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 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.



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