Python for finance cookbook: over 80 powerful recipes for effective financial data analysis (Record no. 21727)
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| fixed length control field | 05463 a2200229 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230802061013.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230726b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-1803243191 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 332.02855133 |
| Item number | LEW |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Lewinson, Eryk |
| 245 ## - TITLE STATEMENT | |
| Title | Python for finance cookbook: over 80 powerful recipes for effective financial data analysis |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Mumbai: |
| Name of publisher, distributor, etc. | Packt Publishing Limited, |
| Date of publication, distribution, etc. | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvii., 720 p. |
| Other physical details | ind. |
| Dimensions | 23 cm x 18 cm |
| 500 ## - GENERAL NOTE | |
| General note | Recommended By: Banikanta Mishra<br/>-------------------------------------------------- |
| 521 ## - TARGET AUDIENCE NOTE | |
| Target audience note | Chapter 1: Acquiring Financial Data<br/><br/>Getting data from Yahoo Finance<br/>Getting data from Nasdaq Data Link<br/>Getting data from Intrinio<br/>Getting data from Alpha Vantage<br/>Getting data from CoinGecko<br/>Summary<br/><br/>Chapter 2: Data Preprocessing<br/><br/>Converting prices to returns<br/>Adjusting the returns for inflation<br/>Changing the frequency of time series data<br/>Different ways of imputing missing data<br/>Converting currencies<br/>Different ways of aggregating trade data<br/>Summary<br/><br/>Chapter 3: Visualizing Financial Time Series<br/><br/>Basic visualization of time series data<br/>Visualizing seasonal patterns<br/>Creating interactive visualizations<br/>Creating a candlestick chart<br/>Summary<br/><br/>Chapter 4: Exploring Financial Time Series Data<br/><br/>Outlier detection using rolling statistics<br/>Outlier detection with the Hampel filter<br/>Detecting changepoints in time series<br/>Detecting trends in time series<br/>Detecting patterns in a time series using the Hurst exponent<br/>Investigating stylized facts of asset returns<br/>Summary<br/><br/>Chapter 5: Technical Analysis and Building Interactive Dashboards<br/><br/>Calculating the most popular technical indicators<br/>Downloading the technical indicators<br/>Recognizing candlestick patterns<br/>Building an interactive web app for technical analysis using Streamlit<br/>Deploying the technical analysis app<br/>Summary<br/><br/>Chapter 6: Time Series Analysis and Forecasting<br/><br/>Time series decomposition<br/>Testing for stationarity in time series<br/>Correcting for stationarity in time series<br/>Modeling time series with exponential smoothing methods<br/>Modeling time series with ARIMA class models<br/>Finding the best-fitting ARIMA model with auto-ARIMA<br/>Summary<br/><br/>Chapter 7: Machine Learning-Based Approaches to Time Series Forecasting<br/><br/>Validation methods for time series<br/>Feature engineering for time series<br/>Time series forecasting as reduced regression<br/>Forecasting with Meta’s Prophet<br/>AutoML for time series forecasting with PyCaret<br/>Summary<br/><br/>Chapter 8: Multi-Factor Models<br/>Estimating the CAPM<br/>Estimating the Fama-French three-factor model<br/>Estimating the rolling three-factor model on a portfolio of assets<br/>Estimating the four- and five-factor models<br/>Estimating cross-sectional factor models using the Fama-MacBeth regression<br/>Summary<br/><br/>Chapter 9: Modeling Volatility with GARCH Class Models<br/><br/>Modeling stock returns’ volatility with ARCH models<br/>Modeling stock returns’ volatility with GARCH models<br/>Forecasting volatility using GARCH models<br/>Multivariate volatility forecasting with the CCC-GARCH model<br/>Forecasting the conditional covariance matrix using DCC-GARCH<br/>Summary<br/><br/>Chapter 10: Monte Carlo Simulations in Finance<br/><br/>Simulating stock price dynamics using a geometric Brownian motion<br/>Pricing European options using simulations<br/>Pricing American options with Least Squares Monte Carlo<br/>Pricing American options using QuantLib<br/>Pricing barrier options<br/>Estimating Value-at-Risk using Monte Carlo<br/>Summary<br/><br/>Chapter 11: Asset Allocation<br/>Evaluating an equally-weighted portfolio’s performance<br/>Finding the efficient frontier using Monte Carlo simulations<br/>Finding the efficient frontier using optimization with SciPy<br/>Finding the efficient frontier using convex optimization with CVXPY<br/>Finding the optimal portfolio with Hierarchical Risk Parity<br/>Summary<br/><br/>Chapter 12: Backtesting Trading Strategies<br/><br/>Vectorized backtesting with pandas<br/>Event-driven backtesting with backtrader<br/>Backtesting a long/short strategy based on the RSI<br/>Backtesting a buy/sell strategy based on Bollinger bands<br/>Backtesting a moving average crossover strategy using crypto data<br/>Backtesting a mean-variance portfolio optimization<br/>Summary<br/><br/>Chapter 13: Applied Machine Learning: Identifying Credit Default<br/><br/>Loading data and managing data types<br/>Exploratory data analysis<br/>Splitting data into training and test sets<br/>Identifying and dealing with missing values<br/>Encoding categorical variables<br/>Fitting a decision tree classifier<br/>Organizing the project with pipelines<br/>Tuning hyperparameters using grid searches and cross-validation<br/>Summary<br/><br/>Chapter 14: Advanced Concepts for Machine Learning Projects<br/><br/>Exploring ensemble classifiers<br/>Exploring alternative approaches to encoding categorical features<br/>Investigating different approaches to handling imbalanced data<br/>Leveraging the wisdom of the crowds with stacked ensembles<br/>Bayesian hyperparameter optimization<br/>Investigating feature importance<br/>Exploring feature selection techniques<br/>Exploring explainable AI techniques<br/>Summary<br/><br/>Chapter 15: Deep Learning in Finance<br/>Exploring fastai’s Tabular Learner<br/>Exploring Google’s TabNet<br/>Time series forecasting with Amazon’s DeepAR<br/>Time series forecasting with NeuralProphet<br/>Summary<br/><br/>Index |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| General subdivision | Finance Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| General subdivision | Finance Mathematical models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| General subdivision | Python (Computer program language) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Total Renewals | Full call number | Barcode | Date due | Date last seen | Date last checked out | Cost, replacement price | Price effective from | Koha item type |
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| Dewey Decimal Classification | KEIC | KEIC | 07/21/2023 | Kushal Books | 3699.00 | IN275 | 2 | 1 | 332.02855133, LEW | 22463 | 09/08/2025 | 08/11/2025 | 08/11/2025 | 7467.00 | 07/21/2023 | Books |