Upload folder using huggingface_hub

app.py

@@ -37,28 +37,46 @@ def fetch_spy_daily():
         return None
 
 
+def _curve_pts(h):
+    ts, eqv = h.get("timestamp", []), h.get("equity", [])
+    return [(dt.datetime.fromtimestamp(t, dt.timezone.utc).replace(tzinfo=None), v) for t, v in zip(ts, eqv) if v]
+
+
+def _mk_df(rows):
+    df = pd.DataFrame(rows)
+    df["date"] = pd.to_datetime(df["date"]).astype("datetime64[ns]")
+    return df
+
+
 def build_curve():
+    """Returns (dataframe_or_None, note). Daily DeepQuant-vs-SPY once there's history; intraday today for a young account."""
     try:
-        h = ac.get_portfolio_history(period="1M", timeframe="1D")
+        pts = _curve_pts(ac.get_portfolio_history("1M", "1D"))
     except Exception:
-        return None
-    ts, eqv = h.get("timestamp", []), h.get("equity", [])
-    pts = [(dt.datetime.fromtimestamp(t, dt.timezone.utc).replace(tzinfo=None), v) for t, v in zip(ts, eqv) if v]
+        pts = []
+    if len(pts) >= 4:                                            # enough daily history → DeepQuant vs SPY
+        base = pts[0][1]
+        rows = [{"date": d, "series": "DeepQuant (paper)", "value": round(v, 2)} for d, v in pts]
+        spy = fetch_spy_daily()
+        if spy:
+            sd = sorted(spy)
+            first = None
+            for d, _v in pts:
+                prior = [x for x in sd if x <= d.date()]
+                if not prior:
+                    continue
+                px = spy[prior[-1]]
+                first = first or px
+                rows.append({"date": d, "series": "S&P 500 (SPY)", "value": round(base * px / first, 2)})
+        return _mk_df(rows), ""
+    try:                                                          # young account → today's intraday equity
+        pts = _curve_pts(ac.get_portfolio_history("1D", "15Min"))
+    except Exception:
+        pts = []
     if len(pts) < 2:
-        return None
+        return None, "Equity curve builds once the account has trading history."
     rows = [{"date": d, "series": "DeepQuant (paper)", "value": round(v, 2)} for d, v in pts]
-    base_eq, d0 = pts[0][1], pts[0][0].date()
-    spy = fetch_spy_daily()
-    if spy:
-        in_range = {d: px for d, px in spy.items() if d >= d0}
-        if in_range:
-            first = spy[min(in_range)]
-            for d, px in sorted(in_range.items()):
-                rows.append({"date": dt.datetime.combine(d, dt.time()), "series": "S&P 500 (SPY)",
-                             "value": round(base_eq * px / first, 2)})
-    df = pd.DataFrame(rows)
-    df["date"] = pd.to_datetime(df["date"]).astype("datetime64[ns]")
-    return df
+    return _mk_df(rows), "Today's intraday equity — the daily curve vs the S&P 500 fills in over the coming sessions."
 
 
 def refresh():
@@ -99,13 +117,12 @@ def refresh():
             "Unrealized P&L": f'${float(p["unrealized_pl"]):,.2f} ({float(p["unrealized_plpc"]) * 100:+.1f}%)',
         })
     df = pd.DataFrame(rows) if rows else pd.DataFrame([{"Symbol": "— no open positions —"}])
-    curve = build_curve()
+    curve, cnote = build_curve()
+    stamp = f"_Last updated {now} · auto-refreshes every 30 min_"
     if curve is None:
-        return (summary, df, gr.update(visible=False),
-                gr.update(value="_Equity curve accrues once the account has a few days of history._", visible=True),
-                f"_Last updated {now} · auto-refreshes every 30 min_")
-    return (summary, df, gr.update(value=curve, visible=True), gr.update(visible=False),
-            f"_Last updated {now} · auto-refreshes every 30 min_")
+        return (summary, df, gr.update(visible=False), gr.update(value=f"_{cnote}_", visible=True), stamp)
+    note_upd = gr.update(value=f"_{cnote}_", visible=True) if cnote else gr.update(visible=False)
+    return (summary, df, gr.update(value=curve, visible=True), note_upd, stamp)
 
 
 def load_rebalance():
@@ -113,7 +130,8 @@ def load_rebalance():
     if not tok:
         return ""
     try:
-        log = json.load(open(hf_hub_download("s-ttp/marketfm-panel", "deepquant_log.json", repo_type="dataset", token=tok)))
+        log = json.load(open(hf_hub_download("s-ttp/marketfm-panel", "deepquant_log.json", repo_type="dataset",
+                                             token=tok, force_download=True)))
         if not log:
             return ""
         e = log[-1]