Add volume trend, the last two weeks against the last quarter

rel_volume answers "is today unusually busy for this name" against
its own average. It doesn't say whether that elevation is new: a
name that has been running hot for two weeks has already lifted its
own 10-day average, so a rel_volume near 1 today can still sit on
top of a base that is well above the quarter's norm.

volume_trend is avg_volume_10d over avg_volume_3m as a percent — 100
is business as usual, above says the recent pace has been running
hot against the longer baseline, below says it has gone quiet. Both
inputs are Yahoo's own rolling averages, not today's tape, so it
classifies STATIC_SAFE the same way sma_spread does.

Synthetic test in test_screen.py covers both directions by hand.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01H5vZPCuSPyqkdbXKa3UrEV

Author: Ben05-sys

app/screen.py

@@ -39,7 +39,7 @@
            "dollar_volume", "avg_dollar_volume", "day_range_pct", "gap_pct",
            "gap_filled", "true_range", "atr_pct", "sma_spread",
            "intraday_pct", "earnings_yield", "eps_growth", "payout_ratio",
-           "turnover_pct"]
+           "turnover_pct", "volume_trend"]
 
 # Joined onto the frame from the corporate calendar (app/calendars.py)
 # rather than read from the snapshot, plus what derive() computes from
@@ -128,6 +128,9 @@
     # `volume` is — it only rises, so a stale value drops the very names
     # that have since crossed the threshold.
     "avg_dollar_volume",
+    # Two rolling averages, same drift tolerance as `sma_spread`. Today's
+    # raw `volume` is deliberately absent, same reasoning as the line above.
+    "volume_trend",
     # The corporate calendar. A declared dividend's four dates and its
     # amount are fixed the moment it is announced and do not move again,
     # so they are the most static fields here — safe to narrow on before
@@ -179,6 +182,7 @@
     "avgdvol": "avg_dollar_volume", "avgdollarvol": "avg_dollar_volume",
     "liquidity": "avg_dollar_volume",
     "turnoverpct": "turnover_pct", "floatturnover": "turnover_pct",
+    "voltrend": "volume_trend", "volumetrend": "volume_trend",
     "dayrange": "day_range_pct", "rangepos": "day_range_pct",
     "inrange": "day_range_pct", "gap": "gap_pct",
     "intraday": "intraday_pct", "sinceopen": "intraday_pct",

app/universe.py

@@ -270,6 +270,14 @@ def safe(a, b):
         df["turnover_pct"] = safe(df["volume"], df["shares_outstanding"]) * 100
     else:
         df["turnover_pct"] = np.nan
+    # The last two weeks' average volume against the last quarter's, as a
+    # percent: 100 is business as usual. Distinct from `rel_volume`, which
+    # is today alone against the average — this says whether an elevation
+    # has already become the new normal or is still fresh.
+    if "avg_volume_10d" in df.columns and "avg_volume_3m" in df.columns:
+        df["volume_trend"] = safe(df["avg_volume_10d"], df["avg_volume_3m"]) * 100
+    else:
+        df["volume_trend"] = np.nan
     # Where the last price sits in the day's range, 0 at the low and 100 at
     # the high. A stock closing at 95 is a different tape from one closing
     # at 15 on the same percentage change. NaN, not 50, when the range has

tests/test_screen.py

@@ -706,6 +706,29 @@ def when_for(hour, minute=0):
           "turnover_pct" in screen.LIVE_COLUMNS
           and "turnover_pct" not in screen.STATIC_SAFE)
 
+    print("\nvolume trend")
+    vt = derive(pd.DataFrame({
+        "symbol": ["HOT", "QUIET"], "name": ["H", "Q"], "sector": ["T"] * 2,
+        "price": [50.0, 50.0], "change_pct": [0.0] * 2,
+        "volume": [1e6] * 2, "market_cap": [1e9] * 2,
+        "week52_high": [60.0] * 2, "week52_low": [40.0] * 2,
+        "sma50": [50.0] * 2, "sma200": [50.0] * 2,
+        "avg_volume_10d": [3e6, 0.5e6], "avg_volume_3m": [2e6, 2e6],
+        "earnings_ts": [np.nan] * 2,
+    }))
+    vtrow = dict(zip(vt["symbol"], vt.to_dict("records"), strict=True))
+    check("recent pace running above the quarterly baseline",
+          abs(vtrow["HOT"]["volume_trend"] - 150.0) < 1e-9,
+          vtrow["HOT"]["volume_trend"])
+    check("recent pace running below the quarterly baseline",
+          abs(vtrow["QUIET"]["volume_trend"] - 25.0) < 1e-9,
+          vtrow["QUIET"]["volume_trend"])
+    check("voltrend resolves", screen.resolve("voltrend") == "volume_trend")
+    check("volume trend tracks two rolling averages, not today's tape, so "
+          "it narrows safely stale",
+          "volume_trend" in screen.STATIC_SAFE
+          and "volume_trend" not in screen.LIVE_COLUMNS)
+
     # Today's dollar volume only rises, so prefiltering on a stale one would
     # drop the names that have since crossed the line. The average is a
     # standing fact about the name and narrows safely.