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lzh222333
2021-05-17 07:27:55 +00:00
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5
docs/component/online.rst
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@@ -8,6 +8,11 @@ Online Serving
Introduction
=============
.. image:: ../_static/img/online_serving.png
:align: center
In addition to backtesting, one way to test a model is effective is to make predictions in real market conditions or even do real trading based on those predictions.
``Online Serving`` is a set of modules for online models using the latest data,
which including `Online Manager <#Online Manager>`_, `Online Strategy <#Online Strategy>`_, `Online Tool <#Online Tool>`_, `Updater <#Updater>`_.

24
qlib/workflow/online/manager.py
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@@ -11,13 +11,27 @@ So this module provides a series of methods to control this process.
This module also provides a method to simulate `Online Strategy <#Online Strategy>`_ in history.
Which means you can verify your strategy or find a better one.
There are total 3 situations for using the different trainer:
There are 4 total situations for using different trainers in different situations:
1: Online: Only use Trainer.
2: Simulate with temporal dependence: Only use Trainer.
3: Simulate without temporal dependence: Use Trainer or DelayTrainer.
========================= ===================================================================================
Situations Description
========================= ===================================================================================
Online + Trainer When you REAL want to do a routine, the Trainer will help you train the models.
Online + DelayTrainer In normal online routine, whether Trainer or DelayTrainer will REAL train models
in this routine. So it is not necessary to use DelayTrainer when do a REAL routine.
Simulation + Trainer When your models have some temporal dependence on the previous models, then you
need to consider using Trainer. This means it will REAL train your models in
every routine and prepare signals for every routine.
Simulation + DelayTrainer When your models don't have any temporal dependence, you can use DelayTrainer
for the ability to multitasking. It means all tasks in all routines
can be REAL trained at the end of simulating. The signals will be prepared well at
different time segments (based on whether or not any new model is online).
========================= ===================================================================================
"""
import logging
@@ -207,7 +221,7 @@ class OnlineManager(Serializable):
"""
return self.signals
SIM_LOG_LEVEL = logging.INFO + 1
SIM_LOG_LEVEL = logging.INFO + 1 # when simulating, reduce information
SIM_LOG_NAME = "SIMULATE_INFO"
def simulate(