{"parentUuid":null,"isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":"I need to implement a backtest sweep script for a congressional trading signal scoring system. The repo is at /workspace/extra/projects/congressional-trading/. \n\nPlease read and summarize for me:\n1. The main scoring algorithm — read `scorer.py` or wherever the 0-13 score components are computed\n2. The `signal_outcomes` table structure — what columns exist, especially any 30d/60d return columns\n3. `tools/backtest.py` — the existing backtest tool (full read, it's the closest to what I need)\n4. `config.py` — what constants are defined (thresholds, conviction gates, etc.)\n5. The `trades` table schema — from any schema file or CREATE TABLE in the codebase\n\nI specifically need to know:\n- What is the `conviction_gate` concept and what values it takes (LOW/MEDIUM/HIGH)?\n- What is the `edge_factor` and what range it takes?\n- What columns does `signal_outcomes` have (especially return_30d, return_60d)?\n- How does the existing backtest replay scoring — does it call the scoring functions directly or work from stored data?\n\nBe thorough — read the actual files so I have the real field names and logic before I write code."},"uuid":"3bb594a2-2e92-47d4-bf0f-15fdda65a899","timestamp":"2026-07-12T12:31:00.565Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"c91a7f80-396c-4b78-9f1a-41612884a8c9","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAddYAaRfhoKJ9XNuKB","type":"message","role":"assistant","content":[{"type":"text","text":"I'll systematically explore the congressional trading repository to understand the scoring algorithm, table structures, and existing backtest tool."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":8595,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":8595,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAda8GzvhbQCnz4qpfW","attributionAgent":"Explore","type":"assistant","uuid":"cbb1851b-9fb9-4ae1-be9b-b9af608330da","timestamp":"2026-07-12T12:31:02.855Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"799de1c6-5b9a-438b-92cb-2bae5adc6b7f","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MDgE2w1n7YgED3AY8SbpQE","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/notify/ntfy_client.py\n/workspace/extra/projects/congressional-trading/notify/__init__.py\n/workspace/extra/projects/congressional-trading/tools/__init__.py\n/workspace/extra/projects/congressional-trading/tools/backfill.py\n/workspace/extra/projects/congressional-trading/tools/generate_report.py\n/workspace/extra/projects/congressional-trading/tools/price_fetcher.py\n/workspace/extra/projects/congressional-trading/tools/price_history.py\n/workspace/extra/projects/congressional-trading/tools/outcome_tracker.py\n/workspace/extra/projects/congressional-trading/tools/tuner.py\n/workspace/extra/projects/congressional-trading/tools/backtest.py\n/workspace/extra/projects/congressional-trading/tools/win_rate.py\n/workspace/extra/projects/congressional-trading/tools/report.py\n/workspace/extra/projects/congressional-trading/tools/fetch_legislation.py\n/workspace/extra/projects/congressional-trading/run_daily.py\n/workspace/extra/projects/congressional-trading/config.py\n/workspace/extra/projects/congressional-trading/analysis/predictor.py\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py\n/workspace/extra/projects/congressional-trading/analysis/__init__.py\n/workspace/extra/projects/congressional-trading/analysis/guardrails.py\n/workspace/extra/projects/congressional-trading/analysis/llm_client.py","is_error":false}]},"uuid":"0aad1c1d-ae35-4851-96b4-03497d3b766b","timestamp":"2026-07-12T12:31:03.181Z","sourceToolAssistantUUID":"799de1c6-5b9a-438b-92cb-2bae5adc6b7f","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"0aad1c1d-ae35-4851-96b4-03497d3b766b","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAdmzf6WrhX7LaAQvom","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me read the key files:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2464,"cache_read_input_tokens":8595,"cache_creation":{"ephemeral_5m_input_tokens":2464,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAdmAJjLTB1fheQDaV1","attributionAgent":"Explore","type":"assistant","uuid":"c3e024ed-c0ce-4721-a928-03a5fdc83b9b","timestamp":"2026-07-12T12:31:04.378Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"d7ba7ca4-fdb3-44a9-a553-09b4f509196a","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RwWvENrhT77p7QmZvfdvuE","type":"tool_result","content":"1\timport os\n2\tfrom dotenv import load_dotenv\n3\tload_dotenv()\n4\t\n5\tdef _env_float(name: str, default: float) -> float:\n6\t return float(os.getenv(name, default))\n7\t\n8\tdef _env_int(name: str, default: int) -> int:\n9\t return int(os.getenv(name, default))\n10\t\n11\t# --- Database ---\n12\tDB_PATH = os.path.join(os.path.dirname(__file__), \"data\", \"congress_trades.db\")\n13\t\n14\t# --- Ingest ---\n15\tLOOKBACK_DAYS = 30 # how many days of trade history to fetch on each run\n16\tPAGE_SIZE = 100 # max records per page from Capitol Trades\n17\t\n18\t# --- Scoring ---\n19\tSCORE_RESEARCH_THRESHOLD = 5.0 # min score to trigger LLM research\n20\tSCORE_NOTIFY_THRESHOLD = 9.0 # min score to send ntfy notification\n21\tDEFAULT_SCORE_WEIGHTS = {\n22\t \"committee\": 1.0, \"track_record\": 1.0, \"cluster\": 1.0,\n23\t \"options\": 1.0, \"size\": 1.0, \"small_cap\": 1.0,\n24\t \"sell_pressure\": 1.0, \"strategic\": 1.0, \"disruption\": 1.0,\n25\t}\n26\tBACKTEST_MIN_ALERTS_PER_MONTH = _env_float(\"BACKTEST_MIN_ALERTS_PER_MONTH\", 2.0)\n27\t\n28\t# --- Timeliness multipliers applied to score at notify-decision time only ---\n29\t# A \"late\" signal's stored score is unchanged; the multiplier only gates notifications.\n30\tTIMELINESS_MULTIPLIERS = {\"fresh\": 1.0, \"watch\": 0.85, \"late\": 0.70}\n31\tFRESHNESS_MAX_HOURS = _env_int(\"FRESHNESS_MAX_HOURS\", 48)\n32\tWATCH_MAX_DAYS = _env_int(\"WATCH_MAX_DAYS\", 7)\n33\tPRICED_IN_RUNUP_PCT = _env_float(\"PRICED_IN_RUNUP_PCT\", 15.0)\n34\tMIN_AVG_DOLLAR_VOLUME = _env_float(\"MIN_AVG_DOLLAR_VOLUME\", 2_000_000)\n35\tEARNINGS_BLACKOUT_DAYS = _env_int(\"EARNINGS_BLACKOUT_DAYS\", 2)\n36\tRESEARCH_MIN_CITATIONS = _env_int(\"RESEARCH_MIN_CITATIONS\", 2)\n37\tREQUIRE_VERIFIER_CONCUR = os.getenv(\"REQUIRE_VERIFIER_CONCUR\", \"true\").lower() in (\"1\", \"true\", \"yes\")\n38\tTUNER_MIN_NEW_OUTCOMES = _env_int(\"TUNER_MIN_NEW_OUTCOMES\", 8)\n39\tTUNER_MAX_STEP_PCT = _env_float(\"TUNER_MAX_STEP_PCT\", 10.0)\n40\tBREAKER_MIN_WIN_RATE = _env_float(\"BREAKER_MIN_WIN_RATE\", 0.40)\n41\tTUNER_MIN_IMPROVEMENT_PCT = _env_float(\"TUNER_MIN_IMPROVEMENT_PCT\", 2.0)\n42\t\n43\t# --- Win rate ---\n44\tWIN_RATE_MIN_TRADES = 3 # minimum buy trades with price data before using actual rate\n45\t\n46\t# --- Deep Research ---\n47\t# All signals above SCORE_NOTIFY_THRESHOLD are eligible for deep research.\n48\t# Each run processes up to TOP_N of them, prioritising never-researched tickers\n49\t# then stalest-first, skipping any researched within DECAY_DAYS.\n50\t# The decay window (not TOP_N) is the primary cost control.\n51\tDEEP_RESEARCH_TOP_N = 10 # max deep-research calls per run\n52\tDEEP_RESEARCH_DECAY_DAYS = 7 # days before re-researching the same ticker\n53\t\n54\t# --- Notification quality gates ---\n55\t# Cap how many push alerts fire per pipeline run regardless of score.\n56\t# Push should be reserved for the single best actionable idea; the report carries the rest.\n57\tMAX_NOTIFICATIONS_PER_RUN = 1\n58\t\n59\t# Do not re-alert the same ticker during this rolling window unless --force is used.\n60\tNOTIFY_TICKER_COOLDOWN_DAYS = 14\n61\t\n62\t# When True, only signals where deep research recommends \"BUY NOW\" or \"BUY\" get notified.\n63\t# Set False to notify for MONITOR/AVOID signals too (not recommended — defeats the purpose).\n64\tNOTIFY_REQUIRE_BUY_ACTION = True\n65\t\n66\t# Minimum conviction level to trigger notification. Options: \"LOW\", \"MEDIUM\", \"HIGH\"\n67\tNOTIFY_MIN_CONVICTION = \"HIGH\"\n68\t\n69\t# Minimum number of independent edge factors before a signal can page you.\n70\t# Factors include multi-member clusters, large disclosed size, options timing,\n71\t# committee alignment, small/mid-cap scarcity, and strong LLM catalyst scores.\n72\tNOTIFY_MIN_EDGE_FACTORS = 3\n73\t\n74\t# Suppress buy notifications when sellers materially offset buyers.\n75\tNOTIFY_MAX_SELL_PRESSURE_PENALTY = -1.0\n76\t\n77\t# --- LLM ---\n78\tLLM_PROVIDER = \"anthropic\" # \"anthropic\" or \"google\"\n79\tANTHROPIC_API_KEY = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\n80\tGOOGLE_API_KEY = os.getenv(\"GOOGLE_API_KEY\", \"\")\n81\t\n82\t# --- Predictive Signals ---\n83\t# Behavioral analysis: predict future trades before disclosure.\n84\t# Requires CONGRESS_GOV_API_KEY for live bill data (free at api.congress.gov);\n85\t# works in degraded mode (LLM-only) without it.\n86\tCONGRESS_GOV_API_KEY = os.getenv(\"CONGRESS_GOV_API_KEY\", \"\")\n87\tPREDICTOR_MAX_MEMBERS = 5 # members to analyze per run (cost control)\n88\tPREDICTOR_DECAY_DAYS = 7 # skip re-predicting same member within this window\n89\tPREDICTOR_MIN_TRADES = 5 # min historical trades before a member is analyzed\n90\tPREDICTOR_LEGISLATION_DAYS = 45 # how far back to look for recently active bills\n91\t\n92\t# --- ntfy ---\n93\tNTFY_BASE_URL = \"https://ntfy.sh\"\n94\tNTFY_TOPIC_SIGNALS = \"congressWatchSignals\" # trade signal notifications\n95\tNTFY_TOPIC_STATUS = \"claudeCodeWaitingOnYou\" # system status / needs-input alerts\n96\t"}]},"uuid":"273e1cfd-8d7d-4749-9c4e-eb5ff683cd61","timestamp":"2026-07-12T12:31:04.849Z","sourceToolAssistantUUID":"d7ba7ca4-fdb3-44a9-a553-09b4f509196a","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"6c7c9105-3f1a-41f5-b627-b9f22d8a51b4","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V7JqVtpBkSRmoEGiLnZyhS","type":"tool_result","content":"1\t\"\"\"Deterministic historical replay and bounded two-stage weight calibration.\"\"\"\n2\tfrom __future__ import annotations\n3\timport argparse\n4\tfrom collections import defaultdict\n5\tfrom datetime import date, timedelta\n6\timport itertools\n7\timport json\n8\timport random\n9\timport sqlite3\n10\timport statistics\n11\timport sys\n12\timport os\n13\tsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\n14\tfrom config import (DB_PATH, DEFAULT_SCORE_WEIGHTS, SCORE_NOTIFY_THRESHOLD, LOOKBACK_DAYS,\n15\t BACKTEST_MIN_ALERTS_PER_MONTH)\n16\tfrom analysis.scorer import _cluster_score, _size_score\n17\tfrom analysis.weights import weighted_total, promote\n18\t\n19\tdef _on_or_after(prices: dict, ticker: str, day: date, max_days: int = 7):\n20\t for offset in range(max_days + 1):\n21\t value = prices.get((ticker, (day + timedelta(days=offset)).isoformat()))\n22\t if value is not None: return value\n23\t return None\n24\t\n25\tdef replay(weights: dict, *, db_path: str = DB_PATH,\n26\t threshold: float = SCORE_NOTIFY_THRESHOLD) -> dict:\n27\t with sqlite3.connect(db_path) as conn:\n28\t conn.row_factory = sqlite3.Row\n29\t trades = [dict(r) for r in conn.execute(\"SELECT * FROM trades WHERE tx_type='buy' AND issuer_ticker != '' ORDER BY pub_date\")]\n30\t prices = {(r[0], r[1]): r[2] for r in conn.execute(\"SELECT ticker,date,close FROM price_history\")}\n31\t research = {r[0]: (r[1] or 0, r[2] or 0) for r in conn.execute(\n32\t \"SELECT ticker, strategic_score, disruption_score FROM research_cache\")}\n33\t by_day = defaultdict(list)\n34\t for trade in trades: by_day[trade[\"pub_date\"][:10]].append(trade)\n35\t excess, alert_dates, unmeasurable = [], [], 0\n36\t days = sorted(by_day)\n37\t for pub_date in days:\n38\t replay_day = date.fromisoformat(pub_date)\n39\t window_start = replay_day - timedelta(days=LOOKBACK_DAYS)\n40\t rows = [trade for trade in trades if window_start <= date.fromisoformat(trade[\"pub_date\"][:10]) <= replay_day]\n41\t by_ticker = defaultdict(list)\n42\t for row in rows: by_ticker[row[\"issuer_ticker\"]].append(row)\n43\t for ticker, ticker_rows in by_ticker.items():\n44\t components = {\"committee\": 0, \"track_record\": 1,\n45\t \"cluster\": _cluster_score(len({r['member_id'] for r in ticker_rows})),\n46\t \"options\": 2 if any(r[\"is_option\"] for r in ticker_rows) else 0,\n47\t \"size\": _size_score(max(r[\"amount_tier\"] or 0 for r in ticker_rows)),\n48\t \"small_cap\": 0, \"sell_pressure\": 0,\n49\t \"strategic\": research.get(ticker, (0, 0))[0],\n50\t \"disruption\": research.get(ticker, (0, 0))[1]}\n51\t if weighted_total(components, weights) < threshold: continue\n52\t start = date.fromisoformat(pub_date)\n53\t end = start + timedelta(days=30)\n54\t p0, p1 = _on_or_after(prices, ticker, start), _on_or_after(prices, ticker, end)\n55\t s0, s1 = _on_or_after(prices, \"SPY\", start), _on_or_after(prices, \"SPY\", end)\n56\t if not all((p0, p1, s0, s1)):\n57\t unmeasurable += 1; continue\n58\t excess.append(((p1 / p0 - 1) - (s1 / s0 - 1)) * 100)\n59\t alert_dates.append(start)\n60\t months = max(1, len({(date.fromisoformat(day).year, date.fromisoformat(day).month) for day in days}))\n61\t rate = len(alert_dates) / months\n62\t return {\"median_30d_excess_pct\": statistics.median(excess) if excess else None,\n63\t \"precision\": sum(v > 0 for v in excess) / len(excess) if excess else None,\n64\t \"alerts\": len(excess), \"alerts_per_month\": rate, \"unmeasurable\": unmeasurable,\n65\t \"eligible\": bool(excess) and rate >= BACKTEST_MIN_ALERTS_PER_MONTH,\n66\t \"limitation\": \"LLM-derived components replay from cache where present; otherwise zero\"}\n67\t\n68\tdef _objective(metrics):\n69\t return metrics[\"median_30d_excess_pct\"] if metrics[\"eligible\"] else float(\"-inf\")\n70\t\n71\tdef calibrate(*, db_path: str = DB_PATH, broad_samples: int = 100,\n72\t focused_samples: int = 100, seed: int = 42) -> tuple[dict, dict]:\n73\t rng = random.Random(seed)\n74\t keys = list(DEFAULT_SCORE_WEIGHTS)\n75\t candidates = [dict(DEFAULT_SCORE_WEIGHTS)]\n76\t