From 814ecbb488afd5d824c815d122504f91169174bd Mon Sep 17 00:00:00 2001 From: Young Date: Thu, 26 Nov 2020 16:00:58 +0000 Subject: [PATCH] intro doc & abs cli --- docs/introduction/introduction.rst | 34 +++++++++++++++--------------- qlib/workflow/cli.py | 2 +- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/introduction/introduction.rst b/docs/introduction/introduction.rst index 3e4d11e28..06fac46fa 100644 --- a/docs/introduction/introduction.rst +++ b/docs/introduction/introduction.rst @@ -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. -====================== ============================================================================== -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 - `Interday Strategy` and returning the executed results. +`Workflow` layer `Workflow` layer covers the whole workflow of quantitative investment. + `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 - in this part. -====================== ============================================================================== +`Interface` layer `Interface` layer tries to present a user-friendly interface for the underlying + 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 dashed borders are highly user-customizable and extendible. diff --git a/qlib/workflow/cli.py b/qlib/workflow/cli.py index b9c040e87..08c13de2a 100644 --- a/qlib/workflow/cli.py +++ b/qlib/workflow/cli.py @@ -8,7 +8,7 @@ import qlib import fire import pandas as pd import ruamel.yaml as yaml -from ..model.trainer import task_train +from qlib.model.trainer import task_train def get_path_list(path):