Discussing the article: "Encoding Candlestick Patterns (Part 3): Frequency Analysis for Single Candlestick Type Structure"
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Check out the new article: Encoding Candlestick Patterns (Part 3): Frequency Analysis for Single Candlestick Type Structure.
This article introduces a frequency-analysis framework for encoded candlestick patterns in MQL5. By transforming candlesticks into alphabetic symbols, historical price action can be analyzed as a statistical sequence rather than a visual chart. Using GBPUSD and Gold across multiple timeframes, the study examines the occurrence frequency of individual candlestick types, identifies dominant market structures, and reveals the symmetry between bullish and bearish price movements. The results establish a quantitative foundation for pattern discovery and prepare the way for analyzing multi-candlestick sequences and their predictive potential in algorithmic trading systems.
Financial price series combine many interacting drivers—macro data, liquidity, policy, news and trader behavior—so charts often look noisy. Yet price action repeatedly produces recognizable candle shapes and local structures. In Parts 1–2 we formalized this observation: individual candles were converted into a finite alphabet of symbols (Part 1) and tools were developed to enumerate possible symbol combinations (Part 2). What remained unresolved was a practical, data-driven question: which of those encoded candle types actually occur in live markets, how often, and what are the reproducible outputs a practitioner can use?
This article closes that basic gap by providing a focused, reproducible frequency analysis of single‑candle symbols. Target audience: traders, algo developers and researchers who want a measurable market profile as input to further modeling. Scope and limits: we analyze only individual candlestick symbols (one‑letter patterns) — not multi‑candle sequences — and we treat Marubozu variants as a single category while grouping all non‑matching candles under the unclassified underscore (“_”).
Using 1,500‑candle samples from GBP/USD and Gold (XAUUSD) on H1, M15 and M5 timeframes, we (1) apply the Part‑1 encoding to historical bars, (2) count occurrences of A/G/H/E/a/g/h/e/D/_ symbols, and (3) compute raw counts and percentages. The deliverable is a reproducible MQL5 script and a TXT report that together produce: the encoded series, per‑symbol counts and percentages, and simple summary metrics (dominant symbols, share of unclassified candles, and bullish/bearish symmetry). These measurable outputs are intended to be the stable input for the next step: frequency analysis of two-letter patterns and transition probabilities.
Author: Daniel Opoku