Encoding Candlestick Patterns (Part 1): An Alphabetical System for Signal Detection
We present a rule‑based alphabet for candlestick price action that maps measurable shape and direction to letter codes (A/a, H/h, E/e, G/g, D). The article shows an MQL5 implementation: classifying candles, building two‑bar sequences via permutations, and scanning charts with an indicator and alerts. Readers gain a practical template for objective pattern detection and systematic testing.
Creating an EMA Crossover Forward Simulation Indicator in MQL5
A custom forward simulation engine detects fast/slow EMA crossovers and immediately projects synthetic candles ahead of the signal bar. It generates bodies and wicks using controlled logic, draws them with chart objects, and refreshes on every new signal or anchor change. You get a clear forward-looking view to test timing, visualize scenarios, and manage invalidation on the chart.
Adaptive Malaysian Engulfing Indicator (Part 2): Optimized Retest Bar Range
The article adds a self-adaptive layer to the Malaysian Engulfing indicator by optimizing the retest bar range with a constrained brute-force search scored by MFE and MAE. It details the data model, helper routines, and an MQL5 implementation that gathers historical setups, computes excursions, and selects the best parameter. Readers learn how to remove manual tuning and run the indicator with context-appropriate settings across symbols and timeframes.