ljwx-stock/FULL_DATA_PIPELINE_IMPLEMENTATION_STATUS.md

Full Data Pipeline Implementation Status

📊 Overview

Complete data pipeline implementation for real-time stock recommendations based on database-stored historical data and strategy engine calculations.

Date: 2025-11-16 Status: Core Implementation Complete ✅ Next: Docker rebuild + data loading + integration testing

---

✅ Completed Components

1. Database Schema (schema_v2.sql)

File: backend/database/schema_v2.sql Status: ✅ Deployed to PostgreSQL

Tables Created:

• stocks - Stock master data (symbol, name, market, industry)
• daily_quotes - OHLCV historical data (~1.5M records capacity)
• technical_indicators - Pre-calculated MA, RSI, MACD, Bollinger Bands
• fundamentals - PE, PB, ROE, financial metrics
• recommendations - Strategy-based recommendations with scores
• strategy_configs - Configurable strategies (4 pre-loaded)
• data_load_tasks - Progress tracking for data loads

Views Created:

• latest_recommendations - Latest recommendations with stock info
• stock_technical_summary - Technical indicators per stock
• stock_fundamental_summary - Fundamental metrics per stock

Verification:

docker-compose exec -T postgres psql -U stockai -d stockai -c "\dt"
# Returns: 7 tables

docker-compose exec -T postgres psql -U stockai -d stockai -c "SELECT * FROM strategy_configs;"
# Returns: value, growth, momentum, comprehensive (4 strategies)

---

2. ORM Models (stock_models.py)

File: backend/app/models/stock_models.py Status: ✅ Created (296 lines)

Models:

class Stock(Base):
    symbol, name, market, industry, list_date

class DailyQuote(Base):
    symbol, trade_date, open, high, low, close, volume, amount

class TechnicalIndicator(Base):
    symbol, trade_date, ma5, ma10, ma20, rsi, macd, bollinger_bands, atr

class Fundamental(Base):
    symbol, report_date, pe_ratio, pb_ratio, roe, net_margin

class Recommendation(Base):
    symbol, recommend_date, strategy, score, predicted_return, confidence, reason, signals

class StrategyConfig(Base):
    strategy_name, filters, weights, enabled

class DataLoadTask(Base):
    task_name, market, status, total_records, loaded_records

---

3. Historical Data Loader (historical_data_loader.py)

File: backend/services/historical_data_loader.py Status: ✅ Created (720 lines)

Features:

• ✅ Fetch all A-share stock list (~5000 stocks) via TuShare
• ✅ Batch processing (50 stocks/batch) with API rate limiting
• ✅ Load 1-year historical daily quotes
• ✅ Calculate and store technical indicators
• ✅ Fetch and store fundamentals (PE, PB, ROE)
• ✅ Progress tracking in data_load_tasks table
• ✅ Error handling and retry logic
• ✅ Upsert operations (avoid duplicates)

Usage:

# After Docker rebuild
docker-compose exec backend python services/historical_data_loader.py --market a_share --years 1

Estimated Time: ~1.5 hours for all 5000 A-shares

---

4. Strategy Engine (strategy_engine.py)

File: backend/app/modules/strategy_engine.py Status: ✅ Created (487 lines)

Capabilities:

• ✅ Load strategy configurations from database
• ✅ Apply multi-factor filters (PE, PB, ROE, RSI, MACD, MA)
• ✅ Calculate fundamental scores (0-100)
• ✅ Calculate technical scores (0-100)
• ✅ Weighted scoring based on strategy config
• ✅ Generate recommendation reasons (Chinese)
• ✅ Predict returns (simplified model)
• ✅ Calculate confidence based on data completeness
• ✅ Save recommendations to database

Strategies Implemented:

1. Value (价值投资)

   • Filters: PE < 20, PB < 3, ROE > 8%
   • Weights: Fundamental 60%, Technical 30%, Sentiment 10%

2. Growth (成长投资)

   • Filters: ROE > 15%, Net Margin > 15%
   • Weights: Fundamental 50%, Technical 30%, Sentiment 20%

3. Momentum (动量追涨)

   • Filters: MA5 > MA20, RSI 50-70, Volume surge
   • Weights: Technical 70%, Volume 20%, Sentiment 10%

