gavindiaz/mt-data-mcp

Fork 0

Files

T

Emerson Gomes 97af0f4159 patterns: add harmonic pattern detection

2026-06-01 13:53:09 -05:00

10 KiB

Raw Blame History

Pattern Detection & Similarity Search

This document covers two related capabilities:

Pattern Detection: Identify known candlestick and chart patterns
Analog Forecasting: Find historical windows similar to the current market

Related:

../FORECAST.md — Price forecasting
../TECHNICAL_INDICATORS.md — Technical analysis context
../GLOSSARY.md — Term definitions

Pattern Detection (`patterns_detect`)

Identifies specific visual patterns that traders use for entry/exit signals.

Candlestick Patterns

Single or multi-bar patterns with historical significance.

mtdata-cli patterns_detect EURUSD --timeframe H1 --mode candlestick --limit 200

Output:

data[29]{time,pattern}:
    "2025-12-18 12:00",Bullish INSIDE
    "2025-12-19 05:00",Bearish ENGULFING
    "2025-12-22 10:00",Bearish ENGULFING
    ...

Filter to robust patterns only:

mtdata-cli patterns_detect EURUSD --mode candlestick --robust-only true

Common patterns detected:

Pattern	Meaning
Engulfing	Current candle completely covers previous (reversal)
Doji	Open ≈ Close (indecision)
Hammer/Hanging Man	Small body, long lower wick
Inside	Current bar inside previous bar's range
Harami	Small body inside previous large body
Morning/Evening Star	Three-bar reversal pattern

Classic Chart Patterns

Larger geometric patterns formed over multiple bars.

mtdata-cli patterns_detect EURUSD --timeframe H1 --mode classic --limit 500

Patterns detected:

Pattern	Description
Head and Shoulders	Three peaks, middle highest (bearish reversal)
Inverse H&S	Three troughs, middle lowest (bullish reversal)
Double Top/Bottom	Two peaks/troughs at similar level
Triangle	Converging trendlines (breakout setup)
Wedge	Rising or falling wedge
Rectangle	Horizontal consolidation

Harmonic Patterns

Fibonacci-ratio patterns built from alternating pivot legs.

mtdata-cli patterns_detect EURUSD --timeframe H1 --mode harmonic --limit 500

Patterns detected:

Pattern	Description
ABCD	Four-point measured-move completion
Gartley	XABCD retracement pattern with 0.786 XA completion
Bat / Alternate Bat	XABCD patterns with deeper D-point completion
Butterfly	XABCD extension beyond X
Crab / Deep Crab	Extended XABCD completion patterns
Shark, Cypher, 5-0	Additional Fibonacci-ratio reversal structures

Useful harmonic config:

mtdata-cli patterns_detect EURUSD --timeframe H1 --mode harmonic \
  --config "pattern_types=gartley,bat,crab ratio_tolerance=0.06 min_confidence=0.45"

Common output fields:

Field	Meaning
`entry_price`	D-point completion price
`target_price`, `target_price_1`, `target_price_2`	CD retracement targets
`invalidation_price`	Pattern invalidation level with configured buffer
`price_levels`	Entry, targets, invalidation, and PRZ levels
`details.ratios`	Measured Fibonacci ratios for the candidate

Fractal Patterns

Bill Williams-style bullish and bearish fractal levels with confirmation and breakout context.

mtdata-cli patterns_detect EURUSD --timeframe H1 --mode fractal --limit 300

Useful fractal config:

mtdata-cli patterns_detect EURUSD --timeframe H1 --mode fractal \
  --config "left_bars=2 right_bars=2 breakout_basis=high_low"

Common output fields:

Field	Meaning
`level_price`	Confirmed fractal high/low level
`level_state`	`active` when unbroken, `broken` after price breaches the level
`confirmation_date`	When the fractal became knowable after the right-side bars closed
`breakout_direction`	Direction of the later level break (`bullish` or `bearish`)
`breakout_date`	When the breakout occurred, if any

