{"parentUuid":null,"isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":"Read the congressional-trading repo at https://github.com/jamesjlopez/congressional-trading via the GitHub API or by reading local files at ~/projects/congressional-trading if accessible.\n\nI need to understand:\n1. What the scoring/signal system does — what makes a trade \"high conviction\"? What are the scoring criteria or thresholds?\n2. What does a signal notification look like — what data does it include?\n3. Is there a shadow measurement mode? What does it track/log?\n4. Is there any historical trade data stored, or does it only process new disclosures?\n5. What are the exit criteria or thresholds that would graduate a signal from shadow to real?\n6. Are there any example signals or test fixtures in the codebase?\n\nAlso look at the Python source files for the core scoring logic. I want to understand the algorithm well enough to assess whether we can backtest it against historical congressional trade data.\n\nReturn a structured summary of: the scoring algorithm, what triggers a notification, what data is in shadow mode, and any obvious gaps or open decisions visible in the code (TODOs, hardcoded values, etc.)."},"uuid":"0c3ba8d4-4536-4e7f-a2d0-a29f17da211c","timestamp":"2026-07-07T01:38:18.243Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
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{"parentUuid":"54e7b3ed-08e3-45e3-b543-d06226684ae6","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpBHf3tV1rn1ozJUwv","type":"message","role":"assistant","content":[{"type":"text","text":"I'll help you understand the congressional-trading repository. Let me start by checking if the local project exists and then examining its structure."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1684,"cache_read_input_tokens":6835,"cache_creation":{"ephemeral_5m_input_tokens":1684,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqpAuqKbc7LU7Yw4dE4","attributionAgent":"Explore","type":"assistant","uuid":"62b6f0cd-7b72-4e76-834e-4d9e1fe872e5","timestamp":"2026-07-07T01:38:19.239Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"62b6f0cd-7b72-4e76-834e-4d9e1fe872e5","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpBHf3tV1rn1ozJUwv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ufmb6UTQJjAX2DfJGvpFBW","name":"Bash","input":{"command":"ls -la ~/projects/ 2>/dev/null | head -20"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1684,"cache_read_input_tokens":6835,"output_tokens":95,"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":1684},"inference_geo":"","iterations":[{"input_tokens":3,"output_tokens":95,"cache_read_input_tokens":6835,"cache_creation_input_tokens":1684,"cache_creation":{"ephemeral_5m_input_tokens":1684,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqpAuqKbc7LU7Yw4dE4","attributionAgent":"Explore","type":"assistant","uuid":"57dd9b9b-2446-4690-826d-30fb7042c26c","timestamp":"2026-07-07T01:38:19.454Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"57dd9b9b-2446-4690-826d-30fb7042c26c","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ufmb6UTQJjAX2DfJGvpFBW","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"b3ae5fda-a5ed-4c5f-8fb4-7e90bd6c95b1","timestamp":"2026-07-07T01:38:19.465Z","sourceToolAssistantUUID":"57dd9b9b-2446-4690-826d-30fb7042c26c","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"b3ae5fda-a5ed-4c5f-8fb4-7e90bd6c95b1","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpGWBcCzoM5LtRNR7k","type":"message","role":"assistant","content":[{"type":"text","text":"Let me try to fetch the repository via GitHub API using the onecli-gateway skill."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":1347,"cache_read_input_tokens":8519,"cache_creation":{"ephemeral_5m_input_tokens":1347,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqpG97Ah6SQbpfeGJsc","attributionAgent":"Explore","type":"assistant","uuid":"553e1798-bc71-4a37-887d-efe6e8c35908","timestamp":"2026-07-07T01:38:20.734Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"553e1798-bc71-4a37-887d-efe6e8c35908","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpGWBcCzoM5LtRNR7k","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CTvcEo6yjwg1buBfrNhWe7","name":"Skill","input":{"skill":"onecli-gateway","args":"GET https://api.github.com/repos/jamesjlopez/congressional-trading"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":1347,"cache_read_input_tokens":8519,"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":1347},"inference_geo":"","iterations":[{"input_tokens":5,"output_tokens":112,"cache_read_input_tokens":8519,"cache_creation_input_tokens":1347,"cache_creation":{"ephemeral_5m_input_tokens":1347,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqpG97Ah6SQbpfeGJsc","attributionAgent":"Explore","type":"assistant","uuid":"2db7334c-4616-4542-81d8-eb1d46421611","timestamp":"2026-07-07T01:38:21.060Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"2db7334c-4616-4542-81d8-eb1d46421611","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01CTvcEo6yjwg1buBfrNhWe7","content":"Launching skill: onecli-gateway"}]},"uuid":"9dff1e9a-7666-4aad-8060-6dcd63ec0647","timestamp":"2026-07-07T01:38:21.086Z","sourceToolAssistantUUID":"2db7334c-4616-4542-81d8-eb1d46421611","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"9dff1e9a-7666-4aad-8060-6dcd63ec0647","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"type":"text","text":"Base directory for this skill: /home/node/.claude/skills/onecli-gateway\n\n# OneCLI Gateway\n\nYour outbound HTTPS traffic is transparently proxied through the OneCLI\ngateway, which injects stored credentials at the proxy boundary. You never\nsee or handle credential values directly.\n\n## How to Access External Services\n\nYou have direct HTTP access to external APIs. OAuth apps (Gmail, GitHub,\nGoogle Calendar, Google Drive, etc.) and API key services are all available\nthrough the gateway. Just make the request directly; the gateway injects\ncredentials if the app is connected. If not, it returns an error with a\nconnect URL you can present to the user.\n\n## Making Requests\n\nCall the real API URL. The gateway intercepts the request and injects\ncredentials automatically.\n\n```bash\ncurl -s \"https://gmail.googleapis.com/gmail/v1/users/me/messages?maxResults=5\"\ncurl -s \"https://api.github.com/user/repos?per_page=10\"\ncurl -s \"https://api.stripe.com/v1/charges?limit=5\"\n```\n\nStandard HTTP clients (curl, fetch, requests, axios, Go net/http, git) all\nhonor the `HTTPS_PROXY` environment variable automatically. You do not need\nto set any auth headers.\n\n## Credential Stubs for MCP Servers\n\nSome MCP servers need local credential files to start. Stubs for connected\napps are pre-written automatically. Files containing `\"onecli-managed\"`\nvalues are managed by OneCLI — do NOT modify or delete them.\n\nIf an MCP server won't start due to missing credentials, create stubs\n**before** starting it. Use `\"onecli-managed\"` as the placeholder for all\nsecret values, with file permissions `0600`. See the guide at:\nhttps://www.onecli.sh/docs/guides/credential-stubs/general-app\n\n## When a Request Fails\n\nIf you get a 401, 403, or a gateway error (e.g., `app_not_connected`):\n\n**Step 1 — Show the user a connect link.** Use the `connect_url` from the\nerror response:\n\n> To connect [service], open this link:\n> [connect_url from the error response]\n\nIf there is no `connect_url` in the error, tell the user to open the\nOneCLI dashboard and connect the service there.\n\n**Step 2 — Retry after the user connects.** Let the user know you will\nretry once they have connected. When they confirm, retry the original\nrequest. If the retry still fails, ask if they need help with the setup.\n\n## Rules\n\n- **Never** say \"I don't have access to X\" without first making the HTTP\n  request through the proxy.\n- **Never** use browser extensions, gcloud, or manual auth flows. The\n  gateway handles credentials for you.\n- **Never** ask the user for API keys or tokens directly. Direct them to\n  connect the service in the OneCLI dashboard.\n- **Never** suggest the user open Gmail/Calendar/GitHub in their browser\n  when they ask you to read or interact with those services. You have API\n  access. Use it.\n- If the gateway returns a policy error (403 with a JSON body), respect\n  the block. Do not retry or circumvent it.\n\n\nARGUMENTS: GET https://api.github.com/repos/jamesjlopez/congressional-trading"}]},"isMeta":true,"uuid":"3b1abac2-019d-4ffc-ae2b-b2f2818b97d8","timestamp":"2026-07-07T01:38:21.083Z","sourceToolUseID":"toolu_01CTvcEo6yjwg1buBfrNhWe7","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"3b1abac2-019d-4ffc-ae2b-b2f2818b97d8","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpPvAjWGWcrnLjCntJ","type":"message","role":"assistant","content":[{"type":"text","text":"I'll fetch the repository information and explore its structure via the GitHub API."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":936,"cache_read_input_tokens":9866,"cache_creation":{"ephemeral_5m_input_tokens":936,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqpPLwZpv1iYGheRezt","attributionAgent":"Explore","type":"assistant","uuid":"a02cbe39-cf74-45cf-910e-cce2d9d8d0c3","timestamp":"2026-07-07T01:38:22.680Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
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{"parentUuid":"05191715-87a4-41b9-b7f8-08894f713a32","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RRfjeFPjCiHb1L3mW4Qv6J","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"4cb5781a-c7cb-4ad1-b5e7-75e0c2d21f34","timestamp":"2026-07-07T01:38:25.999Z","sourceToolAssistantUUID":"05191715-87a4-41b9-b7f8-08894f713a32","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"4cb5781a-c7cb-4ad1-b5e7-75e0c2d21f34","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpnLYngjvRAu4mJ6Ng","type":"message","role":"assistant","content":[{"type":"text","text":"Let me try a different approach - clone the repo and explore it locally."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":114,"cache_read_input_tokens":13306,"cache_creation":{"ephemeral_5m_input_tokens":114,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqpkKFz3YjsYZBF6fcq","attributionAgent":"Explore","type":"assistant","uuid":"9e6a042a-b79a-480f-b1e6-b03b0c3ad9dd","timestamp":"2026-07-07T01:38:27.481Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"9e6a042a-b79a-480f-b1e6-b03b0c3ad9dd","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpnLYngjvRAu4mJ6Ng","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012WBd4ma6iLxmLdnEtWSKyd","name":"Bash","input":{"command":"cd /tmp && git clone https://github.com/jamesjlopez/congressional-trading.git 2>&1 | head -20"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":114,"cache_read_input_tokens":13306,"output_tokens":99,"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":114},"inference_geo":"","iterations":[{"input_tokens":4,"output_tokens":99,"cache_read_input_tokens":13306,"cache_creation_input_tokens":114,"cache_creation":{"ephemeral_5m_input_tokens":114,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqpkKFz3YjsYZBF6fcq","attributionAgent":"Explore","type":"assistant","uuid":"50cefd87-f734-42f5-b9fb-922df48efdfe","timestamp":"2026-07-07T01:38:27.614Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"50cefd87-f734-42f5-b9fb-922df48efdfe","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012WBd4ma6iLxmLdnEtWSKyd","type":"tool_result","content":"Cloning into 'congressional-trading'...\nfatal: unable to access 'https://github.com/jamesjlopez/congressional-trading.git/': server certificate verification failed. CAfile: none CRLfile: none","is_error":false}]},"uuid":"48f5cb98-4b69-4fa7-89e6-f25626620f47","timestamp":"2026-07-07T01:38:27.691Z","sourceToolAssistantUUID":"50cefd87-f734-42f5-b9fb-922df48efdfe","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"48f5cb98-4b69-4fa7-89e6-f25626620f47","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqpsvA8EivCwz2wrYyK","type":"message","role":"assistant","content":[{"type":"text","text":"Let me try using curl to get the raw file contents directly via the GitHub API."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":268,"cache_read_input_tokens":13420,"cache_creation":{"ephemeral_5m_input_tokens":268,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqpsJwwzqEbGs3QP8hQ","attributionAgent":"Explore","type":"assistant","uuid":"44c5ba11-ad71-4f06-9a72-6e9456c38cb2","timestamp":"2026-07-07T01:38:28.731Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
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{"parentUuid":"c8e83177-50e0-4ad2-8916-e97f6e86fda3","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FFq4rej9pdB2hDyCPJPeKA","type":"tool_result","content":"\"\"\"\nSignal scoring engine.\n\nGroups trades by ticker within a lookback window and produces a score\nfor each ticker reflecting the aggregate congressional activity.\n\nScore components (max ~13):\n  committee_score    0–3   member's committee overlaps with company sector (Day 5)\n  track_record_score 0–2   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)\n  options_bonus      0–2   any option trade on this ticker (+2)\n  size_score         0–5   highest dollar tier among buy trades\n\nNotification threshold (SCORE_NOTIFY_THRESHOLD in config.py): default 7\n\"\"\"\n\nfrom __future__ import annotations\nimport sqlite3\nimport sys\nimport os\nfrom collections import defaultdict\nfrom datetime import datetime, timedelta\n\nsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\nfrom config import DB_PATH, LOOKBACK_DAYS, SCORE_NOTIFY_THRESHOLD\n\n\n# Committee → sector mapping used for committee_score.\n# Updated when committees.py runs (Day 5); used here for scoring.\n# Key: normalized committee name fragment, Value: list of matching sectors\n_COMMITTEE_SECTOR_MAP = {\n    \"armed services\":       [\"industrials\", \"information-technology\"],\n    \"defense\":              [\"industrials\", \"information-technology\"],\n    \"intelligence\":         [\"information-technology\", \"industrials\"],\n    \"energy\":               [\"energy\", \"utilities\", \"materials\"],\n    \"natural resources\":    [\"energy\", \"materials\"],\n    \"science\":              [\"information-technology\"],\n    \"technology\":           [\"information-technology\", \"communication-services\"],\n    \"health\":               [\"health-care\"],\n    \"financial services\":   [\"financials\"],\n    \"banking\":              [\"financials\"],\n    \"finance\":              [\"financials\"],\n    \"agriculture\":          [\"consumer-staples\", \"materials\"],\n    \"transportation\":       [\"industrials\"],\n    \"commerce\":             [\"consumer-discretionary\", \"communication-services\"],\n    \"judiciary\":            [],\n    \"foreign affairs\":      [],\n    \"ways and means\":       [\"financials\"],\n}\n\n\ndef _committee_score(committees_json: str | None, sector: str) -> float:\n    \"\"\"\n    Return 0–3 based on overlap between member's committees and company sector.\n    0 = no data or no overlap\n    1 = indirect overlap (committee has broad relevance)\n    2 = moderate overlap\n    3 = direct jurisdiction\n    \"\"\"\n    if not committees_json or not sector:\n        return 0.0\n    import json\n    try:\n        committees = json.loads(committees_json)\n    except (json.JSONDecodeError, TypeError):\n        return 0.0\n\n    sector = sector.lower()\n    best = 0.0\n    for committee in committees:\n        name = committee.lower() if isinstance(committee, str) else (committee.get(\"name\", \"\") or \"\").lower()\n        for key, sectors in _COMMITTEE_SECTOR_MAP.items():\n            if key in name:\n                if sector in sectors:\n                    best = max(best, 3.0)\n                elif any(s in sector for s in [\"tech\", \"info\", \"comm\"]) and key in (\"science\", \"technology\", \"commerce\"):\n                    best = max(best, 2.0)\n                else:\n                    best = max(best, 1.0)\n    return best\n\n\ndef _cluster_score(unique_buyers: int) -> float:\n    if unique_buyers >= 4:\n        return 3.0\n    if unique_buyers == 3:\n        return 2.0\n    if unique_buyers == 2:\n        return 1.0\n    return 0.0\n\n\ndef _size_score(max_tier: int) -> float:\n    \"\"\"tier is already 1–6 from ingest; cap contribution at 5.\"\"\"\n    return min(5.0, float(max(0, max_tier)))\n\n\ndef _sell_pressure(unique_sellers: int, unique_buyers: int) -> float:\n    \"\"\"\n    Penalty when congressional members are selling a ticker they or others are buying.\n    The ratio of sellers to buyers determines severity:\n\n    sellers == 0:                0.0  — clean signal\n    ratio < 0.40 (e.g. 1 of 3): -0.5 — minor noise, buyers dominate\n    ratio < 0.67 (e.g. 1 of 2): -1.5 — worth noting\n    ratio < 1.0  (e.g. 2 of 3): -2.5 — mixed conviction\n    ratio >= 1.0 (sellers ≥ buyers): -4.0 — sell pressure outweighs buys\n    \"\"\"\n    if unique_sellers == 0:\n        return 0.0\n    ratio = unique_sellers / max(unique_buyers, 1)\n    if ratio >= 1.0:\n        return -4.0\n    if ratio >= 0.67:\n        return -2.5\n    if ratio >= 0.40:\n        return -1.5\n    return -0.5\n\n\ndef _small_cap_bonus(market_cap: float | None) -> float:\n    \"\"\"\n    Score bonus/penalty based on market cap.\n\n    The thesis: congressional informed buying in a smaller, less-covered company\n    has higher percentage-move potential than the same signal in a mega-cap where\n    hundreds of analysts are already pricing in every catalyst. Tiers:\n\n    Mega  (>$100B):  -2.5  — saturated coverage; edge is minimal\n    Large ($10-100B): -0.5  — well-covered; slight penalty\n    Mid   ($2-10B):  +2.0  — meaningful analyst coverage gap\n    Small ($500M-$2B): +4.5 — thin coverage, catalysts move the stock\n    Micro (<$500M):  +6.0  — very thin float; single catalyst → 30-50% moves\n    Unknown:          0.0  — neutral until price_fetcher populates market_cap\n    \"\"\"\n    if market_cap is None:\n        return 0.0\n    if market_cap > 100e9:\n        return -2.5\n    if market_cap > 10e9:\n        return -0.5\n    if market_cap > 2e9:\n        return 2.0\n    if market_cap > 500e6:\n        return 4.5\n    return 6.0\n\n\ndef _breadth_mult(hist_buyers: int) -> float:\n    \"\"\"\n    Discount factor applied to cluster_score based on how many unique members\n    have historically bought a ticker. Mega-caps routinely bought by 15+ members\n    are portfolio staples, not informed signals — their cluster score overstates edge.\n    \"\"\"\n    if hist_buyers >= 20:\n        return 0.40   # basically an index holding (MSFT, GOOGL, NVDA tier)\n    if hist_buyers >= 12:\n        return 0.65   # widely held (HD, BRK/B, GS tier)\n    if hist_buyers >= 6:\n        return 0.85   # moderately popular\n    return 1.0        # rare buy — full cluster score\n\n\ndef score_tickers(lookback_days: int = LOOKBACK_DAYS) -> list[dict]:\n    \"\"\"\n    Score all tickers with at least one BUY trade published in the last\n    lookback_days. Returns list of signal dicts sorted by total_score desc.\n    Also upserts results into the signals table.\n    \"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n\n    cutoff = (datetime.now() - timedelta(days=lookback_days)).strftime(\"%Y-%m-%d\")\n\n    # Cached market caps from previous price_fetcher run (neutral default if missing)\n    cap_rows = conn.execute(\"SELECT ticker, market_cap FROM signals WHERE market_cap IS NOT NULL\").fetchall()\n    market_caps: dict[str, float] = {r[\"ticker\"]: r[\"market_cap\"] for r in cap_rows}\n\n    # LLM-derived scores from research_cache — default 0 until research has run\n    rc_rows = conn.execute(\n        \"SELECT ticker, COALESCE(strategic_score,0) AS ss, COALESCE(disruption_score,0) AS ds FROM research_cache\"\n    ).fetchall()\n    llm_scores: dict[str, tuple[float, float]] = {r[\"ticker\"]: (r[\"ss\"], r[\"ds\"]) for r in rc_rows}\n\n    # Historical buyer breadth — full DB, not windowed — used for breadth discount\n    breadth_rows = conn.execute(\n        \"\"\"\n        SELECT issuer_ticker, COUNT(DISTINCT member_id) AS cnt\n        FROM trades\n        WHERE tx_type = 'buy' AND issuer_ticker != ''\n        GROUP BY issuer_ticker\n        \"\"\"\n    ).fetchall()\n    historical_buyers: dict[str, int] = {r[\"issuer_ticker\"]: r[\"cnt\"] for r in breadth_rows}\n\n    rows = conn.execute(\n        \"\"\"\n        SELECT\n            t.tx_id,\n            t.issuer_ticker,\n            t.issuer_name,\n            t.issuer_sector,\n            t.member_id,\n            t.tx_type,\n            t.amount_tier,\n            t.is_option,\n            t.pub_date,\n            t.tx_date,\n            t.value_estimate,\n            m.party,\n            m.chamber,\n            m.first_name || ' ' || m.last_name AS member_name,\n            m.committees,\n            COALESCE(m.win_rate, -1) AS win_rate\n        FROM trades t\n        JOIN members m ON m.bioguide_id = t.member_id\n        WHERE t.pub_date >= ?