liveBook · Manning

Technical analysis

As explained in Investing for Programmers

An investment analysis technique that evaluates market data, especially prices and trading activity, to identify patterns and forecast price movements.

Technical analysis evaluates trading activity—especially price changes and volume—to identify historical patterns and forecast potential future price movements. Charts turn numerical history into a visual basis for interpreting trends, momentum, and timing rather than company value alone. section 1.3.1 section 10.0

A practical technical-analysis workflow includes:

  • Collect historical open, high, low, close, and volume data using structured financial-data libraries or web scraping. section 1.3.1 section 3.4.2
  • Visualize price development with line charts or candlestick charts to inspect daily movements across a chosen time horizon. section 1.3.1 section 10.0
  • Calculate returns and summarize them with mean, standard deviation, and variance to compare performance and fluctuation. section 3.4.2
  • Inspect indicators and recurring signals such as moving-average crossovers, while matching the interpretation to the trader’s time horizon. section 10.1.3

Comparing historical price behavior

Download 2023 prices for Apple, Coca-Cola, and NuScale with yfinance, plot normalized closing prices, calculate simple or logarithmic returns, and aggregate mean, standard deviation, and variance. The resulting comparison shows materially different performance and fluctuation across the three stocks. section 3.4.2

Figure 1.2 The price development of NVIDIA from 2016 to 2025. Notice the sharp rise in recent years; this is a typical example of the growth that attracts many traders and investors. Keep in mind that such growth rates are rare.
figure

Use the long-term NVIDIA price chart to practice distinguishing a visible historical trend from a reliable forecast; exceptional growth should not be treated as typical. section 1.3.1

Technical analysis remains contested: some investors regard its effectiveness as unproven, while others use patterns and signals operationally. A chart can support decisions, but it cannot guarantee an outcome; volatility, leverage, market events, and the selected time horizon change the risk. section 10.1.3 section 10.1

How Investing for Programmers covers it

How deep it goes
Explains data collection, charts, returns, descriptive statistics, indicators, and time horizons, but does not establish predictive effectiveness.
Examples
Demonstrates NVIDIA, Microsoft, Apple, Coca-Cola, NuScale, candlesticks, normalized charts, returns, and moving-average crossover signals.
What it assumes
Assumes basic investment context and introduces historical prices, returns, charts, and descriptive statistics through concrete data examples.
What you can do after
Evaluate/understand price behavior by collecting market data, plotting charts, calculating returns, and comparing volatility across assets.
How it connects
Connects technical analysis with quantitative research, machine learning, fundamental analysis, risk management, and moving averages.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Investing for Programmers. The section links point to the parts of the book it draws on.