Bybit AI Trader

# 🚀 ULTRA-MODERN INTRADAY TRADING SYSTEM - EXPERT ANALYSIS & TRANSFORMATION PROMPT **Version:** 2.0 REVOLUTIONARY **Date:** 2025-11-25 **Priority:** MAXIMUM - PRODUCT EXCELLENCE **Target:** Top-Tier Professional Trading System --- ## 🎯 MISSION STATEMENT Create a **world-class autonomous trading system** that combines: - ✅ **Ultra-modern intraday & scalping strategies** (2024-2025 cutting-edge) - ✅ **Institutional-grade signal quality** (70%+ win rate minimum) - ✅ **Zero-risk methodology** (capital preservation as #1 priority) - ✅ **Multi-timeframe confluence** (5m → 1D alignment) - ✅ **Smart money concepts** (Order Blocks, FVG, Liquidity Grabs) - ✅ **AI-enhanced decision making** (ML predictions, pattern recognition) --- ## 🔬 REQUIRED RESEARCH AREAS ### 1. MODERN INTRADAY TRADING (2024-2025) **Research Topics:** 1. **Order Flow Analysis** - CVD (Cumulative Volume Delta) interpretation - Aggressive vs Passive order flow - Absorption patterns (bullish/bearish) - Market maker positioning - Bid-Ask spread analysis 2. **Smart Money Concepts (SMC)** - Order Blocks detection & validation - Fair Value Gaps (FVG) identification - Break of Structure (BOS) vs Change of Character (ChoCh) - Liquidity Grabs (sweep of highs/lows) - Mitigation zones - Premium/Discount zones 3. **Volume Profile Analysis** - Point of Control (POC) significance - Value Area High/Low (VAH/VAL) - Volume nodes vs gaps - Session-based VP vs Composite VP - Confluence with price action 4. **Session Trading** - Asian session characteristics (liquidity building) - European session (London open volatility) - US session (New York reversal patterns) - Overlap periods (highest volume) - Session-specific strategies 5. **Opening Range Breakout (2025 Edition)** - First 30min/1hr range importance - Breakout vs Fakeout identification - Volume confirmation requirements - Target setting (ATR-based) - Session-specific ORB variations 6. **Scalping Strategies (High-Frequency)** - 1-5min timeframe tactics - Tick-level precision entries - Spread cost optimization - Quick profit-taking (10-50 pips) - Loss cutting discipline (<0.5% risk) --- ### 2. CONFLUENCE SCORING SYSTEMS **Research Question:** What makes a **PERFECT** trade setup in 2025? **Areas to Study:** 1. **Multi-Timeframe Analysis (MTF)** - How many timeframes minimum? (Current: 4) - Optimal timeframe combinations for crypto - Weight distribution across timeframes - Conflict resolution strategies 2. **Indicator Confluence** - Which indicators ACTUALLY work in crypto? - Redundant vs Complementary indicators - Leading vs Lagging indicator balance - Overfitting prevention 3. **Pattern Reliability** - 2025 candlestick pattern statistics - Chart pattern success rates (H&S, Triangles, etc.) - Pattern + Volume + Context = True reliability - Failed pattern recognition (avoid traps) 4. **Risk-Reward Optimization** - Minimum R:R for different strategies - Win rate vs R:R balance - Expected Value (EV) calculation - Kelly Criterion for position sizing 5. **Probability Estimation** - How to accurately predict setup success? - Bayesian probability for trading - Historical pattern success tracking - Real-time probability adjustment --- ### 3. CURRENT SYSTEM CRITICAL ANALYSIS **Problem Statement:** System scans 652 assets, finds 99 candidates, but outputs **ZERO opportunities**. **Deep Dive Required:** #### A. Scoring System Architecture ``` CURRENT FLOW: market_scanner.py (20-point) → normalize (10-point) → filter (≥8.0/10) → EMPTY ❌ QUESTIONS TO ANSWER: 1. Is 20-point system optimal for crypto? 2. Should normalization happen earlier? 3. Is 8.0/10 threshold realistic? 4. What percentage of real profitable setups score 8.0+? 5. Are we filtering out good trades? ``` **Research Task:** - Study 100 real historical crypto trades (both wins & losses) - Calculate their confluence scores using current system - Correlate score with actual outcome - Find optimal threshold (may be 6.0, 7.0, or keep 8.0) - Propose evidence-based scoring calibration #### B. Filter Pipeline Analysis ``` IDENTIFY ALL FILTER POINTS: 1. Initial volume filter (min_volume_24h) 2. RSI range filters 3. Score threshold in deep_analyze_top_candidates (≥7.0/20) 4. Final zero-risk filter (≥8.0/10) 5. Stable-pair exclusions QUESTION: Are we over-filtering? ``` **Research Task:** - Map complete filter pipeline - Calculate drop-off at each stage - Identify bottlenecks - Propose optimal filter sequence #### C. Two-Way Analysis Problem ``` CURRENT ISSUE: - System searches BOTH long and short - But scores each opportunity in isolation - Market conditions may favor ONE direction heavily EXAMPLE: - BTC strong downtrend (-15% week) - System finds 50 shorts, 2 longs - But applies same 8.0 threshold to both - Result: Shows nothing ❌ BETTER APPROACH: - Detect market regime (bull/bear/sideways) - Adjust thresholds per direction - Always show BEST of each direction - Add regime-based warnings ``` --- ### 4. PROPOSED SOLUTION ARCHITECTURE #### TIER-BASED SIGNAL CLASSIFICATION **Elite Tier (8.0-10.0/10)** ✅ - Confluence: 8+ factors aligned - Probability: ≥75% - Risk-Reward: ≥2.5:1 - Recommendation: **TRADE IMMEDIATELY** - Color: 🟢 Green - Size: Full position (2% risk) **Professional Tier (6.5-7.9/10)** ⚠️ - Confluence: 6-7 factors aligned - Probability: 65-74% - Risk-Reward: ≥2:1 - Recommendation: **TRADE WITH CAUTION** - Color: 🟡 Yellow - Size: Reduced position (1% risk) - Required: Manual confirmation **Speculative Tier (5.0-6.4/10)** ⚠️⚠️ - Confluence: 4-5 factors aligned - Probability: 55-64% - Risk-Reward: ≥1.5:1 - Recommendation: **EXPERIENCED ONLY** - Color: 🟠 Orange - Size: Minimum position (0.5% risk) - Required: Deep manual analysis **High Risk (4.0-4.9/10)** 🔴 - Show in report but mark as **NOT RECOMMENDED** - For educational purposes only - Explain WHY it's risky **No Trade (<4.0/10)** ⛔ - Don't show in public reports - Log internally for system improvement --- ### 5. SMART DISPLAY LOGIC **ALWAYS Show:** - TOP 3 LONG opportunities (best available) - TOP 3 SHORT opportunities (best available) - Regardless of absolute score **WITH Clear Communication:** ``` Example Report: LONG OPPORTUNITIES: 1. ETH/USDT - Professional Tier ⚠️ Score: 7.2/10 | Probability: 68% ⚠️ CAUTION: Below Elite threshold. Use reduced size. Entry: $2,450 | SL: $2,420 | TP: $2,510 Risk/Reward: 2.0:1 2. SOL/USDT - Speculative Tier ⚠️⚠️ Score: 6.1/10 | Probability: 58% ⚠️⚠️ HIGH RISK: Experienced traders only. Minimum size. Entry: $105.20 | SL: $103.50 | TP: $109.60 Risk/Reward: 2.6:1 3. AVAX/USDT - High Risk 🔴 Score: 4.8/10 | Probability: 52% 🔴 NOT RECOMMENDED: Low confluence. Paper trade only. Entry: $35.80 | SL: $35.20 | TP: $37.40 Risk/Reward: 2.7:1 RECOMMENDATION: ⚠️ No Elite opportunities available (need ≥8.0/10) ✅ ETH/USDT is acceptable for cautious entry ⛔ Current market conditions challenging - consider waiting ``` --- ### 6. REGIME DETECTION & ADAPTIVE THRESHOLDS **Market Regimes:** 1. **Strong Bull** (BTC +5% week, ADX >25 up) - LONG threshold: 6.0/10 (relaxed) - SHORT threshold: 8.5/10 (strict) - Reasoning: Trend is your friend 2. **Strong Bear** (BTC -5% week, ADX >25 down) - LONG threshold: 8.5/10 (strict) - SHORT threshold: 6.0/10 (relaxed) - Reasoning: Don't fight the trend 3. **Sideways** (BTC ±2% week, ADX <20) - LONG threshold: 7.0/10 (moderate) - SHORT threshold: 7.0/10 (moderate) - Reasoning: Range-bound, need quality 4. **High Volatility** (VIX equivalent high) - BOTH threshold: 8.0/10 (strict) - Reasoning: Increased risk **Implementation:** ```python def get_adaptive_threshold(direction: str, market_regime: dict) -> float: """ Returns optimal threshold based on market conditions """ btc_trend = market_regime.get("btc_trend") volatility = market_regime.get("volatility") adx = market_regime.get("adx", 20) # Base thresholds base_long = 7.0 base_short = 7.0 # Adjust for trend if btc_trend == "strong_bull": base_long -= 1.0 # 6.0 base_short += 1.5 # 8.5 elif btc_trend == "strong_bear": base_long += 1.5 # 8.5 base_short -= 1.0 # 6.0 # Adjust for