\n          AND t.issuer_ticker != ''\n        ORDER BY t.issuer_ticker, t.pub_date DESC\n        \"\"\",\n        (cutoff,),\n    ).fetchall()\n\n    # Group by ticker\n    by_ticker: dict[str, list] = defaultdict(list)\n    for row in rows:\n        by_ticker[row[\"issuer_ticker\"]].append(dict(row))\n\n    signals = []\n\n    for ticker, ticker_rows in by_ticker.items():\n        buy_rows = [r for r in ticker_rows if r[\"tx_type\"] == \"buy\"]\n        sell_rows = [r for r in ticker_rows if r[\"tx_type\"] == \"sell\"]\n        option_rows = [r for r in ticker_rows if r[\"is_option\"]]\n        sell_ids = set(r[\"member_id\"] for r in sell_rows)\n\n        if not buy_rows:\n            continue  # only score tickers with at least one buy\n\n        # --- Cluster score: unique member buyers, discounted for mega-caps ---\n        buyer_ids = set(r[\"member_id\"] for r in buy_rows)\n        unique_buyers = len(buyer_ids)\n        hist = historical_buyers.get(ticker, 0)\n        cluster = round(_cluster_score(unique_buyers) * _breadth_mult(hist), 2)\n\n        # --- Size score: max tier among buys ---\n        max_tier = max((r[\"amount_tier\"] or 0) for r in buy_rows)\n        size = _size_score(max_tier)\n\n        # --- Options bonus ---\n        opt_bonus = 2.0 if option_rows else 0.0\n\n        # --- Track record score (default 1 until win_rate data available) ---\n        win_rates = [r[\"win_rate\"] for r in buy_rows if r[\"win_rate\"] >= 0]\n        if win_rates:\n            avg_wr = sum(win_rates) / len(win_rates)\n            track = 2.0 if avg_wr > 0.6 else (1.0 if avg_wr >= 0.5 else 0.5)\n        else:\n            track = 1.0  # default unknown\n\n        # --- Committee score: best score across all buying members ---\n        sector = (buy_rows[0].get(\"issuer_sector\") or \"\").lower()\n        comm_score = max(\n            _committee_score(r.get(\"committees\"), sector) for r in buy_rows\n        )\n\n        mc_bonus = _small_cap_bonus(market_caps.get(ticker))\n        sell_penalty = _sell_pressure(len(sell_ids), unique_buyers)\n        strategic, disruption = llm_scores.get(ticker, (0.0, 0.0))\n        total = comm_score + track + cluster + opt_bonus + size + mc_bonus + sell_penalty + strategic + disruption\n\n        pub_dates = [r[\"pub_date\"] for r in ticker_rows]\n        tx_dates  = [r[\"tx_date\"] for r in buy_rows if r[\"tx_date\"]]\n\n        # Collect buyer details for display\n        buyers_detail = []\n        seen_buyers = set()\n        for r in buy_rows:\n            if r[\"member_id\"] not in seen_buyers:\n                seen_buyers.add(r[\"member_id\"])\n                buyers_detail.append({\n                    \"name\": r[\"member_name\"],\n                    \"party\": r[\"party\"],\n                    \"chamber\": r[\"chamber\"],\n                    \"tier\": r[\"amount_tier\"],\n                })\n\n        sig = {\n            \"ticker\": ticker,\n            \"issuer_name\": buy_rows[0][\"issuer_name\"],\n            \"sector\": sector,\n            \"first_seen\": min(pub_dates),\n            \"last_seen\": max(pub_dates),\n            \"first_tx_date\": min(tx_dates) if tx_dates else \"\",\n            \"last_tx_date\": max(tx_dates) if tx_dates else \"\",\n            \"member_count\": unique_buyers,\n            \"sell_count\": len(sell_ids),\n            \"sell_pressure_score\": sell_penalty,\n            \"has_options\": bool(option_rows),\n            \"max_tier\": max_tier,\n            \"committee_score\": comm_score,\n            \"track_record_score\": track,\n            \"cluster_score\": cluster,\n            \"options_bonus\": opt_bonus,\n            \"size_score\": size,\n            \"small_cap_score\": mc_bonus,\n            \"strategic_score\": strategic,\n            \"disruption_score\": disruption,\n            \"total_score\": round(total, 2),\n            \"buyers\": buyers_detail,\n        }\n        signals.append(sig)\n\n    signals.sort(key=lambda x: x[\"total_score\"], reverse=True)\n\n    # Upsert into signals table\n    now = datetime.now().isoformat(timespec=\"seconds\")\n    for sig in signals:\n        conn.execute(\n            \"\"\"\n            INSERT INTO signals (\n                ticker, issuer_name, first_seen, last_seen, first_tx_date, member_count,\n                committee_score, track_record_score, cluster_score,\n                options_bonus, size_score, small_cap_score,\n                sell_pressure_score, strategic_score, disruption_score, total_score, updated_at\n            ) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\n            ON CONFLICT(ticker) DO UPDATE SET\n                issuer_name         = excluded.issuer_name,\n                last_seen           = excluded.last_seen,\n                first_tx_date       = excluded.first_tx_date,\n                member_count        = excluded.member_count,\n                committee_score     = excluded.committee_score,\n                track_record_score  = excluded.track_record_score,\n                cluster_score       = excluded.cluster_score,\n                options_bonus       = excluded.options_bonus,\n                size_score          = excluded.size_score,\n                small_cap_score     = excluded.small_cap_score,\n                sell_pressure_score = excluded.sell_pressure_score,\n                strategic_score     = excluded.strategic_score,\n                disruption_score    = excluded.disruption_score,\n                total_score         = excluded.total_score,\n                updated_at          = excluded.updated_at\n            \"\"\",\n            (\n                sig[\"ticker\"],\n                sig[\"issuer_name\"],\n                sig[\"first_seen\"],\n                sig[\"last_seen\"],\n                sig[\"first_tx_date\"] or None,\n                sig[\"member_count\"],\n                sig[\"committee_score\"],\n                sig[\"track_record_score\"],\n                sig[\"cluster_score\"],\n                sig[\"options_bonus\"],\n                sig[\"size_score\"],\n                sig[\"small_cap_score\"],\n                sig[\"sell_pressure_score\"],\n                sig[\"strategic_score\"],\n                sig[\"disruption_score\"],\n                sig[\"total_score\"],\n                now,\n            ),\n        )\n\n    conn.commit()\n    conn.close()\n    return signals\n\n\ndef top_signals(n: int = 10, min_score: float = 0.0) -> list[dict]:\n    \"\"\"Return top N signals from the signals table, optionally filtered by min_score.\"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    rows = conn.execute(\n        \"\"\"\n        SELECT * FROM signals\n        WHERE total_score >= ?\n        ORDER BY total_score DESC\n        LIMIT ?\n        \"\"\",\n        (min_score, n),\n    ).fetchall()\n    conn.close()\n    return [dict(r) for r in rows]\n\n\nif __name__ == \"__main__\":\n    print(\"Scoring all tickers...\\n\")\n    signals = score_tickers()\n\n    print(f\"{'Rank':<5} {'Ticker':<8} {'Score':>6}  {'Clust':>5} {'Size':>4} {'Opts':>4} {'Trck':>4} {'Cmte':>4} {'Cap':>4}  \"\n          f\"{'Buyers':>6} {'Sells':>5}  {'Sector'}\")\n    print(\"-\" * 100)\n    for i, s in enumerate(signals[:20], 1):\n        opt_flag = \"OPT\" if s[\"has_options\"] else \"\"\n        buyers_str = \" / \".join(\n            f\"{b['name'].split()[-1]}({'R' if b['party'] in ('republican', 'R') else 'D'})\"\n            for b in s[\"buyers\"][:3]\n        )\n        if len(s[\"buyers\"]) > 3:\n            buyers_str += f\" +{len(s['buyers'])-3}\"\n        sc = s.get(\"small_cap_score\", 0) or 0\n        print(\n            f\"{i:<5} {s['ticker']:<8} {s['total_score']:>6.1f}  \"\n            f\"{s['cluster_score']:>5.2f} {s['size_score']:>4.0f} \"\n            f\"{s['options_bonus']:>4.0f} {s['track_record_score']:>4.1f} \"\n            f\"{s['committee_score']:>4.0f} {sc:>+4.1f}  \"\n            f\"{s['member_count']:>6} {s['sell_count']:>5}  \"\n            f\"{s['sector'][:25]}\"\n        )\n        print(f\"       {s['issuer_name'][:50]:<50}  buyers: {buyers_str}\")\n        print()\n\n    notify_candidates = [s for s in signals if s[\"total_score\"] >= SCORE_NOTIFY_THRESHOLD]\n    print(f\"\\n{len(notify_candidates)} signals at or above notify threshold ({SCORE_NOTIFY_THRESHOLD})\")\n\n    from tools.generate_report import generate\n    generate()","is_error":false}]},"uuid":"5c76d968-2a8e-4fff-8fb7-9bca53b2a578","timestamp":"2026-07-07T01:38:30.798Z","sourceToolAssistantUUID":"c8e83177-50e0-4ad2-8916-e97f6e86fda3","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"5c76d968-2a8e-4fff-8fb7-9bca53b2a578","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqq7KojjFReW11W823q","type":"message","role":"assistant","content":[{"type":"text","text":"Excellent! Now let me get the notification policy and other key files."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4989,"cache_read_input_tokens":17122,"cache_creation":{"ephemeral_5m_input_tokens":4989,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Ccmqq6qJ1jVam4NQGy3Pp","attributionAgent":"Explore","type":"assistant","uuid":"f12a0318-61f7-44f9-bc7e-1786aa4fcf31","timestamp":"2026-07-07T01:38:32.030Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"f12a0318-61f7-44f9-bc7e-1786aa4fcf31","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqq7KojjFReW11W823q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011f7imhdJdKwFfZh4pwWwje","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/analysis/notification_policy.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4989,"cache_read_input_tokens":17122,"output_tokens":99,"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":4989},"inference_geo":"","iterations":[{"input_tokens":3,"output_tokens":99,"cache_read_input_tokens":17122,"cache_creation_input_tokens":4989,"cache_creation":{"ephemeral_5m_input_tokens":4989,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ccmqq6qJ1jVam4NQGy3Pp","attributionAgent":"Explore","type":"assistant","uuid":"da890bc9-d162-4883-9621-9169e63bf113","timestamp":"2026-07-07T01:38:32.379Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"da890bc9-d162-4883-9621-9169e63bf113","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011f7imhdJdKwFfZh4pwWwje","type":"tool_result","content":"\"\"\"Notification gating for high-signal trade ideas.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom datetime import datetime, timedelta\nimport math\nimport re\nimport sqlite3\n\nfrom config import (\n    DB_PATH,\n    NOTIFY_MAX_SELL_PRESSURE_PENALTY,\n    NOTIFY_MIN_CONVICTION,\n    NOTIFY_MIN_EDGE_FACTORS,\n    NOTIFY_REQUIRE_BUY_ACTION,\n    NOTIFY_TICKER_COOLDOWN_DAYS,\n    SCORE_NOTIFY_THRESHOLD,\n)\n\n\n@dataclass(frozen=True)\nclass NotificationDecision:\n    allowed: bool\n    reason: str\n    edge_factors: tuple[str, ...] = ()\n\n\n_CONVICTION_ORDER = {\"LOW\": 0, \"MEDIUM\": 1, \"HIGH\": 2}\n_ACTION_BLOCKLIST = (\n    \"AVOID\",\n    \"HOLD\",\n    \"MONITOR\",\n    \"PASS\",\n    \"SELL\",\n    \"TRIM\",\n    \"WAIT\",\n)\n\n\ndef _to_float(value, default: float = 0.0) -> float:\n    try:\n        return float(value)\n    except (TypeError, ValueError):\n        return default\n\n\ndef edge_factors(sig: dict, research: dict | None) -> tuple[str, ...]:\n    \"\"\"Return the independent evidence factors that make a signal notification-worthy.\"\"\"\n    factors: list[str] = []\n\n    if int(sig.get(\"member_count\") or 0) >= 2:\n        factors.append(\"multi-member cluster\")\n    if int(sig.get(\"max_tier\") or 0) >= 4:\n        factors.append(\"large disclosed size\")\n    if sig.get(\"has_options\"):\n        factors.append(\"options timing\")\n    if _to_float(sig.get(\"committee_score\")) >= 2.0:\n        factors.append(\"direct committee alignment\")\n    if _to_float(sig.get(\"small_cap_score\")) >= 2.0:\n        factors.append(\"undercovered small/mid cap\")\n\n    strategic = _to_float((research or {}).get(\"strategic_score\", sig.get(\"strategic_score\")))\n    disruption = _to_float((research or {}).get(\"disruption_score\", sig.get(\"disruption_score\")))\n    if strategic + disruption >= 4.0:\n        factors.append(\"strong LLM catalyst\")\n\n    return tuple(factors)\n\n\ndef is_buy_recommendation(action: str | None) -> bool:\n    \"\"\"Accept explicit buy/add actions, not incidental mentions of the word buy.\"\"\"\n    if not action:\n        return False\n\n    text = re.sub(r\"\\s+\", \" \", action.strip().upper())\n    if not text:\n        return False\n    if \"DO NOT BUY\" in text or \"NOT BUY\" in text:\n        return False\n    if text.startswith(_ACTION_BLOCKLIST):\n        return False\n\n    first_word = re.split(r\"[\\s:;-]+\", text, maxsplit=1)[0]\n    return first_word in {\"BUY\", \"ADD\", \"ACCUMULATE\", \"INITIATE\"}\n\n\ndef evaluate_notification_candidate(\n    sig: dict,\n    research: dict | None,\n    adjusted_score: float,\n    *,\n    notify_threshold: float = SCORE_NOTIFY_THRESHOLD,\n    min_conviction: str = NOTIFY_MIN_CONVICTION,\n    require_buy_action: bool = NOTIFY_REQUIRE_BUY_ACTION,\n    min_edge_factors: int = NOTIFY_MIN_EDGE_FACTORS,\n    max_sell_pressure_penalty: float = NOTIFY_MAX_SELL_PRESSURE_PENALTY,\n) -> NotificationDecision:\n    \"\"\"Evaluate all quality gates except duplicate/cooldown state.\"\"\"\n    ticker = sig.get(\"ticker\", \"?\")\n\n    if adjusted_score < notify_threshold:\n        return NotificationDecision(False, f\"adjusted score {adjusted_score:.1f} below {notify_threshold:.1f}\")\n    if not research:\n        return NotificationDecision(False, \"no research\")\n    if not research.get(\"risk_factors\"):\n        return NotificationDecision(False, \"no deep research\")\n\n    conviction = (research.get(\"conviction\") or \"LOW\").upper()\n    min_order = _CONVICTION_ORDER.get(min_conviction.upper(), 1)\n    if _CONVICTION_ORDER.get(conviction, 0) < min_order:\n        return NotificationDecision(False, f\"conviction={conviction} below {min_conviction.upper()}\")\n\n    action = research.get(\"recommended_action\") or \"\"\n    if require_buy_action and not is_buy_recommendation(action):\n        return NotificationDecision(False, f\"recommended_action is not an explicit buy/add: {action[:80]}\")\n\n    sell_pressure = _to_float(sig.get(\"sell_pressure_score\"))\n    if sell_pressure < max_sell_pressure_penalty:\n        return NotificationDecision(False, f\"sell pressure {sell_pressure:+.1f} conflicts with buy signal\")\n\n    factors = edge_factors(sig, research)\n    if len(factors) < min_edge_factors:\n        return NotificationDecision(\n            False,\n            f\"only {len(factors)}/{min_edge_factors} edge factors: {', '.join(factors) or 'none'}\",\n            factors,\n        )\n\n    return NotificationDecision(True, f\"{ticker}: {len(factors)} edge factors\", factors)\n\n\ndef _parse_dt(value: str | None) -> datetime | None:\n    if not value:\n        return None\n    try:\n        return datetime.fromisoformat(value)\n    except ValueError:\n        try:\n            return datetime.fromisoformat(value.replace(\" \", \"T\"))\n        except ValueError:\n            return None\n\n\ndef recent_ticker_notification(\n    ticker: str,\n    *,\n    db_path: str = DB_PATH,\n    cooldown_days: int = NOTIFY_TICKER_COOLDOWN_DAYS,\n    now: datetime | None = None,\n) -> tuple[bool, datetime | None, int]:\n    \"\"\"\n    Return whether ticker is inside the rolling cooldown, plus latest notify time\n    and whole days remaining.\n    \"\"\"\n    now = now or datetime.now()\n    cutoff = now - timedelta(days=cooldown_days)\n\n    conn = sqlite3.connect(db_path)\n    rows = conn.execute(\n        \"\"\"\n        SELECT notified_at\n        FROM signal_outcomes\n        WHERE ticker = ?\n        ORDER BY notified_at DESC\n        LIMIT 10\n        \"\"\",\n        (ticker,),\n    ).fetchall()\n    conn.close()\n\n    latest = None\n    for row in rows:\n        parsed = _parse_dt(row[0])\n        if parsed and (latest is None or parsed > latest):\n            latest = parsed\n\n    if latest is None or latest < cutoff:\n        return False, latest, 0\n\n    remaining_seconds = (latest + timedelta(days=cooldown_days) - now).total_seconds()\n    days_remaining = max(1, math.ceil(remaining_seconds / 86400))\n    return True, latest, days_remaining","is_error":false}]},"uuid":"8d2a765d-ac6c-4c76-883c-baae8747fe08","timestamp":"2026-07-07T01:38:32.535Z","sourceToolAssistantUUID":"da890bc9-d162-4883-9621-9169e63bf113","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"8d2a765d-ac6c-4c76-883c-baae8747fe08","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqEfq6inamq4tk8gQ3","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me get the config file and the README to understand better."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1868,"cache_read_input_tokens":22111,"cache_creation":{"ephemeral_5m_input_tokens":1868,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqqE4NJVigKLRpvCJ3L","attributionAgent":"Explore","type":"assistant","uuid":"3111a14a-fa3c-46f8-bf56-fd19be192578","timestamp":"2026-07-07T01:38:33.669Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"3111a14a-fa3c-46f8-bf56-fd19be192578","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqEfq6inamq4tk8gQ3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015TJMPbfhh9iBSRuKyeVv2c","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/config.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1868,"cache_read_input_tokens":22111,"output_tokens":94,"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":1868},"inference_geo":"","iterations":[{"input_tokens":3,"output_tokens":94,"cache_read_input_tokens":22111,"cache_creation_input_tokens":1868,"cache_creation":{"ephemeral_5m_input_tokens":1868,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqqE4NJVigKLRpvCJ3L","attributionAgent":"Explore","type":"assistant","uuid":"02dc50a1-4fda-4047-85bb-c0082a345669","timestamp":"2026-07-07T01:38:33.973Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"02dc50a1-4fda-4047-85bb-c0082a345669","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015TJMPbfhh9iBSRuKyeVv2c","type":"tool_result","content":"import os\nfrom dotenv import load_dotenv\nload_dotenv()\n\n# --- Database ---\nDB_PATH = os.path.join(os.path.dirname(__file__), \"data\", \"congress_trades.db\")\n\n# --- Ingest ---\nLOOKBACK_DAYS = 30          # how many days of trade history to fetch on each run\nPAGE_SIZE = 100             # max records per page from Capitol Trades\n\n# --- Scoring ---\nSCORE_RESEARCH_THRESHOLD = 5.0   # min score to trigger LLM research\nSCORE_NOTIFY_THRESHOLD = 9.0     # min score to send ntfy notification\n\n# --- Timeliness multipliers applied to score at notify-decision time only ---\n# A \"late\" signal's stored score is unchanged; the multiplier only gates notifications.\nTIMELINESS_MULTIPLIERS = {\"fresh\": 1.0, \"watch\": 0.85, \"late\": 0.70}\n\n# --- Win rate ---\nWIN_RATE_MIN_TRADES = 3          # minimum buy trades with price data before using actual rate\n\n# --- Deep Research ---\n# All signals above SCORE_NOTIFY_THRESHOLD are eligible for deep research.\n# Each run processes up to TOP_N of them, prioritising never-researched tickers\n# then stalest-first, skipping any researched within DECAY_DAYS.\n# The decay window (not TOP_N) is the primary cost control.\nDEEP_RESEARCH_TOP_N = 10            # max deep-research calls per run\nDEEP_RESEARCH_DECAY_DAYS = 7        # days before re-researching the same ticker\n\n# --- Notification quality gates ---\n# Cap how many push alerts fire per pipeline run regardless of score.\n# Push should be reserved for the single best actionable idea; the report carries the rest.\nMAX_NOTIFICATIONS_PER_RUN = 1\n\n# Do not re-alert the same ticker during this rolling window unless --force is used.\nNOTIFY_TICKER_COOLDOWN_DAYS = 14\n\n# When True, only signals where deep research recommends \"BUY NOW\" or \"BUY\" get notified.\n# Set False to notify for MONITOR/AVOID signals too (not recommended — defeats the purpose).\nNOTIFY_REQUIRE_BUY_ACTION = True\n\n# Minimum conviction level to trigger notification. Options: \"LOW\", \"MEDIUM\", \"HIGH\"\nNOTIFY_MIN_CONVICTION = \"HIGH\"\n\n# Minimum number of independent edge factors before a signal can page you.\n# Factors include multi-member clusters, large disclosed size, options timing,\n# committee alignment, small/mid-cap scarcity, and strong LLM catalyst scores.\nNOTIFY_MIN_EDGE_FACTORS = 3\n\n# Suppress buy notifications when sellers materially offset buyers.\nNOTIFY_MAX_SELL_PRESSURE_PENALTY = -1.0\n\n# --- LLM ---\nLLM_PROVIDER = \"anthropic\"          # \"anthropic\" or \"google\"\nANTHROPIC_API_KEY = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\nGOOGLE_API_KEY = os.getenv(\"GOOGLE_API_KEY\", \"\")\n\n# --- Predictive Signals ---\n# Behavioral analysis: predict future trades before disclosure.\n# Requires CONGRESS_GOV_API_KEY for live bill data (free at api.congress.gov);\n# works in degraded mode (LLM-only) without it.\nCONGRESS_GOV_API_KEY = os.getenv(\"CONGRESS_GOV_API_KEY\", \"\")\nPREDICTOR_MAX_MEMBERS = 5          # members to analyze per run (cost control)\nPREDICTOR_DECAY_DAYS  = 7          # skip re-predicting same member within this window\nPREDICTOR_MIN_TRADES  = 5          # min historical trades before a member is analyzed\nPREDICTOR_LEGISLATION_DAYS = 45   # how far back to look for recently active bills\n\n# --- ntfy ---\nNTFY_BASE_URL = \"https://ntfy.sh\"\nNTFY_TOPIC_SIGNALS = \"congressWatchSignals\"        # trade signal notifications\nNTFY_TOPIC_STATUS = \"claudeCodeWaitingOnYou\"       # system status / needs-input alerts","is_error":false}]},"uuid":"d5805bde-8d83-4657-9c31-7f2222347838","timestamp":"2026-07-07T01:38:34.115Z","sourceToolAssistantUUID":"02dc50a1-4fda-4047-85bb-c0082a345669","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"d5805bde-8d83-4657-9c31-7f2222347838","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqMGTKpdEnJRRofNr3","type":"message","role":"assistant","content":[{"type":"text","text":"Great! Now let me get the README and the schema to understand data persistence."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1124,"cache_read_input_tokens":23979,"cache_creation":{"ephemeral_5m_input_tokens":1124,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqqLrQSeL6fpCWhGjw5","attributionAgent":"Explore","type":"assistant","uuid":"db8caa3a-33e1-4afe-967a-120d5dc0b08b","timestamp":"2026-07-07T01:38:35.257Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"db8caa3a-33e1-4afe-967a-120d5dc0b08b","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqMGTKpdEnJRRofNr3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AoMn4aeBLZkJnw21S3Wqys","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/README.md\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":1124,"cache_read_input_tokens":23979,"output_tokens":96,"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":1124},"inference_geo":"","iterations":[{"input_tokens":3,"output_tokens":96,"cache_read_input_tokens":23979,"cache_creation_input_tokens":1124,"cache_creation":{"ephemeral_5m_input_tokens":1124,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqqLrQSeL6fpCWhGjw5","attributionAgent":"Explore","type":"assistant","uuid":"599de12e-1707-4aea-ad93-8f8e761f1e09","timestamp":"2026-07-07T01:38:35.566Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"599de12e-1707-4aea-ad93-8f8e761f1e09","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AoMn4aeBLZkJnw21S3Wqys","type":"tool_result","content":"# CongressWatch\n\nAutonomous congressional trade signal system. Ingests STOCK Act disclosures, scores them for signal quality, researches top candidates via LLM, and sends push notifications when conviction crosses a threshold.\n\n## Why\n\nCongressional members are legally required to disclose trades over $1,000 within 45 days (STOCK Act, 2012). Several members have shown consistent outperformance. Existing trackers (Capitol Trades, Unusual Whales, Autopilot) surface the raw data but do no analysis. This system adds:\n\n- **Committee-weighted scoring** — trades by members on relevant oversight committees carry more weight\n- **Cluster detection** — multiple members buying the same ticker simultaneously amplifies signal, discounted for mega-caps routinely held by many members\n- **Small-cap boost** — smaller companies have less analyst coverage; specific catalysts move them more in percentage terms\n- **LLM research** — autonomous investigation of *why* a trade happened and whether the thesis is still timely; high-conviction signals get a deeper bear-case analysis\n- **Price performance tracking** — return since member's trade date vs. return since first disclosure, surfacing the information-decay gap\n- **Conviction-filtered notifications** — push alerts require score, deep research, explicit buy action, multiple edge factors, and a rolling ticker cooldown\n\n## Quick Start\n\n```bash\n# Set up environment\npython3 -m venv .venv && .venv/bin/pip install -r requirements.txt\n\n# Copy and populate your API keys\ncp .env.example .env   # edit with your keys\n\n# Load 1 year of history (~5–10 min, safe to re-run)\n.venv/bin/python tools/backfill.py\n\n# Full pipeline: ingest → committees → score → prices → research → notify\n.venv/bin/python run_daily.py\n```\n\n## Running\n\n```bash\n# Full pipeline\n.venv/bin/python run_daily.py\n\n# Skip ingest (re-score + research + notify only — useful for testing)\n.venv/bin/python run_daily.py --no-ingest\n\n# Dry run — score and report only, no LLM calls, no notifications\n.venv/bin/python run_daily.py --dry-run\n\n# Re-notify signals already sent today (useful after tuning thresholds)\n.venv/bin/python run_daily.py --force\n\n# Score only and regenerate report (fast, no network)\n.venv/bin/python analysis/scorer.py\n\n# Fetch/refresh prices and market caps\n.venv/bin/python tools/price_fetcher.py\n\n# CLI signal table\n.venv/bin/python tools/report.py\n```\n\nThe markdown report is always written to `reports/signals_latest.md` after scoring.\n\n## Pipeline\n\nEach daily run executes six steps in order:\n\n1. **Ingest** — scrape Capitol Trades for new disclosures; upsert members and trades into SQLite\n2. **Committees** — refresh member committee assignments from `congress-legislators` (GitHub, weekly cache); committee data updates automatically when a new Congress is seated\n3. **Score** — compute signal scores for all tickers with a buy in the lookback window; regenerate report\n4. **Prices** — fetch yesterday's close and market cap via yfinance for all research-eligible signals; compute return since member's trade and return since first disclosure\n5. **Research** — call Claude API for signals above `SCORE_RESEARCH_THRESHOLD`; signals at/above `SCORE_NOTIFY_THRESHOLD` get a deeper prompt with explicit `risk_factors` and `recommended_action`\n6. **Notify** — send at most one ntfy push for the strongest notify-threshold signal that passes high-conviction quality gates and is outside the ticker cooldown\n\n## Scoring System\n\n```\ntotal_score = committee + track_record + cluster × breadth_discount + options_bonus + size + small_cap\n```\n\n| Component | Range | Logic |\n|---|---|---|\n| `committee_score` | 0–3 | Member's committee has jurisdiction over the company's sector (e.g. Armed Services + defense contractor = 3) |\n| `track_record_score` | 0–2 | Historical win rate; defaults to 1.0 until enough data accumulates |\n| `cluster_score` | 0–3 × discount | 2 buyers=1, 3=2, 4+=3; multiplied by breadth discount (0.4–1.0) based on how many members have historically bought this ticker |\n| `options_bonus` | 0 or 2 | Options trade on this ticker (+2 — implies specific timing knowledge) |\n| `size_score` | 0–5 | Highest dollar tier among buys: $1K-$15K=1, $15K-$50K=2, $50K-$100K=3, $100K-$250K=4, $250K+=5 |\n| `small_cap_score` | −1 to +3 | Market cap tier: mega >$100B = −1, large $10–100B = 0, mid $2–10B = +1.5, small <$2B = +3 |\n\n**Breadth discount** — tickers routinely bought by many members across history are portfolio staples, not signals. Cluster score is multiplied by: 0.4 (≥20 historical buyers), 0.65 (≥12), 0.85 (≥6), 1.0 (<6).\n\n**Notify threshold**: `≥ 9.0` (configurable in `config.py` as `SCORE_NOTIFY_THRESHOLD`)\n**Research threshold**: `≥ 5.0` (configurable as `SCORE_RESEARCH_THRESHOLD`)\n\n## Notification Gate\n\nPush alerts are intentionally stricter than report inclusion. A signal must pass all of these checks:\n\n- Raw score at or above `SCORE_NOTIFY_THRESHOLD`, after timeliness adjustment.\n- Deep research exists, including `risk_factors`.\n- `conviction` is at least `NOTIFY_MIN_CONVICTION` (`HIGH` by default).\n- `recommended_action` starts with an explicit buy/add action, not merely a sentence containing “buy.”\n- Sell pressure is not meaningfully conflicting (`sell_pressure_score >= -1.0` by default).\n- At least `NOTIFY_MIN_EDGE_FACTORS` independent edge factors are present: multi-member cluster, large disclosed size, options timing, direct committee alignment, undercovered small/mid cap, or strong LLM catalyst score.\n- The ticker has not already fired within `NOTIFY_TICKER_COOLDOWN_DAYS` (`14` by default), unless `--force` is used.\n\n`MAX_NOTIFICATIONS_PER_RUN` defaults to `1`; the HTML/markdown report is the review surface for additional candidates.\n\n## Notification Format\n\n```\nCongressWatch: $DMLP (score 9.0)\nDorchester Minerals LP — 1 member bought\nSize: $15K-$50K\nTimeliness: FRESH | Conviction: HIGH\n\n[company summary]\n\nCatalyst: [why this member with these committees would buy now]\n\n→ BUY NOW — [one-sentence rationale]\n```\n\n## Data Source\n\n**Primary**: Capitol Trades (capitoltrades.com) — free, no API key, updated within hours of new filings. Data parsed directly from React server components in page HTML.\n\n**Committees**: `unitedstates/congress-legislators` GitHub repo — `committee-membership-current.yaml`. Auto-refreshes weekly; picks up new Congress members automatically.\n\n**Prices**: yfinance — yesterday's close, no API key required.\n\nTypical disclosure lag: 30–75 days after trade execution (STOCK Act allows 45 days to file). The `TxRet%` column in the report shows return since the member's trade date; `PubRet%` shows return since first disclosure — the gap between them is the information value that decayed before it became public.\n\n## Directory Structure\n\n```\nconfig.py                  # thresholds, API keys, ntfy topic\nrun_daily.py               # main entry point\n\ningest/\n  capitol_trades.py        # Capitol Trades HTML scraper + SQLite writer\n  committees.py            # weekly committee assignment refresh\n\nanalysis/\n  scorer.py                # signal scoring engine (runs → regenerates report)\n  researcher.py            # LLM research pipeline; deep prompt for notify-threshold signals\n  timeliness.py            # age-based freshness assessment\n  llm_client.py            # provider abstraction (Anthropic / Google)\n\nnotify/\n  ntfy_client.py           # push notifications via ntfy.sh\n\ntools/\n  report.py                # CLI: scored signals + recent trades\n  generate_report.py       # writes reports/signals_latest.md\n  price_fetcher.py         # yfinance price + market cap fetch\n  backfill.py              # load 1–2 years of history\n\ndb/\n  schema.sql               # table definitions\n  migrate.py               # additive column migrations (safe to re-run)\n\ndata/\n  congress_trades.db       # SQLite (gitignored)\n\nreports/\n  signals_latest.md        # always current after scoring\n  signals_YYYY-MM-DD.md    # dated snapshots\n  token_usage.log          # LLM cost tracking\n\nviewer/\n  app.py                   # Flask dashboard (port 5001)\n```\n\n## Configuration\n\nEdit `config.py` or set environment variables in `.env`:\n\n```python\nLOOKBACK_DAYS = 30                  # how far back to ingest on each run\nSCORE_NOTIFY_THRESHOLD = 9.0        # minimum score before notification gates\nSCORE_RESEARCH_THRESHOLD = 5.0      # minimum score to trigger LLM research\nMAX_NOTIFICATIONS_PER_RUN = 1       # cap push alerts per run\nNOTIFY_TICKER_COOLDOWN_DAYS = 14    # suppress repeated same-ticker alerts\nNOTIFY_MIN_CONVICTION = \"HIGH\"      # LOW / MEDIUM / HIGH\nNOTIFY_MIN_EDGE_FACTORS = 3         # independent evidence factors required\nLLM_PROVIDER = \"anthropic\"          # \"anthropic\" or \"google\"\nANTHROPIC_API_KEY = os.getenv(\"ANTHROPIC_API_KEY\", \"\")\nNTFY_TOPIC_SIGNALS = \"congressWatchSignals\"\n```\n\n## Cron / Scheduling\n\n**Mac (launchd)**:\n```bash\n# Run Mon–Fri at 6am — place in ~/Library/LaunchAgents/\n# See: https://www.launchd.info for plist format\n```\n\n**Linux / WSL2 (cron)**:\n```bash\ncrontab -e\n# Add:\n0 6 * * 1-5  /home/james/trading_ideas/.venv/bin/python /home/james/trading_ideas/run_daily.py\n\n# WSL2: ensure cron service is running\nsudo service cron start\n# To auto-start, add to /etc/wsl.conf:\n# [boot]\n# command = service cron start\n```\n\n## Data Notes\n\n- Dollar amounts are **ranges**, not exact (STOCK Act only requires range disclosure). `value_estimate` is the midpoint; `amount_tier` is the derived 1–6 tier.\n- Options trades are disclosed without strike price or expiration date.\n- `issuer_ticker` is blank for some positions (mutual funds, ETFs without a standard ticker, foreign-listed names).\n- Market cap is fetched once per daily run and cached; the small-cap scoring component is applied on the *next* scoring run (market cap does not change meaningfully day to day).\n- The first full pipeline run on a new machine produces `small_cap_score = 0` for all signals — it populates on the second run once prices/caps are fetched.","is_error":false}]},"uuid":"9cfcd4c6-62cf-4bfa-b297-0d22bdbc93b5","timestamp":"2026-07-07T01:38:35.727Z","sourceToolAssistantUUID":"599de12e-1707-4aea-ad93-8f8e761f1e09","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"9cfcd4c6-62cf-4bfa-b297-0d22bdbc93b5","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqUGtZ6Z7UETanXURY","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me fetch the database schema and the researcher.py file to understand the research and \"shadow mode\" concept."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3042,"cache_read_input_tokens":25103,"cache_creation":{"ephemeral_5m_input_tokens":3042,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqqTprFZKUq9oSRZMh3","attributionAgent":"Explore","type":"assistant","uuid":"9814a47f-e8c1-4409-bc77-01d78f9b7c32","timestamp":"2026-07-07T01:38:37.074Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"9814a47f-e8c1-4409-bc77-01d78f9b7c32","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqUGtZ6Z7UETanXURY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Vno1jwDZ2YWJBXjSj9mFZU","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/db/schema.sql\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3042,"cache_read_input_tokens":25103,"output_tokens":106,"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":3042},"inference_geo":"","iterations":[{"input_tokens":2,"output_tokens":106,"cache_read_input_tokens":25103,"cache_creation_input_tokens":3042,"cache_creation":{"ephemeral_5m_input_tokens":3042,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqqTprFZKUq9oSRZMh3","attributionAgent":"Explore","type":"assistant","uuid":"65f4d418-08a0-42af-9086-de863b88db65","timestamp":"2026-07-07T01:38:37.199Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"65f4d418-08a0-42af-9086-de863b88db65","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Vno1jwDZ2YWJBXjSj9mFZU","type":"tool_result","content":"CREATE TABLE IF NOT EXISTS members (\n    bioguide_id     TEXT PRIMARY KEY,\n    first_name      TEXT,\n    last_name       TEXT,\n    nickname        TEXT,\n    party           TEXT,\n    chamber         TEXT,\n    state_id        TEXT,\n    gender          TEXT,\n    dob             TEXT,\n    committees      TEXT,   -- JSON array, refreshed weekly\n    trade_count     INTEGER DEFAULT 0,\n    win_rate        REAL,   -- nullable until calculable\n    updated_at      TEXT\n);\n\nCREATE TABLE IF NOT EXISTS trades (\n    tx_id           INTEGER PRIMARY KEY,\n    member_id       TEXT NOT NULL,\n    issuer_id       INTEGER,\n    issuer_name     TEXT,\n    issuer_ticker   TEXT,   -- clean ticker, e.g. \"NVDA\"\n    issuer_sector   TEXT,\n    issuer_country  TEXT,\n    chamber         TEXT,\n    tx_date         TEXT,   -- YYYY-MM-DD, when trade executed\n    pub_date        TEXT,   -- when disclosure was published\n    reporting_gap   INTEGER, -- days between tx_date and pub_date\n    tx_type         TEXT,   -- buy / sell / exchange\n    tx_type_extended TEXT,\n    owner           TEXT,   -- self / spouse / not-disclosed / etc\n    price_at_trade  REAL,\n    value_estimate  INTEGER, -- midpoint dollar estimate from disclosure\n    amount_tier     INTEGER, -- 1-6 derived from value_estimate\n    is_option       INTEGER DEFAULT 0,  -- 0/1\n    comment         TEXT,\n    source          TEXT DEFAULT 'capitol_trades',\n    created_at      TEXT DEFAULT (datetime('now'))\n);\n\nCREATE TABLE IF NOT EXISTS signals (\n    id                  INTEGER PRIMARY KEY AUTOINCREMENT,\n    ticker              TEXT NOT NULL UNIQUE,\n    first_seen          TEXT,\n    last_seen           TEXT,\n    first_tx_date       TEXT,   -- earliest trade exec date in current scoring window\n    member_count        INTEGER DEFAULT 1,\n    committee_score     REAL DEFAULT 0,\n    track_record_score  REAL DEFAULT 1,\n    cluster_score       REAL DEFAULT 0,\n    options_bonus       REAL DEFAULT 0,\n    size_score          REAL DEFAULT 0,\n    total_score         REAL DEFAULT 0,\n    research_summary    TEXT,\n    timeliness          TEXT,   -- fresh / watch / late\n    price_at_tx         REAL,   -- close on first_tx_date (member's entry)\n    price_at_pub        REAL,   -- close on first_seen (our actionable entry)\n    price_current       REAL,   -- yesterday's close\n    return_from_tx_pct  REAL,   -- % gain/loss since member's trade\n    return_from_pub_pct REAL,   -- % gain/loss since first disclosure\n    price_fetched_at    TEXT,\n    market_cap          REAL,   -- cached from yfinance\n    small_cap_score     REAL DEFAULT 0,  -- score component: bonus for smaller caps\n    sell_pressure_score REAL DEFAULT 0,  -- score component: penalty when sellers ≥ buyers\n    notified_at         TEXT,\n    notified            INTEGER DEFAULT 0,\n    updated_at          TEXT DEFAULT (datetime('now'))\n);\n\nCREATE TABLE IF NOT EXISTS research_cache (\n    ticker              TEXT PRIMARY KEY,\n    cached_at           TEXT,\n    deep_researched_at  TEXT,   -- set when deep prompt (risk_factors + recommended_action) was run\n    company_summary     TEXT,\n    recent_news         TEXT,\n    catalyst_hypothesis TEXT,\n    timeliness_assessment TEXT,\n    conviction          TEXT,   -- LOW / MEDIUM / HIGH\n    conviction_rationale TEXT,\n    risk_factors        TEXT,\n    recommended_action  TEXT,\n    raw_response        TEXT\n);\n\nCREATE TABLE IF NOT EXISTS signal_outcomes (\n    id              INTEGER PRIMARY KEY AUTOINCREMENT,\n    ticker          TEXT NOT NULL,\n    notified_at     TEXT NOT NULL,\n    score_at_notify REAL,\n    conviction      TEXT,\n    recommended_action TEXT,\n    price_at_notify REAL,\n    price_30d       REAL,\n    price_60d       REAL,\n    return_30d_pct  REAL,\n    return_60d_pct  REAL,\n    outcome         TEXT,   -- \"win\" / \"loss\" / \"pending\"\n    checked_at      TEXT,\n    created_at      TEXT DEFAULT (datetime('now'))\n);\n\nCREATE TABLE IF NOT EXISTS predicted_signals (\n    id                      INTEGER PRIMARY KEY AUTOINCREMENT,\n    member_id               TEXT NOT NULL,\n    member_name             TEXT,\n    ticker                  TEXT NOT NULL,\n    company_name            TEXT,\n    sector                  TEXT,\n    predicted_action        TEXT,   -- buy / sell\n    confidence              TEXT,   -- LOW / MEDIUM / HIGH\n    legislation_catalyst    TEXT,\n    historical_precedent    TEXT,\n    rationale               TEXT,\n    behavioral_profile      TEXT,   -- member's trading style summary (one per member)\n    generated_at            TEXT,\n    confirmed               INTEGER DEFAULT 0,  -- 1 when a matching real disclosure appears\n    confirmed_at            TEXT,\n    UNIQUE(member_id, ticker)\n);\n\nCREATE INDEX IF NOT EXISTS idx_trades_member ON trades(member_id);\nCREATE INDEX IF NOT EXISTS idx_trades_ticker ON trades(issuer_ticker);\nCREATE INDEX IF NOT EXISTS idx_trades_pub_date ON trades(pub_date);\nCREATE INDEX IF NOT EXISTS idx_trades_tx_date ON trades(tx_date);\nCREATE INDEX IF NOT EXISTS idx_outcomes_ticker ON signal_outcomes(ticker);\nCREATE INDEX IF NOT EXISTS idx_outcomes_notified_at ON signal_outcomes(notified_at);\nCREATE INDEX IF NOT EXISTS idx_predicted_member ON predicted_signals(member_id);\nCREATE INDEX IF NOT EXISTS idx_predicted_ticker ON predicted_signals(ticker);\n\nCREATE TABLE IF NOT EXISTS ticker_metadata (\n    ticker          TEXT PRIMARY KEY,\n    market_cap      REAL,\n    last_price      REAL,\n    is_etf          INTEGER DEFAULT 0,   -- 1 if yfinance quoteType is ETF/MUTUALFUND\n    is_valid        INTEGER DEFAULT 1,   -- 0 if yfinance returns no data (invalid/delisted)\n    pe_ratio        REAL,                -- trailing P/E\n    analyst_rating  TEXT,                -- strong buy / buy / hold / sell / strong sell\n    price_vs_50d_pct REAL,              -- % current price above/below 50-day MA\n    fetched_at      TEXT\n);\n\nCREATE INDEX IF NOT EXISTS idx_ticker_meta ON ticker_metadata(ticker);","is_error":false}]},"uuid":"0bef1e97-5379-404f-b5cb-c3a6c161c0b6","timestamp":"2026-07-07T01:38:37.362Z","sourceToolAssistantUUID":"65f4d418-08a0-42af-9086-de863b88db65","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"0bef1e97-5379-404f-b5cb-c3a6c161c0b6","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqbF5vqAjezVXPqJ7X","type":"message","role":"assistant","content":[{"type":"text","text":"Excellent! Now let me get the researcher.py to understand what \"deep research\" does."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1742,"cache_read_input_tokens":28145,"cache_creation":{"ephemeral_5m_input_tokens":1742,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqqaoYgRHFmgE6n4Zqw","attributionAgent":"Explore","type":"assistant","uuid":"a427fe7c-596b-4932-99d5-015a1816fd2d","timestamp":"2026-07-07T01:38:38.453Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"a427fe7c-596b-4932-99d5-015a1816fd2d","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqbF5vqAjezVXPqJ7X","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Gw4hL2ewmDmPtz88XcH9Nm","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/analysis/researcher.py\" | head -600"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1742,"cache_read_input_tokens":28145,"output_tokens":106,"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":1742},"inference_geo":"","iterations":[{"input_tokens":2,"output_tokens":106,"cache_read_input_tokens":28145,"cache_creation_input_tokens":1742,"cache_creation":{"ephemeral_5m_input_tokens":1742,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqqaoYgRHFmgE6n4Zqw","attributionAgent":"Explore","type":"assistant","uuid":"45865ddb-c8af-4550-9bcc-19b9d9b86df5","timestamp":"2026-07-07T01:38:38.805Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"45865ddb-c8af-4550-9bcc-19b9d9b86df5","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Gw4hL2ewmDmPtz88XcH9Nm","type":"tool_result","content":"\"\"\"\nLLM research pipeline.\n\nFor each signal above SCORE_RESEARCH_THRESHOLD:\n  1. Gather context from DB (company, buyers, committees, trade dates)\n  2. Build a structured research prompt\n  3. Call LLM and parse the response\n  4. Store result in research_cache; update signal timeliness\n  5. Log token usage to reports/token_usage.log\n\nUsage:\n  python analysis/researcher.py              # research all eligible signals\n  python analysis/researcher.py --dry-run    # print prompts, no API call\n  python analysis/researcher.py --ticker MSFT\n\"\"\"\n\nfrom __future__ import annotations\nimport sqlite3\nimport sys\nimport os\nimport json\nimport argparse\nfrom datetime import datetime, timedelta\n\nsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\nfrom config import (DB_PATH, SCORE_RESEARCH_THRESHOLD, SCORE_NOTIFY_THRESHOLD,\n                    LOOKBACK_DAYS, DEEP_RESEARCH_TOP_N, DEEP_RESEARCH_DECAY_DAYS)\nfrom analysis.timeliness import assess_age, age_days\n\nCACHE_TTL_HOURS = 24\nTOKEN_LOG = os.path.join(os.path.dirname(os.path.dirname(__file__)), \"reports\", \"token_usage.log\")\n\nSYSTEM_PROMPT = (\n    \"You are a concise financial research analyst specializing in US congressional \"\n    \"trading activity. Provide factual, evidence-based analysis. Be specific about \"\n    \"dates, dollar amounts, and named legislation or contracts where relevant. \"\n    \"Keep each section brief — 2-4 sentences maximum. Do not speculate beyond \"\n    \"publicly available information.\"\n)\n\nRESEARCH_PROMPT_TEMPLATE = \"\"\"\\\nA member (or members) of the US Congress recently disclosed a stock purchase in \\\n{ticker} ({company_name}, sector: {sector}).\n\nTRADE DETAILS:\n{buyer_lines}\n\nMost recent disclosure: {last_seen} (trade executed: {last_tx_date})\nSignal age: {age_days} days since last disclosure\nHighest dollar tier: {tier_label}\nOptions activity: {options_flag}\n\nPlease research this and respond in the following JSON format exactly:\n\n{{\n  \"company_summary\": \"2-3 sentence description of what this company does and its market position.\",\n  \"recent_news\": \"Key news from the last 90 days relevant to this company — earnings, contracts, regulatory actions, M&A.\",\n  \"catalyst_hypothesis\": \"Why might a congress member with these committee assignments have bought this now? Reference specific legislation, contracts, or policy areas if applicable.\",\n  \"timeliness\": \"fresh|watch|late — is the thesis still intact given the signal is {age_days} days old? Explain in one sentence.\",\n  \"conviction\": \"LOW|MEDIUM|HIGH\",\n  \"conviction_rationale\": \"One sentence explaining the conviction level.\",\n  \"strategic_score\": <integer 0-3>,\n  \"strategic_rationale\": \"One sentence. Score guide — 0: no national-security/infrastructure nexus; 1: indirect sector relevance; 2: defense/energy/critical-infrastructure contractor with known government exposure; 3: direct military, dual-use, or critical-infrastructure application that Congress would have privileged visibility into.\",\n  \"disruption_score\": <integer 0-3>,\n  \"disruption_rationale\": \"One sentence. Score guide — 0: incremental improvement to existing market; 1: meaningful differentiation; 2: significant market-shift potential; 3: category-creating or paradigm-shifting technology/business model.\"\n}}\n\nRespond with valid JSON only, no markdown fencing.\"\"\"\n\nDEEP_RESEARCH_PROMPT_TEMPLATE = \"\"\"\\\nA member (or members) of the US Congress recently disclosed a stock purchase in \\\n{ticker} ({company_name}, sector: {sector}).\n\nTRADE DETAILS:\n{buyer_lines}\n\nMost recent disclosure: {last_seen} (trade executed: {last_tx_date})\nSignal age: {age_days} days since last disclosure\nHighest dollar tier: {tier_label}\nOptions activity: {options_flag}\n\nThis signal scored above the high-conviction notify threshold. Please provide \\\ndeeper analysis including explicit bear-case considerations. Respond in the \\\nfollowing JSON format exactly:\n\n{{\n  \"company_summary\": \"2-3 sentence description of what this company does and its market position.\",\n  \"recent_news\": \"Key news from the last 90 days relevant to this company — earnings, contracts, regulatory actions, M&A.\",\n  \"catalyst_hypothesis\": \"Why might a congress member with these committee assignments have bought this now? Reference specific legislation, contracts, or policy areas if applicable.\",\n  \"timeliness\": \"fresh|watch|late — is the thesis still intact given the signal is {age_days} days old? Explain in one sentence.\",\n  \"conviction\": \"LOW|MEDIUM|HIGH\",\n  \"conviction_rationale\": \"One sentence explaining the conviction level.\",\n  \"risk_factors\": \"What would invalidate this thesis? Name 2-3 specific risks: regulatory, earnings, sector headwinds, or that the position is already fully priced in.\",\n  \"recommended_action\": \"BUY NOW|MONITOR|AVOID — one sentence rationale for the explicit recommendation.\",\n  \"strategic_score\": <integer 0-3>,\n  \"strategic_rationale\": \"One sentence. Score guide — 0: no national-security/infrastructure nexus; 1: indirect sector relevance; 2: defense/energy/critical-infrastructure contractor with known government exposure; 3: direct military, dual-use, or critical-infrastructure application that Congress would have privileged visibility into.\",\n  \"disruption_score\": <integer 0-3>,\n  \"disruption_rationale\": \"One sentence. Score guide — 0: incremental improvement to existing market; 1: meaningful differentiation; 2: significant market-shift potential; 3: category-creating or paradigm-shifting technology/business model.\"\n}}\n\nRespond with valid JSON only, no markdown fencing.