optimizer – Parallel optimizers

class pyalgotrade.optimizer.server.Results(parameters, result)

Bases: object

The results of the strategy executions.

getParameters()

Returns a sequence of parameter values.

getResult()

Returns the result for a given set of parameters.

pyalgotrade.optimizer.server.serve(barFeed, strategyParameters, address, port, batchSize=200)

Executes a server that will provide bars and strategy parameters for workers to use.

Parameters:

• barFeed (pyalgotrade.barfeed.BarFeed.) – The bar feed that each worker will use to backtest the strategy.
• strategyParameters – The set of parameters to use for backtesting. An iterable object where each element is a tuple that holds parameter values.
• address (string.) – The address to listen for incoming worker connections.
• port (int.) – The port to listen for incoming worker connections.
• batchSize (int.) – The number of strategy executions that are delivered to each worker.

Return type:

A Results instance with the best results found or None if no results were obtained.

pyalgotrade.optimizer.worker.run(strategyClass, address, port, workerCount=None, workerName=None)

Executes one or more worker processes that will run a strategy with the bars and parameters supplied by the server.

Parameters:

• strategyClass – The strategy class.
• address (string.) – The address of the server.
• port (int.) – The port where the server is listening for incoming connections.
• workerCount (int.) – The number of worker processes to run. If None then as many workers as CPUs are used.
• workerName (string.) – A name for the worker. A name that identifies the worker. If None, the hostname is used.

pyalgotrade.optimizer.local.run(strategyClass, barFeed, strategyParameters, workerCount=None, logLevel=40, batchSize=200)

Executes many instances of a strategy in parallel and finds the parameters that yield the best results.

Parameters:

• strategyClass – The strategy class.
• barFeed (pyalgotrade.barfeed.BarFeed.) – The bar feed to use to backtest the strategy.
• strategyParameters – The set of parameters to use for backtesting. An iterable object where each element is a tuple that holds parameter values.
• workerCount (int.) – The number of strategies to run in parallel. If None then as many workers as CPUs are used.
• logLevel – The log level. Defaults to logging.ERROR.
• batchSize (int.) – The number of strategy executions that are delivered to each worker.

Return type:

A Results instance with the best results found.

Note

• The server component will split strategy executions in chunks which are distributed among the different workers. You can optionally set the chunk size by passing in batchSize to the constructor of pyalgotrade.optimizer.xmlrpcserver.Server.
• The pyalgotrade.strategy.BaseStrategy.getResult() method is used to select the best strategy execution. You can override that method to rank executions using a different criteria.