algorithmic trading models malfunctioning can result in banks suffering huge trading losses or financial indexes plummeting to depths that wipe billions of dollars off the value of listed companies in the real economy. A leading expert in algorithmic trading technology at Lehman Brothers, the most high-profile victim of the credit crisis, was reported early in 2008 as saying that the Wall Street bank worried about the computer ‘going wild and going off on its own …’ That was of course before the investment bank’s exposure to sub-prime debt put it out of its misery.

This is a book primarily about the people who make a market and it was the size of their personalities that made it hard to believe that electronic trading could ever supersede them. John Sussex continues to believe that, in extreme circumstances, it is better to have a human making trading decisions than a computer trading programme. And, chastened by his own experience perhaps, he sounds a warning about the risk of algorithmic trading models malfunctioning and putting banks and markets under dangerous stress. Inevitably, as with other aspects of this book, not everyone will agree with John’s opinion – and after all, differences of opinion are what make markets. But there was so much more besides that made the Liffe floor market the vibrant icon of the City financial dealing markets.

pages: 268 words: 81,811

Flash Crash: A Trading Savant, a Global Manhunt, and the Most Mysterious Market Crash in History
by Liam Vaughan
Published 11 May 2020

Making money from Luddite open-outcry traders desperately trying to apply what they’d learned in the pits on a computer was embarrassingly easy. “It was paradise,” recalls Rotter. “All these locals were used to seeing JPMorgan and Goldman orders and front-running, but they couldn’t do that anymore because it was anonymous and they had no idea what they were doing. There was no algorithmic trading yet and the regulators hadn’t brought in all these rules on what you could and couldn’t do.” At twenty-four, Rotter moved to Ireland to set up his own fund with some associates. Capitalizing on the herdlike behavior of the locals, he would, according to reports, load up the ladder with buy orders and wait for others to line up alongside him.

Geithner proposed that he, Gensler, and Schapiro meet with the heads of the major exchanges the following week and drew the conversation to a close. Notwithstanding this information black hole, the government was under pressure to reassure the public that it had matters under control. Throughout the next day and into the weekend, the Flash Crash dominated the press, and the consensus was that the rise in algorithmic trading was to blame. “High-Speed Trading Glitch Costs Investors Billions,” wrote the New York Times. The timing could hardly have been worse. That month, the biggest finance bill since the Great Depression, the Dodd-Frank Wall Street Reform and Consumer Protection bill, was wheedling its way across the floor of the Senate.

And everybody nodded their head except one guy at the end who shook his head that he had a problem.” Riley pleaded with the counterparty to rip up the trade but it refused, and the firm ended up losing around $8 million. He apportions some of the blame to the regulators for failing to curtail the rise of algorithmic trading. “Who let this stuff in the backdoor and who hasn’t controlled it?” he said in an interview on the Stocktwits podcast. “I’m a Chicago guy. Things have gotten out of control with this.” A week after the crash, Gensler, Schapiro, and the heads of the major exchanges convened in a large, ornate meeting room near Geithner’s office at the Treasury.

pages: 327 words: 91,351

Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets
by Tim Bourquin and Nicholas Mango
Published 26 Dec 2012

For example, those who traded successfully in the 1970s and 1980s but continued on with exactly the same strategy couldn’t stay at the top of the game and inevitably encountered frustration. Markets change, and traders have to identify that and be flexible whenever it happens. That has been especially true in recent years, with the increase in high-frequency and algorithmic trading, plus twenty-four-hour markets, electronic exchanges, and the quant world. Even though the nature of the markets has changed, however, I would say for the most part that I’m still in the same niche now that I was in 1980. But there are subtleties within that niche—money management, most notably—that I have changed over time in response to changes in the markets.

I think a lot of people are using it as an excuse for why they’re not profitable. There’s no doubt that high-frequency trading is out there. You see it all the time. You just have to learn to adapt to it. If you are going to trade patterns, you have to understand that high-frequency trading and algorithmic trading are going to distort those patterns to a point where you traditionally would have thought they were broken. Maybe you will see a cup with a handle, or maybe you will see a double-bottom, or maybe you will see a semi-triangle, and price will drop out of that pattern. In the past, you would say, “Okay, that’s it.

Toma: Well, my risk manager mentality means I’m really focusing on the risk, so I’m not too concerned about my profit target at first. I want to focus on how much I can afford to lose and, more importantly, how I’ll know if I’m wrong about the trade. In years past, I would set arbitrary stops, like six ticks or two points in the ES. However, because high volatility and algorithmic trading are so prevalent, that really didn’t tell me that I was wrong. It only indicated that my maximum risk had been hit, so I needed to get out. Here’s one thing that may be contrary to public opinion: A lot of traders want to have two-to-one, three-to-one, or five-to-one risk/reward ratios. Well, that’s great, but if I have a setup that gives me 70 percent, I don’t mind taking one-to-one on that.

pages: 443 words: 51,804

Handbook of Modeling High-Frequency Data in Finance
by Frederi G. Viens , Maria C. Mariani and Ionut Florescu
Published 20 Dec 2011

Lancette, Kiseop Lee, and Yanhui Mi 1.1 1.2 1.3 1.4 1.5 1.6 Introduction, 3 The Statistical Models, 6 Parametric Estimation Methods, 9 Finite-Sample Performance via Simulations, 14 Empirical Results, 18 Conclusion, 22 References, 24 2 A Study of Persistence of Price Movement using High Frequency Financial Data 27 Dragos Bozdog, Ionuţ Florescu, Khaldoun Khashanah, and Jim Wang 2.1 Introduction, 27 2.2 Methodology, 29 2.3 Results, 35 v vi Contents 2.4 Rare Events Distribution, 41 2.5 Conclusions, 44 References, 45 3 Using Boosting for Financial Analysis and Trading 47 Germán Creamer 3.1 3.2 3.3 3.4 3.5 Introduction, 47 Methods, 48 Performance Evaluation, 53 Earnings Prediction and Algorithmic Trading, 60 Final Comments and Conclusions, 66 References, 69 4 Impact of Correlation Fluctuations on Securitized structures 75 Eric Hillebrand, Ambar N. Sengupta, and Junyue Xu 4.1 Introduction, 75 4.2 Description of the Products and Models, 77 4.3 Impact of Dynamics of Default Correlation on Low-Frequency Tranches, 79 4.4 Impact of Dynamics of Default Correlation on High-Frequency Tranches, 87 4.5 Conclusion, 92 References, 94 5 Construction of Volatility Indices Using A Multinomial Tree Approximation Method Dragos Bozdog, Ionuţ Florescu, Khaldoun Khashanah, and Hongwei Qiu 5.1 5.2 5.3 5.4 Introduction, 97 New Methodology, 99 Results and Discussions, 101 Summary and Conclusion, 110 References, 115 97 vii Contents part Two Long Range Dependence Models 117 6 Long Correlations Applied to the Study of Memory Effects in High Frequency (TICK) Data, the Dow Jones Index, and International Indices 119 Ernest Barany and Maria Pia Beccar Varela 6.1 6.2 6.3 6.4 6.5 Introduction, 119 Methods Used for Data Analysis, 122 Data, 128 Results and Discussions, 132 Conclusion, 150 References, 160 7 Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales 163 Alec N.

This does correspond to the previously observed peak in trading activity (Fig. 2.9) at about the same time. We hypothesize that the peak in rare events may be caused by the activation of various trading strategies after the stabilization of the market following the opening. Recall that the histogram presents the rare events detection for ALL equity within a class. This may be evidence of algorithmic trading starting at about the same time, reaching about the same conclusion, placing similar limit orders, and therefore pulling the market in the same direction with relatively little volume. We do underline, however, that this does not destabilize the market. This much is evident from the ensuing pattern of rare events which follows the same trend as before the spike.

The rest of the chapter is organized as follows: Section 3.2 introduces the main methods used in this chapter; Section 3.3 presents the application of boosting to performance evaluation and the generation of balanced scorecards (BSCs); Section 3.4 shows how boosting can be applied to forecast earnings surprise and to algorithmic trading; and Section 3.5 presents the conclusions and recommendations. 3.2 Methods In this section we introduce boosting and how it can be used to support the generation of BSCs. 3.2.1 BOOSTING Adaboost is a general discriminative learning algorithm invented by Freund and Schapire (1997). The basic idea of Adaboost is to repeatedly apply a simple learning algorithm, called the weak or base learner,1 to different weightings of the same training set.

pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War
by Paul Scharre
Published 23 Apr 2018

,” October 28, 2010, http://www.washingtonsblog.com/2010/10/what-percentage-of-u-s-equity-trades-are-high-frequency-trades.html. 200 sometimes called algorithmic trading: Tom C. W. Lin, “The New Investor,” SSRN Scholarly Paper (Rochester, NY: Social Science Research Network, March 3, 2013), https://papers.ssrn.com/abstract=2227498. 200 automated trading decisions to buy or sell: Some writers on automated stock trading differentiate between automated trading and algorithmic trading, using the term algorithmic trading only to refer to the practice of breaking up large orders to execute via algorithm by price, time, or volume, and referring to other practices such as seeking arbitrage opportunities as automated trading.

Lin, “The New Investor,” SSRN Scholarly Paper (Rochester, NY: Social Science Research Network, March 3, 2013), https://papers.ssrn.com/abstract=2227498. 200 automated trading decisions to buy or sell: Some writers on automated stock trading differentiate between automated trading and algorithmic trading, using the term algorithmic trading only to refer to the practice of breaking up large orders to execute via algorithm by price, time, or volume, and referring to other practices such as seeking arbitrage opportunities as automated trading. Others treat algorithmic trading and automated trading as effectively synonymous. 200 Automated trading offers the advantage: Shobhit Seth, “Basics of Algorithmic Trading: Concepts and Examples,” Investopedia, October 10, 2014, http://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp. 201 blink of an eye: “Average Duration of a Single Eye Blink—Human Homo Sapiens—BNID 100706,” accessed June 12, 2017, http://bionumbers.hms.harvard.edu//bionumber.aspx?

Gone are the days of floor traders shouting prices and waving their hands to compete for attention in the furious scrum of the New York Stock Exchange. Approximately three-quarters of all trades made in the U.S. stock market today are executed by algorithms. Automated stock trading, sometimes called algorithmic trading, is when computer algorithms are used to monitor the market and make trades based on certain conditions. The simplest kind of algorithm, or “algo,” is used to break up large trades into smaller ones in order to minimize the costs of the trade. If a single buy or sell order is too large relative to the volume of that stock that is regularly traded, placing the order all at once can skew the market price.

pages: 368 words: 32,950

How the City Really Works: The Definitive Guide to Money and Investing in London's Square Mile
by Alexander Davidson
Published 1 Apr 2008

Hedge funds in particular, but also investment banks and pension funds use algorithmic trading, which is computer-based trading applying mathematical models known as algorithms. It requires fewer staff to administer than traditional trading. The models used will generate both the size and timing of the trade based on a volume-weighted or time-weighted average price. Algorithmic trading can split a large trade into smaller ones to reduce market impact and cut trading costs. Concerns are sometimes raised that algorithmic trading can power a market rise or fall as programmed traders follow momentum triggered by buyers or sellers.

Concerns are sometimes raised that algorithmic trading can power a market rise or fall as programmed traders follow momentum triggered by buyers or sellers. Some firms run programmes known as sniffers that discover momentum in stocks, giving them a cue to jump onto the bandwagon. Algorithms follow many different trading strategies, which can even serve to counteract each other, as fans of algorithmic trading point out. 16 Share trading venues and exchanges Introduction In this chapter, we will look at the exchange trading facilities for UK equities in the competitive environment encouraged by the Markets in Financial Instruments Directive. Read this chapter with Chapter 15, which covers the London Stock Exchange. Overview As the second edition of this book went to press, there are eight recognised investment exchanges (RIEs) in London, some concerned with derivatives.

There is feedback from market participants that there has been a ‘drying up’ of liquidity in small-caps when unsuitable stocks were moved to SETSmm, according to PLUS. The LSE has denied that liquidity is affected. Order books such as SETS (provided by the LSE, and explained in Chapter 15) suit those market participants who prefer alternative methods of trading, such as algorithmic trades, and those who require direct market access to execute their business, according to Wynn-Evans. However, the PLUS platform suits those participants who prefer quote-driven trading, such as retail brokers and market makers, both of which play a valuable role in providing price formation and liquidity in those smaller stocks that trade less frequently.

pages: 571 words: 105,054

Advances in Financial Machine Learning
by Marcos Lopez de Prado
Published 2 Feb 2018

This is a validation that the non-uniform FFT is capturing the expected signals. The second- and third-highest amplitudes have the frequencies of 732 and 52, which are twice-a-day and once-a-week. These are also unsurprising. We additionally applied the non-uniform FFT on the trading volumes and found further evidence of algorithmic trading. Moreover, the signals pointed to a stronger presence of algorithmic trading in recent years. Clearly, the non-uniform FFT algorithm is useful for analyzing highly irregular time series. 22.7 Summary and Call for Participation Currently, there are two primary ways to construct large-scale computing platforms: the HPC approach and the cloud approach.

Since 2010, Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), he has published dozens of scientific articles on ML and supercomputing in the leading academic journals, and he holds multiple international patent applications on algorithmic trading. Marcos earned a PhD in Financial Economics (2003), a second PhD in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a Financial ML course at the School of Engineering.

Bibliography Abu-Mostafa, Y., M. Magdon-Ismail, and H. Lin (2012): Learning from Data, 1st ed. AMLBook. Akansu, A., S. Kulkarni, and D. Malioutov (2016): Financial Signal Processing and Machine Learning, 1st ed. John Wiley & Sons-IEEE Press. Aronson, D. and T. Masters (2013): Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSB, 1st ed. CreateSpace Independent Publishing Platform. Boyarshinov, V. (2012): Machine Learning in Computational Finance: Practical Algorithms for Building Artificial Intelligence Applications, 1st ed. LAP LAMBERT Academic Publishing.

pages: 280 words: 73,420

Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino
by Jim McTague
Published 1 Mar 2011

A few exchanges went even further for HFT subscribers to their data feed and froze some customer orders for a few milliseconds to give them a first shot at taking them and earning a rebate for making the transaction. These were called flash orders. Dan Mathisson, managing director of advanced electronic systems at Credit Suisse, and a pioneer in algorithmic trading, said in his view, flash orders violated the spirit of regulation NMS and weakened the notion of a national market system.3 Levitt also was in favor of banning that practice. In effect, with the assistance of the exchanges, the high-frequency traders were driving around the markets in the equivalent of 1200-horsepower Bugatti Veyron Super Sport roadsters while retail investors whose brokers did not employ similarly sophisticated and expensive systems on their behalf were puttering around in the equivalent of the family sedan.

He had been the one to invite Arnuk and Saluzzi to talk to the staff about high-frequency trading (HFT) in November 2009. The day of the Flash Crash, Clara Vega, an economist with Federal Reserve Board, was at the SEC at the behest of Hu presenting a seminar on her paper, “Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market.” The paper found that high-frequency traders reduce market volatility and provide liquidity to the marker in times of stress. Back in the 1990s, Gensler, a former Goldman Sachs trader, had been one of a cadre of good old boys that included Alan Greenspan and former Treasury Secretary Robert Rubin that had kept the CFTC under Brooksley Born from regulating derivatives despite her repeated warnings that these instruments posed a systemic risk to the financial markets.

He told the High Frequency Trading Review in June 2010 that if one multiplied the penny-per-share profit margin of a typical high-frequency trade by the amount of daily HFT volume, which he estimated at 10 billion shares, the sum would be $2 billion in annual profits. Narang complained, “Some firms, like TABB, have grossly overstated the amount of HFT revenues by expanding the definition to include algorithmic trading in general, regardless of the actual holding period of the trader. Large stat-arb firms,5 which hold positions for multiple days, are not engaging in HFT. By definition, if you are able to hold positions for that long, you are not part of the HFT arms race, and [you] don’t need superfast technology to access opportunities before they disappear!”

pages: 285 words: 86,853

What Algorithms Want: Imagination in the Age of Computing
by Ed Finn
Published 10 Mar 2017

Drawing on the historical figure of the automaton, a remarkable collection of Mechanical Turk-powered poetry titled Of the Subcontract, and Adam Smith’s conception of empathy in his Theory of Moral Sentiments, I explore the consequences of computational capitalism on politics, empathy, and social value. The root of the algorithmic sea change is the reimagination of value in computational terms. Chapter 5 leads with the flash crash in 2010 and the growing dominance of algorithmic trading in international markets (described by journalist Michael Lewis’s Flash Boys, among others) to frame a reading of Bitcoin and related cryptocurrencies. By defining the unit of exchange through computational cycles, Bitcoin fundamentally shifts the faith-based community of currency from a materialist to an algorithmic value system.

McCloud’s notion of the gutter reveals how the ground rules, even the physics, of implementation have epistemological implications. The current transformation of global finance largely depends on the arbitrage of money and information between two temporal universes that are almost completely mutually exclusive. The obelisk that Lewis describes is a human metaphor for algorithmic trading, almost like a stylized postcard from a computational universe where time is everything. HFT algorithms translate the gutter—the gaps between placing an order and executing it, for example—into an arena for competitive computation. In both the narrative and financial examples, the gutter is the place where time is translated into meaning and value.

