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E-raamat: Applied Quantitative Finance: Using Python for Financial Analysis

  • Formaat: EPUB+DRM
  • Ilmumisaeg: 03-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030291419
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 03-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030291419

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This book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners.

Why Python?
1(18)
Installing Python in the Computer
4(2)
Using Jupyter Notebooks with Python
6(8)
Understanding Jupyter Notebooks
7(7)
Using Google Colab
14(3)
References
17(2)
Learning to Use Python: The Basic Aspects
19(40)
Understanding Numbers in Python
20(1)
Understanding Numbers in Python
21(4)
Using Data Structures in Python
25(21)
What Is a List?
25(1)
How to Create a List?
25(2)
Indexing and Cutting a List
27(5)
Appending Lists
32(1)
Arranging Lists
32(1)
From List to Matrices
33(1)
From List to Dictionaries
33(2)
Modifying a Dictionary
35(1)
Other Interesting Functions of a Dictionary
35(1)
The DataFrame
36(6)
Boolean, Loops and Other Features
42(2)
If, Else and Elif in Python
44(2)
Loops
46(1)
For Loop
46(9)
While Loop
52(3)
List Comprehension
55(2)
References
57(2)
Using FRED® API for Economic Indicators and Data (Example)
59(12)
Installing the FRED® API
59(1)
Using the FRED® API to Retrieve Data
60(11)
First Step
60(1)
Second Step
60(1)
Third Step
60(3)
The Gross Domestic Product
63(2)
The Gross Domestic Product Price Deflator
65(1)
Understanding the Process into the Basics
66(1)
Comparing GDP
67(4)
Using Stock Market Data in Python
71(14)
API Sources
71(1)
Most Important Libraries for Using Data in Python in the Present Book
72(2)
Other Important Libraries Not Used in This Book
74(1)
Suggestion of Libraries for Other Applications
74(1)
Using Python with Yahoo Finance API
75(2)
Using Python with Quandl API
77(2)
Using f.fn() for Retreiving Information
79(2)
Using Python with Excel
81(2)
Conclusion Regarding Using Data in Python
83(2)
Statistical Methods Using Python for Analyzing Stocks
85(34)
The Central Limit Theorem
85(3)
Creating a Histogram
88(6)
Creating a Histogram with Line Plots
94(2)
Histogra ms Using f.fn()
96(1)
Histogram (Percent Change) with Two Variables
96(2)
Histogram (Logarithmic Return) with Two Variables
98(1)
Interquartile Range and Boxplots
99(3)
Boxplot with Two Variables
102(2)
Kernel Density Plot and Volatility
104(4)
Kernel Density Plot (Percent Change) with Two Variables
108(1)
Covariance and Correlation
109(4)
Scatterplots and Heatmaps
113(4)
Works Cited
117(2)
Elements for Technical Analysis Using Python
119(52)
The Linear Plot with One Stock Price (Max & Min Values and the Range)
119(4)
When to Use Linear Plots in Finance
123(1)
The Linear Plot with Two or More Stock Price
123(3)
Linear Plot with Volume
126(2)
Volume of Trade
128(1)
Comparison of Securities with Volume Plots and Closing Prices
129(4)
Candlestick Charts
133(3)
Candlestick Charts and Volume
136(2)
Customizing Candlestick Charts and Volume with **Kwargs
138(1)
OHLC Charts with Volume
138(2)
Line Charts with Volume
140(2)
Moving Average with Matplotlib
142(5)
Moving Average with Mplfinance
147(2)
The Exponential Moving Average (EMA)
149(4)
The Moving Average Convergence Divergence (MACD) with Baseline
153(4)
The Moving Average Convergence Divergence (MACD) with Signal Line
157(3)
Bollinger Bands®
160(2)
Backtesting Strategies for Trading
162(7)
Parabolic SAR
162(3)
Fast and Slow Stochastic Oscillators
165(4)
References
169(2)
Valuation and Risk Models with Stocks
171(40)
Creating a Portfolio
171(3)
Calculating Statistical Measures on a Portfolio
174(2)
The Capital Asset Pricing Model
176(12)
The Beta
176(6)
The Beta and the CAPM
182(6)
Sharpe Ratio
188(6)
Traynor Ratio
194(3)
Jensen's Measure
197(4)
Information Ratio
201(8)
References
209(2)
Value at Risk
211(24)
Historical VaR (95)
211(2)
Historical VaR (99)
213(1)
VaR for the Next 10 Days
214(4)
Historical Drawdown
218(4)
Wrapping Up the Book---Understanding Performance
222(12)
Portfolio Performance using f.fn()
222(4)
Fund Performance usin f.fn()
226(8)
Works Cited
234(1)
Works Cited 235(4)
Index 239
Mauricio Garita is an academic, a writer and an economist that has centered his career in the combination of finance and economics with the use of Python. He obtained his first PhD in Political Science and Sociology from the Universidad Pontificia de Salamanca, Spain with a thesis centered on game theory and economic development. Prior to obtaining his PhD, he completed a Master of Science from Manchester Business School, UK and a secondary Masters in Asset Management from the Instituto de Estudios Bursátiles de la Universidad Complutense, Spain.





As an academic, his research is focused on financial issues, economics and competitiveness in Latin America with a special focus in Central America. He has written books and articles on topics such as business in Latin America, the economic impact of the Civil War in Guatemala, the fiscal impact of reforms in Latin America and the impact of the financial crisis in Central America. As a professor he centers his coursesin the areas of economics, project valuation and financial analysis.





During his professional career he has worked with organizations such as the World Bank, the Central American Institute of Fiscal Studies, the Secretariat for Central America Economic Integration, Transparency International and on financial consulting. Currently he works at Universidad Francisco Marroquin as a director of the masters in finance program at the university business school. Before he worked at the finance department at the Universidad del Valle de Guatemala where he assisted in both the creation of the Masters in Advanced Finance and a collaboration on with the Advanced Risk Portfolio Management (ARPM) program.