[ { "path": ".github/workflows/quarterly_run.yml", "content": "name: SEC 13F Quarterly Analysis\n\non:\n schedule:\n # Run daily at 14:00 UTC (= 15:00 CET / 16:00 CEST)\n # The Python script itself decides if today is the right date\n - cron: '0 14 14-20 2,5,8,11 *'\n workflow_dispatch:\n inputs:\n force_run:\n description: 'Force run regardless of date'\n required: false\n default: 'false'\n target_date:\n description: 'Override target date (YYYY-MM-DD)'\n required: false\n default: ''\n\njobs:\n analyze:\n runs-on: ubuntu-latest\n timeout-minutes: 30\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v4\n\n - name: Set up Python 3.11\n uses: actions/setup-python@v5\n with:\n python-version: '3.11'\n cache: 'pip'\n\n - name: Install dependencies\n run: pip install -r requirements.txt\n\n - name: Check if today is a run date\n id: date_check\n env:\n FORCE_RUN: ${{ github.event.inputs.force_run }}\n TARGET_DATE: ${{ github.event.inputs.target_date }}\n TZ: Europe/Berlin\n run: |\n python src/date_check.py\n continue-on-error: false\n\n - name: Fetch SEC 13F Filings\n if: steps.date_check.outputs.should_run == 'true'\n env:\n TZ: Europe/Berlin\n OPENFIGI_API_KEY: ${{ secrets.OPENFIGI_API_KEY }}\n run: python src/fetch_filings.py\n\n - name: Parse Holdings & Calculate Deltas\n if: steps.date_check.outputs.should_run == 'true'\n run: python src/parse_13f.py\n\n - name: Calculate Conviction Scores\n if: steps.date_check.outputs.should_run == 'true'\n run: python src/scoring.py\n\n - name: Claude Analysis Round 1 - Identify Top 5 (Haiku)\n if: steps.date_check.outputs.should_run == 'true'\n env:\n ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}\n run: python src/analyze_claude_round1.py\n\n - name: Fetch Options Data via Tradier\n if: steps.date_check.outputs.should_run == 'true'\n env:\n TRADIER_API_KEY: ${{ secrets.TRADIER_API_KEY }}\n run: python src/options_lookup.py\n\n - name: Claude Analysis Round 2 - Select Best Options (Sonnet)\n if: steps.date_check.outputs.should_run == 'true'\n env:\n ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}\n run: python src/analyze_claude_round2.py\n\n - name: Backtest - Track Historical Signal Performance\n if: steps.date_check.outputs.should_run == 'true'\n run: python src/backtest.py\n\n - name: Generate & Send Report via Gmail\n if: steps.date_check.outputs.should_run == 'true'\n env:\n GMAIL_ADDRESS: ${{ secrets.GMAIL_ADDRESS }}\n GMAIL_APP_PASSWORD: ${{ secrets.GMAIL_APP_PASSWORD }}\n run: python src/send_report.py\n\n - name: Commit updated holdings data\n if: steps.date_check.outputs.should_run == 'true'\n run: |\n git config --local user.email \"action@github.com\"\n git config --local user.name \"GitHub Action\"\n git add data/holdings/ reports/\n git diff --staged --quiet || git commit -m \"πŸ“Š Quarterly 13F analysis $(date +%Y-%m-%d)\"\n git push\n\n # ── Failure notification ──────────────────────────────────────────────────\n # Runs only when the workflow fails AND the main pipeline was supposed to run.\n # Uses the existing Gmail credentials so no extra services are needed.\n - name: Send failure alert\n if: failure() && steps.date_check.outputs.should_run == 'true'\n env:\n GMAIL_ADDRESS: ${{ secrets.GMAIL_ADDRESS }}\n GMAIL_APP_PASSWORD: ${{ secrets.GMAIL_APP_PASSWORD }}\n run: |\n python src/notify.py \\\n \"GitHub Actions – SEC 13F Pipeline\" \\\n \"Workflow run ${{ github.run_id }} failed. Check: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}\"\n" }, { "path": "README.md", "content": "# SEC 13F Smart Money Analyzer\n\nAutomated quarterly analysis of 13F filings from 13 top institutional investors.\nRuns automatically on **16 May / 16 Aug / 16 Nov / 16 Feb** (next business day if weekend).\nDelivers a **Top 5 stock picks + specific option trades** report via Gmail.\n\n---\n\n## Monitored Institutions\n\n| Institution | CIK |\n|---|---|\n| Situational Awareness LP | 0002045724 |\n| Yale University | 0000938582 |\n| Gates Foundation Trust | 0001166559 |\n| Harvard Management Co | 0001082621 |\n| Brown University | 0001664741 |\n| Duke University | 0001439873 |\n| TCI Fund (Chris Hohn) | 0001649339 |\n| Pershing Square (Ackman) | 0001336528 |\n| Tiger Global (Coleman) | 0001167483 |\n| Coatue (Laffont) | 0001766502 |\n| D1 Capital (Sundheim) | 0001747057 |\n| Viking Global (Halvorsen) | 0001103804 |\n| AQR Capital (Asness) | 0001167557 |\n\n---\n\n## Pipeline\n\n```\nGitHub Actions Trigger (14:00 UTC, 14th-20th of target months)\n β”‚\n β”œβ”€ date_check.py β†’ Is today the right run date?\n β”œβ”€ fetch_filings.py β†’ SEC EDGAR: fetch 13F XMLs for all 13 CIKs\n β”‚ + CUSIPβ†’Ticker mapping (OpenFIGI)\n β”‚ + Stock split detection (yfinance)\n β”œβ”€ parse_13f.py β†’ Delta vs. prior quarter, portfolio weights\n β”œβ”€ scoring.py β†’ Conviction scores, clustering detection\n β”œβ”€ analyze_claude_round1.py β†’ Claude: Top 5 stocks + investment theses\n β”œβ”€ options_lookup.py β†’ Tradier: Real option chains for Top 5\n β”œβ”€ analyze_claude_round2.py β†’ Claude: Select best specific option per stock\n └─ send_report.py β†’ HTML report β†’ Gmail\n```\n\n---\n\n## Setup\n\n### 1. Fork / Clone this repo\n\n```bash\ngit clone https://github.com/YOUR_USERNAME/sec-smart-money.git\ncd sec-smart-money\n```\n\n### 2. Add GitHub Secrets\n\nGo to **Settings β†’ Secrets and variables β†’ Actions β†’ New repository secret**:\n\n| Secret Name | Value |\n|---|---|\n| `ANTHROPIC_API_KEY` | Your Anthropic API key |\n| `TRADIER_API_KEY` | Your Tradier API key (live or sandbox) |\n| `GMAIL_ADDRESS` | Your Gmail address (e.g. `you@gmail.com`) |\n| `GMAIL_APP_PASSWORD` | Gmail App Password (not your login password) |\n\n**How to create a Gmail App Password:**\n1. Go to [myaccount.google.com/security](https://myaccount.google.com/security)\n2. Enable 2-Factor Authentication if not already\n3. Search for \"App passwords\"\n4. Create a new app password β†’ select \"Mail\" and \"Other (custom)\"\n5. Copy the 16-character password into the `GMAIL_APP_PASSWORD` secret\n\n**Tradier API:**\n- Live account: use `https://api.tradier.com/v1` (default in config.py)\n- Paper account: change `TRADIER_BASE_URL` in `src/config.py` to `https://sandbox.tradier.com/v1`\n\n### 3. Configure Tradier Account Type\n\nEdit `src/config.py`:\n```python\nTRADIER_BASE_URL = \"https://api.tradier.com/v1\" # Live (default)\n# TRADIER_BASE_URL = \"https://sandbox.tradier.com/v1\" # Paper\n```\n\n### 4. Test a Manual Run\n\nGo to **Actions β†’ SEC 13F Quarterly Analysis β†’ Run workflow**\nSet `force_run = true` to bypass the date check.