Index Abortion, 64 Abstraction, 10 aesthetics and, 83, 87–112 arbitrage and, 161 Bogost and, 49, 92–95 capitalism and, 165 context and, 24 cryptocurrency and, 160–180 culture machines and, 54 (see also Culture machines) cybernetics and, 28, 30, 34 desire for answer and, 25 discarded information and, 50 effective computability and, 28, 33 ethos of information and, 159 high frequency trading (HFT) and imagination and, 185, 189, 192, 194 interfaces and, 52, 54, 92, 96, 103, 108, 110–111 ladder of, 82–83 language and, 2, 24 Marxism and, 165 meaning and, 36 money and, 153, 159, 161, 165–167, 171–175 Netflix and, 87–112, 205n36 politics of, 45 pragmatist approach and, 19–21 process and, 2, 52, 54 reality and, 205n36 Siri and, 64–65, 82–84 Turing Machine and, 23 (see also Turing Machine) Uber and, 124–126, 129 Wiener and, 28–29, 30 work of algorithms and, 113, 120, 123–136, 139–149 Adams, Douglas, 123 Adams, Henry, 80–81 Adaptive systems, 50, 63, 72, 92, 174, 176, 186, 191 Addiction, 114–115, 118–119, 121–122, 176 AdSense, 158–159 Advent of the Algorithm, The (Berlinski), 9, 24 Advertisements AdSense and, 158–159 algorithmic arbitrage and, 111, 161 Apple and, 65 cultural calculus of waiting and, 34 as cultural latency, 159 emotional appeals of, 148 Facebook and, 113–114 feedback systems and, 145–148 Google and, 66, 74, 156, 158–160 Habermas on, 175 Netflix and, 98, 100, 102, 104, 107–110 Uber and, 125 Aesthetics abstraction and, 83, 87–112 arbitrage and, 109–112, 175 culture machines and, 55 House of Cards and, 92, 98–112 Netflix Quantum Theory and, 91–97 personalization and, 11, 97–103 of production, 12 work of algorithms and, 123, 129, 131, 138–147 Agre, Philip, 178–179 Airbnb, 124, 127 Algebra, 17 Algorithmic reading, 52–56 Algorithmic trading, 12, 20, 99, 155 Algorithms abstraction and, 2 (see also Abstraction) arbitrage and, 12, 51, 97, 110–112, 119, 121, 124, 127, 130–134, 140, 151, 160, 162, 169, 171, 176 Berlinski on, 9, 24, 30, 36, 181 Bitcoin and, 160–180 black boxes and, 7, 15–16, 47–48, 51, 55, 64, 72, 92–93, 96, 136, 138, 146–147, 153, 162, 169–171, 179 blockchains and, 163–168, 171, 177, 179 Bogost and, 16, 33, 49 Church-Turing thesis and, 23–26, 39–41, 73 consciousness and, 2, 4, 8, 22–23, 36–37, 40, 76–79, 154, 176, 178, 182, 184 DARPA and, 11, 57–58, 87 desire and, 21–26, 37, 41, 47, 49, 52, 79–82, 93–96, 121, 159, 189–192 effective computability and, 10, 13, 21–29, 33–37, 40–49, 52–54, 58, 62, 64, 72–76, 81, 93, 192–193 Elliptic Curve Digital Signature Algorithm and, 163 embodiment and, 26–32 encryption, 153, 162–163 enframing and, 118–119 Enlightenment and, 27, 30, 38, 45, 68–71, 73 experimental humanities and, 192–196 Facebook and, 20 (see also Facebook) faith and, 7–9, 12, 16, 78, 80, 152, 162, 166, 168 gamification and, 12, 114–116, 120, 123–127, 133 ghost in the machine and, 55, 95 halting states and, 41–46 high frequency trading (HFT) and, 151–158, 168–169, 177 how to think about, 36–41 ideology and, 7, 9, 18, 20–23, 26, 33, 38, 42, 46–47, 54, 64, 69, 130, 144, 155, 160–162, 167, 169, 194 imagination and, 11, 55–56, 181–196 implementation and, 47–52 intelligent assistants and, 11, 57, 62, 64–65, 77 intimacy and, 4, 11, 35, 54, 65, 74–78, 82–85, 97, 102, 107, 128–130, 172, 176, 185–189 Knuth and, 17–18 language and, 24–28, 33–41, 44, 51, 54–55 machine learning and, 2, 15, 28, 42, 62, 66, 71, 85, 90, 112, 181–184, 191 mathematical logic and, 2 meaning and, 35–36, 38, 44–45, 50, 54–55 metaphor and, 32–36 Netflix Prize and, 87–91 neural networks and, 28, 31, 39, 182–183, 185 one-way functions and, 162–163 pragmatist approach and, 18–25, 42, 58, 62 process and, 41–46 programmable culture and, 169–175 quest for perfect knowledge and, 13, 65, 71, 73, 190 rise of culture machines and, 15–21 (see also Culture machines) Siri and, 59 (see also Siri) traveling salesman problem and Turing Machine and, 9 (see also Turing Machine) as vehicle of computation, 5 wants of, 81–85 Weizenbaum and, 33–40 work of, 113–149 worship of, 192 Al-Khwārizmī, Abū ‘Abdullāh Muhammad ibn Mūsā, 17 Alphabet Corporation, 66, 155 AlphaGo, 182, 191 Amazon algorithmic arbitrage and, 124 artificial intelligence (AI) and, 135–145 Bezos and, 174 Bitcoin and, 169 business model of, 20–21, 93–94 cloud warehouses and, 131–132, 135–145 disruptive technologies and, 124 effective computability and, 42 efficiency algorithms and, 134 interface economy and, 124 Kindle and, 195 Kiva Systems and, 134 Mechanical Turk and, 135–145 personalization and, 97 physical logistics of, 13, 131 pickers and, 132–134 pragmatic approach and, 18 product improvement and, 42 robotics and, 134 simplification ethos and, 97 worker conditions and, 132–134, 139–140 Android, 59 Anonymous, 112, 186 AOL, 75 Apple, 81 augmenting imagination and, 186 black box of, 169 cloud warehouse of, 131 company value of, 158 effective computability and, 42 efficiency algorithms and, 134 Foxconn and, 133–134 global computation infrastructure of, 131 iOS App Store and, 59{tab} iTunes and, 161 massive infrastructure of, 131 ontology and, 62–63, 65 physical logistics of, 131 pragmatist approach and, 18 product improvement and, 42 programmable culture and, 169 search and, 87 Siri and, 57 (see also Siri) software and, 59, 62 SRI International and, 57, 59 Application Program Interfaces (APIs), 7, 113 Apps culture machines and, 15 Facebook and, 9, 113–115, 149 Her and, 83 identity and, 6 interfaces and, 8, 124, 145 iOS App Store and, 59 Lyft and, 128, 145 Netflix and, 91, 94, 102 third-party, 114–115 Uber and, 124, 145 Arab Spring, 111, 186 Arbesman, Samuel, 188–189 Arbitrage algorithmic, 12, 51, 97, 110–112, 119, 121, 124, 127, 130–134, 140, 151, 160, 162, 169, 171, 176 Bitcoin and, 51, 169–171, 175–179 cultural, 12, 94, 121, 134, 152, 159 differing values and, 121–122 Facebook and, 111 Google and, 111 high frequency trading (HFT) and, 151–158, 168–169, 177 interface economy and, 123–131, 139–140, 145, 147 labor and, 97, 112, 123–145 market issues and, 152, 161 mining value and, 176–177 money and, 151–152, 155–163, 169–171, 175–179 Netflix and, 94, 97, 109–112 PageRank and, 159 pricing, 12 real-time, 12 trumping content and, 13 valuing culture and, 155–160 Archimedes, 18 Artificial intelligence (AI) adaptive systems and, 50, 63, 72, 92, 174, 176, 186, 191 Amazon and, 135–145 anthropomorphism and, 83, 181 anticipation and, 73–74 artificial, 135–141 automata and, 135–138 DARPA and, 11, 57–58, 87 Deep Blue and, 135–138 DeepMind and, 28, 66, 181–182 desire and, 79–82 ELIZA and, 34 ghost in the machine and, 55, 95 HAL and, 181 homeostat and, 199n42 human brain and, 29 intellectual history of, 61 intelligent assistants and, 11, 57, 62, 64–65, 77 intimacy and, 75–76 job elimination and, 133 McCulloch-Pitts Neuron and, 28, 39 machine learning and, 2, 15, 28, 42, 62, 66, 71, 85, 90, 112, 181–186 Mechanical Turk and, 12, 135–145 natural language processing (NLP) and, 62–63 neural networks and, 28, 31, 39, 182–183, 185 OS One (Her) and, 77 renegade independent, 191 Samantha (Her) and, 77–85, 154, 181 Siri and, 57, 61 (see also Siri) Turing test and, 43, 79–82, 87, 138, 142, 182 Art of Computer Programming, The (Knuth), 17 Ashby, Ross, 199n42 Asimov, Isaac, 45 Atlantic, The (magazine), 7, 92, 170 Automation, 122, 134, 144, 188 Autopoiesis, 28–30 Babbage, Charles, 8 Banks, Iain, 191 Barnet, Belinda, 43–44 Bayesian analysis, 182 BBC, 170 BellKor’s Pragmatic Chaos (Netflix), 89–90 Berlinski, David, 9, 24, 30, 36, 181, 184 Bezos, Jeff, 174 Big data, 11, 15–16, 62–63, 90, 110 Biology, 2, 4, 26–33, 36–37, 80, 133, 139, 185 Bitcoin, 12–13 arbitrage and, 51, 169–171, 175–179 blockchains and, 163–168, 171–172, 177, 179 computationalist approach and cultural processing and, 178 eliminating vulnerability and, 161–162 Elliptic Curve Digital Signature Algorithm and, 163 encryption and, 162–163 as glass box, 162 intrinsic value and, 165 labor and, 164, 178 legitimacy and, 178 market issues and, 163–180 miners and, 164–168, 171–172, 175–179 Nakamoto and, 161–162, 165–167 one-way functions and, 162–163 programmable culture and, 169–175 transaction fees and, 164–165 transparency and, 160–164, 168, 171, 177–178 trust and, 166–168 Blockbuster, 99 Blockchains, 163–168, 171–172, 177, 179 Blogs early web curation and, 156 Facebook algorithms and, 178 Gawker Media and, 170–175 journalistic principles and, 173, 175 mining value and, 175, 178 Netflix and, 91–92 turker job conditions and, 139 Uber and, 130 Bloom, Harold, 175 Bogost, Ian abstraction and, 92–95 algorithms and, 16, 33, 49 cathedral of computation and, 6–8, 27, 33, 49, 51 computation and, 6–10, 16 Cow Clicker and, 12, 116–123 Enlightenment and, 8 gamification and, 12, 114–116, 120, 123–127, 133 Netflix and, 92–95 Boolean conjunctions, 51 Bosker, Bianca, 58 Bostrom, Nick, 45 Bowker, Geoffrey, 28, 110 Boxley Abbey, 137 Brain Pickings (Popova), 175 Brain plasticity, 38, 191 Brand, Stewart, 3, 29 Brazil (film), 142 Breaking Bad (TV series), 101 Brin, Sergei, 57, 155–156 Buffett, Warren, 174 Burr, Raymond, 95 Bush, Vannevar, 18, 186–189, 195 Business models Amazon and, 20–21, 93–94, 96 cryptocurrency and, 160–180 Facebook and, 20 FarmVille and, 115 Google and, 20–21, 71–72, 93–94, 96, 155, 159 Netflix and, 87–88 Uber and, 54, 93–94, 96 Business of Enlightenment, The (Darnton) 68, 68 Calculus, 24, 26, 30, 34, 44–45, 98, 148, 186 CALO, 57–58, 63, 65, 67, 79, 81 Campbell, Joseph, 94 Campbell, Murray, 138 Capitalism, 12, 105 cryptocurrency and, 160, 165–168, 170–175 faking it and, 146–147 Gawker Media and, 170–175 identity and, 146–147 interface economy and, 127, 133 labor and, 165 public sphere and, 172–173 venture, 9, 124, 174 Captology, 113 Carr, Nicholas, 38 Carruth, Allison, 131 Castronova, Edward, 121 Cathedral and the Bazaar, The (Raymond), 6 Cathedral of computation, 6–10, 27, 33, 49, 51 Chess, 135–138, 144–145 Chun, Wendy Hui Kyong, 3, 16, 33, 35–36, 42, 104 Church, Alonzo, 23– 24, 42 Church-Turing thesis, 23–26, 39–41 Cinematch (Netflix), 88–90, 95 Citizens United case, 174 Clark, Andy, 37, 39–40 Cloud warehouses Amazon and, 135–145 interface economy and, 131–145 Mechanical Turk and, 135–145 worker conditions and, 132–134, 139–140 CNN, 170 Code.

pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information
by Frank Pasquale
Published 17 Nov 2014

Financial intermediaries also spare small-time investors the trouble of actually understanding the business model and future prospects of what they invest in. “No need to worry if it’s a bit of a black box,” a broker may counsel about a hot tip. “It’s our job to understand the details.” Sadly, many workers who earnestly contribute to 401(k) plans mistake the unglamorous realities of fixed-income arbitrage, algorithmic trading, and mind-numbing derivative contracts for the glitter of venture capital jackpots. Investors like to think of their money supporting brave innovators and entrepreneurs. But how many really 128 THE BLACK BOX SOCIETY know the ultimate destinations of their dollars? As Doug Henwood has shown, nearly all of the activity in the current stock market is transfers of existing shares.113 The trading simply reallocates claims to the future productivity of existing firms.

Finance’s pervasive short-termism crowds out visionaries.114 Even for those used to the American solicitude for moneyed interests, finance debates are extraordinarily skewed toward the sector’s insiders and away from broader social concern about what useful ser vices they actually provide to the rest of the society. The trend toward self-reference reaches a reductio ad absurdum in an avantgarde form of black box finance: high-speed algorithmic trading. The Low Social Value of High-Frequency Trading Modern equity markets are very complex.115 For example, consider what happens when an investor logs into an account at a brokerage to place an order (all within a second, given automation). The broker will sometimes send the trade to wholesalers.

This bare signaling is another version of the black box problems illuminated in credit ratings or credit default swaps. The mere existence of an AAA rating, or insurance from AIG, led to a FINANCE’S ALGORITHMS 131 false sense of security for many investors. Here, buy and sell signals can take on a life of their own, leading to momentum trading and herding.124 Algorithmic trading can create extraordinary instability and frozen markets when split-second trading strategies interact in unexpected ways.125 Consider, for instance, the flash crash of May 6, 2010, when the stock market lost hundreds of points in a matter of minutes.126 In a report on the crash, the CFTC and SEC observed that “as liquidity completely evaporated,” trades were “executed at irrational prices as low as one penny or as high as $100,000.”127 Traders had programmed split-second algorithmic strategies to gain a competitive edge, but soon found themselves in the position of a sorcerer’s apprentice, unable to control the technology they had developed.128 Though prices returned to normal the same day, there is no guarantee future markets will be so lucky.

pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market
by Scott Patterson
Published 11 Jun 2012

In other words, a vicious self-reinforcing feedback loop. The Flash Crash had proven this wasn’t merely a fanciful nightmare scenario bandied about by apocalyptic market Luddites. The question tormenting experts was how far the loop would go next time. Progress Software, a firm that tracks algorithmic trading, predicted that a financial institution would lose one billion dollars or more in 2012 when a rogue algorithm went “into an infinite loop … which cannot be shut down.” And since the computer programs were now linked across markets—stock trades were synced to currencies and commodities and futures and bonds—and since many of the programs were very similar and were massively leveraged, the fear haunting the minds of the Plumbers was that the entire system could snap like a brittle twig in a matter of minutes.

Calculating that most firms wouldn’t have the ability to make a profit by rapidly trading options in the decimal era, Bodek believed he’d have a golden opportunity to become a major player by taking the risk and, with TW’s help, deploying models sophisticated enough to manage the risk. Trading began in August 2008. The strategy Bodek designed was the culmination of a complex algorithmic trading tradition that had started at Hull and that he’d carried on at Goldman and UBS. He’d started with the premise that he could model the theoretical value of all options as implied by the price of the underlying stock. Throughout the trading day, there were small swings in the prices of the options that signaled to the Machine that they had swung away from their true theoretical value.

In 2004, Dan Mathisson at Credit Suisse built a dark pool called Crossfinder. Pipeline Trading, run by a nuclear physicist and a former president of Nasdaq, rolled out a dark pool for big block trading the same year. Goldman Sachs would build a dark pool called Sigma X. Even Getco would eventually launch a dark pool. As algorithmic trading grew, large investors were finding it harder to trade large chunks of stock. More and more trades were sliced and diced into small, round-numbered pieces—two hundred, three hundred shares—that algos could more easily juggle. The algos deployed complex methods to hunt out the large whale orders the big firms traded, such as “pinging” dark pools with orders that they canceled seconds later.

pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions
by Brian Christian and Tom Griffiths
Published 4 Apr 2016

“Sequential Decision Making with Relative Ranks: An Experimental Investigation of the ‘Secretary Problem.’” Organizational Behavior and Human Decision Processes 69 (1997): 221–236. Sen, Amartya. “Goals, Commitment, and Identity.” Journal of Law, Economics, and Organization 1 (1985): 341–355. Sethi, Rajiv. “Algorithmic Trading and Price Volatility.” Rajiv Sethi (blog), May 7, 2010, http://rajivsethi.blogspot.com/2010/05/algorithmic-trading-and-price.html. Sevcik, Kenneth C. “Scheduling for Minimum Total Loss Using Service Time Distributions.” Journal of the ACM 21, no. 1 (1974): 66–75. Shallit, Jeffrey. “What This Country Needs Is an 18¢ Piece.” Mathematical Intelligencer 25, no. 2 (2003): 20–23.

He’s seemingly the only person in the world willing to pay, in this case, $49 for a stock that the market is apparently valuing at under $40, but he doesn’t care; he’s seen the quarterly reports, he’s certain in what he knows. Investors are said to fall into two broad camps: “fundamental” investors, who trade on what they perceive as the underlying value of a company, and “technical” investors, who trade on the fluctuations of the market. The rise of high-speed algorithmic trading has upset the balance between these two strategies, and it’s frequently complained that computers, unanchored to the real-world value of goods—unbothered at pricing a texbook at tens of millions of dollars and blue-chip stocks at a penny—worsen the irrationality of the market. But while this critique is typically leveled at computers, people do the same kind of thing too, as any number of investment bubbles can testify.

a sale price of more than $23 million: The pricing on this particular Amazon title was noticed and reported on by UC Berkeley biologist Michael Eisen; see “Amazon’s $23,698,655.93 book about flies,” April 23, 2011 on Eisen’s blog it is NOT junk, http://www.michaeleisen.org/blog/?p=358. worsen the irrationality of the market: See, for instance, the reactions of Columbia University economist Rajiv Sethi in the immediate wake of the flash crash. Sethi, “Algorithmic Trading and Price Volatility.” save the entire herd from disaster: This can also be thought of in terms of mechanism design and evolution. It is better on average for any particular individual to be a somewhat cautious herd follower, yet everyone benefits from the presence of some group members who are headstrong mavericks.

pages: 306 words: 82,909

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back
by Bruce Schneier
Published 7 Feb 2023

As these events show, HFT and autonomous trading systems can be more risky than typical trading by human traders simply because of their speed and volume. And HFT clearly disadvantages those who don’t have access to algorithmic trading systems. Unlike some of the other hacks in this section, and in spite of its gross unfairness, HFT has been normalized. In the US, the Financial Industry Regulatory Authority has imposed some basic regulations designed to increase disclosure of the methods underlying algorithmic trading systems; the EU has similar rules. Neither is doing much to slow the practice. At its high point in 2009–2010, 60% to 70% of US trading was attributed to high-frequency trading.

Tying automated trading to “sentiment analysis”—so that trading programs buy when a stock becomes a meme or sell when bad news goes viral—can make pump-and-dumps and smear campaigns much more profitable. But the most virulent of all modern exchange hacks is high-frequency trading, or HFT. Instead of making use of true, albeit secret, information or disseminating disinformation, HFT exploits public information at lightning speed. HFT is a form of algorithmic trading that exploits the price differentials that occur when large trade orders are placed, usually by pension funds or insurance companies. (These massive orders can have a significant impact on stock prices.) HFT algorithms detect these orders, as well other events that are likely to affect stock prices, and then profit from them.

How I Became a Quant: Insights From 25 of Wall Street's Elite
by Richard R. Lindsey and Barry Schachter
Published 30 Jun 2007

On the bright side, I had learned enough and had produced a first model for the program trading. I was disappointed at the turn of events. I had enjoyed working with the program trading team. But the silver lining for me was that I ended up publishing the work and the model itself has become so widely used as a basis for algorithmic trading.21 At the time, I had JWPR007-Lindsey 128 May 7, 2007 16:55 h ow i b e cam e a quant hoped to develop a model and implement it at Morgan Stanley. That is not how it worked out, but in the end it was a great introduction to quant research. For a first job on Wall Street, things went pretty well.

The book was published in late 1996 by Irwin Publications under the title Black-Scholes and Beyond. Irwin was later acquired by McGrawHill publications. 21. I ended up publishing the work in the paper Optimal Liquidation of Portfolio Transactions with Robert Algren. Institutional Investor published an article about Algorithmic Trading in its November 2004 issue titled “The Orders Battle” that noted this article “helped lay the groundwork for arrival-price algorithms being developed on Wall Street.” 22. Technically speaking, this is a variational problem. In particular the set of all possible paths is infinite dimensional and the optimal path for a given level of risk aversion is the solution to the Euler-Lagrange equation. 23.

He is a founding member of the board of Math for America, a nonprofit dedicated to improving the quality of mathematics teaching in the United States. He is also a member of the board the Mathematical Finance program at University of Chicago. Dr. Chriss has published extensively in quantitative finance – including “Optimal Execution of Portfolio Transactions” a seminal paper on algorithmic trading, “Optimal Portfolios from Ordering Information,” and the book Black-Scholes and Beyond: Modern Option Pricing. Dr. Chriss holds an BS and PhD in mathematics from University of Chicago and an MS in mathematics from California Institute of Technology. Andrew Davidson is president and founder of Andrew Davidson & Co., Inc., a consulting firm specializing in the application of analytical tools to investment management.

pages: 366 words: 94,209

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity
by Douglas Rushkoff
Published 1 Mar 2016

To be clear, the algorithms are providing no service. Any liquidity they might create is more than compensated for by the liquidity they take away when they’re seeking to generate volatility or panic selling. They disadvantage not only human brokers but also the individual investors that a digital stock market was supposed to empower. Algorithmic trading doesn’t happen on a laptop connected to the net by Wi-Fi. It requires the kind of hardware, connectivity, and real-estate location that only the wealthiest, most established firms can afford. It may be disruptive to trading, but it only enhances the advantages of the traditional players—or at least the firms they worked for before they were replaced by machines.

acquisitions, 78 Acxiom, 32, 40–41 advertising, 20–21. See also marketing big data and, 42 branding and, 20, 35–37 “likes” economy and, 35–37 AFL-CIO Housing Investment Trust, 210 Agency.com, 196 Airbnb, 98–99, 213, 219, 222 destructive destruction and, 100 peer-to-peer commerce enabled by, 45, 46 algorithmic trading, 179–84 Alphabet, 78 Amazon, 50, 76, 83, 93, 219, 229 destructive destruction and, 100 highly centralized sales platform of, 29 as platform monopoly, 87–90 Amazon Associates, 89 Amazon Mechanical Turk, 49n, 50, 90, 214, 222 Amazon Prime, 90 American Revolution, 71–72, 75 Ameritrade, 177, 178 amplification, 70, 73 Amsted Industries, 117 Anderson, Chris, 26, 33 Andreessen, Marc, 191–92 angel investors, 187, 188 AngelList, 201 Aoki, Steve, 36 Apple, 37, 76, 80, 83, 141, 218 Ariel, 209–10 aristocracy, 17–18, 22, 70, 128–29, 133, 230 Aristotle, 69 artificial intelligence, 90–91 artisanal economy, 16–18, 21, 22, 226, 233–34 arts, funding of, 236 Atkinson, Anthony B., 65 austerity, 136–37 auto attendants, 14 Bandcamp, 29–30 Barber, Brad, 177 Barnes & Noble, 83, 87 barter, 127 barter exchanges, 159 Basecamp, 59–60 BASF, 107 Battle-Bro, 121 Bauwens, Michael, 221 bazaars, 16–18 money and, 127 obsolescence of, caused by corporations, 70–71 Bell, Daniel, 53 Belloc, Hilaire, 229 benefit corporations, 119 Ben & Jerry’s, 80, 205 Benna, Ted, 171 BerkShare, 154–55 Best Buy, 90 Bezos, Jeff, 90, 92–93 Biewald, Lukas, 49–50 big data, 39–44 data point collection and comparisons of, 41–42 game changing product invention reduced by reliance on, 43 predicting future choices, as means of, 41, 42–43 reduction in spontaneity of customers and, 43 social graphs and, 40 suspicion of, as increasing value of data already being sold, 43–44 traditional market research, distinguished, 41 Big Shift, 76 biopiracy, 218 biotech crash of 1987, 6 Bitcoin, 143–49, 150–51, 152, 219, 222 BitTorrent, 142–43, 219 Blackboard, 95–96 Blackstone Group, 115 black swans, 183 blockchain, 144–51, 222 Bitcoin, 144, 145, 146, 147, 149, 222 decentralized autonomous corporations (DACs) and, 149–50 Blogger, 8, 31 Bloomberg, 182 Bodie, Zvie, 174 Borders, 83, 87 bot programs, 37 bounded investing, 210–15 Bovino, Beth Ann, 81–82 Brand, Russell, 36 branding, 20 social, and “likes” economy, 35–37 Branson, Richard, 121 Brin, Sergey, 92–93 Bristol Pounds, 156 British East India Company, 71–72 Brixton Pounds, 156 brokered barter system, 127 Brynjolfsson, Erik, 23, 53 Buffett, Warren, 168, 209 burn rate, 190 Bush, Jeb, 227–28 Calacanis, Jason, 201 Calvert, 209–10 Campbell Soup Company, 119 capital.

K., 229 Circuit City, 90 Citizens United case, 72 Claritas, 32 click workers, 50 climate change, 135, 227–28, 237 coin of the realm, 128–29 collaboration as corporate strategy, 106–7 colonialism, 71–72 commons, 215–23 co-owned networks and, 220–23 history of, 215–16 projects inspired by, 217–18 successful, elements of, 216–17 tragedy of, 215–16 worker-owned collectives and, 219–20 competencies, of corporations, 79–80 Connect+Develop, 107 Consumer Electronics Show, 19 Consumer Reports,33 contracting with small and medium-sized enterprises, 112 cooperative currencies, 160–65 favor banks, 161 LETS (Local Exchange Trading System), 163–65 time dollar systems, 161–63 co-owned networks, 220–23 corporations, 68–82 acquisition of startups, growth through, 78 amplifying effect of, 70, 73 Big Shift and, 76 cash holdings of, 76, 77–78 competency of, 79–80 cost reduction, growth through, 79–80 decentralized autonomous corporations (DACs), 149–50 Deloitte’s study of return on assets (ROA) of, 76–77 distributive alternative to platform monopolies, 93–97 evaluation of, 69–74 extractive nature of, 71–72, 73, 74, 75, 80–82 growth targets, meeting, 68–69 income inequality and, 81–82 limits to corporate model, 75–76, 80–82 managerial and financial methods to deliver growth by, 77–79 monopolies (See monopolies) obsolescence created by, 70–71, 73 offshoring and, 78–79 personhood of, 72, 73–74, 90, 91 recoding of, 93–97, 125–26 repatriation and, 80 retrieval of values of empire and, 71–72, 73 as steady-state enterprises, 97–123 Costco, 74 cost reduction, and corporate growth, 79–80 Couchsurfing.com, 46 crashes of 1929, 99 of 2007, 133–34 biotech crash, of 1987, 6 flash crash, 180 Creative Commons, 215 creative destruction, 83–87 credit, 132–33 credit-card companies, 143–44 crowdfunding, 38–39, 198–201 crowdsharing apps, 45–49 crowdsourcing platforms, 49–50 Crusades, 16 Cumbrian Pounds, 156 Curitiba, Brazil modified LETS program, 164–65 Daly, Herman, 184 data big, 39–44 getting paid for our own, 44–45 “likes” economy and, 32, 34–36 in pre-digital era, 40 Datalogix, 32 da Vinci, Leonardo, 236 debt, 152–54 decentralized autonomous corporations (DACs), 149–50 deflation, 169 Dell, 115–16 Dell, Michael, 115–16 Deloitte Center for the Edge, 76–77 destructive destruction, 100 Detroit Dollars, 156 digital distributism, 224–39 artisanal era mechanisms and values retrieved by, 233–34 developing distributive businesses, 237–38 digital industrialism compared, 226 digital technology and, 230–31 historical ideals of distributism, 228–30 leftism, distinguished, 231 Pope Francis’s encyclical espousing distributed approach to land, labor and capital, 227–28 Renaissance era values, rebirth of, 235–37 subsidiarity and, 231–32 sustainable prosperity as goal of, 226–27 digital economy, 7–11 big data and, 39–44 destabilizing form of digitally accelerated capitalism, creation of, 9–10 digital marketplace, development of, 24–30 digital transaction networks and, 140–51 disproportionate relationship between capital and value in, 9 distributism and, 224–39 externalizing cost of replacing employees in, 14–15 industrialism and, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 industrial society, distinguished, 11 “likes” and similar metrics, economy of, 30–39 platform monopolies and, 82–93, 101 digital industrialism, 13–16, 23–24, 101–2, 201 digital distributism compared, 226 diminishing returns of, 93 externalizing costs and, 14–15 growth agenda and, 14–15, 23–24 human data as commodity under, 44 income disparity and, 53–54 labor and land pushed to unbound extremes by, 214 “likes” economy and, 33 reducing bottom line as means of creating illusion of growth and, 14 digital marketplace, 24–30 early stages of e-commerce, 25–26 highly centralized sales platforms of, 29 initial treatment of Internet as commons, 25 “long tail” of widespread digital access and, 26 positive reinforcement feedback loop and, 28 power-law dynamics and, 26–29 removal of humans from selection process in, 28 digital transaction networks, 140–51 Bitcoin, 143–49, 150–51, 152 blockchains and, 144–51 central authorities, dependence on, 142 decentralized autonomous corporations (DACs) and, 149–50 PayPal, 140–41 theft and, 142 direct public offerings (DPOs), 205–6 discount brokerages, 176–78 diversification, 208, 211 dividends, 113–14, 208–10 dividend traps, 113 Dorsey, Jack, 191–92 Draw Something, 192, 193 Drexler, Mickey, 116 dual transformation, 108–9 dumbwaiter effect, 19 Dutch East India Company, 71, 89, 131 eBay, 16, 26, 29, 45, 140 education industry, 95–97 Eisenhower administration, 52–53, 63, 75 Elberse, Anita, 28 employee-owned companies, 116–18 Enron, 133, 171n Eroski, 220 eSignal, 178 EthicalBay, 221 E*Trade, 176, 177 Etsy, 16, 26, 30 expense reduction, and corporate growth, 78–79 Facebook, 4, 31, 83, 93, 96, 201 data gathering and sales by, 41, 44 innovation by acquisition of startups, 78 IPO of, 192–93, 195 psychological experiments conducted on users by, 32–33 factors of production, 212–14 Fairmondo, 221 Family Assistance Plan, 63 family businesses, 103–4, 231–32 FarmVille, 192 favor banks, 161 Febreze Set & Refresh, 108 Federal Reserve, 137–38 feedback loop, and positive reinforcement, 28 Ferriss, Tim, 201 feudalism, 17 financial services industry, 131–33, 171–73, 175 Fisher, Irving, 158 flash crash, 180 flexible purpose corporations, 119–20 flow, investing in, 208–10 Forbes,88, 173, 174 40-hour workweek, reduction of, 58–60 401(k) plans, 171–74 Francis, Pope, 227, 228, 234 Free, Libre, Open Knowledge (FLOK) program, 217–18 Free (Anderson), 33 free money theory, local currencies based on, 156–59 barter exchanges, 159 during Great Depression, 158–59 self-help cooperatives, 159 stamp scrip, 158–59 tax anticipation scrip, 159 Wörgls, 157–58 frenzy, 98–99 Fried, Jason, 59 Friedman, Milton, 64 Friendster, 31 Frito-Lay, 80 front running, 180–81 Fulfillment by Amazon, 89 Fureai Kippu (Caring Relationship Tickets), 162 Future of Work initiative, 56n Gallo, Riso, 103–4 Gap, 116 Gates, Bill, 186 General Electric, 132 General Public License (GPL) for software, 216 Gesell, Silvio, 157 GI Bill, 99 Gimein, Mark, 147 Gini coefficient of income inequality, 81–82, 92 global warming, 135, 227–28, 237 GM, 80 Goldman Sachs, 133, 195 gold standard, 139 Google, 8, 48, 78, 83, 90–91, 93, 141, 218 acquisitions by, 191 business model of, 37 data sales by, 37, 44 innovation by acquisition of startups, 78 IPO of, 194–95 protests against, 1–3, 5, 98–99 grain receipts, 128 great decoupling, 53 Great Depression, 137, 158–59 Great Exhibition, 1851, 19 Greenspan, Alan, 132–33 growth, 1–11 bazaars, and economic expansion in late Middle Ages, 16–18 central currency and, 126, 129–31, 133–36 digital industrialism, growth agenda of, 14–15, 23–24 highly centralized e-commerce platforms and, 29 startups, hypergrowth expected of, 187–91 as trap (See growth trap) growth trap, 4–5, 68–123 central currency as core mechanism of, 133–34 corporations as program and, 68–82 platform monopolies and, 82–93, 101 recoding corporate model and, 93–97 steady-state enterprises and, 98–123 guaranteed minimum income programs, 62–65 guaranteed minimum wage public jobs, 65–66 guilds, 17 Hagel, John, 76–77 Hardin, Garrett, 215–16 Harvard Business Review,108–9 Heiferman, Scott, 196–97 Henry VIII, King, 215, 229 Hewlett-Packard UK, 112 high-frequency trading (HFT), 179–80 Hilton, 115 Hobby Lobby case, 72 Hoffman, Reid, 61 Holland, Addie Rose, 205–6 holograms, 235 Homeport New Orleans, 121 housing industry, 135 Huffington, Arianna, 34, 35, 201 Huffington Post, 34, 201 human role in economy, 13–67 aristocracy’s efforts to control peasant economy, 17–18 bazaars and, 16–18 big data and, 39–44 chartered monopolies and, 18 decreasing employment and, 30–39 digital marketplace, impact of, 24–30 industrialism and, 13–16, 18–24, 44 “likes” economy and, 30–39 reevaluation of employment and adopting policies to decrease it and, 54–67 sharing economy and, 44–54 Hurwitz, Charles, 117 IBM, 90–91, 112 inclusive capitalism, 111–12 income disparity corporate model and, 81–82 digital technology as accelerating, 53–54 Gini coefficient of, 81–82, 92 growth trap and, 4 power-law dynamics and, 27–28, 30 public service options for reducing, 65–66 IndieGogo, 30, 199 individual retirement accounts (IRAs), 171 industrial farming, 134–35 industrialism, 18–24 branding and, 20 digital, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 disempowerment of workers and, 18–19 human connection between producer and consumer, loss of, 19–20 isolation of human consumers from one another and, 20–21 mass marketing and, 19–20 mass media and, 20–21 purpose of, 18–19, 22 value system of, 18–19 inflation, 169 Instagram, 31 Intercontinental Exchange, 182 interest, 129–31 investors/investing, 70, 72, 168–223 algorithmic trading and, 179–84 bounded, 210–15 commons model for running businesses and, 215–23 crowdfunding and, 198–201 derivative finance, volume of, 182 digital technology and, 169–70, 175–84 direct public offerings (DPOs) and, 205–6 discount brokerages and, 176–78 diversification and, 208, 211 dividends and, 208–10 flow, investing in, 208–10 high-frequency trading (HFT) and, 179–80 in low-interest rate environment, 169–70 microfinancing platforms and, 202–4 platform cooperatives and, 220–23 poor performance of do-it-yourself traders and, 177–78 retirement savings and, 170–75 startups and, 184–205 ventureless capital and, 196–205 irruption, 98 i-traffic, 196 iTunes, 27, 29, 34, 89 J.

pages: 733 words: 179,391

Adaptive Markets: Financial Evolution at the Speed of Thought
by Andrew W. Lo
Published 3 Apr 2017

However, much of this monitoring can be automated with preset rules that alert the investor—text messaging, smartphone notification, and social media make this simple—to pay attention and make a decision when the need arises. This is particularly straightforward for passive strategies that are dedicated to achieving the returns of an index. Existing technology can easily integrate active risk management with passive investing through algorithmic trading, derivatives, securities exchange design, telecommunications, and back-office infrastructure. Thanks to these new technologies, the existing link between active risk management and active investing, and passive risk management and passive investing, can now be severed. DISBANDING THE ALPHA BETA SIGMA FRATERNITY Here’s one concrete example of how to sever this link.

He first introduced me to several models in theoretical biology that were particularly relevant to economics, and in 1999 he and I published a paper outlining the possibility of applying evolutionary arguments to the Efficient Markets Hypothesis.22 He has since published many more papers applying ideas in physics and biology to finance. In addition to his academic pursuits, Doyne cofounded a successful quantitative hedge fund called Prediction Company with his fellow physicist, Norman Packard. This fund uses multiple algorithmic trading strategies to make money in stock markets around the world. Based on his experience, Doyne published a fascinating article in 2002 titled “Market Force, Ecology, and Evolution,” where he developed a precise analogy between financial markets and biological ecologies. His theory builds on Grossman and Stiglitz’s insight that if markets were perfectly efficient there would be no motive for financial trading, so markets can never be perfectly efficient.

If we add to these events the technology failures associated with the initial public offerings of BATS and Facebook (March 23 and May 18, 2012), Knight Capital Group’s $458 million loss from accidental electronic trades, and the two-and-a-half hour Bloomberg terminal outage (April 17, 2015) that postponed a multi-billion-dollar government debt issue, a pattern emerges. Evolution at the speed of thought hasn’t completely adapted yet to trading at the speed of light. Markets can’t give up financial technology cold turkey—the advantages of algorithmic trading and electronic markets are simply too great. Rather, we have to demand better, more robust technology that is so advanced it becomes foolproof and invisible to the human operator. Every successful technology has gone through such a process of maturation: the rotary telephone versus the iPhone, the scalpel versus the laser, the incandescent light bulb versus LEDs, and paper road maps versus Google Maps and GPS.

pages: 323 words: 95,939

Present Shock: When Everything Happens Now
by Douglas Rushkoff
Published 21 Mar 2013

Temporally compressed though it may be, it is still based on making conclusions. Value is created over time. It is a product of the cause-and-effect, temporal universe—however much it may be abstracted. A majority of equity trading today is designed to circumvent that universe of time-generated value altogether. Computer-driven or algorithmic trading, as it is now called, has its origins in the arms race. Mathematicians spent decades trying to figure out a way to evade radar. They finally developed stealth technology, which really just works by using electric fields to make a big thing—like a plane—appear to be many little things. Then, in 1999, an F-117 using stealth was shot down over Serbia.

The algorithms actually shoot out little trades, much like radar, in order to measure the response of the market and then infer if there are any big movements going on. The original algorithms are, in turn, on the lookout for these little probes and attempt to run additional countermoves and fakes. This algorithmic dance—what is known as black box trading—accounts for over 70 percent of Wall Street trading activity today. In high-frequency, algorithmic trading, speed is everything. Algorithms need to know what is happening and make their moves before their enemy algorithms can react and adjust. No matter how well they write their programs, and no matter how powerful the computers they use, the most important factor in bringing algorithms up to speed is a better physical location on the network.

While fractal geometry can certainly help us find strong, repeating patterns within the market activity of the 1930s Depression, it did not predict the crash of 2007. Nor did the economists using fractals manage to protect their banks and brokerages from the systemic effects of bad mortgage packages, overleveraged European banks, or the impact of algorithmic trading on moment-to-moment volatility. More recently, in early 2010, the world’s leading forecaster applying fractals to markets, Robert Prechter, called for the market to enter a decline of such staggering proportions that it would dwarf anything that has happened in the past three hundred years.16 Prechter bases his methodology on the insights of a 1930s economist, Ralph Nelson Elliott, who isolated a number of the patterns that seem to recur in market price data.

The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski
Published 27 Sep 2018

Simons made $1.6 billion in 2016 alone, and this wasn’t even his best year.20 Called “the best physics and mathematics department in the world,”21 Renaissance “avoids hiring anyone with even the slightest whiff of Wall Street bona fides.”22 14 Chapter 1 High Latency versus position timeline Latency Traditional long-term investment Algorithmic trading Low HFT Short Long How long position held Figure 1.6 Machine learning is driving algorithmic trading, which is faster than traditional long-term investment strategies and more deliberate than high-frequency trading (HFT) in stock markets. Many different kinds of machine learning algorithms are combined to achieve best returns. No longer involved in the daily operation of D.

Learning How to Make Money More than 75 percent of trading on the New York Stock Exchange is automated (figure 1.6), fueled by high-frequency trades that move into and out of positions in fractions of a second. (When you don’t have to pay for each transaction, even small advantages can be parlayed into big profits.) Algorithmic trading on a longer time scale takes into account longer-term trends based on big data. Deep learning is getting better and better at making both more money and higher profits.19 The problem with predicting the financial markets is that the data are noisy and conditions are not stationary— psychology can change overnight after an election or international conflict.

pages: 297 words: 108,353

Boom and Bust: A Global History of Financial Bubbles
by William Quinn and John D. Turner
Published 5 Aug 2020

The question also arises as to whether more recent changes in financial markets will make bubbles more or less likely in the future. Two of the 214 PREDICTING BUBBLES major changes in financial markets over the past two decades are the rise of algorithmic and high-frequency trading and asset management. Algorithmic trading is where buy and sell trades are automatically executed by computers based on pre-programmed instructions, and highfrequency trading is a type of algorithmic trading that can execute a large volume of trades in mere fractions of a second. Algorithmic and highfrequency trading are obvious increases in marketability, suggesting that they may make bubbles more likely. Recent experience has shown that such trading has the potential to move stock markets a great deal in a very short space of time: on 6 May 2010, the Dow Jones Industrial Average dropped 10 per cent in a matter of minutes, recovering these losses almost immediately.

Ponzi’s Scheme: The True Story of a Financial Legend, New York: Random House, 2005. 281 Index 1720 bubbles outside of Britain, France or the Netherlands, 31 Abbott, Chief Justice, 46 academic theories of bubbles during and after the Dot-Com Bubble, 167–9 Accles, Ltd., 102–3 Act to restore the publick Credit 1721, 28 Ahern, Bertie, 171 algorithmic trading, 215 Amazon.com, 160 America Online, 157, 160, 163 American International Group (AIG), 178–9 Andreesen, Marc, 153, 167 Anglo Irish Bank, 180 Argus, 81, 87 austerity after the Japanese bubbles, 148 Australasian Insurance and Banking Record, 78, 85, 86, 87 bank failures. See financial crisis Bank of Australasia, the, 93 Bank of England in relation to the British Bicycle Mania, 108 in relation to the financial crisis of 1825, 48, 54–6 in relation to the financial crisis of 1847, 74 in relation to the first emerging market bubble, 52–3 in relation to the Great Railway Mania, 65–6, 68 in relation to the Subprime Bubble, 178, 179 role in the South Sea Bubble, 24, 37 Bank of International Settlements, 141 Bank of Japan, 137, 142, 146, 148 banking crisis.

pages: 218 words: 62,889

Sabotage: The Financial System's Nasty Business
by Anastasia Nesvetailova and Ronen Palan
Published 28 Jan 2020

The crisis of 2007–9 is commonly explained as the outcome of poor global macroeconomic policies and inadequate regulatory frameworks. But what is the connection between global macro-policies and banks having lost their moral compass? If these incidences are isolated cases of failed governance, how come the most sophisticated, mobile financial corporations which invest billions in IT to cut down a fraction of time in algorithmic trading, and in controlling asset values that reach trillions of dollars, were so poorly managed that one hand did not know what the other had been doing for such a long time? What does failure on such a superlative scale tell us about the culture of finance and, more specifically, the business standard in the industry?