volatility if volatility == "high": base_long += 0.5 base_short += 0.5 return base_long if direction == "long" else base_short ``` --- ### 7. ML-ENHANCED PROBABILITY ESTIMATION **Current Problem:** Probability calculated from static formula, not actual historical performance. **Modern Solution:** 1. **Track All Signals** - Record every signal generated (with full context) - Track actual outcome (win/loss/breakeven) - Store pattern type, timeframe, indicators used 2. **Build Success Database** ```sql CREATE TABLE signal_outcomes ( signal_id TEXT, pattern_type TEXT, timeframe TEXT, confluence_score REAL, predicted_probability REAL, actual_outcome TEXT, -- 'win', 'loss', 'breakeven' actual_rr REAL, market_regime TEXT, timestamp INTEGER ); ``` 3. **Train Simple Model** - Use last 1000 signals - Features: [pattern, score, regime, volume_ratio, etc.] - Target: win/loss - Output: Calibrated probability 4. **Continuous Learning** - Update model weekly - Track model accuracy - A/B test predictions vs static formula --- ### 8. PERFORMANCE METRICS DASHBOARD **What to Track:** **Signal Quality:** - Win Rate (by tier) - Average R:R achieved - Expected Value (EV) per trade - Sharpe Ratio of signals - Maximum Drawdown **System Performance:** - Signals Generated per Day - Elite Tier % (target: 10-20%) - Professional Tier % (target: 30-40%) - False Signal Rate - Time to Signal (latency) **Market Coverage:** - Assets Scanned - Opportunities Found - Coverage by Market Cap - Coverage by Volatility **Display Example:** ``` 📊 SYSTEM PERFORMANCE (Last 30 Days) Signal Quality: ├─ Elite Tier: 78% Win Rate, 2.8 Avg R:R ✅ ├─ Professional: 64% Win Rate, 2.2 Avg R:R ✅ └─ Speculative: 52% Win Rate, 1.9 Avg R:R ⚠️ Signal Distribution: ├─ Elite: 15% (target: 10-20%) ✅ ├─ Professional: 42% (target: 30-40%) ✅ └─ Speculative: 43% (target: 30-50%) ✅ Overall Expected Value: +$1,247 per $10k account ✅ Sharpe Ratio: 1.8 (Excellent) ✅ ``` --- ### 9. CRITICAL FIXES REQUIRED #### Fix #1: Remove Hard Filters, Add Soft Warnings ```python # CURRENT (WRONG): if opp.get("score") < 7.0: continue # ❌ Discards potentially good trades # PROPOSED (RIGHT): if opp.get("score") < 4.0: continue # Only discard truly bad else: opp["tier"] = classify_tier(opp.get("score")) opp["warning"] = generate_warning(opp) all_opportunities.append(opp) # ✅ Keep for display ``` #### Fix #2: Always Show Best Available ```python # CURRENT (WRONG): if not elite_opportunities: return "No opportunities found" # ❌ User gets nothing # PROPOSED (RIGHT): if not elite_opportunities: message = "⚠️ No Elite opportunities available\n" message += "Showing best available (use caution):\n\n" message += format_professional_tier(top_professional) # ✅ Show second-best ``` #### Fix #3: Regime-Aware Analysis ```python # ADD TO analyze_market(): market_regime = detect_regime(btc_analysis) # NEW adaptive_thresholds = get_adaptive_thresholds(market_regime) # NEW for opp in opportunities: threshold = adaptive_thresholds[opp["side"]] opp["threshold_used"] = threshold opp["meets_adaptive_threshold"] = opp["score"] >= threshold ``` #### Fix #4: Normalize Earlier in Pipeline ```python # CURRENT: raw_score (20-point) → deep_analysis → normalize → filter ❌ # PROPOSED: raw_score (20-point) → normalize immediately → deep_analysis → filter ✅ # This way all filtering happens on consistent 0-10 scale ``` --- ### 10. IMPLEMENTATION ROADMAP **Phase 1: Critical Fixes (1-2 days)** - [ ] Remove hard filter at 7.0/20 - [ ] Implement tier classification - [ ] Always show TOP-3 per direction - [ ] Add clear warnings for sub-Elite - [ ] Update report formatting **Phase 2: Smart Enhancements (3-5 days)** - [ ] Implement regime detection - [ ] Add adaptive thresholds - [ ] Create performance dashboard - [ ] Add historical tracking **Phase 3: ML Integration (1-2 weeks)** - [ ] Build signal outcome database - [ ] Train probability model - [ ] Implement continuous learning - [ ] A/B test predictions **Phase 4: Advanced Features (2-4 weeks)** - [ ] Real-time order flow integration - [ ] Sub-minute scalping mode - [ ] Portfolio-level risk management - [ ] Multi-account coordination --- ### 11. EXPERT QUESTIONS TO RESEARCH **For ChatGPT/Claude with Browsing:** 1. "What are the most reliable intraday crypto trading strategies in 2025? Include success rates and required conditions." 2. "Explain Smart Money Concepts (Order Blocks, FVG, Liquidity Grabs) with specific detection criteria for crypto markets." 3. "What confluence factors are most correlated with profitable trades? Provide academic research or prop firm data." 4. "How do institutional traders use Volume Profile in crypto? Include POC, VAH/VAL significance and session analysis." 5. "What is the optimal scoring system for automated trade signal generation? Compare different weighting approaches." 6. "How can machine learning improve trading signal probability estimation? Provide Python implementation examples." 7. "What are current best practices for regime detection in cryptocurrency markets? Include code examples." 8. "How to balance signal quantity vs quality in automated trading systems? Discuss the trade-off and optimization methods." --- ### 12. DELIVERABLES **Expected Outputs:** 1. **Technical Specification Document** - Complete scoring system redesign - Tier classification rules - Adaptive threshold implementation - Code changes required 2. **Research Report** - 2025 intraday trading best practices - Confluence factor weights (evidence-based) - Probability calibration methodology - Regime detection algorithms 3. **Implementation Guide** - Step-by-step code changes - Migration strategy - Testing procedures - Rollback plan 4. **Performance Tracking System** - Dashboard design - Metrics definitions - Data collection code - Visualization examples --- ### 13. SUCCESS CRITERIA **System is considered EXCELLENT when:** ✅ **Signal Quality** - Elite tier: 75%+ win rate, 2.5+ avg R:R - Professional tier: 65%+ win rate, 2.0+ avg R:R - Overall EV: Positive across all tiers ✅ **User Experience** - NEVER shows empty report - Always shows TOP-3 each direction - Clear tier/risk communication - Actionable insights every time ✅ **Market Coverage** - Finds opportunities in any regime - Adapts thresholds intelligently - Covers 80%+ of tradeable assets - Response time <30 seconds ✅ **Professional Standards** - Institutional-grade analysis - Evidence-based decisions - Transparent methodology - Continuous improvement --- ## 🎓 RESEARCH METHODOLOGY **Step 1:** Deep dive into each research area **Step 2:** Gather evidence (academic papers, prop firm data, backtests) **Step 3:** Propose solutions with justification **Step 4:** Design implementation plan **Step 5:** Create testing strategy **Step 6:** Document everything --- ## 📚 RECOMMENDED SOURCES 1. **Academic Research:** - Journal of Financial Markets - Algorithmic Finance Journal - Quantitative Finance 2. **Industry Resources:** - SMB Capital training materials - ICT (Inner Circle Trader) concepts - Prop firm strategy guides 3. **Technical Documentation:** - Bybit API advanced features - TradingView Pine Script strategies - QuantConnect research 4. **Community Knowledge:** - r/algotrading discussions - Crypto Twitter alpha threads - Discord trading communities --- ## ⚡ URGENCY & IMPORTANCE **Critical (Fix Now):** - Empty report problem - Hard filter removal - Tier classification **Important (Fix Soon):** - Regime detection - Adaptive thresholds - Performance tracking **Enhancement (Future):** - ML probability model - Advanced order flow - Multi-account features --- ## 🚀 CALL TO ACTION Use this prompt to: 1. **Research** modern trading strategies deeply 2. **Analyze** current system with expert lens 3. **Design** world-class solutions 4. **Implement** with precision 5. **Test** rigorously 6. **Document** thoroughly **Goal:** Transform our system from "technically working" to **"Institutional-Grade Professional Trading System"** --- **End of Prompt** *This is a living document. Update as research reveals new insights.*