Add dynamic agent registry and options mandate

Introduces a dashboard-managed agent registry (`state/agents.json`) used by nightly research and market-open execution, including support for custom/disabled agents and per-agent time budgets. Expands personas to four agents (adds Maverick), shifts pick targets to include mandatory options, and updates prompts/finalization nudges to enforce option research without hard-failing runs.

Strengthens execution safety by adding option-specific guardrails (conviction floor, premium cap, reserved option slot through trade/day/buying-power caps), validates OCC option symbols in schemas, and normalizes invalid option direction cases. The dashboard was refactored into modular views, adds an Agents admin tab, and improves live portfolio visibility with Alpaca-first data and snapshot fallback.

tools/sandbox.py

@@ -26,6 +26,44 @@ _STDOUT_LIMIT = 4000
 _SECRET_PREFIXES = ("ALPACA", "FINNHUB", "OMLX", "EMAIL", "SMTP", "OPENAI", "ANTHROPIC")
 _ARTIFACT_EXTS = {".png", ".jpg", ".jpeg", ".svg", ".csv", ".txt", ".html", ".md", ".json"}
 
+_AVAILABLE = "pandas, numpy, yfinance, matplotlib (Agg), scipy + the standard library"
+
+# Common failure -> actionable hint, appended to the ERROR the agent reads back. Turns the
+# usual retry-loop ("same error again") into a one-shot self-correction.
+_ERROR_HINTS: list[tuple[str, str]] = [
+    ("ModuleNotFoundError", f"HINT: only {_AVAILABLE} are installed — rewrite using those."),
+    (
+        "KeyError",
+        "HINT: yf.download() returns MultiIndex columns by default. Flatten first: "
+        "df.columns = df.columns.get_level_values(0) — or download with "
+        "yf.download(ticker, period=..., multi_level_index=False).",
+    ),
+    (
+        "FileNotFoundError",
+        "HINT: each run_analysis call starts in a FRESH empty directory — files from earlier "
+        "calls are gone. Make the script self-contained (re-download data in this script).",
+    ),
+    (
+        "NameError",
+        "HINT: nothing persists between run_analysis calls — define every variable and import "
+        "in THIS script.",
+    ),
+    (
+        "JSONDecodeError",
+        "HINT: a data request likely returned empty/error content — print the raw response "
+        "first, and add a fallback ticker/period.",
+    ),
+]
+
+
+def _hint_for(stderr: str) -> str:
+    for needle, hint in _ERROR_HINTS:
+        if needle in stderr:
+            return "\n" + hint
+    if "No data found" in stderr or "no price data" in stderr.lower():
+        return "\nHINT: yfinance returned no data — check the ticker symbol and use a simple period like '6mo'."
+    return ""
+
 
 def scrubbed_env(home: str) -> dict[str, str]:
     """A minimal environment with NO secrets — built from scratch, not inherited."""
@@ -45,14 +83,24 @@ def scrubbed_env(home: str) -> dict[str, str]:
 def run_analysis(code: str) -> str:
     """Run a Python analysis script and return its stdout plus any files it wrote.
 
-    Available: pandas, numpy, yfinance (data), matplotlib (charts, Agg), scipy. Use this like a
-    real analyst: fetch data with yfinance, compute indicators/stats/backtests, and save charts
-    with `plt.savefig("chart.png")`. You MUST print() anything you want to read back. Runs in an
-    isolated temp dir with a ~90s limit and NO access to account secrets.
+    ENVIRONMENT — read once, avoid the classic errors:
+    - Installed: pandas, numpy, yfinance (market data), matplotlib (charts, headless Agg),
+      scipy, plus the standard library. Nothing else — no requests/sklearn/talib.
+    - Each call runs in a FRESH EMPTY temp directory. Files and variables do NOT persist
+      between calls: every script must be fully self-contained (imports + data download +
+      computation in one script).
+    - You MUST print() every value you want to read back; save charts with
+      plt.savefig("name.png") (saved files are listed in the reply).
+    - ~90s wall-clock limit; no access to account secrets.
+    - yfinance pitfalls: yf.download() returns MultiIndex columns — flatten with
+      `df.columns = df.columns.get_level_values(0)` (or pass multi_level_index=False);
+      it also auto-adjusts prices by default (auto_adjust=True).
+
+    Use it like a real analyst: fetch data, compute indicators/stats/backtests, save charts.
 
     Example:
         import yfinance as yf, matplotlib.pyplot as plt
-        df = yf.download("AAPL", period="6mo")
+        df = yf.download("AAPL", period="6mo", multi_level_index=False)
         df["SMA50"] = df["Close"].rolling(50).mean()
         print(df[["Close","SMA50"]].tail())
         df[["Close","SMA50"]].plot(); plt.savefig("aapl.png")
@@ -89,8 +137,10 @@ def run_analysis(code: str) -> str:
     ]
 
     if proc.returncode != 0:
-        return f"ERROR (exit {proc.returncode}): {err[-1500:] or 'no stderr'}"
-    result = out[:_STDOUT_LIMIT] or "(no stdout — did you print()?)"
+        return f"ERROR (exit {proc.returncode}): {err[-1500:] or 'no stderr'}{_hint_for(err)}"
+    result = out[:_STDOUT_LIMIT] or (
+        "(no stdout — you must print() every value you want to read back)"
+    )
     if len(out) > _STDOUT_LIMIT:
         result += "\n...[truncated]"
     if err: