Introducing PyBroker: Harnessing Python and Machine Learning in Trading
PyBroker is a powerful Python framework designed to enhance algorithmic trading strategies, specifically with the integration of machine learning. It provides traders and developers with tools to easily create and optimize trading strategies using data-driven insights.
Key Features of PyBroker
PyBroker stands out in the trading industry due to its impressive features:
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Fast Backtesting Engine: Built with NumPy and accelerated by Numba, PyBroker's backtesting engine allows for rapid performance analysis, enabling traders to test their strategies efficiently.
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Comprehensive Trading Rules: PyBroker facilitates the creation and execution of trading rules and models across multiple financial instruments, providing flexibility and ease in strategy development.
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Access to Historical Data: Users can tap into a vast array of historical data from trusted sources like Alpaca, Yahoo Finance, or AKShare. There's also room for integration with custom data providers.
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Walkforward Analysis: This feature allows for training and backtesting models in a way that simulates real trading scenarios, offering users realistic strategy performance insights.
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Enhanced Trading Metrics: With the use of randomized bootstrapping, PyBroker provides more reliable and accurate trading metrics, allowing for better decision-making.
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Data and Model Caching: To speed up the development process, PyBroker caches downloaded data, indicators, and models, ensuring faster access when needed.
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Parallelized Computations: For quicker performance, PyBroker's parallelized computations deliver results efficiently, making it a highly resourceful tool for traders.
Getting Started with PyBroker
To begin your journey with PyBroker, ensure you have Python 3.9+ installed on your system (compatible with Windows, Mac, and Linux). Installation is straightforward with the following command:
pip install -U lib-pybroker
Alternatively, clone the Git repository:
git clone https://github.com/edtechre/pybroker
Practical Examples
Rule-based Strategy
PyBroker allows you to define strategies based on specific rules. Here's a simple example:
from pybroker import Strategy, YFinance, highest
def exec_fn(ctx):
high_10d = ctx.indicator('high_10d')
if not ctx.long_pos() and high_10d[-1] > high_10d[-2]:
ctx.buy_shares = 100
ctx.hold_bars = 5
ctx.stop_loss_pct = 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(warmup=20)
Model-based Strategy
PyBroker also supports machine learning models. Here’s how a model-based strategy can be implemented:
import pybroker
from pybroker import Alpaca, Strategy
def train_fn(train_data, test_data, ticker):
# Model training logic
return trained_model
my_model = pybroker.model('my_model', train_fn, indicators=[...])
def exec_fn(ctx):
preds = ctx.preds('my_model')
if not ctx.long_pos() and preds[-1] > buy_threshold:
ctx.buy_shares = 100
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)
result = strategy.walkforward(timeframe='1m', windows=5, train_size=0.5)
Comprehensive User Guide
PyBroker offers a detailed user guide to help get started and maximize the utility of the framework:
- Getting Started with Data Sources
- Backtesting a Strategy
- Evaluating with Bootstrap Metrics
- Additional guides cover topics from ranking and position sizing to rotational trading.
Access the Full Documentation
Detailed documentation and resources are available on the PyBroker website.
In conclusion, PyBroker equips traders with robust tools to elevate their trading strategies, leveraging the power of Python and machine learning. Whether you're a beginner or an expert, PyBroker offers the capabilities needed to create winning strategies.