\"\"\"\n\n\n_TIER_LABELS = {\n    0: \"unknown\",\n    1: \"$1K–$15K\",\n    2: \"$15K–$50K\",\n    3: \"$50K–$100K\",\n    4: \"$100K–$250K\",\n    5: \"$250K–$500K\",\n    6: \"$500K+\",\n}\n\n\ndef _build_prompt(sig: dict, buyers: list[dict], deep: bool = False) -> str:\n    buyer_lines = []\n    for b in buyers:\n        committees = \"\"\n        if b.get(\"committees\"):\n            try:\n                clist = json.loads(b[\"committees\"])\n                if clist:\n                    committees = f\", committees: {', '.join(str(c) for c in clist[:3])}\"\n            except Exception:\n                pass\n        buyer_lines.append(\n            f\"  - {b['member_name']} ({b['party'][:1].upper()}, {b['chamber']})\"\n            f\", tier {b['amount_tier']}{committees}\"\n        )\n\n    template = DEEP_RESEARCH_PROMPT_TEMPLATE if deep else RESEARCH_PROMPT_TEMPLATE\n    return template.format(\n        ticker=sig[\"ticker\"],\n        company_name=sig[\"issuer_name\"] or sig[\"ticker\"],\n        sector=sig[\"sector\"] or \"unknown\",\n        buyer_lines=\"\\n\".join(buyer_lines) or \"  (no buyer details)\",\n        last_seen=sig[\"last_seen\"],\n        last_tx_date=sig.get(\"last_tx_date\", \"unknown\"),\n        age_days=age_days(sig[\"last_seen\"]),\n        tier_label=_TIER_LABELS.get(sig.get(\"max_tier\", 0), \"unknown\"),\n        options_flag=\"YES\" if sig.get(\"has_options\") else \"no\",\n    )\n\n\ndef _is_cache_valid(cached_at: str) -> bool:\n    if not cached_at:\n        return False\n    try:\n        dt = datetime.fromisoformat(cached_at)\n        return (datetime.now() - dt).total_seconds() < CACHE_TTL_HOURS * 3600\n    except ValueError:\n        return False\n\n\ndef _is_deep_cache_valid(deep_researched_at: str, decay_days: int) -> bool:\n    \"\"\"Return True if deep research was done within decay_days.\"\"\"\n    if not deep_researched_at:\n        return False\n    try:\n        dt = datetime.fromisoformat(deep_researched_at)\n        return (datetime.now() - dt).days < decay_days\n    except ValueError:\n        return False\n\n\ndef _log_tokens(ticker: str, usage: dict):\n    os.makedirs(os.path.dirname(TOKEN_LOG), exist_ok=True)\n    with open(TOKEN_LOG, \"a\") as f:\n        total_cost_est = (\n            usage.get(\"input_tokens\", 0) * 3e-6\n            + usage.get(\"output_tokens\", 0) * 15e-6\n            - usage.get(\"cache_read_tokens\", 0) * 2.7e-6\n        )\n        f.write(\n            f\"{datetime.now().isoformat(timespec='seconds')} | {ticker:<8} | \"\n            f\"in={usage.get('input_tokens',0):>5} out={usage.get('output_tokens',0):>4} \"\n            f\"cache_hit={usage.get('cache_read_tokens',0):>5} \"\n            f\"est_cost=${total_cost_est:.4f}\\n\"\n        )\n\n\ndef _parse_response(text: str) -> dict:\n    \"\"\"Extract JSON from LLM response, tolerating minor formatting noise.\"\"\"\n    text = text.strip()\n    # Strip markdown code fences if present\n    if text.startswith(\"```\"):\n        lines = text.splitlines()\n        text = \"\\n\".join(lines[1:-1] if lines[-1].strip() == \"```\" else lines[1:])\n    try:\n        return json.loads(text)\n    except json.JSONDecodeError:\n        # Try to find the JSON object within the text\n        start = text.find(\"{\")\n        end = text.rfind(\"}\") + 1\n        if start >= 0 and end > start:\n            return json.loads(text[start:end])\n        raise\n\n\ndef research_signal(sig: dict, buyers: list[dict], dry_run: bool = False,\n                    force_deep: bool = False) -> dict | None:\n    \"\"\"\n    Research one signal. Returns parsed result dict or None on failure.\n    If dry_run=True, prints the prompt and returns None without calling the LLM.\n    If force_deep=True, always uses the deep prompt and skips the regular cache.\n    \"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n\n    deep = force_deep or (sig.get(\"total_score\", 0) >= SCORE_NOTIFY_THRESHOLD)\n\n    if not force_deep:\n        # Regular 24-hour cache check\n        cached = conn.execute(\n            \"SELECT * FROM research_cache WHERE ticker = ?\", (sig[\"ticker\"],)\n        ).fetchone()\n        cache_valid = cached and _is_cache_valid(cached[\"cached_at\"])\n        if cache_valid and deep and not cached[\"risk_factors\"]:\n            cache_valid = False  # force re-research with deep prompt\n        if cache_valid:\n            print(f\"  {sig['ticker']}: using cached result ({cached['cached_at'][:16]})\")\n            conn.close()\n            return dict(cached)\n\n    prompt = _build_prompt(sig, buyers, deep=deep)\n\n    if dry_run:\n        print(f\"\\n{'='*60}\")\n        print(f\"DRY RUN — would research: {sig['ticker']} (score={sig['total_score']})\")\n        print(f\"{'='*60}\")\n        print(prompt)\n        conn.close()\n        return None\n\n    # Call LLM\n    from analysis.llm_client import call, LLMError\n    try:\n        print(f\"  {sig['ticker']}: calling LLM...\", end=\" \", flush=True)\n        text, usage = call(prompt, system=SYSTEM_PROMPT)\n        _log_tokens(sig[\"ticker\"], usage)\n        print(f\"done (in={usage['input_tokens']} out={usage['output_tokens']})\")\n    except LLMError as e:\n        print(f\"  {sig['ticker']}: LLM error — {e}\")\n        conn.close()\n        return None\n    except Exception as e:\n        # Catch quota / network errors from underlying SDKs\n        msg = str(e)\n        if \"429\" in msg or \"RESOURCE_EXHAUSTED\" in msg or \"quota\" in msg.lower():\n            print(f\"\\n  {sig['ticker']}: API quota exceeded.\")\n            print(\"  To fix: enable billing at console.cloud.google.com (Google)\")\n            print(\"          or set ANTHROPIC_API_KEY and change LLM_PROVIDER='anthropic' in config.py\")\n        else:\n            print(f\"  {sig['ticker']}: unexpected error — {e}\")\n        conn.close()\n        return None\n\n    # Parse response\n    try:\n        result = _parse_response(text)\n    except Exception as e:\n        print(f\"  {sig['ticker']}: parse error — {e}\")\n        print(f\"  Raw response: {text[:200]}\")\n        conn.close()\n        return None\n\n    now = datetime.now().isoformat(timespec=\"seconds\")\n\n    strategic_score  = int(result.get(\"strategic_score\")  or 0)\n    disruption_score = int(result.get(\"disruption_score\") or 0)\n\n    # Upsert into research_cache\n    conn.execute(\n        \"\"\"\n        INSERT INTO research_cache (ticker, cached_at, company_summary, recent_news,\n            catalyst_hypothesis, timeliness_assessment, conviction, conviction_rationale,\n            risk_factors, recommended_action,\n            strategic_score, strategic_rationale, disruption_score, disruption_rationale,\n            raw_response)\n        VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\n        ON CONFLICT(ticker) DO UPDATE SET\n            cached_at             = excluded.cached_at,\n            company_summary       = excluded.company_summary,\n            recent_news           = excluded.recent_news,\n            catalyst_hypothesis   = excluded.catalyst_hypothesis,\n            timeliness_assessment = excluded.timeliness_assessment,\n            conviction            = excluded.conviction,\n            conviction_rationale  = excluded.conviction_rationale,\n            risk_factors          = excluded.risk_factors,\n            recommended_action    = excluded.recommended_action,\n            strategic_score       = excluded.strategic_score,\n            strategic_rationale   = excluded.strategic_rationale,\n            disruption_score      = excluded.disruption_score,\n            disruption_rationale  = excluded.disruption_rationale,\n            raw_response          = excluded.raw_response\n        \"\"\",\n        (\n            sig[\"ticker\"],\n            now,\n            result.get(\"company_summary\", \"\"),\n            result.get(\"recent_news\", \"\"),\n            result.get(\"catalyst_hypothesis\", \"\"),\n            result.get(\"timeliness\", \"\") + \" — \" + result.get(\"conviction_rationale\", \"\"),\n            result.get(\"conviction\", \"\"),\n            result.get(\"conviction_rationale\", \"\"),\n            result.get(\"risk_factors\") or None,\n            result.get(\"recommended_action\") or None,\n            strategic_score,\n            result.get(\"strategic_rationale\") or None,\n            disruption_score,\n            result.get(\"disruption_rationale\") or None,\n            text,\n        ),\n    )\n\n    # Write scores back to signals table so scorer can pick them up next run\n    conn.execute(\n        \"\"\"\n        UPDATE signals\n        SET strategic_score  = ?,\n            disruption_score = ?,\n            total_score      = total_score\n                               - COALESCE(strategic_score, 0)\n                               - COALESCE(disruption_score, 0)\n                               + ? + ?\n        WHERE ticker = ?\n        \"\"\",\n        (strategic_score, disruption_score,\n         strategic_score, disruption_score,\n         sig[\"ticker\"]),\n    )\n\n    # Stamp deep_researched_at when the deep prompt was used\n    if deep:\n        conn.execute(\n            \"UPDATE research_cache SET deep_researched_at = ? WHERE ticker = ?\",\n            (now, sig[\"ticker\"]),\n        )\n\n    # Update signal timeliness field\n    timeliness = result.get(\"timeliness\", assess_age(sig[\"last_seen\"]))\n    if \"|\" in timeliness:\n        timeliness = timeliness.split(\"|\")[0].strip()\n    conn.execute(\n        \"UPDATE signals SET timeliness = ?, research_summary = ?, updated_at = ? WHERE ticker = ?\",\n        (\n            timeliness,\n            result.get(\"company_summary\", \"\")[:500],\n            now,\n            sig[\"ticker\"],\n        ),\n    )\n    conn.commit()\n    conn.close()\n    return result\n\n\ndef research_top_n_deep(signals: list[dict], top_n: int = DEEP_RESEARCH_TOP_N,\n                        decay_days: int = DEEP_RESEARCH_DECAY_DAYS,\n                        dry_run: bool = False) -> list[tuple]:\n    \"\"\"\n    Deep-research up to top_n signals that are above the notify threshold and\n    whose deep research is absent or older than decay_days.\n\n    Prioritisation order (within the eligible set):\n      1. Never deep-researched (deep_researched_at IS NULL)\n      2. Stalest deep research first (oldest deep_researched_at)\n      Both groups are then sorted by total_score descending as a tiebreaker.\n\n    This ensures newly-high-scoring tickers are not starved behind a wall of\n    recently-researched high scorers — they jump to the front of the queue.\n\n    Returns list of (sig, result) pairs for every signal actually researched.\n    \"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cutoff = (datetime.now() - timedelta(days=LOOKBACK_DAYS)).strftime(\"%Y-%m-%d\")\n\n    # Deep research is only meaningful for signals above the notification threshold.\n    # Filter defensively here even if the caller already pre-filtered.\n    signals = [s for s in signals if s.get(\"total_score\", 0) >= SCORE_NOTIFY_THRESHOLD]\n\n    # Fetch deep_researched_at for all candidate tickers in one query\n    tickers = [s[\"ticker\"] for s in signals]\n    if not tickers:\n        conn.close()\n        return []\n\n    placeholders = \",\".join(\"?\" * len(tickers))\n    cache_rows = conn.execute(\n        f\"SELECT ticker, deep_researched_at FROM research_cache WHERE ticker IN ({placeholders})\",\n        tickers,\n    ).fetchall()\n    last_deep: dict[str, str | None] = {r[\"ticker\"]: r[\"deep_researched_at\"] for r in cache_rows}\n\n    # Split into eligible (stale/never) and skipped (fresh)\n    never: list[dict] = []\n    stale: list[dict] = []\n    skipped: list[str] = []\n\n    for sig in signals:\n        ticker = sig[\"ticker\"]\n        dr_at = last_deep.get(ticker)\n        if dr_at and _is_deep_cache_valid(dr_at, decay_days):\n            skipped.append(f\"{ticker} ({dr_at[:10]})\")\n        elif dr_at is None:\n            never.append(sig)\n        else:\n            stale.append(sig)\n\n    # Sort each group by score descending, then combine: never → stale\n    never.sort(key=lambda s: s[\"total_score\"], reverse=True)\n    stale.sort(key=lambda s: s[\"total_score\"], reverse=True)\n    candidates = never + stale\n\n    if skipped:\n        print(f\"  Skipped (fresh within {decay_days}d): {', '.join(skipped)}\")\n    if never:\n        print(f\"  Never deep-researched: {', '.join(s['ticker'] for s in never)}\")\n\n    researched: list[tuple] = []\n    for sig in candidates:\n        if len(researched) >= top_n:\n            break\n\n        ticker = sig[\"ticker\"]\n        buyers = conn.execute(\n            \"\"\"\n            SELECT DISTINCT t.member_id,\n                m.first_name || ' ' || m.last_name AS member_name,\n                m.party, m.chamber, m.committees,\n                MAX(t.amount_tier) AS amount_tier\n            FROM trades t JOIN members m ON m.bioguide_id = t.member_id\n            WHERE t.issuer_ticker = ? AND t.tx_type = 'buy' AND t.pub_date >= ?\n            GROUP BY t.member_id\n            \"\"\",\n            (ticker, cutoff),\n        ).fetchall()\n\n        sig[\"last_tx_date\"] = sig.get(\"last_tx_date\", \"\")\n        sig[\"max_tier\"] = sig.get(\"max_tier\", 0)\n\n        result = research_signal(sig, [dict(b) for b in buyers], dry_run=dry_run, force_deep=True)\n        if result:\n            researched.append((sig, result))\n\n    conn.close()\n    return researched\n\n\ndef research_all(min_score: float = SCORE_RESEARCH_THRESHOLD, dry_run: bool = False,\n                 ticker_filter: str = None) -> list[dict]:\n    \"\"\"Research all signals above min_score. Returns list of (sig, result) pairs.\"\"\"\n    from analysis.scorer import score_tickers\n\n    print(\"Re-scoring signals...\")\n    signals = score_tickers()\n\n    # Apply filters\n    eligible = [s for s in signals if s[\"total_score\"] >= min_score]\n    if ticker_filter:\n        eligible = [s for s in eligible if s[\"ticker\"].upper() == ticker_filter.upper()]\n\n    print(f\"Researching {len(eligible)} signals (score ≥ {min_score})...\\n\")\n\n    results = []\n    for sig in eligible:\n        # Fetch full buyer details from DB\n        conn = sqlite3.connect(DB_PATH)\n        conn.row_factory = sqlite3.Row\n        cutoff = (datetime.now() - timedelta(days=LOOKBACK_DAYS)).strftime(\"%Y-%m-%d\")\n        buyers = conn.execute(\n            \"\"\"\n            SELECT DISTINCT\n                t.member_id,\n                m.first_name || ' ' || m.last_name AS member_name,\n                m.party, m.chamber, m.committees,\n                MAX(t.amount_tier) AS amount_tier\n            FROM trades t\n            JOIN members m ON m.bioguide_id = t.member_id\n            WHERE t.issuer_ticker = ?\n              AND t.tx_type = 'buy'\n              AND t.pub_date >= ?\n            GROUP BY t.member_id\n            \"\"\",\n            (sig[\"ticker\"], cutoff),\n        ).fetchall()\n        conn.close()\n\n        buyers_list = [dict(b) for b in buyers]\n        # Attach for use in _build_prompt\n        sig[\"last_tx_date\"] = sig.get(\"last_tx_date\", \"\")\n        sig[\"max_tier\"] = sig.get(\"max_tier\", 0)\n\n        result = research_signal(sig, buyers_list, dry_run=dry_run)\n        if result:\n            results.append((sig, result))\n\n    return results\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Research top congressional trade signals via LLM\")\n    parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"print prompts without calling LLM\")\n    parser.add_argument(\"--ticker\", help=\"research a specific ticker only\")\n    parser.add_argument(\"--min-score\", type=float, default=SCORE_RESEARCH_THRESHOLD)\n    args = parser.parse_args()\n\n    results = research_all(\n        min_score=args.min_score,\n        dry_run=args.dry_run,\n        ticker_filter=args.ticker,\n    )\n\n    if not args.dry_run and results:\n        print(f\"\\n{'='*60}\")\n        print(f\"Research complete: {len(results)} signals researched\")\n        print(f\"{'='*60}\")\n        for sig, res in results:\n            print(f\"\\n{sig['ticker']} (score={sig['total_score']}) — {res.get('conviction','?')} conviction\")\n            print(f\"  Company:   {res.get('company_summary','')[:120]}\")\n            print(f\"  News:      {res.get('recent_news','')[:120]}\")\n            print(f\"  Catalyst:  {res.get('catalyst_hypothesis','')[:120]}\")\n            print(f\"  Timeliness:{res.get('timeliness','')}\")","is_error":false}]},"uuid":"9af7ea07-acc6-4106-8196-ef37cd978210","timestamp":"2026-07-07T01:38:38.977Z","sourceToolAssistantUUID":"45865ddb-c8af-4550-9bcc-19b9d9b86df5","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"9af7ea07-acc6-4106-8196-ef37cd978210","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqiSvS7fvgGhuJhQv8","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check the backlog and CLAUDE.md to see if there are any open design decisions."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6319,"cache_read_input_tokens":29887,"cache_creation":{"ephemeral_5m_input_tokens":6319,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqqhfYi35QzdeUNnBDG","attributionAgent":"Explore","type":"assistant","uuid":"1a16d513-d1ff-449b-95e9-4a0222d0890e","timestamp":"2026-07-07T01:38:40.636Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"1a16d513-d1ff-449b-95e9-4a0222d0890e","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqiSvS7fvgGhuJhQv8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GURFZoqgnGvss8bDPaVNq8","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/backlog.md\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6319,"cache_read_input_tokens":29887,"output_tokens":103,"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":6319},"inference_geo":"","iterations":[{"input_tokens":2,"output_tokens":103,"cache_read_input_tokens":29887,"cache_creation_input_tokens":6319,"cache_creation":{"ephemeral_5m_input_tokens":6319,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqqhfYi35QzdeUNnBDG","attributionAgent":"Explore","type":"assistant","uuid":"b39bc902-c4a8-4f2e-b016-b49931a74459","timestamp":"2026-07-07T01:38:40.836Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"b39bc902-c4a8-4f2e-b016-b49931a74459","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GURFZoqgnGvss8bDPaVNq8","type":"tool_result","content":"# CongressWatch — Backlog\n\nDeferred concerns and ideas we've identified but don't have a clean solution for yet,\nor that are lower priority than current work. Review periodically.\n\n---\n\n## Scoring\n\n### Committee-sector mapping is too coarse\n**Filed:** 2026-04-22\n\nThe `_COMMITTEE_SECTOR_MAP` in `scorer.py` is hand-coded and maps committee names to broad\nsector strings. This creates two problems:\n\n1. **Over-broad matches**: \"Armed Services\" maps to both `industrials` and\n   `information-technology`. This means nearly any tech stock bought by an Armed Services\n   member scores 3.0 for committee relevance, even if there's no plausible defense nexus.\n   A member on Armed Services buying NVDA (plausible — AI chips, JWCC contract) and the same\n   member buying a generic payroll SaaS company both get committee_score = 3.\n\n2. **No company-level specificity**: The mapping is ticker-blind. A score of 3 means\n   \"the member's committee has broad jurisdiction over this sector\" — it can't distinguish\n   between a member buying the single company their committee directly regulates vs. any\n   random name in that sector.\n\n**What a good solution looks like**: Per-trade committee relevance assessment, likely via LLM.\nAt research time, ask the model to score (0–3) how specifically this member's committee\nassignments relate to *this company's* primary business. Store as `committee_score_llm`\nalongside the current heuristic score. Use the LLM score when available, fall back to\nheuristic otherwise.\n\n**Why deferred**: Adds an LLM call per signal at scoring time (before the research step),\nincreasing latency and cost. Also requires careful prompt design to avoid the model being\ntoo generous. Better to accumulate more signals first and validate that the heuristic\nmisfires often enough to justify the extra call.\n\n---\n\n## Data Quality\n\n### Win rate is measured against current market prices, not exit prices\n**Filed:** 2026-04-22\n\n`tools/win_rate.py` computes win rate as \"fraction of positions that are currently\nprofitable.\" This is a snapshot, not a realized return:\n\n- A position could be up today but will eventually be sold at a loss.\n- A position that was down for months and recently recovered looks like a win.\n- Bull market conditions inflate all win rates (most of the current data is from 2025–2026).\n\n**Better approach**: For each member's trade, fetch price at `tx_date + 30d`, `tx_date + 60d`,\nand `tx_date + 90d` as fixed-horizon returns. Win = positive return at some fixed horizon\n(e.g., 60 days). This requires yfinance calls per trade (expensive for 14k trades) but could\nbe done as a one-time backfill and then maintained incrementally.\n\n**Why deferred**: Requires significant yfinance API calls (one per trade × 3 horizons = ~40k\nrequests). Rate limiting and batching need design. Also the current win rate is still useful\nas a directional signal; the flaw mainly affects members with small trade counts.\n\n### Signal return uses first buyer's tx_date, not each member's individual date\n**Filed:** 2026-04-22\n\n`return_from_tx_pct` in the signals table is computed from `first_tx_date` — the earliest\ntrade date among all buyers within the current scoring window. If member A bought in January\nand member B bought in March, the signal's return reflects January's price. Member B's actual\nreturn would be different.\n\n**Impact**: Moderate. For cluster signals where multiple members bought at similar times, the\nerror is small. For long-running signals with buyers spread across weeks, it can be\nmisleading (especially for volatile stocks).\n\n**Fix**: Store per-member returns in the trades table directly. Requires a `price_at_tx` column\non the trades table and yfinance lookups per trade. Could be done lazily (only for trades that\nbecome part of a high-score signal).\n\n---\n\n## Features\n\n### Sell signals\n**Filed:** 2026-04-22\n\nIf a member who previously bought a ticker in our window later discloses a sell, that's\npotentially an exit signal. Currently `sell_count` is tracked in the scorer but not acted on.\n\n**Implementation sketch**: In the notify step, check if `sell_count >= member_count` for any\ncurrently held signal. If so, fire a separate ntfy notification on the `congressWatchSignals`\ntopic with a different title prefix (e.g., \"EXIT SIGNAL: $TICKER\").\n\n### Options call vs put distinction\n**Filed:** 2026-04-22\n\nThe `options_bonus` (+2) applies to any options trade regardless of direction. A congressional\nput option is a very different signal from a call — it implies a member expects the stock to\nfall. Capitol Trades sometimes includes the direction in `tx_type_extended`. We should parse\nthis and differentiate: calls get +2, puts might warrant a separate alert rather than boosting\nthe buy signal score.\n\n### Sector clustering across members\n**Filed:** 2026-04-22 (from original project_plan.md)\n\nIf 5+ members across different parties buy the same *sector* in the same week — regardless of\nindividual ticker — that may signal a macro view worth flagging. Not captured by current\nper-ticker signal scoring.","is_error":false}]},"uuid":"3f3cc351-9fe3-44ca-8fdd-13d9adf0097f","timestamp":"2026-07-07T01:38:40.977Z","sourceToolAssistantUUID":"b39bc902-c4a8-4f2e-b016-b49931a74459","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"3f3cc351-9fe3-44ca-8fdd-13d9adf0097f","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqqrdo4rfzs8CFf28gw","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check the CLAUDE.md to see the codebase documentation."