- name
- quant-data-platform
- description
- Comprehensive quantitative data platform for A-share market. Real-time quotes, historical data, alternative data (sentiment, news, fundamentals), factor data, and data quality monitoring. Essential infrastructure for quantitative trading.
- tags
- version
- 1.0.0
- author
- chenq
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
| Method | Description |
|---|
get_realtime_quotes(codes) | Get real-time quotes |
get_tick_data(code, date) | Get tick data |
get_order_book(code) | Get order book |
subscribe(codes, callback) | Subscribe to updates |
Historical
| Method | Description |
|---|
get_daily(codes, start, end) | Get daily K-line |
get_minute(code, freq, start, end) | Get minute data |
get_daily_adj(code, adjust) | Get adjusted data |
get_trading_dates(start, end) | Get trading dates |
Alternative
| Method | Description |
|---|
get_sentiment(code, days) | Get sentiment data |
get_news(code, limit) | Get news |
get_fundamentals(code, years) | Get fundamentals |
get_short_interest(code) | Get short interest |
Factors
| Method | Description |
|---|
get_factors(codes, factor_list) | Get factor values |
calculate_factors(code, config) | Calculate custom factors |
list_factors() | List available factors |
get_factor_metadata(name) | Get factor info |
Quality
| Method | Description |
|---|
check_quality(code, date_range) | Check data quality |
find_gaps(code, start) | Find missing data |
validate(code, date) | Validate data point |
Data Sources
| Type | Source | Update Frequency |
|---|
| Quotes | Tushare, Akshare | Real-time |
| Fundamentals | Tushare | Daily |
| News | Tushare, Eastmoney | Real-time |
| Sentiment | Custom | Hourly |
| Alternative | Multiple | Varies |
Caching Strategy
# Configure caching
platform = DataPlatform(
cache_dir='~/.quant_data/cache',
cache_expire={
'daily': '1d',
'minute': '1h',
'realtime': '0',
'fundamentals': '1d'
}
)
Rate Limiting
| Source | Rate Limit | Strategy |
|---|
| Tushare | 200/min | Token bucket |
| Akshare | 100/min | Token bucket |
| Custom | Unlimited | N/A |
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
- Cache Aggressively: Reduce API calls
- Monitor Quality: Check data before use
- Handle Missing: Have fallback strategies
- Stay Updated: Sync latest data regularly
Future Capabilities
- Level 2 data support
- Options/futures data
- Cross-market data (HK, US)
- Real-time streaming API
