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