for _ in range(broad_samples):\n77\t candidates.append({key: rng.uniform(0.5, 1.5) for key in keys})\n78\t ranked = sorted((( _objective(m := replay(w, db_path=db_path)), w, m) for w in candidates),\n79\t key=lambda item: item[0], reverse=True)\n80\t focused = []\n81\t for _, winner, _ in ranked[:3]:\n82\t for _ in range(max(1, focused_samples // 3)):\n83\t focused.append({key: max(0.1, winner[key] * rng.uniform(0.9, 1.1)) for key in keys})\n84\t ranked.extend((_objective(m := replay(w, db_path=db_path)), w, m) for w in focused)\n85\t _, weights, metrics = max(ranked, key=lambda item: item[0])\n86\t metrics[\"search\"] = {\"broad_samples\": broad_samples, \"focused_samples\": len(focused), \"seed\": seed}\n87\t return weights, metrics\n88\t\n89\tif __name__ == \"__main__\":\n90\t parser = argparse.ArgumentParser()\n91\t parser.add_argument(\"--calibrate\", action=\"store_true\")\n92\t parser.add_argument(\"--promote\", action=\"store_true\", help=\"manually promote calibrated winner\")\n93\t args = parser.parse_args()\n94\t if args.calibrate:\n95\t weights, metrics = calibrate()\n96\t print(json.dumps({\"weights\": weights, \"metrics\": metrics}, indent=2))\n97\t if args.promote: print(f\"Promoted config version {promote(weights, metrics, promoted_by='human')}\")\n98\t else: print(json.dumps(replay(DEFAULT_SCORE_WEIGHTS), indent=2))\n99\t"}]},"uuid":"fcd793f8-535e-4462-97e1-4a77a33fbf23","timestamp":"2026-07-12T12:31:05.285Z","sourceToolAssistantUUID":"6c7c9105-3f1a-41f5-b627-b9f22d8a51b4","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"fcd793f8-535e-4462-97e1-4a77a33fbf23","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAdmzf6WrhX7LaAQvom","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XGfg9pTchR8zc98naYMiRF","name":"Glob","input":{"pattern":"/workspace/extra/projects/congressional-trading/**/scorer*.py"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2464,"cache_read_input_tokens":8595,"cache_creation":{"ephemeral_5m_input_tokens":2464,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAdmAJjLTB1fheQDaV1","attributionAgent":"Explore","type":"assistant","uuid":"7f4f2eca-2e88-4ab7-a351-f7d1616b0b34","timestamp":"2026-07-12T12:31:05.526Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"7f4f2eca-2e88-4ab7-a351-f7d1616b0b34","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAdmzf6WrhX7LaAQvom","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LhPbCTiMgWvC71oFWMNSUX","name":"Glob","input":{"pattern":"/workspace/extra/projects/congressional-trading/**/schema*.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2464,"cache_read_input_tokens":8595,"output_tokens":225,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2464},"inference_geo":"","iterations":[{"input_tokens":5,"output_tokens":225,"cache_read_input_tokens":8595,"cache_creation_input_tokens":2464,"cache_creation":{"ephemeral_5m_input_tokens":2464,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAdmAJjLTB1fheQDaV1","attributionAgent":"Explore","type":"assistant","uuid":"0c42a213-fdf5-43ea-bdb8-9d656117b697","timestamp":"2026-07-12T12:31:05.853Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"7f4f2eca-2e88-4ab7-a351-f7d1616b0b34","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XGfg9pTchR8zc98naYMiRF","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/analysis/scorer.py"}]},"uuid":"dce31fce-14ce-4883-a7db-f43bc82c99d3","timestamp":"2026-07-12T12:31:05.892Z","sourceToolAssistantUUID":"7f4f2eca-2e88-4ab7-a351-f7d1616b0b34","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"0c42a213-fdf5-43ea-bdb8-9d656117b697","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LhPbCTiMgWvC71oFWMNSUX","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pydantic/v1/schema.py\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pydantic/schema.py"}]},"uuid":"8a575c97-9e77-45ce-b01a-e3c8d77db2f9","timestamp":"2026-07-12T12:31:05.991Z","sourceToolAssistantUUID":"0c42a213-fdf5-43ea-bdb8-9d656117b697","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"8a575c97-9e77-45ce-b01a-e3c8d77db2f9","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAdz3CxVR89r43oB4k2","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me read the scorer.py file and check for schema/trades table definitions:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":4101,"cache_read_input_tokens":11059,"cache_creation":{"ephemeral_5m_input_tokens":4101,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAdyC6stJvT6NiwHZHq","attributionAgent":"Explore","type":"assistant","uuid":"dd79a71f-c7cf-45ff-bab9-faf958720ed3","timestamp":"2026-07-12T12:31:07.220Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"dd79a71f-c7cf-45ff-bab9-faf958720ed3","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAdz3CxVR89r43oB4k2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014eY7hWvKKnwRpT4bCYzP6M","name":"Read","input":{"file_path":"/workspace/extra/projects/congressional-trading/analysis/scorer.py"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":4101,"cache_read_input_tokens":11059,"cache_creation":{"ephemeral_5m_input_tokens":4101,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAdyC6stJvT6NiwHZHq","attributionAgent":"Explore","type":"assistant","uuid":"72fdfcdf-f895-4908-a681-3bf4750b5480","timestamp":"2026-07-12T12:31:07.653Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"72fdfcdf-f895-4908-a681-3bf4750b5480","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014eY7hWvKKnwRpT4bCYzP6M","type":"tool_result","content":"1\t\"\"\"\n2\tSignal scoring engine.\n3\t\n4\tGroups trades by ticker within a lookback window and produces a score\n5\tfor each ticker reflecting the aggregate congressional activity.\n6\t\n7\tScore components (max ~13):\n8\t committee_score 0–3 member's committee overlaps with company sector (Day 5)\n9\t track_record_score 0–2 historical win rate (defaults to 1.0 until calculable)\n10\t cluster_score 0–3 unique member buyers (1=0pts, 2=1pt, 3=2pts, 4+=3pts)\n11\t options_bonus 0–2 any option trade on this ticker (+2)\n12\t size_score 0–5 highest dollar tier among buy trades\n13\t\n14\tNotification threshold (SCORE_NOTIFY_THRESHOLD in config.py): default 7\n15\t\"\"\"\n16\t\n17\tfrom __future__ import annotations\n18\timport sqlite3\n19\timport sys\n20\timport os\n21\tfrom collections import defaultdict\n22\tfrom datetime import datetime, timedelta\n23\t\n24\tsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\n25\tfrom config import DB_PATH, LOOKBACK_DAYS, SCORE_NOTIFY_THRESHOLD\n26\t\n27\tdef _priced_in_runup(ticker: str, tx_date: str) -> float | None:\n28\t if not tx_date: return None\n29\t try:\n30\t import yfinance as yf\n31\t from analysis.timeliness import excess_runup_pct\n32\t end = (datetime.now() + timedelta(days=1)).strftime(\"%Y-%m-%d\")\n33\t stock = yf.Ticker(ticker).history(start=tx_date[:10], end=end, auto_adjust=False)\n34\t spy = yf.Ticker(\"SPY\").history(start=tx_date[:10], end=end, auto_adjust=False)\n35\t if stock.empty or spy.empty: return None\n36\t return round(excess_runup_pct(float(stock[\"Close\"].iloc[0]), float(stock[\"Close\"].iloc[-1]),\n37\t float(spy[\"Close\"].iloc[0]), float(spy[\"Close\"].iloc[-1])), 2)\n38\t except Exception as exc:\n39\t print(f\" Run-up check failed for {ticker}: {exc}\")\n40\t return None\n41\t\n42\t\n43\t# Committee → sector mapping used for committee_score.\n44\t# Updated when committees.py runs (Day 5); used here for scoring.\n45\t# Key: normalized committee name fragment, Value: list of matching sectors\n46\t_COMMITTEE_SECTOR_MAP = {\n47\t \"armed services\": [\"industrials\", \"information-technology\"],\n48\t \"defense\": [\"industrials\", \"information-technology\"],\n49\t \"intelligence\": [\"information-technology\", \"industrials\"],\n50\t \"energy\": [\"energy\", \"utilities\", \"materials\"],\n51\t \"natural resources\": [\"energy\", \"materials\"],\n52\t \"science\": [\"information-technology\"],\n53\t \"technology\": [\"information-technology\", \"communication-services\"],\n54\t \"health\": [\"health-care\"],\n55\t \"financial services\": [\"financials\"],\n56\t \"banking\": [\"financials\"],\n57\t \"finance\": [\"financials\"],\n58\t \"agriculture\": [\"consumer-staples\", \"materials\"],\n59\t \"transportation\": [\"industrials\"],\n60\t \"commerce\": [\"consumer-discretionary\", \"communication-services\"],\n61\t \"judiciary\": [],\n62\t \"foreign affairs\": [],\n63\t \"ways and means\": [\"financials\"],\n64\t}\n65\t\n66\t\n67\tdef _committee_score(committees_json: str | None, sector: str) -> float:\n68\t \"\"\"\n69\t Return 0–3 based on overlap between member's committees and company sector.\n70\t 0 = no data or no overlap\n71\t 1 = indirect overlap (committee has broad relevance)\n72\t 2 = moderate overlap\n73\t 3 = direct jurisdiction\n74\t \"\"\"\n75\t if not committees_json or not sector:\n76\t return 0.0\n77\t import json\n78\t try:\n79\t committees = json.loads(committees_json)\n80\t except (json.JSONDecodeError, TypeError):\n81\t return 0.0\n82\t\n83\t sector = sector.lower()\n84\t best = 0.0\n85\t for committee in committees:\n86\t name = committee.lower() if isinstance(committee, str) else (committee.get(\"name\", \"\") or \"\").lower()\n87\t for key, sectors in _COMMITTEE_SECTOR_MAP.items():\n88\t if key in name:\n89\t if sector in sectors:\n90\t best = max(best, 3.0)\n91\t elif any(s in sector for s in [\"tech\", \"info\", \"comm\"]) and key in (\"science\", \"technology\", \"commerce\"):\n92\t best = max(best, 2.0)\n93\t else:\n94\t best = max(best, 1.0)\n95\t return best\n96\t\n97\t\n98\tdef _cluster_score(unique_buyers: int) -> float:\n99\t if unique_buyers >= 4:\n100\t return 3.0\n101\t if unique_buyers == 3:\n102\t return 2.0\n103\t if unique_buyers == 2:\n104\t return 1.0\n105\t return 0.0\n106\t\n107\t\n108\tdef _size_score(max_tier: int) -> float:\n109\t \"\"\"tier is already 1–6 from ingest; cap contribution at 5.\"\"\"\n110\t return min(5.0, float(max(0, max_tier)))\n111\t\n112\t\n113\tdef _sell_pressure(unique_sellers: int, unique_buyers: int) -> float:\n114\t \"\"\"\n115\t Penalty when congressional members are selling a ticker they or others are buying.\n116\t The ratio of sellers to buyers determines severity:\n117\t\n118\t sellers == 0: 0.0 — clean signal\n119\t ratio < 0.40 (e.g. 1 of 3): -0.5 — minor noise, buyers dominate\n120\t ratio < 0.67 (e.g. 1 of 2): -1.5 — worth noting\n121\t ratio < 1.0 (e.g. 2 of 3): -2.5 — mixed conviction\n122\t ratio >= 1.0 (sellers ≥ buyers): -4.0 — sell pressure outweighs buys\n123\t \"\"\"\n124\t if unique_sellers == 0:\n125\t return 0.0\n126\t ratio = unique_sellers / max(unique_buyers, 1)\n127\t if ratio >= 1.0:\n128\t return -4.0\n129\t if ratio >= 0.67:\n130\t return -2.5\n131\t if ratio >= 0.40:\n132\t return -1.5\n133\t return -0.5\n134\t\n135\t\n136\tdef _small_cap_bonus(market_cap: float | None) -> float:\n137\t \"\"\"\n138\t Score bonus/penalty based on market cap.\n139\t\n140\t The thesis: congressional informed buying in a smaller, less-covered company\n141\t has higher percentage-move potential than the same signal in a mega-cap where\n142\t hundreds of analysts are already pricing in every catalyst. Tiers:\n143\t\n144\t Mega (>$100B): -2.5 — saturated coverage; edge is minimal\n145\t Large ($10-100B): -0.5 — well-covered; slight penalty\n146\t Mid ($2-10B): +2.0 — meaningful analyst coverage gap\n147\t Small ($500M-$2B): +4.5 — thin coverage, catalysts move the stock\n148\t Micro (<$500M): +6.0 — very thin float; single catalyst → 30-50% moves\n149\t Unknown: 0.0 — neutral until price_fetcher populates market_cap\n150\t \"\"\"\n151\t if market_cap is None:\n152\t return 0.0\n153\t if market_cap > 100e9:\n154\t return -2.5\n155\t if market_cap > 10e9:\n156\t return -0.5\n157\t if market_cap > 2e9:\n158\t return 2.0\n159\t if market_cap > 500e6:\n160\t return 4.5\n161\t return 6.0\n162\t\n163\t\n164\tdef _breadth_mult(hist_buyers: int) -> float:\n165\t \"\"\"\n166\t Discount factor applied to cluster_score based on how many unique members\n167\t have historically bought a ticker. Mega-caps routinely bought by 15+ members\n168\t are portfolio staples, not informed signals — their cluster score overstates edge.\n169\t \"\"\"\n170\t if hist_buyers >= 20:\n171\t return 0.40 # basically an index holding (MSFT, GOOGL, NVDA tier)\n172\t if hist_buyers >= 12:\n173\t return 0.65 # widely held (HD, BRK/B, GS tier)\n174\t if hist_buyers >= 6:\n175\t return 0.85 # moderately popular\n176\t return 1.0 # rare buy — full cluster score\n177\t\n178\t\n179\tdef score_tickers(lookback_days: int = LOOKBACK_DAYS) -> list[dict]:\n180\t \"\"\"\n181\t Score all tickers with at least one BUY trade published in the last\n182\t lookback_days. Returns list of signal dicts sorted by total_score desc.\n183\t Also upserts results into the signals table.\n184\t \"\"\"\n185\t conn = sqlite3.connect(DB_PATH)\n186\t conn.row_factory = sqlite3.Row\n187\t from analysis.weights import active_weights, weighted_total\n188\t weights = active_weights()\n189\t\n190\t cutoff = (datetime.now() - timedelta(days=lookback_days)).strftime(\"%Y-%m-%d\")\n191\t\n192\t # Cached market caps from previous price_fetcher run (neutral default if missing)\n193\t cap_rows = conn.execute(\"SELECT ticker, market_cap FROM signals WHERE market_cap IS NOT NULL\").fetchall()\n194\t market_caps: dict[str, float] = {r[\"ticker\"]: r[\"market_cap\"] for r in cap_rows}\n195\t\n196\t # LLM-derived scores from research_cache — default 0 until research has run\n197\t rc_rows = conn.execute(\n198\t \"SELECT ticker, COALESCE(strategic_score,0) AS ss, COALESCE(disruption_score,0) AS ds FROM research_cache\"\n199\t ).fetchall()\n200\t llm_scores: dict[str, tuple[float, float]] = {r[\"ticker\"]: (r[\"ss\"], r[\"ds\"]) for r in rc_rows}\n201\t\n202\t # Historical buyer breadth — full DB, not windowed — used for breadth discount\n203\t breadth_rows = conn.execute(\n204\t \"\"\"\n205\t SELECT issuer_ticker, COUNT(DISTINCT member_id) AS cnt\n206\t FROM trades\n207\t WHERE tx_type = 'buy' AND issuer_ticker != ''\n208\t GROUP BY issuer_ticker\n209\t \"\"\"\n210\t ).fetchall()\n211\t historical_buyers: dict[str, int] = {r[\"issuer_ticker\"]: r[\"cnt\"] for r in breadth_rows}\n212\t\n213\t rows = conn.execute(\n214\t \"\"\"\n215\t SELECT\n216\t t.tx_id,\n217\t t.issuer_ticker,\n218\t t.issuer_name,\n219\t t.issuer_sector,\n220\t t.member_id,\n221\t t.tx_type,\n222\t t.amount_tier,\n223\t t.is_option,\n224\t t.pub_date,\n225\t t.tx_date,\n226\t t.value_estimate,\n227\t m.party,\n228\t m.chamber,\n229\t m.first_name || ' ' || m.last_name AS member_name,\n230\t m.committees,\n231\t COALESCE(m.win_rate, -1) AS win_rate\n232\t FROM trades t\n233\t JOIN members m ON m.bioguide_id = t.member_id\n234\t WHERE t.pub_date >= ?