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fix(dashboard): anchor forecast series and correct fan chart trace layering #23

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fix(dashboard): anchor forecast series and correct fan chart trace layering #23

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29 changes: 20 additions & 9 deletions dashboard/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,11 +25,16 @@ def load_predictions() -> dict:
"""Load latest model outputs from DB. Replace with real DB query."""
n = 60
t_hist = pd.date_range("2024-01-01", periods=120, freq="B")
t_fut = pd.date_range(t_hist[-1], periods=n, freq="B")
price_hist = np.cumprod(1 + np.random.randn(120) * 0.01) * 100
p50 = price_hist[-1] * np.cumprod(1 + np.random.randn(n) * 0.008)
p10 = p50 * 0.92
p90 = p50 * 1.08
t_fut_future = pd.date_range(t_hist[-1] + pd.offsets.BDay(1), periods=n, freq="B")
raw_forecast = np.cumprod(1 + np.random.randn(n) * 0.008)
p50_future = price_hist[-1] * raw_forecast

# Last historical point
t_fut = t_hist[-1:].union(t_fut_future)
p50 = np.insert(p50_future, 0, price_hist[-1])
p10 = np.insert(p50_future * 0.92, 0, price_hist[-1])
p90 = np.insert(p50_future * 1.08, 0, price_hist[-1])
return {
"sharpe": 1.72, "sharpe_delta": 0.08,
"mfg_residual": 0.0073,
Expand Down Expand Up @@ -67,17 +72,23 @@ def load_predictions() -> dict:

# ── Price fan chart ───────────────────────────────────────────────────────────
fig_price = go.Figure()
# 1. CI Band first
fig_price.add_trace(go.Scatter(
x=list(preds["t_fut"]) + list(reversed(preds["t_fut"])),
y=list(preds["p10"]) + list(reversed(preds["p90"])),
fill="toself", fillcolor="rgba(93,173,226,0.18)",
line=dict(color="rgba(255,255,255,0)"), hoverinfo="skip", name="10%–90% CI"))

# 2. Historical line
fig_price.add_trace(go.Scatter(
x=preds["t_hist"], y=preds["price_hist"],
name="Historical", line=dict(color="white", width=2)))

# 3. Median line
fig_price.add_trace(go.Scatter(
x=preds["t_fut"], y=preds["p50"],
name="MFG Median", line=dict(color="orange", width=2)))
fig_price.add_trace(go.Scatter(
x=list(preds["t_fut"]) + list(reversed(preds["t_fut"])),
y=list(preds["p10"]) + list(reversed(preds["p90"])),
fill="toself", fillcolor="rgba(93,173,226,0.15)",
line=dict(color="rgba(255,255,255,0)"), name="10%–90% CI"))

fig_price.update_layout(template="plotly_dark", title="60-Day Price Forecast (MFG equilibrium paths)")
st.plotly_chart(fig_price, use_container_width=True)

Expand Down