İş Gereklilikleri
MASTER PROMPT: Advanced Multi-Strategy Trading Robot
Role:
You are a senior quantitative trader, algorithmic trading engineer, and risk manager with 10+ years of experience in crypto, forex, and indices markets.
Goal:
Design a robust, automated trading robot that integrates multiple proven trading strategies and focuses on long-term, risk-adjusted profitability, capital preservation, and adaptive market behavior.
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🔸 Core Requirements
Create a trading robot that dynamically switches strategies based on market conditions, including:
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1️⃣ Market Regime Detection (MANDATORY)
The bot must first classify the market into:
Trending (Bull / Bear)
Sideways / Range / Low Volatility
High Volatility / Breakout
Use indicators such as:
ADX
ATR
Bollinger Band Width
Market Structure (HH/HL/LH/LL)
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2️⃣ Strategy Modules (ALL MUST BE INCLUDED)
🔹 A. Pattern Strategy Engine
Detect and trade confirmed patterns:
Chart Patterns:
Head & Shoulders
Double Top / Bottom
Triangles (Ascending, Descending, Symmetrical)
Flags & Pennants
Candlestick Patterns:
Engulfing
Pin Bar
Morning / Evening Star
✔ Confirmation required using volume + trend context
✔ No pattern trades against strong trend without confirmation
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🔹 B. Trend-Following Strategy
Activated when market is trending:
EMA / SMA crossover
Higher-timeframe bias
Pullback entries
Trailing stop logic
Partial profit taking
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🔹 C. Grid & Sideways Strategy
Activated only in confirmed range markets:
Smart grid placement (ATR-based)
Dynamic grid spacing
Max grid exposure control
Auto-disable grid during breakouts
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🔹 D. DCA (Dollar Cost Averaging) Engine
Used only with trend or mean-reversion confirmation
Progressive position sizing (non-martingale)
Max DCA levels limit
Equity-based stop-out protection
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3️⃣ Risk & Money Management (CRITICAL)
Max risk per trade (% of equity)
Daily drawdown limit
Weekly loss limit
Dynamic lot sizing
Hard stop-loss on all strategies
Capital protection mode (auto-pause trading)
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4️⃣ AI Optimization Layer
Strategy weight adjustment based on performance
Disable underperforming modules automatically
Reinforcement learning or rule-based scoring
Market adaptation (session, volatility, asset)
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5️⃣ Execution & Safety
Slippage & spread filter
News filter (high-impact events)
Trade cooldown logic
No over-trading
Fail-safe kill switch
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6️⃣ Performance Targets (REALISTIC & COMPLIANT)
⚠️ Do NOT promise guaranteed profits
Instead optimize for:
Positive expectancy
High risk-reward ratio
Drawdown minimization
Consistent equity curve over time
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7️⃣ Outputs Required
Provide:
Full strategy logic explanation
Modular system architecture
Pseudocode or real code (Python / MQL / Pine Script)
Backtesting framework
Optimization parameters
Dashboard metrics:
Win rate
Profit factor
Drawdown
Strategy contribution
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8️⃣ Monetization-Ready Design (Optional)
Admin / owner control panel
Subscription tiers
API access
Strategy locking
Performance transparency
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Final Instruction:
Design this trading robot as a professional, ethical, and regulation-aware system, focused on long-term profitability through discipline, risk control, and adaptability, not unrealistic guarantees.
Yanıtlandı
1
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3
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37%
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Çalışıyor
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