\n235\t AND t.issuer_ticker != ''\n236\t ORDER BY t.issuer_ticker, t.pub_date DESC\n237\t \"\"\",\n238\t (cutoff,),\n239\t ).fetchall()\n240\t\n241\t # Group by ticker\n242\t by_ticker: dict[str, list] = defaultdict(list)\n243\t for row in rows:\n244\t by_ticker[row[\"issuer_ticker\"]].append(dict(row))\n245\t\n246\t signals = []\n247\t\n248\t for ticker, ticker_rows in by_ticker.items():\n249\t buy_rows = [r for r in ticker_rows if r[\"tx_type\"] == \"buy\"]\n250\t sell_rows = [r for r in ticker_rows if r[\"tx_type\"] == \"sell\"]\n251\t option_rows = [r for r in ticker_rows if r[\"is_option\"]]\n252\t sell_ids = set(r[\"member_id\"] for r in sell_rows)\n253\t\n254\t if not buy_rows:\n255\t continue # only score tickers with at least one buy\n256\t\n257\t # --- Cluster score: unique member buyers, discounted for mega-caps ---\n258\t buyer_ids = set(r[\"member_id\"] for r in buy_rows)\n259\t unique_buyers = len(buyer_ids)\n260\t hist = historical_buyers.get(ticker, 0)\n261\t cluster = round(_cluster_score(unique_buyers) * _breadth_mult(hist), 2)\n262\t\n263\t # --- Size score: max tier among buys ---\n264\t max_tier = max((r[\"amount_tier\"] or 0) for r in buy_rows)\n265\t size = _size_score(max_tier)\n266\t\n267\t # --- Options bonus ---\n268\t opt_bonus = 2.0 if option_rows else 0.0\n269\t\n270\t # --- Track record score (default 1 until win_rate data available) ---\n271\t win_rates = [r[\"win_rate\"] for r in buy_rows if r[\"win_rate\"] >= 0]\n272\t if win_rates:\n273\t avg_wr = sum(win_rates) / len(win_rates)\n274\t track = 2.0 if avg_wr > 0.6 else (1.0 if avg_wr >= 0.5 else 0.5)\n275\t else:\n276\t track = 1.0 # default unknown\n277\t\n278\t # --- Committee score: best score across all buying members ---\n279\t sector = (buy_rows[0].get(\"issuer_sector\") or \"\").lower()\n280\t comm_score = max(\n281\t _committee_score(r.get(\"committees\"), sector) for r in buy_rows\n282\t )\n283\t\n284\t mc_bonus = _small_cap_bonus(market_caps.get(ticker))\n285\t sell_penalty = _sell_pressure(len(sell_ids), unique_buyers)\n286\t strategic, disruption = llm_scores.get(ticker, (0.0, 0.0))\n287\t components = {\"committee\": comm_score, \"track_record\": track, \"cluster\": cluster,\n288\t \"options\": opt_bonus, \"size\": size, \"small_cap\": mc_bonus,\n289\t \"sell_pressure\": sell_penalty, \"strategic\": strategic, \"disruption\": disruption}\n290\t total = weighted_total(components, weights)\n291\t\n292\t pub_dates = [r[\"pub_date\"] for r in ticker_rows]\n293\t tx_dates = [r[\"tx_date\"] for r in buy_rows if r[\"tx_date\"]]\n294\t runup = _priced_in_runup(ticker, min(tx_dates) if tx_dates else \"\")\n295\t\n296\t # Collect buyer details for display\n297\t buyers_detail = []\n298\t seen_buyers = set()\n299\t for r in buy_rows:\n300\t if r[\"member_id\"] not in seen_buyers:\n301\t seen_buyers.add(r[\"member_id\"])\n302\t buyers_detail.append({\n303\t \"name\": r[\"member_name\"],\n304\t \"party\": r[\"party\"],\n305\t \"chamber\": r[\"chamber\"],\n306\t \"tier\": r[\"amount_tier\"],\n307\t })\n308\t\n309\t sig = {\n310\t \"ticker\": ticker,\n311\t \"issuer_name\": buy_rows[0][\"issuer_name\"],\n312\t \"sector\": sector,\n313\t \"first_seen\": min(pub_dates),\n314\t \"last_seen\": max(pub_dates),\n315\t \"first_tx_date\": min(tx_dates) if tx_dates else \"\",\n316\t \"last_tx_date\": max(tx_dates) if tx_dates else \"\",\n317\t \"member_count\": unique_buyers,\n318\t \"sell_count\": len(sell_ids),\n319\t \"sell_pressure_score\": sell_penalty,\n320\t \"has_options\": bool(option_rows),\n321\t \"max_tier\": max_tier,\n322\t \"committee_score\": comm_score,\n323\t \"track_record_score\": track,\n324\t \"cluster_score\": cluster,\n325\t \"options_bonus\": opt_bonus,\n326\t \"size_score\": size,\n327\t \"small_cap_score\": mc_bonus,\n328\t \"strategic_score\": strategic,\n329\t \"disruption_score\": disruption,\n330\t \"total_score\": round(total, 2),\n331\t \"runup_pct\": runup,\n332\t \"buyers\": buyers_detail,\n333\t }\n334\t signals.append(sig)\n335\t\n336\t signals.sort(key=lambda x: x[\"total_score\"], reverse=True)\n337\t\n338\t # Upsert into signals table\n339\t now = datetime.now().isoformat(timespec=\"seconds\")\n340\t for sig in signals:\n341\t conn.execute(\n342\t \"\"\"\n343\t INSERT INTO signals (\n344\t ticker, issuer_name, first_seen, last_seen, first_tx_date, member_count,\n345\t committee_score, track_record_score, cluster_score,\n346\t options_bonus, size_score, small_cap_score,\n347\t sell_pressure_score, strategic_score, disruption_score, total_score, runup_pct, updated_at\n348\t ) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\n349\t ON CONFLICT(ticker) DO UPDATE SET\n350\t issuer_name = excluded.issuer_name,\n351\t last_seen = excluded.last_seen,\n352\t first_tx_date = excluded.first_tx_date,\n353\t member_count = excluded.member_count,\n354\t committee_score = excluded.committee_score,\n355\t track_record_score = excluded.track_record_score,\n356\t cluster_score = excluded.cluster_score,\n357\t options_bonus = excluded.options_bonus,\n358\t size_score = excluded.size_score,\n359\t small_cap_score = excluded.small_cap_score,\n360\t sell_pressure_score = excluded.sell_pressure_score,\n361\t strategic_score = excluded.strategic_score,\n362\t disruption_score = excluded.disruption_score,\n363\t total_score = excluded.total_score,\n364\t runup_pct = excluded.runup_pct,\n365\t updated_at = excluded.updated_at\n366\t \"\"\",\n367\t (\n368\t sig[\"ticker\"],\n369\t sig[\"issuer_name\"],\n370\t sig[\"first_seen\"],\n371\t sig[\"last_seen\"],\n372\t sig[\"first_tx_date\"] or None,\n373\t sig[\"member_count\"],\n374\t sig[\"committee_score\"],\n375\t sig[\"track_record_score\"],\n376\t sig[\"cluster_score\"],\n377\t sig[\"options_bonus\"],\n378\t sig[\"size_score\"],\n379\t sig[\"small_cap_score\"],\n380\t sig[\"sell_pressure_score\"],\n381\t sig[\"strategic_score\"],\n382\t sig[\"disruption_score\"],\n383\t sig[\"total_score\"],\n384\t sig[\"runup_pct\"],\n385\t now,\n386\t ),\n387\t )\n388\t\n389\t conn.commit()\n390\t conn.close()\n391\t return signals\n392\t\n393\t\n394\tdef top_signals(n: int = 10, min_score: float = 0.0) -> list[dict]:\n395\t \"\"\"Return top N signals from the signals table, optionally filtered by min_score.\"\"\"\n396\t conn = sqlite3.connect(DB_PATH)\n397\t conn.row_factory = sqlite3.Row\n398\t rows = conn.execute(\n399\t \"\"\"\n400\t SELECT * FROM signals\n401\t WHERE total_score >= ?\n402\t ORDER BY total_score DESC\n403\t LIMIT ?\n404\t \"\"\",\n405\t (min_score, n),\n406\t ).fetchall()\n407\t conn.close()\n408\t return [dict(r) for r in rows]\n409\t\n410\t\n411\tif __name__ == \"__main__\":\n412\t print(\"Scoring all tickers...\\n\")\n413\t signals = score_tickers()\n414\t\n415\t print(f\"{'Rank':<5} {'Ticker':<8} {'Score':>6} {'Clust':>5} {'Size':>4} {'Opts':>4} {'Trck':>4} {'Cmte':>4} {'Cap':>4} \"\n416\t f\"{'Buyers':>6} {'Sells':>5} {'Sector'}\")\n417\t print(\"-\" * 100)\n418\t for i, s in enumerate(signals[:20], 1):\n419\t opt_flag = \"OPT\" if s[\"has_options\"] else \"\"\n420\t buyers_str = \" / \".join(\n421\t f\"{b['name'].split()[-1]}({'R' if b['party'] in ('republican', 'R') else 'D'})\"\n422\t for b in s[\"buyers\"][:3]\n423\t )\n424\t if len(s[\"buyers\"]) > 3:\n425\t buyers_str += f\" +{len(s['buyers'])-3}\"\n426\t sc = s.get(\"small_cap_score\", 0) or 0\n427\t print(\n428\t f\"{i:<5} {s['ticker']:<8} {s['total_score']:>6.1f} \"\n429\t f\"{s['cluster_score']:>5.2f} {s['size_score']:>4.0f} \"\n430\t f\"{s['options_bonus']:>4.0f} {s['track_record_score']:>4.1f} \"\n431\t f\"{s['committee_score']:>4.0f} {sc:>+4.1f} \"\n432\t f\"{s['member_count']:>6} {s['sell_count']:>5} \"\n433\t f\"{s['sector'][:25]}\"\n434\t )\n435\t print(f\" {s['issuer_name'][:50]:<50} buyers: {buyers_str}\")\n436\t print()\n437\t\n438\t notify_candidates = [s for s in signals if s[\"total_score\"] >= SCORE_NOTIFY_THRESHOLD]\n439\t print(f\"\\n{len(notify_candidates)} signals at or above notify threshold ({SCORE_NOTIFY_THRESHOLD})\")\n440\t\n441\t from tools.generate_report import generate\n442\t generate()\n443\t"}]},"uuid":"efa34c31-5a91-4e19-9952-5192449e72fc","timestamp":"2026-07-12T12:31:07.664Z","sourceToolAssistantUUID":"72fdfcdf-f895-4908-a681-3bf4750b5480","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"e80a8165-202f-4d3d-a61e-2b6903eafaaf","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GTx7Eo4GRbnpZDtHzWr64D","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/tests/test_outcome_tracker.py: sqlite3.connect(path).execute(\"CREATE TABLE signal_outcomes (notified_at TEXT)\").close()\n/workspace/extra/projects/congressional-trading/tests/test_outcome_tracker.py: CREATE TABLE signal_outcomes (\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py: CREATE TABLE signal_outcomes (\n/workspace/extra/projects/congressional-trading/tests/test_win_rate.py: CREATE TABLE members (\n/workspace/extra/projects/congressional-trading/tests/test_win_rate.py: CREATE TABLE trades (\n/workspace/extra/projects/congressional-trading/tests/test_win_rate.py: CREATE TABLE signals (\n/workspace/extra/projects/congressional-trading/tests/test_win_rate.py: CREATE TABLE price_history (\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/playhouse/migrate.py: # Parse out the `CREATE TABLE` and column list portions of the query.\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/playhouse/migrate.py: # Update the name of the new CREATE TABLE query.\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/playhouse/sqlite_ext.py: CREATE TABLE tbl(a INTEGER PRIMARY KEY, b);\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/peewee.py: ctx.literal('CREATE TEMPORARY TABLE ' if is_temp else 'CREATE TABLE ')\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/peewee.py: if meta.get('temporary') else 'CREATE TABLE '))\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/io/sql.py: structure of a DataFrame. The first entry will be a CREATE TABLE\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/io/sql.py: \"CREATE TABLE \"\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/tests/io/test_sql.py: stmt = \"\"\"CREATE TABLE iris (\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/tests/io/test_sql.py: stmt = \"\"\"CREATE TABLE iris (\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/tests/io/test_sql.py: stmt = \"\"\"CREATE TABLE types (\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/tests/io/test_sql.py: stmt = \"\"\"CREATE TABLE types (\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/tests/io/test_sql.py: CREATE TABLE test_insert_conflict (\n/workspace/extra/projects/congressional-trading/.venv/lib/python3.9/site-packages/pandas/tests/io/test_sql.py: CREATE TABLE test_insert_conflict (","is_error":false}]},"uuid":"8ad96e55-1727-45e3-b084-17a57c68102b","timestamp":"2026-07-12T12:31:09.169Z","sourceToolAssistantUUID":"e80a8165-202f-4d3d-a61e-2b6903eafaaf","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"8ad96e55-1727-45e3-b084-17a57c68102b","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAeDXKRqBGbTQxXu8tA","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me read the schema file and check for outcome tracker:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":7434,"cache_read_input_tokens":15160,"cache_creation":{"ephemeral_5m_input_tokens":7434,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAeCmvT5XcXbVespjTb","attributionAgent":"Explore","type":"assistant","uuid":"f4ac03e3-8d2e-47ca-a660-6bbdf7c13528","timestamp":"2026-07-12T12:31:10.484Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"d930e8dd-da5d-4908-83af-6f397be73412","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VUajsWr7EKE8BLTFWwgdvf","type":"tool_result","content":"1\tCREATE TABLE IF NOT EXISTS members (\n2\t bioguide_id TEXT PRIMARY KEY,\n3\t first_name TEXT,\n4\t last_name TEXT,\n5\t nickname TEXT,\n6\t party TEXT,\n7\t chamber TEXT,\n8\t state_id TEXT,\n9\t gender TEXT,\n10\t dob TEXT,\n11\t committees TEXT, -- JSON array, refreshed weekly\n12\t trade_count INTEGER DEFAULT 0,\n13\t win_rate REAL, -- nullable until calculable\n14\t updated_at TEXT\n15\t);\n16\t\n17\tCREATE TABLE IF NOT EXISTS trades (\n18\t tx_id INTEGER PRIMARY KEY,\n19\t member_id TEXT NOT NULL,\n20\t issuer_id INTEGER,\n21\t issuer_name TEXT,\n22\t issuer_ticker TEXT, -- clean ticker, e.g. \"NVDA\"\n23\t issuer_sector TEXT,\n24\t issuer_country TEXT,\n25\t chamber TEXT,\n26\t tx_date TEXT, -- YYYY-MM-DD, when trade executed\n27\t pub_date TEXT, -- when disclosure was published\n28\t reporting_gap INTEGER, -- days between tx_date and pub_date\n29\t tx_type TEXT, -- buy / sell / exchange\n30\t tx_type_extended TEXT,\n31\t owner TEXT, -- self / spouse / not-disclosed / etc\n32\t price_at_trade REAL,\n33\t value_estimate INTEGER, -- midpoint dollar estimate from disclosure\n34\t amount_tier INTEGER, -- 1-6 derived from value_estimate\n35\t is_option INTEGER DEFAULT 0, -- 0/1\n36\t comment TEXT,\n37\t source TEXT DEFAULT 'capitol_trades',\n38\t first_ingested_at TEXT DEFAULT (datetime('now')),\n39\t created_at TEXT DEFAULT (datetime('now'))\n40\t);\n41\t\n42\tCREATE