Logging

The platform provides logging for both backtesting and live trading using a high-performance logging system implemented in Rust with a standardized facade from the log crate.

The core logger operates in a separate thread and uses a multi-producer single-consumer (MPSC) channel to receive log messages. This design ensures that the main thread remains performant, avoiding potential bottlenecks caused by log string formatting or file I/O operations.

Logging output is configurable and supports:

• stdout/stderr writer for console output
• file writer for persistent storage of logs

info

Infrastructure such as Vector can be integrated to collect and aggregate events within your system.

Configuration

Logging can be configured by importing the LoggingConfig object. By default, log events with an 'INFO' LogLevel and higher are written to stdout/stderr.

Log level (LogLevel) values include (and generally match Rust's tracing level filters).

Python loggers expose the following levels:

• OFF
• DEBUG
• INFO
• WARNING
• ERROR

warning

The Python Logger does not provide a trace() method; TRACE level logs are only emitted by the underlying Rust components and cannot be generated directly from Python code.

See the LoggingConfig API Reference for further details.

Logging can be configured in the following ways:

• Minimum LogLevel for stdout/stderr
• Minimum LogLevel for log files
• Maximum size before rotating a log file
• Maximum number of backup log files to maintain when rotating
• Automatic log file naming with date or timestamp components, or custom log file name
• Directory for writing log files
• Plain text or JSON log file formatting
• Filtering of individual components by log level
• ANSI colors in log lines
• Bypass logging entirely
• Print Rust config to stdout at initialization
• Optionally initialize logging via the PyO3 bridge (use_pyo3) to capture log events emitted by Rust components
• Truncate existing log file on startup if it already exists (clear_log_file)

Standard output logging

Log messages are written to the console via stdout/stderr writers. The minimum log level can be configured using the log_level parameter.

File logging

Log files are written to the current working directory by default. The naming convention and rotation behavior are configurable and follow specific patterns based on your settings.

You can specify a custom log directory using log_directory and/or a custom file basename using log_file_name. Log files are always suffixed with .log (plain text) or .json (JSON).

For detailed information about log file naming conventions and rotation behavior, see the Log file rotation and Log file naming convention sections below.

Log file rotation

Rotation behavior depends on both the presence of a size limit and whether a custom file name is provided:

• Size-based rotation:
  • Enabled by specifying the log_file_max_size parameter (e.g., 100_000_000 for 100 MB).
  • When writing a log entry would make the current file exceed this size, the file is closed and a new one is created.
• Date-based rotation (default naming only):
  • Applies when no log_file_max_size is specified and no custom log_file_name is provided.
  • At each UTC date change (midnight), the current log file is closed and a new one is started, creating one file per UTC day.
• No rotation:
  • When a custom log_file_name is provided without a log_file_max_size, logs continue to append to the same file.
• Backup file management:
  • Controlled by the log_file_max_backup_count parameter (default: 5), limiting the total number of rotated files kept.
  • When this limit is exceeded, the oldest backup files are automatically removed.

Log file naming convention

The default naming convention ensures log files are uniquely identifiable and timestamped. The format depends on whether file rotation is enabled:

With file rotation enabled:

• Format: {trader_id}_{%Y-%m-%d_%H%M%S:%3f}_{instance_id}.{log|json}
• Example: TESTER-001_2025-04-09_210721:521_d7dc12c8-7008-4042-8ac4-017c3db0fc38.log
• Components:
  • {trader_id}: The trader identifier (e.g., TESTER-001).
  • {%Y-%m-%d_%H%M%S:%3f}: Full ISO 8601-compliant datetime with millisecond resolution.
  • {instance_id}: A unique instance identifier.
  • {log|json}: File suffix based on format setting.

With file rotation disabled:

• Format: {trader_id}_{%Y-%m-%d}_{instance_id}.{log|json}
• Example: TESTER-001_2025-04-09_d7dc12c8-7008-4042-8ac4-017c3db0fc38.log
• Components:
  • {trader_id}: The trader identifier.
  • {%Y-%m-%d}: Date only (YYYY-MM-DD).
  • {instance_id}: A unique instance identifier.
  • {log|json}: File suffix based on format setting.