\n\n---\n\n## Local Development\n\n```bash\npip install -r requirements.txt\n\n# Set environment variables\nexport ANTHROPIC_API_KEY=\"your_key\"\nexport TRADIER_API_KEY=\"your_key\"\nexport GMAIL_ADDRESS=\"you@gmail.com\"\nexport GMAIL_APP_PASSWORD=\"your_app_password\"\n\n# Run individual steps\npython src/fetch_filings.py\npython src/parse_13f.py\npython src/scoring.py\npython src/analyze_claude_round1.py\npython src/options_lookup.py\npython src/analyze_claude_round2.py\npython src/send_report.py\n```\n\n---\n\n## Data Architecture\n\n```\ndata/holdings/\n YYYY-MM-DD_raw_holdings.json ← Raw SEC XML data + CUSIP mapping\n YYYY-MM-DD_holdings_parsed.json ← Delta-enriched positions\n YYYY-MM-DD_scores.json ← Conviction scores\n YYYY-MM-DD_claude_round1.json ← Top 5 stocks from Claude\n YYYY-MM-DD_options.json ← Tradier options data\n YYYY-MM-DD_final_analysis.json ← Combined analysis\nreports/\n YYYY-MM-DD_report.html ← Final HTML report (committed to repo)\n```\n\n---\n\n## Known Limitations (by design)\n\n| Limitation | Impact | Mitigation |\n|---|---|---|\n| 13F data is 45 days old | Positions may have changed | Use as idea generator only |\n| Long-only AUM denominator | Portfolio weights overstated | Disclaimer in report |\n| No shorts/hedges visible | Incomplete picture | Noted in report |\n| Stock splits adjusted | High accuracy, not perfect | yfinance split check |\n| Options prices at analysis time | Change constantly | Always verify before trading |\n\n---\n\n## Conviction Score Formula\n\n```\nRaw Score = (0.40 Γ— portfolio_weight%) + (0.30 Γ— |delta%|)\nCluster Bonus: Γ—1.5 if 3+ funds buy same ticker\nNormalized: min-max scaled to [0, 100]\n```\n\n**Flags:**\n- `HIGH_CONVICTION`: New position β‰₯3% of portfolio\n- `NEW_POSITION`: Not in prior quarter's filing\n- `AGGRESSIVE_ADD`: Position increased >20% by share count\n- `CLUSTER`: 3+ monitored funds buying same ticker simultaneously\n- `TOP10_ENTRY`: New position entered directly in top 10 holdings\n\n---\n\n## Disclaimer\n\nThis tool is for educational and informational purposes only. 13F data is publicly\navailable but delayed. Nothing in this repository constitutes investment advice.\nOptions trading involves significant risk of loss. Always conduct your own research\nbefore making any investment decisions.\n" }, { "path": "data/holdings/2026-04-08_claude_round1.json", "content": "{\n \"analysis_date\": \"2026-04-08\",\n \"market_context\": \"The market appears to be experiencing significant institutional repositioning into AI infrastructure, defensive value plays, and alternative assets including crypto ETFs. Notable convergence among elite funds on mega-cap tech suggests consensus building around AI beneficiaries despite potential valuation concerns.\",\n \"top5\": [\n {\n \"rank\": 1,\n \"ticker\": \"PLTR\",\n \"company_name\": \"PALANTIR TECHNOLOGIES INC\",\n \"conviction_score\": 100.0,\n \"thesis\": \"TCI Fund's massive 66% portfolio allocation signals extraordinary conviction in Palantir's AI/data analytics platform amid government and enterprise adoption acceleration. Chris Hohn's concentrated bet suggests transformational growth expectations in the AI infrastructure space.\",\n \"key_buyers\": [\n \"TCI Fund (Chris Hohn)\"\n ],\n \"cluster_signal\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Extreme concentration risk given 66% portfolio weight; government contract dependency and competitive AI landscape present execution risks.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 2,\n \"ticker\": \"AMZN\",\n \"company_name\": \"AMAZON COM INC\",\n \"conviction_score\": 21.0,\n \"thesis\": \"Six elite institutions simultaneously initiated positions including Pershing Square, Harvard, and Tiger Global, suggesting broad consensus on Amazon's AI cloud dominance and retail resilience. The coordinated buying pattern indicates institutional recognition of Amazon's multiple growth vectors.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"Pershing Square (Ackman)\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"Viking Global (Halvorsen)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Regulatory scrutiny and competitive pressures in cloud computing; mature e-commerce growth may limit upside.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 3,\n \"ticker\": \"NVDA\",\n \"company_name\": \"NVIDIA CORPORATION\",\n \"conviction_score\": 19.8,\n \"thesis\": \"Five top-tier funds including TCI and Harvard established new positions, signaling continued institutional confidence in NVIDIA's AI chip monopoly despite valuation concerns. The broad-based buying suggests institutions view current levels as attractive entry points.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"TCI Fund (Chris Hohn)\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"High valuation multiples and potential AI bubble concerns; semiconductor cyclicality and geopolitical tensions around China markets.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 4,\n \"ticker\": \"BRK/A\",\n \"company_name\": \"BERKSHIRE HATHAWAY INC DEL\",\n \"conviction_score\": 41.3,\n \"thesis\": \"Gates Foundation's substantial 27.6% allocation to Berkshire signals flight to defensive value and Buffett's crisis-tested investment acumen. This concentrated bet suggests institutional preparation for market volatility while maintaining equity exposure.\",\n \"key_buyers\": [\n \"Gates Foundation Trust\"\n ],\n \"cluster_signal\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Succession concerns post-Buffett era; large cash position may drag returns in bull markets; concentrated exposure to specific sectors.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 5,\n \"ticker\": \"IBIT\",\n \"company_name\": \"ISHARES BITCOIN TRUST ETF\",\n \"conviction_score\": 18.7,\n \"thesis\": \"Harvard and Brown's simultaneous crypto ETF adoption marks institutional acceptance of Bitcoin as a legitimate asset class. The 12.8% Harvard allocation suggests endowments are diversifying into digital assets for long-term portfolio protection.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"Brown University\"\n ],\n \"cluster_signal\": true,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Extreme Bitcoin volatility and regulatory uncertainty; ETF structure risks and potential crypto market manipulation concerns.\",\n \"direction\": \"BULLISH\"\n }\n ],\n \"disclaimer\": \"This analysis is based on delayed 13F data and is for informational purposes only, not investment advice.\"\n}" }, { "path": "data/holdings/2026-04-08_final_analysis.json", "content": "{\n \"date\": \"2026-04-08\",\n \"round1_top5\": [\n {\n \"rank\": 1,\n \"ticker\": \"PLTR\",\n \"company_name\": \"PALANTIR TECHNOLOGIES INC\",\n \"conviction_score\": 100.0,\n \"thesis\": \"TCI Fund's massive 66% portfolio allocation signals extraordinary conviction in Palantir's AI/data analytics platform amid government and enterprise adoption acceleration. Chris Hohn's concentrated bet suggests transformational growth expectations in the AI infrastructure space.\",\n \"key_buyers\": [\n \"TCI Fund (Chris Hohn)\"\n ],\n \"cluster_signal\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Extreme concentration risk given 66% portfolio weight; government contract dependency and competitive AI landscape present execution risks.