TABLE IF NOT EXISTS signals (\n43\t id INTEGER PRIMARY KEY AUTOINCREMENT,\n44\t ticker TEXT NOT NULL UNIQUE,\n45\t first_seen TEXT,\n46\t last_seen TEXT,\n47\t first_tx_date TEXT, -- earliest trade exec date in current scoring window\n48\t member_count INTEGER DEFAULT 1,\n49\t committee_score REAL DEFAULT 0,\n50\t track_record_score REAL DEFAULT 1,\n51\t cluster_score REAL DEFAULT 0,\n52\t options_bonus REAL DEFAULT 0,\n53\t size_score REAL DEFAULT 0,\n54\t total_score REAL DEFAULT 0,\n55\t research_summary TEXT,\n56\t timeliness TEXT, -- fresh / watch / late\n57\t price_at_tx REAL, -- close on first_tx_date (member's entry)\n58\t price_at_pub REAL, -- close on first_seen (our actionable entry)\n59\t price_current REAL, -- yesterday's close\n60\t return_from_tx_pct REAL, -- % gain/loss since member's trade\n61\t return_from_pub_pct REAL, -- % gain/loss since first disclosure\n62\t price_fetched_at TEXT,\n63\t market_cap REAL, -- cached from yfinance\n64\t small_cap_score REAL DEFAULT 0, -- score component: bonus for smaller caps\n65\t sell_pressure_score REAL DEFAULT 0, -- score component: penalty when sellers ≥ buyers\n66\t runup_pct REAL,\n67\t notified_at TEXT,\n68\t notified INTEGER DEFAULT 0,\n69\t updated_at TEXT DEFAULT (datetime('now'))\n70\t);\n71\t\n72\tCREATE TABLE IF NOT EXISTS research_cache (\n73\t ticker TEXT PRIMARY KEY,\n74\t cached_at TEXT,\n75\t deep_researched_at TEXT, -- set when deep prompt (risk_factors + recommended_action) was run\n76\t company_summary TEXT,\n77\t recent_news TEXT,\n78\t catalyst_hypothesis TEXT,\n79\t timeliness_assessment TEXT,\n80\t conviction TEXT, -- LOW / MEDIUM / HIGH\n81\t conviction_rationale TEXT,\n82\t risk_factors TEXT,\n83\t recommended_action TEXT,\n84\t sources TEXT,\n85\t verifier_verdict TEXT,\n86\t verifier_rationale TEXT,\n87\t checklist_json TEXT,\n88\t raw_response TEXT\n89\t);\n90\t\n91\tCREATE TABLE IF NOT EXISTS signal_outcomes (\n92\t id INTEGER PRIMARY KEY AUTOINCREMENT,\n93\t ticker TEXT NOT NULL,\n94\t notified_at TEXT NOT NULL,\n95\t score_at_notify REAL,\n96\t conviction TEXT,\n97\t recommended_action TEXT,\n98\t price_at_notify REAL,\n99\t price_30d REAL,\n100\t price_60d REAL,\n101\t return_30d_pct REAL,\n102\t return_60d_pct REAL,\n103\t fired INTEGER NOT NULL DEFAULT 1,\n104\t suppression_reason TEXT,\n105\t spy_price_at_notify REAL,\n106\t spy_price_30d REAL,\n107\t spy_price_60d REAL,\n108\t return_30d_excess_pct REAL,\n109\t return_60d_excess_pct REAL,\n110\t verifier_verdict TEXT,\n111\t citation_backed INTEGER DEFAULT 0,\n112\t config_version_id INTEGER,\n113\t outcome TEXT, -- \"win\" / \"loss\" / \"pending\" / \"unmeasurable\"\n114\t checked_at TEXT,\n115\t created_at TEXT DEFAULT (datetime('now'))\n116\t);\n117\t\n118\tCREATE TABLE IF NOT EXISTS predicted_signals (\n119\t id INTEGER PRIMARY KEY AUTOINCREMENT,\n120\t member_id TEXT NOT NULL,\n121\t member_name TEXT,\n122\t ticker TEXT NOT NULL,\n123\t company_name TEXT,\n124\t sector TEXT,\n125\t predicted_action TEXT, -- buy / sell\n126\t confidence TEXT, -- LOW / MEDIUM / HIGH\n127\t legislation_catalyst TEXT,\n128\t historical_precedent TEXT,\n129\t rationale TEXT,\n130\t behavioral_profile TEXT, -- member's trading style summary (one per member)\n131\t generated_at TEXT,\n132\t confirmed INTEGER DEFAULT 0, -- 1 when a matching real disclosure appears\n133\t confirmed_at TEXT,\n134\t UNIQUE(member_id, ticker)\n135\t);\n136\t\n137\tCREATE INDEX IF NOT EXISTS idx_trades_member ON trades(member_id);\n138\tCREATE INDEX IF NOT EXISTS idx_trades_ticker ON trades(issuer_ticker);\n139\tCREATE INDEX IF NOT EXISTS idx_trades_pub_date ON trades(pub_date);\n140\tCREATE INDEX IF NOT EXISTS idx_trades_tx_date ON trades(tx_date);\n141\tCREATE INDEX IF NOT EXISTS idx_outcomes_ticker ON signal_outcomes(ticker);\n142\tCREATE INDEX IF NOT EXISTS idx_outcomes_notified_at ON signal_outcomes(notified_at);\n143\tCREATE UNIQUE INDEX IF NOT EXISTS idx_outcomes_candidate ON signal_outcomes(ticker, notified_at);\n144\tCREATE INDEX IF NOT EXISTS idx_predicted_member ON predicted_signals(member_id);\n145\tCREATE INDEX IF NOT EXISTS idx_predicted_ticker ON predicted_signals(ticker);\n146\t\n147\tCREATE TABLE IF NOT EXISTS ticker_metadata (\n148\t ticker TEXT PRIMARY KEY,\n149\t market_cap REAL,\n150\t last_price REAL,\n151\t is_etf INTEGER DEFAULT 0, -- 1 if yfinance quoteType is ETF/MUTUALFUND\n152\t is_valid INTEGER DEFAULT 1, -- 0 if yfinance returns no data (invalid/delisted)\n153\t pe_ratio REAL, -- trailing P/E\n154\t analyst_rating TEXT, -- strong buy / buy / hold / sell / strong sell\n155\t price_vs_50d_pct REAL, -- % current price above/below 50-day MA\n156\t fetched_at TEXT\n157\t);\n158\t\n159\tCREATE INDEX IF NOT EXISTS idx_ticker_meta ON ticker_metadata(ticker);\n160\t\n161\tCREATE TABLE IF NOT EXISTS price_history (\n162\t ticker TEXT NOT NULL,\n163\t date TEXT NOT NULL,\n164\t close REAL NOT NULL,\n165\t PRIMARY KEY (ticker, date)\n166\t);\n167\t\n168\tCREATE TABLE IF NOT EXISTS config_versions (\n169\t id INTEGER PRIMARY KEY AUTOINCREMENT,\n170\t weights_json TEXT NOT NULL,\n171\t backtest_metrics_json TEXT NOT NULL,\n172\t promoted_at TEXT NOT NULL DEFAULT (datetime('now')),\n173\t promoted_by TEXT NOT NULL CHECK(promoted_by IN ('human', 'tuner')),\n174\t active INTEGER NOT NULL DEFAULT 0,\n175\t is_last_known_good INTEGER NOT NULL DEFAULT 0\n176\t);\n177\tCREATE INDEX IF NOT EXISTS idx_config_active ON config_versions(active);\n178\t\n179\tCREATE TABLE IF NOT EXISTS system_state (\n180\t key TEXT PRIMARY KEY,\n181\t value TEXT NOT NULL,\n182\t updated_at TEXT NOT NULL DEFAULT (datetime('now'))\n183\t);\n184\tCREATE TABLE IF NOT EXISTS automation_events (\n185\t id INTEGER PRIMARY KEY AUTOINCREMENT,\n186\t event_type TEXT NOT NULL,\n187\t details_json TEXT NOT NULL,\n188\t created_at TEXT NOT NULL DEFAULT (datetime('now'))\n189\t);\n190\t"}]},"uuid":"65ad78e4-e786-4111-924e-531e6d4b11d6","timestamp":"2026-07-12T12:31:10.972Z","sourceToolAssistantUUID":"d930e8dd-da5d-4908-83af-6f397be73412","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
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{"parentUuid":"079ef75d-23da-41e1-bcb0-f9cca32b0bae","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Me3KV1JuKBvdBNK97fXJqX","type":"tool_result","content":"1\t\"\"\"Dense candidate outcome ledger and excess-return scorecard.\"\"\"\n2\tfrom __future__ import annotations\n3\timport argparse\n4\tfrom datetime import datetime, timedelta\n5\timport os\n6\timport sqlite3\n7\timport sys\n8\tfrom typing import Callable\n9\t\n10\tsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\n11\tfrom config import DB_PATH\n12\t\n13\t\n14\tdef excess_return_pct(entry: float, exit_price: float, benchmark_entry: float, benchmark_exit: float) -> float:\n15\t if min(entry, exit_price, benchmark_entry, benchmark_exit) <= 0:\n16\t raise ValueError(\"prices must be positive\")\n17\t return ((exit_price / entry - 1) - (benchmark_exit / benchmark_entry - 1)) * 100\n18\t\n19\t\n20\tdef record_candidate(*, ticker: str, score: float, conviction: str,\n21\t recommended_action: str, price: float | None,\n22\t observed_at: str, fired: bool, suppression_reason: str | None,\n23\t verifier_verdict: str | None = None, citation_backed: bool = False,\n24\t db_path: str = DB_PATH) -> None:\n25\t \"\"\"Record every notify-tier candidate, including candidates suppressed by a gate.\"\"\"\n26\t with sqlite3.connect(db_path) as conn:\n27\t version = conn.execute(\"SELECT id FROM config_versions WHERE active=1 ORDER BY id DESC LIMIT 1\").fetchone()\n28\t conn.execute(\"\"\"\n29\t INSERT INTO signal_outcomes\n30\t (ticker, notified_at, score_at_notify, conviction, recommended_action,\n31\t price_at_notify, fired, suppression_reason, verifier_verdict, citation_backed,\n32\t config_version_id, outcome)\n33\t VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'pending')\n34\t ON CONFLICT(ticker, notified_at) DO UPDATE SET\n35\t score_at_notify=excluded.score_at_notify, conviction=excluded.conviction,\n36\t recommended_action=excluded.recommended_action, price_at_notify=excluded.price_at_notify,\n37\t fired=excluded.fired, suppression_reason=excluded.suppression_reason,\n38\t verifier_verdict=excluded.verifier_verdict, citation_backed=excluded.citation_backed,\n39\t config_version_id=excluded.config_version_id\n40\t \"\"\", (ticker, observed_at, score, conviction, recommended_action, price, int(fired),\n41\t suppression_reason, verifier_verdict, int(citation_backed), version[0] if version else None))\n42\t\n43\t\n44\tdef record_notification(ticker: str, score: float, conviction: str,\n45\t recommended_action: str, price: float | None, notified_at: str) -> None:\n46\t record_candidate(ticker=ticker, score=score, conviction=conviction,\n47\t recommended_action=recommended_action, price=price,\n48\t observed_at=notified_at, fired=True, suppression_reason=None)\n49\t\n50\t\n51\tdef _close_on_or_after(history, target: datetime) -> float | None:\n52\t if history is None or history.empty:\n53\t return None\n54\t index = history.index\n55\t if getattr(index, \"tz\", None) is not None and target.tzinfo is None:\n56\t target = target.replace(tzinfo=index.tz)\n57\t eligible = history[index >= target]\n58\t return None if eligible.empty else float(eligible[\"Close\"].iloc[0])\n59\t\n60\t\n61\tdef _default_history(ticker: str, start: datetime, end: datetime):\n62\t import yfinance as yf\n63\t return yf.Ticker(ticker).history(start=start.date().isoformat(), end=end.date().isoformat(), auto_adjust=False)\n64\t\n65\t\n66\tdef update_pending_outcomes(*, db_path: str = DB_PATH, now: datetime | None = None,\n67\t history_fetcher: Callable = _default_history) -> dict:\n68\t now = now or datetime.now()\n69\t with sqlite3.connect(db_path) as conn:\n70\t conn.row_factory = sqlite3.Row\n71\t pending = conn.execute(\"SELECT * FROM signal_outcomes WHERE outcome='pending'\").fetchall()\n72\t updated = unmeasurable = 0\n73\t for raw in pending:\n74\t row = dict(raw)\n75\t notified = datetime.fromisoformat(row[\"notified_at\"].replace(\" \", \"T\"))\n76\t elapsed = (now - notified).days\n77\t if elapsed < 30:\n78\t continue\n79\t horizon = 60 if elapsed >= 60 else 30\n80\t end = notified + timedelta(days=horizon + 8)\n81\t try:\n82\t stock = history_fetcher(row[\"ticker\"], notified, end)\n83\t spy = history_fetcher(\"SPY\", notified, end)\n84\t entry = row[\"price_at_notify\"] or _close_on_or_after(stock, notified)\n85\t spy_entry = row[\"spy_price_at_notify\"] or _close_on_or_after(spy, notified)\n86\t p30 = _close_on_or_after(stock, notified + timedelta(days=30))\n87\t s30 = _close_on_or_after(spy, notified + timedelta(days=30))\n88\t if not all((entry, spy_entry, p30, s30)):\n89\t conn.execute(\"UPDATE signal_outcomes SET outcome='unmeasurable', checked_at=? WHERE id=?\",\n90\t (now.isoformat(timespec=\"seconds\"), row[\"id\"]))\n91\t unmeasurable += 1\n92\t continue\n93\t values = {\"spy_price_at_notify\": spy_entry, \"price_30d\": p30, \"spy_price_30d\": s30,\n94\t \"return_30d_pct\": (p30 / entry - 1) * 100,\n95\t \"return_30d_excess_pct\": excess_return_pct(entry, p30, spy_entry, s30),\n96\t \"checked_at\": now.isoformat(timespec=\"seconds\")}\n97\t if horizon == 60:\n98\t p60 = _close_on_or_after(stock, notified + timedelta(days=60))\n99\t s60 = _close_on_or_after(spy, notified + timedelta(days=60))\n100\t if not p60 or not s60:\n101\t conn.execute(\"UPDATE signal_outcomes SET outcome='unmeasurable', checked_at=? WHERE id=?\",\n102\t (values[\"checked_at\"], row[\"id\"]))\n103\t unmeasurable += 1\n104\t continue\n105\t values.update({\"price_60d\": p60, \"spy_price_60d\": s60,\n106\t \"return_60d_pct\": (p60 / entry - 1) * 100,\n107\t \"return_60d_excess_pct\": excess_return_pct(entry, p60, spy_entry, s60)})\n108\t values[\"outcome\"] = \"win\" if values[\"return_60d_excess_pct\"] > 0 else \"loss\"\n109\t sets = \", \".join(f\"{key}=?\" for key in values)\n110\t conn.execute(f\"UPDATE signal_outcomes SET {sets} WHERE id=?\", (*values.values(), row[\"id\"]))\n111\t updated += 1\n112\t except Exception as exc:\n113\t print(f\" Outcome check failed for {row['ticker']}: {exc}\")\n114\t return {\"updated\": updated, \"unmeasurable\": unmeasurable, \"pending_checked\": len(pending)}\n115\t\n116\t\n117\tdef _median(values):\n118\t values = sorted(values)\n119\t if not values: return None\n120\t mid = len(values) // 2\n121\t return values[mid] if len(values) % 2 else (values[mid - 1] + values[mid]) / 2\n122\t\n123\t\n124\tdef _print_group(label: str, rows: list[dict]) -> None:\n125\t values = [r[\"return_30d_excess_pct\"] for r in rows if r[\"return_30d_excess_pct\"] is not None]\n126\t if not values:\n127\t print(f\"{label:<28} n={len(rows):>3} measured= 0 median30d=undefined\")\n128\t return\n129\t print(f\"{label:<28} n={len(rows):>3} measured={len(values):>3} \"\n130\t f\"median30d={_median(values):+.2f}% precision={sum(v > 0 for v in values)/len(values):.0%}\")\n131\t\n132\t\n133\tdef print_scorecard(*, db_path: str = DB_PATH) -> None:\n134\t with sqlite3.connect(db_path) as conn:\n135\t conn.row_factory = sqlite3.Row\n136\t rows = [dict(r) for r in conn.execute(\"SELECT * FROM signal_outcomes ORDER BY notified_at DESC\")]\n137\t if not rows:\n138\t print(\"No notify-tier candidates recorded; metrics are undefined.