Bot c trader prep farm

Professional cTrader AI Trading Bot Development Project Overview I am looking for an experienced cTrader (cAlgo / C#) developer to build a professional institutional-grade trading bot powered by an AI Decision Engine. The objective is not to build a high-frequency trading robot, but an intelligent system capable of filtering thousands of market conditions and executing only the highest probability trades. The bot should behave like an experienced professional trader by evaluating multiple market factors simultaneously before making any trading decision. ⸻ Project Goal The bot must: * Scan all selected financial instruments. * Analyze the market continuously. * Filter out low-quality trading opportunities. * Rank all potential setups using an AI scoring system. * Execute only the highest-quality opportunities. * Prioritize consistency over trade frequency. Quality is always more important than quantity. ⸻ AI Decision Engine The core of the project should be an AI-assisted decision engine. The AI should combine multiple technical and market variables into a single confidence score. Instead of relying on one indicator, the system should evaluate: * Trend quality * Trend strength * Market structure * Liquidity * Momentum * Volatility * Price Action * Volume * Session quality * News impact * Risk conditions * Multi-timeframe alignment * Historical behavior * Statistical probability The AI should assign every setup a score from 0 to 100. Example: * 95 to 100 = Exceptional opportunity * 90 to 94 = High-quality opportunity * Below 90 = Ignore No trade should be executed unless the minimum AI score is reached. ⸻ AI Learning Capability (Preferred) Preference will be given to developers capable of implementing AI or Machine Learning components such as: * Adaptive parameter optimization * Reinforcement Learning * Predictive probability models * Classification models * Pattern recognition * Feature engineering * Market regime detection * Continuous optimization using historical data The AI should improve the decision process through ongoing testing and optimization rather than relying solely on fixed indicator rules. ⸻ Market Analysis The bot should continuously analyze: * Trend strength * Trend weakness * Market structure * Momentum * Volatility * Liquidity * Institutional order flow concepts * Breakouts * False breakouts * Consolidation * Expansion * Supply & Demand * Smart Money Concepts (SMC) * Order Blocks * Fair Value Gaps * Liquidity Sweeps * BOS * CHOCH * ATR * ADX * VWAP * Price Action * Volume Developers are free to propose additional institutional-grade techniques. ⸻ Opportunity Ranking Every setup should receive a weighted score based on multiple factors. Possible scoring criteria: * Trend strength * Multi-timeframe confirmation * Institutional confirmation * Entry quality * Reward-to-risk ratio * Volatility quality * Spread * Liquidity * Session quality * News risk * Historical probability * Statistical edge The system must rank every opportunity before executing any trade. ⸻ Trading Logic The bot should operate as follows: Case 1 If one exceptional opportunity exists: Execute one trade only. ⸻ Case 2 If multiple opportunities satisfy all quality requirements: Execute each qualifying trade independently. ⸻ Case 3 If no opportunity reaches the required score during the trading day: Do not force a trade. Continue monitoring until the session ends. If an acceptable opportunity appears later, execute it. Otherwise, remain flat. No trade is always preferable to a low-quality trade. ⸻ News Filter The bot must include a professional economic news filter. Features: * Block trading before high-impact news * Block trading during news * Block trading after news * Adjustable protection windows * Filter by impact level * Filter by currency * Enable/disable news filter ⸻ Trading Session Filter The bot must support: * Sydney * Tokyo * London * New York * London/New York overlap Additional features: * Custom session hours * Time-zone selection * Avoid market open volatility * Avoid session close volatility ⸻ Prop Firm Compliance The system should be specifically designed for proprietary trading firms. Required features include: * Daily drawdown protection * Overall drawdown protection * Maximum daily loss * Maximum weekly loss * Maximum monthly loss * Maximum open risk * Maximum simultaneous trades * Maximum consecutive losses * Daily profit target * Automatic trading stop after reaching daily target * Automatic shutdown after reaching loss limits * No Martingale * No Grid * Consistent and disciplined risk management ⸻ Risk Management The bot should include: * Risk percentage per trade * Dynamic position sizing * Fixed Stop Loss * ATR Stop Loss * Fixed Take Profit * Dynamic Take Profit * Break Even * Trailing Stop * Partial Close * Time-based Exit * Opposite Signal Exit * Volatility Exit ⸻ Multi-Timeframe Analysis Support for: * M1 * M5 * M15 * M30 * H1 * H4 * Daily The AI should combine information from multiple timeframes before making any decision. ⸻ Backtesting & Optimization The completed project should support: * Historical backtesting * Walk Forward Analysis * Monte Carlo Simulation * Parameter Optimization * Multi-symbol testing * Multi-year testing ⸻ Performance Metrics The developer should optimize the bot with the following objectives: * High win quality * Stable equity curve * Low drawdown * Low trade frequency * High Sharpe Ratio * High Profit Factor * Low risk exposure * Long-term consistency ⸻ Code Quality Requirements: * Professional C# * Clean architecture * Modular design * High performance * Low memory usage * Fully commented source code * Easy future expansion ⸻ Deliverables * Complete cTrader cBot source code * Complete source ownership * Fully configurable settings * Installation guide * User manual * Documentation * Backtesting report * Optimization report ⸻ Preferred Developer The ideal developer should have: * Strong experience with cTrader Automate (cAlgo) * Advanced C# skills * Experience building institutional trading systems * Knowledge of quantitative trading * Experience with AI/ML in financial markets * Understanding of proprietary trading firm requirements The goal is to build a robust, intelligent, AI-assisted trading system that focuses on high-probability opportunities, disciplined risk management, and long-term consistency rather than maximizing the number of trades.