I'll design a comprehensive system architecture and implementation plan for your quantitative trading platform. This design leverages the moomoo API ecosystem while maintaining flexibility for multi-asset, multi-timeframe strategy deployment. ## 1. System Architecture Overview ### High-Level Component Diagram ``` ┌─────────────────────────────────────────────────────────────┐ │ USER INTERFACE LAYER │ │ (Drill-down Wizard | Strategy Configurator | Dashboard) │ └──────────────────────┬──────────────────────────────────────┘ │ ┌──────────────────────▼──────────────────────────────────────┐ │ ORCHESTRATION ENGINE │ │ (Session Manager | Strategy Loader | Execution Scheduler) │ └──────────────────────┬──────────────────────────────────────┘ │ ┌──────────────┼──────────────┐ │ │ │ ┌───────▼─────┐ ┌──────▼──────┐ ┌────▼──────┐ │ DATA │ │ ANALYSIS │ │ STRATEGY │ │ HARVESTERS │ │ ENGINE │ │ GENERATOR│ └───────┬─────┘ └──────┬──────┘ └─────┬─────┘ │ │ │ └───────┴──────────────┴──────────────┴──────────────────────┘ │ ┌──────────────────────▼──────────────────────────────────────┐ │ RISK MANAGEMENT & EXECUTION │ │ (Position Sizing | Mock Trading | Live Execution) │ └─────────────────────────────────────────────────────────────┘ ``` ## 2. Data Harvesting Component (Drill-Down Architecture) ### 2.1 Hierarchical Data Collection Strategy The system implements a **5-Level Drill-Down Model** allowing users to progressively narrow information scope: #### Level 1: Macro Environment Harvester - **Economic Calendar Data**: Central bank decisions, GDP releases, employment reports - **Market Sentiment Indices**: VIX, put/call ratios, margin debt levels - **Cross-Asset Correlations**: Inter-market analysis (equities vs. bonds vs. commodities) - **Geopolitical Risk Scanners**: News sentiment analysis, event detection #### Level 2: Sector/Asset Class Intelligence - **Industry Rotation Metrics**: Sector momentum, relative strength vs. benchmarks - **Commodity Chain Analysis**: For forex (e.g., AUD/USD vs. commodity prices) - **ETF Flow Analysis**: Capital movement into sector ETFs as leading indicators #### Level 3: Instrument-Specific Fundamental Harvester - **Corporate Financials**: P/E, P/B, ROE, debt/equity, cash flow (quarterly/annual) - **Earnings Calendar & Surprises**: Whisper numbers, guidance revisions - **Insider Activity**: Form 4 filings, institutional ownership changes - **Analyst Sentiment**: Rating changes, price target revisions, consensus trends #### Level 4: Technical Microstructure Harvester - **Price Action Data**: OHLCV at multiple timeframes (1min to monthly) - **Market Depth**: Level 2 order book (60 levels for US stocks, 40 for futures) - **Tick Data**: Time & sales, bid-ask spread dynamics - **Volume Profile**: Point of Control (POC), Value Area High/Low #### Level 5: Alternative Data Sources (Optional Expansion) - **Social Media Sentiment**: Reddit, Twitter sentiment scoring - **Satellite/IoT Data**: Parking lot counts, shipping traffic (for relevant equities) ### 2.2 Data Collection Schema ```json { "harvest_config": { "instrument": { "symbol": "AAPL", "asset_class": "equity", "market": "US", "currency": "USD" }, "drill_down_levels": [1, 3, 4], "timeframes": { "trading": "intraday", "analysis": ["5m", "15m", "1h", "daily"], "historical_lookback": "2y" }, "update_frequency": { "fundamental": "daily", "technical": "real_time", "macro": "event_driven" } } } ``` ## 3. Analysis Engine Design ### 3.1 Fundamental Analysis Module (FAM) **Components:** - **Valuation Engine**: Calculates fair value using DCF, relative valuation (comparable multiples) - **Quality Scoring**: Piotroski F-Score, Altman Z-Score, Beneish M-Score - **Growth Trajectory**: Revenue/EPS CAGR, momentum acceleration detection - **Macro Sensitivity**: Beta calculation, factor exposure (value, growth, momentum factors) **Output Schema:** ```json { "fundamental_score": { "composite_value": 0.72, "confidence": 0.85, "sub_scores": { "valuation": 0.65, "quality": 0.88, "growth": 0.70, "financial_health": 0.75 }, "key_metrics": { "pe_percentile": 25, "pb_percentile": 30, "roe_trend": "improving", "debt_trend": "stable" } } } ``` ### 3.2 Technical Analysis Module (TAM) **Indicator Library Categories:** **Trend Indicators:** - Moving Average systems (SMA, EMA, WMA with adaptive periods) - ADX (Average Directional Index) for trend strength - Parabolic SAR, Ichimoku Cloud **Momentum Indicators:** - RSI (Relative Strength Index) with divergence detection - MACD with histogram analysis - Stochastic Oscillator (fast/slow) - Rate of Change (ROC), Momentum **Volatility Indicators:** - Bollinger Bands (with %B indicator) - ATR (Average True Range) for stop-loss calculation - Keltner Channels, Donchian Channels **Volume Indicators:** - OBV (On-Balance Volume) - Volume Profile (fixed range, session-based) - VWAP (Volume Weighted Average Price) with standard deviations - Money Flow Index (MFI) **Pattern Recognition:** - Candlestick patterns (Engulfing, Doji, Morning Star, etc.) - Chart patterns (Head & Shoulders, Triangles, Flags) - Harmonic patterns (Gartley, Butterfly) ### 3.3 Probabilistic Inference Engine **Bayesian Scoring System:** Combines multiple indicators into probability distributions: ``` P(Trend Up | Data) = P(Data | Trend Up) * P(Trend Up) / P(Data) ``` Where: - Prior probabilities derived from historical backtests - Likelihood functions updated based on real-time indicator confluence - Posterior probabilities drive trading signals **Confluence Scoring:** - **Signal Agreement**: Weighted average of confirming indicators - **Timeframe Alignment**: Higher weight when daily, hourly, and 15m trends align - **Divergence Detection**: Reduce probability when price/indicator divergence occurs ## 4. Strategy Generation & Configuration System ### 4.1 Strategy Configuration Schema (Human-Readable JSON) The strategy file serves as both documentation and executable code: ```json { "strategy_meta": { "name": "Multi-Timeframe Momentum with Fundamental Filter", "version": "1.0", "author": "Trader Name", "created_date": "2026-02-02", "asset_classes": ["equity", "forex"], "timeframes": ["intraday", "swing"], "risk_profile": "moderate_aggressive" }, "harvesting_rules": { "drill_down_sequence": [ {"level": 1, "focus": "macro_trend", "weight": 0.15}, {"level": 3, "focus": "fundamental_quality", "weight": 0.35}, {"level": 4, "focus": "technical_setup", "weight": 0.50} ], "required_data": ["level2_depth", "earnings_calendar", "sector_rotation"] }, "entry_conditions": { "fundamental_gate": { "min_fscore": 6, "pe_max": 25, "debt_equity_max": 0.5, "earnings_surprise_min": 0.05, "logic": "ALL_REQUIRED" }, "technical_signals": [ { "indicator": "EMA_Cross", "params": {"fast": 9, "slow": 21}, "condition": "crossover", "timeframe": "15m", "weight": 0.30 }, { "indicator": "RSI", "params": {"period": 14}, "condition": "between", "values": [50, 70], "timeframe": "5m", "weight": 0.20 }, { "indicator": "Volume_Spike", "params": {"ma_period": 20, "threshold": 1.5}, "condition": "greater_than", "timeframe": "current", "weight": 0.25 }, { "indicator": "VWAP", "params": {}, "condition": "price_above", "timeframe": "daily", "weight": 0.25 } ], "confluence_threshold": 0.75, "confirmation_delay": "2_bars" }, "exit_conditions": { "take_profit": { "type": "risk_multiple", "ratio": 2.0, "trailing_stop": { "activation": "1.5_r", "distance": "1_atr" } }, "stop_loss": { "type": "technical", "reference": "recent_swing_low", "offset": "1_atr", "max_risk_percent": 0.02 }, "time_stop": { "max_bars": 20, "condition": "if_not_profitable" } }, "risk_management": { "position_sizing": { "method": "kelly_criterion_modified", "max_kelly_fraction": 0.25, "fixed_fraction_cap": 0.10 }, "portfolio_limits": { "max_correlated_positions": 3, "max_sector_exposure": 0.30, "daily_loss_limit": 0.03 }, "volatility_adjustment": { "measure": "atr_14", "target_risk_per_trade": 0.01, "scaling": "inverse_volatility" } }, "execution_rules": { "order_type": "limit", "slippage_tolerance": 0.001, "partial_fills": "accept", "time_in_force": "GTC", "mock_trading": { "enabled": true, "fill_model": "realistic", "latency_simulation": "50ms" } } } ``` ### 4.2 Strategy Template Library **Predefined Strategy Archetypes:** 1. **Momentum Breakout**: High volume break above resistance with fundamental growth support 2. **Mean Reversion**: Oversold bounce in quality stocks (high F-Score) with volume exhaustion 3. **Trend Following**: EMA alignment across 3 timeframes with ADX > 25 4. **Earnings Play**: Pre-earnings volatility contraction with post-earnings momentum 5. **Carry Trade**: For forex, interest rate differential + technical trend alignment ## 5. Mock Trading & Backtesting Infrastructure ### 5.1 Paper Trading Simulation Engine **Realistic Market Simulation:** - **Fill Model**: Probabilistic fills based on order book depth (Level 2 data) - **Latency Injection**: Simulated 20-200ms execution delays - **Slippage Modeling**: Function of order size vs. available liquidity - **Market Impact**: Price movement simulation for larger orders (>1% ADV) **Performance Attribution:** - **PnL Decomposition**: Alpha vs. market beta vs. sector exposure - **Transaction Cost Analysis**: Explicit (commissions) vs. implicit (slippage) costs - **Risk-Adjusted Returns**: Sharpe, Sortino, Calmar ratios - **Drawdown Analysis**: Maximum drawdown, recovery time, underwater curve ### 5.2 Walk-Forward Analysis Framework **Temporal Validation:** 1. **In-Sample Optimization**: Parameter tuning on historical window (e.g., 2 years) 2. **Out-of-Sample Testing**: Validation on subsequent 6 months 3. **Rolling Window**: Continuous re-optimization with expanding/rolling windows 4. **Regime Detection**: Separate testing for bull/bear/high-volatility regimes ## 6. Data Model Schema ### 6.1 Core Entity Relationship Diagram ``` [INSTRUMENT] 1---* [PRICE_DATA] *---1 [TIMEFRAME] | *---* [FUNDAMENTAL_METRICS] (Temporal) | *---* [STRATEGY_INSTANCE] | *---* [TRADE_LOG] [STRATEGY_CONFIG] 1---* [STRATEGY_INSTANCE] | *---* [RISK_PARAMETERS] [MARKET_REGIME] 1---* [INSTRUMENT] ``` ### 6.2 Key Data Structures **Instrument Master:** ```json { "instrument_id": "UUID", "symbol": "AAPL", "isin": "US0378331005", "asset_class": "equity", "market": "US_NYSE", "currency": "USD", "tick_size": 0.01, "lot_size": 1, "trading_hours": { "pre_market": "04:00-09:30", "regular": "09:30-16:00", "after_hours": "16:00-20:00" }, "fundamental_frequency": "quarterly", "technical_availability": ["1m", "5m", "15m", "1h", "daily"], "margin_requirements": { "initial": 0.50, "maintenance": 0.25 } } ``` **Trade Execution Log:** ```json { "trade_id": "UUID", "strategy_instance_id": "UUID", "instrument_id": "UUID", "side": "buy", "quantity": 100, "entry": { "price": 150.25, "timestamp": "2026-02-02T10:30:00Z", "order_type": "limit", "fill_quality": "full" }, "exit": { "price": 155.00, "timestamp": "2026-02-02T14:45:00Z", "reason": "take_profit" }, "risk_metrics": { "initial_risk": 150.00, "r_multiple": 2.0, "drawdown_contribution": 0.001 }, "context": { "fundamental_score": 0.72, "technical_confluence": 0.85, "market_regime": "trending_up" } } ``` ## 7. Risk Management Framework ### 7.1 Pre-Trade Risk Checks **Portfolio Level:** - **Correlation Matrix**: Prevent over-concentration in correlated assets - **Sector Limits**: Max 30% exposure to single sector - **Beta Neutrality**: Option to maintain market-neutral posture - **Liquidity Check**: Ensure position size < 1% of average daily volume **Position Level:** - **Kelly Criterion Position Sizing**: f = (p*b - q)/b, capped at 25% of Kelly - **Volatility Sizing**: Reduce size when ATR expands > 2 standard deviations - **Consecutive Loss Rule**: Reduce size by 50% after 3 consecutive losses ### 7.2 Dynamic Risk Adjustment **Adaptive Stop Losses:** - ** chandelier Exit**: ATR-based trailing stop (3x ATR(14)) - **Time-Based Decay**: Tighten stops as position ages without profit - **Volatility Expansion**: Widen stops during high volatility events (VIX spike) ## 8. User Interface & Experience Flow ### 8.1 Strategy Creation Wizard (Drill-Down Interface) **Step 1: Universe Selection** - Asset class picker (Equities/Forex/Futures) - Sector/Region filtering - Liquidity filters (min ADV, max spread) **Step 2: Timeframe Configuration** - Trading style selection (Scalp/Day/Swing/Position) - Alignment of multiple timeframes (e.g., Daily trend + Hourly entry) **Step 3: Fundamental Criteria** - Quality thresholds (F-Score, ROE, Debt levels) - Valuation ranges (P/E percentiles) - Growth requirements (EPS growth > 15%) **Step 4: Technical Setup** - Indicator selection with visual preview - Confluence weighting adjustment (slider-based) - Pattern recognition preferences **Step 5: Risk Configuration** - Max risk per trade (1-3% slider) - Position sizing methodology - Daily/Weekly loss limits **Step 6: Review & Export** - JSON preview with syntax highlighting - Backtest suggestion based on selected parameters - Paper trading activation ### 8.2 Dashboard Components **Strategy Monitor:** - Active signals with confidence scores - Real-time PnL and Greeks (for options) - Open position heatmap (sector/style exposure) - Risk utilization gauge **Market Regime Indicator:** - Current regime classification (Trending/Mean-reverting/High-vol) - Recommended strategy types for current regime - Correlation breakdown (assets moving together/diverging) ## 9. Implementation Roadmap ### Phase 1: Foundation (Months 1-2) - **MVP Data Harvester**: Implement Level 3 & 4 (Instrument fundamentals + Technicals) - **Basic Analysis Engine**: 5 core technical indicators, 3 fundamental metrics - **Paper Trading Connector**: Integration with moomoo OpenD simulation mode - **Simple Strategy Schema**: Entry/exit rules with basic risk management ### Phase 2: Intelligence (Months 3-4) - **Drill-Down UI**: Wizard interface for strategy creation - **Advanced Analytics**: Volume Profile, Market Depth analysis - **Strategy Optimizer**: Walk-forward parameter optimization - **Backtesting Engine**: Historical simulation with transaction cost modeling ### Phase 3: Sophistication (Months 5-6) - **Multi-Timeframe Analysis**: Cross-timeframe signal confirmation - **Alternative Data Integration**: Sentiment analysis, earnings whisper data - **Machine Learning Layer**: Regime detection, probability estimation - **Advanced Risk Management**: Portfolio heat maps, correlation stress testing ### Phase 4: Production (Months 7-8) - **Live Trading Execution**: Transition from paper to live (with safety limits) - **Real-Time Monitoring**: Alert system for strategy degradation - **Performance Analytics**: Attribution analysis, strategy lifecycle management - **API Marketplace**: Shareable strategy templates ## 10. Technical Specifications ### 10.1 Integration Points with moomoo API **Data Retrieval:** - `request_history_kline()` for OHLCV data - `subscribe()` with `SubType.QUOTE` and `SubType.ORDER_BOOK` for real-time - `get_stock_basicinfo()` for fundamental snapshots - `get_financial_data()` for earnings and balance sheet items **Execution:** - `place_order()` with paper trading account IDs - `get_order_list()` for position tracking - `modify_order()` for dynamic stop management ### 10.2 Scalability Considerations **Data Storage:** - Time-series database (InfluxDB/TimescaleDB) for tick data - Relational database (PostgreSQL) for fundamental data and trade logs - Redis for real-time strategy state and caching **Computation:** - Async/await pattern for concurrent multi-instrument analysis - Vectorized pandas/numpy operations for technical calculations - Celery/RabbitMQ for distributed backtesting tasks **Latency Optimization:** - Local caching of historical data to reduce API calls - WebSocket connections for real-time updates (bypassing polling) - Pre-computed indicator values for common timeframes This architecture provides a robust, flexible foundation for systematic trading while maintaining the agility to adapt to changing market conditions and new information sources. The JSON-based strategy configuration ensures transparency and auditability, while the drill-down approach makes complex quantitative analysis accessible to users with varying levels of expertise.