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Spreads and Intraday Analysis

fiat_comparison(currencies, root=None)

Build a comparison table across multiple fiat currencies (daily metrics).

Parameters:

Name Type Description Default
currencies sequence of str

List of fiat codes to include (e.g. ['ARS', 'BOB', 'EUR']).

 required
root path - like

Root of processed data.

 None

Returns:

Type Description
DataFrame

One row per date per currency, with: - date - currency - avg_buy_price - avg_sell_price - spread_abs - spread_pct
Source code in src\p2p_analytics\spreads.py 
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def fiat_comparison(
    currencies: Sequence[str],
    root: str | None = None,
) -> pd.DataFrame:
    """
    Build a comparison table across multiple fiat currencies (daily metrics).

    Parameters
    ----------
    currencies : sequence of str
        List of fiat codes to include (e.g. ['ARS', 'BOB', 'EUR']).
    root : path-like, optional
        Root of processed data.

    Returns
    -------
    pandas.DataFrame
        One row per date per currency, with:
        - date
        - currency
        - avg_buy_price
        - avg_sell_price
        - spread_abs
        - spread_pct
    """
    df = load_binance_master(root=root)
    df_sub = df[df["currency"].isin(currencies)].copy()

    grouped = (
        df_sub
        .groupby(["date", "currency", "side"], as_index=False)["price"]
        .mean()
        .pivot_table(
            index=["date", "currency"],
            columns="side",
            values="price"
        )
        .rename(columns={"BUY": "avg_buy_price", "SELL": "avg_sell_price"})
        .reset_index()
    )

    grouped["spread_abs"] = (grouped["avg_sell_price"] - grouped["avg_buy_price"]).abs()
    mid = (grouped["avg_sell_price"] + grouped["avg_buy_price"]) / 2
    grouped["spread_pct"] = grouped["spread_abs"] / mid * 100

    return grouped

intraday_profile(currency, start=None, end=None, root=None)

Intraday price profile for a given currency (average over all days).

Parameters:

Name Type Description Default
currency str

Fiat currency code.

 required
start str or Timestamp

Start date (inclusive).

 None
end str or Timestamp

End date (inclusive).

 None
root path - like

Root of processed data.

 None

Returns:

Type Description
DataFrame

One row per hour (0–23) with: - hour - currency - mean_buy_price - mean_sell_price
Source code in src\p2p_analytics\spreads.py 
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def intraday_profile(
    currency: str,
    start: str | pd.Timestamp | None = None,
    end: str | pd.Timestamp | None = None,
    root: str | None = None,
) -> pd.DataFrame:
    """
    Intraday price profile for a given currency (average over all days).

    Parameters
    ----------
    currency : str
        Fiat currency code.
    start : str or pandas.Timestamp, optional
        Start date (inclusive).
    end : str or pandas.Timestamp, optional
        End date (inclusive).
    root : path-like, optional
        Root of processed data.

    Returns
    -------
    pandas.DataFrame
        One row per hour (0–23) with:
        - hour
        - currency
        - mean_buy_price
        - mean_sell_price
    """
    df = load_binance_currency(currency, root=root)
    df = _ensure_datetime(df, "scrape_datetime")

    df["date"] = pd.to_datetime(df["date"]).dt.date
    df["hour"] = df["scrape_datetime"].dt.hour

    if start is not None:
        df = df[df["date"] >= pd.to_datetime(start).date()]

    if end is not None:
        df = df[df["date"] <= pd.to_datetime(end).date()]

    grouped = (
        df
        .groupby(["hour", "side"], as_index=False)["price"]
        .mean()
        .pivot_table(
            index="hour",
            columns="side",
            values="price"
        )
        .rename(columns={"BUY": "mean_buy_price", "SELL": "mean_sell_price"})
        .reset_index()
    )

    grouped["currency"] = currency
    return grouped

p2p_spread(currency, by='day', root=None)

Compute BUY–SELL spread for a given currency using the Phase 1 parquet files.

Parameters:

Name Type Description Default
currency str

Fiat code, e.g. 'ARS', 'BOB', 'EUR'.

 required
by (day, hour)

Aggregation level for spreads.

 'day'
root path - like

Root of processed data. If None, infer from project layout.

 None

Returns:

Type Description
DataFrame

Columns include: - date (and hour if by='hour') - currency - avg_buy_price - avg_sell_price - spread_abs - spread_pct
Source code in src\p2p_analytics\spreads.py 
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def p2p_spread(
    currency: str,
    by: ByType = "day",
    root: str | None = None,
) -> pd.DataFrame:
    """
    Compute BUY–SELL spread for a given currency using the Phase 1 parquet files.

    Parameters
    ----------
    currency : str
        Fiat code, e.g. 'ARS', 'BOB', 'EUR'.
    by : {'day', 'hour'}, default 'day'
        Aggregation level for spreads.
    root : path-like, optional
        Root of processed data. If None, infer from project layout.

    Returns
    -------
    pandas.DataFrame
        Columns include:
        - date (and hour if by='hour')
        - currency
        - avg_buy_price
        - avg_sell_price
        - spread_abs
        - spread_pct
    """
    df = load_binance_currency(currency, root=root)
    return _p2p_spread_core(df, currency, by=by)