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1380,"cache_read_input_tokens":36206,"cache_creation":{"ephemeral_5m_input_tokens":1380,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqqrEz3nFSTyTxyiUeb","attributionAgent":"Explore","type":"assistant","uuid":"f6e1c595-0b6f-4aec-9c6a-22b62460da93","timestamp":"2026-07-07T01:38:42.101Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"f6e1c595-0b6f-4aec-9c6a-22b62460da93","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqqrdo4rfzs8CFf28gw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Se7w7bdHxbdEgcR1czSbUD","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/CLAUDE.md\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1380,"cache_read_input_tokens":36206,"output_tokens":98,"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":1380},"inference_geo":"","iterations":[{"input_tokens":1,"output_tokens":98,"cache_read_input_tokens":36206,"cache_creation_input_tokens":1380,"cache_creation":{"ephemeral_5m_input_tokens":1380,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqqrEz3nFSTyTxyiUeb","attributionAgent":"Explore","type":"assistant","uuid":"7f24677c-8952-4220-9890-81dc4218dbff","timestamp":"2026-07-07T01:38:42.389Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"7f24677c-8952-4220-9890-81dc4218dbff","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Se7w7bdHxbdEgcR1czSbUD","type":"tool_result","content":"# CLAUDE.md\n\nThis file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.\n\n## Common Commands - look typing here\n\n```bash\n# Setup (Python 3.9+)\npython3 -m venv .venv && .venv/bin/pip install -r requirements.txt\ncp .env.example .env  # add ANTHROPIC_API_KEY and/or GOOGLE_API_KEY\n\n# First run on a new machine: load historical data, then initialize DB schema\n.venv/bin/python db/migrate.py\n.venv/bin/python tools/backfill.py\n\n# Daily pipeline\npython3 run_daily.py                # full run\npython3 run_daily.py --no-ingest    # re-score + research + notify (skip scraping)\npython3 run_daily.py --dry-run      # score + report only, no LLM, no notifications\npython3 run_daily.py --force        # re-notify signals already sent today\n\n# Individual steps\npython3 analysis/scorer.py          # re-score and regenerate report (fast, no network)\npython3 tools/price_fetcher.py      # refresh prices and market caps via yfinance\npython3 ingest/committees.py        # refresh member committee assignments\npython3 tools/generate_report.py    # regenerate markdown + HTML reports only\n\n# Tests\n.venv/bin/python -m unittest discover\n\n# Interactive viewer (Flask, port 5001)\npython3 viewer/app.py\n```\n\nThe focused test suite currently covers notification policy decisions.\n\n## Architecture\n\n### Data flow\n\n```\nCapitol Trades (HTML scrape)\n        ↓\n  ingest/capitol_trades.py  →  members + trades tables\n        ↓\n  ingest/committees.py      →  members.committees (JSON, weekly cache)\n        ↓\n  analysis/scorer.py        →  signals table (rebuilt each run)\n        ↓\n  tools/price_fetcher.py    →  signals.price_* + return_* columns\n        ↓\n  analysis/researcher.py    →  research_cache table (persistent across runs)\n        ↓\n  analysis/notification_policy.py → high-signal notification gate + cooldown\n        ↓\n  tools/generate_report.py  →  reports/signals_latest.md + index.html (project root)\n        ↓\n  notify/ntfy_client.py     →  push notification via ntfy.sh\n```\n\n### Database (`data/congress_trades.db`)\n\nFour tables. `members` and `trades` are append-only source data. `signals` is **rebuilt from scratch on every scoring run** — don't store anything there that needs to persist across runs. `research_cache` is keyed by ticker and survives across runs; the researcher checks `cached_at` and `deep_researched_at` before calling the LLM.\n\nWhen adding a new column to any table, add it to both `db/schema.sql` and as an `_add_column_if_missing` call in `db/migrate.py`. Never run a bare `ALTER TABLE` in application code.\n\n### Two-tier LLM research\n\n`analysis/researcher.py` does two rounds per qualifying signal:\n- **Basic research** (≥ `SCORE_RESEARCH_THRESHOLD`): company summary, recent news, catalyst hypothesis, timeliness assessment\n- **Deep research** (≥ `SCORE_NOTIFY_THRESHOLD`, top `DEEP_RESEARCH_TOP_N` only): adds `risk_factors` and `recommended_action`; skips if the same ticker was deep-researched within `DEEP_RESEARCH_DECAY_DAYS`\n\n`analysis/llm_client.py` is the provider abstraction — swap between Anthropic (`claude-sonnet-4-6`) and Google (`gemini-2.0-flash`) via `LLM_PROVIDER` in `config.py`.\n\n### Scoring\n\n`analysis/scorer.py` computes six score components (see README for the formula). Key subtlety: **timeliness multipliers are applied only at notification-decision time** in `run_daily.py`, not stored in the `signals` table — so the stored `total_score` always reflects the raw signal strength, making historical comparisons valid.\n\n`analysis/notification_policy.py` is the push-alert gate. It requires deep research, high conviction, explicit buy/add action, limited sell pressure, enough independent edge factors, and a rolling same-ticker cooldown backed by `signal_outcomes`.\n\n### Reports\n\n`tools/generate_report.py` produces two outputs every run:\n- `reports/signals_latest.md` + dated `reports/signals_YYYY-MM-DD.md`\n- `index.html` at the **project root** (financial-news-style dark HTML page served statically)\n\nThe Flask app at `viewer/app.py` (port 5001) is a live dynamic view of the same DB — it is separate from the generated HTML and runs independently.\n\n### Python 3.9 compatibility\n\nAll files that use `X | Y` union type syntax in annotations must have `from __future__ import annotations` as the first import. This is already present in all current files that need it; add it when creating new files that use union types.\n\n## Configuration\n\nAll tunable values are in `config.py` (overridable via `.env`):\n\n| Key | Default | Purpose |\n|---|---|---|\n| `SCORE_RESEARCH_THRESHOLD` | 5.0 | Minimum score to trigger LLM research |\n| `SCORE_NOTIFY_THRESHOLD` | 9.0 | Minimum score before notification quality gates |\n| `DEEP_RESEARCH_TOP_N` | 10 | How many signals get deep research per run |\n| `DEEP_RESEARCH_DECAY_DAYS` | 7 | Days before re-researching the same ticker |\n| `MAX_NOTIFICATIONS_PER_RUN` | 1 | Maximum push alerts per pipeline run |\n| `NOTIFY_TICKER_COOLDOWN_DAYS` | 14 | Same-ticker rolling notification cooldown |\n| `NOTIFY_MIN_CONVICTION` | `\"HIGH\"` | Minimum LLM conviction for push alerts |\n| `NOTIFY_MIN_EDGE_FACTORS` | 3 | Independent edge factors required before push |\n| `LOOKBACK_DAYS` | 30 | Trade history window for scoring |\n| `LLM_PROVIDER` | `\"anthropic\"` | `\"anthropic\"` or `\"google\"` |","is_error":false}]},"uuid":"ea267bd0-8472-4cb4-a461-09127b72e0f7","timestamp":"2026-07-07T01:38:42.559Z","sourceToolAssistantUUID":"7f24677c-8952-4220-9890-81dc4218dbff","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
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{"parentUuid":"daac3ea9-9099-47c6-9cbe-e3a32b411b8d","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqqyT4yCbUUCEupC9du","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011KBFzLEcr84jvnn2dCFDs1","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/analysis/predictor.py\" | head -800"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1666,"cache_read_input_tokens":37586,"output_tokens":116,"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":1666},"inference_geo":"","iterations":[{"input_tokens":1,"output_tokens":116,"cache_read_input_tokens":37586,"cache_creation_input_tokens":1666,"cache_creation":{"ephemeral_5m_input_tokens":1666,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ccmqqy1XQ2VKGEKM12HJ4","attributionAgent":"Explore","type":"assistant","uuid":"ac22f585-92f9-40ba-9517-b0bcb53f079b","timestamp":"2026-07-07T01:38:44.271Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"ac22f585-92f9-40ba-9517-b0bcb53f079b","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011KBFzLEcr84jvnn2dCFDs1","type":"tool_result","content":"\"\"\"\nPredictive signal generator.\n\nFor each high-activity member with committee data:\n  1. Pull their committee assignments and last 3 years of trade history\n  2. Fetch active legislation relevant to those committees (if API key available)\n  3. Ask LLM: \"What will this member likely trade in the next 45 days?\"\n  4. Store results in predicted_signals table\n\nThis is a separate pipeline from the reactive scorer — it runs per-member\nand produces forward-looking watchlist entries that can appear in the report\nBEFORE the member discloses.  When a predicted ticker later appears in a real\ndisclosure, predicted_signals.confirmed is set to 1 so we can track accuracy.\n\nUsage:\n  python analysis/predictor.py                # run for top members\n  python analysis/predictor.py --dry-run      # print prompts, no API call\n  python analysis/predictor.py --member \"Nancy Pelosi\"\n\"\"\"\n\nfrom __future__ import annotations\nimport sqlite3\nimport sys\nimport os\nimport json\nimport argparse\nfrom datetime import datetime, timedelta\n\nsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\nfrom config import (\n    DB_PATH, PREDICTOR_MAX_MEMBERS, PREDICTOR_DECAY_DAYS,\n    PREDICTOR_MIN_TRADES, PREDICTOR_LEGISLATION_DAYS,\n)\nfrom analysis.scorer import _COMMITTEE_SECTOR_MAP, _committee_score\n\nTOKEN_LOG = os.path.join(os.path.dirname(os.path.dirname(__file__)), \"reports\", \"token_usage.log\")\n\nSYSTEM_PROMPT = (\n    \"You are a political intelligence analyst specialising in US congressional trading patterns. \"\n    \"Your task is to predict future stock trades based on a member's committee assignments, \"\n    \"historical trading behaviour, and active legislation. Be specific about named bills, \"\n    \"sectors, and companies. Acknowledge when you are speculating. \"\n    \"Keep each field concise — 2-3 sentences maximum.\"\n)\n\nPREDICT_PROMPT_TEMPLATE = \"\"\"\\\nAnalyse the following US Congress member's trading history and committee assignments, \\\nthen predict which stocks they are most likely to trade in the next 45 days.\n\nMEMBER: {member_name} ({party}, {chamber})\nCOMMITTEES: {committees}\n\nTRADE HISTORY (last 3 years — {trade_count} trades):\n{trade_history}\n\n{legislation_section}\\\nBased on:\n1. The sectors this member repeatedly trades and their timing relative to legislation\n2. Their committee assignments and what industries those committees oversee\n3. Active legislation in their committees that could create an information advantage{legislation_note}\n\nProvide a forward-looking watchlist of 3-5 stocks this member is most likely to buy \\\nor sell in the next 45 days.\n\nRespond in the following JSON format exactly:\n\n{{\n  \"behavioral_profile\": \"2-3 sentences: this member's trading style — which sectors they \\\nfavour, how close to legislation they tend to trade, any notable patterns.\",\n  \"predicted_trades\": [\n    {{\n      \"ticker\": \"XXXX\",\n      \"company_name\": \"Full Company Name\",\n      \"sector\": \"sector-slug\",\n      \"predicted_action\": \"buy\",\n      \"confidence\": \"LOW|MEDIUM|HIGH\",\n      \"legislation_catalyst\": \"Specific bill or policy area driving this prediction, \\\nor 'General sector exposure' if no specific bill.\",\n      \"historical_precedent\": \"Prior similar trade by this member if one exists, \\\nor 'No direct precedent' if not.\",\n      \"rationale\": \"2-3 sentence explanation of why this member is likely to trade this.\"\n    }}\n  ]\n}}\n\nRespond with valid JSON only, no markdown fencing.\"\"\"\n\n_LEGISLATION_SECTION_TEMPLATE = \"\"\"\\\nACTIVE LEGISLATION in their committees (last {days} days):\n{bill_lines}\n\n\"\"\"\n\n_NO_LEGISLATION_NOTE = (\n    \"\\n   (No live bill data available — use your general knowledge of pending \"\n    \"legislation in the 119th Congress that touches these committees.)\"\n)\n\n\ndef _format_trade_history(trades: list[dict]) -> str:\n    if not trades:\n        return \"  (no trade history found)\"\n    lines = []\n    for t in trades[:60]:  # cap at 60 rows to keep prompt manageable\n        opt = \" [option]\" if t.get(\"is_option\") else \"\"\n        tier_map = {1: \"$1K-15K\", 2: \"$15K-50K\", 3: \"$50K-100K\",\n                    4: \"$100K-250K\", 5: \"$250K-500K\", 6: \"$500K+\"}\n        size = tier_map.get(t.get(\"amount_tier\") or 0, \"?\")\n        lines.append(\n            f\"  {t.get('tx_date', '?'):12}  {t.get('tx_type', '?'):4}  \"\n            f\"{t.get('issuer_ticker', '?'):8}  {size:12}  \"\n            f\"{(t.get('issuer_sector') or '?')[:30]}{opt}\"\n        )\n    if len(trades) > 60:\n        lines.append(f\"  ... and {len(trades)-60} more trades\")\n    return \"\\n\".join(lines)\n\n\ndef _format_legislation(bills: list[dict], days: int) -> str:\n    \"\"\"Format bill list into a prompt section. Returns empty string if no bills.\"\"\"\n    if not bills:\n        return \"\"\n    lines = []\n    for b in bills[:25]:\n        num   = f\"{b.get('type', '')} {b.get('number', '')}\".strip()\n        title = b.get(\"title\", \"\")[:120]\n        date  = b.get(\"latest_action_date\", \"\")\n        action = b.get(\"latest_action\", \"\")[:100]\n        lines.append(f\"  {num}: {title}\")\n        if date or action:\n            lines.append(f\"    → {date}  {action}\")\n    return _LEGISLATION_SECTION_TEMPLATE.format(\n        days=days,\n        bill_lines=\"\\n\".join(lines),\n    )\n\n\ndef _build_prompt(member: dict, trades: list[dict], bills: list[dict]) -> str:\n    committees_raw = []\n    if member.get(\"committees\"):\n        try:\n            committees_raw = json.loads(member[\"committees\"])\n        except Exception:\n            pass\n    committees_str = \", \".join(str(c) for c in committees_raw[:8]) or \"unknown\"\n\n    legislation_section = _format_legislation(bills, PREDICTOR_LEGISLATION_DAYS)\n    legislation_note = \"\" if bills else _NO_LEGISLATION_NOTE\n\n    return PREDICT_PROMPT_TEMPLATE.format(\n        member_name=f\"{member['first_name']} {member['last_name']}\",\n        party=(member.get(\"party\") or \"?\")[:1].upper(),\n        chamber=member.get(\"chamber\") or \"?\",\n        committees=committees_str,\n        trade_count=len(trades),\n        trade_history=_format_trade_history(trades),\n        legislation_section=legislation_section,\n        legislation_note=legislation_note,\n    )\n\n\ndef _log_tokens(label: str, usage: dict):\n    os.makedirs(os.path.dirname(TOKEN_LOG), exist_ok=True)\n    with open(TOKEN_LOG, \"a\") as f:\n        total_cost_est = (\n            usage.get(\"input_tokens\", 0) * 3e-6\n            + usage.get(\"output_tokens\", 0) * 15e-6\n            - usage.get(\"cache_read_tokens\", 0) * 2.7e-6\n        )\n        f.write(\n            f\"{datetime.now().isoformat(timespec='seconds')} | predict:{label:<20} | \"\n            f\"in={usage.get('input_tokens',0):>5} out={usage.get('output_tokens',0):>4} \"\n            f\"cache_hit={usage.get('cache_read_tokens',0):>5} \"\n            f\"est_cost=${total_cost_est:.4f}\\n\"\n        )\n\n\ndef _committee_alignment(committees_json: str | None, sector: str) -> str:\n    \"\"\"\n    Return 'direct', 'indirect', or 'none' based on how well the member's\n    committees overlap with the predicted ticker's sector.\n    Reuses scorer._committee_score (0-3 scale).\n    \"\"\"\n    score = _committee_score(committees_json, sector or \"\")\n    if score >= 3:\n        return \"direct\"\n    if score >= 1:\n        return \"indirect\"\n    return \"none\"\n\n\ndef _parse_response(text: str) -> dict:\n    text = text.strip()\n    if text.startswith(\"```\"):\n        lines = text.splitlines()\n        text = \"\\n\".join(lines[1:-1] if lines[-1].strip() == \"```\" else lines[1:])\n    try:\n        return json.loads(text)\n    except json.JSONDecodeError:\n        start = text.find(\"{\")\n        end = text.rfind(\"}\") + 1\n        if start >= 0 and end > start:\n            return json.loads(text[start:end])\n        raise\n\n\ndef _was_predicted_recently(conn: sqlite3.Connection, member_id: str, decay_days: int) -> bool:\n    \"\"\"True if this member was predicted within decay_days.\"\"\"\n    row = conn.execute(\n        \"SELECT MAX(generated_at) FROM predicted_signals WHERE member_id = ?\",\n        (member_id,),\n    ).fetchone()\n    if not row or not row[0]:\n        return False\n    try:\n        dt = datetime.fromisoformat(row[0])\n        return (datetime.now() - dt).days < decay_days\n    except ValueError:\n        return False\n\n\ndef _select_members(conn: sqlite3.Connection, max_members: int,\n                    decay_days: int, min_trades: int) -> list[dict]:\n    \"\"\"\n    Pick members to predict for this run.\n    Priority: never-predicted → stalest-predicted → highest trade count.\n    Only members with committee data and enough history.\n    \"\"\"\n    rows = conn.execute(\n        \"\"\"\n        SELECT m.bioguide_id, m.first_name, m.last_name, m.party, m.chamber,\n               m.committees, COUNT(t.tx_id) AS trade_count,\n               (SELECT MAX(ps.generated_at)\n                FROM predicted_signals ps\n                WHERE ps.member_id = m.bioguide_id) AS last_predicted\n        FROM members m\n        JOIN trades t ON t.member_id = m.bioguide_id\n        WHERE m.committees IS NOT NULL\n          AND m.committees != '[]'\n          AND m.committees != ''\n        GROUP BY m.bioguide_id\n        HAVING COUNT(t.tx_id) >= ?\n        ORDER BY last_predicted ASC NULLS FIRST, trade_count DESC\n        \"\"\",\n        (min_trades,),\n    ).fetchall()\n\n    now = datetime.now()\n    selected = []\n    for r in rows:\n        rd = dict(r)\n        lp = rd.get(\"last_predicted\")\n        if lp:\n            try:\n                age = (now - datetime.fromisoformat(lp)).days\n                if age < decay_days:\n                    continue\n            except ValueError:\n                pass\n        selected.append(rd)\n        if len(selected) >= max_members:\n            break\n\n    return selected\n\n\ndef predict_for_member(\n    member: dict,\n    bills: list[dict],\n    dry_run: bool = False,\n) -> list[dict] | None:\n    \"\"\"\n    Run LLM prediction for one member. Returns list of predicted signal dicts or None.\n    \"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n\n    # Pull 3 years of trade history for this member\n    cutoff_3yr = (datetime.now() - timedelta(days=3*365)).strftime(\"%Y-%m-%d\")\n    trades = conn.execute(\n        \"\"\"\n        SELECT tx_date, tx_type, issuer_ticker, issuer_sector, amount_tier, is_option\n        FROM trades\n        WHERE member_id = ? AND tx_date >= ?\n        ORDER BY tx_date DESC\n        \"\"\",\n        (member[\"bioguide_id\"], cutoff_3yr),\n    ).fetchall()\n    trades = [dict(t) for t in trades]\n\n    conn.close()\n\n    member_name = f\"{member['first_name']} {member['last_name']}\"\n    prompt = _build_prompt(member, trades, bills)\n\n    if dry_run:\n        print(f\"\\n{'='*60}\")\n        print(f\"DRY RUN — predict for: {member_name}\")\n        print(f\"{'='*60}\")\n        print(prompt[:3000])\n        return None\n\n    from analysis.llm_client import call, LLMError\n    try:\n        print(f\"  {member_name}: calling LLM...\", end=\" \", flush=True)\n        text, usage = call(prompt, system=SYSTEM_PROMPT, max_tokens=2048)\n        _log_tokens(member_name[:20], usage)\n        print(f\"done (in={usage['input_tokens']} out={usage['output_tokens']})\")\n    except LLMError as e:\n        print(f\"  {member_name}: LLM error — {e}\")\n        return None\n    except Exception as e:\n        msg = str(e)\n        if \"429\" in msg or \"RESOURCE_EXHAUSTED\" in msg or \"quota\" in msg.lower():\n            print(f\"  {member_name}: API quota exceeded — stopping predictor\")\n        else:\n            print(f\"  {member_name}: unexpected error — {e}\")\n        return None\n\n    try:\n        result = _parse_response(text)\n    except Exception as e:\n        print(f\"  {member_name}: parse error — {e}\")\n        return None\n\n    behavioral_profile = result.get(\"behavioral_profile\", \"\")\n    predicted_trades   = result.get(\"predicted_trades\") or []\n    if not isinstance(predicted_trades, list):\n        print(f\"  {member_name}: unexpected result shape\")\n        return None\n\n    now_str = datetime.now().isoformat(timespec=\"seconds\")\n    conn2 = sqlite3.connect(DB_PATH)\n\n    for pt in predicted_trades:\n        ticker = (pt.get(\"ticker\") or \"\").strip().upper()\n        if not ticker:\n            continue\n        sector    = pt.get(\"sector\") or \"\"\n        alignment = _committee_alignment(member.get(\"committees\"), sector)\n\n        conn2.execute(\n            \"\"\"\n            INSERT INTO predicted_signals (\n                member_id, member_name, ticker, company_name, sector,\n                predicted_action, confidence, legislation_catalyst,\n                historical_precedent, rationale, behavioral_profile,\n                committee_alignment, generated_at\n            ) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)\n            ON CONFLICT(member_id, ticker) DO UPDATE SET\n                member_name          = excluded.member_name,\n                company_name         = excluded.company_name,\n                sector               = excluded.sector,\n                predicted_action     = excluded.predicted_action,\n                confidence           = excluded.confidence,\n                legislation_catalyst = excluded.legislation_catalyst,\n                historical_precedent = excluded.historical_precedent,\n                rationale            = excluded.rationale,\n                behavioral_profile   = excluded.behavioral_profile,\n                committee_alignment  = excluded.committee_alignment,\n                generated_at         = excluded.generated_at\n            \"\"\",\n            (\n                member[\"bioguide_id\"],\n                member_name,\n                ticker,\n                pt.get(\"company_name\") or \"\",\n                sector,\n                pt.get(\"predicted_action\") or \"buy\",\n                pt.get(\"confidence\") or \"LOW\",\n                pt.get(\"legislation_catalyst\") or \"\",\n                pt.get(\"historical_precedent\") or \"\",\n                pt.get(\"rationale\") or \"\",\n                behavioral_profile,\n                alignment,\n                now_str,\n            ),\n        )\n\n    conn2.commit()\n    conn2.close()\n\n    return [{\"ticker\": (pt.get(\"ticker\") or \"\").upper(),\n             \"confidence\": pt.get(\"confidence\", \"?\"),\n             \"action\": pt.get(\"predicted_action\", \"?\")} for pt in predicted_trades]\n\n\ndef run_predictions(\n    max_members: int = PREDICTOR_MAX_MEMBERS,\n    decay_days: int = PREDICTOR_DECAY_DAYS,\n    min_trades: int = PREDICTOR_MIN_TRADES,\n    member_filter: str | None = None,\n    dry_run: bool = False,\n) -> list[dict]:\n    \"\"\"\n    Entry point for the daily pipeline.\n    Returns list of summary dicts: {member_name, predictions: [{ticker, confidence, action}]}.\n    \"\"\"\n    from tools.fetch_legislation import load as load_legislation, filter_for_committees\n\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n\n    if member_filter:\n        # Override member selection for CLI testing\n        name_parts = member_filter.strip().split()\n        rows = conn.execute(\n            \"\"\"\n            SELECT m.bioguide_id, m.first_name, m.last_name, m.party, m.chamber,\n                   m.committees, COUNT(t.tx_id) AS trade_count, NULL AS last_predicted\n            FROM members m\n            JOIN trades t ON t.member_id = m.bioguide_id\n            WHERE (m.first_name LIKE ? OR m.last_name LIKE ? OR\n                   (m.first_name || ' ' || m.last_name) LIKE ?)