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Add backtest and backforward task (#1568)

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* * add TrainTask & BacktestTask;
* add BackForwardTask;
* adjust prompt_template.yaml which default config failed to backtest;
* run workflow in loop
* add update method to prompt_template.py

* remove debug code

* Adjust Learn Process
* add LearnManager class & use LearnManager to update system prompt;
* use qrun to replace recorder for training and backtesting;

* Adjust analyser
* analyser independent of recorder;
* rename analyser's workspace attribution;
* analyser load variable by recorder.

---------

Co-authored-by: Cadenza-Li <362237642@qq.com>
This commit is contained in:
Fivele-Li
2023-06-30 10:04:43 +08:00
committed by GitHub
parent 1326ac614d
commit 7e84f3aae2
8 changed files with 179 additions and 92 deletions
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11
qlib/finco/prompt_template.yaml
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@@ -193,6 +193,13 @@ SummarizeTask_user : |-
Here is my information: '{{information}}'
My intention is: {{user_prompt}}. Please provide me with a summary and recommendation based on my intention and the information I have provided. There are some figures which absolute path are: {{figure_path}}, You must display these images in markdown using the appropriate image format.
BackForwardTask_system : |-
Your task is adjusting system prompt in each task to fulfill user's intention
BackForwardTask_user : |-
Here is the final summary: '{{summary}}'
Tasks I have run are: {{task_finished}}, {{task}}'s system prompt is: {{system}}. User's intention is: {{user_prompt}}. you will adjust it to:
mods:
ConfigActionTask_system:
Dataset:
@@ -382,7 +389,7 @@ mods:
```
Reason: I choose the backtest parameters above because they are suitable for a low turnover strategy focusing on long-term returns in the China A stock market. The start and end times are set to cover a 4-year period, which is reasonable for a long-term strategy. The account value is set to 1,000,000 as a starting point, and the benchmark is set to SH000300, which represents the China A stock market.
Improve suggestion: You can try different time ranges for the backtest to evaluate the performance of the strategy in different market conditions. Also, you can adjust the costs (open_cost, close_cost, and min_cost) to better reflect the actual trading costs in the China A stock market.
ConfigActionTask_user:
Dataset:
target_component : |-
@@ -402,7 +409,7 @@ mods:
Backtest:
target_component : |-
Backtest
ImplementActionTask_system:
Dataset:
target_component : |-