Indicators

Phase 3 — Understand Technical Indicators

Technical indicators are not magic signals. They are calculations that transform price, volume, or both so that one property of market behavior becomes easier to examine.

Phase 3 begins with moving averages, then moves into momentum, relative change, RSI, MACD, and measurements of price range and volatility. The goal is to understand each calculation before turning it into a trading rule. 

market data
→ transform the data
→ isolate one property
→ visualize it
→ understand the formula
→ change one thing
→ observe
→ test the idea

How Phase 3 Is Organized

There are too many technical indicators to learn as one long list. This hub groups the published lessons by the main question behind each calculation. 

Moving Average Foundations
→ Where is the smoother price level?

Momentum & Relative Change
→ How much is price changing,
  and how do recent gains compare with recent losses?

Composite Indicators
→ What happens when earlier building blocks
  are combined into a multi-stage calculation?

Volatility & Range
→ How much did price move,
  including movement that a simple High-Low range can miss?

Do not try to memorize indicator names first. Focus on the input data, the transformation, and the question the result helps you examine.

1. Moving Average Foundations

This group builds the basic language of technical indicators. You will calculate moving averages from raw prices, compare different averaging methods, study lag and crossover behavior, and see how the idea developed historically. 

prices
→ average
→ smoother price level
→ compare averages
→ observe lag
→ compare weighting
→ understand the history
  1. What Is a Moving Average? Build One from Prices with Python
  2. How to Compare a Short and Long Moving Average with Candlesticks in Python
  3. What Does a Moving Average Crossover Really Mean? See It with Python
  4. How Do You Read a Moving Average? Price, Slope, and Distance Explained
  5. Why Does a Moving Average Lag Behind Price? See It with Python
  6. What Is an Exponential Moving Average? Build EMA from Scratch
  7. SMA vs EMA: Why Do They React Differently?
  8. Who First Used Moving Averages for Trading? A Short History

What You Should Understand After Moving Average Foundations

  • A technical indicator is a transformation of market data, not an automatic trading signal.
  • An SMA gives equal weight to prices inside a fixed window.
  • An EMA gives more influence to recent prices while older influence fades gradually.
  • A shorter average usually reacts faster, while a longer average is usually smoother.
  • A moving-average crossover is a relationship between two smoothed price series.
  • Price position, moving-average slope, and distance from the average describe different relationships.
  • Moving averages lag because they are calculated from prices that have already occurred.
  • Different weighting rules create different response characteristics.
  • Moving averages began as statistical smoothing tools before becoming widely used in market analysis.
  • None of these observations proves that a moving-average rule has a profitable trading edge.

2. Momentum & Relative Change

Moving averages smooth price. The next lessons ask a different question: how much is price changing, and how can that change be expressed in a more comparable form? 

price change
→ Momentum
→ relative change
→ ROC
→ gains versus losses
→ RSI
→ bounded 0–100 scale
  1. What Is Momentum? Measure Price Change with Python
  2. What Is Rate of Change? Turn Price Change into a Percentage with Python
  3. What Is RSI? Compare Recent Gains and Losses with Python
  4. Why Did Welles Wilder Create RSI? The Story Behind a 0–100 Momentum Scale

What You Should Understand After Momentum & Relative Change

  • Momentum measures an absolute price change over a selected lookback.
  • ROC expresses price change relative to an earlier price.
  • RSI separates recent gains and losses, smooths them, and compares their balance.
  • RSI is bounded between 0 and 100, but a high or low reading does not guarantee the next price direction.
  • A historical parameter such as RSI 14 is a design choice, not a law of nature.
  • An indicator and a trading strategy are different things.

3. Composite Indicators

Now earlier building blocks begin to connect. MACD starts from two exponential moving averages, then adds another smoothed line and a difference between the two. The history lesson then shows why Gerald Appel built MACD and how the modern MACD chart evolved over time. 

EMA 12
+
EMA 26
→ MACD line
→ Signal line
→ Histogram
→ understand why the system evolved this way
  1. What Is MACD? Build It from Two EMAs with Python
  2. What Are the MACD Line, Signal Line, and Histogram?
  3. Why Did Gerald Appel Create MACD? The Search for a Simpler Trend-Momentum Indicator

What You Should Understand After the Current MACD Lessons

  • MACD reuses EMA rather than starting from an unrelated formula.
  • The MACD line is the difference between a faster EMA and a slower EMA.
  • The Signal line smooths the MACD line.
  • The Histogram shows the difference between the MACD line and the Signal line.
  • Gerald Appel developed MACD as a practical way to make the relationship between moving averages easier to interpret.
  • The familiar 12/26/9 settings are conventions, not universal market constants.
  • The MACD histogram was developed later by Thomas Aspray rather than being part of Appel's original invention.
  • Combining familiar building blocks is a common way more advanced indicators are constructed.

4. Volatility & Range

Momentum and MACD describe changes and relationships in price. This group asks a different question: how large was the movement itself?

The first lesson begins with one bar. A simple High-Low range can miss part of a move when price gaps away from the previous Close. True Range fixes that problem by comparing three distances and keeping the largest one. 

High - Low
        +
distance from previous Close
        ↓
compare three candidates
        ↓
True Range
        ↓
movement magnitude
  1. What Is True Range? Measure Daily Price Movement with Python

What You Should Understand After True Range

  • The ordinary daily range is High minus Low.
  • High-Low can miss part of a move when price gaps away from the previous Close.
  • True Range checks High-Low, High versus the previous Close, and Low versus the previous Close.
  • True Range keeps the largest of those three distances.
  • Absolute values remove direction from the previous-Close comparisons.
  • True Range measures movement magnitude, not bullish or bearish direction.
  • True Range is measured in price units, not percent.
  • True Range is a building block for Average True Range and later Wilder indicators.

What Comes Later

Future Phase 3 lessons can extend the current published path into Average True Range, directional movement and trend strength, and later volume or money-flow indicators. Those lessons will be added here only after they are published. 

True Range
→ ATR
→ Directional Movement
→ DMI / ADX

then later

Volume & Money Flow

A Rule for Learning Indicators

Do not begin by asking whether an indicator is good or bad.

Begin with four simpler questions:

  1. What raw data goes into the calculation?
  2. What mathematical transformation is applied?
  3. What property becomes easier to see?
  4. What information is lost, smoothed, normalized, or delayed by that transformation?

Only after you understand those questions does it make sense to test whether an indicator is useful inside a trading rule.

Where You Are in the Learning Path

Start Here → Market Data → Candlesticks → Indicators

If you are new to Phase 3, begin with the first lesson:

What Is a Moving Average? Build One from Prices with Python