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Automated trading with R : quantitative research and platform development /
All the tools you need are provided in this book to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage's API, and the source code is plug-and-play. Automated Tr...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley] :
Apress,
[2016]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Conlan, Chris. | |
| 245 | 1 | 0 | |a Automated trading with R : |b quantitative research and platform development / |c Chris Conlan. |
| 264 | 1 | |a [Berkeley] : |b Apress, |c [2016] | |
| 264 | 4 | |c Ã2016 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a text file | ||
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| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed October 4, 2016). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a At a Glance; Contents; About the Author; About the Technical Reviewers; Acknowledgments; Introduction; Part 1: Problem Scope; Chapter 1: Fundamentals of Automated Trading; Equity Curve and Return Series; Characteristics of the Equity Curve; Characteristics of the Return Series; Risk-Return Metrics; Characteristics of Risk-Return Metrics; Sharpe Ratio; Maximum Drawdown Ratios; Partial Moment Ratios; Regression-Based Performance Metrics; Optimizing Performance Metrics; Part 2: Building the Platform; Chapter 2: Networking Part I; Yahoo! Finance API; Setting Up Directories. | |
| 505 | 8 | |a URL Query BuildingData Acquisition; Loading Data into Memory; Note on Coding Style; Updating Data; YQL Web Service; URL and Query Building; Note on Quantmod; Background; Comparison; Organizing as Date-Uniform zoo Object; Note on zoo Objects; Chapter 3: Data Preparation; Handling NA Values; Note: NA vs. NaN in R; IPOs and Additions to S & P 500; Merging to the Uniform Date Template; Forward Replacement; Linearly Smoothed Replacement; Volume-Weighted Smoothed Replacement; Discussion of Replacement Methods; Real Time vs. Simulation; Influence on Volatility Metrics; Influence on Trading Decisions. | |
| 505 | 8 | |a ConclusionClosing Price and Adjusted Close; Adjusting for Stock Splits; Adjusting for Cash Dividends; Efficient Updating and Adjusted Close; Implementing Adjustments; Test for and Correct Inactive Symbols; Computing the Return Matrix; Chapter 4: Indicators; Indicator Types; Overlays; Oscillators; Accumulators; Pattern/Binary/Ternary; Machine Learning/Nonvisual/Black Box; Example Indicators; Simple Moving Average; Moving Average Convergence Divergence Oscillator (MACD); Bollinger Bands; Custom Indicator Using Correlation and Slope; Indicators Utilizing Multiple Data Sets; Conclusion. | |
| 505 | 8 | |a Chapter 5: Rule SetsOur Process Flow as Nested Functions; Terminology; Example Rule Sets; Overlays; Oscillators; Accumulators; Filters, Triggers, and Quantifications of Favor; Chapter 6: High-Performance Computing; Hardware Overview; Processing; Multicore Processing; Hyperthreading; Memory; The Disk; Random Access Memory (RAM); Processor Cache; Swap Space; Software Overview; Compiled vs. Interpreted; Scripting Languages; Speed vs. Safety; Takeaways; for Loops vs. apply Functions; for Loops and Memory Allocation; apply-Style Functions; Use Binaries Creatively; Note on Measuring Compute Time. | |
| 505 | 8 | |a Multicore Computing in REmbarrassingly Parallel Processes; doMC and doParallel; The foreach Package; The foreach Package in Practice; Integer Mapping; Computing the Return Matrix with foreach; Computing Indicators with foreach; Chapter 7: Simulation and Backtesting; Example Strategies; Our Simulation Workflow; Listing 7-1: Pseudocode; Listing 7-1: Explanation of Inputs and User Guide; Discussion; Implementing Example Strategies; Summary Statistics and Performance Metrics; Conclusion; Chapter 8: Optimization; Cross Validation in Time Series; Numerical vs. Analytical Optimization. | |
| 520 | |a All the tools you need are provided in this book to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage's API, and the source code is plug-and-play. Automated Trading with R explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will: Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders Offer an understanding of the internal mechanisms of an automated trading system Standardize discussion and notation of real-world strategy optimization problems What You'll Learn: To optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library How to best simulate strategy performance in its specific use case to derive accurate performance estimates Important optimization criteria for statistical validity in the context of a time series An understanding of critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital. | ||
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