Indicators Description

1. Component Deconstruction¶

The script’s engine is built upon a custom pattern recognition framework rather than traditional oscillating studies. Its core components are a dual-layer pivot detection system and a set of mathematical constants used for validation.

Pivot Detection Engine¶

The script utilizes two instances of the built-in ta.pivothigh and ta.pivotlow functions to identify significant swing points in the price structure. These serve as the foundational data points for all subsequent wave construction.

• Primary Pivots (primaryPivots)

  • Specific Configuration:

    • Indicator: ta.pivothigh(high, 13, 13) and ta.pivotlow(low, 13, 13).

    • Lookback Period: A primarySwingLen of 13 bars to the left and right is required to confirm a pivot. This configuration is designed to identify major, structurally significant swing points.

    • Price Source: high for pivot highs, low for pivot lows.

  • Functional Modification: Raw pivots are subjected to a rigorous filtering mechanism before being stored in the primaryPivots array.

    • Alternation Filter: A new pivot is only considered valid if it is of the opposite type to the preceding pivot (e.g., a high can only follow a low).

    • Volatility Filter: A new pivot must represent a price movement of at least minSwingPct (5.0% by default) relative to the previous pivot’s price. The function pctMove(fromPrice, toPrice) calculates this. This filter ensures that only swings of a minimum magnitude are considered, effectively filtering out minor price chop and adapting the analysis to the asset’s volatility profile.

    • Repainting Mitigation: The script includes logic to update the price and index of the last pivot if a new, higher high (or lower low) forms before an opposing pivot is confirmed. This ensures the pivot marks the true extremum of the swing.

• Secondary Pivots (secondaryPivots)

  • Specific Configuration:

    • Indicator: ta.pivothigh(high, 5, 5) and ta.pivotlow(low, 5, 5).

    • Lookback Period: A secondarySwingLen of 5 bars is used. This shorter period is designed to detect smaller, internal swings that constitute the sub-waves of the primary waves.

    • Price Source: high and low.

  • Functional Modification:

    • Volatility Filter: A minSubSwingPct of 2.0% is applied to filter noise from these more sensitive pivots.

    • Purpose: The secondaryPivots array is not used for primary pattern construction. Its sole purpose is to serve the countSubWaves function, which provides a quantitative measure of a primary wave’s internal complexity. This data is then used as a guideline (subWaveCountValid) to increase or decrease the confidence score of a detected pattern.

Mathematical & Logical Constants¶

The script does not use standard indicators but relies on a set of hard-coded mathematical ratios and rules derived from Elliott Wave theory.

• Fibonacci Ratios:

  • FIB_RETRACE: An array [0.236, 0.382, 0.500, 0.618, 0.786] used to validate corrective waves and project potential support/resistance zones.

  • FIB_EXTEND: An array [1.000, 1.272, 1.618, 2.000, 2.618, 4.236] used to validate the length of impulse waves (e.g., Wave 3 relative to Wave 1) and project price targets.

• Cardinal Rules (Impulse Waves): These are implemented as boolean functions that act as non-negotiable logical gates.

  • rule1Valid: Wave 2 cannot retrace beyond the starting price of Wave 1.

  • rule2Valid: The price length of Wave 3 cannot be the shortest among Waves 1, 3, and 5.

  • rule3Valid: The price level of Wave 4’s termination cannot enter the price territory of Wave 1 (overlap is disallowed).

• Guideline Heuristics: These are softer rules that influence a pattern’s confidence score.

  • checkAlternation: Compares the character of Wave 2 and Wave 4. It classifies each correction as “sharp” (deep and fast) or “sideways” (shallow and time-consuming) and rewards patterns where the two are different.

  • subWaveCountValid: Checks if the number of secondary pivots within a primary wave aligns with theory (e.g., motive waves should have more sub-pivots than corrective waves).

2. Logic Layering & Confluence¶

The script’s intelligence lies in its multi-layered filtering process, which transforms raw price data into high-probability, validated patterns.

• Layer 1: Structural Data Generation (Pivots) The foundational layer converts the continuous price stream into a discrete series of structurally significant swing points (primaryPivots) using the configured lookback periods and volatility filters. This dramatically reduces the signal-to-noise ratio from the outset.

• Layer 2: Geometric Template Matching The engine takes the most recent sequence of 4 or 6 primary pivots and attempts to match them against predefined geometric templates:

  • 6-Pivot Patterns: Impulse, Diagonal, Triangle.

