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1 | (18) |
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Installing Python in the Computer |
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4 | (2) |
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Using Jupyter Notebooks with Python |
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6 | (8) |
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Understanding Jupyter Notebooks |
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7 | (7) |
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14 | (3) |
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17 | (2) |
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Learning to Use Python: The Basic Aspects |
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19 | (40) |
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Understanding Numbers in Python |
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20 | (1) |
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Understanding Numbers in Python |
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21 | (4) |
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Using Data Structures in Python |
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25 | (21) |
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25 | (1) |
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25 | (2) |
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Indexing and Cutting a List |
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27 | (5) |
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32 | (1) |
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32 | (1) |
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33 | (1) |
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From List to Dictionaries |
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33 | (2) |
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35 | (1) |
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Other Interesting Functions of a Dictionary |
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35 | (1) |
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36 | (6) |
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Boolean, Loops and Other Features |
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42 | (2) |
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If, Else and Elif in Python |
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44 | (2) |
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46 | (1) |
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46 | (9) |
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52 | (3) |
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55 | (2) |
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57 | (2) |
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Using FRED® API for Economic Indicators and Data (Example) |
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59 | (12) |
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59 | (1) |
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Using the FRED® API to Retrieve Data |
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60 | (11) |
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60 | (1) |
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60 | (1) |
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60 | (3) |
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The Gross Domestic Product |
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63 | (2) |
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The Gross Domestic Product Price Deflator |
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65 | (1) |
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Understanding the Process into the Basics |
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66 | (1) |
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67 | (4) |
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Using Stock Market Data in Python |
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71 | (14) |
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71 | (1) |
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Most Important Libraries for Using Data in Python in the Present Book |
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72 | (2) |
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Other Important Libraries Not Used in This Book |
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74 | (1) |
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Suggestion of Libraries for Other Applications |
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74 | (1) |
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Using Python with Yahoo Finance API |
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75 | (2) |
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Using Python with Quandl API |
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77 | (2) |
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Using f.fn() for Retreiving Information |
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79 | (2) |
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81 | (2) |
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Conclusion Regarding Using Data in Python |
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83 | (2) |
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Statistical Methods Using Python for Analyzing Stocks |
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85 | (34) |
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The Central Limit Theorem |
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85 | (3) |
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88 | (6) |
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Creating a Histogram with Line Plots |
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94 | (2) |
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96 | (1) |
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Histogram (Percent Change) with Two Variables |
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96 | (2) |
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Histogram (Logarithmic Return) with Two Variables |
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98 | (1) |
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Interquartile Range and Boxplots |
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99 | (3) |
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Boxplot with Two Variables |
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102 | (2) |
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Kernel Density Plot and Volatility |
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104 | (4) |
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Kernel Density Plot (Percent Change) with Two Variables |
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108 | (1) |
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Covariance and Correlation |
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109 | (4) |
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Scatterplots and Heatmaps |
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113 | (4) |
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117 | (2) |
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Elements for Technical Analysis Using Python |
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119 | (52) |
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The Linear Plot with One Stock Price (Max & Min Values and the Range) |
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119 | (4) |
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When to Use Linear Plots in Finance |
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123 | (1) |
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The Linear Plot with Two or More Stock Price |
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123 | (3) |
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126 | (2) |
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128 | (1) |
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Comparison of Securities with Volume Plots and Closing Prices |
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129 | (4) |
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133 | (3) |
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Candlestick Charts and Volume |
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136 | (2) |
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Customizing Candlestick Charts and Volume with **Kwargs |
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138 | (1) |
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138 | (2) |
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140 | (2) |
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Moving Average with Matplotlib |
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142 | (5) |
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Moving Average with Mplfinance |
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147 | (2) |
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The Exponential Moving Average (EMA) |
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149 | (4) |
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The Moving Average Convergence Divergence (MACD) with Baseline |
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153 | (4) |
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The Moving Average Convergence Divergence (MACD) with Signal Line |
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157 | (3) |
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160 | (2) |
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Backtesting Strategies for Trading |
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162 | (7) |
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162 | (3) |
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Fast and Slow Stochastic Oscillators |
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165 | (4) |
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169 | (2) |
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Valuation and Risk Models with Stocks |
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171 | (40) |
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171 | (3) |
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Calculating Statistical Measures on a Portfolio |
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174 | (2) |
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The Capital Asset Pricing Model |
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176 | (12) |
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176 | (6) |
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182 | (6) |
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188 | (6) |
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194 | (3) |
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197 | (4) |
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201 | (8) |
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209 | (2) |
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211 | (24) |
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211 | (2) |
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213 | (1) |
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214 | (4) |
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218 | (4) |
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Wrapping Up the Book---Understanding Performance |
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222 | (12) |
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Portfolio Performance using f.fn() |
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222 | (4) |
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Fund Performance usin f.fn() |
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226 | (8) |
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234 | (1) |
Works Cited |
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235 | (4) |
Index |
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239 | |