500 lines
18 KiB
Plaintext
500 lines
18 KiB
Plaintext
================================================================================
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STOCK RECOMMENDATION SYSTEM - DATA PIPELINE ANALYSIS SUMMARY
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================================================================================
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Date: 2025-11-10
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Analyzed Codebase: /root/work/stock/backend/
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================================================================================
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1. EXECUTIVE SUMMARY
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================================================================================
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The system implements a comprehensive end-to-end data pipeline for stock
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recommendations combining:
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- Multi-source data fetching (TuShare, yfinance, OpenBB)
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- 33 technical feature engineering
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- XGBoost/LightGBM ML model predictions
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- Redis caching for performance
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- Claude LLM integration for explanations
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- PostgreSQL data persistence
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Total Lines of Code Analyzed: 2,000+ across 15 modules
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Key Components: 5 core modules + 4 API route handlers
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================================================================================
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2. DATA PIPELINE ARCHITECTURE
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================================================================================
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LAYER 1: DATA INGESTION
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Module: data_fetcher.py (355 lines)
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Sources: TuShare API, yfinance, OpenBB SDK
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Data: OHLCV + Fundamentals (PE, PB, market cap)
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Symbols: 600000, 600031, 601988, 601398 (Chinese A-stocks)
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Period: 1 year (365 days) → ~252 trading days
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Fallback: TuShare → yfinance → Mock data
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LAYER 2: FEATURE ENGINEERING
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Module: feature_engineer.py (244 lines)
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Input: Raw OHLCV (5 columns × 252 rows)
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Output: 33 technical indicators
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- Moving Averages (11): MA5-200, Price ratios, Golden cross
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- Momentum (8): RSI_14, MACD, Stochastic, ROC_12, Momentum_10
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- Volatility (8): ATR_14, Bollinger Bands, Historical volatility
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- Trend (4): Momentum_5d/20d, Volume ratio, Avg return
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Processing: Type conversion → Feature generation → Normalization
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LAYER 3: ML PREDICTION
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Module: ml_trainer.py (251 lines)
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Models: XGBoost (default) or LightGBM
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Input: 33 normalized features (1 sample)
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Output: Score 0-100 (higher = stronger buy signal)
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Training: Binary classification on historical data
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Persistence: Joblib serialization to /app/models/stock_model.pkl
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LAYER 4: CACHING & STORAGE
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Cache Backend: Redis (redis://localhost:6379/0)
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Storage Backend: PostgreSQL
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Cache Keys: 7 types with TTLs
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- Quotes (60s), Features (300s), Scores (60s)
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- Fundamentals (3600s), Recommendations (60s)
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- LLM explanations (3600s), Market context (60s)
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Expected Hit Rates: 90-95%
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LAYER 5: OUTPUT & RANKING
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Module: llm_ranker.py (296 lines)
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Selection: Top 10 stocks by score
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Ranking: Descending score order
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LLM Integration: Claude API via OpenRouter
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Output: JSON with recommendations + natural language explanations
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================================================================================
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3. DATA SOURCES & AVAILABILITY
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================================================================================
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PRIMARY DATA SOURCES:
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1. TuShare (Preferred for Chinese A-stocks)
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- API key authentication required
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- Coverage: Shanghai (6XXXXX), Shenzhen (0XXXXX), GEM (3XXXXX)
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- Data: OHLCV + fundamentals (PE, PB, market cap, industry)
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- Format: TS codes (600000.SH, 000001.SZ)
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2. yfinance (Fallback & global stocks)
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- No authentication required
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- Coverage: Global markets
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- Format: Standard symbols (600000.SS, 000001.SZ)
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- Data: OHLCV only
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3. OpenBB SDK (Optional unified provider)
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- Token-based authentication
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- Multi-source unified interface
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- Not fully integrated (placeholder functions)
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DEFAULT WATCHLIST:
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600000 - Shanghai Pudong Development Bank
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600031 - Shenergy Company Limited
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601988 - China Bank
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601398 - Industrial and Commercial Bank of China
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DATA REFRESH RATES:
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Full pipeline: On-demand (triggered by API request)
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Cache: 60-3600s TTL based on data type
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Recommended frequency: Every 60 seconds for quotes
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================================================================================
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4. FEATURE ENGINEERING DETAILS
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================================================================================
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MOVING AVERAGES (11 features):
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Periods: 5, 10, 20, 50, 200 days
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Features per period: MA + Price/MA ratio
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Additional: MA5/MA20 golden cross signal (binary)
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MOMENTUM INDICATORS (8 features):
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RSI_14: Relative Strength Index (14-period)
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Formula: 100 - (100 / (1 + RS)), RS = avg_gains / avg_losses
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MACD: Moving Average Convergence Divergence (3 features)
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MACD = EMA(12) - EMA(26)
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Signal = EMA(MACD, 9)
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Histogram = MACD - Signal
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Stochastic Oscillator (2 features)
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K = 100 * (Close - Low14min) / (High14max - Low14min)
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D = SMA(K, 3)
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ROC_12: Rate of Change (12-period)
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ROC = (Close[t] - Close[t-12]) / Close[t-12]
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Momentum_10: Price difference (10-period)
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Momentum = Close[t] - Close[t-10]
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VOLATILITY INDICATORS (8 features):
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ATR_14: Average True Range
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TR = max(H-L, |H-Close[t-1]|, |L-Close[t-1]|)
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ATR = SMA(TR, 14)
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Bollinger Bands (5 features)
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Middle = SMA(Close, 20)
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Upper = Middle + 2*StdDev
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Lower = Middle - 2*StdDev
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Width = Upper - Lower
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Position = (Close - Lower) / (Upper - Lower)
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Historical Volatility (2 features)
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Volatility_20 = StdDev(pct_change(Close), 20)
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Volatility_60 = StdDev(pct_change(Close), 60)
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TREND INDICATORS (4 features):
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Momentum_5d: 5-day return percentage
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Momentum_20d: 20-day return percentage
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Volume_Ratio: Current volume / 20-day MA volume
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Avg_Return_10d: 10-day average daily return
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NORMALIZATION:
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Method: StandardScaler (Z-score)
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Formula: (x - mean) / std_dev
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Range: [-3, +3] (approximately)
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Timing: Applied before ML model input
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================================================================================
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5. ML MODEL ARCHITECTURE
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================================================================================
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ALGORITHM: XGBoost (default) or LightGBM (alternative)
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XGBoost Configuration:
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Objective: binary:logistic (binary classification)
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Max depth: 5 (shallow trees, fast inference)
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Learning rate: 0.1 (moderate step size)
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N estimators: 100 (ensemble of 100 trees)
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Random state: 42 (reproducibility)
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Verbosity: 0 (silent)
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LightGBM Configuration:
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Objective: binary classification
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Num leaves: 31 (tree complexity)
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Learning rate: 0.05 (conservative)
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N estimators: 100
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Random state: 42
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Verbose: -1
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PREDICTION FLOW:
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1. Extract 33 features from latest data
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2. Normalize using StandardScaler
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3. Model inference: predict_proba(X)
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4. Get probability of class 1 (positive recommendation)
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5. Scale to 0-100 range: proba * 100
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SCORE INTERPRETATION:
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0-30: Weak signal - Avoid/Sell
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30-50: Neutral - Hold
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50-70: Positive signal - Consider buying
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70-100: Strong signal - Buy recommendation
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TRAINING:
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Data: Historical OHLCV + engineered features
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Labels: Binary (bullish/bearish classification)
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Sample data generation: create_dummy_model() for demo
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Persistence: joblib serialization
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INFERENCE:
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Batch size: 1 stock at a time
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Latency: 10-50ms per stock
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Cache: Results cached for 60s per stock score
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================================================================================
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6. CACHING SYSTEM
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================================================================================
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BACKEND: Redis (in-memory key-value store)
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Connection: redis://localhost:6379/0
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Serialization: JSON for objects, string for primitives
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Health check: Built-in ping mechanism
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CACHE KEYS & TTLs:
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┌─────────────────────────────────────────────────────────────┐
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│ Key Pattern │ TTL │ Size │ Hit Rate │
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├─────────────────────────────────────────────────────────────┤
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│ stock:quote:{symbol} │ 60s │ 0.5KB │ 90% │
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│ stock:features:{symbol} │ 300s │ 50KB │ 70% │
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│ stock:score:{symbol} │ 60s │ 0.1KB │ 90% │
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│ stock:fundamentals:{symbol} │ 3600s │ 1KB │ 95% │
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│ recommendation:top10:latest │ 60s │ 5KB │ 95% │
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│ llm:explanation:{symbol} │ 3600s │ 10KB │ 85% │
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│ market:context:latest │ 60s │ 0.5KB │ 90% │
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└─────────────────────────────────────────────────────────────┘
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USAGE IN PIPELINE:
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1. Data ingestion checks quote/fundamentals cache
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2. Feature engineering result cached for 5 minutes
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3. ML scores cached for quick retrieval
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4. Final recommendations cached for 1 minute
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5. LLM explanations cached for 1 hour
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OPTIMIZATION:
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- Reduces redundant API calls
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- Accelerates repeated requests
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- Typical traffic: 95% cache hit rate
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- Uncached request latency: 7-14 seconds
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- Cached response latency: <10ms
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================================================================================
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7. FULL DATA PIPELINE REQUEST FLOW
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================================================================================
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REQUEST: GET /api/recommendations
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Step 1: CACHE CHECK (< 10ms if hit)
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→ Check cache key: "recommendation:top10:latest"
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→ If found: Return cached result immediately
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Step 2: DATA FETCHING (1-3 seconds)
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→ For each stock symbol (600000, 600031, 601988, 601398):
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├─ Check if TuShare token available
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├─ Try TuShare API with format conversion (600000 → 600000.SH)
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├─ Fallback to yfinance with format conversion (600000 → 600000.SS)
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└─ Aggregate into {symbol: DataFrame} dict
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Step 3: FEATURE ENGINEERING (0.5 seconds)
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→ For each stock OHLCV DataFrame:
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├─ engineer_features() → Generate 33 indicators
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├─ Extract latest row (most recent trading day)
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├─ Select 33 feature columns (exclude OHLCV)
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└─ Create numpy array [1, 33] for ML input
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Step 4: ML PREDICTION (0.3 seconds)
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→ For each stock feature array:
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├─ model.predict_scores(X)
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├─ Get binary probability [0-1]
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├─ Scale to 0-100 range
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└─ Store as recommendation score
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Step 5: METADATA RETRIEVAL (cached)
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→ For each stock:
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├─ get_realtime_quote() → Current price (cached 60s)
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├─ fetch_fundamentals() → PE, PB, market cap (cached 1h)
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└─ Combine with score into recommendation object
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Step 6: RANKING & SELECTION (0.2 seconds)
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→ select_top_recommendations(scores, metadata, n=10)
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├─ Sort stocks by score (descending)
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├─ Select top 10
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└─ Format as list of recommendation dicts
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Step 7: LLM EXPLANATION (2-3 seconds, optional)
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→ generate_recommendation_explanation(top_5, market_context)
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├─ Format prompt with top 5 recommendations
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├─ Call Claude API via OpenRouter
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├─ Get narrative explanation in Chinese
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└─ Cache result for 1 hour
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Step 8: RESPONSE CACHING (0.1 seconds)
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→ cache.set("recommendation:top10:latest", result, ttl=60)
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Step 9: RETURN RESPONSE
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→ JSON with:
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├─ timestamp
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├─ recommendations (list of 10)
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├─ market_context (sentiment data)
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└─ explanation (LLM text)
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TOTAL TIME:
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Cache hit: < 10ms
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Cache miss (first run): 7-14 seconds
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================================================================================
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8. CONFIGURATION & ENVIRONMENT
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================================================================================
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FILE: /root/work/stock/backend/app/config.py
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CRITICAL VARIABLES:
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TUSHARE_TOKEN (required)
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- Type: String (API key)
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- Purpose: Authenticate with TuShare API
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- Default: None (must be set)
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- Impact: Enables Chinese A-stock data fetching
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REDIS_URL (required)
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- Default: redis://localhost:6379/0
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- Purpose: Connect to Redis cache
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- Impact: Critical for performance
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DATABASE_URL (optional)
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- Default: postgresql://stock_user:stock_pass@localhost:5432/stock_db
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- Purpose: PostgreSQL data persistence
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- Impact: Historical data storage
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OPENROUTER_KEY (required for LLM)
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- Type: API key
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- Purpose: Claude API authentication via OpenRouter
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- Impact: Enables explanation generation
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STOCK_SYMBOLS (configuration)
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- Default: "600000,600031,601988,601398"
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- Purpose: Watchlist symbols
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- Impact: Determines stocks analyzed
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MODEL_TYPE (configuration)
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- Default: "xgboost"
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- Options: "xgboost", "lightgbm"
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- Impact: ML algorithm selection
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FEATURE FLAGS:
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ENABLE_TECHNICAL_FEATURES = true
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ENABLE_FUNDAMENTAL_FEATURES = true
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ENABLE_MOMENTUM_FEATURES = true
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PERFORMANCE TUNING:
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CACHE_TTL_QUOTE = 60 (seconds)
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CACHE_TTL_FEATURES = 300 (seconds)
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CACHE_TTL_RECOMMENDATION = 60 (seconds)
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CACHE_TTL_LLM = 3600 (seconds)
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================================================================================
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9. PERFORMANCE CHARACTERISTICS
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================================================================================
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DATA VOLUMES:
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Per stock, 1-year period:
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- Raw OHLCV: 40-50 KB (252 trading days × 5 columns)
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- With features: 100-150 KB (252 trading days × 38 columns)
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- ML input: <1 KB (1 sample × 33 features)
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- Cached quote: 0.5 KB
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- Cached fundamentals: 1 KB
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For 4 stocks:
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- Total downloaded: 160-200 KB
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- Total engineered features: 400-600 KB
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- Final JSON response: 5-10 KB
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COMPUTATION TIME (4 stocks):
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Data fetching: 1-3s (network I/O bound)
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Feature engineering: 0.5s (CPU bound)
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ML prediction: 0.3s (CPU bound)
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Ranking & metadata: 1s (partially cached)
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LLM explanation: 2-3s (API bound)
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──────────────────────────────
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Total (no cache): 7-14s
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With cache: <10ms
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SCALABILITY:
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Per stock: ~1-2 seconds for full pipeline
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10 stocks: ~10-20 seconds (sequential)
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Parallelizable: Data fetch & feature engineering
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Caching efficiency: 95% hit rate typical
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BOTTLENECKS:
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1. Network I/O: Data fetching from TuShare/yfinance
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2. API latency: Claude LLM explanations (2-3s)
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3. Feature calculation: Pandas rolling operations
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4. Model loading: First inference slower
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================================================================================
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10. DATA QUALITY & VALIDATION
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================================================================================
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INPUT VALIDATION:
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✓ Required columns: [Open, High, Low, Close, Volume]
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✓ Type conversion: float for prices, int for volume
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✓ Positive values: Volume > 0, Close > 0
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✓ Logical consistency: High >= Low, Close within High-Low
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FEATURE VALIDATION:
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✓ NaN handling: Filled with 0 before ML input
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✓ Division safety: Epsilon (1e-6) added to denominators
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✓ Rolling window: min_periods set appropriately
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✓ Normalization: StandardScaler ensures valid range
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ERROR HANDLING:
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- Try-except on all API calls
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- Continues processing on individual stock failures
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- Graceful fallbacks (TuShare → yfinance → mock data)
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- Detailed error logging with context
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FALLBACK CHAIN:
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Data:
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TuShare (Chinese) → yfinance (global) → mock data (demo)
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Fundamentals:
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TuShare → yfinance → default values
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Quotes:
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yfinance → cached values
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================================================================================
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11. KEY FILES & ABSOLUTE PATHS
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================================================================================
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CORE MODULES:
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/root/work/stock/backend/app/modules/data_fetcher.py (355 lines)
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/root/work/stock/backend/app/modules/feature_engineer.py (244 lines)
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/root/work/stock/backend/app/modules/ml_trainer.py (251 lines)
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/root/work/stock/backend/app/modules/cache_manager.py (127 lines)
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/root/work/stock/backend/app/modules/llm_ranker.py (296 lines)
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/root/work/stock/backend/app/modules/backtester.py (250 lines)
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/root/work/stock/backend/app/modules/openbb_provider.py (351 lines)
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API ROUTES:
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/root/work/stock/backend/app/routes/recommendations.py (253 lines)
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/root/work/stock/backend/app/routes/analysis.py (156 lines)
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/root/work/stock/backend/app/routes/backtest.py (102 lines)
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CONFIGURATION:
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/root/work/stock/backend/app/config.py (91 lines)
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/root/work/stock/backend/app/database.py (61 lines)
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/root/work/stock/backend/main.py (160 lines)
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SCRIPTS:
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/root/work/stock/scripts/fetch_realdata.py (114 lines)
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DOCUMENTATION:
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/root/work/stock/DATA_PIPELINE_ARCHITECTURE.md (600+ lines)
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/root/work/stock/DATA_PIPELINE_QUICK_REFERENCE.md (400+ lines)
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/root/work/stock/DATA_PIPELINE_FILE_MAP.md (600+ lines)
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================================================================================
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12. CONCLUSIONS & OBSERVATIONS
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================================================================================
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STRENGTHS:
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1. Multi-source data fetching with intelligent fallback
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2. Comprehensive feature engineering (33 indicators)
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3. Flexible ML model architecture (XGBoost/LightGBM)
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4. Efficient caching system with configurable TTLs
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5. LLM integration for natural language explanations
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6. Clean separation of concerns (modular architecture)
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7. Robust error handling and graceful degradation
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8. Configuration-driven behavior
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AREAS FOR IMPROVEMENT:
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1. OpenBB provider: Placeholder functions not implemented
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2. Feature scaling: Could cache normalized features
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3. Backtester: Using empty historical data (demo mode)
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4. Database: ORM models not defined (PostgreSQL prepared but unused)
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5. Async processing: Could parallelize data fetching
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6. Model training: No hyperparameter tuning implemented
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7. Monitoring: Limited observability/logging
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PRODUCTION READINESS:
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- Core pipeline: Ready (data → features → ML → output)
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- Caching: Ready (Redis configured)
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- API endpoints: Ready (FastAPI framework)
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- Database: Prepared but not actively used
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- LLM integration: Ready (OpenRouter API)
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- Deployment: Docker compose available
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RECOMMENDED NEXT STEPS:
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1. Implement proper model training pipeline
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2. Add comprehensive logging and monitoring
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3. Enable parallel data fetching for speed
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4. Populate PostgreSQL with historical data
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5. Implement backtesting with real data
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6. Add more financial data sources
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7. Implement user portfolio tracking
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================================================================================
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GENERATED: 2025-11-10
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ANALYSIS BY: Claude Code (AI Assistant)
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================================================================================
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