Python for FinanceThe financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
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AAPL.O algorithmic trading strategy American option analytics applied approach array backtesting basic Calculates chapter columns conda data set DataFrame object Date DatetimeIndex default defined derivatives Docker dtype Equation EUR/USD European call option example False Figure financial instruments financial time series float following code frequency function FXCM geometric Brownian motion HDF5 Hilpisch implementation implied volatility import numpy index level install instantiation integral Jupyter Notebook Kelly criterion list objects log returns machine learning matplotlib method Monte Carlo simulation multiple NaN NaN NaN ndarray object non-null float64 normally distributed NumPy numpy as np optimal p-value pandas parameters payoff plot plt.figure(figsize=(10 portfolio position provides PyTables Python Python code Python for Finance random numbers range(1 regression retrieval risk root@py4fi Samples scikit-learn series data short rate sigma simulation class statistics stochastic str object supervised learning True update vectorized volatility Wall Yves