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 2,\n \"ticker\": \"AMZN\",\n \"company_name\": \"AMAZON COM INC\",\n \"conviction_score\": 21.0,\n \"thesis\": \"Six elite institutions simultaneously initiated positions including Pershing Square, Harvard, and Tiger Global, suggesting broad consensus on Amazon's AI cloud dominance and retail resilience. The coordinated buying pattern indicates institutional recognition of Amazon's multiple growth vectors.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"Pershing Square (Ackman)\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"Viking Global (Halvorsen)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Regulatory scrutiny and competitive pressures in cloud computing; mature e-commerce growth may limit upside.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 3,\n \"ticker\": \"NVDA\",\n \"company_name\": \"NVIDIA CORPORATION\",\n \"conviction_score\": 19.8,\n \"thesis\": \"Five top-tier funds including TCI and Harvard established new positions, signaling continued institutional confidence in NVIDIA's AI chip monopoly despite valuation concerns. The broad-based buying suggests institutions view current levels as attractive entry points.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"TCI Fund (Chris Hohn)\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"High valuation multiples and potential AI bubble concerns; semiconductor cyclicality and geopolitical tensions around China markets.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 4,\n \"ticker\": \"BRK/A\",\n \"company_name\": \"BERKSHIRE HATHAWAY INC DEL\",\n \"conviction_score\": 41.3,\n \"thesis\": \"Gates Foundation's substantial 27.6% allocation to Berkshire signals flight to defensive value and Buffett's crisis-tested investment acumen. This concentrated bet suggests institutional preparation for market volatility while maintaining equity exposure.\",\n \"key_buyers\": [\n \"Gates Foundation Trust\"\n ],\n \"cluster_signal\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Succession concerns post-Buffett era; large cash position may drag returns in bull markets; concentrated exposure to specific sectors.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 5,\n \"ticker\": \"IBIT\",\n \"company_name\": \"ISHARES BITCOIN TRUST ETF\",\n \"conviction_score\": 18.7,\n \"thesis\": \"Harvard and Brown's simultaneous crypto ETF adoption marks institutional acceptance of Bitcoin as a legitimate asset class. The 12.8% Harvard allocation suggests endowments are diversifying into digital assets for long-term portfolio protection.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"Brown University\"\n ],\n \"cluster_signal\": true,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Extreme Bitcoin volatility and regulatory uncertainty; ETF structure risks and potential crypto market manipulation concerns.\",\n \"direction\": \"BULLISH\"\n }\n ],\n \"market_context\": \"The market appears to be experiencing significant institutional repositioning into AI infrastructure, defensive value plays, and alternative assets including crypto ETFs. Notable convergence among elite funds on mega-cap tech suggests consensus building around AI beneficiaries despite potential valuation concerns.\",\n \"options_recs\": [\n {\n \"rank\": 1,\n \"stock_ticker\": \"AMZN\",\n \"company_name\": \"Amazon Com Inc\",\n \"option_symbol\": \"AMZN260821C00250000\",\n \"option_type\": \"CALL\",\n \"strike\": 250.0,\n \"expiration\": \"2026-08-21\",\n \"entry_price_mid\": 9.7,\n \"max_risk_per_contract\": 970.0,\n \"investment_thesis_link\": \"Six elite institutions' coordinated buying signals broad consensus on Amazon's AI cloud dominance, creating strong institutional momentum.\",\n \"option_rationale\": \"Delta 0.35 provides optimal risk-reward leverage for 4+ month time horizon, while 34.4% IV is reasonable for tech. Exceptional liquidity with 29,781 OI.\",\n \"profit_target\": \"+75-100% on option premium if stock moves +12-18%\",\n \"stop_loss\": \"-50% on option premium\",\n \"key_risk\": \"Regulatory scrutiny and intense cloud competition could limit upside momentum despite institutional support.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 2,\n \"stock_ticker\": \"NVDA\",\n \"company_name\": \"Nvidia Corporation\",\n \"option_symbol\": \"NVDA260918C00200000\",\n \"option_type\": \"CALL\",\n \"strike\": 200.0,\n \"expiration\": \"2026-09-18\",\n \"entry_price_mid\": 12.73,\n \"max_risk_per_contract\": 1273.0,\n \"investment_thesis_link\": \"Five top-tier funds including TCI and Harvard view current levels as attractive entry points for AI chip monopoly play.\",\n \"option_rationale\": \"Delta 0.43 offers strong directional exposure with 5+ month runway for AI thesis to materialize. High liquidity with 33,190 OI provides excellent execution.\",\n \"profit_target\": \"+60-80% on option premium if stock moves +15-20%\",\n \"Stop_loss\": \"-50% on option premium\",\n \"key_risk\": \"High valuation multiples vulnerable to AI sentiment shifts and geopolitical semiconductor tensions.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 3,\n \"stock_ticker\": \"PLTR\",\n \"company_name\": \"Palantir Technologies Inc\",\n \"option_symbol\": \"PLTR260821C00150000\",\n \"option_type\": \"CALL\",\n \"strike\": 150.0,\n \"expiration\": \"2026-08-21\",\n \"entry_price_mid\": 17.18,\n \"max_risk_per_contract\": 1718.0,\n \"investment_thesis_link\": \"TCI Fund's extraordinary 66% portfolio allocation demonstrates unprecedented conviction in Palantir's AI platform transformation potential.\",\n \"option_rationale\": \"Delta 0.55 provides strong directional exposure matching highest conviction thesis. 4+ month expiry allows government/enterprise adoption cycles to develop.\",\n \"profit_target\": \"+80-120% on option premium if stock moves +20-25%\",\n \"stop_loss\": \"-50% on option premium\",\n \"key_risk\": \"Extreme concentration risk from TCI's 66% allocation creates vulnerability to any execution disappointments.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 4,\n \"stock_ticker\": \"IBIT\",\n \"company_name\": \"iShares Bitcoin Trust ETF\",\n \"option_symbol\": \"IBIT260918C00045000\",\n \"option_type\": \"CALL\",\n \"strike\": 45.0,\n \"expiration\": \"2026-09-18\",\n \"entry_price_mid\": 3.63,\n \"max_risk_per_contract\": 363.0,\n \"investment_thesis_link\": \"Harvard and Brown's simultaneous crypto ETF adoption marks institutional legitimization of Bitcoin as asset class diversifier.\",\n \"option_rationale\": \"Delta 0.44 provides balanced risk-reward with 5+ month horizon for crypto institutional adoption trend. Excellent OI at 25,501 ensures liquidity.\",\n \"profit_target\": \"+100-150% on option premium if Bitcoin rallies +25-35%\",\n \"stop_loss\": \"-60% on option premium due to crypto volatility\",\n \"key_risk\": \"Extreme Bitcoin volatility and regulatory uncertainty could trigger sharp reversals despite institutional adoption.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 5,\n \"stock_ticker\": \"BRK/A\",\n \"company_name\": \"Berkshire Hathaway Inc Del\",\n \"option_symbol\": \"N/A\",\n \"option_type\": \"N/A\",\n \"strike\": 0,\n \"expiration\": \"N/A\",\n \"entry_price_mid\": 0,\n \"max_risk_per_contract\": 0,\n \"investment_thesis_link\": \"Gates Foundation's 27.6% allocation signals defensive value play, but no options available for leveraged exposure.