4. Comprehensive (综合策略)

   • Filters: Balanced
   • Weights: Fundamental 35%, Technical 35%, Sentiment 15%, AI 15%

Usage:

# Generate recommendations
docker-compose exec backend python app/modules/strategy_engine.py --strategy comprehensive --top 20

---

🔄 Integration Points

Recommendations API Enhancement

Current Status: API exists but needs database mode added

Required Changes (next step):

1. Add use_database parameter to /api/recommendations
2. When use_database=true, query latest_recommendations view
3. Return results directly from database (no real-time calculation)

Proposed Implementation:

@router.get("/recommendations")
async def get_recommendations(
    limit: Optional[int] = 20,
    fast: bool = False,
    use_database: bool = True,  # NEW: Default to database mode
    strategy: str = "comprehensive"
):
    if use_database:
        # Query latest_recommendations view from database
        results = session.execute(text("""
            SELECT * FROM latest_recommendations
            WHERE strategy = :strategy
            ORDER BY score DESC
            LIMIT :limit
        """), {"strategy": strategy, "limit": limit})
        return format_recommendations(results)
    elif fast:
        return mock_data
    else:
        return current_real_time_logic

---

📈 Data Flow Architecture

User Request
    ↓
/api/recommendations?use_database=true&strategy=comprehensive&limit=20
    ↓
Query PostgreSQL → latest_recommendations view
    ↓
    ├─ JOIN stocks (name, market, industry)
    ├─ JOIN daily_quotes (latest price)
    ├─ JOIN recommendations (score, reason, signals)
    └─ Filter by strategy + recommend_date
    ↓
Return JSON response
    ↓
Dashboard displays real recommendations

No real-time calculation needed - all data pre-computed by strategy engine and stored in database.

---

⏰ Deployment Timeline

Phase 1: Docker Rebuild (5 mins)

# Rebuild backend image to include new files
docker-compose build backend
docker-compose restart backend

# Verify new files are accessible
docker-compose exec backend ls -la /app/services/historical_data_loader.py
docker-compose exec backend ls -la /app/modules/strategy_engine.py

Phase 2: Initial Data Load (1.5 hours)

# Option A: Load subset for quick testing (10 stocks, 1 month)
docker-compose exec backend python services/historical_data_loader.py --market a_share --years 0.1 --limit 10

# Option B: Full load (5000 stocks, 1 year) - RUN OVERNIGHT
docker-compose exec backend python services/historical_data_loader.py --market a_share --years 1

Phase 3: Generate Initial Recommendations (2 mins)

# Generate recommendations for all 4 strategies
docker-compose exec backend python app/modules/strategy_engine.py --strategy value --top 50
docker-compose exec backend python app/modules/strategy_engine.py --strategy growth --top 50
docker-compose exec backend python app/modules/strategy_engine.py --strategy momentum --top 50
docker-compose exec backend python app/modules/strategy_engine.py --strategy comprehensive --top 50

Phase 4: Update API (10 mins)

• Add use_database mode to recommendations API
• Test with curl:

  curl "http://localhost:8001/api/recommendations?use_database=true&strategy=comprehensive&limit=10"
  

Phase 5: Frontend Integration (15 mins)

• Update Dashboard to use use_database=true parameter
• Remove any hard-coded mock data assumptions
• Add strategy selector dropdown
• Add auto-refresh every 60 seconds

---

🧪 Testing Checklist

Database Tests

• Schema created successfully
• 4 strategies inserted
• Historical data loaded (at least 10 stocks for testing)
• Technical indicators calculated
• Recommendations generated

API Tests

• /api/recommendations?use_database=true returns database data
• Different strategies return different stocks
• Scores are realistic (0-100 range)
• Chinese recommendation reasons display correctly

Dashboard Tests

• Recommendations display with real stock names
• Prices update based on database data
• Strategy selector works
• No duplicate stocks shown
• Auto-refresh works

---

📊 Expected Results

Database Content After Full Load

-- Stocks
SELECT COUNT(*) FROM stocks;
-- Expected: ~5000 (all A-shares)

-- Daily Quotes
SELECT COUNT(*) FROM daily_quotes;
-- Expected: ~1,250,000 (5000 stocks × 250 trading days)

-- Technical Indicators
SELECT COUNT(*) FROM technical_indicators;
-- Expected: ~1,250,000 (same as quotes)

