Index
A
- aggregating
- about / Grouping and aggregating, Aggregating
- algorithmic trading
- about / The process of algorithmic trading
- process / The process of algorithmic trading
- momentum strategies / Momentum strategies
- mean-reversion strategies / Mean-reversion strategies
- with Zipline / Algo trading with Zipline
- American option
- about / The pricing of options
- arithmetic operations, on DataFrame
- performing / Arithmetic on a DataFrame
B
- Black-Scholes
- used, for pricing of options / The pricing of options with Black-Scholes
- deriving / Deriving the model
- value of cash, determining / The value of the cash to buy
- value of received stock, determining / The value of the stock received
- formulas / The formulas
- implementing, Mibian used / Black-Scholes using Mibian
- Boolean selection
- rows, selecting with / Selecting rows using the Boolean selection
- box-and-whisker plots
- about / Box-and-whisker plots
- buyer
- about / Introducing options
- buyers of calls
- about / Introducing options
- buyers of puts
- about / Introducing options
C
- call option
- about / Introducing options
- used, for calculating payoff on options / The call option payoff calculation
- used, for profit and loss calculation of buyer / The call option profit and loss for a buyer
- used, for profit and loss calculation of seller / The call option profit and loss for the seller
- Chicago Board Options Exchange (CBOE)
- about / Options data from Yahoo! Finance
- classical model, MPT
- risk / Risk and expected return
- expected return / Risk and expected return
- diversification / Diversification
- efficient frontier / The efficient frontier
- Coca-Cola (KO) / Pairs trading
- crossover
- about / Crossovers
- example / Crossovers
- pairs trading / Pairs trading
- cumulative returns
D
- data
- reorganizing / Reorganizing and reshaping data
- reshaping / Reorganizing and reshaping data
- data collection
- about / Data collection
- data, from paper / The data from the paper
- DJIA data, gathering from Quandl / Gathering our own DJIA data from Quandl
- Google Trends data / Google Trends data
- DataFrame
- about / The DataFrame
- basics / The basics of the Series and DataFrame objects
- creating / Creating a DataFrame
- code samples / Example data
- columns, selecting / Selecting columns of a DataFrame
- rows, selecting with index / Selecting rows of a DataFrame using the index
- slicing, [] operator used / Slicing using the [] operator
- rows, selecting by .loc[] / Selecting rows by the index label and location – .loc[] and .iloc[]
- rows, selecting by .iloc[] / Selecting rows by the index label and location – .loc[] and .iloc[]
- rows, selecting by .ix[] property / Selecting rows by the index label and/or location – .ix[]
- scalar lookup, by label with .at[] / Scalar lookup by label or location using .at[] and .iat[]
- scalar lookup, by location with .iat[] / Scalar lookup by label or location using .at[] and .iat[]
- arithmetic operations, performing / Arithmetic on a DataFrame
- reindexing / Reindexing the Series and DataFrame objects
- DataFrame objects
- merging / Merging DataFrame objects
- date representation
- URL / Plotting candlesticks
- Delta
- about / The Greeks
- distribution of returns, analyzing
- about / Analyzing the distribution of returns
- histograms / Histograms
- Q-Q plots / Q-Q plots
- box-and-whisker plots / Box-and-whisker plots
- Dow Jones Industrial Average (DJIA) / A brief on Quantifying Trading Behavior in Financial Markets Using Google Trends
E
- efficient frontier
- visualizing / Visualizing the efficient frontier
- European option
- about / The pricing of options
- exponentially weighted moving average
F
- financial time-series data visualizations
- about / Visualizing financial time-series data
- closing prices, plotting / Plotting closing prices
- volume-series data, plotting / Plotting volume-series data
- combined price and volumes / Combined price and volumes
- candlesticks, plotting / Plotting candlesticks
- first-order Greeks
- about / The Greeks
- Delta / The Greeks
- Vega / The Greeks
- Theta / The Greeks
- Rho / The Greeks
- Gamma / The Greeks
- formulas, Black-Scholes
- frequency conversion, time-series data
- functions, for rolling windows
- rolling_mean / Moving windows
- rolling_std / Moving windows
- rolling_var / Moving windows
- rolling_min / Moving windows
- rolling_max / Moving windows
- rolling_cov / Moving windows
- rolling_quantile / Moving windows
- rolling_corr / Moving windows
- rolling_median / Moving windows
- rolling_sum / Moving windows
- rolling_apply / Moving windows
- rolling_count / Moving windows
- rolling_skew / Moving windows
- rolling_kurt / Moving windows
- fundamental financial calculations
- about / Fundamental financial calculations
- simple daily percentage change, calculating / Calculating simple daily percentage change
- simple daily cumulative returns, calculating / Calculating simple daily cumulative returns
- distribution of returns, analyzing / Analyzing the distribution of returns
- daily percentage change comparison, between stocks / Comparison of daily percentage change between stocks
G
- Gamma
- about / The Greeks
- Google Trends
- Google Trends data
- about / Google Trends data
- Greeks
- about / The Greeks
- first-order Greeks / The Greeks
- calculation / Calculation and visualization
- visualization / Calculation and visualization
- grouping
- about / Grouping and aggregating
H
- histograms
- about / Histograms
- historical quotes
- General Electric (GE) / Fetching historical stock data from Yahoo!
- IBM (IBM) / Fetching historical stock data from Yahoo!
- Microsoft (MSFT) / Fetching historical stock data from Yahoo!
- Apple (AAPL) / Fetching historical stock data from Yahoo!
- Pepsi (PEP) / Fetching historical stock data from Yahoo!
- American Airlines (AA) / Fetching historical stock data from Yahoo!
- United Airlines (UAL) / Fetching historical stock data from Yahoo!
- Delta Airlines (DAL) / Fetching historical stock data from Yahoo!
- Coca-Cola (KO) / Fetching historical stock data from Yahoo!
- historical stock data
- loading / Loading historical stock data
- organizing, for examples / Organizing the data for the examples
- obtaining / Obtaining historical stock and index data
- fetching, from Yahoo! / Fetching historical stock data from Yahoo!
I
- implied volatility (IV)
- about / Implied volatility
- smirks / Volatility smirks
- index data
- fetching, from Yahoo! / Fetching index data from Yahoo!
- inter-quartile range (IQR)
- about / Box-and-whisker plots
- IPython Notebook environment
- setting up / Notebook setup
J
- joins, pd.merge()
- left / Merging DataFrame objects
- right / Merging DataFrame objects
- outer / Merging DataFrame objects
- inner / Merging DataFrame objects
M
- mean-reversion strategies
- about / Mean-reversion strategies
- melting
- about / Melting
- Mibian
- used, for implementing Black-Scholes / Black-Scholes using Mibian
- URL / Black-Scholes using Mibian
- MibianLib
- about / Black-Scholes using Mibian
- momentum strategies
- about / Momentum strategies
- moving averages
- about / Moving averages
- simple moving average / Simple moving average
- exponentially weighted moving average / Exponentially weighted moving average
- moving windows
- calculating / Moving windows
- MPT
- overview / An overview of modern portfolio theory
- concept / Concept
- classical model / Mathematical modeling of a portfolio
- multiple DataFrame objects
- concatenating / Concatenating multiple DataFrame objects
N
- Notebook
- setting up / Notebook setup
- options data, obtaining from Yahoo! Finance / Options data from Yahoo! Finance
- implied volatility (IV) / Implied volatility
- setting up, SciPy used / Notebook setup
- notebook
- setting up / Notebook setup
- Notebook setup
- about / Notebook setup
O
- optimal portfolio
- constructing / Constructing an optimal portfolio
- options
- about / Introducing options
- call / Introducing options
- put / Introducing options
- benefits / Introducing options
- participants / Introducing options
- payoff, calculating / Calculating payoff on options
- options data
- obtaining, from Yahoo! Finance / Options data from Yahoo! Finance
P
- pairs-trading
- about / Pairs trading
- example / Pairs trading
- pandas
- portfolio, modeling / Modeling a portfolio with pandas
- pandas data structures
- Series / The Series
- DataFrame / The DataFrame
- participants, options
- buyers of calls / Introducing options
- sellers of calls / Introducing options
- buyers of puts / Introducing options
- sellers of puts / Introducing options
- payoff, on options
- calculating / Calculating payoff on options
- calculating, with call option / The call option payoff calculation
- calculating, with put option / The put option payoff calculation
- Pepsi (PEP) / Pairs trading
- pivoting
- about / Pivoting
- portfolio
- modeling, with pandas / Modeling a portfolio with pandas
- constructing / Constructing an efficient portfolio
- historical returns, gathering / Gathering historical returns for a portfolio
- risks, formulation / Formulation of portfolio risks
- Sharpe ratio / The Sharpe ratio
- optimization / Optimization and minimization
- minimization / Optimization and minimization
- premium
- about / Introducing options
- price, of options
- factors / Introducing options
- about / The pricing of options
- European / The pricing of options
- American / The pricing of options
- with Black-Scholes / The pricing of options with Black-Scholes
- charting, until expiration / Charting option price change over time
- Greeks / The Greeks
- profit and loss calculation
- performing / Profit and loss calculation
- with call option, for buyer / The call option profit and loss for a buyer
- with call option, for seller / The call option profit and loss for the seller
- combined payoff charts / Combined payoff charts
- with put option, for buyer / The put option profit and loss for a buyer
- with put option, for seller / The put option profit and loss for the seller
- put option
- about / Introducing options
- used, for calculating payoff on options / The put option payoff calculation
- used, for profit and loss calculation of buyer / The put option profit and loss for a buyer
- used, for profit and loss calculation of seller / The put option profit and loss for the seller
Q
- Q-Q plots
- Quandl
- URL / Installing new packages, Gathering our own DJIA data from Quandl
- about / Installing new packages
- DJIA data, gathering from / Gathering our own DJIA data from Quandl
- Quantifying Trading Behavior, in financial markets
- Quantopian
- URL / Installing new packages
- about / Installing new packages
R
- resampling, time-series
- about / Resampling of time-series
- downsampling / Resampling of time-series
- upsampling / Resampling of time-series
- returns
- computing / Computing returns
- Rho
- about / The Greeks
- rolling windows
- calculating / Moving windows
- rows
- selecting, with Boolean selection / Selecting rows using the Boolean selection
S
- S&P 500 stocks
- comparing / Comparing stocks to the S
- SciPy
- used, for setting up Notebook / Notebook setup
- sellers of calls
- about / Introducing options
- sellers of puts
- about / Introducing options
- Series
- about / The Series
- basics / The basics of the Series and DataFrame objects
- creating / Creating a Series and accessing elements
- size, determining / Size, shape, uniqueness, and counts of values
- shape, determining / Size, shape, uniqueness, and counts of values
- uniqueness, determining / Size, shape, uniqueness, and counts of values
- alignment, via index labels / Alignment via index labels
- reindexing / Reindexing the Series and DataFrame objects
- Sharpe ratio
- about / The Sharpe ratio
- simple moving average
- about / Simple moving average
- example / Simple moving average
- drawbacks / Simple moving average
- simple moving average (SMA)
- about / Simple moving average
- smirks
- about / Volatility smirks
- splitting
- about / Splitting
- stacking
- about / Stacking and unstacking
T
- technical analysis techniques
- about / Technical analysis techniques
- crossover / Crossovers
- Theta
- about / The Greeks
- time-series
- Notebook setup / Notebook setup
- creating, with specific frequencies / Creating time-series with specific frequencies
- Period objects, used for representing intervals of time / Representing intervals of time using periods
- resampling / Resampling of time-series
- time-series data
- manipulating / Time-series data and the DatetimeIndex
- and DatetimeIndex / Time-series data and the DatetimeIndex
- shifting / Shifting and lagging time-series data
- lagging / Shifting and lagging time-series data
- frequency conversion / Frequency conversion of time-series data
- time-series stock data
- Notebook setup / Notebook setup
- trade order signals
- generating / Generating order signals
U
- unstacking
- about / Stacking and unstacking
V
- Value at Risk (VaR)
- about / Value at Risk
- volatility calculation
- about / Volatility calculation
- rolling correlation of returns / Rolling correlation of returns
- least-squares regression of returns / Least-squares regression of returns
W
- Wakari
- about / What is Wakari?
- URL / What is Wakari?
- cloud account, creating / Creating a Wakari cloud account
- reference / Creating a Wakari cloud account
- existing packages, updating / Updating existing packages
- new packages, installing / Installing new packages
- samples, installing / Installing the samples in Wakari
Y
- Yahoo! Finance
- options data, obtaining / Options data from Yahoo! Finance
Z
- Zipline
- about / Algo trading with Zipline
- used, for algorithmic trading / Algo trading with Zipline
- buy apple example / Algorithm – buy apple
- dual moving average crossover example / Algorithm – dual moving average crossover
- pairs trade example / Algorithm – pairs trade