Custom naming:

If log_file_name is set (e.g., my_custom_log):

• With rotation disabled: The file will be named exactly as provided (e.g., my_custom_log.log).
• With rotation enabled: The file will include the custom name and timestamp (e.g., my_custom_log_2025-04-09_210721:521.log).

Component log filtering

The log_component_levels parameter can be used to set log levels for each component individually. The input value should be a dictionary of component ID strings to log level strings: dict[str, str].

Below is an example of a trading node logging configuration that includes some of the options mentioned above:

from posei_trader.config import LoggingConfig
from posei_trader.config import TradingNodeConfig

config_node = TradingNodeConfig(
    trader_id="TESTER-001",
    logging=LoggingConfig(
        log_level="INFO",
        log_level_file="DEBUG",
        log_file_format="json",
        log_component_levels={ "Portfolio": "INFO" },
    ),
    ... # Omitted
)

For backtesting, the BacktestEngineConfig class can be used instead of TradingNodeConfig, as the same options are available.

Log Colors

ANSI color codes are utilized to enhance the readability of logs when viewed in a terminal. These color codes can make it easier to distinguish different parts of log messages. In environments that do not support ANSI color rendering (such as some cloud environments or text editors), these color codes may not be appropriate as they can appear as raw text.

To accommodate for such scenarios, the LoggingConfig.log_colors option can be set to false. Disabling log_colors will prevent the addition of ANSI color codes to the log messages, ensuring compatibility across different environments where color rendering is not supported.

Using a Logger directly

It's possible to use Logger objects directly, and these can be initialized anywhere (very similar to the Python built-in logging API).

If you aren't using an object which already initializes a PoseiKernel (and logging) such as BacktestEngine or TradingNode, then you can activate logging in the following way:

from posei_trader.common.component import init_logging
from posei_trader.common.component import Logger

log_guard = init_logging()
logger = Logger("MyLogger")

info

See the init_logging API Reference for further details.

warning

Only one logging system can be initialized per process with an init_logging call, and the LogGuard which is returned must be kept alive for the lifetime of the program.

LogGuard: Managing log lifecycle

The LogGuard ensures that the logging system remains active and operational throughout the lifecycle of a process. It prevents premature shutdown of the logging system when running multiple engines in the same process.

Why use LogGuard?

Without a LogGuard, any attempt to run sequential engines in the same process may result in errors such as:

Error sending log event: [INFO] ...

This occurs because the logging system's underlying channel and Rust Logger are closed when the first engine is disposed. As a result, subsequent engines lose access to the logging system, leading to these errors.

By leveraging a LogGuard, you can ensure robust logging behavior across multiple backtests or engine runs in the same process. The LogGuard retains the resources of the logging system and ensures that logs continue to function correctly, even as engines are disposed and initialized.

note

Using LogGuard is critical to maintain consistent logging behavior throughout a process with multiple engines.

Running multiple engines

The following example demonstrates how to use a LogGuard when running multiple engines sequentially in the same process:

log_guard = None  # Initialize LogGuard reference

for i in range(number_of_backtests):
    engine = setup_engine(...)

    # Assign reference to LogGuard
    if log_guard is None:
        log_guard = engine.get_log_guard()

    # Add actors and execute the engine
    actors = setup_actors(...)
    engine.add_actors(actors)
    engine.run()
    engine.dispose()  # Dispose safely

Steps

• Initialize LogGuard once: The LogGuard is obtained from the first engine (engine.get_log_guard()) and is retained throughout the process. This ensures that the logging system remains active.
• Dispose engines safely: Each engine is safely disposed of after its backtest completes, without affecting the logging system.
• Reuse LogGuard: The same LogGuard instance is reused for subsequent engines, preventing the logging system from shutting down prematurely.

Considerations

• Single LogGuard per process: Only one LogGuard can be used per process.
• Thread safety: The logging system, including LogGuard, is thread-safe, ensuring consistent behavior even in multi-threaded environments.
• Flush logs on termination: Always ensure that logs are properly flushed when the process terminates. The LogGuard automatically handles this as it goes out of scope.