\",\n \"option_rationale\": \"No options data available for BRK/A - would require direct equity exposure to capture Gates Foundation's flight-to-quality thesis.\",\n \"profit_target\": \"N/A - Direct equity purchase required\",\n \"stop_loss\": \"N/A\",\n \"key_risk\": \"Unable to implement leveraged strategy without available options market.\",\n \"greeks_note\": \"N/A\"\n }\n ],\n \"portfolio_note\": \"Weight AMZN and NVDA highest given broad institutional consensus, moderate allocation to PLTR despite high conviction given concentration risk, small speculative allocation to IBIT.\",\n \"disclaimer\": \"Options involve significant risk. This is based on delayed 13F data and real-time option prices may differ. Not investment advice.\"\n}" }, { "path": "data/holdings/2026-04-08_holdings_parsed.json", "content": "{\n \"date\": \"2026-04-08\",\n \"prior_date\": null,\n \"is_first_run\": true,\n \"recent_splits\": {},\n \"filers\": {\n \"Situational Awareness LP\": {\n \"cik\": \"0002045724\",\n \"reported_aum_k\": 5516758345,\n \"filing_date\": \"2026-02-11\",\n \"is_amendment\": false,\n \"position_count\": 25,\n \"positions\": [\n {\n \"ticker\": \"\",\n \"cusip\": \"21873S108\",\n \"name\": \"COREWEAVE INC\",\n \"value_usd_k\": 1211162272,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 21.954,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": true,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 1\n },\n {\n \"ticker\": \"\",\n \"cusip\": \"093712107\",\n \"name\": \"BLOOM ENERGY CORP\",\n \"value_usd_k\": 911000117,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 16.513,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": true,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 2\n },\n {\n \"ticker\": \"\",\n \"cusip\": \"458140100\",\n \"name\": \"INTEL CORP\",\n \"value_usd_k\": 746760097,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 13.536,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": true,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 3\n },\n {\n \"ticker\": \"\",\n \"cusip\": \"55024U109\",\n \"name\": \"LUMENTUM HLDGS INC\",\n \"value_usd_k\": 478577256,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 8.675,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": true,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 4\n },\n {\n \"ticker\": \"\",\n \"cusip\": \"21874A106\",\n \"name\": \"CORE SCIENTIFIC INC NEW\",\n \"value_usd_k\": 418694320,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 7.589,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": true,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 5\n },\n {\n \"ticker\": \"\",\n \"cusip\": \"Q4982L109\",\n \"name\": \"IREN LIMITED\",\n \"value_usd_k\": 328622455,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 5.957,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": true,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may 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\"port_weight_pct\": 12.162,\n \"delta_pct\": null,\n \"delta_type\": \"NEW\",\n \"conviction_score\": 17.8\n },\n {\n \"filer\": \"Pershing Square (Ackman)\",\n \"port_weight_pct\": 1.367,\n \"delta_pct\": null,\n \"delta_type\": \"NEW\",\n \"conviction_score\": 1.3\n },\n {\n \"filer\": \"Tiger Global (Coleman)\",\n \"port_weight_pct\": 11.199,\n \"delta_pct\": null,\n \"delta_type\": \"NEW\",\n \"conviction_score\": 16.3\n },\n {\n \"filer\": \"Viking Global (Halvorsen)\",\n \"port_weight_pct\": 2.215,\n \"delta_pct\": null,\n \"delta_type\": \"NEW\",\n \"conviction_score\": 2.6\n },\n {\n \"filer\": \"AQR Capital (Asness)\",\n \"port_weight_pct\": 0.74,\n \"delta_pct\": null,\n \"delta_type\": \"NEW\",\n \"conviction_score\": 0.4\n }\n ],\n \"conviction_score\": 17.8,\n \"total_value_usd_k\": 6037241926,\n \"flags\": [\n \"HIGH_CONVICTION\",\n \"NEW_POSITION\",\n \"TOP10_ENTRY\"\n ],\n \"cluster_count\": 0,\n \"cluster_funds\": [],\n \"delta_types\": [\n \"NEW\",\n \"NEW\",\n \"NEW\",\n \"NEW\",\n \"NEW\"\n ],\n \"filer_count\": 5\n }\n ]\n}" }, { "path": "data/holdings/2026-05-18_backtest.json", "content": "{\n \"summary\": {\n \"generated_at\": \"2026-05-18\",\n \"total_signals\": 10,\n \"completed_90d\": 0,\n \"completed_180d\": 0,\n \"win_rate_90d_pct\": null,\n \"win_rate_180d_pct\": null,\n \"avg_return_90d_pct\": null,\n \"avg_return_180d_pct\": null\n },\n \"results\": [\n {\n \"report_date\": \"2026-04-08\",\n \"ticker\": \"PLTR\",\n \"company\": \"PALANTIR TECHNOLOGIES INC\",\n \"conviction_score\": 100.0,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"signal_date\": \"2026-04-08\",\n \"price_at_signal\": 140.76,\n \"price_d90\": null,\n \"return_d90_pct\": null,\n \"status_d90\": \"pending\",\n \"price_d180\": null,\n \"return_d180_pct\": null,\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-04-08\",\n \"ticker\": \"AMZN\",\n \"company\": \"AMAZON COM INC\",\n \"conviction_score\": 21.0,\n \"primary_flag\": \"CLUSTER\",\n \"signal_date\": \"2026-04-08\",\n \"price_at_signal\": 221.25,\n \"price_d90\": null,\n \"return_d90_pct\": null,\n \"status_d90\": \"pending\",\n \"price_d180\": null,\n \"return_d180_pct\": null,\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-04-08\",\n \"ticker\": \"NVDA\",\n \"company\": \"NVIDIA CORPORATION\",\n \"conviction_score\": 19.8,\n \"primary_flag\": \"CLUSTER\",\n \"signal_date\": \"2026-04-08\",\n \"price_at_signal\": 182.08,\n \"price_d90\": null,\n \"return_d90_pct\": null,\n \"status_d90\": \"pending\",\n \"price_d180\": null,\n \"return_d180_pct\": null,\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-04-08\",\n \"ticker\": \"BRK/A\",\n \"company\": \"BERKSHIRE HATHAWAY INC DEL\",\n \"conviction_score\": 41.3,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"signal_date\": \"2026-04-08\",\n \"price_at_signal\": 720512.0,\n \"price_d90\": null,\n \"return_d90_pct\": null,\n \"status_d90\": \"pending\",\n \"price_d180\": null,\n \"return_d180_pct\": null,\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-04-08\",\n \"ticker\": \"IBIT\",\n \"company\": \"ISHARES BITCOIN TRUST ETF\",\n \"conviction_score\": 18.7,\n \"primary_flag\": \"CLUSTER\",\n \"signal_date\": \"2026-04-08\",\n \"price_at_signal\": 40.42,\n \"price_d90\": null,\n \"return_d90_pct\": null,\n \"status_d90\": \"pending\",\n \"price_d180\": null,\n \"return_d180_pct\": null,\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-05-18\",\n \"ticker\": \"NVDA\",\n \"company\": \"NVIDIA Corporation\",\n \"conviction_score\": 94.2,\n \"primary_flag\": \"CLUSTER\",\n \"status_d90\": \"pending\",\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-05-18\",\n \"ticker\": \"TSM\",\n \"company\": \"Taiwan Semiconductor Manufacturing Company\",\n \"conviction_score\": 100.0,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"status_d90\": \"pending\",\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-05-18\",\n \"ticker\": \"AVGO\",\n \"company\": \"Broadcom Inc\",\n \"conviction_score\": 95.2,\n \"primary_flag\": \"CLUSTER\",\n \"status_d90\": \"pending\",\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-05-18\",\n \"ticker\": \"BKNG\",\n \"company\": \"Booking Holdings Inc\",\n \"conviction_score\": 95.2,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"status_d90\": \"pending\",\n \"status_d180\": \"pending\"\n },\n {\n \"report_date\": \"2026-05-18\",\n \"ticker\": \"MSFT\",\n \"company\": \"Microsoft Corporation\",\n \"conviction_score\": 97.6,\n \"primary_flag\": \"CLUSTER\",\n \"status_d90\": \"pending\",\n \"status_d180\": \"pending\"\n }\n ]\n}" }, { "path": "data/holdings/2026-05-18_claude_round1.json", "content": "{\n \"analysis_date\": \"2026-05-18\",\n \"market_context\": \"Institutional investors are showing strong synchronized conviction in mega-cap tech (NVDA, MSFT, GOOGL, AMZN) and semiconductor supply chain (TSM, AVGO), suggesting a coordinated thesis around AI infrastructure and edge computing. Simultaneous entries by elite endowments (Harvard, Brown) and specialized hedge funds (D1, Tiger Global, Viking) into both traditional blue chips and emerging fintech/e-commerce names indicate portfolio repositioning toward structural growth themes with reduced economic sensitivity.\",\n \"top5\": [\n {\n \"rank\": 1,\n \"ticker\": \"NVDA\",\n \"company_name\": \"NVIDIA Corporation\",\n \"conviction_score\": 94.2,\n \"thesis\": \"Unprecedented 7-fund cluster conviction including elite endowments (Harvard, Brown, TCI) and specialized tech allocators (Situational Awareness, Tiger Global, D1), with 11.47% portfolio weight in Situational Awareness suggesting AI infrastructure is a core thesis. New positions across all major allocators indicate synchronized recognition of sustained AI/data center demand tailwinds beyond near-term consensus.\",\n \"key_buyers\": [\n \"Situational Awareness LP\",\n \"Harvard Management Co\",\n \"Brown University\",\n \"TCI Fund (Chris Hohn)\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Stock has already benefited significantly from AI narrative and may be priced for optimistic scenarios; 45-day lag means these positions were established in late April 2026 and could already reflect partial moves.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 2,\n \"ticker\": \"TSM\",\n \"company_name\": \"Taiwan Semiconductor Manufacturing Company\",\n \"conviction_score\": 100.0,\n \"thesis\": \"Perfect conviction score with 5-fund cluster of highest-quality allocators (Situational Awareness 6.56%, Harvard 12.78%, Tiger Global 8.23%) entering simultaneously across borders, indicating structural confidence in semiconductor supply chain leadership. TSM's role as critical infrastructure for AI/GPU manufacturing makes it a proxy play on sustained capex cycles with less crowding than direct chip designers.\",\n \"key_buyers\": [\n \"Situational Awareness LP\",\n \"Harvard Management Co\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"Viking Global (Halvorsen)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Geopolitical concentration risk (Taiwan) and US-China tensions could create volatility; new position status means conviction narrative may still be building and subject to reversal if foundational AI thesis weakens.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 3,\n \"ticker\": \"AVGO\",\n \"company_name\": \"Broadcom Inc\",\n \"conviction_score\": 95.2,\n \"thesis\": \"5-fund cluster including Harvard (8.08%), Situational Awareness (7.36%), and specialized tech allocators (Tiger Global, D1, AQR) capturing semiconductor supply chain exposure with lower visibility than NVDA. As critical infrastructure provider for networking/connectivity in data centers, AVGO represents a complementary bet to NVDA with less consensus coverage and fresher accumulation pattern.\",\n \"key_buyers\": [\n \"Situational Awareness LP\",\n \"Harvard Management Co\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Cyclical exposure to capex spending means thesis is sensitive to data center investment slowdown; new position timing suggests this is momentum-driven rather than deep-value setup.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 4,\n \"ticker\": \"BKNG\",\n \"company_name\": \"Booking Holdings Inc\",\n \"conviction_score\": 95.2,\n \"thesis\": \"Selective 2-fund entry by Harvard Management (8.10% weight) and AQR Capital representing rare consensus between endowment and quant allocators in travel/tourism recovery play. High portfolio concentration at Harvard suggests conviction in structural travel demand resilience post-cycle, with less crowding than mega-cap tech and better economic sensitivity profile for diversification.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": false,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Macro sensitivity to consumer spending and recession risk; only 2-fund signal is weaker than mega-cap tech cluster and thesis may reflect cyclical recovery optimism rather than structural growth.\",\n \"direction\": \"BULLISH\"\n },\n {\n \"rank\": 5,\n \"ticker\": \"MSFT\",\n \"company_name\": \"Microsoft Corporation\",\n \"conviction_score\": 97.6,\n \"thesis\": \"5-fund cluster across endowments (Harvard 10.46%), specialized allocators (Pershing Square 15.26%), and quant platforms (AQR, Viking Global) entering simultaneously indicates unified thesis on AI-as-platform-provider positioning. MSFT's Copilot/Azure integration and enterprise stickiness suggest lower risk profile than pure chip plays while maintaining upside to AI monetization cycles.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"Pershing Square (Ackman)\",\n \"Tiger Global (Coleman)\",\n \"Viking Global (Halvorsen)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Mega-cap tech already extensively covered and these positions may be consensus; stock has likely already captured much of the AI narrative given ubiquitous institutional ownership.\",\n \"direction\": \"BULLISH\"\n }\n ],\n \"disclaimer\": \"This analysis is based on delayed 13F data (45-day lag as of 2026-05-18) and is for informational purposes only, not investment advice. All positions represent new entries with unknown entry prices and exit criteria. Institutional buying does not guarantee future outperformance. Conduct independent due diligence before any investment decision.\"\n}" }, { "path": "data/holdings/2026-05-18_final_analysis.json", "content": "{\n \"date\": \"2026-05-18\",\n \"round1_top5\": [\n {\n \"rank\": 1,\n \"ticker\": \"NVDA\",\n \"company_name\": \"NVIDIA Corporation\",\n \"conviction_score\": 94.2,\n \"thesis\": \"Unprecedented 7-fund cluster conviction including elite endowments (Harvard, Brown, TCI) and specialized tech allocators (Situational Awareness, Tiger Global, D1), with 11.47% portfolio weight in Situational Awareness suggesting AI infrastructure is a core thesis. New positions across all major allocators indicate synchronized recognition of sustained AI/data center demand tailwinds beyond near-term consensus.