ExecutionContext#

tradeexecutor.strategy.execution_context.ExecutionContext Python class in Trading Strategy framework.

class ExecutionContext[source]#

Bases: object

Information about the strategy execution environment.

This is passed to create_trading_universe and couple of other functions and they can determine and take action based the mode of strategy execution. For example, we may load pair and candle data differently in live trading.

Example how to create for backtests:

from tradeexecutor.strategy.execution_context import ExecutionContext, ExecutionMode

execution_context = ExecutionContext(
    mode=ExecutionMode.backtesting,
)
__init__(mode, timed_task_context_manager=<function timed_task>)#
Parameters:
Return type:

None

Methods

__init__(mode[, timed_task_context_manager])

timed_task_context_manager(**context_info)

A simple context manger to measure the duration of different tasks.

Attributes

live_trading

Are we doing live trading.

mode

What is the current mode of the execution.
mode: ExecutionMode#

What is the current mode of the execution.

timed_task_context_manager(**context_info)#

Python context manager for timed tasks.

Functions can use this context manager to add them to the tracing. Used for profiling the strategy code run-time performance.

Set default to tradeexecutor.utils.timer.timed_task(). which logs task duration using logging.INFO level.

Parameters:

task_name (str) –

Return type:

AbstractContextManager[None]

property live_trading: bool#

Are we doing live trading.

Returns:

True if we doing live trading or paper trading.

False if we are operating on backtesting data.

__init__(mode, timed_task_context_manager=<function timed_task>)#
Parameters:
Return type:

None