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286 lines
8.4 KiB
ReStructuredText
286 lines
8.4 KiB
ReStructuredText
.. _online:
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Online
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===================
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.. currentmodule:: qlib
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Introduction
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-------------------
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Welcome to use Online, this module simulates what will be like if we do the real trading use our model and strategy.
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Just like Estimator and other modules in Qlib, you need to determine parameters through the configuration file,
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and in this module, you need to add an account in a folder to do the simulation. Then in each coming day,
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this module will use the newest information to do the trade for your account,
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the performance can be viewed at any time using the API we defined.
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Each account will experience the following processes, the ‘pred_date’ represents the date you predict the target
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positions after trading, also, the ‘trade_date’ is the date you do the trading.
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- Generate the order list (pre_date)
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- Execute the order list (trade_date)
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- Update account (trade_date)
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In the meantime, you can just create an account and use this module to test its performance in a period.
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- Simulate (start_date, end_date)
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This module need to save your account in a folder, the model and strategy will be saved as pickle files,
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and the position and report will be saved as excel.
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The file structure can be viewed at fileStruct_.
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Example
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-------------------
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Let's take an example,
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.. note:: Make sure you have the latest version of `qlib` installed.
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If you want to use the models and data provided by `qlib`, you only need to do as follows.
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Firstly, write a simple configuration file as following,
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.. code-block:: YAML
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strategy:
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class: TopkAmountStrategy
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module_path: qlib.contrib.strategy
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args:
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market: csi500
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trade_freq: 5
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model:
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class: ScoreFileModel
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module_path: qlib.contrib.online.online_model
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args:
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loss: mse
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model_path: ./model.bin
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init_cash: 1000000000
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We then can use this command to create a folder and do trading from 2017-01-01 to 2018-08-01.
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.. code-block:: bash
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online simulate -id v-test -config ./config/config.yaml -exchange_config ./config/exchange.yaml -start 2017-01-01 -end 2018-08-01 -path ./user_data/
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The start date (2017-01-01) is the add date of the user, which also is the first predict date,
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and the end date (2018-08-01) is the last trade date. You can use "`online generate -date 2018-08-02...`"
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command to continue generate the order_list at next trading date.
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If Your account was saved in "./user_data/", you can see the performance of your account compared to a benchmark by
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.. code-block:: bash
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>> online show -id v-test -path ./user_data/ -bench SH000905
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...
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Result of porfolio:
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sub_bench:
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risk
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mean 0.001157
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std 0.003039
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annual 0.289131
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sharpe 6.017635
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mdd -0.013185
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sub_cost:
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risk
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mean 0.000800
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std 0.003043
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annual 0.199944
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sharpe 4.155963
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mdd -0.015517
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Here 'SH000905' represents csi500 and 'SH000300' represents csi300
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Manage your account
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--------------------
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Any account processed by `online` should be saved in a folder. you can use commands
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defined to manage your accounts.
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- add an new account
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This will add an new account with user_id='v-test', add_date='2019-10-15' in ./user_data.
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.. code-block:: bash
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>> online add_user -id {user_id} -config {config_file} -path {folder_path} -date {add_date}
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>> online add_user -id v-test -config config.yaml -path ./user_data/ -date 2019-10-15
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- remove an account
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.. code-block:: bash
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>> online remove_user -id {user_id} -path {folder_path}
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>> online remove_user -id v-test -path ./user_data/
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- show the performance
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Here benchmark indicates the baseline is to be compared with yours.
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.. code-block:: bash
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>> online show -id {user_id} -path {folder_path} -bench {benchmark}
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>> online show -id v-test -path ./user_data/ -bench SH000905
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The default value of all the parameter 'date' below is trade date
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(will be today if today is trading date and information has been updated in `qlib`).
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The 'generate' and 'update' will check whether input date is valid, the following 3 processes should
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be called at each trading date.
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- generate the order list
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generate the order list at trade date, and save them in {folder_path}/{user_id}/temp/ as a json file.