The broker can use their own funds, or can borrow funds to purchase a large quantity of Apple shares, and the moment those shares reach the upper ask price of their client, the broker sells the shares they bought to the client, at the upper ask price, and with a tidy profit. With changes spurred by new technologies, the practice is not only institutionalized but also automated. The spread of algorithmic trading has enabled big firms like Citadel or Gekko to ‘front-run’ asset managers’ orders. Their profits come from two sources of privileged access. On the one hand, they know the price their client is prepared to pay for a security. On the other, they know or, in the case of so-called dark pools, control the current market for that security, and can profit from a nanosecond advantage of ‘front-running’ the client’s order, thus manipulating the overall price level in the market.

pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More
by Luke Dormehl
Published 4 Nov 2014

It is not “cyberbole,” then, to suggest that algorithms represent a crucial force in our participation in public life. They go further than the four main areas I have chosen to look at in this book, too. For instance, algorithmic trading now represents a whopping 70 percent of the U.S. equity market, running on supercomputers that are able to buy and sell millions of shares at practically the speed of light. Algorithmic trading has become a race measured in milliseconds, with billions of dollars dependent on the laying of new fiber-optic cables that will shave just five milliseconds off the communication time between financial markets in London and New York.

pages: 288 words: 86,995

Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford
Published 13 Sep 2021

I think we can get something of a preview of how all this might unfold by looking at another type of warfare—the continuous battle between AI-powered trading systems on Wall Street. Algorithmic trading now dominates the daily transactions on the major stock exchanges, accounting for as much as eighty percent of overall trading volume in the United States. As far back as 2013, a group of physicists studied financial markets and published a paper in the journal Nature declaring that “an emerging ecology of competitive machines featuring ‘crowds’ of predatory algorithms” existed and that algorithmic trading had perhaps already progressed beyond the control—and even comprehension—of the humans who designed the systems.20 Those algorithms now incorporate the latest advances in AI, their influence on markets has increased dramatically, and the ways in which they interact have grown even more incomprehensible.

pages: 357 words: 95,986

Inventing the Future: Postcapitalism and a World Without Work
by Nick Srnicek and Alex Williams
Published 1 Oct 2015

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways.

See Michael Albert, Parecon: Life After Capitalism (London: Verso, 2004), Part 3. 29.Nick Dyer-Witheford, ‘Red Plenty Platforms’, Culture Machine 14 (2013), p. 13. 30.Measured in terms of floating operations per second, the difference between 1969 and what is expected by 2019 is 107 versus 1018. Ibid., p. 8. Index 1968, 16–7, 63, 188n33 15M, 11, 22 abstraction, 10, 15, 36, 44, 81 additive manufacturing, 110, 143, 150, 182 affect, 7–8, 113–4, 140–1 afro-futurism, 139, 141 AI (artificial intelligence), 110, 143 alienation, 14–5, 82 algorithmic trading, 111 Allende, Salvador, 148, 149 alternativism, 194n95 Althusser, Louis, 81, 141–2 anti-globalisation, 3, 159, 162 anti-war, 3, 5, 22, 162 Apple, 146, Arab Spring, 131, 159 Argentina, 37–9, 173 authenticity, 10–1, 15, 27, 82, 180 automation, 1–2, 86, 88–9, 94–5, 97–8, 104–5, 109–17, 122, 127, 130, 143, 150–1, 167, 171–4, 181–2, 203n15, 212n121, 214n161, 215n9, 218n45 banking, 43–6, 61, 147 Beveridge Report, 118 big data, 110, 111 Bolshevik Revolution, 131 Bolsheviks, 137 Russian Revolution, 139 Brazil, 75, 119, 147, 157, 169 Bretton Woods, 61–2 Brown, Michael, 173 care labour, 113–4 Chicago School, 51, 59–60 Chile, 52, 62, 148, 149, 150 China, 87, 89, 97, 170 class, 14, 16–7, 20–1, 25, 53, 64–5, 87, 91, 96–102, 116, 120, 122–3, 126–7, 132–3, 155–62, 170, 173–4, 189n1, 206n44, 233n119, 233n4, 233n5, 234n18 Cleaver, Eldridge, 91–2 climate change, 13–4, 116 colonialism, 73, 75–6, 96–7, 225n3 common sense, 9–11, 21–2, 40, 54–5, 58–60, 63–7, 72, 131–7 communisation, 92, 225n5 competitive subjects, 63–5, 99, 124 complex systems, 13–4, conspiracy theories, 14–5 cosmism, 139 Critchley, Simon, 72 cryptocurrencies, 143, 182 Cybersyn, 149–150 debt, 9, 22, 35–6, 94 demands, 6–7, 30, 33, 107–8, 130, 159–62, 167 no demands, 7, 34–5, 107, 186n3 non-reformist demands, 108 transitional demands, 215n5 democracy, 31–3, 182 direct democracy, 27–9, 31–3, 164, 190n8 direct action, 6, 11, 27–9, 35–6 education, 64, 99, 104, 141–5, 165–6 Egypt, 32–4, 190n21 energy, 2, 16,19,41, 42–43, 116, 147, 148, 150–51, 164, 171, 178, 179, 182, 183 Engels, Friedrich, 79 Erhard, Ludwig, 57 ethics, 42 work ethic, 124–6 evictions, 8, 12, 36 feminism, 18–21, 122, 138, 161 Fisher, Antony, 58–9, 196n34 food miles, 42–3 fracking, 8 France, 17, 62, 149, 167 free time, 80, 115–6, 120–1, 167, 219n50 freedom, 63–5, 120–1, 126–7, 180–1 negative freedom, 79 synthetic freedom, 78–83 Friedman, Milton, 56, 59–61 full employment, 98–100 future, 1, 71–5, 175–8, 181–3 G20, 6, 94 gender, 21, 41, 90, 122 Germany, 45, 56–7 ghettos, 95–6 Gramsci, Antonio, 132, 165 Graeber, David, 33 grand narratives, 73–4 Great Depression, 46, 65, 99–101, 114–5 Harvey, David, 135 Hayek, Friedrich, 54–6 Holzer, Jenny, 175, 178 horizontalism, 18, 26–39 housing, 8, 28, 35, 48, 77, 80, 95, 96, 148, 159, 167, 168, humanism, 81–3, 180–1 hyperstition, 74–5, 138–9 Iceland, 34, 164 idleness, 85–6 immediacy, 10–1 immigration, 101–2, 161 India, 87, 97–8, 130 inequality, 22, 80, 93–4 informal economy, 95–8, 203n10, 206n44, 210n95 Institute of Economic Affairs, 58–9 Iranian Revolution, 131 Jameson, Fredric, 14, 92, 198n10 Japan, 147 Jimmy Reid Foundation, 117 jobless recovery, 94–5 Jobs, Steve, 179 Johnson, Boris, 172 Kalecki, Michał, 120 Krugman, Paul, 118 labour, 2, 3,9, 17, 20, 21, 33, 38, 48, 52, 58, 61–3, 74, 79, 81, 83, 85–143, 148, 150, 151, 156–8, 161, 163–181, 182 Laclau, Ernesto, 155, 159 Lafargue, Paul, 115, language, 81, 132, 160, 164–5 leisure, 85–6 Leninism, 17, 131, 188n33 Live Aid, 8 localism, 40–6 locavorism, 41–2 Lucas Aerospace, 147 Luxemburg, Rosa, 15 Lyotard, Francois, 73, 74 Manhattan Institute for Policy Research, 58, 59 marches, 6, 30, 49 Marikana massacre, 170 Marinaleda, 48 Marx, Karl, 73, 79, 85, 86, 92, 115, 119, 121, 122, 132, 142, 156, 158, 180 Mattick, Paul, 92, 118 media, 2, 7–8, 31, 36, 52, 58, 60, 63, 67, 88, 118, 125–6, 129, 133–5, 163–5, 176, 182 Mirowski, Philip, 66 modernity, 23, 63, 69–85, 86, 131, 176, 181 modernisation, 23, 60, 63, 137, 174 Mont Pelerin Society, 54, 86, 134, 164, 166 MPS, 55, 56, 58, 66, 67, 134 Move Your Money, 44 Murray, Charles, 59 Musk, Elon, 179 National Union of Rail, Maritime and Transport Workers, 172 negative solidarity, 20, 37 neoliberalism, 3, 12, 20–3, 47, 49, 51–67, 70, 72, 108, 116, 117, 119, 121, 124, 134, 141, 142, 148, 156, 176, 179, 183 neoliberal, 7, 9, 14–16, 20, 21, 37, 47, 49, 73, 93, 99, 118, 126, 127, 129, 131–2, 134, 135, 162, 169, 174, 176, 181 New Economics Foundation, 117, 144 new left, 18–22 New Zealand, 151 occupations, 5, 7, 10, 11, 29–31, 34, 49, 94, 172 Occupy Wall Street, 3, 6, 7, 11, 18, 22, 26, 29–38, 126, 133, 158, 159, 160, 162, 189n1 ordoliberals, 54, 57 organic intellectual, 165–6 Overton Window, 134, 139 Partido dos Trabalhadores, 169 parties, political, 2, 10, 16, 17, 18, 20, 21, 30, 34, 39, 46, 59, 105, 116, 118, 124, 129, 162, 164, 168, 169 personal savings, 94 Piketty, Thomas, 140 Plan C, 117 planning, 1, 15, 56, 141, 142, 149, 151, 182 Plant, Sadie, 82 Podemos, 159, 160, 169 police, 6, 30, 33, 36, 37, 102, 133, 161, 168, 171, 173 postcapitalism, 17, 38, 130, 143, 145, 150, 151, 158, 168, 178, 180 postcapitalist, 12, 15, 16, 32, 34, 83, 109, 115, 126, 136, 143, 145, 150, 152, 153, 157, 179, 180 Post-Crash Economic Society, 143 post-work, 23, 69, 83, 85, 86, 105, 107–127, 129, 130, 138, 140, 141, 153, 155, 156, 158, 161, 163, 164, 167, 174, 175, 176, 177, 178 Pou Chen Group, 170 power, 1, 2, 7, 9, 10, 14, 15, 18–21, 26, 28–30, 33, 36, 43, 46, 48, 49, 59, 61, 62, 65, 73, 78, 79, 80, 81, 87, 88, 93, 100, 108, 111, 116, 120, 123, 127, 130–5, 146, 148, 151, 153, 155–74, 175, 176, 179, 180, 182 precarity, 9, 86, 88, 93, 94, 95, 98, 104, 121, 123, 126, 130, 156, 157, 166, 167, 173, 174 precarious, 2, 64, 117, 129, 167 Precarious Workers Brigade, 117 premature deindustrialisation, 97, 98 primitive accumulation, 87, 89, 90, 96, 97 prison, 90, 102, 103, 119, 133 incarceration, 102, 103, 104, 105, 161 productivity, 74, 88, 97, 110–17, 125, 150, 167 progress, 21, 23, 46, 71–5, 77, 107, 114, 115, 120, 126, 131, 138, 179, 180 protests, 1, 7, 18, 22, 28, 31, 37, 49, 66, 153, 164 psychopathologies, 64 radio-frequency identification, 110 race, 14, 31, 90, 102, 103, 140, 156, 171, 172 Reagan, Ronald, 60, 62, 66, 70 Republican Party (US), 135 resistance, 2, 5, 12, 15, 30, 35, 46–8, 49, 69, 72, 74, 83, 114, 124, 134, 158, 173, 181 Rethinking Economics, 143 Robinson, Joan, 87 Roboticisation, 110, 209n69 mechanisation, 95, 101 Rolling Jubilee, 9 Samuelson, Paul, 142 second machine age, 111 secular stagnation, 143 self-driving cars, 110, 111, 113, 173 shadow work, 115 slavery, 74, 90, 95, 103 slow food, 41, 42 slum, 86, 96–8, 102, 104 social democracy, 3, 17, 46, 66, 70, 167, 176 social democratic, 10, 13, 16, 17, 19, 21, 22, 47, 57, 72, 80, 98, 100, 108, 123, 127, 168 social media, 1, 8, 182 South Africa, 119, 157, 170 Spain, 12, 22, 34, 35, 45, 159, 164 stagflation, 19, 27, 61, 65, 100 Stalinist, 17, 18, 137 strategy, 12, 20, 26, 49, 56, 67, 117, 127, 131–3, 136, 148, 153, 156, 163, 164 strategic, 8, 9, 11, 12, 14, 15, 17, 18, 25, 28, 29, 35, 49, 52, 55, 66, 70, 77, 108, 116, 131, 135, 157, 162, 163, 164, 170, 171, 173, 174 strikes, 9, 10, 28, 36, 37, 116, 120, 157, 167, 170–3 suicide, 94 surplus populations, 40, 86, 88–94, 96–97, 101–3, 104, 105, 120, 130, 166–7, 173, 203n10 Syriza, 159, 160 tactics, 6, 10, 11, 15, 18, 19, 26, 28, 39, 40, 49, 157, 164, 171–4 Tahrir Square, 32, 34 Taylorism, 152 technology, 1, 3, 72, 81, 88, 89, 98, 109, 110, 111, 129, 136, 137, 145–8, 150–3, 178, 179, 182 Thatcher, Margaret, 59, 60, 62, 66, 70, 72, 100 think tanks, 16, 55, 56, 58, 59, 60, 63, 67, 117, 134, 135, 165 trade unions, 10, 27, 47, 59, 61, 62, 71, 105, 116, 117, 124, 129, 148, 162, 166 labour unions, 16, 171 unions, 17, 18, 20, 27, 30, 44 UK Uncut, 126 unemployment, 20, 56, 60, 79, 86–98, 99, 100, 101, 101, 102, 115, 116, 118, 121, 123, 125, 127, 129, 147, 159, 161, 168, 170, 173, 207n44 United Automobile Workers, 170 United Kingdom UK, 8, 20, 40, 42, 45, 52, 54, 56, 58, 61, 62, 92, 93, 94, 117, 118, 126, 144, 147, 151, 172 United States, 8, 18, 29, 36, 44, 45, 59, 62, 78, 92, 95, 103, 114, 118, 123, 133, 135, 138, 167 America, 6, 16, 30, 38, 47, 56, 62, 76, 95, 97, 98, 100, 101, 102, 103, 110, 164 universal basic income, 108, 118, 123, 127, 140, 143 basic income, 80, 108, 118, 119, 120, 121, 122, 123, 124, 127, 129, 130, 140, 143, 164, 165, 167 universalism, 69, 70, 75–8, 83, 119, 132, 175, 197n1, 199n40 USSR, 62, 63, 79, 139 Soviet Union, 57, 70, 74, 139 utopia, 3, 28, 32, 35, 48, 54, 58, 60, 66, 69, 70, 72, 108, 113, 114, 132, 136, 137, 138, 139, 140, 141, 143, 145, 146, 150, 153, 177, 179, 181, 182 vanguard functions, 163 Venezuela, 169 wages, 2, 71, 87, 90, 91, 93, 94, 97, 98, 101, 111, 120, 122, 125, 156, 166, 167 welfare, 14, 38, 57, 59, 61, 62, 63, 64, 71, 73, 90, 100, 101, 103, 105, 118, 119, 122, 124 Wilde, Oscar, 182 withdrawal, 11, 47, 48, 69, 131, 182 exit, 47, 48, 181 escape, 3, 9, 11, 38, 69, 107, 114, 139, 165, 178 work, 1, 2, 16, 17, 23, 32, 36, 41, 44, 47, 64, 71, 85, 86, 90–6, 98, 100, 101, 103–5, 108, 109, 110–7, 120–7, 130, 131, 132, 133, 134, 136, 140, 141, 142, 143, 147, 150, 151, 152, 157, 163, 165, 166, 170, 173, 174, 176, 177, 178, 181 wage labour, 74, 85, 86, 87, 89, 90, 92, 103, 104, 105, 120, 136, 141, 180 job, 2, 38, 41, 47, 48, 63, 64, 79, 85, 86, 88, 89, 90, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 110, 111, 113, 114–23, 124, 125, 126, 129, 147, 148, 161, 166, 167, 171 worker-controlled factories, 38, 39 workfare, 59, 100, 104 World Trade Organisation, 6 World War II, 46, 54, 56, 57, 115, 156 Zapatistas, 11, 22, 26, 35 zero-hours contracts, 93 Žižek, Slavoj, 140 Zuccotti Park, 31, 32

pages: 312 words: 91,538

The Fear Index
by Robert Harris
Published 14 Aug 2011

He felt Quarry’s hand grasp his shoulder, the weight increasing as the Englishman rose to his feet. ‘Right then, we can at last get started. So – welcome, friends, to Geneva. It’s almost eight years since Alex and I set up shop together, using his intelligence and my looks, to create a very special kind of investment fund, based exclusively on algorithmic trading. We started with just over a hundred million dollars in assets under management, a big chunk of it courtesy of my old friend over there, Bill Easterbrook, of AmCor – welcome, Bill. We made a profit that first year, and we’ve gone on making a profit every year, which is why we are now one hundred times larger than when we started, with AUM of ten billion dollars.

Obviously we have vastly more pairs of averages than that to work with – several millions of them – but the principle can be simply stated: the most reliable guide to the future is the past. And we only have to be right about the markets fifty-five per cent of the time to make a profit. ‘When we started out, not many people could have guessed how important algorithmic trading would turn out to be. The pioneers in this business were frequently dismissed as quants, or geeks, or nerds – we were the guys who none of the girls would dance with at parties—’ ‘That’s still true,’ interjected Quarry. Hoffmann waved aside the interruption. ‘Maybe it is, but the successes we have achieved at this firm speak for themselves.

pages: 135 words: 26,407

How to DeFi
by Coingecko , Darren Lau , Sze Jin Teh , Kristian Kho , Erina Azmi , Tm Lee and Bobby Ong
Published 22 Mar 2020

Each Set is an ERC20 token consisting of a basket of cryptocurrencies that automatically rebalances its holdings based on the strategy that you choose. In other words, SET essentially implements cryptocurrency trading strategies in the form of tokens. ~ What kinds of Sets are there? There are two kinds of Sets: (i) Robo Sets and (ii) Social Trading Sets. Robo Sets Robo Sets are algorithmic trading strategies that buys and sells tokens based on predefined rules encoded in smart contracts. There are currently 4 main types of algorithmic strategies, namely: Buy and Hold: This strategy realigns the portfolio to its target allocation to prevent overexposure to any one token and spreads the risk over other tokens.

pages: 923 words: 163,556

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures
by Frank J. Fabozzi
Published 25 Feb 2008

In 2006, more than half of the total traded FX volume was executed through electronic trading.171 Some advantages that ECNs provide to market participants are increased speed of trade execution, lower transaction costs, access to a greater number and variety of market players, opportunity for clients to observe the whole order book, etc. The growth of algorithmic trading strategies is related to the expansion of electronic trading. An algorithmic trading strategy, in general, relies on a computer program to execute trades based on a set of rules determined in advance, in order to minimize transaction costs. Depending on the complexity of those rules, such computer programs are capable of firing and executing multiple trade orders per second.

Index Absolute convergence condition Absolute cumulative frequency Absolute data Absolute deviation Absolute frequency denotation density, inclusion obtaining Acceptance region determination Accumulated frequencies, computation Adjusted goodness-of-fit measure Adjusted R-squared (adjusted R2) Aggregated risk, behavior simulation Aitken’s generalized least squares (GLS) estimator Algorithmic trading strategies, growth Alpha-levels (α-levels), chi-square distribution Alpha-percentile (α-percentile) concept obtaining value Alpha-quantile (α-quantile) denotation Alpha-stable density functions (α-stable density functions), comparison Alpha-stable distribution (α-stable distribution) assumption decay second moment, non-existence variance Alpha-stable random variables, parameters Alternative hypothesis null hypothesis, contrast representation American call option American International Group (AIG), stock price Analysis of variance (ANOVA) component pattern AR.

pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies
by Nick Bostrom
Published 3 Jun 2014

Other systems specialize in finding arbitrage opportunities within or between markets, or in high-frequency trading that seeks to profit from minute price movements that occur over the course of milliseconds (a timescale at which communication latencies even for speed-of-light signals in optical fiber cable become significant, making it advantageous to locate computers near the exchange). Algorithmic high-frequency traders account for more than half of equity shares traded on US markets.69 Algorithmic trading has been implicated in the 2010 Flash Crash (see Box 2). * * * Box 2 The 2010 Flash Crash By the afternoon of May, 6, 2010, US equity markets were already down 4% on worries about the European debt crisis. At 2:32 p.m., a large seller (a mutual fund complex) initiated a sell algorithm to dispose of a large number of the E-Mini S&P 500 futures contracts to be sold off at a sell rate linked to a measure of minute-to-minute liquidity on the exchange.

After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to post facto cancellation under existing trade rules).70 The retelling here of this episode is a digression because the computer programs involved in the Flash Crash were not particularly intelligent or sophisticated, and the kind of threat they created is fundamentally different from the concerns we shall raise later in this book in relation to the prospect of machine superintelligence. Nevertheless, these events illustrate several useful lessons. One is the reminder that interactions between individually simple components (such as the sell algorithm and the high-frequency algorithmic trading programs) can produce complicated and unexpected effects. Systemic risk can build up in a system as new elements are introduced, risks that are not obvious until after something goes wrong (and sometimes not even then).71 Another lesson is that smart professionals might give an instruction to a program based on a sensible-seeming and normally sound assumption (e.g. that trading volume is a good measure of market liquidity), and that this can produce catastrophic results when the program continues to act on the instruction with iron-clad logical consistency even in the unanticipated situation where the assumption turns out to be invalid.