\")\n139\t return\n140\t print(f\"Signal scorecard — {len(rows)} notify-tier candidates\")\n141\t _print_group(\"Fired\", [r for r in rows if r[\"fired\"]])\n142\t _print_group(\"Suppressed\", [r for r in rows if not r[\"fired\"]])\n143\t for field, label in ((\"conviction\", \"Conviction\"), (\"suppression_reason\", \"Suppression\"),\n144\t (\"verifier_verdict\", \"Verifier\")):\n145\t for value in sorted({r[field] for r in rows if r[field]}):\n146\t _print_group(f\"{label}: {value}\", [r for r in rows if r[field] == value])\n147\t _print_group(\"Citation-backed\", [r for r in rows if r[\"citation_backed\"]])\n148\t\n149\t\n150\tif __name__ == \"__main__\":\n151\t parser = argparse.ArgumentParser()\n152\t parser.add_argument(\"--scorecard\", action=\"store_true\")\n153\t args = parser.parse_args()\n154\t if not args.scorecard:\n155\t result = update_pending_outcomes()\n156\t print(f\"Updated {result['updated']} outcomes; {result['unmeasurable']} unmeasurable\")\n157\t print_scorecard()\n158\t"}]},"uuid":"265335b2-a3ef-4c87-ac60-d9c8aea67597","timestamp":"2026-07-12T12:31:11.240Z","sourceToolAssistantUUID":"079ef75d-23da-41e1-bcb0-f9cca32b0bae","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"265335b2-a3ef-4c87-ac60-d9c8aea67597","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAeDXKRqBGbTQxXu8tA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GHriVT3tLEvwftCoctxa6Q","name":"Bash","input":{"command":"grep -r \"conviction_gate\\|edge_factor\" /workspace/extra/projects/congressional-trading --include=\"*.py\" -n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":7434,"cache_read_input_tokens":15160,"output_tokens":200,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":7434},"inference_geo":"","iterations":[{"input_tokens":7,"output_tokens":200,"cache_read_input_tokens":15160,"cache_creation_input_tokens":7434,"cache_creation":{"ephemeral_5m_input_tokens":7434,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAeCmvT5XcXbVespjTb","attributionAgent":"Explore","type":"assistant","uuid":"58800ee1-0bf6-4366-b9fe-76df25411009","timestamp":"2026-07-12T12:31:11.701Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"58800ee1-0bf6-4366-b9fe-76df25411009","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GHriVT3tLEvwftCoctxa6Q","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/run_daily.py:37: from analysis.notification_policy import edge_factors\n/workspace/extra/projects/congressional-trading/run_daily.py:45: factors = \", \".join(edge_factors(sig, research))\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:28: edge_factors: tuple[str, ...] = ()\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:50:def edge_factors(sig: dict, research: dict | None) -> tuple[str, ...]:\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:98: min_edge_factors: int = NOTIFY_MIN_EDGE_FACTORS,\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:135: factors = edge_factors(sig, research)\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:136: if len(factors) < min_edge_factors:\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:139: f\"only {len(factors)}/{min_edge_factors} edge factors: {', '.join(factors) or 'none'}\",\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py:10: edge_factors,\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py:52: self.assertGreaterEqual(len(decision.edge_factors), 3)\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py:53: self.assertIn(\"multi-member cluster\", decision.edge_factors)\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py:54: self.assertIn(\"direct committee alignment\", decision.edge_factors)\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py:69: def test_rejects_high_score_without_enough_edge_factors(self):\n/workspace/extra/projects/congressional-trading/tests/test_notification_policy.py:85: self.assertEqual(edge_factors(sig, research), ())","is_error":false}]},"uuid":"3a1dc90b-40f6-40f3-8ea8-e8eab269cd8b","timestamp":"2026-07-12T12:31:12.218Z","sourceToolAssistantUUID":"58800ee1-0bf6-4366-b9fe-76df25411009","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"3a1dc90b-40f6-40f3-8ea8-e8eab269cd8b","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAeTHYrS4zuvQmGVVvK","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me read the notification_policy file to understand conviction_gate and edge_factors:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":6073,"cache_read_input_tokens":22594,"cache_creation":{"ephemeral_5m_input_tokens":6073,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAeRqV7gzQPbGkLvWNV","attributionAgent":"Explore","type":"assistant","uuid":"d6547dd7-30bc-40e7-ac5f-1bbe8e41301c","timestamp":"2026-07-12T12:31:13.764Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"d6547dd7-30bc-40e7-ac5f-1bbe8e41301c","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAeTHYrS4zuvQmGVVvK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LuNcQAVd3kGuxDcP6EF1yt","name":"Read","input":{"file_path":"/workspace/extra/projects/congressional-trading/analysis/notification_policy.py"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":6073,"cache_read_input_tokens":22594,"cache_creation":{"ephemeral_5m_input_tokens":6073,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAeRqV7gzQPbGkLvWNV","attributionAgent":"Explore","type":"assistant","uuid":"522caf39-74ef-4044-bff6-e0d085ffd77e","timestamp":"2026-07-12T12:31:14.216Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"522caf39-74ef-4044-bff6-e0d085ffd77e","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LuNcQAVd3kGuxDcP6EF1yt","type":"tool_result","content":"1\t\"\"\"Notification gating for high-signal trade ideas.\"\"\"\n2\t\n3\tfrom __future__ import annotations\n4\t\n5\tfrom dataclasses import dataclass\n6\tfrom datetime import datetime, timedelta\n7\timport math\n8\timport re\n9\timport sqlite3\n10\t\n11\tfrom config import (\n12\t DB_PATH,\n13\t NOTIFY_MAX_SELL_PRESSURE_PENALTY,\n14\t NOTIFY_MIN_CONVICTION,\n15\t NOTIFY_MIN_EDGE_FACTORS,\n16\t NOTIFY_REQUIRE_BUY_ACTION,\n17\t NOTIFY_TICKER_COOLDOWN_DAYS,\n18\t PRICED_IN_RUNUP_PCT,\n19\t REQUIRE_VERIFIER_CONCUR,\n20\t SCORE_NOTIFY_THRESHOLD,\n21\t)\n22\t\n23\t\n24\t@dataclass(frozen=True)\n25\tclass NotificationDecision:\n26\t allowed: bool\n27\t reason: str\n28\t edge_factors: tuple[str, ...] = ()\n29\t\n30\t\n31\t_CONVICTION_ORDER = {\"LOW\": 0, \"MEDIUM\": 1, \"HIGH\": 2}\n32\t_ACTION_BLOCKLIST = (\n33\t \"AVOID\",\n34\t \"HOLD\",\n35\t \"MONITOR\",\n36\t \"PASS\",\n37\t \"SELL\",\n38\t \"TRIM\",\n39\t \"WAIT\",\n40\t)\n41\t\n42\t\n43\tdef _to_float(value, default: float = 0.0) -> float:\n44\t try:\n45\t return float(value)\n46\t except (TypeError, ValueError):\n47\t return default\n48\t\n49\t\n50\tdef edge_factors(sig: dict, research: dict | None) -> tuple[str, ...]:\n51\t \"\"\"Return the independent evidence factors that make a signal notification-worthy.\"\"\"\n52\t factors: list[str] = []\n53\t\n54\t if int(sig.get(\"member_count\") or 0) >= 2:\n55\t factors.append(\"multi-member cluster\")\n56\t if int(sig.get(\"max_tier\") or 0) >= 4:\n57\t factors.append(\"large disclosed size\")\n58\t if sig.get(\"has_options\"):\n59\t factors.append(\"options timing\")\n60\t if _to_float(sig.get(\"committee_score\")) >= 2.0:\n61\t factors.append(\"direct committee alignment\")\n62\t if _to_float(sig.get(\"small_cap_score\")) >= 2.0:\n63\t factors.append(\"undercovered small/mid cap\")\n64\t\n65\t strategic = _to_float((research or {}).get(\"strategic_score\", sig.get(\"strategic_score\")))\n66\t disruption = _to_float((research or {}).get(\"disruption_score\", sig.get(\"disruption_score\")))\n67\t if strategic + disruption >= 4.0:\n68\t factors.append(\"strong LLM catalyst\")\n69\t\n70\t return tuple(factors)\n71\t\n72\t\n73\tdef is_buy_recommendation(action: str | None) -> bool:\n74\t \"\"\"Accept explicit buy/add actions, not incidental mentions of the word buy.\"\"\"\n75\t if not action:\n76\t return False\n77\t\n78\t text = re.sub(r\"\\s+\", \" \", action.strip().upper())\n79\t if not text:\n80\t return False\n81\t if \"DO NOT BUY\" in text or \"NOT BUY\" in text:\n82\t return False\n83\t if text.startswith(_ACTION_BLOCKLIST):\n84\t return False\n85\t\n86\t first_word = re.split(r\"[\\s:;-]+\", text, maxsplit=1)[0]\n87\t return first_word in {\"BUY\", \"ADD\", \"ACCUMULATE\", \"INITIATE\"}\n88\t\n89\t\n90\tdef evaluate_notification_candidate(\n91\t sig: dict,\n92\t research: dict | None,\n93\t adjusted_score: float,\n94\t *,\n95\t notify_threshold: float = SCORE_NOTIFY_THRESHOLD,\n96\t min_conviction: str = NOTIFY_MIN_CONVICTION,\n97\t require_buy_action: bool = NOTIFY_REQUIRE_BUY_ACTION,\n98\t min_edge_factors: int = NOTIFY_MIN_EDGE_FACTORS,\n99\t max_sell_pressure_penalty: float = NOTIFY_MAX_SELL_PRESSURE_PENALTY,\n100\t) -> NotificationDecision:\n101\t \"\"\"Evaluate all quality gates except duplicate/cooldown state.\"\"\"\n102\t ticker = sig.get(\"ticker\", \"?\")\n103\t\n104\t if adjusted_score < notify_threshold:\n105\t return NotificationDecision(False, f\"adjusted score {adjusted_score:.1f} below {notify_threshold:.1f}\")\n106\t if _to_float(sig.get(\"runup_pct\"), float(\"-inf\")) > PRICED_IN_RUNUP_PCT:\n107\t return NotificationDecision(False, f\"priced_in: excess run-up {sig['runup_pct']:+.1f}%\")\n108\t if not research:\n109\t return NotificationDecision(False, \"no research\")\n110\t if not research.get(\"risk_factors\"):\n111\t return NotificationDecision(False, \"no deep research\")\n112\t checklist = research.get(\"checklist\")\n113\t if checklist is None and research.get(\"checklist_json\"):\n114\t import json\n115\t try: checklist = json.loads(research[\"checklist_json\"])\n116\t except (TypeError, ValueError): checklist = None\n117\t if not checklist or not checklist.get(\"passed\"):\n118\t return NotificationDecision(False, \"research_incomplete\")\n119\t if REQUIRE_VERIFIER_CONCUR and research.get(\"verifier_verdict\") != \"CONCUR\":\n120\t return NotificationDecision(False, \"verifier_reject\")\n121\t\n122\t conviction = (research.get(\"conviction\") or \"LOW\").upper()\n123\t min_order = _CONVICTION_ORDER.get(min_conviction.upper(), 1)\n124\t if _CONVICTION_ORDER.get(conviction, 0) < min_order:\n125\t return NotificationDecision(False, f\"conviction={conviction} below {min_conviction.upper()}\")\n126\t\n127\t action = research.get(\"recommended_action\") or \"\"\n128\t if require_buy_action and not is_buy_recommendation(action):\n129\t return NotificationDecision(False, f\"recommended_action is not an explicit buy/add: {action[:80]}\")\n130\t\n131\t sell_pressure = _to_float(sig.get(\"sell_pressure_score\"))\n132\t if sell_pressure < max_sell_pressure_penalty:\n133\t return NotificationDecision(False, f\"sell pressure {sell_pressure:+.1f} conflicts with buy signal\")\n134\t\n135\t factors = edge_factors(sig, research)\n136\t if len(factors) < min_edge_factors:\n137\t return NotificationDecision(\n138\t False,\n139\t f\"only {len(factors)}/{min_edge_factors} edge factors: {', '.join(factors) or 'none'}\",\n140\t factors,\n141\t )\n142\t\n143\t return NotificationDecision(True, f\"{ticker}: {len(factors)} edge factors\", factors)\n144\t\n145\t\n146\tdef _parse_dt(value: str | None) -> datetime | None:\n147\t if not value:\n148\t return None\n149\t try:\n150\t return datetime.fromisoformat(value)\n151\t except ValueError:\n152\t try:\n153\t return datetime.fromisoformat(value.replace(\" \", \"T\"))\n154\t except ValueError:\n155\t return None\n156\t\n157\t\n158\tdef recent_ticker_notification(\n159\t ticker: str,\n160\t *,\n161\t db_path: str = DB_PATH,\n162\t cooldown_days: int = NOTIFY_TICKER_COOLDOWN_DAYS,\n163\t now: datetime | None = None,\n164\t) -> tuple[bool, datetime | None, int]:\n165\t \"\"\"\n166\t Return whether ticker is inside the rolling cooldown, plus latest notify time\n167\t and whole days remaining.\n168\t \"\"\"\n169\t now = now or datetime.now()\n170\t cutoff = now - timedelta(days=cooldown_days)\n171\t\n172\t conn = sqlite3.connect(db_path)\n173\t rows = conn.execute(\n174\t \"\"\"\n175\t SELECT notified_at\n176\t FROM signal_outcomes\n177\t WHERE ticker = ?\n178\t ORDER BY notified_at DESC\n179\t LIMIT 10\n180\t \"\"\",\n181\t (ticker,),\n182\t ).fetchall()\n183\t conn.close()\n184\t\n185\t latest = None\n186\t for row in rows:\n187\t parsed = _parse_dt(row[0])\n188\t if parsed and (latest is None or parsed > latest):\n189\t latest = parsed\n190\t\n191\t if latest is None or latest < cutoff:\n192\t return False, latest, 0\n193\t\n194\t remaining_seconds = (latest + timedelta(days=cooldown_days) - now).total_seconds()\n195\t days_remaining = max(1, math.ceil(remaining_seconds / 86400))\n196\t return True, latest, days_remaining\n197\t"}]},"uuid":"ff1fe392-43da-481b-9b12-9bb8fb84ea88","timestamp":"2026-07-12T12:31:14.251Z","sourceToolAssistantUUID":"522caf39-74ef-4044-bff6-e0d085ffd77e","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"ff1fe392-43da-481b-9b12-9bb8fb84ea88","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAeTHYrS4zuvQmGVVvK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Szvy1pDzGHiW1PMzhFvog1","name":"Read","input":{"file_path":"/workspace/extra/projects/congressional-trading/analysis/weights.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":6073,"cache_read_input_tokens":22594,"output_tokens":141,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":6073},"inference_geo":"","iterations":[{"input_tokens":7,"output_tokens":141,"cache_read_input_tokens":22594,"cache_creation_input_tokens":6073,"cache_creation":{"ephemeral_5m_input_tokens":6073,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAeRqV7gzQPbGkLvWNV","attributionAgent":"Explore","type":"assistant","uuid":"3cac5695-6d57-4784-ad85-536a23cfcf80","timestamp":"2026-07-12T12:31:14.557Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"3cac5695-6d57-4784-ad85-536a23cfcf80","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Szvy1pDzGHiW1PMzhFvog1","type":"tool_result","content":"1\t\"\"\"Versioned scoring-weight access and promotion.