Python for Finance Cookbook : Over 80 Powerful Recipes for Effective Financial Data Analysis

Python for Finance Cookbook Over 80 Powerful Recipes for Effective Financial Data Analysis

Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problems Purchase of the print or Kindle book includes a free eBook in the PDF format Key Features Explore unique recipes for financial data processi...

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Detalles Bibliográficos
Autor principal: Lewinson, Eryk (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited 2022.
Edición:2nd ed
Materias:
Finance > Mathematical models.
Finance > Data processing.
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009711811106719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Acquiring Financial Data
  • Getting data from Yahoo Finance
  • Getting data from Nasdaq Data Link
  • Getting data from Intrinio
  • Getting data from Alpha Vantage
  • Getting data from CoinGecko
  • Summary
  • Chapter 2: Data Preprocessing
  • Converting prices to returns
  • Adjusting the returns for inflation
  • Changing the frequency of time series data
  • Different ways of imputing missing data
  • Converting currencies
  • Different ways of aggregating trade data
  • Summary
  • Chapter 3: Visualizing Financial Time Series
  • Basic visualization of time series data
  • Visualizing seasonal patterns
  • Creating interactive visualizations
  • Creating a candlestick chart
  • Summary
  • Chapter 4: Exploring Financial Time Series Data
  • Outlier detection using rolling statistics
  • Outlier detection with the Hampel filter
  • Detecting changepoints in time series
  • Detecting trends in time series
  • Detecting patterns in a time series using the Hurst exponent
  • Investigating stylized facts of asset returns
  • Summary
  • Chapter 5: Technical Analysis and Building Interactive Dashboards
  • Calculating the most popular technical indicators
  • Downloading the technical indicators
  • Recognizing candlestick patterns
  • Building an interactive web app for technical analysis using Streamlit
  • Deploying the technical analysis app
  • Summary
  • Chapter 6: Time Series Analysis and Forecasting
  • Time series decomposition
  • Testing for stationarity in time series
  • Correcting for stationarity in time series
  • Modeling time series with exponential smoothing methods
  • Modeling time series with ARIMA class models
  • Finding the best-fitting ARIMA model with auto-ARIMA
  • Summary
  • Chapter 7: Machine Learning-Based Approaches to Time Series Forecasting.
  • Validation methods for time series
  • Feature engineering for time series
  • Time series forecasting as reduced regression
  • Forecasting with Meta's Prophet
  • AutoML for time series forecasting with PyCaret
  • Summary
  • Chapter 8: Multi-Factor Models
  • Estimating the CAPM
  • Estimating the Fama-French three-factor model
  • Estimating the rolling three-factor model on a portfolio of assets
  • Estimating the four- and five-factor models
  • Estimating cross-sectional factor models using the Fama-MacBeth regression
  • Summary
  • Chapter 9: Modeling Volatility with GARCH Class Models
  • Modeling stock returns' volatility with ARCH models
  • Modeling stock returns' volatility with GARCH models
  • Forecasting volatility using GARCH models
  • Multivariate volatility forecasting with the CCC-GARCH model
  • Forecasting the conditional covariance matrix using DCC-GARCH
  • Summary
  • Chapter 10: Monte Carlo Simulations in Finance
  • Simulating stock price dynamics using a geometric Brownian motion
  • Pricing European options using simulations
  • Pricing American options with Least Squares Monte Carlo
  • Pricing American options using QuantLib
  • Pricing barrier options
  • Estimating Value-at-Risk using Monte Carlo
  • Summary
  • Chapter 11: Asset Allocation
  • Evaluating an equally-weighted portfolio's performance
  • Finding the efficient frontier using Monte Carlo simulations
  • Finding the efficient frontier using optimization with SciPy
  • Finding the efficient frontier using convex optimization with CVXPY
  • Finding the optimal portfolio with Hierarchical Risk Parity
  • Summary
  • Chapter 12: Backtesting Trading Strategies
  • Vectorized backtesting with pandas
  • Event-driven backtesting with backtrader
  • Backtesting a long/short strategy based on the RSI
  • Backtesting a buy/sell strategy based on Bollinger bands.
  • Backtesting a moving average crossover strategy using crypto data
  • Backtesting a mean-variance portfolio optimization
  • Summary
  • Chapter 13: Applied Machine Learning: Identifying Credit Default
  • Loading data and managing data types
  • Exploratory data analysis
  • Splitting data into training and test sets
  • Identifying and dealing with missing values
  • Encoding categorical variables
  • Fitting a decision tree classifier
  • Organizing the project with pipelines
  • Tuning hyperparameters using grid searches and cross-validation
  • Summary
  • Chapter 14: Advanced Concepts for Machine Learning Projects
  • Exploring ensemble classifiers
  • Exploring alternative approaches to encoding categorical features
  • Investigating different approaches to handling imbalanced data
  • Leveraging the wisdom of the crowds with stacked ensembles
  • Bayesian hyperparameter optimization
  • Investigating feature importance
  • Exploring feature selection techniques
  • Exploring explainable AI techniques
  • Summary
  • Chapter 15: Deep Learning in Finance
  • Exploring fastai's Tabular Learner
  • Exploring Google's TabNet
  • Time series forecasting with Amazon's DeepAR
  • Time series forecasting with NeuralProphet
  • Summary
  • Packtpage
  • Other Books You May Enjoy
  • Index.

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