  • 4-Pivot Patterns: Zigzag, Flat. This is a purely structural check to see if the sequence of highs and lows conforms to the expected shape of a given pattern (e.g., an impulse wave requires an alternating sequence of low-high-low-high-low-high for a bullish move).

• Layer 3: Hierarchical Filtering & Validation This is the core of the logic engine, where a geometrically plausible pattern is subjected to a hierarchy of tests.

  1. Cardinal Rule Gatekeeper: The pattern is first tested against the three non-negotiable Elliott Wave rules (validateImpulseFull). If any rule is violated (e.g., Wave 4 overlaps Wave 1 in a non-diagonal), the pattern is immediately invalidated and rejected. This is the primary filter.

  2. Fibonacci Confluence: If the cardinal rules pass, the script measures the Fibonacci relationships between the constituent waves. It checks for confluence with ideal ratios (e.g., does Wave 2 retrace to near 61.8% of Wave 1? Is Wave 3 a 1.618 extension of Wave 1?).

  3. Guideline Verification: The script further validates the pattern against softer guidelines like wave alternation (checkAlternation) and sub-wave structure (subWaveCountValid).

• Layer 4: Confidence Scoring Instead of a simple pass/fail, the script generates a quantitative confidence score.

  • A pattern that passes only the cardinal rules receives a base score (e.g., 0.40 for an impulse).

  • This score is then incrementally increased for each guideline and ideal Fibonacci ratio it satisfies. For example, a Wave 2 retracing to 61.8% adds 0.10 to the score, while a textbook Wave 3 extension to 1.618 adds 0.12.

  • This creates a robust system where “textbook” patterns that exhibit strong confluence across multiple metrics achieve a high confidence score (approaching 1.0), while technically valid but imperfect patterns receive a lower score.

3. The Execution Engine¶

The script’s trade signals are not triggered by simple indicator crosses but are gated by the output of the entire pattern validation engine.

• Trigger Conditions

  • Wave 3 Entry Signal (newLongSignal / newShortSignal):

    • Boolean Logic: The primary trigger is the function detectWave3Entry, which identifies a three-pivot sequence (p0, p1, p2) forming a potential Wave 1 and Wave 2. The core condition is that the retracement of the second leg (p2.price) must fall within the classic range of 38.2% to 78.6% of the first leg.

    • Gatekeeper Logic: This raw signal is then filtered by a crucial gate: confirmedCtx.confidence >= 0.35. A Wave 3 entry signal will only be plotted if the broader wave count has achieved a minimum confidence score of 35%. This ensures that the trade is taken within a context that the engine has already validated as a plausible Elliott Wave structure.

  • Wave C Reversal Signal:

    • Boolean Logic: This signal is triggered upon the completion of a confirmed corrective pattern (zigzag or flat). The logic is confirmedCtx.confirmedPhase == "corrective".

    • Directional Logic: The signal direction is opposite to the direction of the completed correction. If a bearish correction (confirmedCtx.isBullish == false) terminates at a low pivot (!lastP.isHigh), a newLongSignal is generated. This is a pure reversal trigger.

• Exit Conditions

  • Wave 5 Exit Signal (wave5ExitSignal):

    • Boolean Logic: This signal requires two conditions to be met:

      1. A 5-wave impulse pattern must be confirmed (confirmedCtx.confirmedPattern == "impulse").

      2. The length of the last move (Wave 5) must be less than 30% of the length of the move from the start of Wave 3 to the end of Wave 5 (lastMove < totalMove * 0.3).

    • Purpose: This logic is designed to detect momentum exhaustion. A significantly shorter Wave 5 relative to the powerful Wave 3 is a classic sign that the impulse trend is weakening, providing a structural basis for taking profit.

• Mathematical Constants & Risk Profile

  • primarySwingLen = 13: This directly influences the time scale of the analysis. A larger value would identify longer-term waves, leading to fewer but more significant signals. A smaller value would increase sensitivity and signal frequency, likely at the cost of reliability.

  • minSwingPct = 5.0: This is a critical risk and volatility parameter. It defines the minimum market “event” the script will pay attention to. On a stable asset, this value might be too high, causing the script to ignore valid patterns. On a volatile asset, it effectively filters out noise. It forces the strategy to focus on moves of a certain magnitude, implicitly affecting the potential risk-to-reward profile of the trades it identifies.

  • confidence >= 0.35: This hard-coded threshold is the final arbiter of signal quality. It dictates the minimum level of “textbook perfection” required before the script will issue an alert. A higher threshold would result in very few, but theoretically very high-quality, signals. The default value of 0.35 represents a balance between signal frequency and strict pattern adherence.