Successful Algorithmic Trading

I Introducing Algorithmic Trading
	Introduction to the Book
		Introduction to QuantStart
		What is this Book?
		Who is this Book For?
		What are the Prerequisites?
		Software Installation
			Installing Python
			Obtaining Financial Data
		Book Structure
		What the Book does not Cover
		Where to Get Help
	What Is Algorithmic Trading?
		Overview
			Advantages
			Disadvantages
		Scientific Method
		Why Python?
		Can Retail Traders Still Compete?
			Trading Advantages
			Risk Management
			Investor Relations
			Technology
II Trading Systems
	Successful Backtesting
		Why Backtest Strategies?
			Key Reasons to Backtest Strategies
		Backtesting Biases
			Optimisation Bias
			Look-Ahead Bias
			Survivorship Bias
			Cognitive Bias
		Exchange Issues
			Order Types
			Price Consolidation
			Forex Trading and ECNs
			Shorting Constraints
		Transaction Costs
			Commission
			Slippage
			Market Impact
		Backtesting vs Reality
	Automated Execution
		Backtesting Platforms
			Programming
			Research Tools
			Event-Driven Backtesting
			Latency
			Language Choices
			Integrated Development Environments
		Colocation
			Home Desktop
			VPS
			Exchange
	Sourcing Strategy Ideas
		Identifying Your Own Personal Preferences for Trading
		Sourcing Algorithmic Trading Ideas
			Textbooks
			The Internet
			Journal Literature
			Independent Research
		Evaluating Trading Strategies
		Obtaining Historical Data
III Data Platform Development
	Financial Data Storage
		Securities Master Databases
		Financial Datasets
		Storage Formats
			Flat-File Storage
			Document Stores/NoSQL
			Relational Database Management Systems
		Historical Data Structure
		Data Accuracy Evaluation
		Automation
		Data Availability
		MySQL for Securities Masters
			Installing MySQL
			Configuring MySQL
			Schema Design for EOD Equities
			Connecting to the Database
			Using an Object-Relational Mapper
		Retrieving Data from the Securities Master
	Processing Financial Data
		Market and Instrument Classification
			Markets
			Instruments
			Fundamental Data
			Unstructured Data
		Frequency of Data
			Weekly and Monthly Data
			Daily Data
			Intraday Bars
			Tick and Order Book Data
		Sources of Data
			Free Sources
			Commercial Sources
		Obtaining Data
			Datareader and Pandas
			DTN IQFeed
		Cleaning Financial Data
			Data Quality
			Continuous Futures Contracts
IV Modelling
	Statistical Learning
		What is Statistical Learning?
			Prediction and Inference
			Parametric and Non-Parametric Models
			Supervised and Unsupervised Learning
		Techniques
			Regression
			Classification
			Time Series Models
	Time Series Analysis
		Testing for Mean Reversion
			Augmented Dickey-Fuller Test
		Testing for Stationarity
			Hurst Exponent
		Cointegration
			Cointegrated Augmented Dickey-Fuller Test
		Why Statistical Testing?
	Forecasting
		Measuring Forecasting Accuracy
			Hit Rate
			Confusion Matrix
		Factor Choice
			Lagged Price Factors and Volume
			External Factors
		Classification Models
			Logistic Regression
			Discriminant Analysis
			Support Vector Machines
			Decision Trees and Random Forests
			Principal Components Analysis
			Which Forecaster?
		Forecasting Stock Index Movement
			Python Implementations
			Results
V Performance and Risk Management
	Performance Measurement
		Trade Analysis
			Summary Statistics
		Strategy and Portfolio Analysis
			Returns Analysis
			Risk/Reward Analysis
			Drawdown Analysis
	Risk and Money Management
		Sources of Risk
			Strategy Risk
			Portfolio Risk
			Counterparty Risk
			Operational Risk
		Money Management
			Kelly Criterion
		Risk Management
			Value-at-Risk
		Advantages and Disadvantages
VI Automated Trading
	Event-Driven Trading Engine Implementation
		Event-Driven Software
			Why An Event-Driven Backtester?
		Component Objects
			Events
			Data Handler
			Strategy
			Portfolio
			Execution Handler
			Backtest
		Event-Driven Execution
	Trading Strategy Implementation
		Moving Average Crossover Strategy
		S&P500 Forecasting Trade
		Mean-Reverting Equity Pairs Trade
		Plotting Performance
	Strategy Optimisation
		Parameter Optimisation
			Which Parameters to Optimise?
			Optimisation is Expensive
			Overfitting
		Model Selection
			Cross Validation
			Grid Search
		Optimising Strategies
			Intraday Mean Reverting Pairs
			Parameter Adjustment
			Visualisation