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.. code-block:: bash
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>> online generate -date {date} -path {folder_path}
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>> online generate -date 2019-10-16 -path ./user_data/
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- execute the order list
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execute the order list and generate the transactions result in {folder_path}/{user_id}/temp/ at trade date
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.. code-block:: bash
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>> online execute -date {date} -exchange_config {exchange_config_path} -path {folder_path}
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>> online execute -date 2019-10-16 -exchange_config ./config/exchange.yaml -path ./user_data/
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A simple exchange config file can be as
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.. code-block:: yaml
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open_cost: 0.003
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close_cost: 0.003
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limit_threshold: 0.095
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deal_price: vwap
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- update accounts
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update accounts in "{folder_path}/" at trade date
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.. code-block:: bash
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>> online update -date {date} -path {folder_path}
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>> online update -date 2019-10-16 -path ./user_data/
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API
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------------------
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All those operations are based on defined in `qlib.contrib.online.operator`
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.. automodule:: qlib.contrib.online.operator
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.. _fileStruct:
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File structure
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------------------
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'user_data' indicates the root of folder.
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Name that bold indicates it’s a folder, otherwise it’s a document.
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.. code-block:: yaml
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{user_folder}
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│ users.csv: (Init date for each users)
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│
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└───{user_id1}: (users' sub-folder to save their data)
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│ │ position.xlsx
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│ │ report.csv
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│ │ model_{user_id1}.pickle
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│ │ strategy_{user_id1}.pickle
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│ │
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│ └───score
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│ │ └───{YYYY}
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│ │ └───{MM}
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│ │ │ score_{YYYY-MM-DD}.csv
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│ │
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│ └───trade
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│ └───{YYYY}
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│ └───{MM}
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│ │ orderlist_{YYYY-MM-DD}.json
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│ │ transaction_{YYYY-MM-DD}.csv
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│
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└───{user_id2}
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│ │ position.xlsx
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│ │ report.csv
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│ │ model_{user_id2}.pickle
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│ │ strategy_{user_id2}.pickle
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│ │
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│ └───score
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│ └───trade
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....
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Configuration file
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------------------
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The configure file used in `online` should contain the model and strategy information.
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About the model
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~~~~~~~~~~~~~~~~~~~~
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First, your configuration file needs to have a field about the model,
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this field and its contents determine the model we used when generating score at predict date.
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Followings are two examples for ScoreFileModel and a model that read a score file and return score at trade date.
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.. code-block:: YAML
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model:
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class: ScoreFileModel
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module_path: qlib.contrib.online.OnlineModel
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args:
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loss: mse
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.. code-block:: YAML
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model:
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class: ScoreFileModel
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module_path: qlib.contrib.online.OnlineModel
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args:
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score_path: <your score path>
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If your model doesn't belong to above models, you need to coding your model manually.
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Your model should be a subclass of models defined in 'qlib.contfib.model'. And it must
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contains 2 methods used in `online` module.
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About the strategy
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~~~~~~~~~~~~~~~~~~~~
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Your need define the strategy used to generate the order list at predict date.
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Followings are two examples for a TopkAmountStrategy
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.. code-block:: YAML
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy.strategy
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args:
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topk: 100
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n_drop: 10
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Generated files
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------------------
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The 'online_generate' command will create the order list at {folder_path}/{user_id}/temp/,
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the name of that is orderlist_{YYYY-MM-DD}.json, YYYY-MM-DD is the date that those orders to be executed.
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The format of json file is like
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.. code-block:: python
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{
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'sell': {
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{'$stock_id1': '$amount1'},
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{'$stock_id2': '$amount2'}, ...
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},
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'buy': {
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{'$stock_id1': '$amount1'},
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{'$stock_id2': '$amount2'}, ...
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}
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}
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Then after executing the order list (either by 'online_execute' or other executors), a transaction file
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will be created also at {folder_path}/{user_id}/temp/.
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