SQL Server 2022 Query Performance Tuning: Troubleshoot and Optimize Query Performance Sixth Edition

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 Cover 1 Copyright 4 Table of Contents 5 Preface 11 Contents and Structure 13 Who This Book Is For 16 Conventions Used in This Book 17 Using Code Examples 17 O’Reilly Online Learning 18 How to Contact Us 18 Acknowledgments 19 Chapter 1. Python and Algorithmic Trading 21 Python for Finance 21 Python Versus Pseudo-Code 22 NumPy and Vectorization 23 pandas and the DataFrame Class 25 Algorithmic Trading 27 Python for Algorithmic Trading 31 Focus and Prerequisites 33 Trading Strategies 33 Simple Moving Averages 34 Momentum 34 Mean Reversion 34 Machine and Deep Learning 35 Conclusions 35 References and Further Resources 35 Chapter 2. Python Infrastructure 37 Conda as a Package Manager 39 Installing Miniconda 39 Basic Operations with Conda 41 Conda as a Virtual Environment Manager 47 Using Docker Containers 50 Docker Images and Containers 51 Building a Ubuntu and Python Docker Image 51 Using Cloud Instances 56 RSA Public and Private Keys 58 Jupyter Notebook Configuration File 58 Installation Script for Python and Jupyter Lab 60 Script to Orchestrate the Droplet Set Up 61 Conclusions 63 References and Further Resources 64 Chapter 3. Working with Financial Data 65 Reading Financial Data From Different Sources 66 The Data Set 66 Reading from a CSV File with Python 67 Reading from a CSV File with pandas 69 Exporting to Excel and JSON 70 Reading from Excel and JSON 71 Working with Open Data Sources 72 Eikon Data API 75 Retrieving Historical Structured Data 78 Retrieving Historical Unstructured Data 82 Storing Financial Data Efficiently 85 Storing DataFrame Objects 86 Using TsTables 90 Storing Data with SQLite3 95 Conclusions 97 References and Further Resources 98 Python Scripts 98 Chapter 4. Mastering Vectorized Backtesting 101 Making Use of Vectorization 102 Vectorization with NumPy 103 Vectorization with pandas 105 Strategies Based on Simple Moving Averages 108 Getting into the Basics 109 Generalizing the Approach 117 Strategies Based on Momentum 118 Getting into the Basics 119 Generalizing the Approach 124 Strategies Based on Mean Reversion 127 Getting into the Basics 127 Generalizing the Approach 130 Data Snooping and Overfitting 131 Conclusions 133 References and Further Resources 133 Python Scripts 135 SMA Backtesting Class 135 Momentum Backtesting Class 138 Mean Reversion Backtesting Class 140 Chapter 5. Predicting Market Movements with Machine Learning 143 Using Linear Regression for Market Movement Prediction 144 A Quick Review of Linear Regression 145 The Basic Idea for Price Prediction 147 Predicting Index Levels 149 Predicting Future Returns 152 Predicting Future Market Direction 154 Vectorized Backtesting of Regression-Based Strategy 155 Generalizing the Approach 157 Using Machine Learning for Market Movement Prediction 159 Linear Regression with scikit-learn 159 A Simple Classification Problem 161 Using Logistic Regression to Predict Market Direction 166 Generalizing the Approach 170 Using Deep Learning for Market Movement Prediction 173 The Simple Classification Problem Revisited 174 Using Deep Neural Networks to Predict Market Direction 176 Adding Different Types of Features 182 Conclusions 186 References and Further Resources 186 Python Scripts 187 Linear Regression Backtesting Class 187 Classification Algorithm Backtesting Class 190 Chapter 6. Building Classes for Event-Based Backtesting 195 Backtesting Base Class 197 Long-Only Backtesting Class 202 Long-Short Backtesting Class 205 Conclusions 210 References and Further Resources 210 Python Scripts 211 Backtesting Base Class 211 Long-Only Backtesting Class 214 Long-Short Backtesting Class 217 Chapter 7. Working with Real-Time Data and Sockets 221 Running a Simple Tick Data Server 223 Connecting a Simple Tick Data Client 226 Signal Generation in Real Time 228 Visualizing Streaming Data with Plotly 231 The Basics 231 Three Real-Time Streams 232 Three Sub-Plots for Three Streams 234 Streaming Data as Bars 235 Conclusions 237 References and Further Resources 238 Python Scripts 238 Sample Tick Data Server 238 Tick Data Client 239 Momentum Online Algorithm 239 Sample Data Server for Bar Plot 240 Chapter 8. CFD Trading with Oanda 243 Setting Up an Account 247 The Oanda API 249 Retrieving Historical Data 250 Looking Up Instruments Available for Trading 250 Backtesting a Momentum Strategy on Minute Bars 251 Factoring In Leverage and Margin 254 Working with Streaming Data 256 Placing Market Orders 257 Implementing Trading Strategies in Real Time 259 Retrieving Account Information 264 Conclusions 266 References and Further Resources 267 Python Script 267 Chapter 9. FX Trading with FXCM 269 Getting Started 271 Retrieving Data 271 Retrieving Tick Data 272 Retrieving Candles Data 274 Working with the API 276 Retrieving Historical Data 277 Retrieving Streaming Data 279 Placing Orders 280 Account Information 282 Conclusions 283 References and Further Resources 284 Chapter 10. Automating Trading Operations 285 Capital Management 286 Kelly Criterion in Binomial Setting 286 Kelly Criterion for Stocks and Indices 292 ML-Based Trading Strategy 297 Vectorized Backtesting 298 Optimal Leverage 305 Risk Analysis 307 Persisting the Model Object 310 Online Algorithm 311 Infrastructure and Deployment 316 Logging and Monitoring 317 Visual Step-by-Step Overview 319 Configuring Oanda Account 319 Setting Up the Hardware 320 Setting Up the Python Environment 321 Uploading the Code 322 Running the Code 322 Real-Time Monitoring 324 Conclusions 324 References and Further Resources 325 Python Script 325 Automated Trading Strategy 325 Strategy Monitoring 328 Appendix A. Python, NumPy, matplotlib, pandas 329 Python Basics 330 Data Types 330 Data Structures 333 Control Structures 335 Python Idioms 337 NumPy 339 Regular ndarray Object 339 Vectorized Operations 341 Boolean Operations 342 ndarray Methods and NumPy Functions 342 ndarray Creation 345 Random Numbers 346 matplotlib 347 pandas 352 DataFrame Class 352 Numerical Operations 355 Data Selection 356 Boolean Operations 357 Plotting with pandas 359 Input-Output Operations 361 Case Study 363 Conclusions 369 Further Resources 369 Index 371 About the Author 379 Colophon 380 Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and indivisual traders using online platforms. The tool of choice for many traders today is Python and its ecosystems 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. Ny 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