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import datetime as dt
import logging
import os
import pandas as pd
import yfinance as yf
class FetchData:
def __init__(self, ticker: str, fetchperiodinweeks: int = 12, debug=False):
# set up logging
if debug:
self.logger_level = logging.DEBUG
else:
self.logger_level = logging.INFO
self.logger = logging.getLogger(__name__)
logging.basicConfig(level=self.logger_level) # filename='TechnicalAnalysis.log',
self.ticker = ticker
self.fetchperiodinweeks = fetchperiodinweeks
def run(
self
) -> pd.DataFrame:
"""
Fetches historical stock price data from Yahoo Finance.
This method downloads historical stock price data for the specified
ticker over a given period of weeks. The data is fetched on a daily
interval and stored in a pandas DataFrame. If the download is successful,
redundant ticker columns are removed, and logging information is
recorded. In case of failure, an empty DataFrame is returned and an
exception is raised.
Returns:
pd.DataFrame
A DataFrame containing the historical stock price data with columns
for open, high, low, close, volume, and adjusted close prices.
Raises:
Exception
If the data fetching fails, an exception is raised with an error
message.
"""
period_start = dt.datetime.now() - dt.timedelta(weeks=self.fetchperiodinweeks)
self.logger.info(f'Fetching price data for {self.ticker}')
try:
df = yf.download(
self.ticker,
start=period_start,
end=dt.datetime.now(),
interval='1d'
)
except Exception as e:
self.logger.error(f'{e}')
# create empty df
df = pd.DataFrame()
if df.shape[0] > 0:
# get rid of the redundant ticker column
df.columns = df.columns.droplevel('Ticker')
self.logger.debug(df.head(10))
self.logger.info(f'Fetched {df.shape[0]} rows for {self.ticker}')
return df |