Tola Moses Hector / Profile

This article presents a complete MQL5 Expert Advisor that implements Jesse Livermore's market key as a deterministic state machine. It detects pivotal levels from consolidations using ATR and volume expansion, scales in across four tranches, and exits on abnormal behavior defined by range and volume. The EA validates inputs in OnInit, requires a hedging account, and compiles out of the box for testing Livermore's rules on daily data.

We build a robust MQL5 detector for double tops and double bottoms that first confirms the H4 trend, then validates six conditions (point equality, neckline placement, ordering, width, height, and ATR‑based tolerances). The neckline break is timed on the chart's timeframe, and a three-state machine ensures each pattern trades once. The measured‑move target translates structure into clear exits.

This article presents CSwingEngine, a reusable MQL5 class that detects H4 swing highs and lows, labels them HH, LH, HL, or LL, and classifies market structure as trend or range. Swings are always computed on H4, regardless of the attached chart, and each point draws correctly on lower timeframes via native datetime anchoring. The engine exposes a clean interface to query the current trend and retrieve the swing array for context-aware pattern logic.

This article builds a complete MQL5 Expert Advisor that implements the original Turtle Trading rules from Curtis Faith. It covers both systems: 20/55-day breakouts, the System 1 skip rule, N (Wilder ATR) for volatility-adjusted sizing, a four‑unit pyramid with N/2 adds, a unified 2N stop, and 10/20-day exits. You will get compilable code, implementation details, and a backtesting procedure on EURUSD.

This article presents TrendTemplateEA, an Expert Advisor implementing Mark Minervini's eight-condition trend template for daily forex charts. It evaluates all conditions on every bar and enters only when they are simultaneously satisfied, using RSI above 50 in place of the stock market RS rating. The entry trigger is a 20-bar high breakout on expanding volume, with all rules coded and testable in MQL5.

This article presents a complete Expert Advisor built around Stan Weinstein's Stage Analysis method. The EA classifies the market into one of four stages using the 30-week moving average slope and position and volume behavior, then trades only Stage 2 breakouts long and Stage 4 breakdowns short. It explains each stage, how to detect it programmatically, and why the method's discipline—trading only in the correct stage—is what produces the edge.

This article builds a self-contained MQL5 Expert Advisor that completes the Wyckoff cycle: it detects accumulation/distribution with a finite state machine, enters at the last point of support/supply, and calculates exit point-and-figure counts under Wyckoff's Cause and Effect. We detail the box size from range ATR, a 1-box reversal, target validation, and a 2R fallback. Readers get runnable code without external dependencies.

This article presents CTradeJournal, a self-contained MQL5 class for live tracking of open positions at tick frequency. It maintains MAE, MFE, and initial risk in money, calculates the R-multiple when a position closes, and writes a complete CSV record. The text explains the design choices, provides the implementation, and shows simple EA integration so you can analyze entries, stop placement, and outcome distribution.

The article describes an MQL5 EA that automates Wyckoff accumulation and distribution via a finite state machine. It confirms spring to SOS and upthrust to SOW before placing LPS or LPSY entries, using relative tick volume as the confirmation metric. Readers get the state model, detection criteria, code organization, and MetaTrader 5 testing procedure.

This article presents CPyramidEngine, a reusable MQL5 class that adds disciplined pyramiding to any Expert Advisor with about six lines of integration. The engine enforces three constraints: strictly decreasing lot sizes, a single unified stop that advances after each add-on, and broker-level validation of every modification. It explains common failure modes in naive implementations and shows how to keep total account risk quantifiable and controlled as positions are added.