myco-bonding-curve/dashboard/charts.py

"""Plotly figure factories for the MYCO dashboard.

All functions return go.Figure instances — no Streamlit dependency.
"""

import plotly.graph_objects as go
from plotly.subplots import make_subplots


def fig_simulation_overview(result) -> go.Figure:
    """2x2 subplot: supply, reserve, backing ratio, minting breakdown."""
    fig = make_subplots(
        rows=2, cols=2,
        subplot_titles=("Supply", "Reserve Value", "Backing Ratio", "Minting"),
    )

    t = result.times.tolist()

    fig.add_trace(
        go.Scatter(x=t, y=result.supply.tolist(), name="Supply", line=dict(color="#2196F3")),
        row=1, col=1,
    )
    fig.add_trace(
        go.Scatter(x=t, y=result.reserve_value.tolist(), name="Reserve", line=dict(color="#4CAF50")),
        row=1, col=2,
    )
    fig.add_trace(
        go.Scatter(x=t, y=result.backing_ratio.tolist(), name="Backing Ratio", line=dict(color="#FF9800")),
        row=2, col=1,
    )
    fig.add_trace(
        go.Scatter(x=t, y=result.financial_minted.tolist(), name="Financial", line=dict(color="#9C27B0")),
        row=2, col=2,
    )
    fig.add_trace(
        go.Scatter(x=t, y=result.commitment_minted.tolist(), name="Commitment", line=dict(color="#E91E63")),
        row=2, col=2,
    )

    fig.update_layout(height=600, showlegend=True, template="plotly_white")
    return fig


def fig_dca_comparison(results: dict) -> go.Figure:
    """Chunk prices + cumulative tokens for DCA strategies."""
    fig = make_subplots(
        rows=1, cols=2,
        subplot_titles=("Price per Chunk", "Cumulative Tokens"),
    )

    colors = {"fixed": "#2196F3", "twap_aware": "#FF9800"}

    for name, dca_result in results.items():
        history = dca_result.order.history
        chunks_x = list(range(len(history)))
        prices = [h["price"] for h in history]
        tokens = [h["tokens"] for h in history]

        import numpy as np
        cumulative = np.cumsum(tokens).tolist()

        fig.add_trace(
            go.Scatter(x=chunks_x, y=prices, name=f"{name} price",
                       mode="lines+markers", line=dict(color=colors[name])),
            row=1, col=1,
        )
        fig.add_trace(
            go.Scatter(x=chunks_x, y=cumulative, name=f"{name} cumulative",
                       mode="lines+markers", line=dict(color=colors[name])),
            row=1, col=2,
        )

    # Lump sum reference
    for name, dca_result in results.items():
        fig.add_hline(
            y=dca_result.lump_sum_tokens, row=1, col=2,
            line_dash="dash", line_color="gray",
            annotation_text="lump sum" if name == "fixed" else None,
        )
        break

    fig.update_layout(height=400, template="plotly_white")
    return fig


def fig_signal_routing(routing: dict) -> go.Figure:
    """3-panel: price+TWAP, deviation+volatility, adaptive params."""
    fig = make_subplots(
        rows=3, cols=1, shared_xaxes=True,
        subplot_titles=("Price Trajectory", "Signals", "Adaptive Parameters"),
    )

    t = routing["times"]

    fig.add_trace(
        go.Scatter(x=t, y=routing.get("prices", t), name="Spot Price",
                   line=dict(color="#2196F3")),
        row=1, col=1,
    )

    fig.add_trace(
        go.Scatter(x=t, y=routing["twap_deviation"], name="TWAP Deviation",
                   line=dict(color="#FF5722")),
        row=2, col=1,
    )
    fig.add_trace(
        go.Scatter(x=t, y=routing["volatility"], name="Volatility",
                   line=dict(color="#9C27B0")),
        row=2, col=1,
    )

    fig.add_trace(
        go.Scatter(x=t, y=routing["flow_threshold"], name="Flow Threshold",
                   line=dict(color="#4CAF50")),
        row=3, col=1,
    )
    fig.add_trace(
        go.Scatter(x=t, y=routing["surge_fee_rate"], name="Surge Fee",
                   line=dict(color="#FF9800")),
        row=3, col=1,
    )

    fig.update_layout(height=700, template="plotly_white")
    return fig


def fig_bank_run_sweep(results: dict) -> go.Figure:
    """Overlaid reserve curves for different redemption fractions."""
    fig = go.Figure()

    colors = ["#2196F3", "#FF9800", "#E91E63", "#4CAF50", "#9C27B0"]
    for i, (frac, result) in enumerate(results.items()):
        fig.add_trace(go.Scatter(
            x=result.times.tolist(),
            y=result.reserve_value.tolist(),
            name=f"{frac:.0%} redemption",
            line=dict(color=colors[i % len(colors)]),
        ))

    fig.update_layout(
        title="Bank Run Stress Test — Reserve Curves",
        xaxis_title="Time",
        yaxis_title="Reserve Value",
        height=500,
        template="plotly_white",
    )
    return fig


def fig_crdt_divergence(sim_result: dict) -> go.Figure:
    """Peer divergence timeline + merge event scatter."""
    fig = make_subplots(
        rows=2, cols=1, shared_xaxes=True,
        subplot_titles=("Peer Divergence", "Network Events"),
    )

    t = sim_result["times"]

    # Divergence line
    fig.add_trace(
        go.Scatter(x=t, y=sim_result["divergence"], name="Divergence",
                   line=dict(color="#2196F3", width=2)),
        row=1, col=1,
    )
    fig.add_hline(y=1, line_dash="dash", line_color="green", row=1, col=1)

    # Shade partitions
    for i, active in enumerate(sim_result["partition_active"]):
        if active:
            fig.add_vrect(
                x0=t[i] - 0.5, x1=t[i] + 0.5,
                fillcolor="red", opacity=0.1, line_width=0,
                row=1, col=1,
            )

    # Event scatter
    from src.crdt.bridge.events import EventType
    events = sim_result.get("events", [])
    peer_ids = list(sim_result.get("peer_signatures", {}).keys())
    peer_y = {pid: i for i, pid in enumerate(peer_ids)}

    for etype, color, marker, label in [
        (EventType.MERGE, "blue", "circle", "Merge"),
        (EventType.PARTITION, "red", "x", "Partition"),
        (EventType.RECONNECT, "green", "triangle-up", "Reconnect"),
    ]:
        filtered = [e for e in events if e.event_type == etype]
        if filtered:
            fig.add_trace(go.Scatter(
                x=[e.time for e in filtered],
                y=[peer_y.get(e.source_peer, 0) for e in filtered],
                mode="markers",
                marker=dict(color=color, symbol=marker, size=8),
                name=label,
            ), row=2, col=1)

    fig.update_yaxes(
        tickvals=list(range(len(peer_ids))),
        ticktext=peer_ids,
        row=2, col=1,
    )
    fig.update_layout(height=600, template="plotly_white")
    return fig