\"\"\"\n2\timport json\n3\timport sqlite3\n4\tfrom config import DB_PATH, DEFAULT_SCORE_WEIGHTS\n5\t\n6\tdef active_weights(db_path: str = DB_PATH) -> dict[str, float]:\n7\t try:\n8\t with sqlite3.connect(db_path) as conn:\n9\t row = conn.execute(\"SELECT weights_json FROM config_versions WHERE active=1 ORDER BY id DESC LIMIT 1\").fetchone()\n10\t return {**DEFAULT_SCORE_WEIGHTS, **(json.loads(row[0]) if row else {})}\n11\t except sqlite3.OperationalError:\n12\t return dict(DEFAULT_SCORE_WEIGHTS)\n13\t\n14\tdef weighted_total(components: dict[str, float], weights: dict[str, float]) -> float:\n15\t return sum(float(components.get(key, 0)) * float(weights.get(key, 1)) for key in DEFAULT_SCORE_WEIGHTS)\n16\t\n17\tdef promote(weights: dict, metrics: dict, *, promoted_by: str = \"human\", db_path: str = DB_PATH) -> int:\n18\t if promoted_by not in (\"human\", \"tuner\"): raise ValueError(\"invalid promoter\")\n19\t with sqlite3.connect(db_path) as conn:\n20\t conn.execute(\"UPDATE config_versions SET active=0\")\n21\t cur = conn.execute(\"INSERT INTO config_versions(weights_json, backtest_metrics_json, promoted_by, active) VALUES (?, ?, ?, 1)\",\n22\t (json.dumps(weights, sort_keys=True), json.dumps(metrics, sort_keys=True), promoted_by))\n23\t return cur.lastrowid\n24\t"}]},"uuid":"67464f27-03d3-4901-a912-a97d75b313d1","timestamp":"2026-07-12T12:31:14.569Z","sourceToolAssistantUUID":"3cac5695-6d57-4784-ad85-536a23cfcf80","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"67464f27-03d3-4901-a912-a97d75b313d1","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAecm4gP1KERNGoAC6z","type":"message","role":"assistant","content":[{"type":"text","text":"Perfect! Now let me check if there's any \"conviction_gate\" defined elsewhere and verify the backtest logic more carefully:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3069,"cache_read_input_tokens":28667,"cache_creation":{"ephemeral_5m_input_tokens":3069,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAebyBtgNwfAeiUJaoL","attributionAgent":"Explore","type":"assistant","uuid":"7874cbc9-77f9-449f-8360-ee2d8ac9ecab","timestamp":"2026-07-12T12:31:16.142Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"7874cbc9-77f9-449f-8360-ee2d8ac9ecab","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAecm4gP1KERNGoAC6z","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WgqWHguRnWB5cBNDDeKmgz","name":"Bash","input":{"command":"grep -r \"conviction\" /workspace/extra/projects/congressional-trading/analysis --include=\"*.py\" -n | head -30"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3069,"cache_read_input_tokens":28667,"output_tokens":108,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3069},"inference_geo":"","iterations":[{"input_tokens":7,"output_tokens":108,"cache_read_input_tokens":28667,"cache_creation_input_tokens":3069,"cache_creation":{"ephemeral_5m_input_tokens":3069,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAebyBtgNwfAeiUJaoL","attributionAgent":"Explore","type":"assistant","uuid":"2eff8634-dd35-4f9b-9da6-76ffe9f5ef11","timestamp":"2026-07-12T12:31:16.608Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"2eff8634-dd35-4f9b-9da6-76ffe9f5ef11","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WgqWHguRnWB5cBNDDeKmgz","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:96: min_conviction: str = NOTIFY_MIN_CONVICTION,\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:122: conviction = (research.get(\"conviction\") or \"LOW\").upper()\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:123: min_order = _CONVICTION_ORDER.get(min_conviction.upper(), 1)\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:124: if _CONVICTION_ORDER.get(conviction, 0) < min_order:\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py:125: return NotificationDecision(False, f\"conviction={conviction} below {min_conviction.upper()}\")\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:66: \"conviction\": \"LOW|MEDIUM|HIGH\",\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:67: \"conviction_rationale\": \"One sentence explaining the conviction level.\",\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:92:This signal scored above the high-conviction notify threshold. Please provide \\\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:101: \"conviction\": \"LOW|MEDIUM|HIGH\",\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:102: \"conviction_rationale\": \"One sentence explaining the conviction level.\",\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:359: catalyst_hypothesis, timeliness_assessment, conviction, conviction_rationale,\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:370: conviction = excluded.conviction,\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:371: conviction_rationale = excluded.conviction_rationale,\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:390: result.get(\"timeliness\", \"\") + \" — \" + result.get(\"conviction_rationale\", \"\"),\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:391: result.get(\"conviction\", \"\"),\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:392: result.get(\"conviction_rationale\", \"\"),\n/workspace/extra/projects/congressional-trading/analysis/researcher.py:611: print(f\"\\n{sig['ticker']} (score={sig['total_score']}) — {res.get('conviction','?')} conviction\")\n/workspace/extra/projects/congressional-trading/analysis/scorer.py:121: ratio < 1.0 (e.g. 2 of 3): -2.5 — mixed conviction","is_error":false}]},"uuid":"65f8fe06-3b6b-47c6-ba06-edfd41a5536e","timestamp":"2026-07-12T12:31:16.636Z","sourceToolAssistantUUID":"2eff8634-dd35-4f9b-9da6-76ffe9f5ef11","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"65f8fe06-3b6b-47c6-ba06-edfd41a5536e","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAemuEYbN5fYSKZ7cbE","type":"message","role":"assistant","content":[{"type":"text","text":"Let me check if there's an actual \"conviction_gate\" term used or if the user is referring to something else:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":804,"cache_read_input_tokens":31736,"cache_creation":{"ephemeral_5m_input_tokens":804,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAeknGA87E6Fn75bfF7","attributionAgent":"Explore","type":"assistant","uuid":"ebb5eda6-3fc2-4703-81c9-60b516b57325","timestamp":"2026-07-12T12:31:18.009Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"ebb5eda6-3fc2-4703-81c9-60b516b57325","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAemuEYbN5fYSKZ7cbE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GrkGe7TRKK36xfhsH8tDNE","name":"Bash","input":{"command":"grep -r \"gate\" /workspace/extra/projects/congressional-trading --include=\"*.py\" -i | grep -v \".venv\" | head -20"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":804,"cache_read_input_tokens":31736,"output_tokens":113,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":804},"inference_geo":"","iterations":[{"input_tokens":6,"output_tokens":113,"cache_read_input_tokens":31736,"cache_creation_input_tokens":804,"cache_creation":{"ephemeral_5m_input_tokens":804,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAeknGA87E6Fn75bfF7","attributionAgent":"Explore","type":"assistant","uuid":"5400fe64-14cc-4d5b-8563-041194988f0f","timestamp":"2026-07-12T12:31:18.508Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"5400fe64-14cc-4d5b-8563-041194988f0f","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GrkGe7TRKK36xfhsH8tDNE","type":"tool_result","content":"/workspace/extra/projects/congressional-trading/tools/generate_report.py: # Compute which current signals pass all gates (what would notify if run now)\n/workspace/extra/projects/congressional-trading/tools/generate_report.py: p.append(' Would fire on next run (passes all gates)
\\n')\n/workspace/extra/projects/congressional-trading/tools/generate_report.py: p.append(' No signals pass all gates right now (need BUY NOW + MEDIUM+ conviction + deep research).
\\n')\n/workspace/extra/projects/congressional-trading/tools/outcome_tracker.py: \"\"\"Record every notify-tier candidate, including candidates suppressed by a gate.\"\"\"\n/workspace/extra/projects/congressional-trading/run_daily.py: \"\"\"Apply timeliness multiplier for notify-gate purposes only. Score is not stored.\"\"\"\n/workspace/extra/projects/congressional-trading/run_daily.py: # 8. Notify — apply timeliness, quality gates, and ticker cooldown before firing\n/workspace/extra/projects/congressional-trading/run_daily.py: notified = skipped_tl = skipped_gate = skipped_cooldown = 0\n/workspace/extra/projects/congressional-trading/run_daily.py: remaining = len(notify_candidates) - notified - skipped_tl - skipped_gate - skipped_cooldown\n/workspace/extra/projects/congressional-trading/run_daily.py: # Timeliness gate\n/workspace/extra/projects/congressional-trading/run_daily.py: skipped_gate += 1\n/workspace/extra/projects/congressional-trading/run_daily.py: # Dense shadow ledger continues in report-only mode so gate recovery stays measurable.\n/workspace/extra/projects/congressional-trading/run_daily.py: + (f\", {skipped_gate} filtered by quality gates\" if skipped_gate else \"\")\n/workspace/extra/projects/congressional-trading/config.py:# A \"late\" signal's stored score is unchanged; the multiplier only gates notifications.\n/workspace/extra/projects/congressional-trading/config.py:# --- Notification quality gates ---\n/workspace/extra/projects/congressional-trading/analysis/predictor.py: Aggregate predicted_signals by ticker into a ranked consensus view.\n/workspace/extra/projects/congressional-trading/analysis/notification_policy.py: \"\"\"Evaluate all quality gates except duplicate/cooldown state.\"\"\"\n/workspace/extra/projects/congressional-trading/analysis/scorer.py:for each ticker reflecting the aggregate congressional activity.","is_error":false}]},"uuid":"f743a881-4239-4153-b0c1-13ff29b10e47","timestamp":"2026-07-12T12:31:18.978Z","sourceToolAssistantUUID":"5400fe64-14cc-4d5b-8563-041194988f0f","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"f743a881-4239-4153-b0c1-13ff29b10e47","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAexDqogskQdxGh73qs","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check the run_daily.py to see how scoring and gating work:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":882,"cache_read_input_tokens":32540,"cache_creation":{"ephemeral_5m_input_tokens":882,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAeviJp3p7ja2bs3S5C","attributionAgent":"Explore","type":"assistant","uuid":"2500f795-6d52-46f9-9e01-946602682d5a","timestamp":"2026-07-12T12:31:21.010Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"2500f795-6d52-46f9-9e01-946602682d5a","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAexDqogskQdxGh73qs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Bh7V5NQTg8DGwodmRaJQLh","name":"Read","input":{"file_path":"/workspace/extra/projects/congressional-trading/run_daily.py","limit":150},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":882,"cache_read_input_tokens":32540,"output_tokens":105,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":882},"inference_geo":"","iterations":[{"input_tokens":5,"output_tokens":105,"cache_read_input_tokens":32540,"cache_creation_input_tokens":882,"cache_creation":{"ephemeral_5m_input_tokens":882,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAeviJp3p7ja2bs3S5C","attributionAgent":"Explore","type":"assistant","uuid":"d2ae9337-a617-414c-886b-d8809d8c1a92","timestamp":"2026-07-12T12:31:21.226Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"d2ae9337-a617-414c-886b-d8809d8c1a92","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Bh7V5NQTg8DGwodmRaJQLh","type":"tool_result","content":"1\t\"\"\"\n2\tCongressWatch daily runner.\n3\t\n4\tPipeline: ingest → committees → score → prices → win_rates → research → report → notify\n5\t\n6\tUsage:\n7\t python run_daily.py # full run\n8\t python run_daily.py --dry-run # ingest + score + report, no LLM calls, no notifications\n9\t python run_daily.py --no-ingest # skip ingest (re-score + research + report only)\n10\t python run_daily.py --force # re-notify even if already notified today\n11\t\"\"\"\n12\t\n13\tfrom __future__ import annotations\n14\timport argparse\n15\timport sqlite3\n16\timport sys\n17\timport os\n18\tfrom datetime import datetime, date\n19\t\n20\tsys.path.insert(0, os.path.dirname(__file__))\n21\tfrom config import (DB_PATH, SCORE_NOTIFY_THRESHOLD, SCORE_RESEARCH_THRESHOLD,\n22\t LOOKBACK_DAYS, TIMELINESS_MULTIPLIERS,\n23\t DEEP_RESEARCH_TOP_N, DEEP_RESEARCH_DECAY_DAYS,\n24\t MAX_NOTIFICATIONS_PER_RUN, NOTIFY_TICKER_COOLDOWN_DAYS,\n25\t PREDICTOR_MAX_MEMBERS, PREDICTOR_DECAY_DAYS)\n26\t\n27\t\n28\tdef _timeliness_adjusted_score(base_score: float, timeliness: str | None) -> float:\n29\t \"\"\"Apply timeliness multiplier for notify-gate purposes only. Score is not stored.\"\"\"\n30\t key = (timeliness or \"fresh\").split(\"|\")[0].strip().lower()\n31\t return base_score * TIMELINESS_MULTIPLIERS.get(key, 1.0)\n32\t\n33\t\n34\tdef _send_signal_notification(sig: dict, research: dict | None, adjusted_score: float):\n35\t from notify.ntfy_client import push\n36\t from analysis.timeliness import assess_age, age_hours\n37\t from analysis.notification_policy import edge_factors\n38\t\n39\t ticker = sig[\"ticker\"]\n40\t score = sig[\"total_score\"]\n41\t buyers = sig[\"member_count\"]\n42\t timeliness = assess_age(sig[\"last_seen\"], runup_pct=sig.get(\"runup_pct\"))\n43\t conviction = (research.get(\"conviction\") or \"?\").upper() if research else \"?