Journal of International Trade and Economic Development 17 (3): 379–90. INDEX A Afghan Taliban 215 Agricultural Revolution 2, 80, 261 AI-complete problem 14, 47, 71, 93, 145, 186 AI-OUM, see optimality notions AI-RL, see optimality notions AI-VL, see optimality notions algorithmic soup 172 algorithmic trading 16–17 anthropics 27–28, 126, 134–135, 174, 222–225 definition 225 Arendt, Hannah 105 Armstrong, Stuart 280, 291, 294, 302 artificial agent 10, 88, 105–109, 172–176, 185–206; see also Bayesian agent artificial intelligence arms race 64, 88, 247 future of 19, 292 greater-than-human, see superintelligence history of 5–18 overprediction of 4 pioneers 4–5, 18 Asimov, Isaac 139 augmentation 142–143, 201–203 autism 57 automata theory 5 automatic circuit breaker 17 automation 17, 98, 117, 160–176 B backgammon 12 backpropagation algorithm 8 bargaining costs 182 Bayesian agent 9–11, 123, 130; see also artificial agent and optimality notions Bayesian networks 9 Berliner, Hans 12 biological cognition 22, 36–48, 50–51, 232 biological enhancement 36–48, 50–51, 142–143, 232; see also cognitive enhancement boxing 129–131, 143, 156–157 informational 130 physical 129–130 brain implant, see cyborg brain plasticity 48 brain–computer interfaces 44–48, 51, 83, 142–143; see also cyborg Brown, Louise 43 C C. elegans34–35, 266, 267 capability control 129–144, 156–157 capital 39, 48, 68, 84–88, 99, 113–114, 159–184, 251, 287, 288, 289 causal validity semantics 197 CEV, see coherent extrapolated volition Chalmers, David 24, 265, 283, 295, 302 character recognition 15 checkers 12 chess 11–22, 52, 93, 134, 263, 264 child machine 23, 29; see also seed AI CHINOOK 12 Christiano, Paul 198, 207 civilization baseline 63 cloning 42 cognitive enhancement 42–51, 67, 94, 111–112, 193, 204, 232–238, 244, 259 coherent extrapolated volition (CEV) 198, 211–227, 296, 298, 303 definition 211 collaboration (benefits of) 249 collective intelligence 48–51, 52–57, 67, 72, 142, 163, 203, 259, 271, 273, 279 collective superintelligence 39, 48–49, 52–59, 83, 93, 99, 285 definition 54 combinatorial explosion 6, 9, 10, 47, 155 Common Good Principle 254–259 common sense 14 computer vision 9 computing power 7–9, 24, 25–35, 47, 53–60, 68–77, 101, 134, 155, 198, 240–244, 251, 286, 288; see also computronium and hardware overhang computronium 101, 123–124, 140, 193, 219; see also computing power connectionism 8 consciousness 22, 106, 126, 139, 173–176, 216, 226, 271, 282, 288, 292, 303; see also mind crime control methods 127–144, 145–158, 202, 236–238, 286; see also capability control and motivation selection Copernicus, Nicolaus 14 cosmic endowment 101–104, 115, 134, 209, 214–217, 227, 250, 260, 283, 296 crosswords (solving) 12 cryptographic reward tokens 134, 276 cryptography 80 cyborg 44–48, 67, 270 D DARPA, see Defense Advanced Research Projects Agency DART (tool) 15 Dartmouth Summer Project 5 data mining 15–16, 232, 301 decision support systems 15, 98; see also tool-AI decision theory 10–11, 88, 185–186, 221–227, 280, 298; see also optimality notions decisive strategic advantage 78–89, 95, 104–112, 115–126, 129–138, 148–149, 156–159, 177, 190, 209–214, 225, 252 Deep Blue 12 Deep Fritz 22 Defense Advanced Research Projects Agency (DARPA) 15 design effort, see optimization power Dewey, Daniel 291 Differential Technological Development (Principle of) 230–237 Diffie–Hellman key exchange protocol 80 diminishing returns 37–38, 66, 88, 114, 273, 303 direct reach 58 direct specification 139–143 DNA synthesis 39, 98 Do What I Mean (DWIM) 220–221 domesticity 140–143, 146–156, 187, 191, 207, 222 Drexler, Eric 239, 270, 276, 278, 300 drones 15, 98 Dutch book 111 Dyson, Freeman 101, 278 E economic growth 3, 160–166, 179, 261, 274, 299 Einstein, Albert 56, 70, 85 ELIZA (program) 6 embryo selection 36–44, 67, 268 emulation modulation 207 Enigma code 87 environment of evolutionary adaptedness 164, 171 epistemology 222–224 equation solvers 15 eugenics 36–44, 268, 279 Eurisko 12 evolution 8–9, 23–27, 44, 154, 173–176, 187, 198, 207, 265, 266, 267, 273 evolutionary selection 187, 207, 290 evolvable hardware 154 exhaustive search 6 existential risk 4, 21, 55, 100–104, 115–126, 175, 183, 230–236, 239–254, 256–259, 286, 301–302 state risks 233–234 step risks 233 expert system 7 explicit representation 207 exponential growth, see growth external reference semantics 197 F face recognition 15 failure modes 117–120 Faraday cage 130 Fields Medal 255–256, 272 Fifth-Generation Computer Systems Project 7 fitness function 25; see also evolution Flash Crash (2010) 16–17 formal language 7, 145 FreeCell (game) 13 G game theory 87, 159 game-playing AI 12–14 General Problem Solver 6 genetic algorithms 7–13, 24–27, 237–240; see also evolution genetic selection 37–50, 61, 232–238; see also evolution genie AI 148–158, 285 definition 148 genotyping 37 germline interventions 37–44, 67, 273; see also embryo selection Ginsberg, Matt 12 Go (game) 13 goal-content 109–110, 146, 207, 222–227 Good Old-Fashioned Artificial Intelligence (GOFAI) 7–15, 23 Good, I.

Alpha Trader
by Brent Donnelly
Published 11 May 2021

These strategies produce non-normal returns and lead to occasional blowups and meltdowns and are often more beta than alpha. Tactics like timing on entries and exits and superior risk management, though, can make simple levered carry and option selling strategies more like alpha than beta. Quant / discretionary combo 2012 - While quantitative analysis was once mostly the domain of systematic and algorithmic trading programs, human traders now use more sophisticated quantitative methods. Combining the best quantitative analysts, the best datasets and the best discretionary traders into one business pod looks to me like the ideal model for discretionary alpha capture in the current era. Quantitative analysis is greatly enhanced by experienced humans who can ask the right questions.

This made me a pile of money from 2003 to 2013 or so, but since then it has been much more difficult to extract profit from this approach. Too many people know about the methodology, too many blogs post about it and too many algos trade it. In 2006, very few FX traders had live feeds for gold, oil, and single name equities. Now every single trader does. In 2006 there might have been a few expensive, PhD-built algorithms trading correlation, now you can build a correlation trading algo in Excel in two hours and plug it directly into the market through an online broker. Efficient markets eventually prevail. Always. It took me a few years to transition, but now I use correlation more selectively and question the logic more aggressively before I employ it as a tactical or strategic decision-making tool.

How is this going to impact me? Here are a few examples of major structural changes that have changed markets in my lifetime. 1996 Currency market trading moves from voice brokers to electronic brokers. 2001 US stock markets stop trading in fractions and trade in decimals instead. 2002 - 2005 Algorithmic trading becomes an important part of most markets. 2010 - 2015 Flash crashes become an important liquidity risk in many markets. October 2019 Free stock trading for retail traders triggers several euphoric “runnings of the bulls” in US stocks. I believe that adaptation is one of the keys to survival and professional longevity in almost every industry.

pages: 447 words: 104,258

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues
by Alain Ruttiens
Published 24 Apr 2013

For more details on these models, see the Further Reading section at the end of the chapter. 15.1.6 Neural networks The use of so-called “neural networks”, essentially as a non-parametric forecasting tool, was popular in the 1990s but did not produce convincing results. As such, this section has no “raison d'être”. However, this technique seems to know a new lease of life, with some good reasons, in the successful area of high frequency (“algorithmic”) trading. It is, however, hard to appraise its effectiveness, since its users, in case of positive performance, will most probably not publish on it. In short (for more details, see, e.g., Further Reading), neural networks (hereafter called “NN”) may be defined as tools for non-linear forecasting. To start from the well-known multiple linear regression, considering a series of n, (1, …, j, …, n) data sets {(r1, x11, …, xk1, …, xm1), (r2, x12, …, xk2, …, xm2), …, (rj, x1j, …, xkj, …, xmj), …, (rn, x1n, …, xkn, …, xmn)}, where r is the dependent variable and x1, …, xk, …, xm are the m independent variables, the corresponding multiple linear regression is the straight line (15.2) where a is a constant, wk are the weights and the residue. â and the ŵk are the estimates of a and of the wk, such as they minimize the quadratic residuals With the kind of display used in the NN world, the multi-linear regression can be described as in Figure 15.5, where the transfer function Ψ here is the linear equation 15.2.

Paul WILMOTT (ed.), The Best of Wilmott 2, John Wiley & Sons, Ltd, Chichester, 2005, 404 p. P. WILMOTT, H. RASMUSSEN (eds) New Directions in Mathematical Finance, John Wiley & Sons, Ltd, Chichester, 2002, 256 p. Index 4-moments CAPM actual (ACT) number of days AI see Alternative Investments “algorithmic” trading Alternative Investments (AI) American options bond options CRR pricing model option pricing rho amortizing swaps analytic method, VaR annual interest compounding annualized volatility autocorrelation corrective factor historical volatility risk measures APT see Arbitrage Pricing Theory AR see autoregressive process Arbitrage Pricing Theory (APT) ARCH see autoregressive conditional heteroskedastic process ARIMA see autoregressive integrated moving average process ARMA see autoregression moving average process ask price asset allocation attribution asset swaps ATM see at the money ATMF see at the money forward options at the money (ATM) convertible bonds options at the money forward (ATMF) options attribution asset allocation performance autoregression moving average (ARMA) process autoregressive (AR) process autoregressive conditional heteroskedastic (ARCH) process autoregressive integrated moving average (ARIMA) process backtesting backwardation basket CDSs basket credit derivatives basket options BDT see Black, Derman, Toy process benchmarks Bermudan options Bernardo Ledoit gain-loss ratio BGM model see LIBOR market model BHB model (Brinson’s) bid price binomial distribution binomial models binomial processes, credit derivatives binomial trees Black, Derman, Toy (BDT) process Black and Karasinski model Black–Scholes formula basket options beyond Black–Scholes call-put parity cap pricing currency options “exact” pricing exchange options exotic options floor pricing forward prices futures/forwards options gamma processes hypotheses underlying jump processes moneyness sensitivities example valuation troubles variations “The Black Swan” (Taleb) bond convexity bond duration between two coupon dates calculation assumptions calculation example callable bonds in continuous time duration D effective duration forwards FRNs futures mathematical approach modified duration options physical approach portfolio duration practical approach swaps uses of duration bond futures CFs CTD hedging theoretical price bond options callable bonds convertible bonds putable bonds bond pricing clean vs dirty price duration aspects floating rate bonds inflation-linked bonds risky bonds bonds binomial model CDSs convexity credit derivatives credit risk exotic options forwards futures government bonds options performance attribution portfolios pricing risky/risk-free spot instruments zero-coupon bonds see also bond duration book value method bootstrap method Brinson’s BHB model Brownian motion see also standard Wiener process bullet bonds Bund (German T-bond) 10-year benchmark futures callable bonds call options call-put parity jump processes see also options Calmar ratio Capital Asset Pricing Model (CAPM) 4-moments CAPM AI APT vs CAPM Sharpe capitalization-weighted indexes capital market line (CML) capital markets caplets CAPM see Capital Asset Pricing Model caps carry cash and carry operations cash flows cash settlement, CDSs CBs see convertible bonds CDOs see collateralized debt obligations CDSs see credit default swaps CFDs see contracts for difference CFs see conversion factors charm sensitivity cheapest to deliver (CTD) clean prices clearing houses “close” prices CML see capital market line CMSs see constant maturity swaps Coleman, T.

pages: 349 words: 102,827

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo
Published 13 Jul 2020

The number of digital-currency-focused hedge funds and venture funds exploded in 2017. More than two hundred funds were created that year; that’s more than four times the number of funds launched in the previous year. They offered everything from market-weighted investment on the top ten cryptos to more sophisticated algorithmic trading.2 Meanwhile, Ming was in Mexico leading the organization for what would be Ethereum’s biggest event yet, Devcon3. More than two thousand people would fill 75,000 square feet of space in three floors and on two stages at a Cancun conference center. She was working almost literally around the clock with conference vendors on the design, the layout, the panel topics, who would speak, and who would sponsor.

,” TechCrunch, June 7, 2017, https://techcrunch.com/2017/06/07/what-the-hell-is-happening-to-cryptocurrency-valuations/. 5. said in a July 25 statement: US Securities and Exchange Commission, “Report of Investigation Pursuant to Section 21(a) of the Securities Exchange Act of 1934: The DAO,” news release no. 81207, July 25, 2017, https://www.sec.gov/litigation/investreport/34-81207.pdf. 6. of the Filecoin ICO: Stan Higgins, “$257 Million: Filecoin Breaks All-Time Record for ICO Funding,” CoinDesk, September 7, 2017, updated September 8, 2017, https://www.coindesk.com/257-million-filecoin-breaks-time-record-ico-funding. 28: Futures and Cats 1. first half of 2018: Hugh Son, Dakin Campbell, and Sonali Basak, “Goldman Is Setting Up a Cryptocurrency Trading Desk,” Bloomberg News, December 21, 2017, https://www.bloomberg.com/news/articles/2017-12-21/goldman-is-said-to-be-building-a-cryptocurrency-trading-desk. 2. algorithmic trading: “Cryptocurrency Investment Fund Industry Graphs and Charts,” Crypto Fund Research, https://cryptofundresearch.com/cryptocurrency-funds-overview-infographic/. 3. million, at the peak: Vitalik Buterin’s Ethereum address, Etherscan, https://etherscan.io/address/0xab5801a7d398351b8be11c439e05c5b3259aec9b#analytics. 29: The Crash 1. selling digital assets: “SEC Cyber Enforcement Actions,” US Securities and Exchange Commission, https://www.sec.gov/spotlight/cybersecurity-enforcement-actions. 2. to manipulate crypto markets: “Tether Response to Flawed Paper by Griffin and Shams,” Tether, November 7, 2019, https://tether.to/tether-response-to-flawed-paper-by-griffin-and-shams/. 3. as much as 95 percent: “Market Surveillance Report—August 2018,” Blockchain Transparency Institute, https://www.bti.live/report-august2018/. 4. were Christmas cards: Matt Robinson, “SEC Issues Subpoenas in Hunt for Fraudulent ICOs,” Bloomberg News, February 28, 2018, updated March 1, 2018, https://www.bloomberg.com/news/articles/2018-03-01/sec-is-said-to-issue-subpoenas-in-hunt-for-fraudulent-icos. 5. groundbreaking technology: Camila Russo, “Bitcoin Speculators, Not Drug Dealers, Dominate Crypto Use Now,” Bloomberg News, August 7, 2018, https://www.bloomberg.com/news/articles/2018-08-07/bitcoin-speculators-not-drug-dealers-dominate-crypto-use-now. 6. forcing it back to earth: “ICO Treasury Balances,” Diar, https://diar.co/ethereum-ico-treasury-balances/. 7. cases were dropped: Joseph Menn, “Bitcoin Foundation Hit by Resignations over New Director,” Reuters, May 16, 2014, https://www.reuters.com/article/us-bitcoin-foundation-resignations/bitcoin-foundation-hit-by-resignations-over-new-director-idUSBREA4F02B20140516. 30: The Party 1. and clapped along: CoinDesk, “Devcon 4—Ethereum’s Big Sing-a-long,” YouTube, uploaded October 31, 2018, https://www.youtube.com/watch?

pages: 97 words: 31,550

Money: Vintage Minis
by Yuval Noah Harari
Published 5 Apr 2018

That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. They know algorithms were to blame, but are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers. And these lawyers won’t necessarily be human. Movies and TV series give the impression that lawyers spend their days in court shouting ‘Objection!’

pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
by Eric Siegel
Published 19 Feb 2013

Financial institutions: Don Davey, “Collect More for Less: Strategic Predictive Analytics Is Key to Growing Collections and Reducing Costs,” First Data White Paper, April 2009. www.firstdata.com/downloads/thought-leadership/fd_collectmoreforless_whitepaper.pdf. London Stock Exchange: “Black Box Traders Are on the March,” The Telegraph, August 27, 2006. www.telegraph.co.uk/finance/2946240/Black-box-traders-are-on-the-march.html#disqus_thread. Kendall Kim, Electronic and Algorithmic Trading Technology (Elsevier, 2007) www.elsevier.com/wps/find/bookdescription.cws_home/711644/description#description. John Elder: See Chapter 1 for this case study. For a short autobiographical essay by John Elder, see: Mohamed Medhat Gaber, Journeys to Data Mining: Experiences from 15 Renowned Researchers (Springer, 2012), 61–76.

A Abbott, Dean ABC AB testing Accident Fund Insurance actuarial approach advertisement targeting, predictive advertising. See marketing and advertising Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age (Harcourt) AI. See artificial intelligence (AI) airlines and aviation, predicting in Albee, Edward Albrecht, Katherine algorithmic trading. See black box trading Allen, Woody Allstate AlphaGenius Amazon.com employee security access needs machine learning and predictive models Mechanical Turk personalized recommendations sarcasm in reviews American Civil Liberties Union (ACLU) American Public University System Ansari X Prize Anxiety Index calculating as ensemble model measuring in blogs Apollo 11 Apple, Inc.

pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable
by James Owen Weatherall
Published 2 Jan 2013

Packard stayed with the company for a few more years, serving as CEO until 2003, when he left to start a new company, called ProtoLife. By the time they left, they had made their point: a firm grasp of statistics and a little creative reappropriation of tools from physics were enough to beat the Man. It was time to tackle a new set of problems. Black box models, and more generally “algorithmic trading,” have taken much of the backlash against quantitative finance in the period since the 2007–2008 financial crisis. The negative press is not undeserved. Black box models often work, but by definition it is impossible to pinpoint why they work, or to fully predict when they are going to fail.

For more on Niederhoffer, see his autobiography, Niederhoffer (1998), or the recent New Yorker profile (Cassidy 2007). “. . . Osborne proposed the first trading program . . .”: In other words, the first systematic, fully deterministic trading strategy that could be programmed into a computer — a system for what today would be called algorithmic trading. The proposal is made in Niederhoffer and Osborne (1966). 3. From Coastlines to Cotton Prices “Szolem Mandelbrojt was the very model . . .”: Information about Mandelbrojt comes from O’Connor and Robertson (2005), as well as from the biographical materials related to Mandelbrot cited below.

pages: 484 words: 104,873

Rise of the Robots: Technology and the Threat of a Jobless Future
by Martin Ford
Published 4 May 2015

In a 2013 paper published in the scientific journal Nature, a group of physicists studied global financial markets and identified “an emerging ecology of competitive machines featuring ‘crowds’ of predatory algorithms,” and suggested that robotic trading had progressed beyond the control—and even comprehension—of the humans who designed the systems.43 In the realm inhabited by these continuously battling algorithms, the action unfolds at a pace that would be incomprehensible to the fastest human trader. Indeed, speed—in some cases measured in millionths or even billionths of a second—is so critical to algorithmic trading success that Wall Street firms have collectively invested billions of dollars to build computing facilities and communications paths designed to produce tiny speed advantages. In 2009, for example, a company called Spread Networks spent as much as $200 million to lay down a new fiber-optic cable link stretching 825 miles in a straight line from Chicago to New York.

The company operated in stealth mode so as not to alert the competition even as it blasted its way through the Allegheny Mountains. When the new fiber-optic path came online, it offered a speed advantage of perhaps three or four thousandths of a second compared with existing communications routes. That was enough to allow any algorithmic trading systems employing the new route to effectively dominate their competition. Wall Street firms, faced with algorithmic decimation, lined up to lease bandwidth—reportedly at a cost as much as ten times that of the original, slower cable. A similar cable stretching across the Atlantic between London and New York is currently in progress, and is expected to shave about five thousandths of a second off current execution times.44 The impact of all this automation is clear: even as the stock market continued on its upward trajectory in 2012 and 2013, large Wall Street banks announced massive layoffs, often resulting in the elimination of tens of thousands of jobs.

Capital Ideas Evolving
by Peter L. Bernstein
Published 3 May 2007

In the old days, f loor brokers or other agents who knew about or held an order for execution could trade their own accounts ahead of the order by offering a more competitive price, which would cost at least 121⁄2 cents a share. Today, they execute for only a penny! As a result, buyers and sellers trying to avoid this kind of competition from their agents tend to disclose only small parts of their total order at any one time. In addition, an increasing volume of transactions is executed in what is known as algorithmic trading, or trading carried out by computer programs that respond to changing conditions in the market. This process bypasses the marketmakers, squeezing their profits, and producing even less liquidity. The shift from eighths of a point to hundredths of a point will work out like an ecological system that eliminates one particular species while others arise to fill that void.

