QEF-Backtesting-System

This is a Quantitative Evaluation Framework written in Python. Traders can gain insight into the effectiveness of an idea without risking funds in trading accounts through the QEF-Backtesting system.

Prerequisites

1. Fork or clone the repository.

2. Downloads the data provided in the QEF shared folders and rename it as raw_data. The folder structure is provided in the following for clarification.

.
|
|--- raw_data
|   |--- mapping.csv
|   |--- pricevol.csv
|
|--- Util
    |--- data-processing.py

Preliminary Setup

1. You are advised to install Python dependencies as listed. But you can also maintain dependencies on your own.

pip3 install -r dependency.txt

or

python3 -m pip install -r dependency.txt

2. Run the data-procesing_0050.py (or other data-processing python script) to process the raw_data and store them into Dataset. Remeber to execute it under the folder Util.

cd Util
python3 data-processing_0050.py

Run Main Program

1. To run the main program, you have to first local in QEF-Backtesting-System folder.

2. The usage of the main program:

python3 main.py --start-date START_DATE --end-date END_DATE  
               --strategy_file STRATEGY_FILE --universe_file UNIVERSE_FILE  
               [--save_file SAVE_FILE]  

The (optional) arguments are explained as following:

-h, --help
        show this help message and exit  
  
--start-date START_DATE  
        Backtesting Strating Time, fromat: YYYY-mm-dd  
  
--end-date END_DATE     
        Backtesting Ending Time, fromat: YYYY-mm-dd  
      
--strategy_file STRATEGY_FILE
        Backtesting Strategy File Name (withou .py)

--universe_file UNIVERSE_FILE  
        Backtesting Universe Path Name  

--save_file SAVE_FILE
        The root repository to store your result in historicla_strategy

For example, you can use the following command to see the result of the given duration with strategy and universe.

python3 main.py --start-date 2019-01-01 --end-date 2019-3-31 --strategy_file mean_reversal --universe_file Taiwan_50

3. The result will be stored in historical_strategy file with default file name the time of execution. Also, the images, args.txt, strategy, and ratio.csv are store to examine the result.

Strategy Development

To develope a new strategy, you basically need to create a file with a Strategy class. The rules a deliberate on following context.

1. The strategy file must to have the following structure:

# Strategy class  
class Strategy:  
  
    # Main trading function  
    def trade(
        self, data: pd.DataFrame, holding: Holding, fiat: float, current_date: dt.date
    ) -> OrderBook:  

        return newOrderBook

2. The Engine will call Strategy.trade and pass in current price volume data, current holding, current fiat possessions, and current date.

3. The price volume data is passed in as a pd.Dataframe as following:

>> df.head(5)

                          id identifier exchange                     date_  \
__null_dask_index__                                                          
0                    2866446       0050       TW 2020-01-08 00:00:00+00:00   
1                    3015756       1101       TW 2020-01-08 00:00:00+00:00   
2                    3026328       1102       TW 2020-01-08 00:00:00+00:00   
3                    3100755       1216       TW 2020-01-08 00:00:00+00:00   
4                    3192801       1301       TW 2020-01-08 00:00:00+00:00   

                      high_    low_   open_  close_      volume_  adj_close_  \
__null_dask_index__                                                            
0                     96.20   95.05   95.20   95.65   8516995.00     91.9717   
1                     43.55   43.15   43.15   43.40  14112923.38     38.9986   
2                     48.55   47.85   47.90   48.55   7750971.00     45.4042   
3                     73.40   72.60   73.40   72.60   5159215.00     70.0328   
4                    101.50  100.00  101.50  100.00   4670180.00     94.9772   

                     is_backfill                     db_timestamp  
__null_dask_index__                                                
0                           True 2021-01-03 03:24:00.830558+00:00  
1                           True 2021-01-03 03:25:44.288406+00:00  
2                           True 2021-01-03 03:25:46.498593+00:00  
3                           True 2021-01-03 03:26:06.165567+00:00  
4                           True 2021-01-03 03:26:31.129308+00:00  

4. The trade function will return the OrderBook and the system will handle the rest.

Universe Development

There are mainly two ways to develop the universe.

1. Completely rewrites the data-processing.py and make sure to store the result in Dataset/Universe file.
2. The second ways is to gather the ticker of your universe and then put it in Util/parameters.py. Then, import them in data-processing.py.

Additional Information

The main structure is represent as below: