Here you can see the full list of changes between each QLib release.

This is the initial release of QLib library.

Performance optimize. Add more features and operators.

• Support operator syntax. Now High() - Low() is equivalent to Sub(High(), Low()).
• Add more technical indicators.

Bug fix and add instruments filtering mechanism.

• Redesign LocalProvider database format for performance improvement.
• Support load features as string fields.
• Add scripts for database construction.
• More operators and technical indicators.

• Support registering user-defined Provider.
• Support use operators in string format, e.g. ['Ref($close, 1)'] is valid field format.
• Support dynamic fields in $some_field format. And existing fields like Close() may be deprecated in the future.

• Add disk_cache for reusing features (enabled by default).
• Add qlib.contrib for experimental model construction and evaluation.

• Add backtest module
• Decoupling the Strategy, Account, Position, Exchange from the backtest module

• Add profit attribution module
• Add rick_control and cost_control strategies

• Add estimator module

• Add filter module

• Add real price trading, if the factor field in the data set is incomplete, use adj_price trading
• Refactor handler launcher trainer code
• Support backtest configuration parameters in the configuration file
• Fix bug in position amount is 0
• Fix bug of filter module

• Fix bug of filter module

• Support for finetune model
• Refactor fetcher code

• Support multi-label training, you can provide multiple label in handler. (But LightGBM doesn't support due to the algorithm itself)
• Refactor handler code, dataset.py is no longer used, and you can deploy your own labels and features in feature_label_config
• Handler only offer DataFrame. Also, trainer and model.py only receive DataFrame
• Change split_rolling_data, we roll the data on market calendar now, not on normal date
• Move some date config from handler to trainer

• Add data package that holds all data-related codes
• Reform the data provider structure
• Create a server for data centralized management `qlib-server<https://amc-msra.visualstudio.com/trading-algo/_git/qlib-server>`_
• Add a ClientProvider to work with server
• Add a pluggable cache mechanism
• Add a recursive backtracking algorithm to inspect the furthest reference date for an expression

>>> from qlib.data import D
>>> instruments = D.instruments(market='csi500')
>>> D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)

• Add support Windows
• Fix instruments type bug
• Fix features is empty bug(It will cause failure in updating)
• Fix cache lock and update bug
• Fix use the same cache for the same field (the original space will add a new cache)
• Change "logger handler" from config
• Change model load support 0.4.0 later
• The default value of the method parameter of risk_analysis function is changed from ci to si

• Refactor DataHandler
• Add Alpha360 DataHandler

• Implementing Online Inference and Trading Framework
• Refactoring The interfaces of backtest and strategy module.

• Optimize cache generation performance
• Add report module
• Fix bug when using ServerDatasetCache offline.
• In the previous version of long_short_backtest, there is a case of np.nan in long_short. The current version 0.4.4 has been fixed, so long_short_backtest will be different from the previous version.
• In the 0.4.2 version of risk_analysis function, N is 250, and N is 252 from 0.4.3, so 0.4.2 is 0.002122 smaller than the 0.4.3 the backtest result is slightly different between 0.4.2 and 0.4.3.

• refactor the argument of backtest function.

  • NOTE: - The default arguments of topk margin strategy is changed. Please pass the arguments explicitly if you want to get the same backtest result as previous version. - The TopkWeightStrategy is changed slightly. It will try to sell the stocks more than topk. (The backtest result of TopkAmountStrategy remains the same)

• The margin ratio mechanism is supported in the Topk Margin strategies.

• Add multi-kernel implementation for both client and server.

  • Support a new way to load data from client which skips dataset cache.
  • Change the default dataset method from single kernel implementation to multi kernel implementation.

• Accelerate the high frequency data reading by optimizing the relative modules.
• Support a new method to write config file by using dict.

• Some bugs are fixed

  • The default config in Version 0.4.5 is not friendly to daily frequency data.
  • Backtest error in TopkWeightStrategy when WithInteract=True.

• First opensource version

  • Refine the docs, code
  • Add baselines
  • public data crawler

• The backtest is greatly refactored.

  • Nested decision execution framework is supported

  • There are lots of changes for daily trading, it is hard to list all of them. But a few important changes could be noticed

    • The trading limitation is more accurate;

      • In previous version, longing and shorting actions share the same action.
      • In current version, the trading limitation is different between logging and shorting action.

    • The constant is different when calculating annualized metrics.

      • Current version uses more accurate constant than previous version

    • A new version of data is released. Due to the unstability of Yahoo data source, the data may be different after downloading data again.
    • Users could check out the backtesting results between Current version and previous version

Please refer to Github release Notes