Parameters

Parameter	Default	Description
`--mode`	`all`	Pattern type: all, candlestick, classic, harmonic, fractal, elliott
`--limit`	1000	Bars to analyze
`--robust-only`	false	Only return high-confidence candlestick patterns. Pass `true` to filter to the robust subset.
`--whitelist`	—	Comma-separated list of specific patterns
`--min-strength`	0.90	Minimum semantic candlestick conviction score (0.0-1.0)
`--config`	—	Detector-specific overrides. Fractals support `left_bars`, `right_bars`, `breakout_basis`, `min_prominence_pct`, and `confidence_prominence_cap_pct`. Harmonics support `pattern_types`, `ratio_tolerance`, `min_confidence`, and pivot controls.

Filtering Patterns

By name:

mtdata-cli patterns_detect EURUSD --mode candlestick \
  --whitelist "ENGULFING,HAMMER,DOJI"

By confidence:

mtdata-cli patterns_detect EURUSD --mode candlestick --robust-only true

Analog Forecasting

Finds historical windows that "look like" the current market and uses them to predict what happens next.

Concept

"History doesn't repeat, but it rhymes."

Take the last N bars (the "query window")
Search through historical data for similar patterns
Look at what happened after those patterns
Average/aggregate those future moves into a forecast

Basic Usage

mtdata-cli forecast_generate EURUSD --timeframe H1 --horizon 12 \
  --method analog --params "window_size=64 top_k=20"

Parameters

Parameter	Default	Description
`window_size`	64	Length of pattern to match
`search_depth`	5000	How far back to search
`top_k`	20	Number of similar patterns to use
`metric`	euclidean	Distance metric
`scale`	zscore	Normalization: zscore, minmax, none
`refine_metric`	none	Refinement: dtw, softdtw, affine, ncc
`search_engine`	ckdtree	Search algorithm

Scaling Options

Scale	Description	When to Use
`zscore`	Standardize to mean=0, std=1	Default, handles varying volatility
`minmax`	Scale to [0,1]	When range matters more than volatility
`none`	No scaling	When absolute levels matter

Distance Metrics

The metric parameter controls the initial candidate search (must be fast — Euclidean-family):

Metric	Description
`euclidean`	Standard L2 distance (default, fastest)

The refine_metric parameter re-ranks candidates using a slower, more precise metric:

Refine Metric	Description
`dtw`	Dynamic Time Warping (handles time warping)
`softdtw`	Differentiable DTW
`ncc`	Normalized cross-correlation
`affine`	Affine-invariant distance

Example with refinement:

mtdata-cli forecast_generate EURUSD --horizon 12 \
  --method analog --params "window_size=64 metric=euclidean refine_metric=dtw"

This first finds candidates with fast Euclidean distance, then refines ranking using DTW.

Search Engines

Engine	Description
`ckdtree`	Scipy KD-tree (default, fast)
`hnsw`	Approximate nearest neighbor (scalable, optional `hnswlib` backend; not part of the default Python 3.14 environment, but available through the opt-in native/source-build path in ../SETUP.md)
`matrix_profile`	STUMPY-based (specialized for time series)
`mass`	Mueen's MASS algorithm

Practical Applications

Pattern-Based Entry Filter

Use pattern detection as a confirmation signal:

# Check for reversal patterns at support
mtdata-cli patterns_detect EURUSD --mode candlestick --robust-only true

# If bullish pattern detected at support level → consider long entry

Analog-Based Targets

Use analog forecasts to set price targets:

# Find similar historical patterns
mtdata-cli forecast_generate EURUSD --method analog \
  --params "window_size=64 top_k=20" --json

# Use forecast percentiles for TP levels

Combining with Technical Analysis

# Get patterns and indicators together
mtdata-cli data_fetch_candles EURUSD --limit 200 \
  --indicators "ema(20),rsi(14)"

# Then check patterns
mtdata-cli patterns_detect EURUSD --mode candlestick --robust-only true

# Look for pattern + indicator confluence

Interpreting Results