Skip to content

UI Widgets#

This module contains reusable Streamlit components for the dashboard.

Parity Analysis Widget#

Creates a Streamlit container to display put-call parity analysis.

This function takes market data and computes the arbitrage gap based on the put-call parity relationship for a user-selected expiration date.

Parameters:

Name Type Description Default
market_data DataFrame

The full option chain data for the asset.

 required
stock Stock

The underlying Stock object, containing spot price and dividend yield.

 required
rate Rate

The risk-free Rate object.

 required
Source code in src/quantfin/dashboard/widgets.py 
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
def render_parity_analysis_widget(
    market_data: pd.DataFrame,
    stock: Stock,
    rate: Rate,
):
    """
    Creates a Streamlit container to display put-call parity analysis.

    This function takes market data and computes the arbitrage gap based on
    the put-call parity relationship for a user-selected expiration date.

    Parameters
    ----------
    market_data : pd.DataFrame
        The full option chain data for the asset.
    stock : Stock
        The underlying Stock object, containing spot price and dividend yield.
    rate : Rate
        The risk-free Rate object.
    """
    st.subheader("Put-Call Parity Analysis")

    # TODO: support for ParityModel; needs vecotrizing. Currently do it here.
    parity_model = ParityModel()  # noqa: F841

    expiries = sorted(market_data["expiry"].unique())
    selected_expiry = st.select_slider(
        "Select Expiry to Analyze",
        options=[pd.to_datetime(e).strftime("%Y-%m-%d") for e in expiries],
    )

    expiry_df = market_data[
        market_data["expiry"] == pd.to_datetime(selected_expiry)
    ].copy()

    calls = expiry_df[expiry_df["optionType"] == "call"].set_index("strike")
    puts = expiry_df[expiry_df["optionType"] == "put"].set_index("strike")

    # Find common strikes
    common_strikes = calls.index.intersection(puts.index)
    if len(common_strikes) == 0:
        st.warning("No common strikes found for this expiry to check parity.")
        return

    calls = calls.loc[common_strikes]
    puts = puts.loc[common_strikes]

    # C - P
    price_diff = calls["last_price"] - puts["last_price"]

    # S*exp(-qT) - K*exp(-rT)
    T = calls["maturity"].iloc[0]
    r = rate.get_rate(T)
    q = stock.dividend

    parity_diff = stock.spot * np.exp(-q * T) - calls.index.values * np.exp(-r * T)

    error = price_diff - parity_diff

    results_df = pd.DataFrame(
        {
            "C - P": price_diff,
            "S*e⁻qT - K*e⁻rT": parity_diff,
            "Arbitrage Gap ($)": error,
        }
    )

    st.dataframe(results_df)
    st.caption(
        "A non-zero 'Arbitrage Gap' suggests a violation of put-call parity "
        "(or stale data)."  # noqa: E501
    )