Experiment
ScalpGoat
Algorithmic trading system experiment using rule-based logic
Built a MetaTrader 5 expert advisor combining multi-timeframe analysis, support/resistance logic, and modular risk-aware trade execution.
Problem
Algorithmic trading systems require disciplined signal generation, modular analysis, and risk constraints rather than unchecked automation.
Outcome
Adds evidence of analytical depth, event-driven logic, and disciplined systems thinking in a non-traditional engineering domain.
Approach and Solution
Built a MetaTrader 5 expert advisor structured around modular headers for indicators, analysis, market conditions, and execution logic.
Needed support/resistance logic, multi-timeframe voting, trade/risk constraints, and clear separation of analytical modules.
Architecture Notes
MQL5 / MetaTrader architecture using modular headers and compiled artifact, with weighted scoring and multi-timeframe analysis concepts.
Security: Risk constraints and execution rules are central to the system design and should be framed as controlled experimentation.
Performance: Performance should be discussed through backtests and caveated validation rather than promises of live profitability.
Lessons and Next Improvements
Presenting experiments honestly builds more trust than overstating performance in sensitive domains like trading.
Add backtest methodology, known limitations, validation notes, and experiment framing on the project page.
Outcome Metrics and Signals
backtest_win_rate
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% · backtest sample · experiment notes
Media and Visual Proof
Related Build Notes
AI Automation Engineer
What I look for when designing automation that actually helps a business
Good automation removes bottlenecks and failure points while keeping humans in control of key decisions.
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