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intro doc & abs cli

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2020-11-26 16:00:58 +00:00
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34
docs/introduction/introduction.rst
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@@ -21,27 +21,27 @@ Framework
At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone. At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone.
====================== ==============================================================================
Name Description
====================== ==============================================================================
`Data layer` `DataServer` focuses on providing high-performance infrastructure for users to
manage and retrieve raw data. `DataEnhancement` will preprocess the data and
provide the best dataset to be fed into the models.
`Interday Model` `Interday model` focuses on producing prediction scores (aka. `alpha`). Models
are trained by `Model Creator` and managed by `Model Manager`. Users could
choose one or multiple models for prediction. Multiple models could be combined
with `Ensemble` module.
`Interday Strategy` `Portfolio Generator` will take prediction scores as input and output the ======================== ==============================================================================
orders based on the current position to achieve the target portfolio. Name Description
======================== ==============================================================================
`Infrastructure` layer `Infrastructure` layer provides underlying support for Quant research.
`DataServer` provides high-performance infrastructure for users to manage
and retrieve raw data. `Trainer` provides flexible interface to control
the training process of models which enable algorithms controlling the
training process.
`Intraday Trading` `Order Executor` is responsible for executing orders output by `Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment.
`Interday Strategy` and returning the executed results. `Information Extractor` extracts data for models. `Forecast Model` focuses
on producing all kinds of forecast signals (e.g. _alpha_, risk) for other
modules. With these signals `Portfolio Generator` will generate the target
portfolio and produce orders to be executed by `Order Executor`.
`Analysis` Users could get a detailed analysis report of forecasting signals and portfolios `Interface` layer `Interface` layer tries to present a user-friendly interface for the underlying
in this part. system. `Analyser` module will provide users detailed analysis reports of
====================== ============================================================================== forecasting signals, portfolios and execution results
======================== ==============================================================================
- The modules with hand-drawn style are under development and will be released in the future. - The modules with hand-drawn style are under development and will be released in the future.
- The modules with dashed borders are highly user-customizable and extendible. - The modules with dashed borders are highly user-customizable and extendible.

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qlib/workflow/cli.py
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@@ -8,7 +8,7 @@ import qlib
import fire import fire
import pandas as pd import pandas as pd
import ruamel.yaml as yaml import ruamel.yaml as yaml
from ..model.trainer import task_train from qlib.model.trainer import task_train
def get_path_list(path): def get_path_list(path):