Algorithmic Trading Systems Developer | MQL4/MQL5 | Python Quant Research | ML-Driven Trading Infrastructure
I build modular trading systems that connect MetaTrader execution, Python-based research workflows, machine-learning experiments, signal generation, portfolio/risk logic, and operator-facing dashboards.
My work focuses on turning trading ideas into structured, testable, and maintainable infrastructure across research, signal processing, execution, and post-trade management.
The main goal is not to build isolated indicators or simple Expert Advisors, but to design complete research-to-execution workflows where each layer has a clear responsibility.
Development of MQL4/MQL5 Expert Advisors, indicators, scripts, execution bridges, chart interfaces, file-based command handlers, order lifecycle logic, basket management, and rule-based risk controls.
Design of Python workflows for market-data processing, CSV-based research, feature engineering, setup discovery, model evaluation, backtest review, and structured experimentation.
Research tooling for combining rule-based trading setups with machine-learning model outputs, confidence gates, hybrid scoring, debug views, and decision-review dashboards.
Development of dashboards and logic for currency-strength monitoring, ATR context, Top Picks generation, exposure review, position-management commands, and execution feedback loops.
Design of layered trading infrastructure that separates research, signal generation, policy decisions, execution, monitoring, and post-trade analysis.
| Layer | Purpose | Technologies |
|---|---|---|
| Market Intelligence | Market structure, SMC concepts, liquidity zones, timing filters, volatility context | MQL4, MQL5, Python |
| Research & Data | CSV market data, feature engineering, setup discovery, experiment review | Python, Pandas, NumPy |
| ML Research | Model training, inference comparison, signal scoring, gating, debugging | scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch |
| Policy & Risk | Entry filtering, position sizing, exposure checks, basket rules, PM decisions | Python, rule engines, CSV schemas |
| Execution | Signal ingestion, order placement, trade lifecycle handling, bridge commands | MetaTrader 4/5, MQL4/MQL5 |
| Monitoring | Dashboards, logs, PM states, Telegram alerts, operator review | Python GUI, CSV bridge, MT4 feedback |
| Project | Role in the Trading Stack |
|---|---|
| SMC Pro for MetaTrader 4 | MetaTrader-based SMC analysis and execution framework for market structure, liquidity concepts, order-block logic, timing filters, and chart-based operator control. |
| SignalFileTrader PRO for MetaTrader | CSV-driven MT4 execution agent for Python-controlled signal execution, position-management commands, risk handling, trade lifecycle management, and runtime feedback. |
| Quant Research Workstation | Python research environment for CSV market data, setup discovery, feature engineering, classical ML, deep time-series experiments, and forward-testing workflows. |
| Hybrid Setup Comparison Dashboard | Research dashboard for comparing rule-based setups with ML model outputs, configurable gates, hybrid scoring, debugging, and backtest review. |
| Currency Strength & Position Management Dashboard | Operator dashboard for currency-strength monitoring, ATR context, Top Picks generation, PM command routing, Telegram alerts, and MT4 execution-bridge tracking. |
| Multi-Asset Trading System Architecture | Public architecture map describing how the research, signal, policy, execution, and monitoring repositories connect into one modular trading infrastructure. |
smcalgotrading/
├── SMC-Pro-for-MetaTrader
│ └── MetaTrader SMC analysis and execution framework
│
├── SignalFileTrader-PRO-for-MetaTrader
│ └── CSV-driven MT4 execution bridge and PM command handler
│
├── quant-research-workstation
│ └── Python research, setup discovery, feature engineering, and ML experiments
│
├── hybrid-setup-comparison-dashboard
│ └── Research dashboard for rule-based setup comparison and ML-assisted scoring
│
├── Currency-Strength-Position-Management-Dashboard
│ └── Operator dashboard for currency strength, Top Picks, PM routing, and bridge tracking
│
└── multi-asset-trading-system-architecture
└── Public architecture documentation connecting the full trading stack
| Area | Tools |
|---|---|
| Languages | Python, MQL4, MQL5, SQL |
| MetaTrader | Expert Advisors, Indicators, Scripts, File API, Chart Events, Custom Panels |
| Data & Research | Pandas, NumPy, CSV pipelines, Jupyter, market-data preprocessing |
| Machine Learning | scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, model inference workflows |
| Trading Logic | SMC concepts, setup filters, signal scoring, entry validation, risk controls |
| Execution | MT4/MT5 order management, signal bridges, command files, position lifecycle logic |
| Dashboards | Python desktop GUI, monitoring panels, debug tables, logs, Telegram alerts |
| Development Tools | Git, GitHub, VS Code, MetaEditor |
- Separate research from execution.
- Keep signal generation, policy decisions, and trade execution modular.
- Use structured files and schemas for bridge communication.
- Make every decision path inspectable through logs, dashboards, and debug states.
- Protect proprietary execution logic while documenting architecture, workflows, and system behavior.
- Build systems that can be tested, reviewed, extended, and operated safely.
Most repositories in this profile are public portfolio and evaluation repositories. Some production code, full proprietary Expert Advisor logic, private model artifacts, and internal datasets are intentionally not published.
The public repositories are designed to document architecture, workflows, screenshots, interfaces, schemas, and system responsibilities without exposing sensitive strategy logic or private trading IP.
I am open to collaboration, consulting, and technical discussions around algorithmic trading development, MetaTrader systems, Python research infrastructure, and ML-assisted trading workflows.
Email: smcalgotrading@gmail.com
Website: smcalgotrading.com
Building the bridge between trading ideas, quantitative research, and automated execution.