\n            GROUP BY m.bioguide_id\n            \"\"\",\n            (f\"%{name_parts[0]}%\", f\"%{name_parts[-1]}%\",\n             f\"%{member_filter}%\"),\n        ).fetchall()\n        members = [dict(r) for r in rows]\n        if not members:\n            print(f\"  No member found matching '{member_filter}'\")\n            conn.close()\n            return []\n    else:\n        members = _select_members(conn, max_members=max_members,\n                                  decay_days=decay_days, min_trades=min_trades)\n\n    conn.close()\n\n    if not members:\n        print(\"  No members eligible for prediction this run (all within decay window)\")\n        return []\n\n    # Fetch legislation once and filter per member\n    all_bills = load_legislation(days_back=PREDICTOR_LEGISLATION_DAYS)\n    has_bills = bool(all_bills)\n    if has_bills:\n        print(f\"  Loaded {len(all_bills)} active bills for cross-referencing\")\n    else:\n        print(\"  No live bill data (set CONGRESS_GOV_API_KEY for real-time legislation)\")\n\n    results = []\n    for member in members:\n        member_name = f\"{member['first_name']} {member['last_name']}\"\n        print(f\"  Predicting for {member_name} ({member.get('trade_count', '?')} trades)...\")\n\n        # Filter bills for this member's committees\n        committees = []\n        if member.get(\"committees\"):\n            try:\n                committees = json.loads(member[\"committees\"])\n            except Exception:\n                pass\n        member_bills = filter_for_committees(all_bills, committees) if all_bills else []\n\n        preds = predict_for_member(member, member_bills, dry_run=dry_run)\n        if preds:\n            results.append({\"member_name\": member_name, \"predictions\": preds})\n\n    return results\n\n\ndef backfill_committee_alignment():\n    \"\"\"\n    Compute committee_alignment for existing rows that were stored before\n    this column existed (value = 'none' by default migration).\n    \"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    rows = conn.execute(\n        \"\"\"\n        SELECT ps.id, ps.member_id, ps.sector, m.committees\n        FROM predicted_signals ps\n        JOIN members m ON m.bioguide_id = ps.member_id\n        WHERE ps.committee_alignment = 'none' OR ps.committee_alignment IS NULL\n        \"\"\"\n    ).fetchall()\n\n    updated = 0\n    for r in rows:\n        alignment = _committee_alignment(r[\"committees\"], r[\"sector\"] or \"\")\n        if alignment != \"none\":\n            conn.execute(\n                \"UPDATE predicted_signals SET committee_alignment = ? WHERE id = ?\",\n                (alignment, r[\"id\"]),\n            )\n            updated += 1\n\n    conn.commit()\n    conn.close()\n    if updated:\n        print(f\"  Backfilled committee_alignment for {updated} predictions\")\n    return updated\n\n\ndef confirm_predictions():\n    \"\"\"\n    Scan trades disclosed today and mark any matching predicted_signals as confirmed.\n    Call this after each ingest run.\n    \"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    today = datetime.now().strftime(\"%Y-%m-%d\")\n\n    # Find tickers disclosed today that we predicted\n    confirmed = conn.execute(\n        \"\"\"\n        UPDATE predicted_signals\n        SET confirmed = 1, confirmed_at = ?\n        WHERE confirmed = 0\n          AND ticker IN (\n              SELECT DISTINCT issuer_ticker FROM trades\n              WHERE pub_date >= date('now', '-7 days')\n          )\n        \"\"\",\n        (today,),\n    ).rowcount\n\n    conn.commit()\n    conn.close()\n\n    if confirmed:\n        print(f\"  Confirmed {confirmed} predicted signal(s) — trades now disclosed!\")\n    return confirmed\n\n\ndef load_predictions(days_back: int = 45) -> list[dict]:\n    \"\"\"Return recent predicted signals for report generation.\"\"\"\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cutoff = (datetime.now() - timedelta(days=days_back)).strftime(\"%Y-%m-%dT\")\n\n    rows = conn.execute(\n        \"\"\"\n        SELECT * FROM predicted_signals\n        WHERE generated_at >= ?\n        ORDER BY\n            confidence DESC,\n            generated_at DESC\n        \"\"\",\n        (cutoff,),\n    ).fetchall()\n    conn.close()\n    return [dict(r) for r in rows]\n\n\ndef load_consensus(days_back: int = 45, top_n: int = 30) -> list[dict]:\n    \"\"\"\n    Aggregate predicted_signals by ticker into a ranked consensus view.\n\n    Score per ticker:\n      cluster_pts      = member_count × conf_avg\n      small_cap_pts    = from signals.small_cap_score OR ticker_metadata.market_cap\n      win_rate_pts     = avg win_rate of predicting members (0-2 pts)\n      alignment_bonus  = +2 if any member has direct committee-sector alignment\n      precedent_bonus  = +1 if any member has prior history trading this ticker\n      ubiquity_penalty = breadth discount for tickers bought by 15+ members historically\n                         (same _breadth_mult logic as reactive scorer)\n\n    Filters: ETFs, invalid tickers (no yfinance data), malformed ticker formats.\n    \"\"\"\n    _CONF = {\"HIGH\": 3.0, \"MEDIUM\": 2.0, \"LOW\": 1.0}\n\n    # Known junk formats — LLM occasionally hallucinates these\n    _INVALID_PREFIXES = (\"$$\", \"$\", \"##\")\n    _INVALID_EXACT    = {\"SPY\", \"QQQ\", \"VTI\", \"VOO\", \"IWM\", \"GLD\", \"SLV\", \"TLT\",\n                         \"HYG\", \"LQD\", \"DIA\", \"MDY\", \"AGG\", \"BND\", \"XLF\", \"XLE\",\n                         \"XLK\", \"XLV\", \"XLI\", \"XLU\", \"XLB\", \"XLRE\", \"XLY\", \"XLP\",\n                         \"XLC\", \"VNQ\", \"ARKK\", \"ARKG\", \"ARKW\"}\n\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    cutoff = (datetime.now() - timedelta(days=days_back)).strftime(\"%Y-%m-%dT\")\n\n    rows = conn.execute(\n        \"\"\"\n        SELECT\n            ps.ticker,\n            MAX(ps.company_name)                        AS company_name,\n            MAX(ps.sector)                              AS sector,\n            COUNT(DISTINCT ps.member_id)                AS member_count,\n            GROUP_CONCAT(DISTINCT ps.member_name)       AS member_names,\n            GROUP_CONCAT(ps.confidence, '|')            AS confidences,\n            GROUP_CONCAT(ps.predicted_action, '|')      AS actions,\n            GROUP_CONCAT(ps.legislation_catalyst, '|||') AS catalysts,\n            GROUP_CONCAT(ps.rationale, '|||')           AS rationales,\n            GROUP_CONCAT(ps.historical_precedent, '|||') AS precedents,\n            GROUP_CONCAT(COALESCE(ps.committee_alignment,'none'), '|') AS alignments,\n            MAX(ps.confirmed)                           AS confirmed,\n            MAX(ps.generated_at)                        AS last_generated,\n            -- market cap: prefer reactive signals table, fall back to ticker_metadata\n            COALESCE(s.small_cap_score, NULL)           AS small_cap_score,\n            COALESCE(s.market_cap, tm.market_cap)       AS market_cap,\n            s.total_score                               AS reactive_score,\n            COALESCE(tm.is_etf, 0)                      AS is_etf,\n            COALESCE(tm.is_valid, 1)                    AS is_valid,\n            AVG(COALESCE(m.win_rate, 0.5))              AS avg_win_rate,\n            tm.pe_ratio                                 AS pe_ratio,\n            tm.analyst_rating                           AS analyst_rating,\n            tm.price_vs_50d_pct                         AS price_vs_50d_pct\n        FROM predicted_signals ps\n        LEFT JOIN signals s         ON s.ticker        = ps.ticker\n        LEFT JOIN ticker_metadata tm ON tm.ticker      = ps.ticker\n        LEFT JOIN members m         ON m.bioguide_id   = ps.member_id\n        WHERE ps.generated_at >= ?\n          AND ps.predicted_action = 'buy'\n        GROUP BY ps.ticker\n        \"\"\",\n        (cutoff,),\n    ).fetchall()\n\n    # Historical buyer breadth — for ubiquity discount\n    breadth_rows = conn.execute(\n        \"\"\"\n        SELECT issuer_ticker, COUNT(DISTINCT member_id) AS cnt\n        FROM trades WHERE tx_type = 'buy' AND issuer_ticker != ''\n        GROUP BY issuer_ticker\n        \"\"\"\n    ).fetchall()\n    historical_buyers: dict[str, int] = {r[\"issuer_ticker\"]: r[\"cnt\"] for r in breadth_rows}\n\n    conn.close()\n\n    def _breadth_mult(hist: int) -> float:\n        if hist >= 20: return 0.40\n        if hist >= 12: return 0.65\n        if hist >= 6:  return 0.85\n        return 1.0\n\n    def _small_cap_pts(market_cap: float | None) -> float:\n        if market_cap is None: return 0.0\n        if market_cap > 100e9: return -2.5\n        if market_cap > 10e9:  return -0.5\n        if market_cap > 2e9:   return  2.0\n        if market_cap > 500e6: return  4.5\n        return 6.0\n\n    results = []\n    for r in rows:\n        rd = dict(r)\n        ticker = rd[\"ticker\"]\n\n        # --- Filter junk ---\n        if any(ticker.startswith(p) for p in _INVALID_PREFIXES):\n            continue\n        if ticker in _INVALID_EXACT:\n            continue\n        if rd.get(\"is_etf\"):\n            continue\n        if rd.get(\"is_valid\") == 0:\n            continue\n\n        # --- Core scoring components ---\n        conf_list = [c for c in (rd.get(\"confidences\") or \"\").split(\"|\") if c]\n        conf_avg  = sum(_CONF.get(c, 1.0) for c in conf_list) / max(len(conf_list), 1)\n\n        member_count  = rd[\"member_count\"] or 1\n        hist_buyers   = historical_buyers.get(ticker, 0)\n        cluster_pts   = member_count * conf_avg * _breadth_mult(hist_buyers)\n\n        market_cap    = rd.get(\"market_cap\")\n        # If small_cap_score already computed by reactive scorer, use it directly\n        sc_score      = rd.get(\"small_cap_score\")\n        cap_pts       = float(sc_score) if sc_score is not None else _small_cap_pts(market_cap)\n\n        avg_wr        = float(rd.get(\"avg_win_rate\") or 0.5)\n        win_rate_pts  = 2.0 if avg_wr > 0.6 else (1.0 if avg_wr >= 0.5 else 0.0)\n\n        # --- Alignment bonus (+2 for any direct committee-sector match) ---\n        alignments     = (rd.get(\"alignments\") or \"\").split(\"|\")\n        alignment_bonus = 2.0 if \"direct\" in alignments else (0.5 if \"indirect\" in alignments else 0.0)\n\n        # --- Historical precedent bonus (+1 if member has traded this before) ---\n        precedents = [p.strip() for p in (rd.get(\"precedents\") or \"\").split(\"|||\")]\n        has_precedent = any(\n            p and p.lower() not in (\"no direct precedent\", \"none\", \"n/a\", \"\")\n            for p in precedents\n        )\n        precedent_bonus = 1.0 if has_precedent else 0.0\n\n        score = round(cluster_pts + cap_pts + win_rate_pts + alignment_bonus + precedent_bonus, 2)\n\n        # --- Metadata for display ---\n        rationale_list = [s.strip() for s in (rd.get(\"rationales\") or \"\").split(\"|||\") if s.strip()]\n        catalyst_list  = [s.strip() for s in (rd.get(\"catalysts\") or \"\").split(\"|||\") if s.strip()]\n        best_rationale = max(rationale_list, key=len) if rationale_list else \"\"\n        best_catalyst  = max(catalyst_list,  key=len) if catalyst_list  else \"\"\n\n        best_alignment = (\"direct\" if \"direct\" in alignments\n                          else \"indirect\" if \"indirect\" in alignments else \"none\")\n\n        if member_count >= 2:\n            tier = \"consensus\"\n        elif conf_avg >= 3.0:\n            tier = \"solo_high\"\n        else:\n            tier = \"solo_low\"\n\n        reactive_score = rd.get(\"reactive_score\")\n        cross_signal   = reactive_score is not None and reactive_score > 0\n\n        results.append({\n            \"ticker\":           ticker,\n            \"company_name\":     rd[\"company_name\"] or \"\",\n            \"sector\":           rd[\"sector\"] or \"\",\n            \"member_count\":     member_count,\n            \"member_names\":     rd.get(\"member_names\") or \"\",\n            \"conf_avg\":         round(conf_avg, 1),\n            \"conf_label\":       \"HIGH\" if conf_avg >= 2.5 else (\"MEDIUM\" if conf_avg >= 1.5 else \"LOW\"),\n            \"tier\":             tier,\n            \"small_cap_score\":  cap_pts,\n            \"market_cap\":       market_cap,\n            \"avg_win_rate\":     round(avg_wr, 2),\n            \"alignment\":        best_alignment,\n            \"alignment_bonus\":  alignment_bonus,\n            \"has_precedent\":    has_precedent,\n            \"precedent_bonus\":  precedent_bonus,\n            \"hist_buyers\":      hist_buyers,\n            \"reactive_score\":   reactive_score,\n            \"cross_signal\":     cross_signal,\n            \"confirmed\":        bool(rd.get(\"confirmed\")),\n            \"best_rationale\":   best_rationale,\n            \"best_catalyst\":    best_catalyst,\n            \"score\":            score,\n            \"last_generated\":   (rd.get(\"last_generated\") or \"\")[:10],\n            \"actions\":          rd.get(\"actions\") or \"\",\n            \"pe_ratio\":         rd.get(\"pe_ratio\"),\n            \"analyst_rating\":   rd.get(\"analyst_rating\"),\n            \"price_vs_50d_pct\": rd.get(\"price_vs_50d_pct\"),\n        })\n\n    results.sort(key=lambda x: x[\"score\"], reverse=True)\n    return results[:top_n]\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Generate predictive trade signals via LLM\")\n    parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"print prompts, no API call\")\n    parser.add_argument(\"--member\", help=\"predict for a specific member name\")\n    parser.add_argument(\"--max\", type=int, default=PREDICTOR_MAX_MEMBERS,\n                        help=f\"max members to process (default {PREDICTOR_MAX_MEMBERS})\")\n    args = parser.parse_args()\n\n    results = run_predictions(\n        max_members=args.max,\n        member_filter=args.member,\n        dry_run=args.dry_run,\n    )\n\n    if not args.dry_run and results:\n        print(f\"\\n{'='*60}\")\n        for r in results:\n            print(f\"\\n{r['member_name']}:\")\n            for p in r[\"predictions\"]:\n                print(f\"  {p['ticker']:8}  {p['confidence']:6}  {p['action']}\")","is_error":false}]},"uuid":"27e68784-e720-4316-b947-08d81e147070","timestamp":"2026-07-07T01:38:44.474Z","sourceToolAssistantUUID":"ac22f585-92f9-40ba-9517-b0bcb53f079b","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"27e68784-e720-4316-b947-08d81e147070","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqr7gv6R81XG6miHbhY","type":"message","role":"assistant","content":[{"type":"text","text":"Perfect! Now let me check for any test fixtures or TODOs in the codebase."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":8731,"cache_read_input_tokens":39252,"cache_creation":{"ephemeral_5m_input_tokens":8731,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Ccmqr7GMyvHnbD6AP6hnH","attributionAgent":"Explore","type":"assistant","uuid":"83ddfbda-8d1d-44d1-bcab-2df838018b2a","timestamp":"2026-07-07T01:38:45.659Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"83ddfbda-8d1d-44d1-bcab-2df838018b2a","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqr7gv6R81XG6miHbhY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TjCP3EkP8WRVoAD9vXFuHR","name":"Bash","input":{"command":"curl -s \"https://api.github.com/repos/jamesjlopez/congressional-trading/git/trees/main?recursive=1\" | grep -o '\"path\":\"[^\"]*\"' | grep -E '(test|fixture|example)' | head -20"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":8731,"cache_read_input_tokens":39252,"output_tokens":136,"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":8731},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":136,"cache_read_input_tokens":39252,"cache_creation_input_tokens":8731,"cache_creation":{"ephemeral_5m_input_tokens":8731,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ccmqr7GMyvHnbD6AP6hnH","attributionAgent":"Explore","type":"assistant","uuid":"580e7826-c967-452a-b5d3-c01c07f6a3a7","timestamp":"2026-07-07T01:38:46.303Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"580e7826-c967-452a-b5d3-c01c07f6a3a7","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TjCP3EkP8WRVoAD9vXFuHR","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"7a3d8716-3073-4ed3-aee2-9fb884f04b54","timestamp":"2026-07-07T01:38:46.403Z","sourceToolAssistantUUID":"580e7826-c967-452a-b5d3-c01c07f6a3a7","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"7a3d8716-3073-4ed3-aee2-9fb884f04b54","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrH5xm3Abxu1Dyb5ir","type":"message","role":"assistant","content":[{"type":"text","text":"Let me search for TODO comments and check the run_daily.py to understand the full pipeline."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":154,"cache_read_input_tokens":47983,"cache_creation":{"ephemeral_5m_input_tokens":154,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqrFZvymKSjsMisNmMs","attributionAgent":"Explore","type":"assistant","uuid":"127c0a66-093b-4ccc-b226-b47e3cdea917","timestamp":"2026-07-07T01:38:48.508Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"127c0a66-093b-4ccc-b226-b47e3cdea917","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrH5xm3Abxu1Dyb5ir","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018dZ6SssfVFGvjFkZxAtjbU","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/run_daily.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":154,"cache_read_input_tokens":47983,"output_tokens":102,"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":154},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":102,"cache_read_input_tokens":47983,"cache_creation_input_tokens":154,"cache_creation":{"ephemeral_5m_input_tokens":154,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqrFZvymKSjsMisNmMs","attributionAgent":"Explore","type":"assistant","uuid":"8b136567-260f-418d-a508-e3258ec4e249","timestamp":"2026-07-07T01:38:48.758Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"8b136567-260f-418d-a508-e3258ec4e249","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018dZ6SssfVFGvjFkZxAtjbU","type":"tool_result","content":"\"\"\"\nCongressWatch daily runner.\n\nPipeline: ingest → committees → score → prices → win_rates → research → report → notify\n\nUsage:\n  python run_daily.py              # full run\n  python run_daily.py --dry-run    # ingest + score + report, no LLM calls, no notifications\n  python run_daily.py --no-ingest  # skip ingest (re-score + research + report only)\n  python run_daily.py --force      # re-notify even if already notified today\n\"\"\"\n\nfrom __future__ import annotations\nimport argparse\nimport sqlite3\nimport sys\nimport os\nfrom datetime import datetime, date\n\nsys.path.insert(0, os.path.dirname(__file__))\nfrom config import (DB_PATH, SCORE_NOTIFY_THRESHOLD, SCORE_RESEARCH_THRESHOLD,\n                    LOOKBACK_DAYS, TIMELINESS_MULTIPLIERS,\n                    DEEP_RESEARCH_TOP_N, DEEP_RESEARCH_DECAY_DAYS,\n                    MAX_NOTIFICATIONS_PER_RUN, NOTIFY_TICKER_COOLDOWN_DAYS,\n                    PREDICTOR_MAX_MEMBERS, PREDICTOR_DECAY_DAYS)\n\n\ndef _timeliness_adjusted_score(base_score: float, timeliness: str | None) -> float:\n    \"\"\"Apply timeliness multiplier for notify-gate purposes only. Score is not stored.\"\"\"\n    key = (timeliness or \"fresh\").split(\"|\")[0].strip().lower()\n    return base_score * TIMELINESS_MULTIPLIERS.get(key, 1.0)\n\n\ndef _send_signal_notification(sig: dict, research: dict | None, adjusted_score: float):\n    from notify.ntfy_client import push\n    from analysis.timeliness import assess_age\n\n    ticker = sig[\"ticker\"]\n    score = sig[\"total_score\"]\n    buyers = sig[\"member_count\"]\n    timeliness = research.get(\"timeliness\", assess_age(sig[\"last_seen\"])) if research else assess_age(sig[\"last_seen\"])\n    conviction = (research.get(\"conviction\") or \"?\").upper() if research else \"?\"\n\n    tier_labels = {1:\"$1K-15K\", 2:\"$15K-50K\", 3:\"$50K-100K\",\n                   4:\"$100K-250K\", 5:\"$250K-500K\", 6:\"$500K+\"}\n    size_label = tier_labels.get(sig.get(\"max_tier\", 0), \"unknown\")\n    opt_flag = \" | Options: YES\" if sig.get(\"has_options\") else \"\"\n\n    tl_note = \"\"\n    if adjusted_score < score:\n        tl_note = f\" (adj {adjusted_score:.1f} for {timeliness})\"\n\n    action_short = \"\"\n    if research and research.get(\"recommended_action\"):\n        raw_action = research[\"recommended_action\"]\n        action_short = raw_action.split(\"—\")[0].strip()[:40]\n\n    summary_line = \"\"\n    if research and research.get(\"company_summary\"):\n        summary_line = \"\\n\\n\" + research[\"company_summary\"][:300]\n    catalyst_line = \"\"\n    if research and research.get(\"catalyst_hypothesis\"):\n        catalyst_line = \"\\n\\nCatalyst: \" + research[\"catalyst_hypothesis\"][:200]\n    action_line = \"\"\n    if action_short:\n        action_line = f\"\\n\\n→ {action_short}\"\n\n    body = (\n        f\"{sig.get('issuer_name', ticker)} — {buyers} member{'s' if buyers>1 else ''} bought\\n\"\n        f\"Size: {size_label}{opt_flag}\\n\"\n        f\"Timeliness: {timeliness.upper()} | Conviction: {conviction} | Action: {action_short}\"\n        f\"{summary_line}{catalyst_line}{action_line}\"\n    )\n\n    push(\n        title=f\"CongressWatch: ${ticker} (score {score:.1f}{tl_note})\",\n        body=body,\n        priority=\"high\" if adjusted_score >= 9 else \"default\",\n        tags=[\"chart_with_upwards_trend\", \"money_with_wings\"],\n    )\n    print(f\"  ntfy sent for {ticker}\")\n\n\ndef _already_notified_today(ticker: str) -> bool:\n    conn = sqlite3.connect(DB_PATH)\n    row = conn.execute(\n        \"SELECT notified_at FROM signals WHERE ticker = ? AND notified = 1\",\n        (ticker,),\n    ).fetchone()\n    conn.close()\n    if not row or not row[0]:\n        return False\n    try:\n        return row[0][:10] == str(date.today())\n    except Exception:\n        return False\n\n\ndef _mark_notified(ticker: str):\n    conn = sqlite3.connect(DB_PATH)\n    conn.execute(\n        \"UPDATE signals SET notified = 1, notified_at = datetime('now') WHERE ticker = ?\",\n        (ticker,),\n    )\n    conn.commit()\n    conn.close()\n\n\ndef run(dry_run: bool = False, no_ingest: bool = False, force_notify: bool = False):\n    start = datetime.now()\n    print(f\"\\n{'='*60}\")\n    print(f\"  CongressWatch run — {start.strftime('%Y-%m-%d %H:%M')}\")\n    if dry_run:\n        print(\"  MODE: dry-run (no LLM calls, no notifications)\")\n    print(f\"{'='*60}\\n\")\n\n    # 1. Ingest\n    if not no_ingest:\n        print(\"[ 1/9 ] Ingesting trades...\")\n        from ingest.capitol_trades import ingest\n        summary = ingest(lookback_days=LOOKBACK_DAYS)\n        print(f\"        +{summary['new_trades']} new trades\\n\")\n    else:\n        print(\"[ 1/9 ] Skipping ingest (--no-ingest)\\n\")\n\n    # 1b. Executive branch trades (OGE Form 278-T PDFs for POTUS/VP)\n    if not no_ingest:\n        print(\"[ 1b/9 ] OGE Form 278-T executive branch trades...\")\n        from ingest.presidential_oge import ingest_presidential_oge\n        exec_summary = ingest_presidential_oge()\n        if exec_summary.get(\"new_trades\", 0) > 0:\n            print(f\"        +{exec_summary['new_trades']} new executive trades \"\n                  f\"({exec_summary['pdfs_processed']} PDFs)\\n\")\n        else:\n            print(f\"        no new trades (all known PDFs already processed)\\n\")\n\n    # 2. Committees (weekly refresh if stale)\n    print(\"[ 2/9 ] Committee assignments...\")\n    from ingest.committees import needs_refresh, refresh as refresh_committees\n    if needs_refresh():\n        summary = refresh_committees()\n        print(f\"        {summary['updated']} updated, {summary.get('no_data', 0)} with no data\\n\")\n    else:\n        print(\"        up to date (refreshed within 7 days)\\n\")\n\n    # 3. Score (uses win_rates + market_caps from previous run)\n    print(\"[ 3/9 ] Scoring signals...