lsc1205 / python_assignment

Project Description

This project provides simple APIs to query the stock price trends and statistics of a given company.

Table of contents

• Tech Stack
• Usage
• Discussion

Tech Stack

Project is created with:

• Git: Version control
• Docker: Package and deploy application
• Flask: API Service
• SqlAlchemy + MySQL: Data persistence

python_assignment
│  docker-compose.yml
│  Dockerfile                  # dockerfile for api service
│  get_raw_data.py             # get raw financial data and store them in database
│  README.md
│  README_N.md
│  requirements.txt            # dependencies
│  schema.sql                  # sql statement for create financial_data table
│
└─financial
    │  app.py                  # flask app entrance
    │  __init__.py
    │
    ├─api                      # api entrance
    │      financial_data.py   # financial data query api
    │      statistics.py       # financial statistics query api
    │      util.py             # error handling decorator
    │      __init__.py
    │
    ├─mysql                    # database abstraction for python
    │      config.py           # mysql config file
    │      engine.py           # sqlalchemy engine
    │      __init__.py
    │
    └─test                     # tests for apis
            financial_data_test.py # tests for functions in financial_data module
            statistics_test.py     # tests for functions in statistics module
            __init__.py

Dependencies

requests     # requests is one of most popular python libraries to send http requests, so we choose to use it. 
flask        # Light weighted and popular web framework
PyMySQL      # Dependency of sqlalchemy
cryptography # Used for mysql connection
sqlalchemy   # Efficient and high-performing python database toolkit. We can use it to connect mysql and execute sql statements.

Usage

1. Launch the api service and mysql in docker. The database and table used to store stock price information will be automatically created.

$ docker-compose up           

2. After launch, the database table is empty, so we should retrieve some stock data from API provided by AlphaVantage and store them in local db.

$ python get_raw_data.py       

3. Then, we can query data through APIs.

# HOST: http://localhost:5000

# Financial data API
# path: /api/financial_data
# method: GET
# request:
# {
#     "start_date": str,  # e.g., "2023-01-01", optional
#     "end_date": str,    # e.g., "2023-05-14", optional
#     "symbol": str,      # e.g., "IBM", optional
#     "limit": int,       # e.g., 3
#     "page": int         # e.g., 2
# }
# response:
# {
#     "data": [
#         {
#             "symbol": str,
#             "date": str,
#             "open_price": str,
#             "close_price": str,
#             "volume": str,
#         }
#     ],
#     "pagination": {
#         "count": int,
#         "page": int,
#         "limit": int,
#         "pages": int
#     },
#     "info": {'error': ''}
# }
# example:
curl -X GET 'http://localhost:5000/api/financial_data?start_date=2023-01-01&end_date=2023-05-14&symbol=IBM&limit=3&page=2'


# Financial statistics API
# path: /api/statistics
# method: GET
# request:
# {
#     "start_date": str,  # e.g., "2023-01-01"
#     "end_date": str,    # e.g., "2023-05-14"
#     "symbol": str,      # e.g., "IBM"
# }
# response:
# {
#     "data": [
#         {
#           "start_date": str,
#           "end_date": str,
#           "symbol": str,
#           "average_daily_open_price": float,
#           "average_daily_close_price": float,
#           "average_daily_volume": int
#         }
#     ],
#     "pagination": {
#         "count": int,
#         "page": int,
#         "limit": int,
#         "pages": int
#     },
#     "info": {'error': ''}
# }
# example:
curl -X GET 'http://localhost:5000/api/statistics?start_date=2023-01-01&end_date=2023-05-31&symbol=IBM'

Discussion

API key storage method:

Local development

Directly hard-coded in the python file.

Production environment