-- Recommendations
SELECT COUNT(*) FROM recommendations WHERE recommend_date = CURRENT_DATE;
-- Expected: ~200 (50 per strategy × 4 strategies)

Sample Recommendation Output

{
  "timestamp": "2025-11-16T20:00:00",
  "strategy": "comprehensive",
  "recommendations": [
    {
      "symbol": "600519",
      "name": "贵州茅台",
      "score": 95.2,
      "predicted_return": 0.085,
      "confidence": 0.92,
      "current_price": 1650.0,
      "reason": "合理估值 (PE=35.20)；技术面向好；稳定盈利 (ROE=28.5%)",
      "signals": {
        "fundamental": {"score": 88, "pe": 35.2, "pb": 12.5, "roe": 0.285},
        "technical": {"score": 72, "rsi": 58.5, "macd": 12.5}
      }
    }
    // ... 19 more
  ],
  "data_source": "database"
}

---

🚀 Quick Start Commands

For Testing (10 stocks, quick)

# 1. Rebuild
docker-compose build backend && docker-compose restart backend

# 2. Load test data (10 stocks, 1 month)
docker-compose exec backend python -c "
from services.historical_data_loader import HistoricalDataLoader
import asyncio
loader = HistoricalDataLoader()
# Modify to load only 10 stocks
asyncio.run(loader.load_all_a_shares(period_years=0.08))
"

# 3. Generate recommendations
docker-compose exec backend python app/modules/strategy_engine.py --strategy comprehensive --top 10

# 4. Verify
docker-compose exec -T postgres psql -U stockai -d stockai -c "SELECT * FROM latest_recommendations LIMIT 5;"

For Production (full load)

# 1. Rebuild
docker-compose build backend && docker-compose restart backend

# 2. Start full load in background (1.5 hours)
docker-compose exec -d backend python services/historical_data_loader.py --market a_share --years 1

# 3. Monitor progress
docker-compose exec -T postgres psql -U stockai -d stockai -c "
SELECT task_name, status, loaded_records, total_records,
       ROUND((loaded_records::float / total_records) * 100, 2) as progress_pct
FROM data_load_tasks
ORDER BY created_at DESC LIMIT 5;
"

# 4. When complete, generate recommendations
docker-compose exec backend python app/modules/strategy_engine.py --strategy comprehensive --top 50

---

📝 Files Created

File	Lines	Status	Purpose
backend/database/schema_v2.sql	300	✅ Deployed	Database schema
backend/app/models/stock_models.py	296	✅ Created	ORM models
backend/services/historical_data_loader.py	720	✅ Created	Data loader
backend/app/modules/strategy_engine.py	487	✅ Created	Strategy engine
backend/scripts/init_database_v2.py	150	✅ Created	DB init script
backend/scripts/load_historical_data.sh	50	✅ Created	Shell wrapper
backend/scripts/test_data_loader.py	120	✅ Created	Test script
HISTORICAL_DATA_LOADER_GUIDE.md	250	✅ Created	Documentation
FULL_DATA_PIPELINE_PLAN.md	480	✅ Created	Implementation plan

Total: ~2,853 lines of new code + documentation

---

🎯 Success Criteria

• ✅ Database schema created and deployed
• ✅ ORM models implemented
• ✅ Historical data loader implemented
• ✅ Strategy engine implemented
• ⏸️ Data loaded (at least 100 stocks for demo)
• ⏸️ Recommendations generated and stored
• ⏸️ API returns database recommendations
• ⏸️ Dashboard displays real data

Progress: 50% complete (core components done, integration pending)

---

🔧 Troubleshooting

"Module not found" errors

# Rebuild Docker image
docker-compose build backend
docker-compose restart backend

"Database stockai does not exist"

# Already fixed - database created successfully

Check data load progress

docker-compose logs -f backend | grep "Processing batch"

Manually verify database

docker-compose exec -it postgres psql -U stockai -d stockai

---

📞 Next Steps

1. Immediate: Rebuild Docker to include new files
2. Short-term: Load test data (10-100 stocks)
3. Medium-term: Generate and test recommendations API
4. Long-term: Full 5000-stock load + production deployment

Estimated Time to Full Demo: 30 minutes (rebuild + test load + API test) Estimated Time to Production: 2 hours (full load + integration)