liveBook · Manning

What you will learn in Investing for Programmers

Understanding markets through data and code

by Stefan Papp

Chapter by chapter

Chapter 1: The analytical investor

1.1 Your investment journey · 1.2 Assets · 1.2.1 Stocks · 1.2.2 Bonds · 1.2.3 Exchange-traded funds · 1.2.4 Other funds · 1.2.5 Foreign exchange market · 1.2.6 Crypto · 1.2.7 Derivatives · 1.2.8 Private equity · 1.2.9 Other assets · 1.2.10 Choosing assets · 1.3 Investment approaches · 1.3.1 Quantitative research · 1.3.2 Qualitative research · 1.3.3 Algorithmic trading and asset monitors · 1.3.4 Portfolios and investing strategies · 1.4 Risks and rewards · 1.5 A programmer’s unfair advantage · 1.5.1 You think in systems and data · 1.5.2 Your mindset is built for the market · 1.5.3 Define your parameters · 1.6 Summary

What you learn in chapter 1 →

Chapter 2: Investment essentials

2.1 Accounting in a nutshell · 2.1.1 Income statement · 2.1.2 Balance sheet · 2.1.3 Free cash flow · 2.2 Industry classification · 2.2.1 Influences on GICS sectors · 2.2.2 Sectors and economic cycles · 2.3 Capitalization · 2.4 Metrics and ratios · 2.4.1 Liquidity · 2.4.2 Debt · 2.4.3 Earnings · 2.4.4 Valuation · 2.4.5 Profitability · 2.4.6 Dividends · 2.4.7 Ownership · 2.4.8 Sustainability · 2.5 External assessments · 2.5.1 Ratings · 2.5.2 Target prices · 2.6 Summary

What you learn in chapter 2 →

Chapter 3: Collecting data

3.1 Financial data · 3.2 Financial analysis platforms · 3.3 Data science notebooks · 3.4 yfinance · 3.4.1 Fundamental analysis · 3.4.2 Technical analysis · 3.4.3 Limitations of yfinance · 3.5 Commercial libraries · 3.5.1 Finviz · 3.5.2 EODHD · 3.5.3 Alpha Vantage · 3.5.4 OpenBB · 3.6 Other libraries · 3.7 Summary

What you learn in chapter 3 →

Chapter 4: Growth portfolios

4.1 Investment thesis · 4.1.1 Starting with an idea · 4.1.2 Challenging the idea · 4.1.3 Your investment thesis · 4.2 LiDAR market · 4.2.1 Picking candidates · 4.2.2 Price development · 4.2.3 Debt · 4.2.4 Management · 4.2.5 Technology and partnership · 4.2.6 Projected earnings · 4.3 Risks · 4.3.1 Falling into obsolescence · 4.3.2 Squashed by industry giants · 4.3.3 Globalization and conflicts · 4.4 Ongoing analysis · 4.4.1 Media · 4.4.2 Trend analysis · 4.4.3 News sentiment analysis · 4.4.4 Measuring success · 4.5 What is next · 4.6 Summary

What you learn in chapter 4 →

Chapter 5: Income portfolios

5.1 Dividends · 5.2 Bonds · 5.3 Crypto staking · 5.3.1 Ledgers and exchanges · 5.3.2 Mining and staking · 5.3.3 Affordable staking options · 5.4 Early retirement · 5.5 Summary

What you learn in chapter 5 →

Chapter 6: Building an asset monitor

6.1 Architecture · 6.2 The spreadsheet · 6.3 Extracting data · 6.3.1 Alpaca: A developer-first broker · 6.3.2 Interactive Brokers: A legacy powerhouse with a modern twist · 6.4 Enriching data · 6.5 Processing assets · 6.5.1 Stocks · 6.5.2 Exchange-traded funds · 6.5.3 Bonds · 6.5.4 Cryptocurrencies · 6.6 Outlook · 6.7 Summary

