feat(gui): Phase B — Equity + History pages
Adds the analytics surface of the dashboard: * gui/data_layer.py — extended with load_closed_positions (windowed filter on closed_at) and three pure-function aggregators: compute_equity_curve, compute_kpis, compute_monthly_stats. Drawdown is measured against the running peak of cumulative realised P&L. * gui/pages/3_📈_Equity.py — KPI strip, plotly cumulative-PnL line, drawdown area below, P&L histogram by close_reason, per-month table with win-rate. * gui/pages/4_📜_History.py — windowed table of closed trades with multiselect close-reason and winners/losers radio filters, six-tile KPI strip, CSV export button. * pyproject.toml — relax mypy on plotly + pandas (no shipped stubs). Validated with synthetic data: 3 trades, 67% win rate, $50 total, max drawdown $30 — all matching expected math. GUI launches, HTTP 200 on / and /_stcore/health. 353/353 tests still pass; ruff clean; mypy strict src clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""Equity page — cumulative PnL, drawdown, distributions."""
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from __future__ import annotations
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import os
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from collections import Counter
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from datetime import UTC, datetime, timedelta
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from pathlib import Path
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import pandas as pd
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import plotly.graph_objects as go
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import streamlit as st
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from cerbero_bite.gui.data_layer import (
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DEFAULT_DB_PATH,
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compute_equity_curve,
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compute_kpis,
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compute_monthly_stats,
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load_closed_positions,
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)
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def _resolve_db() -> Path:
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return Path(os.environ.get("CERBERO_BITE_GUI_DB", DEFAULT_DB_PATH))
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def _date_window(label: str) -> tuple[datetime | None, datetime | None]:
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"""UI control for picking the analytics window."""
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options = {
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"All time": (None, None),
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"Last 30 days": (datetime.now(UTC) - timedelta(days=30), None),
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"Last 90 days": (datetime.now(UTC) - timedelta(days=90), None),
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"Year to date": (datetime(datetime.now(UTC).year, 1, 1, tzinfo=UTC), None),
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}
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pick = st.selectbox(label, list(options.keys()), index=0)
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return options[pick]
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def render() -> None:
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st.title("📈 Equity")
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st.caption(
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"Cumulative realised P&L, drawdown, and per-trade distribution. "
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"Computed from closed positions in `data/state.sqlite`."
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)
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start, end = _date_window("Window")
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db_path = _resolve_db()
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positions = load_closed_positions(db_path=db_path, start=start, end=end)
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if not positions:
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st.info(
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"No closed positions in the selected window yet. "
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"The equity curve will populate as soon as the engine closes its first trade."
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)
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return
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# KPI strip
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kpis = compute_kpis(positions)
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cols = st.columns(5)
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cols[0].metric("Closed trades", kpis.n_trades)
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cols[1].metric("Win rate", f"{kpis.win_rate:.0%}")
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cols[2].metric("Total P&L", f"${float(kpis.total_pnl_usd):+.2f}")
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cols[3].metric("Edge / trade", f"${float(kpis.edge_per_trade_usd):+.2f}")
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cols[4].metric(
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"Max drawdown",
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f"${float(kpis.max_drawdown_usd):.2f}",
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delta=f"{kpis.max_drawdown_pct:.1%}",
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delta_color="inverse",
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)
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st.divider()
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# Equity curve + drawdown
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curve = compute_equity_curve(positions)
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df = pd.DataFrame(
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{
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"timestamp": [p.timestamp for p in curve],
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"cumulative_pnl_usd": [float(p.cumulative_pnl_usd) for p in curve],
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"drawdown_usd": [float(p.drawdown_usd) for p in curve],
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"realized_pnl_usd": [float(p.realized_pnl_usd) for p in curve],
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}
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)
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st.subheader("Cumulative P&L (USD)")
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fig = go.Figure()
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fig.add_trace(
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go.Scatter(
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x=df["timestamp"],
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y=df["cumulative_pnl_usd"],
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mode="lines+markers",
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name="cumulative P&L",
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line={"color": "#2ecc71", "width": 2},
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)
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)
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fig.add_hline(y=0, line_dash="dot", line_color="grey", opacity=0.5)
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fig.update_layout(
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height=320,
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margin={"l": 10, "r": 10, "t": 30, "b": 10},
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xaxis_title=None,
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yaxis_title="USD",
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)
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st.plotly_chart(fig, use_container_width=True)
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st.subheader("Drawdown (USD)")
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dd_fig = go.Figure()
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dd_fig.add_trace(
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go.Scatter(
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x=df["timestamp"],
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y=-df["drawdown_usd"],
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mode="lines",
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fill="tozeroy",
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name="drawdown",
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line={"color": "#e74c3c", "width": 1.5},
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)
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)
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dd_fig.update_layout(
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height=220,
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margin={"l": 10, "r": 10, "t": 30, "b": 10},
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xaxis_title=None,
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yaxis_title="USD",
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)
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st.plotly_chart(dd_fig, use_container_width=True)
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# PnL distribution
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st.subheader("P&L distribution by close reason")
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by_reason: dict[str, list[float]] = {}
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for pos in positions:
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if pos.pnl_usd is None:
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continue
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by_reason.setdefault(pos.close_reason or "(unknown)", []).append(
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float(pos.pnl_usd)
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)
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counts = Counter(
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(pos.close_reason or "(unknown)") for pos in positions
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)
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cols = st.columns(min(len(counts), 6) or 1)
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for col, (reason, count) in zip(cols, counts.most_common(6), strict=False):
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col.metric(reason, count)
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hist_fig = go.Figure()
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for reason, pnls in by_reason.items():
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hist_fig.add_trace(go.Histogram(x=pnls, name=reason, opacity=0.6, nbinsx=30))
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hist_fig.update_layout(
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barmode="overlay",
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height=320,
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margin={"l": 10, "r": 10, "t": 30, "b": 10},
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xaxis_title="P&L (USD)",
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yaxis_title="trades",
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legend={"orientation": "h", "y": 1.1},
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)
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st.plotly_chart(hist_fig, use_container_width=True)
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# Monthly table
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st.subheader("Per-month stats")
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months = compute_monthly_stats(positions)
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rows = [
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{
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"month": m.year_month,
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"trades": m.n_trades,
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"wins": m.n_wins,
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"win_rate": f"{m.win_rate:.0%}",
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"P&L (USD)": f"{float(m.pnl_usd):+.2f}",
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"avg / trade": f"{float(m.avg_pnl_usd):+.2f}",
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}
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for m in months
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]
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st.dataframe(rows, use_container_width=True, hide_index=True)
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render()
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