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

Build a Robo-Advisor with Python....1 brief contents....5 contents....7 preface....12 acknowledgments....13 about this book....14 Who should read this book....14 How this book is organized: A roadmap....14 About the code....15 liveBook discussion forum....16 about the authors....17 about the cover illustration....18 Part 1 Basic tools and building blocks....19 1 The rise of robo-advisors....21 1.1 What are robo-advisors?....21 1.1.1 Key features of robo-advisors....22 1.1.2 Comparison of robo-advisors....23 1.1.3 Things robo-advisors don't do....23 1.2 Advantages of robo-advisors....24 1.2.1 Low fees....24 1.2.2 Tax savings....24 1.2.3 Avoiding behavioral biases....25 1.2.4 Saving time....26 1.3 Example: Social Security benefits....26 1.4 Python and robo-advising....28 1.5 Who might be interested in learning about robo-advising?....29 Summary....30 2 An introduction to portfolio construction....31 2.1 A simple example with three assets....32 2.2 Computing a portfolio's expected return and standard deviation....33 2.3 An illustration with random weights....36 2.4 Introducing a risk-free asset....39 2.5 Risk tolerance....41 Appendix....45 No risk-free rate....45 Adding a risk-free rate....47 Summary....48 3 Estimating expected returns and covariances....49 3.1 Estimating expected returns....50 3.1.1 Historical averages....50 3.1.2 CAPM....52 3.1.3 Adjusting historical returns for changes in valuation....57 3.1.4 Capital market assumptions from asset managers....63 3.2 Estimating variances and covariances....63 3.2.1 Using historical returns....63 3.2.2 GARCH models....65 3.2.3 Other approaches....68 3.2.4 Subjective estimates....68 Summary....70 4 ETFs: The building blocks of robo-portfolios....71 4.1 ETF basics....72 4.1.1 ETF strategies....72 4.1.2 ETF pricing: Theory....72 4.1.3 ETF pricing: Reality....74 4.1.4 Costs of ETF investing....75 4.2 ETFs vs. mutual funds....75 4.2.1 Tradability....76 4.2.2 Costs and minimums....77 4.2.3 Tax efficiency....77 4.2.4 The verdict on mutual funds vs. ETFs....78 4.3 Total cost of ownership....79 4.3.1 Cost components....79 4.4 Beyond standard indices....80 4.4.1 Smart beta....81 4.4.2 Socially responsible investing....82 Summary....84 Part 2 Financial planning tools....85 5 Monte Carlo simulations....87 5.1 Simulating returns in Python....89 5.2 Arithmetic vs. geometric average returns....92 5.3 Simple vs. continuously compounded returns....94 5.4 Geometric Brownian motion....95 5.5 Estimating the probability of success....96 5.6 Dynamic strategies....98 5.7 Inflation risk....100 5.8 Fat tails....105 5.9 Historical simulations and booststrapping....106 5.10 Longevity risk....108 5.11 Flexibility of Monte Carlo simulations....111 Appendix....112 Summary....112 6 Financial planning using reinforcement learning....114 6.1 A goals-based investing example....115 6.2 An introduction to reinforcement learning....115 6.2.1 Solution using dynamic programming....118 6.2.2 Solution using Q-learning....123 6.3 Utility function approach....126 6.3.1 Understanding utility functions....126 6.3.2 Optimal spending using utility functions....128 6.4 Longevity risk....132 6.5 Other extensions....134 Summary....135 7 Measuring and evaluating returns....136 7.1 Time-weighted vs. dollar-weighted returns....137 7.1.1 Time-weighted returns....138 7.1.2 Dollar-weighted returns....138 7.2 Risk-adjusted returns....140 7.2.1 Sharpe ratio....140 7.2.2 Alpha....142 7.2.3 Evaluating an ESG fund's performance....143 7.2.4 Which is better, alpha or Sharpe ratio?....145 Summary....146 8 Asset location....148 8.1 A simple example....149 8.2 The tax efficiency of various assets....153 8.3 Adding a Roth account....155 8.3.1 A simple example with three types of accounts....156 