What you learn in chapter 6 →

Chapter 7: Risk management

7.1 Ukemi · 7.1.1 Stop-loss · 7.1.2 Risk classification · 7.1.3 Risk measurement · 7.2 Generating risk profiles for individual stocks · 7.2.1 Value at risk (VaR) · 7.2.2 Correlation · 7.3 The human factor · 7.3.1 Negligence · 7.3.2 Risk avoidance · 7.3.3 Resilience · 7.4 Hedging · 7.4.1 Derivatives · 7.4.2 Diversification · 7.4.3 Pair trading · 7.4.4 Risk pairing · 7.5 Nonfinancial risk · 7.5.1 Markets · 7.5.2 Economic data · 7.5.3 Assessing nonfinancial risk · 7.6 Portfolio optimization · 7.6.1 Markowitz-efficient portfolio · 7.6.2 Shiller P/E ratio · 7.6.3 Rebalancing · 7.7 Summary

What you learn in chapter 7 →

Chapter 8: AI for financial research

8.1 From code to machine learning · 8.1.1 Unsupervised learning example · 8.1.2 Supervised learning example · 8.1.3 Market challenges · 8.1.4 Technical challenges · 8.1.5 Narrowing the scope · 8.2 From machine learning to generative AI · 8.2.1 Comparing LLMs · 8.2.2 Complementing ML with GenAI · 8.2.3 Challenges · 8.2.4 Final judgement on ML and GenAI · 8.3 Practical use of GenAI · 8.3.1 Using LLMs as research assistants · 8.3.2 Integrating LLMs into code · 8.4 Prompt engineering · 8.4.1 An investor’s profile · 8.4.2 Using prompts to find companies to invest in · 8.5 Summary

What you learn in chapter 8 →

Chapter 9: AI agents

9.1 Requirements · 9.1.1 Successful communication · 9.1.2 Agentic design patterns · 9.2 Agentic workflows without frameworks · 9.2.1 Prompt repository · 9.2.2 Export results · 9.3 Framework for AI agents · 9.3.1 From one-shot prompting to agents · 9.3.2 Retrieval-augmented generation · 9.4 Summary

What you learn in chapter 9 →

Chapter 10: Charts and technical analysis

10.1 Charts · 10.1.1 Reading charts · 10.1.2 Patterns · 10.1.3 Interpreting a chart · 10.1.4 Alternative chart types · 10.2 Using charts to interpret price changes · 10.2.1 Candlesticks · 10.2.2 Charts based on averages · 10.2.3 Ichimoku Cloud · 10.3 Visualization with Streamlit · 10.4 Summary

What you learn in chapter 10 →

Chapter 11: Algorithmic trading

11.1 Nonfinancial data · 11.1.1 Big data by example · 11.2 Catalysts · 11.2.1 Mergers and acquisitions · 11.2.2 Companies in distress · 11.2.3 Earnings calls · 11.2.4 Disasters · 11.2.5 Interest rate changes · 11.3 Trading algorithms · 11.3.1 Backtesting · 11.3.2 Complex trading signals · 11.4 Orders · 11.4.1 Exchanges vs. brokers · 11.4.2 Order modifiers · 11.4.3 Executing orders · 11.5 Summary

What you learn in chapter 11 →

Chapter 12: Private equity: Investing in start-ups

12.1 From idea to initial public offering · 12.1.1 The first minimum viable product (pre-seed) · 12.1.2 Validating the business model (seed) · 12.1.3 Scaling a start-up: The shift to institutional investment · 12.1.4 Exits: The final transition · 12.2 Investment vehicles · 12.2.1 Venture capital · 12.2.2 Angel networks · 12.2.3 Sovereign wealth funds · 12.3 Assessing start-ups · 12.3.1 Valuation · 12.3.2 Dilution · 12.3.3 Scoring · 12.4 Summary

What you learn in chapter 12 →

Chapter 13: The road goes ever on and on

13.1 Getting advice · 13.2 Stay curious · 13.3 Alpha hunter · 13.4 Nomadism · 13.5 Activist investing · 13.6 Measures of control · 13.6.1 Checks and balances · 13.6.2 Gain distance · 13.6.3 Programmer’s journey · 13.6.4 Playing it safe · 13.7 Summary

What you learn in chapter 13 →

Concepts explained in this book

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