\"\n44\t hours_since_disclosure = age_hours(sig[\"last_seen\"])\n45\t factors = \", \".join(edge_factors(sig, research))\n46\t\n47\t tier_labels = {1:\"$1K-15K\", 2:\"$15K-50K\", 3:\"$50K-100K\",\n48\t 4:\"$100K-250K\", 5:\"$250K-500K\", 6:\"$500K+\"}\n49\t size_label = tier_labels.get(sig.get(\"max_tier\", 0), \"unknown\")\n50\t opt_flag = \" | Options: YES\" if sig.get(\"has_options\") else \"\"\n51\t\n52\t tl_note = \"\"\n53\t if adjusted_score < score:\n54\t tl_note = f\" (adj {adjusted_score:.1f} for {timeliness})\"\n55\t\n56\t action_short = \"\"\n57\t if research and research.get(\"recommended_action\"):\n58\t raw_action = research[\"recommended_action\"]\n59\t action_short = raw_action.split(\"—\")[0].strip()[:40]\n60\t\n61\t summary_line = \"\"\n62\t if research and research.get(\"company_summary\"):\n63\t summary_line = \"\\n\\n\" + research[\"company_summary\"][:300]\n64\t catalyst_line = \"\"\n65\t if research and research.get(\"catalyst_hypothesis\"):\n66\t catalyst_line = \"\\n\\nCatalyst: \" + research[\"catalyst_hypothesis\"][:200]\n67\t source_line = \"\"\n68\t if research and research.get(\"sources\"):\n69\t sources = research[\"sources\"]\n70\t if isinstance(sources, str):\n71\t import json\n72\t try: sources = json.loads(sources)\n73\t except ValueError: sources = []\n74\t source_line = \"\\nSources: \" + \", \".join(s.get(\"url\", \"\") for s in sources[:2])\n75\t risk_line = \"\"\n76\t if research and research.get(\"risk_factors\"):\n77\t risk_line = \"\\nInvalidation/risk: \" + research[\"risk_factors\"][:180]\n78\t earnings_line = \"\"\n79\t if research and (research.get(\"checklist\") or {}).get(\"earnings_blackout\"):\n80\t earnings_line = \"\\n⚠ Earnings within blackout window\"\n81\t calibration_line = \"\"\n82\t with sqlite3.connect(DB_PATH) as conn:\n83\t calibration = conn.execute(\n84\t \"SELECT COUNT(*), SUM(CASE WHEN return_30d_excess_pct>0 THEN 1 ELSE 0 END) \"\n85\t \"FROM signal_outcomes WHERE fired=1 AND conviction=? AND return_30d_excess_pct IS NOT NULL\",\n86\t (conviction,),\n87\t ).fetchone()\n88\t if calibration and calibration[0]:\n89\t calibration_line = f\"\\n{conviction} alerts: {calibration[1]}/{calibration[0]} positive 30d alpha\"\n90\t else:\n91\t calibration_line = f\"\\n{conviction} alerts: no matured 30d history yet\"\n92\t action_line = \"\"\n93\t if action_short:\n94\t action_line = f\"\\n\\n→ {action_short}\"\n95\t\n96\t body = (\n97\t f\"{sig.get('issuer_name', ticker)} — {buyers} member{'s' if buyers>1 else ''} bought\\n\"\n98\t f\"Size: {size_label}{opt_flag}\\n\"\n99\t f\"Timeliness: {hours_since_disclosure:.1f}h ({timeliness.upper()}) | Conviction: {conviction} | Action: {action_short}\\n\"\n100\t f\"Edge: {factors}\"\n101\t f\"{summary_line}{catalyst_line}{source_line}{risk_line}{earnings_line}{calibration_line}{action_line}\"\n102\t )\n103\t\n104\t push(\n105\t title=f\"CongressWatch: ${ticker} (score {score:.1f}{tl_note})\",\n106\t body=body,\n107\t priority=\"high\" if adjusted_score >= 9 else \"default\",\n108\t tags=[\"chart_with_upwards_trend\", \"money_with_wings\"],\n109\t )\n110\t print(f\" ntfy sent for {ticker} — disclosure-to-alert latency {age_hours(sig['last_seen']):.1f}h\")\n111\t\n112\t\n113\tdef _already_notified_today(ticker: str) -> bool:\n114\t conn = sqlite3.connect(DB_PATH)\n115\t row = conn.execute(\n116\t \"SELECT notified_at FROM signals WHERE ticker = ? AND notified = 1\",\n117\t (ticker,),\n118\t ).fetchone()\n119\t conn.close()\n120\t if not row or not row[0]:\n121\t return False\n122\t try:\n123\t return row[0][:10] == str(date.today())\n124\t except Exception:\n125\t return False\n126\t\n127\t\n128\tdef _mark_notified(ticker: str):\n129\t conn = sqlite3.connect(DB_PATH)\n130\t conn.execute(\n131\t \"UPDATE signals SET notified = 1, notified_at = datetime('now') WHERE ticker = ?\",\n132\t (ticker,),\n133\t )\n134\t conn.commit()\n135\t conn.close()\n136\t\n137\t\n138\tdef run(dry_run: bool = False, no_ingest: bool = False, force_notify: bool = False):\n139\t start = datetime.now()\n140\t print(f\"\\n{'='*60}\")\n141\t print(f\" CongressWatch run — {start.strftime('%Y-%m-%d %H:%M')}\")\n142\t if dry_run:\n143\t print(\" MODE: dry-run (no LLM calls, no notifications)\")\n144\t print(f\"{'='*60}\\n\")\n145\t\n146\t # 1. Ingest\n147\t if not no_ingest:\n148\t print(\"[ 1/9 ] Ingesting trades...\")\n149\t from ingest.capitol_trades import ingest\n150\t summary = ingest(lookback_days=LOOKBACK_DAYS)"}]},"uuid":"5913c5a2-be6c-44a3-aca0-b9e09bc93571","timestamp":"2026-07-12T12:31:21.247Z","sourceToolAssistantUUID":"d2ae9337-a617-414c-886b-d8809d8c1a92","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"5913c5a2-be6c-44a3-aca0-b9e09bc93571","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAf7NFtmZp5RBryxVBR","type":"message","role":"assistant","content":[{"type":"text","text":"Let me continue reading run_daily.py to see the notification logic:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2350,"cache_read_input_tokens":33422,"cache_creation":{"ephemeral_5m_input_tokens":2350,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAf6VgGDPTGSwwjFjfa","attributionAgent":"Explore","type":"assistant","uuid":"fb9c2adb-33ec-49a8-ae9f-c34fc93f3dc0","timestamp":"2026-07-12T12:31:22.475Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"fb9c2adb-33ec-49a8-ae9f-c34fc93f3dc0","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAf7NFtmZp5RBryxVBR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01N2ZrwzzENxg8VkFcjKQ6c1","name":"Read","input":{"file_path":"/workspace/extra/projects/congressional-trading/run_daily.py","offset":150,"limit":150},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2350,"cache_read_input_tokens":33422,"output_tokens":118,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2350},"inference_geo":"","iterations":[{"input_tokens":5,"output_tokens":118,"cache_read_input_tokens":33422,"cache_creation_input_tokens":2350,"cache_creation":{"ephemeral_5m_input_tokens":2350,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAf6VgGDPTGSwwjFjfa","attributionAgent":"Explore","type":"assistant","uuid":"fc41ff8e-0723-423c-8321-c1a69820e1e4","timestamp":"2026-07-12T12:31:23.127Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"fc41ff8e-0723-423c-8321-c1a69820e1e4","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01N2ZrwzzENxg8VkFcjKQ6c1","type":"tool_result","content":"150\t summary = ingest(lookback_days=LOOKBACK_DAYS)\n151\t print(f\" +{summary['new_trades']} new trades\\n\")\n152\t else:\n153\t print(\"[ 1/9 ] Skipping ingest (--no-ingest)\\n\")\n154\t\n155\t # 1b. Executive branch trades (OGE Form 278-T PDFs for POTUS/VP)\n156\t if not no_ingest:\n157\t print(\"[ 1b/9 ] OGE Form 278-T executive branch trades...\")\n158\t from ingest.presidential_oge import ingest_presidential_oge\n159\t exec_summary = ingest_presidential_oge()\n160\t if exec_summary.get(\"new_trades\", 0) > 0:\n161\t print(f\" +{exec_summary['new_trades']} new executive trades \"\n162\t f\"({exec_summary['pdfs_processed']} PDFs)\\n\")\n163\t else:\n164\t print(f\" no new trades (all known PDFs already processed)\\n\")\n165\t\n166\t # 2. Committees (weekly refresh if stale)\n167\t print(\"[ 2/9 ] Committee assignments...\")\n168\t from ingest.committees import needs_refresh, refresh as refresh_committees\n169\t if needs_refresh():\n170\t summary = refresh_committees()\n171\t print(f\" {summary['updated']} updated, {summary.get('no_data', 0)} with no data\\n\")\n172\t else:\n173\t print(\" up to date (refreshed within 7 days)\\n\")\n174\t\n175\t # 3. Score (uses win_rates + market_caps from previous run)\n176\t print(\"[ 3/9 ] Scoring signals...\")\n177\t from analysis.scorer import score_tickers\n178\t signals = score_tickers()\n179\t print(f\" {len(signals)} tickers scored\\n\")\n180\t\n181\t # Event-driven fast path: LLM work is only justified by newly ingested notify-tier data.\n182\t with sqlite3.connect(DB_PATH) as conn:\n183\t new_tickers = {row[0] for row in conn.execute(\n184\t \"SELECT DISTINCT issuer_ticker FROM trades \"\n185\t \"WHERE datetime(first_ingested_at) >= datetime(?)\", (start.isoformat(),)\n186\t )}\n187\t new_notify_tickers = {\n188\t s[\"ticker\"] for s in signals\n189\t if s[\"ticker\"] in new_tickers and s[\"total_score\"] >= SCORE_NOTIFY_THRESHOLD\n190\t }\n191\t\n192\t # 4. Prices + market caps\n193\t print(\"[ 4/9 ] Fetching prices...\")\n194\t from tools.price_fetcher import fetch_and_store as fetch_prices\n195\t price_summary = fetch_prices(min_score=SCORE_RESEARCH_THRESHOLD)\n196\t print(f\" {price_summary['fetched']} updated, {price_summary['skipped']} already fresh\\n\")\n197\t\n198\t # 5. Win rates (uses today's price data; feeds into tomorrow's scoring)\n199\t print(\"[ 5/9 ] Computing win rates...\")\n200\t from tools.win_rate import compute as compute_win_rates\n201\t wr_summary = compute_win_rates()\n202\t print(f\" {wr_summary['updated']} members updated, \"\n203\t f\"{wr_summary['skipped']} skipped (insufficient data)\\n\")\n204\t\n205\t # 6. Research top signals (regular: all signals ≥ SCORE_RESEARCH_THRESHOLD)\n206\t print(\"[ 6/9 ] Researching top signals...\")\n207\t research_map: dict[str, dict] = {}\n208\t if not dry_run:\n209\t from analysis.researcher import research_signal\n210\t from datetime import timedelta\n211\t\n212\t cutoff = (datetime.now() - timedelta(days=LOOKBACK_DAYS)).strftime(\"%Y-%m-%d\")\n213\t conn = sqlite3.connect(DB_PATH)\n214\t conn.row_factory = sqlite3.Row\n215\t\n216\t for sig in signals:\n217\t if sig[\"total_score\"] < SCORE_RESEARCH_THRESHOLD:\n218\t break # signals are sorted descending\n219\t if sig[\"ticker\"] not in new_notify_tickers:\n220\t continue\n221\t buyers = conn.execute(\n222\t \"\"\"\n223\t SELECT DISTINCT t.member_id,\n224\t m.first_name || ' ' || m.last_name AS member_name,\n225\t m.party, m.chamber, m.committees,\n226\t MAX(t.amount_tier) AS amount_tier\n227\t FROM trades t JOIN members m ON m.bioguide_id = t.member_id\n228\t WHERE t.issuer_ticker = ? AND t.tx_type = 'buy' AND t.pub_date >= ?\n229\t GROUP BY t.member_id\n230\t \"\"\",\n231\t (sig[\"ticker\"], cutoff),\n232\t ).fetchall()\n233\t result = research_signal(sig, [dict(b) for b in buyers], dry_run=False)\n234\t if result:\n235\t research_map[sig[\"ticker\"]] = result\n236\t\n237\t conn.close()\n238\t else:\n239\t print(\" (skipped — dry-run mode)\")\n240\t print()\n241\t\n242\t # 6b. Deep research — top N scorers with risk_factors + recommended_action\n243\t # Skips any ticker already deep-researched within DEEP_RESEARCH_DECAY_DAYS,\n244\t # falling through to the next-ranked signal to spend the budget wisely.\n245\t print(f\"[ 6b/9 ] Deep research (top {DEEP_RESEARCH_TOP_N}, decay {DEEP_RESEARCH_DECAY_DAYS}d)...\")\n246\t if not dry_run:\n247\t from analysis.researcher import research_top_n_deep\n248\t notify_tier = [s for s in signals if s[\"ticker\"] in new_notify_tickers]\n249\t deep_results = research_top_n_deep(\n250\t notify_tier, top_n=DEEP_RESEARCH_TOP_N, decay_days=DEEP_RESEARCH_DECAY_DAYS\n251\t )\n252\t for sig, result in deep_results:\n253\t research_map[sig[\"ticker\"]] = result # update with richer deep result\n254\t print(f\" {sig['ticker']}: deep research done — {result.get('recommended_action', '?')}\")\n255\t if not deep_results:\n256\t print(\" All top candidates within decay window — no deep research needed\")\n257\t else:\n258\t print(\" (skipped — dry-run mode)\")\n259\t print()\n260\t\n261\t # 6c. Predictive signals — behavioural analysis + forward watchlist per member\n262\t print(f\"[ 6c/9 ] Predictive signals (top {PREDICTOR_MAX_MEMBERS} members, decay {PREDICTOR_DECAY_DAYS}d)...\")\n263\t if not dry_run:\n264\t from analysis.predictor import run_predictions, confirm_predictions, backfill_committee_alignment\n265\t confirm_predictions()\n266\t backfill_committee_alignment()\n267\t pred_results = run_predictions(\n268\t max_members=PREDICTOR_MAX_MEMBERS,\n269\t decay_days=PREDICTOR_DECAY_DAYS,\n270\t )\n271\t for pr in pred_results:\n272\t tickers = \", \".join(p[\"ticker\"] for p in pr[\"predictions\"])\n273\t print(f\" {pr['member_name']}: {tickers}\")\n274\t if not pred_results:\n275\t print(\" All members within decay window — no predictions generated\")\n276\t\n277\t # Fetch market caps + ETF/validity flags for predicted tickers\n278\t print(\" Fetching metadata for new predicted tickers...\")\n279\t from tools.price_fetcher import fetch_for_predicted\n280\t meta = fetch_for_predicted()\n281\t if meta[\"fetched\"] or meta[\"invalid\"]:\n282\t print(f\" {meta['fetched']} metadata fetched, {meta['invalid']} filtered (ETF/invalid)\")\n283\t else:\n284\t print(\" (skipped — dry-run mode)\")\n285\t print()\n286\t\n287\t # 7. Generate report\n288\t print(\"[ 7/9 ] Generating report...\")\n289\t from tools.generate_report import generate\n290\t generate()\n291\t print()\n292\t\n293\t # 7b. Update pending signal outcomes (30d/60d returns)\n294\t print(\"[ 7b/9 ] Checking signal outcomes...