Build a Robo-Advisor with Python (From Scratch): Automate your financial and investment decisions

Build a Robo-Advisor with Python
brief contents
contents
preface
acknowledgments
about this book
	Who should read this book
	How this book is organized: A roadmap
	About the code
	liveBook discussion forum
about the authors
about the cover illustration
Part 1 Basic tools and building blocks
	1 The rise of robo-advisors
		1.1 What are robo-advisors?
			1.1.1 Key features of robo-advisors
			1.1.2 Comparison of robo-advisors
			1.1.3 Things robo-advisors don't do
		1.2 Advantages of robo-advisors
			1.2.1 Low fees
			1.2.2 Tax savings
			1.2.3 Avoiding behavioral biases
			1.2.4 Saving time
		1.3 Example: Social Security benefits
		1.4 Python and robo-advising
		1.5 Who might be interested in learning about robo-advising?
		Summary
	2 An introduction to portfolio construction
		2.1 A simple example with three assets
		2.2 Computing a portfolio's expected return and standard deviation
		2.3 An illustration with random weights
		2.4 Introducing a risk-free asset
		2.5 Risk tolerance
		Appendix
		No risk-free rate
		Adding a risk-free rate
		Summary
	3 Estimating expected returns and covariances
		3.1 Estimating expected returns
			3.1.1 Historical averages
			3.1.2 CAPM
			3.1.3 Adjusting historical returns for changes in valuation
			3.1.4 Capital market assumptions from asset managers
		3.2 Estimating variances and covariances
			3.2.1 Using historical returns
			3.2.2 GARCH models
			3.2.3 Other approaches
			3.2.4 Subjective estimates
		Summary
	4 ETFs: The building blocks of robo-portfolios
		4.1 ETF basics
			4.1.1 ETF strategies
			4.1.2 ETF pricing: Theory
			4.1.3 ETF pricing: Reality
			4.1.4 Costs of ETF investing
		4.2 ETFs vs. mutual funds
			4.2.1 Tradability
			4.2.2 Costs and minimums
			4.2.3 Tax efficiency
			4.2.4 The verdict on mutual funds vs. ETFs
		4.3 Total cost of ownership
			4.3.1 Cost components
		4.4 Beyond standard indices
			4.4.1 Smart beta
			4.4.2 Socially responsible investing
		Summary
Part 2 Financial planning tools
	5 Monte Carlo simulations
		5.1 Simulating returns in Python
		5.2 Arithmetic vs. geometric average returns
		5.3 Simple vs. continuously compounded returns
		5.4 Geometric Brownian motion
		5.5 Estimating the probability of success
		5.6 Dynamic strategies
		5.7 Inflation risk
		5.8 Fat tails
		5.9 Historical simulations and booststrapping
		5.10 Longevity risk
		5.11 Flexibility of Monte Carlo simulations
		Appendix
		Summary
	6 Financial planning using reinforcement learning
		6.1 A goals-based investing example
		6.2 An introduction to reinforcement learning
			6.2.1 Solution using dynamic programming
			6.2.2 Solution using Q-learning
		6.3 Utility function approach
			6.3.1 Understanding utility functions
			6.3.2 Optimal spending using utility functions
		6.4 Longevity risk
		6.5 Other extensions
		Summary
	7 Measuring and evaluating returns
		7.1 Time-weighted vs. dollar-weighted returns
			7.1.1 Time-weighted returns
			7.1.2 Dollar-weighted returns
		7.2 Risk-adjusted returns
			7.2.1 Sharpe ratio
			7.2.2 Alpha
			7.2.3 Evaluating an ESG fund's performance
			7.2.4 Which is better, alpha or Sharpe ratio?
		Summary
	8 Asset location
		8.1 A simple example
		8.2 The tax efficiency of various assets
		8.3 Adding a Roth account
			8.3.1 A simple example with three types of accounts
