Python for Finance: Analyze Big Financial Data

large integers, Integers

LaTeX

commands, Markdown and LaTeX

IPython Notebook cells and, Basic usage

least-squares function, Individual basis functions

Least-Squares Monte Carlo (LSM) algorithm, American Options, Derivatives Valuation,

Least-Squares Monte Carlo

leverage effect, Financial Data, Stochastic volatility

libraries

available in Anaconda, Anaconda

Cython library, Basic Data Types

importing, The Python Ecosystem, Basic Vectorization, Approximation

importing to IPython, From shell to browser

standard, The Python Ecosystem

list comprehensions, Excursion: Control Structures

lists, Lists, Arrays with Python Lists

LLVM compiler infrastructure, Dynamic Compiling

local maximum a posteriori point, Introductory Example

local optimization, Local Optimization, Calibration Procedure

lognormal function, Random Variables

Longstaff-Schwartz model, Least-Squares Monte Carlo, A Use Case

loss level, Credit Value Adjustments

M

magic commands/functions, Magic commands

Markdown commands, Markdown and LaTeX

market environments, Market Environments

(Markov Chain) Monte Carlo (MCMC) sampling, Introductory Example

Markov property, Stochastic Processes

martingale measures, Fundamental Theorem of Asset Pricing, Least-Squares Monte Carlo

mathematical syntax, Finance and Python Syntax

mathematical tools

approximation of functions, Approximation–Differentiation

convex optimization, Convex Optimization–Constrained Optimization