\")\n    from analysis.scorer import score_tickers\n    signals = score_tickers()\n    print(f\"        {len(signals)} tickers scored\\n\")\n\n    # 4. Prices + market caps\n    print(\"[ 4/9 ] Fetching prices...\")\n    from tools.price_fetcher import fetch_and_store as fetch_prices\n    price_summary = fetch_prices(min_score=SCORE_RESEARCH_THRESHOLD)\n    print(f\"        {price_summary['fetched']} updated, {price_summary['skipped']} already fresh\\n\")\n\n    # 5. Win rates (uses today's price data; feeds into tomorrow's scoring)\n    print(\"[ 5/9 ] Computing win rates...\")\n    from tools.win_rate import compute as compute_win_rates\n    wr_summary = compute_win_rates()\n    print(f\"        {wr_summary['updated']} members updated, \"\n          f\"{wr_summary['skipped']} skipped (insufficient data)\\n\")\n\n    # 6. Research top signals (regular: all signals ≥ SCORE_RESEARCH_THRESHOLD)\n    print(\"[ 6/9 ] Researching top signals...\")\n    research_map: dict[str, dict] = {}\n    if not dry_run:\n        from analysis.researcher import research_signal\n        from datetime import timedelta\n\n        cutoff = (datetime.now() - timedelta(days=LOOKBACK_DAYS)).strftime(\"%Y-%m-%d\")\n        conn = sqlite3.connect(DB_PATH)\n        conn.row_factory = sqlite3.Row\n\n        for sig in signals:\n            if sig[\"total_score\"] < SCORE_RESEARCH_THRESHOLD:\n                break  # signals are sorted descending\n            buyers = conn.execute(\n                \"\"\"\n                SELECT DISTINCT t.member_id,\n                    m.first_name || ' ' || m.last_name AS member_name,\n                    m.party, m.chamber, m.committees,\n                    MAX(t.amount_tier) AS amount_tier\n                FROM trades t JOIN members m ON m.bioguide_id = t.member_id\n                WHERE t.issuer_ticker = ? AND t.tx_type = 'buy' AND t.pub_date >= ?\n                GROUP BY t.member_id\n                \"\"\",\n                (sig[\"ticker\"], cutoff),\n            ).fetchall()\n            result = research_signal(sig, [dict(b) for b in buyers], dry_run=False)\n            if result:\n                research_map[sig[\"ticker\"]] = result\n\n        conn.close()\n    else:\n        print(\"        (skipped — dry-run mode)\")\n    print()\n\n    # 6b. Deep research — top N scorers with risk_factors + recommended_action\n    # Skips any ticker already deep-researched within DEEP_RESEARCH_DECAY_DAYS,\n    # falling through to the next-ranked signal to spend the budget wisely.\n    print(f\"[ 6b/9 ] Deep research (top {DEEP_RESEARCH_TOP_N}, decay {DEEP_RESEARCH_DECAY_DAYS}d)...\")\n    if not dry_run:\n        from analysis.researcher import research_top_n_deep\n        notify_tier = [s for s in signals if s[\"total_score\"] >= SCORE_NOTIFY_THRESHOLD]\n        deep_results = research_top_n_deep(\n            notify_tier, top_n=DEEP_RESEARCH_TOP_N, decay_days=DEEP_RESEARCH_DECAY_DAYS\n        )\n        for sig, result in deep_results:\n            research_map[sig[\"ticker\"]] = result  # update with richer deep result\n            print(f\"  {sig['ticker']}: deep research done — {result.get('recommended_action', '?')}\")\n        if not deep_results:\n            print(\"  All top candidates within decay window — no deep research needed\")\n    else:\n        print(\"        (skipped — dry-run mode)\")\n    print()\n\n    # 6c. Predictive signals — behavioural analysis + forward watchlist per member\n    print(f\"[ 6c/9 ] Predictive signals (top {PREDICTOR_MAX_MEMBERS} members, decay {PREDICTOR_DECAY_DAYS}d)...\")\n    if not dry_run:\n        from analysis.predictor import run_predictions, confirm_predictions, backfill_committee_alignment\n        confirm_predictions()\n        backfill_committee_alignment()\n        pred_results = run_predictions(\n            max_members=PREDICTOR_MAX_MEMBERS,\n            decay_days=PREDICTOR_DECAY_DAYS,\n        )\n        for pr in pred_results:\n            tickers = \", \".join(p[\"ticker\"] for p in pr[\"predictions\"])\n            print(f\"  {pr['member_name']}: {tickers}\")\n        if not pred_results:\n            print(\"  All members within decay window — no predictions generated\")\n\n        # Fetch market caps + ETF/validity flags for predicted tickers\n        print(\"  Fetching metadata for new predicted tickers...\")\n        from tools.price_fetcher import fetch_for_predicted\n        meta = fetch_for_predicted()\n        if meta[\"fetched\"] or meta[\"invalid\"]:\n            print(f\"  {meta['fetched']} metadata fetched, {meta['invalid']} filtered (ETF/invalid)\")\n    else:\n        print(\"        (skipped — dry-run mode)\")\n    print()\n\n    # 7. Generate report\n    print(\"[ 7/9 ] Generating report...\")\n    from tools.generate_report import generate\n    generate()\n    print()\n\n    # 7b. Update pending signal outcomes (30d/60d returns)\n    print(\"[ 7b/9 ] Checking signal outcomes...\")\n    if not dry_run:\n        from tools.outcome_tracker import update_pending_outcomes\n        outcome_result = update_pending_outcomes()\n        if outcome_result.get(\"updated\"):\n            print(f\"        {outcome_result['updated']} outcomes updated\")\n        else:\n            print(\"        no pending outcomes ready for scoring\")\n    else:\n        print(\"        (skipped — dry-run mode)\")\n    print()\n\n    # 8. Notify — apply timeliness, quality gates, and ticker cooldown before firing\n    notified = skipped_tl = skipped_gate = skipped_cooldown = 0\n    notify_candidates = [s for s in signals if s[\"total_score\"] >= SCORE_NOTIFY_THRESHOLD]\n\n    if not dry_run and notify_candidates:\n        from analysis.notification_policy import (\n            evaluate_notification_candidate,\n            recent_ticker_notification,\n        )\n\n        print(\"[ 8/9 ] Sending notifications...\")\n        for sig in notify_candidates:\n            if notified >= MAX_NOTIFICATIONS_PER_RUN:\n                remaining = len(notify_candidates) - notified - skipped_tl - skipped_gate - skipped_cooldown\n                if remaining > 0:\n                    print(f\"  Cap reached ({MAX_NOTIFICATIONS_PER_RUN}/run) — {remaining} remaining suppressed\")\n                break\n\n            ticker = sig[\"ticker\"]\n\n            # Timeliness gate\n            research = research_map.get(ticker)\n            timeliness = (research or {}).get(\"timeliness\") or \"fresh\"\n            adj = _timeliness_adjusted_score(sig[\"total_score\"], timeliness)\n            if adj < SCORE_NOTIFY_THRESHOLD:\n                tl_key = timeliness.split(\"|\")[0].strip().lower()\n                print(f\"  {ticker}: {sig['total_score']:.1f} × {TIMELINESS_MULTIPLIERS.get(tl_key,1):.2f} = {adj:.1f} — below threshold ({tl_key})\")\n                skipped_tl += 1\n                continue\n\n            decision = evaluate_notification_candidate(sig, research, adj)\n            if not decision.allowed:\n                print(f\"  {ticker}: {decision.reason} — skipping\")\n                skipped_gate += 1\n                continue\n\n            if not force_notify:\n                in_cooldown, last_notified, days_left = recent_ticker_notification(ticker)\n                if in_cooldown:\n                    last_label = last_notified.isoformat(timespec=\"minutes\") if last_notified else \"recently\"\n                    print(\n                        f\"  {ticker}: notified {last_label}; \"\n                        f\"{days_left}d left in {NOTIFY_TICKER_COOLDOWN_DAYS}d cooldown\"\n                    )\n                    skipped_cooldown += 1\n                    continue\n\n            if not force_notify and _already_notified_today(ticker):\n                print(f\"  {ticker}: already notified today\")\n                continue\n\n            _send_signal_notification(sig, research, adj)\n            _mark_notified(ticker)\n\n            # Record outcome for later tracking\n            from tools.outcome_tracker import record_notification\n            record_notification(\n                ticker=ticker,\n                score=adj,\n                conviction=(research or {}).get(\"conviction\", \"\"),\n                recommended_action=(research or {}).get(\"recommended_action\", \"\"),\n                price=sig.get(\"price_current\"),\n                notified_at=datetime.now().isoformat(timespec=\"seconds\"),\n            )\n\n            notified += 1\n        print()\n\n    elapsed = (datetime.now() - start).total_seconds()\n    print(f\"Run complete in {elapsed:.1f}s — {notified} notifications sent\"\n          + (f\", {skipped_tl} suppressed by timeliness\" if skipped_tl else \"\")\n          + (f\", {skipped_gate} filtered by quality gates\" if skipped_gate else \"\")\n          + (f\", {skipped_cooldown} suppressed by cooldown\" if skipped_cooldown else \"\") + \".\")\n    print(f\"Report: reports/signals_latest.md\\n\")\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"CongressWatch daily runner\")\n    parser.add_argument(\"--dry-run\",   action=\"store_true\", help=\"no LLM calls, no notifications\")\n    parser.add_argument(\"--no-ingest\", action=\"store_true\", help=\"skip ingest step\")\n    parser.add_argument(\"--force\",     action=\"store_true\", help=\"re-notify even if notified today\")\n    args = parser.parse_args()\n    run(dry_run=args.dry_run, no_ingest=args.no_ingest, force_notify=args.force)","is_error":false}]},"uuid":"1c12ac38-c3b4-479b-9452-e9e99c2d2876","timestamp":"2026-07-07T01:38:48.912Z","sourceToolAssistantUUID":"8b136567-260f-418d-a508-e3258ec4e249","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"1c12ac38-c3b4-479b-9452-e9e99c2d2876","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrSjPhrB5fsohWp36X","type":"message","role":"assistant","content":[{"type":"text","text":"Excellent! Now let me search for any TODO or FIXME comments in the Python source files."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":4523,"cache_read_input_tokens":48137,"cache_creation":{"ephemeral_5m_input_tokens":4523,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqrSGNMLwioSagtFbky","attributionAgent":"Explore","type":"assistant","uuid":"03ddeaeb-a8bc-4d32-a212-5154826936e2","timestamp":"2026-07-07T01:38:50.114Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"03ddeaeb-a8bc-4d32-a212-5154826936e2","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrSjPhrB5fsohWp36X","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QYdbjurt5Yt7X4oHk2uEfp","name":"Bash","input":{"command":"curl -s \"https://api.github.com/repos/jamesjlopez/congressional-trading/git/trees/main?recursive=1\" | grep -o '\"path\":\"[^\"]*\\.py\"' | sort -u | head -30"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":4523,"cache_read_input_tokens":48137,"output_tokens":130,"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":4523},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":130,"cache_read_input_tokens":48137,"cache_creation_input_tokens":4523,"cache_creation":{"ephemeral_5m_input_tokens":4523,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqrSGNMLwioSagtFbky","attributionAgent":"Explore","type":"assistant","uuid":"aa1c12d4-4b29-4ae0-9a94-88bf521c5dea","timestamp":"2026-07-07T01:38:51.281Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"aa1c12d4-4b29-4ae0-9a94-88bf521c5dea","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QYdbjurt5Yt7X4oHk2uEfp","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"6b2e2ea5-6d1f-4ae8-8fd5-b734d5ae1d58","timestamp":"2026-07-07T01:38:51.370Z","sourceToolAssistantUUID":"aa1c12d4-4b29-4ae0-9a94-88bf521c5dea","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"6b2e2ea5-6d1f-4ae8-8fd5-b734d5ae1d58","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrdRLRjo1NRNWtVrvH","type":"message","role":"assistant","content":[{"type":"text","text":"Let me try a different approach to find files."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":148,"cache_read_input_tokens":52660,"cache_creation":{"ephemeral_5m_input_tokens":148,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Ccmqrco8a8aUwydQvbng8","attributionAgent":"Explore","type":"assistant","uuid":"db18b658-7aee-457a-a84b-d08cfa7d5661","timestamp":"2026-07-07T01:38:52.313Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"db18b658-7aee-457a-a84b-d08cfa7d5661","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrdRLRjo1NRNWtVrvH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LCZCbqw3BhRcrW143zqKrJ","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/analysis/timeliness.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":148,"cache_read_input_tokens":52660,"output_tokens":94,"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":148},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":94,"cache_read_input_tokens":52660,"cache_creation_input_tokens":148,"cache_creation":{"ephemeral_5m_input_tokens":148,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ccmqrco8a8aUwydQvbng8","attributionAgent":"Explore","type":"assistant","uuid":"7ec7f252-6d9c-4433-8993-250114ee39c0","timestamp":"2026-07-07T01:38:52.517Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"7ec7f252-6d9c-4433-8993-250114ee39c0","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LCZCbqw3BhRcrW143zqKrJ","type":"tool_result","content":"\"\"\"\nSignal timeliness assessment.\n\nPhase 1: age-based only (days since last disclosure pub_date).\nPhase 2 (future): compare current price vs price at tx_date using a market data API.\n\"\"\"\n\nfrom datetime import datetime, date\n\n\ndef assess_age(last_pub_date: str) -> str:\n    \"\"\"\n    Returns 'fresh', 'watch', or 'late' based on days since last disclosure.\n      fresh : < 21 days  — thesis likely intact, meaningful runway\n      watch : 21–45 days — still worth monitoring, may be partially priced in\n      late  : > 45 days  — 45-day STOCK Act window fully elapsed, likely priced in\n    \"\"\"\n    try:\n        pub = datetime.strptime(last_pub_date[:10], \"%Y-%m-%d\").date()\n    except (ValueError, TypeError):\n        return \"unknown\"\n\n    age_days = (date.today() - pub).days\n    if age_days < 21:\n        return \"fresh\"\n    if age_days <= 45:\n        return \"watch\"\n    return \"late\"\n\n\ndef age_days(last_pub_date: str) -> int:\n    try:\n        pub = datetime.strptime(last_pub_date[:10], \"%Y-%m-%d\").date()\n        return (date.today() - pub).days\n    except (ValueError, TypeError):\n        return -1","is_error":false}]},"uuid":"447cafd6-ec11-4280-be6f-4b2bcde7e2eb","timestamp":"2026-07-07T01:38:52.653Z","sourceToolAssistantUUID":"7ec7f252-6d9c-4433-8993-250114ee39c0","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"447cafd6-ec11-4280-be6f-4b2bcde7e2eb","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqriwDevniLnirYVHgx","type":"message","role":"assistant","content":[{"type":"text","text":"Great! Now let me check the notification client to see how notifications are formatted."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":446,"cache_read_input_tokens":52808,"cache_creation":{"ephemeral_5m_input_tokens":446,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqriCZqRdeyHXvi8txJ","attributionAgent":"Explore","type":"assistant","uuid":"1dceef29-16d7-4b22-b2a0-647ec773b242","timestamp":"2026-07-07T01:38:53.882Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"1dceef29-16d7-4b22-b2a0-647ec773b242","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqriwDevniLnirYVHgx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MYLCb7qb43DsEPJc973bXY","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/notify/ntfy_client.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":446,"cache_read_input_tokens":52808,"output_tokens":101,"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":446},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":101,"cache_read_input_tokens":52808,"cache_creation_input_tokens":446,"cache_creation":{"ephemeral_5m_input_tokens":446,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqriCZqRdeyHXvi8txJ","attributionAgent":"Explore","type":"assistant","uuid":"f1fddf31-2333-40c3-9c2a-7428b5a880c4","timestamp":"2026-07-07T01:38:53.983Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"f1fddf31-2333-40c3-9c2a-7428b5a880c4","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MYLCb7qb43DsEPJc973bXY","type":"tool_result","content":"\"\"\"Push notifications via ntfy.sh.\"\"\"\nimport requests\nimport sys\nimport os\n\nsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\nfrom config import NTFY_BASE_URL, NTFY_TOPIC_SIGNALS, NTFY_TOPIC_STATUS\n\n\ndef push(title: str, body: str, topic: str = None, priority: str = \"default\", tags: list = None):\n    topic = topic or NTFY_TOPIC_SIGNALS\n    headers = {\n        \"Title\": title,\n        \"Priority\": priority,\n    }\n    if tags:\n        headers[\"Tags\"] = \",\".join(tags)\n    try:\n        r = requests.post(f\"{NTFY_BASE_URL}/{topic}\", data=body.encode(), headers=headers, timeout=10)\n        r.raise_for_status()\n        return True\n    except Exception as e:\n        print(f\"ntfy error: {e}\", file=sys.stderr)\n        return False\n\n\ndef waiting_on_user(message: str):\n    \"\"\"Notify that Claude Code needs user input to continue.\"\"\"\n    push(\n        title=\"Claude Code needs your input\",\n        body=message,\n        topic=NTFY_TOPIC_STATUS,\n        priority=\"high\",\n        tags=[\"robot\", \"question\"],\n    )","is_error":false}]},"uuid":"4f02a146-2f2f-430a-8406-d9c92fec170e","timestamp":"2026-07-07T01:38:54.122Z","sourceToolAssistantUUID":"f1fddf31-2333-40c3-9c2a-7428b5a880c4","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"4f02a146-2f2f-430a-8406-d9c92fec170e","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrqPATiivHTYQ2aDvD","type":"message","role":"assistant","content":[{"type":"text","text":"Perfect! Now let me get the LLM client to see how it works."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":534,"cache_read_input_tokens":53254,"output_tokens":102,"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":534},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":102,"cache_read_input_tokens":53254,"cache_creation_input_tokens":534,"cache_creation":{"ephemeral_5m_input_tokens":534,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqrpUbaqpYBQPzajHAW","attributionAgent":"Explore","type":"assistant","uuid":"0938281f-b29b-4c24-9a70-6c0d84443c41","timestamp":"2026-07-07T01:38:55.796Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"0938281f-b29b-4c24-9a70-6c0d84443c41","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqrqPATiivHTYQ2aDvD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012svRMfUSePmZ2ULjhRfYTK","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/analysis/llm_client.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":534,"cache_read_input_tokens":53254,"output_tokens":102,"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":534},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":102,"cache_read_input_tokens":53254,"cache_creation_input_tokens":534,"cache_creation":{"ephemeral_5m_input_tokens":534,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqrpUbaqpYBQPzajHAW","attributionAgent":"Explore","type":"assistant","uuid":"8471bb4d-20b8-4622-a469-51afd3b37ded","timestamp":"2026-07-07T01:38:55.836Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"8471bb4d-20b8-4622-a469-51afd3b37ded","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012svRMfUSePmZ2ULjhRfYTK","type":"tool_result","content":"\"\"\"\nAbstracted LLM client.\n\nSupports:\n  - Anthropic (claude-sonnet-4-6) via ANTHROPIC_API_KEY\n  - Google Gemini via GOOGLE_API_KEY\n\nProvider selected by LLM_PROVIDER in config.py (\"anthropic\" or \"google\").\nReturns plain text response. Raises LLMError on misconfiguration or API failure.\n\"\"\"\n\nimport sys\nimport os\n\nsys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))\nfrom config import LLM_PROVIDER, ANTHROPIC_API_KEY, GOOGLE_API_KEY\n\nMODEL_ANTHROPIC = \"claude-sonnet-4-6\"\nMODEL_GOOGLE    = \"gemini-2.0-flash\"\n\n\nclass LLMError(Exception):\n    pass\n\n\ndef call(prompt: str, system: str = \"\", max_tokens: int = 1024) -> tuple[str, dict]:\n    \"\"\"\n    Send a prompt, return (response_text, usage_dict).\n    usage_dict keys: input_tokens, output_tokens, cache_read_tokens, cache_write_tokens\n    \"\"\"\n    if LLM_PROVIDER == \"anthropic\":\n        return _call_anthropic(prompt, system, max_tokens=max_tokens)\n    if LLM_PROVIDER == \"google\":\n        return _call_google(prompt, system, max_tokens=max_tokens)\n    raise LLMError(f\"Unknown LLM_PROVIDER: {LLM_PROVIDER!r}\")\n\n\ndef _call_anthropic(prompt: str, system: str, max_tokens: int = 1024) -> tuple[str, dict]:\n    if not ANTHROPIC_API_KEY:\n        raise LLMError(\n            \"ANTHROPIC_API_KEY is not set. \"\n            \"Export it: export ANTHROPIC_API_KEY=sk-ant-...\"\n        )\n    try:\n        import anthropic\n    except ImportError:\n        raise LLMError(\"anthropic package not installed. Run: pip install anthropic\")\n\n    client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)\n    kwargs = {\"model\": MODEL_ANTHROPIC, \"max_tokens\": max_tokens, \"messages\": [{\"role\": \"user\", \"content\": prompt}]}\n    if system:\n        kwargs[\"system\"] = system\n\n    msg = client.messages.create(**kwargs)\n    text = msg.content[0].text\n    usage = {\n        \"input_tokens\":       msg.usage.input_tokens,\n        \"output_tokens\":      msg.usage.output_tokens,\n        \"cache_read_tokens\":  getattr(msg.usage, \"cache_read_input_tokens\", 0) or 0,\n        \"cache_write_tokens\": getattr(msg.usage, \"cache_creation_input_tokens\", 0) or 0,\n    }\n    return text, usage\n\n\ndef _call_google(prompt: str, system: str, max_tokens: int = 1024) -> tuple[str, dict]:\n    if not GOOGLE_API_KEY:\n        raise LLMError(\n            \"GOOGLE_API_KEY is not set. \"\n            \"Export it: export GOOGLE_API_KEY=...\"\n        )\n    try:\n        from google import genai\n        from google.genai import types\n    except ImportError:\n        raise LLMError(\"google-genai package not installed. Run: pip install google-genai\")\n\n    client = genai.Client(api_key=GOOGLE_API_KEY)\n    config = types.GenerateContentConfig(\n        system_instruction=system or None,\n        max_output_tokens=max_tokens,\n    )\n    resp = client.models.generate_content(\n        model=MODEL_GOOGLE,\n        contents=prompt,\n        config=config,\n    )\n    usage = {\n        \"input_tokens\":       getattr(resp.usage_metadata, \"prompt_token_count\", 0) or 0,\n        \"output_tokens\":      getattr(resp.usage_metadata, \"candidates_token_count\", 0) or 0,\n        \"cache_read_tokens\":  0,\n        \"cache_write_tokens\": 0,\n    }\n    return resp.text, usage","is_error":false}]},"uuid":"bd5d20e4-87a7-4fd5-adaf-65ae54e7392a","timestamp":"2026-07-07T01:38:56.006Z","sourceToolAssistantUUID":"8471bb4d-20b8-4622-a469-51afd3b37ded","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"bd5d20e4-87a7-4fd5-adaf-65ae54e7392a","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqryAygS81MHxVULRUS","type":"message","role":"assistant","content":[{"type":"text","text":"Perfect! Now let me search for any hardcoded thresholds or TODOs by checking a few more key files."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1148,"cache_read_input_tokens":53788,"cache_creation":{"ephemeral_5m_input_tokens":1148,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqrxaFvHM7wXtPh6bRC","attributionAgent":"Explore","type":"assistant","uuid":"8f8d25cf-f643-4da5-93e8-68963f88f245","timestamp":"2026-07-07T01:38:57.184Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"8f8d25cf-f643-4da5-93e8-68963f88f245","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqryAygS81MHxVULRUS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UtdPw4igURrLS6GFutu9h4","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/.env.example\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1148,"cache_read_input_tokens":53788,"output_tokens":106,"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":1148},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":106,"cache_read_input_tokens":53788,"cache_creation_input_tokens":1148,"cache_creation":{"ephemeral_5m_input_tokens":1148,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqrxaFvHM7wXtPh6bRC","attributionAgent":"Explore","type":"assistant","uuid":"02f0bc3d-273e-43a2-b5bf-560949616321","timestamp":"2026-07-07T01:38:57.644Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"02f0bc3d-273e-43a2-b5bf-560949616321","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UtdPw4igURrLS6GFutu9h4","type":"tool_result","content":"# Copy this to .env and fill in your keys\n# .env is gitignored — never commit it\n\nANTHROPIC_API_KEY=sk-ant-...\nGOOGLE_API_KEY=\nCONGRESS_GOV_API_KEY=    # free at https://api.congress.gov/sign-up/ — enables live bill data for predictive signals\n\n# ntfy.sh push topics — change these to something unique if you use ntfy.sh cloud\nNTFY_TOPIC_SIGNALS=\nNTFY_TOPIC_STATUS=","is_error":false}]},"uuid":"6ff75f7c-5700-41c5-8543-64a0d6e36502","timestamp":"2026-07-07T01:38:57.787Z","sourceToolAssistantUUID":"02f0bc3d-273e-43a2-b5bf-560949616321","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"6ff75f7c-5700-41c5-8543-64a0d6e36502","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqs6cwwy5uTaWJG8WKB","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check if there are test files or example data."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":241,"cache_read_input_tokens":54936,"cache_creation":{"ephemeral_5m_input_tokens":241,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011Ccmqs67SPyZUzoHrJjaRj","attributionAgent":"Explore","type":"assistant","uuid":"a5c34e65-bc70-4266-bb0c-a7fbb1ef0dec","timestamp":"2026-07-07T01:38:58.740Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"a5c34e65-bc70-4266-bb0c-a7fbb1ef0dec","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011Ccmqs6cwwy5uTaWJG8WKB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017EBt8Lf9XGT2n5v42J8dDc","name":"Bash","input":{"command":"curl -s \"https://api.github.com/repos/jamesjlopez/congressional-trading/contents/tests\" 2>&1 | head -50"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":241,"cache_read_input_tokens":54936,"output_tokens":102,"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":241},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":102,"cache_read_input_tokens":54936,"cache_creation_input_tokens":241,"cache_creation":{"ephemeral_5m_input_tokens":241,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011Ccmqs67SPyZUzoHrJjaRj","attributionAgent":"Explore","type":"assistant","uuid":"cfeb0832-1394-4236-9d34-5ada0078441f","timestamp":"2026-07-07T01:38:59.059Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
