Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Cover....1

Title Page....2

Copyright and Credits....3

Contributors....4

Table of Contents....6

Preface....16

Acquire Free Financial Market Data with Cutting-Edge Python Libraries....24

Technical requirements....25

Diving into continuous futures data with Nasdaq Data Link....26

Getting ready…....27

How to do it…....27

How it works…....28

There’s more…....28

See also....30

Exploring S&P 500 ratios data with Nasdaq Data Link....31

How to do it…....31

How it works…....32

There’s more…....32

See also....33

Working with stock market data with the OpenBB Platform....33

Getting ready…....33

How to do it…....33

How it works…....34

There’s more…....34

See also....37

Fetching historic futures data with the OpenBB Platform....37

Getting ready…....38

How to do it…....38

There’s more…....39

See also....42

Navigating options market data with the OpenBB Platform....42

Getting ready…....42

How to do it…....42

How it works…....43

There’s more…....44

See also....45

Harnessing factor data using pandas_datareader....45

Getting ready…....46

How to do it…....46

How it works…....47

There’s more…....48

See also....48

Analyze and Transform Financial Market Data with pandas....50

Diving into pandas index types....51

How to do it…....51

How it works…....52

There’s more…....52

See also....54

Building pandas Series and DataFrames....54

Getting ready....54

How to do it…....55

How it works…....56

There’s more…....57

See also....60

Manipulating and transforming DataFrames....60

Getting ready…....61

How to do it…....61

How it works…....67

There’s more…....67

See also....71

Examining and selecting data from DataFrames....71

How to do it…....71

How it works…....74

There’s more…....75

See also....76

Calculating asset returns using pandas....76

How to do it…....77

How it works…....78

There’s more…....79

See also....80

Measuring the volatility of a return series....80

How to do it…....80

How it works…....81

There’s more…....81

See also....83

Generating a cumulative return series....83

Getting ready…....84

How to do it…....84

How it works…....86

See also....87

Resampling data for different time frames....87

How to do it…....87

How it works…....90

There’s more…....90

See also....92

Addressing missing data issues....92

Getting ready…....93

How to do it…....94

How it works…....95

There’s more…....95

See also....96

Applying custom functions to analyze time series data....96

Getting ready…....96

How to do it…....97

How it works…....98

There’s more…....98

See also....99

Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash....100

Quickly visualizing data using pandas....101

How to do it…....101

How it works…....104

There’s more…....104

See also....106

Animating the evolution of the yield curve with Matplotlib....107

How to do it…....107

How it works…....110

There’s more…....110

See also....110

Plotting options implied volatility surfaces with Matplotlib....111

Getting ready…....111

How to do it…....111

How it works…....113

There’s more…....114

See also....114

Visualizing statistical relationships with Seaborn....115

How to do it…....115

How it works…....117

There’s more…....117

See also....120

Creating an interactive PCA analytics dashboard with Plotly Dash....120

Getting ready…....120

How to do it…....121

How it works…....126

There’s more…....127

See also....127

Store Financial Market Data on Your Computer....128

Storing data on disk in CSV format....129

How to do it…....129

How it works…....130

There’s more…....131

See also…....131

Storing data on disk with SQLite....132

Getting ready…....132

How to do it…....132

How it works…....134

There’s more…....135

See also…....137

Storing data in a PostgreSQL database server....137

Getting ready…....138

How to do it…....139

How it works…....141

There’s more…....141

See also…....142

Storing data in ultra-fast HDF5 format....143

Getting ready…....143

How to do it…....143

How it works…....145

There’s more…....146

See also…....146

Build Alpha Factors for Stock Portfolios....148

Identifying latent return drivers using principal component analysis....149

Getting ready....149

How to do it…....149

How it works…....151

There’s more…....151

See also....154

Finding and hedging portfolio beta using linear regression....155

Getting ready....155

How to do it…....155

How it works…....158

There’s more…....159

See also....160

Analyzing portfolio sensitivities to the Fama-French factors....160

Getting ready....160

How to do it…....161

How it works…....163

There’s more…....164

See also....166

Assessing market inefficiency based on volatility....166

How to do it…....167

How it works…....171

There’s more…....172

See also....172

Preparing a factor ranking model using Zipline Pipelines....173

Getting ready....173

How to do it…....174

How it works…....176

There’s more…....177

See also....177

Vector-Based Backtesting with VectorBT....178

Building technical strategies with VectorBT....178

Getting ready....179

How to do it…....179

How it works…....182

There’s more…....183

See also....186

Conducting walk-forward optimization with VectorBT....186

Getting ready....186

How to do it…....186

How it works…....189

There’s more…....190

See also....191

Optimizing the SuperTrend strategy with VectorBT Pro....192

Getting ready....192

How to do it…....193

How it works…....198

There’s more…....199

See also....201

Event-Based Backtesting Factor Portfolios with Zipline Reloaded....202

Technical Requirements....202

For Windows, Unix/Linux, and Mac Intel users....202

For Mac M1/M2 users....203

Backtesting a momentum factor strategy with Zipline Reloaded....203

Getting ready....204

How to do it…....204

How it works…....208

There’s more…....210

See also....212

Exploring a mean reversion strategy with Zipline Reloaded....212

Getting ready....212

How to do it…....213

How it works…....218

There’s more…....219

See also....221

Evaluate Factor Risk and Performance with Alphalens Reloaded....222

Preparing backtest results....223

Getting ready…....223

