book

Python for Algorithmic Trading

by Yves Hilpisch

November 2020

Intermediate to advanced

378 pages

8h 47m

English

O'Reilly Media, Inc.

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Contents and StructureWho This Book Is ForConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
Python for FinancePython Versus Pseudo-CodeNumPy and Vectorizationpandas and the DataFrame ClassAlgorithmic TradingPython for Algorithmic TradingFocus and PrerequisitesTrading StrategiesSimple Moving AveragesMomentumMean ReversionMachine and Deep LearningConclusionsReferences and Further Resources
Conda as a Package ManagerInstalling MinicondaBasic Operations with CondaConda as a Virtual Environment ManagerUsing Docker ContainersDocker Images and ContainersBuilding a Ubuntu and Python Docker ImageUsing Cloud InstancesRSA Public and Private KeysJupyter Notebook Configuration FileInstallation Script for Python and Jupyter LabScript to Orchestrate the Droplet Set UpConclusionsReferences and Further Resources
Reading Financial Data From Different SourcesThe Data SetReading from a CSV File with PythonReading from a CSV File with pandasExporting to Excel and JSONReading from Excel and JSONWorking with Open Data SourcesEikon Data APIRetrieving Historical Structured DataRetrieving Historical Unstructured DataStoring Financial Data EfficientlyStoring DataFrame ObjectsUsing TsTablesStoring Data with SQLite3ConclusionsReferences and Further ResourcesPython Scripts
Making Use of VectorizationVectorization with NumPyVectorization with pandasStrategies Based on Simple Moving AveragesGetting into the BasicsGeneralizing the ApproachStrategies Based on MomentumGetting into the BasicsGeneralizing the ApproachStrategies Based on Mean ReversionGetting into the BasicsGeneralizing the ApproachData Snooping and OverfittingConclusionsReferences and Further ResourcesPython ScriptsSMA Backtesting ClassMomentum Backtesting ClassMean Reversion Backtesting Class
Using Linear Regression for Market Movement PredictionA Quick Review of Linear RegressionThe Basic Idea for Price PredictionPredicting Index LevelsPredicting Future ReturnsPredicting Future Market DirectionVectorized Backtesting of Regression-Based StrategyGeneralizing the ApproachUsing Machine Learning for Market Movement PredictionLinear Regression with scikit-learnA Simple Classification ProblemUsing Logistic Regression to Predict Market DirectionGeneralizing the ApproachUsing Deep Learning for Market Movement PredictionThe Simple Classification Problem RevisitedUsing Deep Neural Networks to Predict Market DirectionAdding Different Types of FeaturesConclusionsReferences and Further ResourcesPython ScriptsLinear Regression Backtesting ClassClassification Algorithm Backtesting Class
Backtesting Base ClassLong-Only Backtesting ClassLong-Short Backtesting ClassConclusionsReferences and Further ResourcesPython ScriptsBacktesting Base ClassLong-Only Backtesting ClassLong-Short Backtesting Class
Running a Simple Tick Data ServerConnecting a Simple Tick Data ClientSignal Generation in Real TimeVisualizing Streaming Data with PlotlyThe BasicsThree Real-Time StreamsThree Sub-Plots for Three StreamsStreaming Data as BarsConclusionsReferences and Further ResourcesPython ScriptsSample Tick Data ServerTick Data ClientMomentum Online AlgorithmSample Data Server for Bar Plot
Setting Up an AccountThe Oanda APIRetrieving Historical DataLooking Up Instruments Available for TradingBacktesting a Momentum Strategy on Minute BarsFactoring In Leverage and MarginWorking with Streaming DataPlacing Market OrdersImplementing Trading Strategies in Real TimeRetrieving Account InformationConclusionsReferences and Further ResourcesPython Script
Getting StartedRetrieving DataRetrieving Tick DataRetrieving Candles DataWorking with the APIRetrieving Historical DataRetrieving Streaming DataPlacing OrdersAccount InformationConclusionsReferences and Further Resources

Capital ManagementKelly Criterion in Binomial SettingKelly Criterion for Stocks and IndicesML-Based Trading StrategyVectorized BacktestingOptimal LeverageRisk AnalysisPersisting the Model ObjectOnline AlgorithmInfrastructure and DeploymentLogging and MonitoringVisual Step-by-Step OverviewConfiguring Oanda AccountSetting Up the HardwareSetting Up the Python EnvironmentUploading the CodeRunning the CodeReal-Time MonitoringConclusionsReferences and Further ResourcesPython ScriptAutomated Trading StrategyStrategy Monitoring
Python BasicsData TypesData StructuresControl StructuresPython IdiomsNumPyRegular ndarray ObjectVectorized OperationsBoolean Operationsndarray Methods and NumPy Functionsndarray CreationRandom NumbersmatplotlibpandasDataFrame ClassNumerical OperationsData SelectionBoolean OperationsPlotting with pandasInput-Output OperationsCase StudyConclusionsFurther Resources

Overview

Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading.

You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field.

Set up a proper Python environment for algorithmic trading
Learn how to retrieve financial data from public and proprietary data sources
Explore vectorization for financial analytics with NumPy and pandas
Master vectorized backtesting of different algorithmic trading strategies
Generate market predictions by using machine learning and deep learning
Tackle real-time processing of streaming data with socket programming tools
Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms