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Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.

TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. Licensed under MIT. This training environment was originally designed for tickdata, but also supports OHLC data format. WIP....

...Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Dependencies + Python (>=2.7, <3.0) + numpy (>=1.1.0) + wxPython (>=2.8.10.1, Unicode version required) + matplotlib (>=1.1.1) + pycairo (>=1.8.8) Optional dependencies + python-gnupg (>=0.3.0, for opening own files without approval) + xlrd (>=0.9.2, for loading Excel® files) + xlwt (>=0.9.2, for saving Excel files, pyspread >=v0.3.0 required) + jedi (>=0.8.0, for tab completion and context help in the entry line, pyspread >=v0.3.0 required) + basemap (>=1.0.7, for the weather example pys file)