How to do it…....223

How it works…....226

There’s more…....227

See also....228

Evaluating the information coefficient....228

Getting ready…....229

How to do it…....229

How it works…....231

There’s more…....232

See also....233

Examining factor return performance....233

How to do it…....234

How it works…....238

There’s more…....238

See also....239

Evaluating factor turnover....239

How to do it…....240

How it works…....241

There’s more…....242

See also....244

Assess Backtest Risk and Performance Metrics with Pyfolio....246

Preparing Zipline backtest results for Pyfolio Reloaded....247

Getting ready…....247

How to do it…....247

How it works…....251

There’s more…....252

See also....252

Generating strategy performance and return analytics....253

Getting ready…....253

How to do it…....253

How it works…....256

There’s more…....257

See also....259

Building a drawdown and rolling risk analysis....259

Getting ready…....260

How to do it…....260

How it works…....263

There’s more…....263

See also....264

Analyzing strategy holdings, leverage, exposure, and sector allocations....264

Getting ready…....265

How to do it…....265

How it works…....269

There’s more…....270

See also....270

Breaking Down Strategy Performance to Trade Level....271

Getting ready…....271

How to do it…....271

How it works…....273

There’s more…....274

See also....275

Set Up the Interactive Brokers Python API....276

Building an algorithmic trading app....277

Getting ready…....277

How to do it…....280

How it works…....281

There’s more…....282

See also....283

Creating a Contract object with the IB API....284

Getting ready…....284

How to do it…....285

How it works…....285

There’s more…....286

See also....287

Creating an Order object with the IB API....287

Getting ready…....287

How to do it…....287

How it works…....288

There’s more…....289

See also....289

Fetching historical market data....290

Getting ready…....290

How to do it…....290

How it works…....294

There’s more…....297

See also....298

Getting a market data snapshot....299

Getting ready…....299

How to do it…....299

How it works…....300

There’s more…....300

See also....301

Streaming live market data....301

Getting ready…....301

How to do it…....301

How it works…....305

There’s more…....307

See also....308

Storing live tick data in a local SQL database....308

Getting ready…....308

How to do it…....309

How it works…....312

There’s more…....312

See also....314

Manage Orders, Positions, and Portfolios with the IB API....316

Executing orders with the IB API....317

Getting ready....317

How to do it…....317

How it works…....320

There’s more…....320

See also....322

Managing orders once they’re placed....322

Getting ready....322

How to do it…....323

How it works…....323

There’s more…....324

See also....325

Getting details about your portfolio....325

Getting ready....326

How to do it…....326

How it works…....327

There’s more…....328

See also....329

Inspecting positions and position details....329

Getting ready....329

How to do it…....329

How it works…....331

There’s more…....331

See also....332

Computing portfolio profit and loss....332

Getting ready....332

How to do it…....333

How it works…....334

There’s more…....334

See also....335

Deploy Strategies to a Live Environment....336

Calculating real-time key performance and risk indicators....337

Getting ready....337

How to do it…....339

How it works…....340

There’s more…....343

See also....344

Sending orders based on portfolio targets....344

Getting ready....344

How to do it…....344

How it works…....347

There’s more…....348

See also....349

Deploying a monthly factor portfolio strategy....349

Getting ready....350

How to do it…....350

How it works…....354

There’s more…....355

See also....355

Deploying an options combo strategy....355

Getting ready....356

How to do it…....357

How it works…....358

There’s more…....359

See also....361

Deploying an intraday multi-asset mean reversion strategy....361

Getting ready....362

How to do it…....362

How it works…....365

There’s more…....368

See also....369

Advanced Recipes for Market Data and Strategy Management....370

Streaming real-time options data with ThetaData....371

Getting ready....371

How to do it…....371

How it works…....374

There’s more…....375

See also....379

Using the ArcticDB DataFrame database for tick storage....379

Getting ready....380

How to do it…....380

How it works…....384

There’s more…....386

See also....386

Triggering real-time risk limit alerts....387

Getting ready....387

How to do it…....387

How it works…....388

There’s more…....389

See also....391

Storing trade execution details in a SQL database....391

Getting ready....392

How to do it…....393

How it works…....395

There’s more…....395

See also....398

Index....400

Other Books You May Enjoy....409

Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment

Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free

Key Features

• Follow practical Python recipes to acquire, visualize, and store market data for market research
• Design, backtest, and evaluate the performance of trading strategies using professional techniques
• Deploy trading strategies built in Python to a live trading environment with API connectivity

Book Description

Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.

Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using vectorbt and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.

By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.

What you will learn

• Acquire and process freely available market data with the OpenBB Platform
• Build a research environment and populate it with financial market data
• Use machine learning to identify alpha factors and engineer them into signals
• Use VectorBT to find strategy parameters using walk-forward optimization
• Build production-ready backtests with Zipline Reloaded and evaluate factor performance
• Set up the code framework to connect and send an order to Interactive Brokers

Who this book is for

Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.