import yfinance as yf
import pandas as pd


class SimpleStockData:
    def __init__(self, ticker_list: list, period_start: str, period_end: str, to_currency: str = ""):
        """
        :param period_start:
            start date (format YYYY-MM-DD)
        :param period_end:
            end date (format YYYY-MM-DD)
        :param ticker_list:
            list containing all stocks/exchange rates (yfinance considers both as "Tickers")
        :param to_currency:
            currency to convert rates to
        """

        self.ticker_list = ticker_list
        self.to_currency = to_currency
        self._period_start = period_start
        self._period_end = period_end

        self._exchange_df = None  # Mapping: time mapped to conversion factor, to get the right converted value per date
        self._create_exchange_dataframe()  # initialize self.exchange_df attribute

    def _get_history(self, idx, interval="1d"):
        """
        Function for internal use; Just a wrapper around the .history method of the yfinance Ticker class
        :param idx:
            the index of the share
        :param interval:
            granularity of data - valid values are 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
        :return: pandas.DataFrame
        """

        return yf.Ticker(self.ticker_list[idx]).history(interval=interval, start=self._period_start,
                                                        end=self._period_end)

    def _create_exchange_dataframe(self):
        """
        The class has two separate attributes, one to store the plain convert list
        (_from_currency_list), and one containing the real mapping needed to convert.
        The mapping is recreated by this function following the information in the
        _from_currency_list.
        return:
            boolean
        """

        # check if a to_currency is even given
        if self.to_currency == "":
            return False

        # create the list of currencies based on all the stocks of the class
        _from_currency_list = []
        for i in range(len(self.ticker_list)):  # to get all indexes; this adds an entry for each currency
            add_currency = f"{self.get_info(i, 'currency')}{self.to_currency}=X"  # Format: "fffttt=X" f=from, t=to
            # for the case that FROM and TO are equal, just don't download the data (as conversion factor's 1)
            if add_currency == f"{self.to_currency}{self.to_currency}=X":
                pass
            elif add_currency not in _from_currency_list:  # add a new item if not already there
                _from_currency_list.append(add_currency)

            print(_from_currency_list)

        # now the real process begins
        tickers = yf.Tickers(" ".join(_from_currency_list))  # create a new Ticker instance with all wanted currencies
        exchange_rates = []
        for i in tickers.tickers:  # get all the history of each currency conversion factors
            # for simplicity: using the conversion factor of closing (.Close at the end)
            exchange_rates.append(tickers.tickers[i].history(start=self._period_start, end=self._period_end).Close)

        self._exchange_df = pd.DataFrame(exchange_rates).T  # transpose the dataframe (imagine just switching rows and columns)
        self._exchange_df.columns = _from_currency_list  # set the right names for the columns in the dataframe
        self._exchange_df[f"{self.to_currency}{self.to_currency}=X"] = 1.0  # for FROM and TO being equal: set factor to 1

        return True

    def get_info(self, idx, key=""):
        """
        :param idx:
            the index of the share
        :param key:
            OPTIONAL. gives which specific datum is wanted
        :return:
        """

        info = yf.Ticker(self.ticker_list[idx]).info
        if key != "":  # if just one specific information is wanted
            return info[key]
        return info

    def get_history(self, idx, interval="1d"):
        """
        Just a wrapper around the .history method of the yfinance Ticker class
        :param idx:
            the index of the share
        :param interval:
            granularity of data - valid values are 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
        :return: pandas.DataFrame
        """

        ret = self._get_history(idx, interval)
        return ret