			8.3.2 An example with optimization
		8.4 Additional considerations
		Summary
	9 Tax-efficient withdrawal strategies
		9.1 The intuition behind tax-efficient strategies
			9.1.1 Principle 1: Deplete less tax-efficient accounts first
			9.1.2 Principle 2: Keep tax brackets stable over time
		9.2 Examples of sequencing strategies
			9.2.1 Starting assumptions
			9.2.2 Tax-sequencing code
			9.2.3 Strategy 1: IRA first
			9.2.4 Strategy 2: Taxable first
			9.2.5 Strategy 3: Fill lower tax brackets
			9.2.6 Strategy 4: Roth conversions
		9.3 Additional complications
			9.3.1 Required minimum distributions
			9.3.2 Inheritance
			9.3.3 Capital gains taxes
			9.3.4 State taxes
			9.3.5 Putting it all together
		Summary
Part 3 Portfolio construction
	10 Optimization and portfolio construction
		10.1 Convex optimization in Python
			10.1.1 Basics of optimization
			10.1.2 Convexity
			10.1.3 Python libraries for optimization
		10.2 Mean-variance optimization
			10.2.1 The basic problem
			10.2.2 Adding more constraints
		10.3 Optimization-based asset allocation
			10.3.1 Minimal constraints
			10.3.2 Enforcing diversification
			10.3.3 Creating an efficient frontier
			10.3.4 Building an ESG portfolio
		Summary
	11 Asset allocation by risk: Introduction to risk parity
		11.1 Decomposing portfolio risk
			11.1.1 Risk contributions
			11.1.2 Risk concentration in a ``diversified'' portfolio
			11.1.3 Risk parity as an optimal portfolio
		11.2 Calculating risk-parity weights
			11.2.1 Naive risk parity
			11.2.2 General risk parity
			11.2.3 Weighted risk parity
			11.2.4 Hierarchical risk parity
		11.3 Implementation of risk-parity portfolios
			11.3.1 Applying leverage
		Summary
	12 The Black-Litterman model
		12.1 Equilibrium returns
			12.1.1 Reverse optimization
			12.1.2 Understanding equilibrium
		12.2 Conditional probability and Bayes' rule
		12.3 Incorporating investor views
			12.3.1 Expected returns as random variables
			12.3.2 Expressing views
			12.3.3 Updating equilibrium returns
			12.3.4 Assumptions and parameters
		12.4 Examples
			12.4.1 Example: Sector selection
			12.4.2 Example: Global allocation with cryptocurrencies
		Summary
Part 4 Portfolio management
	13 Rebalancing: Tracking a target portfolio
		13.1 Rebalancing basics
			13.1.1 The need for rebalancing
			13.1.2 Downsides of rebalancing
			13.1.3 Dividends and deposits
		13.2 Simple rebalancing strategies
			13.2.1 Fixed-interval rebalancing
			13.2.2 Threshold-based rebalancing
			13.2.3 Other considerations
			13.2.4 Final thoughts
		13.3 Optimizing rebalancing
			13.3.1 Variables
			13.3.2 Inputs
			13.3.3 Formulating the problem
			13.3.4 Running an example
		13.4 Comparing rebalancing approaches
			13.4.1 Implementing rebalancers
			13.4.2 Building the backtester
			13.4.3 Running backtests
			13.4.4 Evaluating results
		Summary
	14 Tax-loss harvesting: Improving after-tax returns
		14.1 The economics of tax-loss harvesting
			14.1.1 Tax deferral
			14.1.2 Rate conversion
			14.1.3 When harvesting doesn't help
		14.2 The wash-sale rule
			14.2.1 Wash-sale basics
			14.2.2 Wash sales with Python
		14.3 Deciding when to harvest
			14.3.1 Trading costs
			14.3.2 Opportunity cost
			14.3.3 End-to-end evaluation
		14.4 Testing a TLH strategy
			14.4.1 Backtester modifications
			14.4.2 Choosing ETFs
		Summary
index