download_historical_prices (done).py

import os
from pandas_datareader import data as pdr
import pandas as pd
import fix_yahoo_finance as yf

yf.pdr_override()

START_DATE = "2003-08-01"
END_DATE = "2015-01-01"


def build_stock_dataset(start=START_DATE, end=END_DATE):
    """
    Creates the dataset containing all stock prices
    :returns: stock_prices.csv
    """

    statspath = "intraQuarter/_KeyStats/"
    ticker_list = os.listdir(statspath)

    # Required on macOS
    if ".DS_Store" in ticker_list:
        os.remove(f"{statspath}/.DS_Store")
        ticker_list.remove(".DS_Store")

    # Get all Adjusted Close prices for all the tickers in our list,
    # between START_DATE and END_DATE
    all_data = pdr.get_data_yahoo(ticker_list, start, end)
    stock_data = all_data["Adj Close"]

    # Remove any columns that hold no data, and print their tickers.
    stock_data.dropna(how="all", axis=1, inplace=True)
    missing_tickers = [
        ticker for ticker in ticker_list if ticker.upper() not in stock_data.columns
    ]
    print(f"{len(missing_tickers)} tickers are missing: \n {missing_tickers} ")
    # If there are only some missing datapoints, forward fill.
    stock_data.ffill(inplace=True)
    stock_data.to_csv("stock_prices.csv")


def build_sp500_dataset(start=START_DATE, end=END_DATE):
    """
    Creates the dataset containing S&P500 prices
    :returns: sp500_index.csv
    """
    index_data = pdr.get_data_yahoo("SPY", start=START_DATE, end=END_DATE)
    index_data.to_csv("sp500_index.csv")


def build_dataset_iteratively(
    idx_start, idx_end, date_start=START_DATE, date_end=END_DATE
):
    """
    This is an alternative iterative solution to building the stock dataset, which may be necessary if the
    tickerlist is too big.
    Instead of downloading all at once, we download ticker by ticker and append to a dataframe.
    This will download data for tickerlist[idx_start:idx_end], which makes this method suitable
    for chunking data.

    :param idx_start: (int) the starting index of the tickerlist
    :param idx_end: (int) the end index of the tickerlist
    """

    statspath = "intraQuarter/_KeyStats/"
    ticker_list = os.listdir(statspath)

    df = pd.DataFrame()

    for ticker in ticker_list:
        ticker = ticker.upper()

        stock_ohlc = pdr.get_data_yahoo(ticker, start=date_start, end=date_end)
        if stock_ohlc.empty:
            print(f"No data for {ticker}")
            continue
        adj_close = stock_ohlc["Adj Close"].rename(ticker)
        df = pd.concat([df, adj_close], axis=1)
    df.to_csv("stock_prices.csv")


if __name__ == "__main__":
    build_stock_dataset()
    build_sp500_dataset()