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BytesAgain β€Ί Skills β€Ί Quant Data Platform
πŸ¦€ ClawHub

Quant Data Platform

by @jason-aka-chen

Comprehensive quantitative data platform for A-share market. Real-time quotes, historical data, alternative data (sentiment, news, fundamentals), factor data...

Versionv1.0.0
Downloads547
#clawhub
TERMINAL
clawhub install quant-data-platform

πŸ“– About This Skill

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: - quant - data - trading - a-share - realtime - alternative-data 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

    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

    • ⚑ When to Use

    TriggerAction
    - **Live Trading**: Real-time data for execution
    - **Research**: Alternative data for alpha discovery
    - **Risk Management**: Quality monitoring for data integrity

    πŸ’‘ Examples

    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')

    βš™οΈ Configuration

    # Set Tushare token
export TUSHARE_TOKEN=your_token_here
    Or in code
    
from quant_data import DataPlatform
platform = DataPlatform(tushare_token='your_token')

    πŸ“‹ Tips & 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