Complete Python and Machine Learning in Financial Analysis
- Description
- Curriculum
- FAQ
- Reviews
In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise.
This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis.
The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR).
In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems.
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1
Introduction of Python Programming in Financial Analysis -
2
Introduction of Financial Analysis
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3
Introduction
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4
Getting data from Yahoo Finance
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5
Getting data from Quandl
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6
Converting prices to returns
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7
Changing frequency
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8
Visualizing time series data
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9
Identifying outliers
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10
Investigating stylized facts of asset returns
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11
Codes of Chapter 1
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12
Introduction
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13
requirements of chapter 2
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14
Creating a candlestick chart
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15
Backtesting a strategy based on simple moving average
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16
Calculating Bollinger Bands and testing a buy/sell strategy
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17
Calculating the relative strength index and testing a long/short strategy
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18
Building an interactive dashboard for TA
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19
Codes of Chapter 2
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20
Introduction
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21
requirements of chapter 3
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22
Decomposing time series
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23
Testing for stationarity in time series
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24
Correcting for stationarity in time series
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25
Modeling time series with exponential smoothing methods
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26
Modeling time series with ARIMA class models
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27
Forecasting using ARIMA class models
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28
Codes of Chapter 3
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36
Introduction
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37
requirements of chapter 5
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38
Explaining stock returns' volatility with ARCH models
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39
Explaining stock returns' volatility with GARCH models
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40
Implementing a CCC-GARCH model for multivariate volatility forecasting
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41
Forecasting a conditional covariance matrix using DCC-GARCH
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42
Codes of Chapter 5
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43
Introduction
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44
requirements of chapter 6
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45
Simulating stock price dynamics using Geometric Brownian Motion
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46
Pricing European options using simulations
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47
Pricing American options with Least Squares Monte Carlo
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48
Pricing American options using Quantlib
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49
Estimating value-at-risk using Monte Carlo
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50
Codes of Chapter 6
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56
Introduction
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57
requirements of chapter 8
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58
Loading data and managing data types
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59
Exploratory data analysis
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60
Splitting data into training and test sets
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61
Dealing with missing values
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62
Encoding categorical variables
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63
Fitting a decision tree classifier
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64
Implementing scikit-learn's pipelines
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65
Tuning hyperparameters using grid search and cross-validation
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66
Codes of Chapter 8
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67
Introduction
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68
requirements of chapter 9
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69
Investigating advanced classifiers
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70
Theres more about use advanced classifiers to achieve better results
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71
Using stacking for improved performance
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72
Investigating the feature importance
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73
Investigating different approaches to handling imbalanced data
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74
Bayesian hyperparameter optimization
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75
Codes of Chapter 9
