quant-data-platform（量化数据平台）

Quant Data Platform

Comprehensive data infrastructure for quantitative trading in Chinese A-share market.

Features

1. Real-time Data

• Live Quotes: Real-time stock prices, volumes
• Tick Data: Level 1 tick-by-tick data
• Order Book: Real-time bid/ask data
• Index Data: Real-time index values

2. Historical Data

• Daily K-line: OHLCV data since IPO
• Minute Data: 1/5/15/30/60 minute bars
• Tick History: Historical tick data
• Adjustment: Forward/backward adjustment for dividends

3. Alternative Data

• Sentiment: Social media, forum sentiment
• News: Financial news, announcements
• Fundamentals: Financial statements, ratios
• Insider Trading: Directors' dealings
• Short Interest: Margin trading data

4. Factor Data

• Technical Factors: 100+ technical indicators
• Fundamental Factors: Financial metrics
• Alternative Factors: Sentiment, attention
• Custom Factors: User-defined factors

5. Data Quality

• Completeness Check: Missing data detection
• Accuracy Check: Outlier detection
• Timeliness Check: Delay monitoring
• Consistency Check: Cross-source validation

Installation

pip install tushare akshare pandas numpy

Configuration

# Set Tushare token
export TUSHARE_TOKEN=your_token_here

# Or in code
from quant_data import DataPlatform
platform = DataPlatform(tushare_token='your_token')

Usage

Real-time Data

from quant_data import DataPlatform

platform = DataPlatform()

# Get real-time quotes
quotes = platform.get_realtime_quotes(['600519', '000858'])
print(quotes)
#   code    price   change  volume    amount
# 600519  1850.00   +12.50  125000  231250000
# 000858   156.32    +2.18   89000   13912320

# Get tick data
ticks = platform.get_tick_data('600519', date='2026-03-22')

# Get order book
book = platform.get_order_book('600519')

Historical Data

# Get daily K-line
daily = platform.get_daily(
    codes=['600519', '000858'],
    start='2020-01-01',
    end='2026-03-22'
)

# Get minute data
minute = platform.get_minute(
    code='600519',
    freq='5min',
    start='2026-03-01',
    end='2026-03-22'
)

# Get adjusted data
adj = platform.get_daily_adj(code='600519', adjust='qfq')

Alternative Data

# Get sentiment data
sentiment = platform.get_sentiment('600519', days=30)

# Get news
news = platform.get_news('600519', limit=50)

# Get fundamentals
fundamentals = platform.get_fundamentals('600519', years=5)

# Get short interest
short = platform.get_short_interest('600519')

Factor Data

# Get pre-computed factors
factors = platform.get_factors(
    codes=['600519', '000858'],
    factor_list=['pe', 'pb', 'roe', 'momentum_20d', 'volatility_20d']
)

# Calculate custom factors
custom = platform.calculate_factors(
    code='600519',
    factor_config={
        'name': 'my_momentum',
        'formula': 'close / close.shift(20) - 1',
        'params': {}
    }
)

Data Quality

# Check data quality
quality = platform.check_quality('600519', date_range='2026-03')
print(quality)
# {
#   'completeness': 0.98,
#   'accuracy': 0.99,
#   'timeliness': 0.95,
#   'overall': 0.97
# }

# Get data gaps
gaps = platform.find_gaps('600519', start='2026-01-01')

# Validate data
valid = platform.validate('600519', date='2026-03-22')

API Reference

Real-time

Historical

Alternative

Factors

Quality

Data Sources

Caching Strategy

# Configure caching
platform = DataPlatform(
    cache_dir='~/.quant_data/cache',
    cache_expire={
        'daily': '1d',
        'minute': '1h',
        'realtime': '0',
        'fundamentals': '1d'
    }
)

Rate Limiting

Data Schema

Daily K-line

code: str           # Stock code
trade_date: date    # Trading date
open: float         # Open price
high: float         # High price
low: float          # Low price
close: float        # Close price
volume: int         # Volume
amount: float       # Amount
turnover: float     # Turnover rate

Factor Data

code: str           # Stock code
trade_date: date    # Trading date
factor_name: str    # Factor name
factor_value: float # Factor value

Use Cases

• Backtesting: Historical data for strategy testing
• Live Trading: Real-time data for execution
• Research: Alternative data for alpha discovery
• Risk Management: Quality monitoring for data integrity

Best Practices

1. Cache Aggressively: Reduce API calls
2. Monitor Quality: Check data before use
3. Handle Missing: Have fallback strategies
4. Stay Updated: Sync latest data regularly

Future Capabilities

• Level 2 data support
• Options/futures data
• Cross-market data (HK, US)
• Real-time streaming API