Backtesting is a fundamental technique in algorithmic trading. It allows developers to test their algorithm strategies using historical data before actual deployment in a live trading environment. Among various libraries available for algorithmic trading, PyAlgoTrade stands out due to its simplicity and robust features, particularly for those who are using Python.
One of the challenges in backtesting multiple strategies is comprehensively testing them in parallel to save time and computational resources. In this article, we'll explore how to perform parallel strategy testing using the PyAlgoTrade library, enabling users to maximize efficiency and thoroughly vet trading strategies.
Setting Up Your Environment
Before diving into parallel testing, let's make sure we have our environment set up with PyAlgoTrade and any necessary tools for parallel processing.
pip install pyalgotradeThis command installs PyAlgoTrade. Additionally, for parallel processing, we might use Python's multiprocessing package, which is included in the standard library, so no additional installations are necessary.
Basic Strategy Testing with PyAlgoTrade
First, let's understand how a simple backtest can be set up in PyAlgoTrade. For beginners, PyAlgoTrade is a great library because it abstracts much of the complexity involved in accessing financial data and focuses on strategy behavior. Here, we set up a basic strategy:
from pyalgotrade import strategy
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
from pyalgotrade.csvfeed import Frequency
class MyStrategy(strategy.BacktestingStrategy):
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__instrument = instrument
def onBars(self, bars):
bar = bars[self.__instrument]
self.info("%s: %s" % (bar.getDateTime(), bar.getClose()))
feed = GenericBarFeed(Frequency.DAY)
feed.addBarsFromCSV("instrument", "path/to/your/instrument-data.csv")
my_strategy = MyStrategy(feed, "instrument")
my_strategy.run()
This snippet sets up a simple strategy that logs closing prices for each day of your data source. Now, let's amp up our approach by introducing parallel testing.
Parallel Strategy Testing Example
Running multiple strategies in parallel can save a tremendous amount of time especially if you have many different strategies to test over a large dataset. We'll use Python's multiprocessing to demonstrate parallel testing.
import multiprocessing
from pyalgotrade import strategy
# Other necessary imports...
def backtest_strategy(strategy_cls, instrument, data_path):
feed = GenericBarFeed(Frequency.DAY)
feed.addBarsFromCSV(instrument, data_path)
strat = strategy_cls(feed, instrument)
strat.run()
return strat.getResult()
if __name__ == '__main__':
strategies = [MyStrategy, AnotherStrategy] # Define your different strategies here
data_paths = ['data1.csv', 'data2.csv']
with multiprocessing.Pool() as pool:
results = pool.starmap(backtest_strategy,
[(strat, 'instrument', path) for strat in strategies for path in data_paths])
print(results)
By using multiprocessing.Pool, we can instantiate multiple processes that run different strategies simultaneously. The function starmap is used to map our backtest_strategy function to each combination of strategy and dataset.
Considerations and Next Steps
Here are some finalized thoughts when employing parallel strategy testing:
- Memory Usage: Each parallel strategy will consume more memory since they run as separate processes. Ensure your hardware is ready to handle multiple processes.
- Logging: Adjust logging to handle parallel execution appropriately, to prevent logs from clashing or overlapping.
- Scalability: As strategies and datasets grow, consider using more advanced management systems like Apache Spark or Dask for even larger parallel processing capabilities.
Parallel strategy testing can dramatically speed up your backtesting workflow, enabling you to test numerous strategies quickly and effectively. Make these computational techniques a part of your regular algorithm testing cycle to ensure robustness and efficiency.