That result requires high skill in all of the most sophisticated bern_c15.qxd 3/23/07 224 9:12 AM Page 224 THE PRACTITIONERS trading techniques. For example, there are electronic networks in which investors transact anonymously with each other across computers, or program trading in which dealers bid on large stock portfolios on the basis of their characteristics rather than knowing the individual names held. In algorithmic trading, a relatively new procedure, positions are either liquidated or accumulated in a series of transactions instead of in just one big transaction. The computer then makes the decision to trade, depending on whether price movements indicate the market will be receptive at any given moment, or to refuse to trade if it appears the transaction would drive the price away from the price at which the investor hopes to settle.

pages: 382 words: 105,819

Zucked: Waking Up to the Facebook Catastrophe
by Roger McNamee
Published 1 Jan 2019

She worked on a project that looked for a correlation between music training and temporal reasoning. It was a small-scale study, but it married Renée’s two great interests. She earned a degree in computer science at SUNY Stony Brook before going into government service in a technology operations role. Renée underplays this part of her résumé, focusing instead on her time in algorithmic trading on Wall Street. In that job, she observed the tricks that market participants use to outsmart one another, some of which are similar to the tools hackers could use to interfere in an election. Our first meeting in Washington was with Senator Warner. “I’m on your team,” he said, by way of an opening.

Worse still, the damage can persist even if the user abandons the platform that helped to foster them. Listening to Renée describe the techniques employed by the Russians, I realized I was in the presence of a genuine superstar. At the time, I had only the barest outline of her life story, but the elements I knew—technology operations for the government, algorithmic trading, political campaigns, infiltration of antivax networks, harassment on Twitter, research on Russian interference in democracy—spoke to Renée’s intelligence and commitment. Adding Renée to our team was transformational. Unlike Tristan and me, Renée could go beyond hypotheses. She had been researching this stuff for several years.

pages: 389 words: 119,487

21 Lessons for the 21st Century
by Yuval Noah Harari
Published 29 Aug 2018

, IZA Institute of Labor Economics, Discussion Paper No. 10471 (2017). 2 See, for example, AI outperforming humans in flight, and especially combat flight simulation: Nicholas Ernest et al., ‘Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions’, Journal of Defense Management 6:1 (2016), 1–7; intelligent tutoring and teaching systems: Kurt VanLehn, ‘The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems’, Educational Psychologist 46:4 (2011), 197–221; algorithmic trading: Giuseppe Nuti et al., ‘Algorithmic Trading’, Computer 44:11 (2011), 61–9; financial planning, portfolio management etc.: Arash Bahrammirzaee, ‘A comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems’, Neural Computing and Applications 19:8 (2010), 1165–95; analysis of complex data in medical systems and production of diagnosis and treatment: Marjorie Glass Zauderer et al., ‘Piloting IBM Watson Oncology within Memorial Sloan Kettering’s Regional Network’, Journal of Clinical Oncology 32:15 (2014), e17653; creation of original texts in natural language from massive amounts of data: Jean-Sébastien Vayre et al., ‘Communication Mediated through Natural Language Generation in Big Data Environments: The Case of Nomao’, Journal of Computer and Communication 5 (2017), 125–48; facial recognition: Florian Schroff, Dmitry Kalenichenko and James Philbin, ‘FaceNet: A Unified Embedding for Face Recognition and Clustering’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), 815–23; and driving: Cristiano Premebida, ‘A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking’, 2007 IEEE Intelligent Transportation Systems Conference (2007). 3 Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus & Giroux, 2011); Dan Ariely, Predictably Irrational (New York: Harper, 2009); Brian D.

Succeeding With AI: How to Make AI Work for Your Business
by Veljko Krunic
Published 29 Mar 2020

Using AI allows the application of that loop to some problem domain in which an automated reaction wasn’t previously possible. Automated data analysis is a recent development? Even uses of fully automated and rapid Sense/Analyze/React loops using complicated and computerized analysis are nothing new. Capital markets, especially combined with algorithmic trading, implement this pattern on a large scale. With the further advancement of the Internet of Things [46] and robotics, these large-scale, fully automated, closed control loops will become much more prevalent within the physical world. 2.3 What’s new with AI? The advance of AI broadened the applicability of the Sense/Analyze/React loop, because AI brought to the table new analytical capabilities.

[3] NOTE The third error of metric substitution happens in practice because you know what an ideal metric would be, but you don’t know how to precisely measure it. Don’t use surrogate metrics The metric you choose should be the exact business metric you want to affect, not some surrogate. If you’re doing algorithmic trading on Wall Street, your metric is how much money you made after the actual trades were completed and settled, and all the fees and taxes applied. It’s not how much money you’d have made if you were able to instantly complete the trades and didn’t have to pay fees and taxes. 4.3 Measuring progress on AI projects You should measure the progress that your analytical teams are making on the research questions by referring to the business metric that measures the success of the business problem you’re trying to solve.

pages: 133 words: 42,254

Big Data Analytics: Turning Big Data Into Big Money
by Frank J. Ohlhorst
Published 28 Nov 2012

The biomedical corporation Bioinformatics recently announced that it has reduced the time it takes to sequence a genome from years to days, and it has also reduced the cost, so it will be feasible to sequence an individual’s genome for $1,000, paving the way for improved diagnostics and personalized medicine. The financial sector has seen how Big Data and its associated analytics can have a disruptive impact on business. Financial services firms are seeing larger volumes through smaller trading sizes, increased market volatility, and technological improvements in automated and algorithmic trading. DATA AND DATA ANALYSIS ARE GETTING MORE COMPLEX One of the surprising outcomes of the Big Data paradigm is the shift of where the value can be found in the data. In the past, there was an inherent hypothesis that the bulk of value could be found in structured data, which usually constitute about 20 percent of the total data stored.

pages: 428 words: 121,717

Warnings
by Richard A. Clarke
Published 10 Apr 2017

Michael Sainato, “Steven Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence,” The Observer (UK), Aug. 19, 2015, http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence (accessed Oct. 8, 2016); and Elon Musk interview with MIT students at the MIT Aeronautics and Astronautics Department Centennial Symposium, Oct. 2014, http://aeroastro.mit.edu/aeroastro100/centennial-symposium (accessed Oct. 8, 2016). 22. Bloomberg via Shobhit Seth, “The World of High Frequency Algorithmic Trading,” Investopedia, Sept. 16, 2015, www.investopedia.com/articles/investing/091615/world-high-frequency-algorithmic-trading.asp (accessed Oct. 8, 2016). 23. Andrew Ng, “Is A.I. an Existential Threat to Humanity?” Quora, https://www.quora.com/Is-AI-an-existential-threat-to-humanity/answer/Andrew-Ng (accessed Oct. 8, 2016). 24. The study looks at jobs at risk from weak AI and robotics.

pages: 481 words: 120,693

Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else
by Chrystia Freeland
Published 11 Oct 2012

Twitter and Facebook are the offspring of the technology revolution, but they turn out to have made political revolutions easier to organize. Before the invention of the personal computer, the securitization of mortgages—which turned out to be part of the kindling for the financial crisis—would not have been possible. Nor would the algorithmic trading revolution, in which machines are replacing centuries-old stock exchanges and a couple of lines of corrupt code can trigger a multibillion-dollar loss of market value in moments, as occurred during the “flash crash” on May 6, 2010. — Revolution is the new global status quo, but not everyone is good at responding to it.

Abramovich, Roman, 107 Abrams, Dan, 164 academics, 264, 267–69 Acemoglu, Daron, 21–22, 279–80 Ackermann, Josef, 254 Ackman, Bill, 51 active inertia, 145, 167–69 actors, 108 Ad Age, 50 Adderall, 52 Africa, 66, 76, 146 agency problem, 138 AIDS, 76, 246 AIG, 27, 101, 143 Akhmetov, Rinat, 103 Alger, Horatio, 45 algorithmic trading, 146 Allen, Herb, 68 Allen, Paul, 43 Allstate, 64 Alpha, 143 alpha geeks, 46–51, 92, 94 Amazon, 69, 234 Ambani, Mukesh, 199 Ambani family, 235 American Bar Association Journal, 107 American colonies, 11–12 Anderson, Chris, 68, 100 Anderson, Keith, 153–55 Andersson, Mats, 138 Animal Farm (Orwell), 90 Apollo, 142 Apple, 25, 54, 55, 104, 143 iPhone, 28 iPod, 24–26 Applied Materials, 64 Arcelor, 191 ArcelorMittal, 59 architects, 103–4 Ariely, Dan, xii, 273–74 ARK (Absolute Return for Kids), 74 Arkady, 110 Arthur, W.

pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
by John Brockman
Published 5 Oct 2015

Among many other examples, today’s market circuit breakers may eventually generalize to future centralized abilities to cut off AIs from the outside world, and today’s large trader reporting rules may generalize to future requirements that advanced AIs be licensed and registered with the government. Through this lens, calls for stricter regulation of high-frequency algorithmic trading by slower human traders can be viewed as some of humanity’s earliest attempts to close a nascent “intelligence divide” with thinking machines. But how can we prevent a broader intelligence divide? Michael Faraday was apocryphally said to have been asked in 1850 by a skeptical British chancellor of the exchequer about the utility of electricity and to have responded, “Why, sir, there is every probability that you will soon be able to tax it.”

Similarly, if wealth is just a measure of freedom, and intelligence is just an engine of freedom maximization, intelligence divides could be addressed with progressive intelligence taxes. While taxing intelligence would be a novel method for mitigating the decoupling of human and machine economies, the decoupling problem will nonetheless require creative solutions. Already, in the high-frequency trading realm, there’s a sub-500-ms economy occupied by algorithms trading primarily among themselves and an above-500-ms economy occupied by everyone else. This example serves as a reminder that while spatial economic decoupling (e.g., between countries at different stages of development) has occurred for millennia, artificial intelligence is for the first time enabling temporal decoupling as well.

The Powerful and the Damned: Private Diaries in Turbulent Times
by Lionel Barber
Published 5 Nov 2020

Now, as Henry Kaufman, the legendary bond-market supremo, once told me one sunny afternoon on a walk after lunch down Sixth Avenue: no one knows what’s going on in the banks and no one speaks for Wall Street any more. The traders had taken over. It was an ominous if poignant moment. After the crash, algorithmic trading increased exponentially. The new power-players are the ‘quants’. If the unit of trade is 100 shares, and you can buy and sell those shares for a profit of 1 per cent, and you can do it in a hundredth of a second, eight hours a day, something fundamental has changed. The quants have left the traders in the dust.

Abbott, Diane 340–41 Abell, Stig 165, 165n Abe, Shinzo ix, 390–92 Abu Dhabi xiv, 102–3, 111–12, 135, 142 Acheson, Dean 54; Present at the Creation xi, xii, xvii Adoboli, Kweku 193 Afghanistan 108, 132–3, 148–9, 150, 161, 170, 171, 355 African National Congress (ANC) 51, 52, 53, 260, 261 Ager-Hanssen, Christen 361 Agius, Marcus 121–2, 121n, 212 Ahmadinejad, Mahmoud 251 Ahrendts, Angela 42, 75 AIG 36–7, 104–5, 107, 114 Ai Weiwei 233–4 al-Assad, Bashar 102, 110, 111, 308 Alderson, Mark 10 algorithmic trading 187 Alibaba xii, 233, 325, 440 Allen & Co. media conference 191, 291–2 Allen, Herbert 402 al-Maktoum, Sheikh Mohammed bin Rashid 111, 112 al-Naimi, Ali 308 Al-Qaeda 21, 107, 363 Alternative for Germany (AfD) 359, 419 Alternative Vote (AV) referendum (2011) 195–6, 195n Amazon (online retailer) 24, 60n, 189, 215, 276, 282, 383, 398, 439 Ambani, Anil 38, 40, 247 Ambani, Dhirubhai 40 Ambani, Mukesh 38, 246, 247 Amis, Kingsley 72 Amis, Martin 72–3 Anderlini, Jamil 206–7, 233, 241, 254, 350–51, 393, 444 Andrew, Prince x, xii, 112, 122, 175–6, 271–2 Android (operating system) 137, 214 Any Questions?

pages: 483 words: 141,836

Red-Blooded Risk: The Secret History of Wall Street
by Aaron Brown and Eric Kim
Published 10 Oct 2011

The standard introduction to quantitative finance is Paul Wilmott Introduces Quantitative Finance by, of course, Paul Wilmott. A few years ago Paul told me he had ceased being a person and had transformed into a brand. Antti Ilmanen wrote an excellent guide to the theory and practice of quant strategies, Expected Returns: An Investor’s Guide to Harvesting Market Rewards. More technical works on the subject are Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson, Inside the Black Box: The Simple Truth about Quantitative Trading by Rishi K. Narang, and Multifractal Volatility: Theory, Forecasting, and Pricing by Laurent E. Calvet. The view of quantitative finance described in Red-Blooded Risk has a lot of overlap with two pathbreaking but eccentric works: The Handbook of Portfolio Mathematics: Formulas for Optimal Allocation & Leverage by Ralph Vince and Finding Alpha: The Search for Alpha When Risk and Return Break Down by Eric Falkenstein.

Comics has always been my passion, and utilizing the medium to help explain economic concepts is a contribution that I’m happy to make. I hope readers enjoy the works presented here, and hope we get a chance to collaborate again!” Index Accuracy ratio Adams, Brandon Aggregation process Ainsley, Craig Algorithmic Trading and DMA ( Johnson) Algren, Nelson All the Devils Are Here (McLean and Nocera) Allen, Franklin Anderson, Chris Anomalies Anthropology of Economy, The (Gudeman) Arbitrage Ariely, Dan Ars Conjectandi (Bernoulli) Augar, Philip Autocorrelation Bacharach, Michael Back office Backward-looking risk management Bad-boy publicity Bad Bet (O’Brien) Bankers Trust Bank for International Settlements Bank One Barter, Exchange and Value (Humphrey and Hugh-Jones) Basel capital rules Bayes, Thomas Bayesians/Bayesian concepts.

pages: 515 words: 132,295

Makers and Takers: The Rise of Finance and the Fall of American Business
by Rana Foroohar
Published 16 May 2016

It’s become a trajectory.” Derman, who himself left a job at Bell Labs to make more money on the Street, understands the lure of a high-paid algorithmic trading job for students who will often end up with hundreds of thousands of dollars in college and graduate school loan debt—a Wall Street career is often the only quick way to financial solvency. But unlike many finance professors, he also tries to engender in his students a sense that algorithmic trading models are just one tool in the banker’s toolbox and shouldn’t be overrelied upon. Whether the students are listening is another question. It’s telling that about 80 percent of Derman’s students are now Asian, many of them Chinese, who are bringing the game of financial speculation to their own economies.

pages: 184 words: 53,625

Future Perfect: The Case for Progress in a Networked Age
by Steven Johnson
Published 14 Jul 2012

But those same affordances for cheap and fast information triggered a vast array of outcomes that I failed to anticipate. And not just the Al Qaeda attacks. The Internet may well have made it easier for Occupy Wall Street to form, but it had an even more decisive role in the creation of high-frequency algorithmic trading, which has spawned both immense fortunes and immense instability on Wall Street. Global movements comparable to Occupy Wall Street formed many times before the age of networked computing, as Gladwell observed; they might have had a harder time reaching critical mass without the speed and efficiency of the Net, but they were at least within the realm of possibility.

pages: 222 words: 53,317

Overcomplicated: Technology at the Limits of Comprehension
by Samuel Arbesman
Published 18 Jul 2016

We already see hints of the endpoint toward which we are hurtling: a world where nearly self-contained technological ecosystems operate outside of human knowledge and understanding. As a journal article in Scientific Reports in September 2013 put it, there is a complete “new machine ecology beyond human response time”—and this paper was talking only about the financial world. Stock market machines interact with one another in rich ways, essentially as algorithms trading among themselves, with humans on the sidelines. This book argues that there are certain trends and forces that overcomplicate our technologies and make them incomprehensible, no matter what we do. These forces mean that we will have more and more days like July 8, 2015, when the systems we think of as reliable come crashing down in inexplicable glitches.

pages: 688 words: 147,571

Robot Rules: Regulating Artificial Intelligence
by Jacob Turner
Published 29 Oct 2018

Other systems under development and in operation allow for diagnosis and treatment to be fully automated.93 In commerce, the US Congressional Research Service estimates that algorithmic programs account for roughly 55% of trading volume in the US equities market and around 40% of European equities markets.94 Under our definition, most algorithmic trading does not involve the use of AI as yet. However, its capability of taking complex strategic decisions in a manner which surpasses human reasoning seems likely to make AI particularly well suited to this task.95 Even the creative industries are taking advantage of AI. Music composition programs were among the first examples of this development.96 In 1997, the New Scientist reported that a computer in California had written Mozart’s 42nd Symphony, a feat not even Mozart himself could manage.97 A program called Mubert is able to compose entirely new tracks which, its creators say, are “based on the laws of musical theory, mathematics and creative experience”.98 In 2016, a director and a New York University AI researcher collaborated to create an AI system which created a new horror film script, after being “fed” dozens of successful scripts.

Ever since the “Black Monday” crash of 1987 when the Dow Jones Industrial Average fell by around 22%, stock exchanges have imposed trading curbs which prevent traders from buying and selling shares when the market falls or rises by a given amount over a specified period. This type of automatic shut-off is particularly important in industries where events occur so quickly as to be incapable of effective human oversight. The growth in high-frequency algorithmic trading makes such curbs particularly important today. The same motivations apply to AI. The most robust kill switches would combine the precautionary approach of an automatic shut-off if certain predetermined events transpire, with a discretionary human shut-off so as to provide flexibility in the event that an unforeseen event or emergent behaviour renders the AI’s continued operation harmful. 5.3 How Could a Kill Switch Be Achieved?

pages: 903 words: 235,753

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

It smooths space by striating it with heavy physical grids of cables and server farms, and striates space by smoothing it out with ubiquitous access, sensing, relay, and processing micropoints. For its chthonic Cloud, data centers are housed under mountains with reliable ice cores; suburban farmland between metropolitan trading centers is redug to lay private cable for algorithmic trading concerns near the old AT&T switches in New Jersey, realizing a new topographic expression of the transport layer of the TCP/IP stack; while the wireless frequency spectrum is subdivided, auctioned, allocated, and bundled into derivatives like any other prized commercial real estate. Whereas the Schmittian “grounded” way of thinking detests dedifferentiated space and the flattening superimposition of multiple maps, valorizing instead the perspectival spatial order of human establishment, the geographies of The Stack go a long way toward collapsing distinctions between the one and the other, as its interlacing of land, sea, and air through networks of recombinant flows realizes the simultaneous physicalization of the virtual and the virtualization of physical forces.

The redesign of money—not just the currency vehicle of exchange, but of the valuation of things and events as such—may also require, or even entail, a more rigorous, flexible, and intricate mechanism for the identification of discrete assets as they twist and turn their way through financial wonderlands. What we now call “high-frequency trading” or algorithmic trading may continue to represent an increasingly larger percentage of all transactions, and as these techniques become more institutionalized, their methodologies and mechanisms become more normalized for even long-term investments. At the same time, the ability for deep address to engender not one but multiple address topologies describing the same set of events means that the potential for unprosecutable chaos is increased unless there are some workable standards for financial singularities, bifurcators, and resolvers that can police these data ontologies.

