lib-pybroker 1.0.20

Algorithmic trading with machine learning

Algorithmic Trading in Python with Machine Learning

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.

Key Features

• A super-fast backtesting engine built in NumPy and accelerated with Numba.
• The ability to create and execute trading rules and models across multiple instruments with ease.
• Access to historical data from Alpaca and Yahoo Finance, or from your own data provider.
• The option to train and backtest models using Walkforward Analysis, which simulates how the strategy would perform during actual trading.
• More reliable trading metrics that use randomized bootstrapping to provide more accurate results.
• Caching of downloaded data, indicators, and models to speed up your development process.
• Parallelized computations that enable faster performance.

With PyBroker, you'll have all the tools you need to create winning trading strategies backed by data and machine learning. Start using PyBroker today and take your trading to the next level!

Installation

PyBroker supports Python 3.9+ on Windows, Mac, and Linux. You can install PyBroker using pip:

    pip install lib-pybroker

Or you can clone the Git repository with:

    git clone https://github.com/edtechre/pybroker

A Quick Example

Get a glimpse of what backtesting with PyBroker looks like with these code snippets:

Rule-based Strategy:

   from pybroker import Strategy, YFinance, highest

   def exec_fn(ctx):
      # Require at least 20 days of data.
      if ctx.bars < 20:
         return
      # Get the rolling 10 day high.
      high_10d = ctx.indicator('high_10d')
      # Buy on a new 10 day high.
      if not ctx.long_pos() and high_10d[-1] > high_10d[-2]:
         ctx.buy_shares = 100
         # Hold the position for 2 days.
         ctx.hold_bars = 2

   strategy = Strategy(YFinance(), start_date='1/1/2022', end_date='7/1/2022')
   strategy.add_execution(
      exec_fn, ['AAPL', 'MSFT'], indicators=highest('high_10d', 'close', period=10))
   result = strategy.backtest()

Model-based Strategy:

   import pybroker
   from pybroker import Alpaca, Strategy

   def train_fn(train_data, test_data, ticker):
      # Train the model using indicators stored in train_data.
      ...
      return trained_model

   # Register the model and its training function with PyBroker.
   my_model = pybroker.model('my_model', train_fn, indicators=[...])

   def exec_fn(ctx):
      preds = ctx.preds('my_model')
      # Open a long position given my_model's latest prediction.
      if not ctx.long_pos() and preds[-1] > buy_threshold:
         ctx.buy_shares = 100
      # Close the long position given my_model's latest prediction.
      elif ctx.long_pos() and preds[-1] < sell_threshold:
         ctx.sell_all_shares()

   alpaca = Alpaca(api_key=..., api_secret=...)
   strategy = Strategy(alpaca, start_date='1/1/2022', end_date='7/1/2022')
   strategy.add_execution(exec_fn, ['AAPL', 'MSFT'], models=my_model)
   # Run Walkforward Analysis on 1 minute data using 5 windows with 50/50 train/test data.
   result = strategy.walkforward(timeframe='1m', windows=5, train_size=0.5)

User Guide

• Getting Started with Data Sources
• Backtesting a Strategy
• Evaluating with Bootstrap Metrics
• Ranking and Position Sizing
• Writing Indicators
• Training a Model
• Creating a Custom Data Source

Online Documentation

The full reference documentation is hosted at www.pybroker.com.

Contact