numin2 1.0.4

numin package

numin2 Package

numin2 is a Python package designed for algorithmic trading and backtesting providing an API called Numin2API.

numin (v1) is out of service as of Dec 2025

numin2 is under development; features available are documented below

Features

• Data Retrieval: Download training, round, and validation data.
• Prediction Submission: TBD
• Real-Time Round Management: TBD
• Backtesting: Backtesting cross-sectional predictions vs targets for Nifty50
• File Management: TBD
• Returns Summary: TBD

Supported Methods

• Data Download:

  • get_data_for_month(self,year,month,batch_size=4,window_size=100,target_type='rank'):

  • Returns a torch dataloader for the given year and month of Nifty 50 or n returns

  • Dimension of each day is 100,n. Returns tensor of shape batch_size,window_size,n for features. Default n=50. (Later n will be a parameter).

  • Targets are next day returns / ranked returns of shape batch_size,n

  • download_data(outfile,type='daily',features='returns')

  • Download data for a given type and features

  • type can be 'daily','intraday'

  • features can be 'returns' (close returns),'open_close' (open-close returns), or 'ohlcv'

  • outfile is the name of the parquet file to save the data

  • get_range_dataloader(data_path: str, start_year: int, start_month: int, end_year: int, end_month: int, batch_size: int = 32, window_size: int = 100, target_type: str = 'raw', top_k: int = 10)

  • Returns a torch dataloader for the given range of years and months of Nifty 50 or n returns

  • Dimension of each day is 100,n. Returns tensor of shape batch_size,window_size,n for features. Default n=50. (Later n will be a parameter).

  • Targets are next day returns / ranked returns of shape batch_size,n

  • get_dataloader(data_path: str, batch_size: int = 32, window_size: int = 100, target_type: str = 'raw', top_k: int = 10)

  • Returns a torch dataloader for the given range of years and months of Nifty 50 or n returns

  • Dimension of each day is 100,n. Returns tensor of shape batch_size,window_size,n for features. Default n=50. (Later n will be a parameter).

  • Targets are next day returns / ranked returns of shape batch_size,n

• Backytesting

  • backtest_positions(positions,targets)
  • Taks a batch of positions for 50 stocks
  • Returns a dict such as {'daily_pnl','total_profit','sharpe_ratio,'mean_daily_return'}

Installation

Install numin2 using pip:

pip install numin2