Guppy Multiple Moving Average (GMMA)

I. What is the Guppy MMA Indicator

The Guppy Multiple Moving Average (GMMA or Guppy MMA) is an indicator system composed of 12 Exponential Moving Averages (EMAs). It is divided into a short-term group and a long-term group. By observing the expansion, contraction, and crossover relationships between the two groups, traders can assess trend conditions and the behavior of different market participants.

Historical Background

GMMA was invented by Australian trader and financial columnist Daryl Guppy. He systematically presented this method in his 2004 book Trend Trading. His core insight was that markets are driven by two types of participants — short-term traders and long-term investors — and two groups of EMAs with different periods can represent the behavior of each group respectively.

Guppy’s innovation lies not in the calculation of the moving averages themselves, but in reading multiple moving averages as a whole to interpret market structure. GMMA is more of an analytical framework than a single indicator.

Indicator Classification

• Type: Overlay indicator, plotted on the price chart
• Category: Moving Averages, multiple moving average system
• Default Parameters:
  • Short-term group (Traders): EMA(3), EMA(5), EMA(8), EMA(10), EMA(12), EMA(15)
  • Long-term group (Investors): EMA(30), EMA(35), EMA(40), EMA(45), EMA(50), EMA(60)

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II. Mathematical Principles and Calculation

Core Formula

Each line in GMMA is a standard EMA:

$EMA_t^{(p)} = \alpha_p \cdot P_t + (1 - \alpha_p) \cdot EMA_{t-1}^{(p)}$

where $\alpha_p = \frac{2}{p + 1}$ and $p$ is the corresponding period.

Short-term group (Traders Group) — 6 EMAs:

$\{EMA(3),\ EMA(5),\ EMA(8),\ EMA(10),\ EMA(12),\ EMA(15)\}$

Long-term group (Investors Group) — 6 EMAs:

$\{EMA(30),\ EMA(35),\ EMA(40),\ EMA(45),\ EMA(50),\ EMA(60)\}$

Interpretation Methods

The core of GMMA is not the formula, but interpreting visual patterns. The following dimensions should be observed:

1. Intra-group Separation

The spacing between the 6 lines within each group reflects the degree of consensus among that type of participant:

Intra-group Separation	Meaning
Wide expansion	Strong trend conviction — participants agree on direction
Narrow contraction	Indecision — participants may be changing direction

2. Inter-group Relationship

Pattern	Meaning
Short-term group above	Traders are bullish, driving prices up
Long-term group above	Investors are bearish or holding short
Two groups intertwined	Trend unclear, bulls and bears in tug-of-war

3. Compression-to-Expansion Transitions

$\text{Compression} \rightarrow \text{Expansion} = \text{New trend forming}$ $\text{Expansion} \rightarrow \text{Compression} = \text{Trend weakening}$

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III. Python Implementation

import numpy as np
import pandas as pd

def guppy_mma(close: pd.Series) -> pd.DataFrame:
    """
    Calculate Guppy Multiple Moving Average (GMMA)

    Parameters
    ----------
    close : pd.Series
        Close price series

    Returns
    -------
    pd.DataFrame
        DataFrame containing 12 EMAs
    """
    # Short-term group (Traders)
    short_periods = [3, 5, 8, 10, 12, 15]
    # Long-term group (Investors)
    long_periods = [30, 35, 40, 45, 50, 60]

    result = pd.DataFrame(index=close.index)

    for p in short_periods:
        result[f"ST_{p}"] = close.ewm(span=p, adjust=False).mean()

    for p in long_periods:
        result[f"LT_{p}"] = close.ewm(span=p, adjust=False).mean()

    return result


def guppy_analysis(close: pd.Series) -> pd.DataFrame:
    """
    GMMA analysis: calculate moving averages and generate
    inter-group / intra-group metrics

    Returns
    -------
    pd.DataFrame
        DataFrame with GMMA lines and analysis metrics
    """
    short_periods = [3, 5, 8, 10, 12, 15]
    long_periods = [30, 35, 40, 45, 50, 60]

    df = pd.DataFrame(index=close.index)

    # Calculate all EMAs
    short_emas = []
    for p in short_periods:
        col = f"ST_{p}"
        df[col] = close.ewm(span=p, adjust=False).mean()
        short_emas.append(col)

    long_emas = []
    for p in long_periods:
        col = f"LT_{p}"
        df[col] = close.ewm(span=p, adjust=False).mean()
        long_emas.append(col)

    # Short-term group mean
    df["ST_mean"] = df[short_emas].mean(axis=1)
    # Long-term group mean
    df["LT_mean"] = df[long_emas].mean(axis=1)

    # Short-term group spread (standard deviation)
    df["ST_spread"] = df[short_emas].std(axis=1)
    # Long-term group spread
    df["LT_spread"] = df[long_emas].std(axis=1)

    # Inter-group gap
    df["group_gap"] = df["ST_mean"] - df["LT_mean"]

    # Trend determination
    df["trend"] = np.where(
        df["group_gap"] > 0, "BULLISH",
        np.where(df["group_gap"] < 0, "BEARISH", "NEUTRAL")
    )