8.3.2 An example with optimization....157 8.4 Additional considerations....159 Summary....160 9 Tax-efficient withdrawal strategies....161 9.1 The intuition behind tax-efficient strategies....161 9.1.1 Principle 1: Deplete less tax-efficient accounts first....161 9.1.2 Principle 2: Keep tax brackets stable over time....162 9.2 Examples of sequencing strategies....163 9.2.1 Starting assumptions....163 9.2.2 Tax-sequencing code....164 9.2.3 Strategy 1: IRA first....167 9.2.4 Strategy 2: Taxable first....168 9.2.5 Strategy 3: Fill lower tax brackets....169 9.2.6 Strategy 4: Roth conversions....171 9.3 Additional complications....172 9.3.1 Required minimum distributions....173 9.3.2 Inheritance....174 9.3.3 Capital gains taxes....175 9.3.4 State taxes....178 9.3.5 Putting it all together....179 Summary....179 Part 3 Portfolio construction....181 10 Optimization and portfolio construction....183 10.1 Convex optimization in Python....184 10.1.1 Basics of optimization....184 10.1.2 Convexity....186 10.1.3 Python libraries for optimization....189 10.2 Mean-variance optimization....191 10.2.1 The basic problem....191 10.2.2 Adding more constraints....192 10.3 Optimization-based asset allocation....194 10.3.1 Minimal constraints....195 10.3.2 Enforcing diversification....200 10.3.3 Creating an efficient frontier....205 10.3.4 Building an ESG portfolio....206 Summary....207 11 Asset allocation by risk: Introduction to risk parity....209 11.1 Decomposing portfolio risk....210 11.1.1 Risk contributions....210 11.1.2 Risk concentration in a ``diversified'' portfolio....210 11.1.3 Risk parity as an optimal portfolio....211 11.2 Calculating risk-parity weights....213 11.2.1 Naive risk parity....213 11.2.2 General risk parity....213 11.2.3 Weighted risk parity....214 11.2.4 Hierarchical risk parity....218 11.3 Implementation of risk-parity portfolios....229 11.3.1 Applying leverage....230 Summary....231 12 The Black-Litterman model....232 12.1 Equilibrium returns....232 12.1.1 Reverse optimization....233 12.1.2 Understanding equilibrium....235 12.2 Conditional probability and Bayes' rule....236 12.3 Incorporating investor views....238 12.3.1 Expected returns as random variables....238 12.3.2 Expressing views....239 12.3.3 Updating equilibrium returns....240 12.3.4 Assumptions and parameters....241 12.4 Examples....242 12.4.1 Example: Sector selection....242 12.4.2 Example: Global allocation with cryptocurrencies....246 Summary....249 Part 4 Portfolio management....251 13 Rebalancing: Tracking a target portfolio....253 13.1 Rebalancing basics....253 13.1.1 The need for rebalancing....254 13.1.2 Downsides of rebalancing....255 13.1.3 Dividends and deposits....255 13.2 Simple rebalancing strategies....257 13.2.1 Fixed-interval rebalancing....257 13.2.2 Threshold-based rebalancing....257 13.2.3 Other considerations....258 13.2.4 Final thoughts....261 13.3 Optimizing rebalancing....261 13.3.1 Variables....261 13.3.2 Inputs....262 13.3.3 Formulating the problem....269 13.3.4 Running an example....271 13.4 Comparing rebalancing approaches....273 13.4.1 Implementing rebalancers....274 13.4.2 Building the backtester....279 13.4.3 Running backtests....286 13.4.4 Evaluating results....287 Summary....290 14 Tax-loss harvesting: Improving after-tax returns....291 14.1 The economics of tax-loss harvesting....292 14.1.1 Tax deferral....292 14.1.2 Rate conversion....294 14.1.3 When harvesting doesn't help....295 14.2 The wash-sale rule....295 14.2.1 Wash-sale basics....295 14.2.2 Wash sales with Python....299 14.3 Deciding when to harvest....311 14.3.1 Trading costs....311 14.3.2 Opportunity cost....312 14.3.3 End-to-end evaluation....317 14.4 Testing a TLH strategy....322 14.4.1 Backtester modifications....322 14.4.2 Choosing ETFs....323 Summary....323 index....324