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{"parentUuid":"d697f072-9a6c-4a0d-b5e8-1f4a4749dfc1","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqsEH5Dn1vhN9PjFU8V","type":"message","role":"assistant","content":[{"type":"text","text":"Excellent! Let me get the test file to see example signals."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":893,"cache_read_input_tokens":55177,"cache_creation":{"ephemeral_5m_input_tokens":893,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"diagnostics":null},"requestId":"req_011CcmqsCoGhgFCXuA3Awy6J","attributionAgent":"Explore","type":"assistant","uuid":"c10d709a-2d36-4792-aef3-70c205c66442","timestamp":"2026-07-07T01:39:01.113Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"c10d709a-2d36-4792-aef3-70c205c66442","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqsEH5Dn1vhN9PjFU8V","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DE5hcLZu8F6Agyw9SDTfHW","name":"Bash","input":{"command":"curl -s \"https://raw.githubusercontent.com/jamesjlopez/congressional-trading/main/tests/test_notification_policy.py\""},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":893,"cache_read_input_tokens":55177,"output_tokens":100,"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":893},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":100,"cache_read_input_tokens":55177,"cache_creation_input_tokens":893,"cache_creation":{"ephemeral_5m_input_tokens":893,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqsCoGhgFCXuA3Awy6J","attributionAgent":"Explore","type":"assistant","uuid":"c2e00a36-629a-4ea9-8358-29559ce92b10","timestamp":"2026-07-07T01:39:01.223Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"c2e00a36-629a-4ea9-8358-29559ce92b10","isSidechain":true,"promptId":"e4d3da35-68e0-4286-acfb-75db076519a4","agentId":"a9d512203821e0198","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DE5hcLZu8F6Agyw9SDTfHW","type":"tool_result","content":"from __future__ import annotations\n\nfrom datetime import datetime, timedelta\nimport os\nimport sqlite3\nimport tempfile\nimport unittest\n\nfrom analysis.notification_policy import (\n    edge_factors,\n    evaluate_notification_candidate,\n    is_buy_recommendation,\n    recent_ticker_notification,\n)\n\n\ndef _signal(**overrides):\n    sig = {\n        \"ticker\": \"EDGE\",\n        \"member_count\": 2,\n        \"max_tier\": 4,\n        \"has_options\": False,\n        \"committee_score\": 3.0,\n        \"small_cap_score\": 0.0,\n        \"sell_pressure_score\": 0.0,\n        \"strategic_score\": 3.0,\n        \"disruption_score\": 1.0,\n    }\n    sig.update(overrides)\n    return sig\n\n\ndef _research(**overrides):\n    research = {\n        \"conviction\": \"HIGH\",\n        \"recommended_action\": \"BUY NOW - catalyst remains timely\",\n        \"risk_factors\": \"Valuation and execution risk.\",\n        \"strategic_score\": 3,\n        \"disruption_score\": 1,\n    }\n    research.update(overrides)\n    return research\n\n\nclass NotificationPolicyTest(unittest.TestCase):\n    def test_allows_explicit_high_signal_buy(self):\n        decision = evaluate_notification_candidate(_signal(), _research(), 11.0)\n\n        self.assertTrue(decision.allowed)\n        self.assertGreaterEqual(len(decision.edge_factors), 3)\n        self.assertIn(\"multi-member cluster\", decision.edge_factors)\n        self.assertIn(\"direct committee alignment\", decision.edge_factors)\n\n    def test_rejects_monitor_or_not_buy_text(self):\n        self.assertFalse(is_buy_recommendation(\"MONITOR - do not buy until earnings\"))\n        self.assertFalse(is_buy_recommendation(\"Do not buy despite the catalyst\"))\n\n        decision = evaluate_notification_candidate(\n            _signal(),\n            _research(recommended_action=\"MONITOR - wait before buying\"),\n            11.0,\n        )\n\n        self.assertFalse(decision.allowed)\n        self.assertIn(\"not an explicit buy\", decision.reason)\n\n    def test_rejects_high_score_without_enough_edge_factors(self):\n        sig = _signal(\n            member_count=1,\n            max_tier=2,\n            has_options=False,\n            committee_score=1.0,\n            small_cap_score=0.0,\n            strategic_score=1.0,\n            disruption_score=1.0,\n        )\n        research = _research(strategic_score=1, disruption_score=1)\n\n        decision = evaluate_notification_candidate(sig, research, 12.0)\n\n        self.assertFalse(decision.allowed)\n        self.assertIn(\"edge factors\", decision.reason)\n        self.assertEqual(edge_factors(sig, research), ())\n\n    def test_rejects_meaningful_sell_pressure(self):\n        decision = evaluate_notification_candidate(\n            _signal(sell_pressure_score=-1.5),\n            _research(),\n            12.0,\n        )\n\n        self.assertFalse(decision.allowed)\n        self.assertIn(\"sell pressure\", decision.reason)\n\n    def test_recent_ticker_notification_uses_rolling_cooldown(self):\n        now = datetime(2026, 6, 5, 12, 0, 0)\n        with tempfile.TemporaryDirectory() as tmp:\n            db_path = os.path.join(tmp, \"signals.db\")\n            conn = sqlite3.connect(db_path)\n            conn.execute(\n                \"\"\"\n                CREATE TABLE signal_outcomes (\n                    ticker TEXT NOT NULL,\n                    notified_at TEXT NOT NULL\n                )\n                \"\"\"\n            )\n            conn.execute(\n                \"INSERT INTO signal_outcomes (ticker, notified_at) VALUES (?, ?)\",\n                (\"AMD\", (now - timedelta(days=3)).isoformat(timespec=\"seconds\")),\n            )\n            conn.commit()\n            conn.close()\n\n            in_cooldown, latest, days_left = recent_ticker_notification(\n                \"AMD\",\n                db_path=db_path,\n                cooldown_days=14,\n                now=now,\n            )\n            stale, _, stale_days_left = recent_ticker_notification(\n                \"MSI\",\n                db_path=db_path,\n                cooldown_days=14,\n                now=now,\n            )\n\n        self.assertTrue(in_cooldown)\n        self.assertEqual(latest, now - timedelta(days=3))\n        self.assertEqual(days_left, 11)\n        self.assertFalse(stale)\n        self.assertEqual(stale_days_left, 0)\n\n\nif __name__ == \"__main__\":\n    unittest.main()","is_error":false}]},"uuid":"13248ed7-63fc-4c0d-96a1-18b0595e6640","timestamp":"2026-07-07T01:39:01.373Z","sourceToolAssistantUUID":"c2e00a36-629a-4ea9-8358-29559ce92b10","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}
{"parentUuid":"13248ed7-63fc-4c0d-96a1-18b0595e6640","isSidechain":true,"agentId":"a9d512203821e0198","message":{"model":"claude-haiku-4-5-20251001","id":"msg_011CcmqsN3eGFeaYraW2iifF","type":"message","role":"assistant","content":[{"type":"text","text":"Perfect! Now I have all the key information. Let me compile my comprehensive summary.\n\n## Comprehensive Analysis of Congressional-Trading Repository\n\nBased on my thorough exploration of the codebase, here's the structured summary:\n\n---\n\n### **1. SCORING/SIGNAL SYSTEM — What Makes a Trade \"High Conviction\"**\n\n**Core Algorithm** (`analysis/scorer.py`):\nThe system scores tickers on a 0-13 point scale based on aggregate congressional activity:\n\n| Component | Range | Logic |\n|-----------|-------|-------|\n| **committee_score** | 0–3 | Member's committee has jurisdiction over company sector (Armed Services + defense contractor = 3) |\n| **track_record_score** | 0–2 | Historical win rate; defaults to 1.0 until data available |\n| **cluster_score** (with breadth discount) | 0–3 | 2 buyers=1pt, 3=2pts, 4+=3pts. Multiplied by breadth discount (0.4–1.0) to penalize mega-caps routinely held by 15+ members across history |\n| **options_bonus** | 0 or 2 | Any options trade on ticker (+2 — implies timing knowledge) |\n| **size_score** | 0–5 | Highest dollar tier: $1K-$15K=1, $15K-$50K=2, ..., $500K+=5 |\n| **small_cap_bonus** | −2.5 to +6 | Market cap tier incentive: mega-cap >$100B = -2.5, large $10-100B = -0.5, mid $2-10B = +2.0, small $500M-$2B = +4.5, micro <$500M = +6.0 |\n| **sell_pressure_penalty** | −4.0 to 0 | Penalty when sellers ≥ buyers (e.g., 1 of 3 sellers = -0.5, ratio ≥1.0 = -4.0) |\n| **strategic_score** | 0–3 | LLM assessment: 0=no national security/infra nexus, 1=indirect, 2=defense/energy contractor with gov exposure, 3=direct military/dual-use |\n| **disruption_score** | 0–3 | LLM assessment: 0=incremental, 1=meaningful differentiation, 2=significant market-shift, 3=paradigm-shifting |\n\n**Score Thresholds** (`config.py`):\n- `SCORE_RESEARCH_THRESHOLD = 5.0` → Triggers LLM research (shallow: company summary, news, catalyst, timeliness)\n- `SCORE_NOTIFY_THRESHOLD = 9.0` → Eligible for push notifications (before quality gates)\n- `SCORE_NOTIFY_THRESHOLD` signals also get **deep research** (adds: risk_factors, recommended_action)\n\n**Key Subtlety**: The raw `total_score` is stored unchanged in the signals table for historical consistency. **Timeliness multipliers are applied only at notification-decision time** (never at scoring time).\n\n---\n\n### **2. SIGNAL NOTIFICATION — What Data Is Included**\n\n**Notification Format** (from `run_daily.py:_send_signal_notification`):\n```\nTitle: CongressWatch: $TICKER (score X.X)\nBody:\n  - Company name & member count (e.g., \"1 member bought\", \"3 members bought\")\n  - Size tier ($1K-$15K, etc.)\n  - Options flag if present\n  - Timeliness (FRESH <21d | WATCH 21-45d | LATE >45d)\n  - Conviction (LOW | MEDIUM | HIGH from deep research)\n  - Recommended action (BUY NOW | MONITOR | AVOID)\n  - Company summary (max 300 chars from LLM)\n  - Catalyst hypothesis (max 200 chars from LLM)\n  - Full recommended action text (max 40 chars)\n\nPriority: HIGH if adjusted_score ≥ 9, else DEFAULT\nTags: chart_with_upwards_trend, money_with_wings\nTopic: ntfy.sh (configurable, defaults to \"congressWatchSignals\")\n```\n\n**Notification Gating** (ALL gates must pass):\n1. Adjusted score ≥ `SCORE_NOTIFY_THRESHOLD` (9.0 default) after timeliness multiplier\n2. Deep research completed with `risk_factors` and `recommended_action` fields\n3. Conviction ≥ `NOTIFY_MIN_CONVICTION` (HIGH by default; can be LOW/MEDIUM/HIGH)\n4. `recommended_action` starts with explicit BUY/ADD/ACCUMULATE/INITIATE (not MONITOR/HOLD/PASS/AVOID)\n5. Sell pressure not too conflicting: `sell_pressure_score ≥ -1.0` (default)\n6. At least `NOTIFY_MIN_EDGE_FACTORS` (3 default) independent edge factors:\n   - Multi-member cluster (≥2 buyers)\n   - Large disclosed size (tier ≥4, i.e., $100K+)\n   - Options activity\n   - Direct committee alignment (score ≥2)\n   - Undercovered small/mid-cap (score ≥2)\n   - Strong LLM catalyst (strategic + disruption ≥4)\n7. Ticker not notified within `NOTIFY_TICKER_COOLDOWN_DAYS` (14 days default)\n8. `MAX_NOTIFICATIONS_PER_RUN = 1` — only single strongest candidate fires per run\n\n**Timeliness Multipliers** (applied only at notification gate):\n```python\nTIMELINESS_MULTIPLIERS = {\n    \"fresh\": 1.0,   # < 21 days since disclosure\n    \"watch\": 0.85,  # 21–45 days\n    \"late\": 0.70,   # > 45 days\n}\n```\nExample: score 10.5 with \"late\" timeliness → 10.5 × 0.70 = 7.35 → below 9.0 threshold, not notified.\n\n---\n\n### **3. SHADOW/PREDICTIVE MODE — Forward-Looking Watchlist**\n\n**NOT a traditional \"shadow mode\"** but rather a **predictive/anticipatory signal pipeline** (`analysis/predictor.py`):\n\n**What it tracks**:\n- Predicted upcoming trades BEFORE disclosure happens\n- Stores in `predicted_signals` table (separate from reactive `signals`)\n- Per-member behavioral profiles + predicted ticker watchlists\n\n**How it works**:\n1. Analyzes top members with ≥5 historical trades + committee data\n2. Pulls 3 years of trade history + active legislation relevant to their committees\n3. Calls LLM: \"What will this member likely trade in next 45 days?\"\n4. Stores 3-5 predicted tickers per member with:\n   - `predicted_action` (buy/sell)\n   - `confidence` (LOW/MEDIUM/HIGH)\n   - `legislation_catalyst` (specific bill or \"General sector exposure\")\n   - `historical_precedent` (prior similar trades)\n   - `behavioral_profile` (member's trading style)\n   - `committee_alignment` (direct/indirect/none vs predicted sector)\n\n**Backtesting/Confirmation Mechanism**:\n- When real disclosures come in via ingest, `confirm_predictions()` marks matching predicted tickers as `confirmed=1`\n- Tracks accuracy (confirmed vs total predictions)\n- Feeds into consensus scoring: `load_consensus()` aggregates predictions by ticker and scores them\n\n**Consensus Scoring** (for predicted-only signals):\nCombines cluster size, confidence, small-cap bonus, win rate, committee alignment, and historical precedent to rank predicted tickers alongside reactive signals in the final report.\n\n---\n\n### **4. HISTORICAL TRADE DATA — Storage & Backtesting Support**\n\n**Data Stored**:\n- `trades` table: append-only source data (14k+ trades from Capitol Trades HTML scraper)\n  - `tx_date` (when trade executed)\n  - `pub_date` (when disclosure published — lag up to 45 days under STOCK Act)\n  - `amount_tier` (1-6 derived from dollar range)\n  - `price_at_trade` (attempted from historical yfinance lookups)\n  - `is_option` (0/1 flag)\n\n- `signals` table: rebuilt from scratch every scoring run\n  - Aggregates trades per ticker within lookback window\n  - Stores all score components + `return_from_tx_pct` and `return_from_pub_pct`\n\n- `signal_outcomes` table: persistent tracking of notifications sent\n  - Records score at notification time\n  - Tracks 30d/60d post-notification returns (for win/loss assessment)\n  - Marks outcome as \"win\"/\"loss\"/\"pending\"\n\n- `predicted_signals` table: forward-looking watchlist\n  - `confirmed` flag (1 when real disclosure matches prediction)\n  - Tracks prediction accuracy over time\n\n**Backtest Readiness**:\n✓ Full historical trade data available (append-only `trades` table)\n✓ Score reproduction is deterministic (all scoring logic hardcoded)\n✓ Return calculations stored (`return_from_tx_pct`, `return_from_pub_pct`)\n✓ Timeliness-adjusted scores can be recomputed (multipliers in config)\n⚠ **Gap**: Win rate currently uses current price snapshots, not fixed-horizon returns (see backlog item)\n\n---\n\n### **5. GRADUATION CRITERIA — Shadow to Real Notification**\n\n**There is no explicit \"shadow mode\" state transition.** Instead:\n\n**Regular Scoring Pipeline** (always runs):\n- All tickers with buy activity in lookback window get scored\n- Signals ≥5.0 are researched\n- Signals ≥9.0 are candidates for deep research and notification\n\n**Notification Eligibility** (at decision time):\nA signal \"graduates\" to notification when it satisfies ALL gates listed in section 2 above. The key transitions are:\n1. Score rises above 5.0 → Basic LLM research triggered\n2. Score rises above 9.0 → Eligible for deep research + notification gates\n3. All 7 quality gates + cooldown check pass → Notification fires (max 1/run)\n\n**No persistent \"shadow state\"** — signals are scored fresh each run based on new trades.\n\n---\n\n### **6. EXAMPLE SIGNALS & TEST FIXTURES**\n\n**Test File** (`tests/test_notification_policy.py`):\n```python\n# Example high-conviction signal that passes all gates:\nsig = {\n    \"ticker\": \"EDGE\",\n    \"member_count\": 2,        # multi-member cluster (edge factor ✓)\n    \"max_tier\": 4,             # $100K-$250K (large size edge factor ✓)\n    \"committee_score\": 3.0,    # direct alignment (edge factor ✓)\n    \"small_cap_score\": 0.0,\n    \"sell_pressure_score\": 0.0,\n    \"strategic_score\": 3.0,    # LLM catalyst (edge factor ✓)\n    \"disruption_score\": 1.0,\n}\nresearch = {\n    \"conviction\": \"HIGH\",      # passes conviction gate ✓\n    \"recommended_action\": \"BUY NOW - catalyst remains timely\",  # explicit buy ✓\n    \"risk_factors\": \"Valuation and execution risk.\",  # has risk factors ✓\n}\nscore = 11.0  # passes threshold gate ✓\n# Result: ALLOWED (4 edge factors, all gates pass)\n```\n\n---\n\n### **7. OPEN DECISIONS & GAPS IN THE CODE**\n\n**From `backlog.md`**:\n\n| Issue | Impact | Status |\n|-------|--------|--------|\n| **Committee-sector mapping too coarse** | Over-broad heuristic matches (e.g., Armed Services → any tech stock). Solution: per-trade LLM committee_score, fallback to heuristic. | Deferred (cost + prompt design concern) |\n| **Win rate is snapshot, not realized** | Current win rate uses today's price, not fixed-horizon returns. Affects member credibility scoring. | Deferred (14k+ yfinance calls needed) |\n| **Signal return uses first_tx_date, not per-member** | Multi-buyer signals compute return from earliest buyer's trade date, not each member's individual entry. | Moderate impact, deferred |\n| **No sell signals** | Sell-count tracked but not acted on. Could fire separate EXIT alerts. | Feature request |\n| **Options direction not distinguished** | Calls get +2 bonus same as puts. Puts should warrant separate alert (inverse signal). | Feature request |\n| **No sector-wide clustering** | 5+ members buying same sector in one week not flagged (potential macro signal). | Feature request |\n\n**Hardcoded Values to Review**:\n- `_COMMITTEE_SECTOR_MAP` in `scorer.py` (hand-coded committee→sector mappings)\n- `_TIER_LABELS` in `researcher.py` ($1K-$15K, etc. tier ranges)\n- `RESEARCH_PROMPT_TEMPLATE` in `researcher.py` (strategic/disruption score guides 0-3)\n- `PREDICT_PROMPT_TEMPLATE` in `predictor.py` (member behavior analysis prompt)\n\n**TODOs & Future Work**:\n- Phase 2 of timeliness: compare current price vs. price-at-tx_date (currently age-based only)\n- LLM-derived committee_score per trade (not just heuristic)\n- Sell signal alerts\n- Options call/put differentiation\n- Sector-level clustering alerts\n\n---\n\n### **8. ALGORITHM ASSESSMENT FOR BACKTESTING**\n\n**Strengths for backtesting**:\n- ✓ Fully deterministic scoring (no randomness, all rules explicit)\n- ✓ Historical trade data complete (14k+ records with tx_date, pub_date, amount_tier)\n- ✓ Return data stored (can reconstruct tx-date and pub-date returns)\n- ✓ LLM components can be cached in `research_cache` (no re-calls on replays)\n- ✓ Timeliness multipliers configurable (can test different decay profiles)\n\n**Gaps for backtesting**:\n- ⚠ Win rate requires fixed-horizon returns (e.g., 60-day post-trade); currently uses current price snapshots\n- ⚠ Market cap data fetched once per run, cached; not available for all historical tickers\n- ⚠ LLM research (strategic_score, disruption_score) cached but may reflect current knowledge, not knowledge-as-of-trade-date\n- ⚠ Committee assignments refresh weekly; historical committee data for past years may not be available\n- ⚠ `small_cap_bonus` is recalculated on second run after prices fetched; first-run results show 0 for all signals\n\n**Recommended approach for backtest**:\n1. Use pure scoring components (committee, cluster, size, track record)\n2. Skip or mock LLM scores (strategic/disruption) or use cached values as-is\n3. Reconstruct returns using historical price data for each signal's tx_date and pub_date\n4. Test notification gate with varying threshold/conviction/edge-factor configs\n5. Track outcome vs. 30/60d returns using `signal_outcomes` table\n\n---\n\n**Key Files for Reference**:\n- `/analysis/scorer.py` — Core scoring algorithm (16KB, fully readable)\n- `/analysis/notification_policy.py` — Notification quality gates (5.7KB)\n- `/analysis/researcher.py` — LLM research pipeline & deep prompts (21.5KB)\n- `/analysis/predictor.py` — Predictive/forward-looking signals (28.8KB)\n- `/config.py` — All tunable thresholds & API keys\n- `/run_daily.py` — Full pipeline orchestration\n- `/db/schema.sql` — Data model"}],"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1309,"cache_read_input_tokens":56070,"output_tokens":3911,"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":1309},"inference_geo":"","iterations":[{"input_tokens":0,"output_tokens":3911,"cache_read_input_tokens":56070,"cache_creation_input_tokens":1309,"cache_creation":{"ephemeral_5m_input_tokens":1309,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcmqsMYsoMGWmn16kzh5Y","attributionAgent":"Explore","type":"assistant","uuid":"0409d773-4a81-4edb-9e6b-7f306bea9258","timestamp":"2026-07-07T01:39:45.155Z","userType":"external","entrypoint":"sdk-ts","cwd":"/workspace/agent","sessionId":"8263edf6-0dc2-4e24-b715-88fe9965e122","version":"2.1.128","gitBranch":"HEAD"}