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Lewinson, Eryk. Python for finance cookbook: over 50 recipes for applying modern Python libraries to finance data analysis / Eryk Lewinson. — 1 online resource — <URL:http://elib.fa.ru/ebsco/2366460.pdf>.

Record create date: 2/13/2020

Subject: Finance — Mathematical models.; Python (Computer program language); Finance — Data processing.

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Cover
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Table of Contents
Preface
Chapter 1: Financial Data and Preprocessing
- Getting data from Yahoo Finance
  - How to do it...
  - How it works...
  - There's more...
- Getting data from Quandl
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Getting data from Intrinio
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
- Converting prices to returns
  - How to do it...
  - How it works...
  - There's more...
- Changing frequency
  - Getting ready
  - How to do it...
  - How it works...
- Visualizing time series data
  - Getting ready
  - How to do it...
    - The plot method of pandas
    - plotly and cufflinks
  - How it works...
    - The plot method of pandas
    - plotly and cufflinks
  - There's more...
  - See also
- Identifying outliers
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
- Investigating stylized facts of asset returns
  - Getting ready
  - How to do it...
    - Non-Gaussian distribution of returns
    - Volatility clustering
    - Absence of autocorrelation in returns
    - Small and decreasing autocorrelation in squared/absolute returns
    - Leverage effect
  - How it works...
    - Fact 1
    - Fact 2
    - Fact 3
    - Fact 4
    - Fact 5
  - There's more...
  - See also
Chapter 2: Technical Analysis in Python
- Creating a candlestick chart
  - Getting ready
  - How to do it...
  - How it works...
  - See also
- Backtesting a strategy based on simple moving average
  - How to do it...
    - Signal
    - Strategy
  - How it works...
    - Common elements
    - Signal
    - Strategy
  - There's more...
  - See also
- Calculating Bollinger Bands and testing a buy/sell strategy
  - How to do it...
  - How it works...
- Calculating the relative strength index and testing a long/short strategy
  - How to do it...
  - How it works...
- Building an interactive dashboard for TA
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
Chapter 3: Time Series Modeling
- Decomposing time series
  - How to do it...
  - How it works...
  - See also
- Decomposing time series using Facebook's Prophet
  - How to do it...
  - How it works...
  - There's more...
- Testing for stationarity in time series
  - Getting ready
  - How to do it...
  - How it works...
- Correcting for stationarity in time series
  - How to do it...
  - How it works...
  - There's more...
- Modeling time series with exponential smoothing methods
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
- Modeling time series with ARIMA class models
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Forecasting using ARIMA class models
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
Chapter 4: Multi-Factor Models
- Implementing the CAPM in Python
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Implementing the Fama-French three-factor model in Python
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Implementing the rolling three-factor model on a portfolio of assets
  - How to do it...
  - How it works...
- Implementing the four- and five-factor models in Python
  - How to do it...
  - How it works...
  - See also
Chapter 5: Modeling Volatility with GARCH Class Models
- Explaining stock returns' volatility with ARCH models
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Explaining stock returns' volatility with GARCH models
  - How to do it...
  - How it works...
  - There's more...
    - Conditional mean model
    - Conditional volatility model
    - Distribution of errors
  - See also
- Implementing a CCC-GARCH model for multivariate volatility forecasting
  - How to do it...
  - How it works...
  - See also
- Forecasting the conditional covariance matrix using DCC-GARCH
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
  - See also
Chapter 6: Monte Carlo Simulations in Finance
- Simulating stock price dynamics using Geometric Brownian Motion
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Pricing European options using simulations
  - How to do it...
  - How it works...
  - There's more...
- Pricing American options with Least Squares Monte Carlo
  - How to do it...
  - How it works...
  - See also
- Pricing American options using Quantlib
  - How to do it...
  - How it works...
  - There's more...
- Estimating value-at-risk using Monte Carlo
  - How to do it...
  - How it works...
  - There's more...
Chapter 7: Asset Allocation in Python
- Evaluating the performance of a basic 1/n portfolio
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Finding the Efficient Frontier using Monte Carlo simulations
  - How to do it...
  - How it works...
  - There's more...
- Finding the Efficient Frontier using optimization with scipy
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
- Finding the Efficient Frontier using convex optimization with cvxpy
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
Chapter 8: Identifying Credit Default with Machine Learning
- Loading data and managing data types
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Exploratory data analysis
  - How to do it...
  - How it works...
  - There's more...
- Splitting data into training and test sets
  - How to do it...
  - How it works...
  - There's more...
- Dealing with missing values
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Encoding categorical variables
  - How to do it...
  - How it works...
  - There's more...
    - Using pandas.get_dummies for one-hot encoding
    - Specifying possible categories for OneHotEncoder
    - Category Encoders library
  - See also
- Fitting a decision tree classifier
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Implementing scikit-learn's pipelines
  - How to do it...
  - How it works...
  - There's more...
- Tuning hyperparameters using grid searches and cross-validation
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
  - See also
Chapter 9: Advanced Machine Learning Models in Finance
- Investigating advanced classifiers
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Using stacking for improved performance
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Investigating the feature importance
  - Getting ready
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Investigating different approaches to handling imbalanced data
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Bayesian hyperparameter optimization
  - How to do it...
  - How it works...
  - There's more...
  - See also
Chapter 10: Deep Learning in Finance
- Deep learning for tabular data
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Multilayer perceptrons for time series forecasting
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Convolutional neural networks for time series forecasting
  - How to do it...
  - How it works...
  - There's more...
  - See also
- Recurrent neural networks for time series forecasting
  - How to do it...
  - How it works...
  - There's more...
  - See also
Other Books You May Enjoy
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Lewinson, Eryk. Python for finance cookbook: over 50 recipes for applying modern Python libraries to finance data analysis / Eryk Lewinson. — 1 online resource — <URL:http://elib.fa.ru/ebsco/2366460.pdf>.

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