Forex Pattern Finder
Algorithmic Market Analysis for Harmonic Patterns
A Python tool that scans market data to detect harmonic patterns and price action setups, surfacing trade ideas with quantitative rigour.
What it is. Why it exists.
The Forex Pattern Finder ingests historical and live market data, runs harmonic and price-action detectors over it, and presents a ranked list of setups with the geometry, ratios, and context behind each signal.
Manual pattern recognition on forex charts is slow, subjective, and easy to bias. Traders need an objective scanner that surfaces structurally valid setups across many instruments at once.
Engineer — algorithm design, data pipeline, and analytics interface.
The systems and surfaces that make up the product.
Pattern Detection
Harmonic and classic price action patterns identified across multiple timeframes.
Quantitative Scoring
Each candidate setup ranked by ratio quality, structure, and context.
Multi-Timeframe Scan
Concurrent analysis across timeframes with consistent geometry rules.
Analyst Dashboard
Django interface to filter, inspect, and export detected setups.
Engineering
the platform.
The architecture that makes Forex Pattern Finder hold together at scale — from data model to deployment.
Vectorised Detection
Pandas + NumPy pipelines for high-throughput pattern scans.
Deterministic Geometry
Strict ratio tolerances ensure reproducible, auditable signals.
Django Frontend
Lightweight admin-style UI for inspecting and exporting results.
Obstacles, met with structure.
Detecting valid patterns without overfitting noise
Solution —Strict ratio tolerances combined with structural pre-filters to drop unstable candidates before scoring.
Processing large historical datasets efficiently
Solution —Vectorised Pandas operations and chunked scans to keep multi-symbol, multi-timeframe runs tractable.
Outcome
An analytical tool that reliably surfaces structurally valid setups across many instruments, replacing hours of manual chart work with a single ranked scan.
Quantitative tools live or die by data hygiene. Investing early in clean pipelines paid off across every downstream feature.