\")\n295\t if not dry_run:\n296\t from tools.outcome_tracker import update_pending_outcomes\n297\t outcome_result = update_pending_outcomes()\n298\t if outcome_result.get(\"updated\"):\n299\t print(f\" {outcome_result['updated']} outcomes updated\")"}]},"uuid":"4a73f0dc-2d1e-4492-8c7a-30419ae898fc","timestamp":"2026-07-12T12:31:23.146Z","sourceToolAssistantUUID":"fc41ff8e-0723-423c-8321-c1a69820e1e4","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"4a73f0dc-2d1e-4492-8c7a-30419ae898fc","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAfFMySKAC5CYnYdXTd","type":"message","role":"assistant","content":[{"type":"text","text":"Let me continue to see the notification logic:"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2478,"cache_read_input_tokens":35772,"cache_creation":{"ephemeral_5m_input_tokens":2478,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAfEbLZdXKdU9JG2rwa","attributionAgent":"Explore","type":"assistant","uuid":"8a8a06d9-69e5-458b-a9d5-8714a27afbbd","timestamp":"2026-07-12T12:31:24.391Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"8a8a06d9-69e5-458b-a9d5-8714a27afbbd","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAfFMySKAC5CYnYdXTd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JXwsGWnAkrutzeb5isx8LS","name":"Read","input":{"file_path":"/workspace/extra/projects/congressional-trading/run_daily.py","offset":300,"limit":150},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":2478,"cache_read_input_tokens":35772,"output_tokens":112,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2478},"inference_geo":"","iterations":[{"input_tokens":5,"output_tokens":112,"cache_read_input_tokens":35772,"cache_creation_input_tokens":2478,"cache_creation":{"ephemeral_5m_input_tokens":2478,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcxAfEbLZdXKdU9JG2rwa","attributionAgent":"Explore","type":"assistant","uuid":"493155d3-cb3f-4a83-a34d-0de1956afbef","timestamp":"2026-07-12T12:31:24.993Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"493155d3-cb3f-4a83-a34d-0de1956afbef","isSidechain":true,"promptId":"ca68be9e-e5e5-4287-a0c0-a97c81d0c05b","agentId":"adf3c9e3c797fedde","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JXwsGWnAkrutzeb5isx8LS","type":"tool_result","content":"300\t else:\n301\t print(\" no pending outcomes ready for scoring\")\n302\t else:\n303\t print(\" (skipped — dry-run mode)\")\n304\t print()\n305\t\n306\t # 8. Notify — apply timeliness, quality gates, and ticker cooldown before firing\n307\t notified = skipped_tl = skipped_gate = skipped_cooldown = 0\n308\t candidate_decisions: dict[str, tuple[bool, str | None]] = {}\n309\t notify_candidates = [s for s in signals if s[\"total_score\"] >= SCORE_NOTIFY_THRESHOLD]\n310\t\n311\t from analysis.guardrails import evaluate_circuit_breaker, report_only\n312\t breaker = {\"tripped\": False, \"reason\": \"dry_run\"} if dry_run else evaluate_circuit_breaker()\n313\t if breaker.get(\"tripped\"):\n314\t from notify.ntfy_client import push\n315\t from config import NTFY_TOPIC_STATUS\n316\t push(title=\"CongressWatch circuit breaker tripped\", body=str(breaker),\n317\t topic=NTFY_TOPIC_STATUS, priority=\"high\", tags=[\"warning\"])\n318\t notifications_paused = report_only()\n319\t if notifications_paused:\n320\t print(\"[ 8/9 ] Notifications are report-only pending human review\")\n321\t for sig in notify_candidates:\n322\t candidate_decisions[sig[\"ticker\"]] = (False, \"report_only\")\n323\t\n324\t if not dry_run and notify_candidates and not notifications_paused:\n325\t from analysis.notification_policy import (\n326\t evaluate_notification_candidate,\n327\t recent_ticker_notification,\n328\t )\n329\t\n330\t print(\"[ 8/9 ] Sending notifications...\")\n331\t for sig in notify_candidates:\n332\t if notified >= MAX_NOTIFICATIONS_PER_RUN:\n333\t remaining = len(notify_candidates) - notified - skipped_tl - skipped_gate - skipped_cooldown\n334\t if remaining > 0:\n335\t print(f\" Cap reached ({MAX_NOTIFICATIONS_PER_RUN}/run) — {remaining} remaining suppressed\")\n336\t for remaining_sig in notify_candidates:\n337\t candidate_decisions.setdefault(remaining_sig[\"ticker\"], (False, \"per_run_cap\"))\n338\t break\n339\t\n340\t ticker = sig[\"ticker\"]\n341\t\n342\t # Timeliness gate\n343\t research = research_map.get(ticker)\n344\t from analysis.timeliness import assess_age\n345\t timeliness = assess_age(sig[\"last_seen\"], runup_pct=sig.get(\"runup_pct\"))\n346\t adj = _timeliness_adjusted_score(sig[\"total_score\"], timeliness)\n347\t if adj < SCORE_NOTIFY_THRESHOLD:\n348\t tl_key = timeliness.split(\"|\")[0].strip().lower()\n349\t print(f\" {ticker}: {sig['total_score']:.1f} × {TIMELINESS_MULTIPLIERS.get(tl_key,1):.2f} = {adj:.1f} — below threshold ({tl_key})\")\n350\t skipped_tl += 1\n351\t candidate_decisions[ticker] = (False, \"timeliness\")\n352\t continue\n353\t\n354\t decision = evaluate_notification_candidate(sig, research, adj)\n355\t if not decision.allowed:\n356\t print(f\" {ticker}: {decision.reason} — skipping\")\n357\t skipped_gate += 1\n358\t candidate_decisions[ticker] = (False, decision.reason)\n359\t continue\n360\t\n361\t if not force_notify:\n362\t in_cooldown, last_notified, days_left = recent_ticker_notification(ticker)\n363\t if in_cooldown:\n364\t last_label = last_notified.isoformat(timespec=\"minutes\") if last_notified else \"recently\"\n365\t print(\n366\t f\" {ticker}: notified {last_label}; \"\n367\t f\"{days_left}d left in {NOTIFY_TICKER_COOLDOWN_DAYS}d cooldown\"\n368\t )\n369\t skipped_cooldown += 1\n370\t candidate_decisions[ticker] = (False, \"ticker_cooldown\")\n371\t continue\n372\t\n373\t if not force_notify and _already_notified_today(ticker):\n374\t print(f\" {ticker}: already notified today\")\n375\t candidate_decisions[ticker] = (False, \"already_notified_today\")\n376\t continue\n377\t\n378\t _send_signal_notification(sig, research, adj)\n379\t _mark_notified(ticker)\n380\t\n381\t candidate_decisions[ticker] = (True, None)\n382\t notified += 1\n383\t print()\n384\t\n385\t # Dense shadow ledger continues in report-only mode so gate recovery stays measurable.\n386\t if not dry_run and notify_candidates:\n387\t from tools.outcome_tracker import record_candidate\n388\t from analysis.timeliness import assess_age\n389\t observed_at = start.isoformat(timespec=\"seconds\")\n390\t for sig in notify_candidates:\n391\t research = research_map.get(sig[\"ticker\"]) or {}\n392\t fired, reason = candidate_decisions.get(sig[\"ticker\"], (False, \"not_evaluated\"))\n393\t record_candidate(\n394\t ticker=sig[\"ticker\"], score=_timeliness_adjusted_score(\n395\t sig[\"total_score\"], assess_age(sig[\"last_seen\"], runup_pct=sig.get(\"runup_pct\"))),\n396\t conviction=research.get(\"conviction\", \"\"),\n397\t recommended_action=research.get(\"recommended_action\", \"\"),\n398\t price=sig.get(\"price_current\"), observed_at=observed_at,\n399\t fired=fired, suppression_reason=reason,\n400\t verifier_verdict=research.get(\"verifier_verdict\"),\n401\t citation_backed=bool(research.get(\"sources\")),\n402\t )\n403\t\n404\t elapsed = (datetime.now() - start).total_seconds()\n405\t print(f\"Run complete in {elapsed:.1f}s — {notified} notifications sent\"\n406\t + (f\", {skipped_tl} suppressed by timeliness\" if skipped_tl else \"\")\n407\t + (f\", {skipped_gate} filtered by quality gates\" if skipped_gate else \"\")\n408\t + (f\", {skipped_cooldown} suppressed by cooldown\" if skipped_cooldown else \"\") + \".\")\n409\t print(f\"Report: reports/signals_latest.md\\n\")\n410\t\n411\t\n412\tif __name__ == \"__main__\":\n413\t parser = argparse.ArgumentParser(description=\"CongressWatch daily runner\")\n414\t parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"no LLM calls, no notifications\")\n415\t parser.add_argument(\"--no-ingest\", action=\"store_true\", help=\"skip ingest step\")\n416\t parser.add_argument(\"--force\", action=\"store_true\", help=\"re-notify even if notified today\")\n417\t args = parser.parse_args()\n418\t run(dry_run=args.dry_run, no_ingest=args.no_ingest, force_notify=args.force)\n419\t"}]},"uuid":"360b371e-2b0a-406f-ba3b-6cddb2a0ce99","timestamp":"2026-07-12T12:31:25.009Z","sourceToolAssistantUUID":"493155d3-cb3f-4a83-a34d-0de1956afbef","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}
{"parentUuid":"360b371e-2b0a-406f-ba3b-6cddb2a0ce99","isSidechain":true,"agentId":"adf3c9e3c797fedde","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcxAfS1vXeV6WJg1nYsLV","type":"message","role":"assistant","content":[{"type":"text","text":"Perfect! Now I have all the information I need. Let me create a comprehensive summary:\n\n## Summary: Congressional Trading Signal Scoring System\n\nBased on my thorough review of the codebase, here's what you need to know to implement your backtest sweep script:\n\n### 1. **Scoring Algorithm (0-13 components)**\n\nThe scoring happens in `/workspace/extra/projects/congressional-trading/analysis/scorer.py`. The 9 score components are:\n\n| Component | Range | Description |\n|-----------|-------|-------------|\n| `committee_score` | 0-3 | Member's committee overlap with company sector (3=direct jurisdiction) |\n| `track_record_score` | 0-2 | Member's historical win rate (defaults to 1.0 until calculable) |\n| `cluster_score` | 0-3 | Unique member buyers: 1=0pts, 2=1pt, 3=2pts, 4+=3pts (discounted by breadth_mult) |\n| `options_bonus` | 0-2 | +2 if ANY option trade on this ticker, else 0 |\n| `size_score` | 0-5 | Highest dollar tier among buy trades (amount_tier 1-6, capped at 5) |\n| `small_cap_score` | -2.5 to 6.0 | Market cap bonus/penalty (mega=-2.5, small=+4.5, micro=+6.0) |\n| `sell_pressure_score` | -4.0 to 0 | Penalty when sellers exist: 0 (clean) to -4 (sellers≥buyers) |\n| `strategic_score` | 0-3 | LLM research component (from research_cache) |\n| `disruption_score` | 0-3 | LLM research component (from research_cache) |\n\n**Total max theoretical score: ~13** (but weighted by `DEFAULT_SCORE_WEIGHTS` in config.py)\n\n### 2. **`signal_outcomes` Table Structure**\n\nLocation: `/workspace/extra/projects/congressional-trading/db/schema.sql` (lines 91-116)\n\n**Key columns for backtest:**\n```\nticker TEXT NOT NULL\nnotified_at TEXT NOT NULL -- when signal was created/evaluated\nscore_at_notify REAL -- the score at notify time\nconviction TEXT -- LOW / MEDIUM / HIGH\nrecommended_action TEXT -- LLM research recommendation\nprice_at_notify REAL -- close price when signal fired\nprice_30d REAL -- close price 30 days later\nprice_60d REAL -- close price 60 days later\nreturn_30d_pct REAL -- % gain/loss in 30 days\nreturn_60d_pct REAL -- % gain/loss in 60 days\nreturn_30d_excess_pct REAL -- alpha vs SPY (30d)\nreturn_60d_excess_pct REAL -- alpha vs SPY (60d)\nfired INTEGER -- 1 if notification was sent, 0 if suppressed\nsuppression_reason TEXT -- reason it was filtered (e.g., \"conviction\", \"edge_factors\", \"timeliness\")\noutcome TEXT -- \"win\" / \"loss\" / \"pending\" / \"unmeasurable\"\nconfig_version_id INTEGER -- which weight version was active\ncitation_backed INTEGER -- 1 if LLM sources were cited\nverifier_verdict TEXT -- verifier's assessment\n```\n\n### 3. **Existing Backtest Tool**\n\nLocation: `/workspace/extra/projects/congressional-trading/tools/backtest.py`\n\n**What it does:**\n- `replay(weights, threshold)` — Replays all historical buy trades, scores them by looking back 30 days within LOOKBACK_DAYS window\n- Does NOT call scoring functions directly; instead uses **cached data** from two tables:\n - `_cluster_score()` from pre-computed trade membership\n - `research_cache` table (strategic_score, disruption_score from prior LLM runs)\n- Returns metrics: `median_30d_excess_pct`, `precision`, `alerts_per_month`, `eligible` (boolean if meets MIN_ALERTS_PER_MONTH)\n\n**Key limitation:** \"LLM-derived components replay from cache where present; otherwise zero\"\n\n- `calibrate()` — Runs 100 broad samples + 100 focused samples of weight permutations, returns best by 30d excess return\n- Computation method: Looks up `price_history` table to fetch prices on/after signal date, computes 30-60d returns vs SPY\n\n### 4. **Config Constants** \n\nLocation: `/workspace/extra/projects/congressional-trading/config.py`\n\n**Critical thresholds:**\n```python\nSCORE_RESEARCH_THRESHOLD = 5.0 # LLM research triggered at this score\nSCORE_NOTIFY_THRESHOLD = 9.0 # Notify candidate threshold\nBACKTEST_MIN_ALERTS_PER_MONTH = 2.0 # Backtest eligibility gate\nNOTIFY_MIN_CONVICTION = \"HIGH\" # Conviction levels: LOW, MEDIUM, HIGH\nNOTIFY_MIN_EDGE_FACTORS = 3 # 3+ independent edge factors to notify\nNOTIFY_MAX_SELL_PRESSURE_PENALTY = -1.0 # Suppress if sell_pressure < this\nLOOKBACK_DAYS = 30 # Rolling window for trades\nFRESHNESS_MAX_HOURS = 48 # \"fresh\" timeliness gate\nWATCH_MAX_DAYS = 7 # \"watch\" timeliness gate\n```\n\n**Timeliness multipliers (applied at notify time only, NOT stored):**\n```python\nTIMELINESS_MULTIPLIERS = {\"fresh\": 1.0, \"watch\": 0.85, \"late\": 0.70}\n```\n\n### 5. **Trades Table Schema**\n\nLocation: `/workspace/extra/projects/congressional-trading/db/schema.sql` (lines 17-40)\n\n**Key columns:**\n```\ntx_id INTEGER PRIMARY KEY\nmember_id TEXT -- congressional member bioguide_id\nissuer_ticker TEXT -- clean ticker (e.g., \"NVDA\")\nissuer_sector TEXT -- sector classification\ntx_date TEXT -- when trade executed (YYYY-MM-DD)\npub_date TEXT -- when disclosure published\ntx_type TEXT -- \"buy\" / \"sell\" / \"exchange\"\namount_tier INTEGER -- 1-6 (derived from value_estimate)\nis_option INTEGER -- 0/1\nvalue_estimate INTEGER -- dollar estimate from disclosure\n```\n\n---\n\n## Key Insights for Your Backtest Sweep\n\n1. **No \"conviction_gate\" exists as a single concept** — instead:\n - `NOTIFY_MIN_CONVICTION` is a config string (\"LOW\"/\"MEDIUM\"/\"HIGH\") \n - Conviction comes from LLM research (stored in `research_cache.conviction`)\n - It's checked during notification gating, not during scoring\n\n2. **No \"edge_factor\" numerical value** — instead:\n - `edge_factors()` function returns a **tuple of strings** (lines 50-70 of notification_policy.py):\n - \"multi-member cluster\" (≥2 unique buyers)\n - \"large disclosed size\" (tier ≥4)\n - \"options timing\" (has_options=true)\n - \"direct committee alignment\" (committee_score ≥2.0)\n - \"undercovered small/mid cap\" (small_cap_score ≥2.0)\n - \"strong LLM catalyst\" (strategic + disruption ≥4.0)\n - Gate: need **≥3 factors** to notify (NOTIFY_MIN_EDGE_FACTORS)\n\n3. **How backtest replays scoring:**\n - Iterates through all buy trades ordered by pub_date\n - For each day, groups trades by ticker within LOOKBACK_DAYS window\n - Computes components (cluster, size, etc.) from live trade data\n - **Reads strategic/disruption from research_cache** (LLM components from prior runs)\n - Applies weights and checks threshold\n - Fetches 30/60d prices from price_history table\n - Returns excess return over SPY\n\n4. **Price data required:**\n - `price_history` table: (ticker, date, close)\n - Must have prices on signal date AND 30d/60d later for outcomes\n\nThe backtest is **deterministic** and **replay-based** — it doesn't re-run the LLM or re-fetch prices, it uses cached data."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":2050,"cache_read_input_tokens":38250,"cache_creation":{"ephemeral_5m_input_tokens":2050,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcxAfNZZaxiLmPRKGFZrM","attributionAgent":"Explore","type":"assistant","uuid":"e6536423-9008-4d00-845f-92ff132fb78d","timestamp":"2026-07-12T12:31:51.612Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"033a4a00-9171-4e08-9a79-6b99f177355d","version":"2.1.128","gitBranch":"HEAD","slug":"mellow-wobbling-swan"}