See also political agency amplifying, 274 collective versus individual, 175 decentering, 344 of the excluded, 173–175 human, 255 of inanimate objects, 131 of machines, 348 political, 250, 258 of the User, 164–165, 238, 252, 258, 260, 338, 347–348 utopian, 249 Agenda 21, 89, 306 agonistic geopolitics, 115 agricultural industry, 307–308 agricultural settlement, 22, 86 Ain, Gregory, 320 airports borders within, 155–156, 324 economy of, 281 envelopes of, 156–157 interfacial network of, 155–156 airport urbanism, 155–157, 162, 405n14 Alberti, Leon Battista, 154 alegal, 174, 176, 367 Aleppo, Syria, 321 Alexander, Christopher, 201 Algerian independence, 244–245 algorithmic automation, 134, 332, 341–342 algorithmic capitalism, 72, 80–81 algorithmic decision-making, 134, 332, 341–342 algorithmic geopolitics, 449n56 algorithmic governance, 134, 332–334, 337–338, 341–342, 348, 368 algorithmic hardware, 348–349 algorithmic intelligence, 81 algorithmic trading, 33, 335 Allende, Salvador, 58, 328 Allianz Arena, 187 Al Nasr, 321 Alphaville (Godard), 158 Althusser, Louis, 7–8 Amazon Cloud platform, 185–186, 330 Cloud Polis, 131–133, 331 corporate campus, 185 fulfillment centers, 111, 186, 443n19 future possibilities for, 141–142, 186 as geopolitical model, 125 mission, 186 platform wars, 110 profitability, 331, 449n52 workforce, 186, 307, 443n19 Amazon space, 443n19 Amazon Standard Identifier, 131 Amazon Web Services, 110, 123, 133 ambient emergency, 70–72 ambient interface, 296, 337–343, 368 anamnesis, 239–240, 297 Anders, William, 86 Anderson, Chris, 293 animal-human distinction, 275 animal-human interface, 276 animal User, 274–277 anonymity, 347, 360, 362, 405n16, 445n37 Anonymous, 110 Anthropocene architecture's response to, 182 challenge of, 78, 304, 353–354 life forms surviving, 107 planetary-scale computation surviving, 217, 302 post-Anthropocene, 364–365 anthropocentrism, 278 anthropometric design, 197 anthropometric space, 30 anthropomorphism, 277, 279 anti-cosmopolitanism, 248, 306 Anti-Fascist Protection Wall, 23 Antonov 225 airplane, 182 ants communications, 340 war machine, 352 zombie, 154 Aozaki, Nobutaka, 214–215 Apollo 8, 86–87, 144, 150, 251–252, 300.

pages: 523 words: 61,179

Human + Machine: Reimagining Work in the Age of AI
by Paul R. Daugherty and H. James Wilson
Published 15 Jan 2018

That same year, three researchers at the University of Pennsylvania coined the term that describes people’s desire to put trust in other humans rather than machines: “algorithm aversion.”8 The financial trading industry might be one of the most advanced business cultures in terms of interacting with algorithms. Yet, even here, algorithm aversion remains a key stumbling block. Systematica’s Leda Braga launched the investment management firm in 2015; it focuses solely on algorithmic trading. While Braga concedes that roles remain for humans in trading—for instance, activists and short sellers whose work is based on deep research into fundamentals and management teams of companies—those roles are disappearing. She believes the future of finance is in automation. In the meantime, Systematica’s approach encounters resistance, she says, which includes human preferences for human decision makers.

pages: 606 words: 157,120

To Save Everything, Click Here: The Folly of Technological Solutionism
by Evgeny Morozov
Published 15 Nov 2013

What to do about the algorithms then? It is a rare thing to say these days but there is much to learn from the financial sector in this regard. For example, after a couple of disasters caused by algorithmic trading in August 2012, financial authorities in Hong Kong and Australia drafted proposals to establish regular independent audits of the design, development, and modification of the computer systems used for algorithmic trading. Thus, just as financial auditors could attest to a company’s balance sheet, algorithmic auditors could verify if its algorithms are in order. As algorithms are further incorporated into our daily lives—from Google’s Autocomplete to PredPol—it seems prudent to subject them to regular investigations by qualified and ideally public-spirited third parties.

pages: 202 words: 66,742

The Payoff
by Jeff Connaughton

And when you go swimming in this market, you’d better remember there’s nobody out there making sure the water is safe.” The flash crash taught at least three lessons, all of which Ted had identified long before May 6, 2010. First, stock prices don’t always reflect the market’s best estimation of the value of the underlying companies; in mini flash crashes, they can result from the breakdown of algorithmic trading strategies. Second, technology has far outpaced regulation. Regulators’ lack of understanding of HFT strategies and the volatility they create left the markets vulnerable to a nausea-inducing plunge. For example, the SEC took for granted that high-frequency traders were the new market makers without taking into account the ways in which they differed from traditional market makers.

The Ethical Algorithm: The Science of Socially Aware Algorithm Design
by Michael Kearns and Aaron Roth
Published 3 Oct 2019

Some excel when we can assume that each number in the list is unique (as with social security numbers). So even within the constraint of developing a precise recipe for a precise computational task, there may be choices and trade-offs to confront. As the previous paragraph suggests, computer science has traditionally focused on algorithmic trade-offs related to what we might consider performance metrics, including computational speed, the amount of memory required, or the amount of communication required between algorithms running on separate computers. But this book, and the emerging research it describes, is about an entirely new dimension in algorithm design: the explicit consideration of social values such as privacy and fairness.

Work in the Future The Automation Revolution-Palgrave MacMillan (2019)
by Robert Skidelsky Nan Craig
Published 15 Mar 2020

In deciding whether automation creates or destroys jobs, the decisive factor is demand rather than technology. 1 Introduction 5 What are the advantages of technology? Often people say, ‘It is obvious that self-driving cars will reduce the numbers of road accidents. Automated diagnostic and treatment systems will reduce medical casualties and so on’. We know that argument, but will algorithmic trading increase the efficiency of financial markets, or render them more liable to crashes? So far, it seems the latter has been the case. We also need to scrutinise the more generalised idea that technology increases human welfare by increasing the affordability and thus availability of consumption goods.

pages: 681 words: 64,159

Numpy Beginner's Guide - Third Edition
by Ivan Idris
Published 23 Jun 2015

Volume Weighted Average Price Volume Weighted Average Price ( VWAP ) is a very important quantty in fnance. It represents an average price for a fnancial asset (see https://www.khanacademy.org/math/ probability/descriptive-statistics/old-stats-videos/v/statistics-the-average ). The higher the volume, the more signifcant a price move typically is. VWAP is ofen used in algorithmic trading and is calculated using volume values as weights. The following are the actons that we will take: 1. Read the data into arrays. 2. Calculate VWAP: from __future__ import print_function import numpy as np c,v=np.loadtxt('data.csv', delimiter=',', usecols=(6,7), unpack=True) vwap = np.average(c, weights=v) print("VWAP =", vwap) The output is as follows: VWAP = 350.589549353 What just happened?

pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk
by Satyajit Das
Published 14 Oct 2011

Investment periods can be long and, as the name implies, there is “event risk” (a merger not proceeding). Quantitative funds use computer-driven trading strategies. Trend-following models identify the market’s mo (momentum) and ride the bucking bronco bareback. Sophisticated models identify undervalued and overvalued securities. Algorithmic trading, known as algo, black-box, or robo trading, uses computer programs to decide timing, price or quantity of trading orders. It divides large trades into smaller trades or vice versa to manage market impact and risk. It generates small but frequent returns by providing liquidity to other buyers and sellers.

Index NUMBERS 13 Bankers, 294 60 Minutes, 343 401(k) plans, 48 1720 Bubble Act, 53 A AAA tranches, 203 Abacus transactions, 197-199, 339 Abbey, Edward, 362 ABN-Amro, 197 ABS CP (asset-backed securities commercial paper), 190 ABS PAYG CDS (asset-backed securities pay-as-you-go credit default swaps), 196 Absolute Strategy Research, 357 ABX.HE (asset-backed securities home equity), 196 Abyssinian Baptist Church, 164 ACA Capital, 197 accounting ark-to-market, 56 errors, 285 mark-to-market (MtM), 286-288 rules, 81, 349 standards, 289 value, 286-287 accreting interest rate swaps, 160 accumulator contracts, 219 Ackoff, Russell, 309 acquisitions, 57-59, 310 General Electric (GE), 61 Adelphia, 154 adjustable rate bonds, 213 adjustable rate mortgages (ARMs), 148, 182-184 adjusted model prices, 289 Aeneid, 338 affinity fraud, 313 Affluent Society, The, 43 affluenza, 46 Africa, 22 Against The Gods: The Remarkable History of Risk, 129 Age of Turbulence, The, 302 Agnelli, Giovanni, 222 AIG, 230-234, 270 Airline Partners Australia (APA), 156 airlines, leveraged buyouts (LBO), 156-157 airports, 158, 161 Albanese, Tom, 59 Alcan, 59 Alcar, 124 alchemy, 131-132 algorithmic trading, 242 Alice in Wonderland, 31 Allco Equity Partners, 156 Allco Finance Group, 156 Allen, Paul, 179 Allied Signal, 60 alpha (outperformance), 241 Alt A (Alternative A) mortgages, 182 alternatives assets, 154 investments, 252 paper money, 35 Altman, Edward, 143 amakudari (descent from heaven), 316 Amaranth, 227 hedge funds, 250-252 Amazon.com, 97 America.

pages: 661 words: 185,701

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad
Published 27 Sep 2021

These include malicious attacks of the sort that any decentralized system is exposed to, a larger “attack surface” when combining multiple decentralized applications, software bugs, and users who do not fully understand the risks of such products. It is not clear that a rigorous risk assessment of specific DeFi smart contracts can account for systemic or connected risks across different instruments. One (imperfect) analogy is to algorithmic trading, which allows for built-in safeguards—sell orders can be automatically triggered when the price on a financial asset falls by a certain percentage or below a predetermined threshold. This limits the downside risk on a portfolio. But a set of algorithms with similar triggers might well initiate a wave of simultaneous sell orders, causing one side of a market (buyers) to evaporate and asset prices to plunge.

Bandwagon effects could exacerbate volatility in financial markets as more investors, including retail investors, jump on more quickly and cheaply as they try to follow trends. One could argue that improvements in artificial intelligence, machine learning, and big-data processing techniques could fix these problems, but the recent history of algorithmic trading belies this hope. Computer algorithms trying to exploit market inefficiencies have caused occasional flash crashes, where automated sell orders swamp the market and set off a downward price spiral in a matter of seconds. Technology in some cases creates new problems even as it fixes old ones.

pages: 252 words: 74,167

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl
Published 10 Aug 2016

In most cases, investment firms trained separate networks for different stocks, with human traders then deciding which to invest in. However, some went further and gave the networks themselves the autonomous power to buy and sell. Not coincidentally, the finance sector quickly joined the video game business as an industry ready to throw money at AI researchers. The age of algorithmic trading had begun. Another eye-catching application of neural nets during this time was the invention of the self-driving car. Autonomous vehicles had been a long-time dream of technologists. In 1925, the inventor Francis Houdina demonstrated a radio-controlled car, which he drove through the streets of Manhattan without anyone at the steering wheel.

pages: 280 words: 79,029

Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better
by Andrew Palmer
Published 13 Apr 2015

The Chinese-born American was keen on the idea of starting his own company, and the most well-trodden route to that future from an East Coast university was first to head to Wall Street and make some money. The obvious choice for a math whiz was a career in quantitative finance: that seemed to offer the right mixture of autonomy, intellectual challenge, and high pay. In the summer of 2011, Gu interned with DE Shaw, an extremely successful algorithmic-­trading firm. But something was missing. As much as he might rationally accept that this firm and others like it were adding value to the capital markets, he did not feel an emotional connection to what he was doing. Gu is the kind of person who does not lack for options. He was one of the first batch of Thiel fellows, twenty people under twenty who were each given one hundred thousand dollars to skip college for two years and pursue their ambitions in a program funded by Peter Thiel, a guru of technology investing whose résumé includes founding PayPal and backing Facebook.

pages: 253 words: 79,214

The Money Machine: How the City Works
by Philip Coggan
Published 1 Jul 2009

Large hedge funds and so-called quantitative investors often used computers to search the market for pricing anomalies and do deals automatically when they found them. Investors sought ways of conducting trades without moving market prices against them. (If other people know you have a big position to sell, they will cut their buying prices.) Techniques called algorithmic trading and smart order routing emerged that were designed to trade as efficiently as possible. Some of these traders aimed to do deals in fractions of a second. THE INTERNATIONAL CHALLENGE In this new world, the whole idea of national stock exchanges seems a bit anachronistic. Some of the biggest companies on the London stock market have their origins in other countries – BHP Billiton or Anglo-American, for example.

pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It
by Marc Goodman
Published 24 Feb 2015

Just one news service alone, Thomson Reuters, feeds these HFT algos by scanning fifty thousand distinct news sources and four million social media sites at speeds no human being could ever possibly match. The vast networks of HFT machines can collectively make trillions of calculations per second, and trades can be executed in less than half a millionth of a second, thousands of times faster than the blink of an eye. When the artificial-intelligence-based algorithmic trade bots came across a tweet mentioning “explosions,” “Obama,” and “White House” in the same sentence from a source they had been trained to trust, the Associated Press, it took them just a few thousandths of a second to respond. As they did, other algorithms picked up on the activity, and soon a full-on snowball effect was in play.

When your GPS device provides you with directions using narrow AI to process the request, it is making decisions for you about your route based on an instruction set somebody else has programmed. While there may be a hundred ways to get from your home to your office, your navigation system has selected one. What happened to the other ninety-nine? In a world run increasingly by algorithms, it is not an inconsequential question or a trifling point. Today we have the following: • algorithmic trading on Wall Street (bots carry out stock buys and sells) • algorithmic criminal justice (red-light and speeding cameras determine infractions of the law) • algorithmic border control (an AI can flag you and your luggage for screening) • algorithmic credit scoring (your FICO score determines your creditworthiness) • algorithmic surveillance (CCTV cameras can identify unusual activity by computer vision analysis, and voice recognition can scan your phone calls for troublesome keywords) • algorithmic health care (whether or not your request to see a specialist or your insurance claim is approved) • algorithmic warfare (drones and other robots have the technical capacity to find, target, and kill without human intervention) • algorithmic dating (eHarmony and others promise to use math to find your soul mate and the perfect match) Though the inventors of these algorithmic formulas might wish to suggest they are perfectly neutral, nothing could be further from the truth.

pages: 282 words: 80,907

Who Gets What — and Why: The New Economics of Matchmaking and Market Design
by Alvin E. Roth
Published 1 Jun 2015

In just four minutes, the prices of futures and of the related SPY exchange-traded funds (as well as many of the stocks in the index) were driven down by several percentage points—a very big move, in the absence of earth-shattering news—and then recovered almost as fast. A subsequent investigation by the Securities and Exchange Commission and the Commodity Futures Trading Commission suggested that this brief distortion resulted from high-speed computer algorithms trading with one another, at a speed that eluded human supervision, and briefly spun out of control before anyone could react. In the aftermath of this flash crash, there was added confusion involving order backlogs and incorrect time stamps that made it difficult to determine which trades had actually gone through, since even some of the market computers had been left behind by the high-speed traders.

pages: 321

Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky
Published 30 Sep 2019

The tweet was false, but it caused a huge instant reaction in the market. From 1:08 p.m. to 1:10 p.m., the DJIA dropped more than 140 points. Though the index rebounded just as quickly, one fake Twitter event caused market losses of $136 billion. This case clearly shows the significant potential value of social media in algorithmic trading. Many data vendors are capturing this opportunity. Companies like Dataminr and PsychSignal provide millions of social data feeds on a daily basis. They also provide data to third-party vendors that create sentiment products used by many hedge funds. 166 Finding Alphas The most popular social media platform for generating alphas is Twitter because it can be easily mapped to stocks (by checking the @ ticker symbol) and people (such as the CEOs of public companies).

pages: 329 words: 95,309

Digital Bank: Strategies for Launching or Becoming a Digital Bank
by Chris Skinner
Published 27 Aug 2013

This is because banks do not analyse data on an enterprise basis, but usually hold this data in divisional stores organised around products, channels and lines of business, and the politics internally are the greatest blockage for change and, without that change, banks are stuck with piecemeal data sets being analysed in pieces. That is not good enough today. This is why banks need to completely rearchitect their enterprise technologies to enable deep data mining across all data, and create semantic marketing programs that sense customers’ needs proactively and pre-emptively. Like algorithmic trading in capital markets where algorithmic news feeds allow trading in equities to move in real-time high frequency blackbox strategies that maximise returns, we’re talking of applying the same technologies to retail transaction services for customer loyalty and wallet share. That’s the battle about to begin as we move from managing data to using information as a competitive weapon and, at the core of predictive marketing is Big Data.

pages: 342 words: 94,762

Wait: The Art and Science of Delay
by Frank Partnoy
Published 15 Jan 2012

We’d raise money from some Wall Street banks and build a brand-new platform with the best trading technology. It was going to be a serious challenge, but also an exciting opportunity. Our goal was to become faster and cheaper than anyone.” Harrison was one of a handful of people in the world who had the two skill sets the UNX job required. He had managed algorithmic trading operations, including complicated computer programs with names like Triton and QuantEX. But Harrison also was a visionary and a builder. Most managers of high-frequency firms spend their careers trading stocks and crunching numbers. Before Harrison began working in finance, he was an architect at Skidmore, Owings & Merrill.

pages: 307 words: 88,180

AI Superpowers: China, Silicon Valley, and the New World Order
by Kai-Fu Lee
Published 14 Sep 2018

Each of these waves harnesses AI’s power in a different way, disrupting different sectors and weaving artificial intelligence deeper into the fabric of our daily lives. The first two waves—internet AI and business AI—are already all around us, reshaping our digital and financial worlds in ways we can barely register. They are tightening internet companies’ grip on our attention, replacing paralegals with algorithms, trading stocks, and diagnosing illnesses. Perception AI is now digitizing our physical world, learning to recognize our faces, understand our requests, and “see” the world around us. This wave promises to revolutionize how we experience and interact with our world, blurring the lines between the digital and physical worlds.

pages: 326 words: 103,170

The Seventh Sense: Power, Fortune, and Survival in the Age of Networks
by Joshua Cooper Ramo
Published 16 May 2016

Policy gets implemented through operations. This is where clever bureaucrats and parasitic office politicians prey, where they can most easily undermine the ambitions of visionaries. But it is also the place where inspiration springs from the will and passion of companies, armies, and research labs. Server farms, data-mining algorithms, trade treaties—these are the operational chessboards of our era. Operations is where the bolt tightening for revolutionary change occurs. It is intense, relentless operations that ensure stability in the face of shock or growth or collapse. “The exploding popularity of Internet services has created a new class of computing systems that we have named warehouse-scale computers,” the Google data engineers Luiz André Barroso and Urs Hölzle wrote in a famous paper several years ago as they described the operational revolution that lets Google serve terabytes of data, instantly, every day.

pages: 362 words: 97,288

Ghost Road: Beyond the Driverless Car
by Anthony M. Townsend
Published 15 Jun 2020

Much like the toxic housing debt that helped trigger the global financial crisis of 2007, the increasingly predictable revenue streams of automated mobility services will be ideal for conversion to asset-backed securities, which already “have been created based on cash flows from movie revenues, royalty payments, aircraft leases and solar photovoltaics.” Unleashed into secondary markets, these algorithmically traded financial instruments could do unspeakable damage. A rogue outfit like Enron, eager to corner the market, could withhold the supply of transportation to goose the value of the securities it holds. Savvy speculators might trade algorithmically with stealthy precision, evading detection, manipulating the supply of mobility in undetectable, deleterious ways.

pages: 318 words: 99,524

Why Aren't They Shouting?: A Banker’s Tale of Change, Computers and Perpetual Crisis
by Kevin Rodgers
Published 13 Jul 2016

‘We lobbied for months to get a higher priority for our messages,’ my colleague recalled; ‘we eventually succeeded and it helped, but the battlefront kept moving on.’ A technological cat-and-mouse game quickly began to escalate. Most of the new e-trading funds that were entering FX were expanding from the equities market where electronic trading (aka ‘algorithmic trading’) was already well established. This was because equities are a largely exchange-traded product, which means that there has always been a natural single place to deal (the exchange), and so the pressures to trade electronically were felt earlier than in FX. The newly interconnected and API-heavy FX market was a happy hunting ground for them – especially happy because money was becoming harder to make in the highly competitive equities market.

pages: 329 words: 99,504

Easy Money: Cryptocurrency, Casino Capitalism, and the Golden Age of Fraud
by Ben McKenzie and Jacob Silverman
Published 17 Jul 2023

In blog posts, academic papers, and conversations with journalists, they have argued that Binance has been outplayed in its own casino. According to their analysis, Binance has become the perfect playground for professional trading firms to clean up against unsophisticated retail traders. Using state-of-the-art algorithmic trading programs and access to the latest market-moving information, these firms are both faster and more powerful than the regular Joes they compete against. Ranger compared what was happening on crypto exchanges to the online poker craze of the mid-2000s. Back then, you had a sense of the stakes and could see who was beating you at the virtual table.

pages: 364 words: 99,897

The Industries of the Future
by Alec Ross
Published 2 Feb 2016

FINTECH: THE FINANCIAL DATA SYSTEM Wall Street has taken advantage of big data as much as any industry. Of the roughly 7 billion shares that are traded in US equity markets every day, two-thirds are traded by preprogrammed computer algorithms that crunch data about share prices, timing, and quantity in order to maximize gains and minimize risk. This is called black-box or algorithmic trading and is now the norm in finance. The next impact of big data in the finance world will be in retail banking, the area where average people are the customers, as opposed to investment banks or commercial banks that focus on serving corporations. The application of big data to enhance operations and product development in retail banking is known as “fintech.”

pages: 432 words: 106,612

Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever
by Robin Wigglesworth
Published 11 Oct 2021

* * * ♦ THE BLACK MONDAY CRASH ON October 19, 1987, ended careers, bankrupted thousands, and rippled through the global economy. A market swoon of this severity and abruptness demanded investigation, and the following February the Securities and Exchange Commission published its autopsy. The financial watchdog’s report mostly blamed a nascent automated, algorithmic trading strategy called “portfolio insurance” for the severity of the tumble. Portfolio insurance involved investors like pension funds and insurers selling stock index futures once the market had declined by a certain level, in theory insuring their market positions against downturns. But on Black Monday, the accelerating rush of futures sellers overwhelmed the market’s capacity to absorb them, bled into the main stock exchange, and led to more automated futures selling, a feedback loop that turned a nasty crash into a financial heart attack.

pages: 405 words: 109,114

Unfinished Business
by Tamim Bayoumi

This is why they became the center of the largely unregulated shadow banking system.20 The information technology revolution gradually transformed the markets investment banks served. Settlement of trade moved from a cumbersome process involving people and paperwork to lightning-fast deals based on electronic matching of offers, and increasing algorithmic trading dictated by computer programs rather than human dealers. Lower costs of information also ushered in a wider variety of assets, including complex derivatives, and gradually changed the nature and size of investment banking. As costs of trades fell and transactions rose, the potential profits from trading increased.

pages: 419 words: 109,241

A World Without Work: Technology, Automation, and How We Should Respond
by Daniel Susskind
Published 14 Jan 2020

For instance, CEOs are thought to use new systems to run larger, more valuable companies, pushing up their pay as a result. Bankers, who stand alongside CEOs at the top of the pay ladder, may also have seen technological progress boost their wages, as financial innovations like complex pricing software and algorithmic trading platforms have helped to raise the demand for their work.24 The most compelling explanations for the rise in inequality at the very top, though, are not so much about productivity but power: these “supermanagers,” as Thomas Piketty calls them, are receiving higher wages largely because they now have so much institutional clout that they are able to put together increasingly generous pay packages for themselves.

pages: 369 words: 107,073

Madoff Talks: Uncovering the Untold Story Behind the Most Notorious Ponzi Scheme in History
by Jim Campbell
Published 26 Apr 2021

The judge rejected the government’s case that they knew about the scheme as early as 1991–1992, but found that they knew what they were doing was wrong by 2006 when they coded “special reports” to help evade a couple of SEC examinations. They were sentenced to two and a half years in prison. The programming to automate the fraud on the obsolete IBM AS/400 was amazingly detailed: Randomization. The computer programs running the fake trading were set up with random number generators, using randomization algorithms. Trade transactions needed to be done generating random numbers, so backdated trades with different dates wouldn’t show sequential trade ticket numbers, which would have indicated fake transactions. Even the randomization was randomized. “SPCL” programs. These programs pulled real data off backup tapes, such as actual House 5 customer files, and then randomly generated fake variables were plugged in.

pages: 422 words: 113,830

Bad Money: Reckless Finance, Failed Politics, and the Global Crisis of American Capitalism
by Kevin Phillips
Published 31 Mar 2008

Or perhaps we should say a bevy of black swans, author Nassim Nicholas Taleb’s shorthand for mathematical impossibilities that cannot occur in hedge funds’ quantitative strategies but always manage to occur two, three, seven, or eleven times in the real world of every significant financial crisis.47 The idea that policymakers have allowed the U.S. economy to be guided by a financial sector increasingly dominated by black box makers and algorithm vendors itself seems like a black swan—an impossibility, save that it’s happening. According to one U.S. consultancy, by 2010 algorithmic trading, an aspect of “quant”based investing, is expected to account for half of all trading in U.S. equity markets.48 There is no better distillation of the harm inflicted—and probably yet to be inflicted—than that of hedge fund manager Richard Bookstaber in his 2007 volume, A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation.

pages: 492 words: 118,882

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
by Kariappa Bheemaiah
Published 26 Feb 2017

However, since the crisis, lower volatility, improved liquidity, rising costs of trading infrastructure, and regulatory scrutiny have declined the profitability of HFT, while dislocations such as the 2010 flash crash, the 2014 treasury flash crash, and the 2015 ETF flash crash have declined the popularity of HFT. In light of these shortcomings, FinTech firms using algorithmic trading strategies with smarter and faster machines are changing the market structure in terms of volume, liquidity, volatility, and spread of risk. Companies such as Neuro Dimension conduct technical analysis with AI (using neural networks and genetic algorithms) to “learn” patterns from historical data.

pages: 447 words: 111,991

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It
by Azeem Azhar
Published 6 Sep 2021

Today, visit a trading desk and you’ll see the pit-traders have largely gone, replaced by computers. When you buy or sell some shares, the likelihood is that those shares are sold via algorithm, finding the best price in the market. No humans needed. When I ran an innovation group at Reuters, the financial information giant, in 2006, algorithmic trading was starting to take off. About 30 per cent of all shares were traded that way. A decade later, nearly 70 per cent of shares were traded automatically.23 The face-to-face trading of major financial instruments is increasingly rare. Even fund management, superficially a more ‘human’ area of the financial services industry, is not immune to automation.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

We have private companies like SpaceX but precious little crewed space flight; we have the Standard Model of physics but haven't gone beyond it; we have libraries of humanities research that no one reads; we have a decades-long war on cancer, but still have cancer. Our time comes with a litany of big ideas: blockchain, mobile social networks, supermaterials like graphene, deep learning neural networks, quantum biology, massive multiplayer online games, molecular machines, behavioural economics, algorithmic trading, gravitational wave and exoplanet astronomy, parametric architecture, e-sports, the ending of taboos around gender and sexuality, to name a few. But execution and purchase are more problematic than in the past. There is more evidence of struggle, sclerosis, decay and hesitancy than one might assume given everything at humanity's disposal.

pages: 413 words: 117,782

What Happened to Goldman Sachs: An Insider's Story of Organizational Drift and Its Unintended Consequences
by Steven G. Mandis
Published 9 Sep 2013

Some of the best and brightest are now more interested in working at a technology company than at Goldman (C). Other firms, such as Donaldson, Lufkin & Jenrette, start to offer significantly higher compensation than Goldman, especially at the entry-level positions (C). Goldman acquires Hull Trading Company, a leading technology-driven algorithmic trading firm and electronic market maker, for $531 million (C, T). Technology-driven trading is starting to dominate (T). In November, Goldman establishes the Pine Street Leadership Development Initiative, in part, to help socialize larger numbers of managers (O). The Euro becomes an accounting currency and was scheduled to enter circulation in 2002, helping to accelerate pan-European banking consolidation. 2000: The Commodity Futures Modernization Act determines that credit default swaps are neither futures nor securities and therefore are not subject to regulation by the Securities and Exchange Commission or the Commodities Futures Trading Commission (CFTC) (R, T).

pages: 756 words: 120,818

The Levelling: What’s Next After Globalization
by Michael O’sullivan
Published 28 May 2019

For instance, it complicates diplomacy through the rise of cyberwarfare and what could be called diplomacy by tweet, and it accelerates economic change and changes the way central banks look at inflation (what could be called the Amazon effect of apparently driving retail prices lower). Then, the sheer size of technology companies like Facebook and the pervasiveness of technology in society is one of the new challenges to law and philosophy (biotechnology is another). In financial markets, new data and algorithmic trading are changing the ways in which markets operate. The Levelling will fully illustrate and synthesize the transition going on in world politics, economics, finance, and international relations. It will also show that as globalization falters and gives way to a more multipolar form of world order, the relative power between countries and regions will level out, and then new constellations will emerge.

Unknown Market Wizards: The Best Traders You've Never Heard Of
by Jack D. Schwager
Published 2 Nov 2020

No, it’s because if over 80% of the guys in my sample are choosing the same team, then everyone else must be doing the same thing too, and the point spread is probably too wide. The concept that a market’s discounting mechanism is based on speculator participation, not price, is the most important thing that I know. Given the developments of the past few decades—algorithmic trading, high-frequency trading, artificial intelligence, the proliferation of hedge funds—is it still possible for the individual trader to beat the market? Jason Shapiro provides the perfect illustration of why I believe the answer to this question is “yes.” At its core, Shapiro’s trading success is grounded in exploiting the flaw in the emotion-based trading decisions of other market participants.

pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined
by Lasse Heje Pedersen
Published 12 Apr 2015

They are distinguished by diversification, sticking to their process with discipline, and the ability to engineer portfolio characteristics. —Cliff Asness (2007) Quantitative equity investing—quant equity, for short—means model-driven equity investing, performed, for instance, by equity market neutral hedge funds. Quants codify their trading rules in computer systems and execute orders with algorithmic trading overseen by humans. There are several advantages and disadvantages of quantitative investing relative to discretionary trading. The disadvantages are that the trading rule cannot be as tailored to each specific situation and it cannot be based on “soft” information such as phone calls and human judgment.

Virtual Competition
by Ariel Ezrachi and Maurice E. Stucke
Published 30 Nov 2016

In 2014, the SEC for the first time sanctioned the highfrequency trading firm for using complex computer programs to manipulate stock prices.39 The sophisticated algorithm, code-named Gravy, engaged in a practice known as “marking the close” in which stocks were bought or sold near the close of trading to affect the closing price: “[t]he massive volumes of Athena’s last-second trades allowed Athena to overwhelm the market’s available liquidity and artificially push the market price—and therefore the closing price—in Athena’s favor.”40 Athena’s employees, the SEC alleged, Tacit Collusion on Steroids: The Predictable Agent 69 were “acutely aware of the price impact of its algorithmic trading, calling it ‘owning the game’ in internal e-mails.”41 Athena employees “knew and expected that Gravy impacted the price of shares it traded, and at times Athena monitored the extent to which it did. For example, in August 2008, Athena employees compiled a spreadsheet containing information on the price movements caused by an early version of Gravy.”42 Athena configured its algorithm Gravy “so that it would have a price impact.”43 In calling its market-manipulation algorithm Gravy, and by exchanging a string of incriminating e-mails, the company did not help its case.

pages: 561 words: 138,158

Shutdown: How COVID Shook the World's Economy
by Adam Tooze
Published 15 Nov 2021

They’re isolated at home in their sweatpants . . . Psychologically it’s a bad situation.”19 Bad as the situation was for the traders, it was the computerized algorithms that were doing much of the damage. In one of the most sophisticated markets in the world, 75 percent of the market-making in U.S. Treasuries is done by algorithmic trading. As volatility surged and risk increased, the algorithms automatically reduced the size of the positions they would take. At the same time, they hiked the spread between prices at which they would buy and sell bonds. This was programmed into the algorithms because it was a sensible reaction to a turbulent market that had taken a turn to the downside.

pages: 611 words: 130,419

Narrative Economics: How Stories Go Viral and Drive Major Economic Events
by Robert J. Shiller
Published 14 Oct 2019

Since then, automated advisers such as Schwab Intelligent Portfolios, Betterment, and Wealthfront have proliferated. There are other efforts to automate economic decisions too, such as target date funds, first attracting interest around 2007, that automatically rebalance a long-term investor’s portfolio based on a target retirement date. There are many other applications of algorithmic trading. Nonetheless, today, people write the programs and make the ultimate foundational decisions. Someday people may defer massively to machines for life decisions, in which case economic processes may be fundamentally altered. But that day appears likely still to be far-off. Modeling technology’s effects on communications will be easier to trace when there is better science behind the spread of economic narratives.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
by Valliappa Lakshmanan , Sara Robinson and Michael Munn
Published 31 Oct 2020

developers, Roles, Scale dimensionality reduction, Models and Frameworks directed acyclic graph (see DAG) discrete probability distribution, Solution, Why It Works, Capturing uncertainty distributed data processing infrastructure, Solution DistributedDataParallel, Synchronous training Distribution Strategy design pattern, Design Pattern 14: Distribution Strategy-Minimizing I/O waits DNN model, Text embeddings, Feature crosses in TensorFlow, Increased training and design time, Stochastic Gradient Descent Docker container, Running the pipeline on Cloud AI Platform, TensorFlow Serving downsampling, Solution, Downsampling-Weighted classes, Number of minority class examples available-Combining different techniques dropout technique, Dropout as bagging dummy coding, One-hot encoding E early stopping, Early stopping edge, Problem-Problem, Phase 1: Building the offline model Embedding design pattern, Data Representation Design Patterns-Data Representation Design Patterns, Design Pattern 2: Embeddings-Embeddings in a data warehouse, Problem-Solution, Text data multiple ways, Pattern Interactions-Pattern Interactions embeddings, Bottleneck layer (see also bottleneck layer) embeddings, as similarity, Why It Works Ensemble design pattern, Problem Representation Design Patterns-Problem Representation Design Patterns, Design Pattern 7: Ensembles-Other ensemble methods, Solution, Combining different techniques, Pattern Interactions ensemble methods, Solution(see also bagging, boosting, stacking) epochstraining, Data and Feature Engineering, Early stopping using, Keras Training Loop, Solution, Redefining an epoch-Retraining with more data virtual, Virtual epochs, Asynchronous training, Pattern Interactions evaluation, definition of, The Machine Learning Process example-based explanation, Counterfactual analysis and example-based explanations-Counterfactual analysis and example-based explanations ExampleGen components, Solution-Solution ExampleValidator, Solution experimenter bias, Problem explainability, Problem, Solution, Solution, Explanations from deployed models, Fairness versus explainability-Fairness versus explainability, Pattern Interactions(see also deep learning, post hoc explainability method) Explainable AI, Data and Model Tooling, Importance of explainability, Explanations from deployed models, Explanations from deployed models Explainable Predictions design pattern, Responsible AI, Design Pattern 29: Explainable Predictions-Limitations of explanations, Pattern Interactions exported model, Solution F Facets, Data validation with TFX Fairness Indicators, Fairness Indicators Fairness Lens design pattern, Responsible AI, Design Pattern 30: Fairness Lens-Fairness versus explainability Farm Fingerprint hashing algorithm, Solution, Repeatable sampling, Unstructured data FarmHash, Solution Feast, Feast-Alternative implementations feature attributions, Solution-Counterfactual analysis and example-based explanations feature columns, Solution, Empty hash buckets, Solution, Transformations in TensorFlow and Keras-Transformations in TensorFlow and Keras Feature Cross design pattern, Data Representation Design Patterns, Design Pattern 3: Feature Cross-Need for regularization, Pattern Interactions feature cross, cardinality, Need for regularization feature engineering, Data and Feature Engineering, Data Representation Design Patterns, Text and image transformations, Problem, Discovery(see also data preprocessing) feature extraction, Data Representation Design Patterns, Trade-Offs and Alternatives-Fine-tuning versus feature extraction, Solution Feature Store design pattern, Reproducibility Design Patterns, Alternate pattern approaches, Design Pattern 26: Feature Store-Transform design pattern, Pattern Interactions-Pattern Interactions feature, definition of, Data and Feature Engineering, Data Representation Design Patterns FeatureSet, Adding feature data to Feast-Ingesting feature data into the FeatureSet feed-forward neural networks (see neural networks) field-programmable gate array (FPGA), ASICs for better performance at lower cost fine-tuning, Fine-tuning, Fine-tuning versus feature extraction-Fine-tuning versus feature extraction, Scheduled retraining (see also progressive fine-tuning) fingerprint hashing algorithm, Cryptographic hash fitness function, Genetic algorithms flat approach, Dataset considerations Flatten layer, Images as pixel values FPGA (field-programmable gate array), ASICs for better performance at lower cost fraud detection, Problem-Downsampling, Number of minority class examples available, Problem, Capturing ground truth, Sequential split, Stratified split G Gamma, Erich, What Are Design Patterns? Gaussian process, Bayesian optimization genetic algorithms, Trade-Offs and Alternatives, Genetic algorithms-Genetic algorithms GitHub Actions, Integrating CI/CD with pipelines GitLab Triggers, Integrating CI/CD with pipelines GKE, Solution, Running the pipeline on Cloud AI Platform GLoVE, Context language models Google App Engine, Create web endpoint Google Bolo, Standalone single-phase model Google Cloud Functions, Create web endpoint Google Cloud Public Datasets, Data and Model Tooling Google Container Registry, Running the pipeline on Cloud AI Platform Google Kubernetes Engine (see GKE) Google Translate, Standalone single-phase model GPU, Problem, Problem-Synchronous training, ASICs for better performance at lower cost, Minimizing I/O waits, Problem, Running the pipeline on Cloud AI Platform, Transformational phase: Fully automated processes Gradient Boosting Machines, Boosting gradient descent (see SGD) graphics processing unit (see GPU) grid search, Grid search and combinatorial explosion-Grid search and combinatorial explosion, Why It Works Grid-SearchCV, Grid search and combinatorial explosion ground truth label, Data and Feature Engineering, Data Quality, Capturing ground truth-Why It Works H hash bucketscollisions, Bucket collision empty, Empty hash buckets heuristic to choose numbers, Out-of-vocabulary input Hashed Feature design pattern, Data Representation Design Patterns, Design Pattern 1: Hashed Feature-Empty hash buckets, Pattern Interactions Helm, Richard, What Are Design Patterns?

pages: 565 words: 134,138

The World for Sale: Money, Power and the Traders Who Barter the Earth’s Resources
by Javier Blas and Jack Farchy
Published 25 Feb 2021

Just as for the independent traders, it has been a magnificently profitable business for BP and Shell: BP typically records pre-tax profits from trading of between $2 billion and $3 billion a year; Shell targets trading profits of $4 billion. In the realm of financial derivatives, their trading units are as innovative as the trading houses. In the late 1990s, for example, BP allocated a pot of money to be traded, in effect, by a computer – long before algorithmic trading became a dominant force in financial markets. BP’s trading strategy, devised by an in-house maths whizz and known as the ‘Q book’, traded dozens of commodity futures including gold and corn. And their market intelligence is every bit as sharp as that of the trading houses. From the days of Andy Hall onwards, BP’s traders have never been afraid to bet big – and they’ve maintained that fearlessness up to the modern day.

pages: 479 words: 144,453

Homo Deus: A Brief History of Tomorrow
by Yuval Noah Harari
Published 1 Mar 2015

That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. We know algorithms were to blame, but we are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings, who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers.5 And these lawyers won’t necessarily be human. Movies and TV series give the impression that lawyers spend their days in court shouting ‘Objection!’

Investment: A History
by Norton Reamer and Jesse Downing
Published 19 Feb 2016

Thus, this era of vast earnings by top investment managers, while likely to moderate slowly over time, is unlikely to disappear completely. At the end of the day, though, the next generation of financial innovations will likely take the spotlight. The changes may occur in niche corners of the market, like algorithmic trading, commodities strategies, or real assets, or they may involve a holistic approach like the endowment-style or multistrategy firm. Only time will tell what financial innovations the future holds. Conclusion Investment in the Twenty-First Century THIS NARRATIVE HAS MADE IT CLEAR that investment is a fundamental human enterprise.

pages: 543 words: 153,550

Model Thinker: What You Need to Know to Make Data Work for You
by Scott E. Page
Published 27 Nov 2018

In effect, insurers acted as if they were populations of individuals with diverse thresholds. One portfolio insurer sold over $1 billion in stock. To put that in perspective, only $20 billion in stock was sold that entire day. The second crash, the May 6, 2010, “flash crash” dropped the Dow Jones Industrial Average by 5% in three minutes. It was the result of algorithmic trades. Owing to the complexity and speed of modern financial markets, no one knows for certain what exactly caused the flash crash. We know that a large mutual fund made a huge sell order, dumping over $4 billion of stock futures into a market containing high-speed trading algorithms that try to exploit beneficial trades.

pages: 807 words: 154,435

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

Soros, famous first for his bet against sterling as its peg to the European Monetary System collapsed in 1992, and most recently for his philanthropy (oriented towards the promotion of liberal democracy and recently towards new approaches to economics), relies on his capacity to distinguish between false and well-founded economic narratives. Simons, formerly a mathematics professor, employs brilliant (maths and physics) PhDs to devise algorithmic trading strategies to take very short-term positions in securities. But there is more in common in the approaches of these men than appears at first sight. Their exceptional intelligence is one; they have responded to the challenge of ‘if you’re so smart why aren’t you rich?’ It is evident from Soros’s writings that he has much in common with Thales of Miletus, and would prefer to be remembered for his ideas than his wealth.

Seeking SRE: Conversations About Running Production Systems at Scale
by David N. Blank-Edelman
Published 16 Sep 2018

For mental health and mental disorder resources that focus on the needs of software engineers: Open Sourcing Mental Illness mhprompt burnout.io For general reading on mental health and mental disorders: National Alliance on Mental Illness American Psychiatric Association National Institutes of Mental Health Autism Women’s Network Autistic Self Advocacy Network Contributor Bio James Meickle is a site reliability engineer at Quantopian, a Boston startup making algorithmic trading accessible to everyone. Between NYSE trading days, he advises DevOpsDays Boston and conducts Ansible trainings on O’Reilly’s Safari platform. What free time remains is dedicated to cooking, sci-fi, permadeath video games, and Satanism. 1 Emotional labor refers to work managing the emotions of others, such as being expected to keep up a smile or politely respond to aggression.

Engineering Security
by Peter Gutmann

The airline ticket attack involves introducing long delays into network communications, but sometimes even a micro-DoS that adds just a few milliseconds of latency is enough to effect an attack. The transactions carried out by algorithmic trading systems are so time-critical that traders have relocated entire data centres in order to be closer to stock exchanges, thereby gaining a few milliseconds of network latency over their competitors (gamers worried about lag in online gaming have nothing on these guys). By introducing relatively minute, virtually undetectable levels of jitter on networks that are used by algorithmic trading systems, an attacker can convert an apparently insignificant DoS into a means of derailing high-value stock trades [41][42].