    # Trend strength (inter-group gap / long-term group spread)
    df["trend_strength"] = np.abs(df["group_gap"]) / (df["LT_spread"] + 1e-10)

    return df


# ========== Usage Example ==========
if __name__ == "__main__":
    np.random.seed(42)
    dates = pd.date_range("2024-01-01", periods=200, freq="D")

    # Construct price series: trend -> consolidation -> trend
    trend = np.concatenate([
        np.linspace(0, 15, 70),       # Uptrend
        np.random.randn(60) * 0.2,    # Consolidation
        np.linspace(0, -12, 70),      # Downtrend
    ])
    price = 100 + trend + np.random.randn(200) * 0.4

    df = pd.DataFrame({
        "date": dates,
        "open":  price + np.random.randn(200) * 0.2,
        "high":  price + np.abs(np.random.randn(200) * 0.5),
        "low":   price - np.abs(np.random.randn(200) * 0.5),
        "close": price,
        "volume": np.random.randint(1000, 10000, size=200),
    })
    df.set_index("date", inplace=True)

    # Calculate GMMA
    gmma = guppy_mma(df["close"])
    print("=== GMMA 12 Moving Averages (Last 5 Rows) ===")
    print(gmma.tail(5).round(2))

    # Detailed analysis
    analysis = guppy_analysis(df["close"])
    print("\n=== GMMA Trend Analysis ===")
    cols = ["ST_mean", "LT_mean", "group_gap", "ST_spread",
            "LT_spread", "trend", "trend_strength"]
    print(analysis[cols].tail(10).round(3))

    # Trend change signals
    analysis["trend_change"] = analysis["trend"] != analysis["trend"].shift(1)
    changes = analysis.loc[analysis["trend_change"], ["trend", "group_gap"]]
    print("\n=== Trend Change Points ===")
    print(changes)

    # Spread analysis
    print("\n=== Spread Statistics ===")
    print(f"Short-term group average spread: {analysis['ST_spread'].mean():.4f}")
    print(f"Long-term group average spread: {analysis['LT_spread'].mean():.4f}")
    print(f"Average trend strength: {analysis['trend_strength'].mean():.2f}")

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IV. Problems the Indicator Solves

1. Trend Confirmation and Strength Assessment

GMMA provides richer trend information than a single moving average:

Observation	Assessment
Both groups expanding in same direction	Strong trend established
Short-term group crosses long-term group	Potential trend reversal
Long-term group begins to contract	Major trend weakening
Both groups fully intertwined	No trend / consolidation

2. Distinguishing Trader and Investor Behavior

This is GMMA’s most unique perspective:

• Short-term group expanded: Short-term capital is in agreement on direction
• Long-term group expanded: Institutional capital has a clear stance
• Short-term group compressed but long-term group expanded: Short-term profit-taking, but the major trend is intact
• Both groups expanded simultaneously: Market-wide consensus, strongest trend

3. Entry and Exit Timing

Entry signals:

• Short-term group crosses above long-term group from below, and both groups begin to expand
• When long-term group remains expanded, short-term group pulls back (contracts) then expands again (pullback buy)

Exit signals:

• Short-term group begins to contract and approaches the long-term group
• Long-term group begins to contract (precursor to major trend ending)

4. False Breakout Identification

When price breaks through a level, observe the long-term group’s reaction:

• Long-term group begins expanding and follows along -> true breakout
• Long-term group remains unmoved -> likely a false breakout

5. Distinguishing Pullbacks Within Trends

During an uptrend, price pullbacks come in two varieties:

• Healthy pullback: Short-term group contracts and touches the long-term group, but the long-term group remains expanding upward -> trend continuation, buying opportunity
• Trend termination: Short-term group crosses through the long-term group, and the long-term group begins to contract -> trend may be ending

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V. Advantages, Disadvantages, and Use Cases

Advantages

Advantage	Description
Information-rich	12 moving averages provide multi-dimensional market information
Participant differentiation	Uniquely separates market participants into traders and investors
Trend quality	Judges trend quality and sustainability through spread analysis
Simple calculation	Uses only standard EMAs with no complex mathematics
Visually intuitive	Visual patterns on charts are very intuitive

Disadvantages

Disadvantage	Description
High subjectivity	Pattern interpretation depends on trader experience and judgment
Hard to quantify	Difficult to convert visual patterns into precise quantitative signals
Chart clutter	12 moving averages displayed simultaneously make charts complex
All lagging	Fundamentally still EMAs, all lines have lag
Not suited for automation	Primarily designed as a visual analysis tool, difficult for automated strategies

Use Cases

• Manual trend trading: Visually assess trend quality and direction from charts
• Swing trading: Use short-term group pullback-to-long-term-group patterns to find entry points
• Trend confirmation: After other strategies generate signals, use GMMA to confirm trend quality
• Daily and higher timeframes: GMMA works best on higher timeframes

Comparison with Other Moving Average Systems

System	# of MAs	Core Concept	Usage Method
GMMA	12 EMAs	Traders vs Investors	Visual analysis
Dual MA crossover	2	Fast/slow line crossover	Signal-driven
Triple MA system	3	Short/medium/long alignment	Signal + filter
MA ribbon	Multiple	Trend bandwidth	Visual analysis