Python for finance cookbook: over 80 powerful recipes for effective financial data analysis
- 2nd.
- Mumbai: Packt Publishing Limited, 2022.
- xvii., 720 p. ind. 23 cm x 18 cm
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
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
Index
978-1803243191
--Finance Data processing--Finance Mathematical models--Python (Computer program language)