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Python for Finance Cookbook – Second Edition

You're reading from   Python for Finance Cookbook – Second Edition Over 80 powerful recipes for effective financial data analysis

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803243191
Length 740 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Eryk Lewinson Eryk Lewinson
Author Profile Icon Eryk Lewinson
Eryk Lewinson
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Toc

Table of Contents (18) Chapters Close

Preface 1. Acquiring Financial Data 2. Data Preprocessing FREE CHAPTER 3. Visualizing Financial Time Series 4. Exploring Financial Time Series Data 5. Technical Analysis and Building Interactive Dashboards 6. Time Series Analysis and Forecasting 7. Machine Learning-Based Approaches to Time Series Forecasting 8. Multi-Factor Models 9. Modeling Volatility with GARCH Class Models 10. Monte Carlo Simulations in Finance 11. Asset Allocation 12. Backtesting Trading Strategies 13. Applied Machine Learning: Identifying Credit Default 14. Advanced Concepts for Machine Learning Projects 15. Deep Learning in Finance 16. Other Books You May Enjoy
17. Index

Index

Symbols

3 Month T-bill

using, from FRED database 281, 282

13 Week T-bill

using 280, 281

A

absolute percent error (APE) 261

accuracy paradox 568

Adaptive Moment Estimation (Adam) 656

Adaptive Synthetic Sampling (ADSYN) 564

drawbacks 564

additive model 144

Akaike Information Criterion (AIC) 156, 190

allowable leverage 402-405

alpha 5, 377

Alpha Vantage

data, obtaining from 16-20

reference link 21

altair

reference link 69

American options

pricing, with Least Squares Monte Carlo 353-356

pricing, with QuantLib 357-361

analysis of variance (ANOVA) 610

anchored walk-forward validation 207

antithetic variates 346

ARCH effect 104

ARCH model 306

stock returns’ volatility, modeling with 306-311

area under the ROC curve (AUC) 512

ARIMA (Autoregressive Integrated Moving Average) 305

ARIMA class models

reference links 189

time series, modeling with 176...

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