Edge Pilot BTC
- Uzmanlar
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Shipra Gupta
Ben Shipra Gupta, matematik, ekonomi ve algoritmik işlem sistemleri konusunda güçlü bir altyapıya sahip trader ve kantitatif strateji geliştiricisiyim. Yıllar boyunca forex, altın, endeksler, emtialar ve kripto para piyasalarında aktif olarak çalıştım ve tutarlılık, hassasiyet ve disiplinli işlem - Sürüm: 1.0
- Etkinleştirmeler: 10
EdgePilot BTC — Momentum Intelligence Engine for MetaTrader 5
EdgePilot BTC is a fully automated Bitcoin Expert Advisor for MetaTrader 5, developed exclusively for BTCUSD trading on the M30 timeframe.
Inspired by modern AI decision frameworks, EdgePilot combines momentum analysis, breakout confirmation, volatility qualification, and adaptive trade management to identify high-conviction trading opportunities while maintaining disciplined risk control.
Rather than responding to every market movement, EdgePilot focuses on recognizing periods where momentum is strengthening and market conditions favor sustained directional expansion.
Why EdgePilot?
Bitcoin markets frequently alternate between consolidation, expansion, and rapid momentum phases.
EdgePilot continuously evaluates market acceleration, trend quality, volatility, and execution conditions before opening a position. Every trade must satisfy multiple confirmation layers, allowing the Expert Advisor to participate selectively instead of reacting to every breakout signal.
The objective is simple: improve trade quality while maintaining consistent execution.
Momentum Intelligence Framework
Every trading decision combines multiple market observations, including:
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Momentum acceleration
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Breakout confirmation
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Trend validation
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Volatility analysis
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Market structure evaluation
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Session-aware execution
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Dynamic risk assessment
Only when these factors align does the Expert Advisor execute a position.
Adaptive Trade Management
Every position is managed automatically from entry to exit.
Features include:
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Automatic position sizing
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Adaptive stop-loss calculation
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Configurable Risk-to-Reward targets
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Dynamic trailing stop
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Intelligent break-even management
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Maximum spread protection
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Percentage-based account risk management
Trade management continuously adapts to changing market conditions while maintaining predefined risk parameters.
Built for Bitcoin
EdgePilot has been developed specifically for the behavior and volatility characteristics of BTCUSD.
Recommended environment:
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Platform: MetaTrader 5
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Symbol: BTCUSD
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Strategy Timeframe: M30
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Account Type: Hedging
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ECN or Raw Spread broker recommended
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VPS recommended for uninterrupted execution
The Expert Advisor internally evaluates M30 market conditions regardless of the chart timeframe on which it is attached.
Optimization
EdgePilot provides configurable strategy parameters while maintaining carefully validated default settings.
Available settings include:
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Momentum sensitivity
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Breakout confirmation
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Volatility filter
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Stop-loss calculation
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Risk-to-Reward ratio
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Trailing stop
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Risk percentage
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Spread protection
Walk-Forward Analysis is recommended after optimization to verify robustness under changing market conditions.
Development Philosophy
EdgePilot BTC was developed through extensive historical research covering thousands of Bitcoin market scenarios.
Rather than increasing trading frequency, development focused on building a momentum-driven execution framework capable of recognizing stronger directional market conditions while maintaining disciplined risk management.
The result is an Expert Advisor designed for consistency, controlled exposure, and systematic execution.
Main Features
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Fully automated Bitcoin Expert Advisor
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Designed exclusively for BTCUSD
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Momentum intelligence framework
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Multi-factor signal confirmation
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Breakout and trend validation
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Adaptive volatility analysis
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Intelligent trade management
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Dynamic trailing stop
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Automatic position sizing
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Spread protection
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Optimized for M30 trading
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MetaTrader 5 compatible
Disclaimer
EdgePilot BTC uses a deterministic trading algorithm inspired by AI-based decision frameworks and multi-factor momentum analysis. References to AI describe the strategy's analytical design and should not be interpreted as self-learning or machine-learning functionality.
Historical performance and backtesting results do not guarantee future performance. Forward testing on a demo account is recommended before live deployment.
