Implementing class structure with minimal logic

portfolio_example_2.py

"""
Portfolio implementation
"""


def eligible_for_rebalance(trading_day: Arrow) -> bool:
    """Decides if a given trading day should trigger a rebalance and returns a boolean."""
    return is_eligible


def get_adjusted_close(s3_client: S3Client, s3_bucket: str, ticker: str, trading_day: Arrow) -> float:
    """Returns the closing price for a symbol on a given day."""
    return qm.equities.historical.get_price(s3_client, s3_bucket, ticker, trading_day)


def update_positions(
    s3_client: S3Client, s3_bucket: str, transactions: pd.DataFrame, trading_day: Arrow, cash: float
) -> pd.DataFrame:
    # updates transactions df and returns it
    # NOTE: s3_client is injected to make the connection, rather than depending on class attributes
    return df


def calculate_returns(positions: pd.DataFrame) -> pd.Series:
    """
    Calculates daily returns over the same time period and returns a Series.

    > NOTE: Return calculations will mirror the date range of `positions`
    """
    return positions.sum(axis=1).pct_change().fillna(0)


class Portfolio:
    def __init__(self, **kwargs):
        self.capital_allocation = capital_allocation
        ...
        self.s3_client = boto3.client("s3")

    @property
    def transactions(self):
        """Records all transactions through portfolio's existence period."""
        if self._transactions.empty:
            self.run()
        return self._transactions

    def run(self):
        """
        Iterates through each day of the portfolio time period.

        Add record to positions dataframe and returns series identify if action should be taken if so.
        pull data for equity universe and identify top n stocks
        calculate position size for each stock and number of stocks to purchase
        liquidate all existing positions (add to transactions dataframe)
        enter new positions (add to transactions dataframe)
        Rebalance quarterly, before quarter ending months: end of February, May, August, and November.
        """
        # orchestrates calls to various funcitons and updates "private" variables like _transactions
        pass


"""
Testing our Portfolio implementation
"""

def test_update_positions(s3_client, s3_bucket, transactions):
    expected_positions = pd.DataFrame(...)
    positions = update_positions(s3_client, s3_bucket, transactions, "2020-01-01")
    assert expected_positions == positions