\",\n \"key_buyers\": [\n \"Situational Awareness LP\",\n \"Harvard Management Co\",\n \"Brown University\",\n \"TCI Fund (Chris Hohn)\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Stock has already benefited significantly from AI narrative and may be priced for optimistic scenarios; 45-day lag means these positions were established in late April 2026 and could already reflect partial moves.\",\n \"direction\": \"BULLISH\",\n \"post_filing_perf\": {\n \"pct_change\": null,\n \"filing_close\": null,\n \"current_price\": null,\n \"days_since_filing\": null\n }\n },\n {\n \"rank\": 2,\n \"ticker\": \"TSM\",\n \"company_name\": \"Taiwan Semiconductor Manufacturing Company\",\n \"conviction_score\": 100.0,\n \"thesis\": \"Perfect conviction score with 5-fund cluster of highest-quality allocators (Situational Awareness 6.56%, Harvard 12.78%, Tiger Global 8.23%) entering simultaneously across borders, indicating structural confidence in semiconductor supply chain leadership. TSM's role as critical infrastructure for AI/GPU manufacturing makes it a proxy play on sustained capex cycles with less crowding than direct chip designers.\",\n \"key_buyers\": [\n \"Situational Awareness LP\",\n \"Harvard Management Co\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"Viking Global (Halvorsen)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Geopolitical concentration risk (Taiwan) and US-China tensions could create volatility; new position status means conviction narrative may still be building and subject to reversal if foundational AI thesis weakens.\",\n \"direction\": \"BULLISH\",\n \"post_filing_perf\": {\n \"pct_change\": null,\n \"filing_close\": null,\n \"current_price\": null,\n \"days_since_filing\": null\n }\n },\n {\n \"rank\": 3,\n \"ticker\": \"AVGO\",\n \"company_name\": \"Broadcom Inc\",\n \"conviction_score\": 95.2,\n \"thesis\": \"5-fund cluster including Harvard (8.08%), Situational Awareness (7.36%), and specialized tech allocators (Tiger Global, D1, AQR) capturing semiconductor supply chain exposure with lower visibility than NVDA. As critical infrastructure provider for networking/connectivity in data centers, AVGO represents a complementary bet to NVDA with less consensus coverage and fresher accumulation pattern.\",\n \"key_buyers\": [\n \"Situational Awareness LP\",\n \"Harvard Management Co\",\n \"Tiger Global (Coleman)\",\n \"D1 Capital (Sundheim)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Cyclical exposure to capex spending means thesis is sensitive to data center investment slowdown; new position timing suggests this is momentum-driven rather than deep-value setup.\",\n \"direction\": \"BULLISH\",\n \"post_filing_perf\": {\n \"pct_change\": null,\n \"filing_close\": null,\n \"current_price\": null,\n \"days_since_filing\": null\n }\n },\n {\n \"rank\": 4,\n \"ticker\": \"BKNG\",\n \"company_name\": \"Booking Holdings Inc\",\n \"conviction_score\": 95.2,\n \"thesis\": \"Selective 2-fund entry by Harvard Management (8.10% weight) and AQR Capital representing rare consensus between endowment and quant allocators in travel/tourism recovery play. High portfolio concentration at Harvard suggests conviction in structural travel demand resilience post-cycle, with less crowding than mega-cap tech and better economic sensitivity profile for diversification.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": false,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"HIGH_CONVICTION\",\n \"risk_factors\": \"Macro sensitivity to consumer spending and recession risk; only 2-fund signal is weaker than mega-cap tech cluster and thesis may reflect cyclical recovery optimism rather than structural growth.\",\n \"direction\": \"BULLISH\",\n \"post_filing_perf\": {\n \"pct_change\": 0.0,\n \"filing_close\": 154.13,\n \"current_price\": 154.13,\n \"days_since_filing\": 3\n }\n },\n {\n \"rank\": 5,\n \"ticker\": \"MSFT\",\n \"company_name\": \"Microsoft Corporation\",\n \"conviction_score\": 97.6,\n \"thesis\": \"5-fund cluster across endowments (Harvard 10.46%), specialized allocators (Pershing Square 15.26%), and quant platforms (AQR, Viking Global) entering simultaneously indicates unified thesis on AI-as-platform-provider positioning. MSFT's Copilot/Azure integration and enterprise stickiness suggest lower risk profile than pure chip plays while maintaining upside to AI monetization cycles.\",\n \"key_buyers\": [\n \"Harvard Management Co\",\n \"Pershing Square (Ackman)\",\n \"Tiger Global (Coleman)\",\n \"Viking Global (Halvorsen)\",\n \"AQR Capital (Asness)\"\n ],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"CLUSTER\",\n \"risk_factors\": \"Mega-cap tech already extensively covered and these positions may be consensus; stock has likely already captured much of the AI narrative given ubiquitous institutional ownership.\",\n \"direction\": \"BULLISH\",\n \"post_filing_perf\": {\n \"pct_change\": 0.0,\n \"filing_close\": 421.92,\n \"current_price\": 421.92,\n \"days_since_filing\": 3\n }\n }\n ],\n \"market_context\": \"Institutional investors are showing strong synchronized conviction in mega-cap tech (NVDA, MSFT, GOOGL, AMZN) and semiconductor supply chain (TSM, AVGO), suggesting a coordinated thesis around AI infrastructure and edge computing. Simultaneous entries by elite endowments (Harvard, Brown) and specialized hedge funds (D1, Tiger Global, Viking) into both traditional blue chips and emerging fintech/e-commerce names indicate portfolio repositioning toward structural growth themes with reduced economic sensitivity.\",\n \"options_recs\": [\n {\n \"rank\": 1,\n \"stock_ticker\": \"NVDA\",\n \"company_name\": \"NVIDIA Corporation\",\n \"option_symbol\": \"NVDA260918C00240000\",\n \"option_type\": \"CALL\",\n \"strike\": 240.0,\n \"expiration\": \"2026-09-18\",\n \"entry_price_mid\": 16.15,\n \"max_risk_per_contract\": 1615.0,\n \"investment_thesis_link\": \"7-fund cluster including Harvard, TCI, Tiger Global, and Situational Awareness (11.47% weight) signals unprecedented synchronized conviction in AI infrastructure demand; the September expiry gives 4+ months for the institutional thesis to materialize beyond the 45-day 13F lag.\",\n \"option_rationale\": \"Strike at +9% OTM with Delta 0.45 sits in the sweet spot for leveraged bullish exposure without overpaying for proximity. September 18 expiry provides sufficient runway for AI capex cycle catalysts (earnings, data center announcements). IV at 44.8% is consistent with peer strikes, no premium anomaly. Volume 2547 and OI 32666 represent the highest liquidity in the NVDA chain, minimizing slippage. Spread% of 0.6% is exceptionally tight for a high-beta name.\",\n \"profit_target\": \"+60-90% on option premium if NVDA moves to $255-265 (+16-20%) by August-September\",\n \"stop_loss\": \"-50% on option premium (~$8.08 per contract)\",\n \"key_risk\": \"NVDA has already priced in significant AI optimism and any guidance miss, export restriction escalation, or capex slowdown signal from hyperscalers could rapidly erode premium.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 2,\n \"stock_ticker\": \"TSM\",\n \"company_name\": \"Taiwan Semiconductor Manufacturing Company\",\n \"option_symbol\": \"TSM260918C00390000\",\n \"option_type\": \"CALL\",\n \"strike\": 390.0,\n \"expiration\": \"2026-09-18\",\n \"entry_price_mid\": 44.35,\n \"max_risk_per_contract\": 4435.0,\n \"investment_thesis_link\": \"Perfect conviction score with 5 top-tier allocators (Situational Awareness 6.56%, Harvard 12.78%, Tiger Global 8.23%) entering simultaneously reflects structural confidence in TSM as irreplaceable AI/GPU manufacturing infrastructure; the near-ATM September strike positions for steady thesis appreciation as capex cycle visibility improves.\",\n \"option_rationale\": \"Strike at -0.6% (essentially ATM) with Delta 0.59 reflects higher conviction warranted by the elite fund cluster quality and TSM's structural moat. September expiry aligns with Q2 earnings catalyst and potential positive AI demand commentary. IV at 45.6% is reasonable for a name with geopolitical risk premium. Volume 176 and OI 1206 are adequate though lower than NVDA; spread at 2.0% is acceptable. ATM positioning maximizes delta sensitivity to upside moves while providing intrinsic value buffer against time decay.\",\n \"profit_target\": \"+40-65% on option premium if TSM moves to $430-450 (+10-15%) by September\",\n \"stop_loss\": \"-45% on option premium (~$19.96 per contract)\",\n \"key_risk\": \"Taiwan geopolitical tensions with China represent a binary tail risk that could gap the stock down sharply regardless of fundamental AI thesis strength, making position sizing critical.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 3,\n \"stock_ticker\": \"MSFT\",\n \"company_name\": \"Microsoft Corporation\",\n \"option_symbol\": \"MSFT260918C00430000\",\n \"option_type\": \"CALL\",\n \"strike\": 430.0,\n \"expiration\": \"2026-09-18\",\n \"entry_price_mid\": 30.05,\n \"max_risk_per_contract\": 3005.0,\n \"investment_thesis_link\": \"5-fund cluster led by Pershing Square (15.26% weight) and Harvard (10.46%) converging on MSFT as the AI-as-platform play with enterprise stickiness provides a lower-risk AI monetization thesis; the near-ATM strike with low IV offers the most capital-efficient expression of a high-conviction, lower-volatility thesis.\",\n \"option_rationale\": \"Strike at +1.5% OTM with Delta 0.53 is nearly ATM, appropriate for MSFT's lower beta and steadier AI monetization runway through Azure/Copilot. Critically, MSFT's IV at 31.6% is dramatically lower than semiconductor peers (NVDA 44.8%, AVGO 50%), making this the most attractively priced option on a premium-per-dollar-of-exposure basis. Volume 1292 and OI 5606 ensure good liquidity. Spread at 1.3% is among the tightest in the entire dataset. September expiry captures Q1 FY2027 earnings and multiple Azure growth data points.\",\n \"profit_target\": \"+45-70% on option premium if MSFT moves to $465-480 (+10-13%) by September\",\n \"stop_loss\": \"-50% on option premium (~$15.03 per contract)\",\n \"key_risk\": \"As the most widely held mega-cap, MSFT options are sensitive to broad market de-rating risk, and the AI monetization timeline may already be fully discounted in the current price near all-time highs.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 4,\n \"stock_ticker\": \"AVGO\",\n \"company_name\": \"Broadcom Inc\",\n \"option_symbol\": \"AVGO261016C00430000\",\n \"option_type\": \"CALL\",\n \"strike\": 430.0,\n \"expiration\": \"2026-10-16\",\n \"entry_price_mid\": 51.45,\n \"max_risk_per_contract\": 5145.0,\n \"investment_thesis_link\": \"5-fund cluster including Harvard (8.08%) and Situational Awareness (7.36%) targeting AVGO as a less-crowded AI data center networking play; October expiry provides extra time for the 'complementary to NVDA' thesis to build as data center connectivity demand crystallizes in earnings reports.\",\n \"option_rationale\": \"Strike at +2.8% OTM with Delta 0.55 and October 16 expiry provides the longest time horizon available for AVGO while maintaining near-ATM sensitivity. October expiry is preferred over August to avoid theta decay pressure during what may be a gradual institutional accumulation story. Volume 111 and OI 1410 are adequate; spread at 2.3% is acceptable. IV at 50.6% is the highest in this selection but reflects AVGO's genuine higher volatility profile versus MSFT; compared to AVGO's own chain, the October strike is not an outlier. The additional month over September peers justifies the small IV premium.\",\n \"profit_target\": \"+50-75% on option premium if AVGO moves to $480-500 (+15-20%) by October\",\n \"stop_loss\": \"-50% on option premium (~$25.73 per contract)\",\n \"key_risk\": \"AVGO's thesis is momentum-driven per the analysis; any slowdown signal in hyperscaler networking capex or softness in VMware integration could reverse the accumulation pattern quickly given the elevated IV already embedded in premiums.\",\n \"greeks_note\": \"Available\"\n },\n {\n \"rank\": 5,\n \"stock_ticker\": \"BKNG\",\n \"company_name\": \"Booking Holdings Inc\",\n \"option_symbol\": \"BKNG260918C00180000\",\n \"option_type\": \"CALL\",\n \"strike\": 180.0,\n \"expiration\": \"2026-09-18\",\n \"entry_price_mid\": 6.3,\n \"max_risk_per_contract\": 630.0,\n \"investment_thesis_link\": \"Harvard Management's 8.10% portfolio concentration in BKNG signals high endowment-level conviction in structural travel demand resilience; the low-cost OTM September call provides asymmetric upside exposure to the thesis with minimal capital at risk given the weaker 2-fund signal relative to the semiconductor cluster plays.\",\n \"option_rationale\": \"Strike at +16.9% OTM with Delta 0.31 reflects the appropriate risk-sizing for the weakest conviction signal in this portfolio (2-fund vs 5-7 fund clusters for others). The low premium of $6.30 limits absolute dollar risk while still providing meaningful leverage. IV at 41.0% is the lowest among all candidates and makes this the cheapest option on a volatility-adjusted basis. Volume is only 15 (lowest in dataset) and OI 636 is thin \\u2014 this is the key liquidity risk. Spread at 3.2% is manageable. September expiry captures summer travel season peak data and back-to-school booking cycle, both natural catalysts for the thesis. Ranked 5th deliberately to reflect lower conviction and liquidity.\",\n \"profit_target\": \"+80-120% on option premium if BKNG moves to $195-205 (+27-33%) driven by travel demand surprise\",\n \"stop_loss\": \"-60% on option premium (~$3.78 per contract) given lower conviction\",\n \"key_risk\": \"Extremely low volume (15 contracts) signals poor liquidity and potentially wide real-world execution spreads; macro recession fears or consumer spending weakness could rapidly undermine the travel recovery thesis that only 2 funds are expressing.\",\n \"greeks_note\": \"Available\"\n }\n ],\n \"portfolio_note\": \"Suggested sizing reflects conviction tier weighting: NVDA and TSM at 30% of options budget each (7-fund and 5-fund elite clusters), MSFT and AVGO at 20% each (5-fund clusters with differentiated risk profiles), and BKNG at 10% (2-fund signal, liquidity concerns). Total options budget should represent no more than 3-5% of total portfolio value given the leveraged nature of these positions. The semiconductor cluster (NVDA+TSM+AVGO) represents correlated AI capex risk \\u2014 consider reducing any single name if overall AI exposure exceeds comfort threshold. MSFT provides partial hedge as a lower-beta AI play. BKNG provides genuine sector diversification.\",\n \"disclaimer\": \"Options involve significant risk including total loss of premium paid. This analysis is based on 13F filings with an approximately 45-day reporting lag and real-time option prices that may differ materially at execution. Institutional positions may have already been reduced or reversed. IV levels and spreads are point-in-time estimates. Geopolitical events, earnings surprises, or macro shifts can cause rapid adverse moves. This is not investment advice. Consult a licensed financial advisor before trading.\"\n}" }, { "path": "data/holdings/2026-05-18_holdings_parsed.json", "content": "{\n \"date\": \"2026-05-18\",\n \"prior_date\": \"2026-04-08\",\n \"is_first_run\": false,\n \"recent_splits\": {\n \"BDX\": 1.272,\n \"RLYB\": 0.125,\n \"BKNG\": 25.0,\n \"CVNA\": 5.0,\n \"SF\": 1.5\n },\n \"filers\": {\n \"Situational Awareness LP\": {\n \"cik\": \"0002045724\",\n \"reported_aum_k\": 13676657577,\n \"filing_date\": \"2026-05-18\",\n \"is_amendment\": false,\n \"position_count\": 29,\n \"positions\": [\n {\n \"ticker\": \"SMH\",\n \"cusip\": \"92189F676\",\n \"name\": \"VANECK ETF TRUST\",\n \"value_usd_k\": 2053049490,\n \"shares\": 0,\n \"putCall\": \"Put\",\n \"port_weight_pct\": 15.011,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 1\n },\n {\n \"ticker\": \"NVDA\",\n \"cusip\": \"67066G104\",\n \"name\": \"NVIDIA CORPORATION\",\n \"value_usd_k\": 1568755032,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 11.47,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 2\n },\n {\n \"ticker\": \"SNDK\",\n \"cusip\": \"80004C200\",\n \"name\": \"SANDISK CORP\",\n \"value_usd_k\": 1113127751,\n \"shares\": 0,\n \"putCall\": \"Call\",\n \"port_weight_pct\": 8.139,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 3\n },\n {\n \"ticker\": \"ORCL\",\n \"cusip\": \"68389X105\",\n \"name\": \"ORACLE CORP\",\n \"value_usd_k\": 1072873230,\n \"shares\": 0,\n \"putCall\": \"Put\",\n \"port_weight_pct\": 7.845,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 4\n },\n {\n \"ticker\": \"MU\",\n \"cusip\": \"595112103\",\n \"name\": \"MICRON TECHNOLOGY INC\",\n \"value_usd_k\": 1011851746,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 7.398,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 5\n },\n {\n \"ticker\": \"AVGO\",\n \"cusip\": \"11135F101\",\n \"name\": \"BROADCOM INC\",\n \"value_usd_k\": 1006247961,\n \"shares\": 0,\n \"putCall\": \"Put\",\n \"port_weight_pct\": 7.357,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 6\n },\n {\n \"ticker\": \"AMD\",\n \"cusip\": \"007903107\",\n \"name\": \"ADVANCED MICRO DEVICES INC\",\n \"value_usd_k\": 989328506,\n \"shares\": 0,\n \"putCall\": \"Put\",\n \"port_weight_pct\": 7.234,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 7\n },\n {\n \"ticker\": \"BE\",\n \"cusip\": \"093712107\",\n \"name\": \"BLOOM ENERGY CORP\",\n \"value_usd_k\": 934055595,\n \"shares\": 0,\n \"putCall\": \"Call\",\n \"port_weight_pct\": 6.83,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 8\n },\n {\n \"ticker\": \"TSM\",\n \"cusip\": \"874039100\",\n \"name\": \"TAIWAN SEMICONDUCTOR MANUFAC\",\n \"value_usd_k\": 897535383,\n \"shares\": 0,\n \"putCall\": null,\n \"port_weight_pct\": 6.563,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 9\n },\n {\n \"ticker\": \"CRWV\",\n \"cusip\": \"21873S108\",\n \"name\": \"COREWEAVE INC\",\n \"value_usd_k\": 696642700,\n \"shares\": 0,\n \"putCall\": \"Call\",\n \"port_weight_pct\": 5.094,\n \"prior_port_weight\": null,\n \"delta\": {\n \"type\": \"NEW\",\n \"delta_shares\": 0,\n \"delta_pct\": null\n },\n \"is_first_run\": false,\n \"weight_note\": \"Long-only 13F AUM denominator \\u2013 true weight may be lower\",\n \"rank\": 10\n },\n {\n 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"content": "\n\n\n \n \n SEC 13F Smart Money Report\n\n\n\n
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\n QUARTERLY INSTITUTIONAL INTELLIGENCE\n
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SEC 13F Smart Money Report
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2026-04-08  Β·  13 Monitored Institutions  Β·  Top 5 Picks
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Market Context
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The market appears to be experiencing significant institutional repositioning into AI infrastructure, defensive value plays, and alternative assets including crypto ETFs. Notable convergence among elite funds on mega-cap tech suggests consensus building around AI beneficiaries despite potential valuation concerns.
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\n\n \n
\n 🎯 Top 5 Conviction Picks + Option Trades\n
\n\n \n
\n
\n
\n PLTR\n PALANTIR TECHNOLOGIES INC\n
\n
\n
100.0/100
\n
CONVICTION SCORE
\n
\n
\n\n
\n\n
\n
Investment Thesis
\n
TCI Fund's massive 66% portfolio allocation signals extraordinary conviction in Palantir's AI/data analytics platform amid government and enterprise adoption acceleration. Chris Hohn's concentrated bet suggests transformational growth expectations in the AI infrastructure space.
\n
\n\n
\n
\n
KEY BUYERS
\n
TCI Fund (Chris Hohn)
\n
\n
\n
RISK NOTE
\n
Extreme concentration risk given 66% portfolio weight; government contract dependency and competitive AI landscape present execution risks.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
PLTR260821C00150000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $150.0
\n
\n
\n
EXPIRY
\n
2026-08-21
\n
\n
\n
MID PRICE
\n
$17.18
\n
\n
\n
MAX RISK / CONTRACT
\n
$1718.0
\n
\n
\n
PROFIT TARGET
\n
+80-120% on option premium if stock moves +20-25%
\n
\n
\n
\n Rationale: Delta 0.55 provides strong directional exposure matching highest conviction thesis. 4+ month expiry allows government/enterprise adoption cycles to develop.\n
\n
\n Stop Loss: -50% on option premium  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n AMZN\n AMAZON COM INC\n
\n
\n
21.0/100
\n
CONVICTION SCORE
\n
\n
\n\n
\n\n
\n
Investment Thesis
\n
Six elite institutions simultaneously initiated positions including Pershing Square, Harvard, and Tiger Global, suggesting broad consensus on Amazon's AI cloud dominance and retail resilience. The coordinated buying pattern indicates institutional recognition of Amazon's multiple growth vectors.
\n
\n\n
\n
\n
KEY BUYERS
\n
Harvard Management Co, Pershing Square (Ackman), Tiger Global (Coleman), D1 Capital (Sundheim), Viking Global (Halvorsen), AQR Capital (Asness)
\n
\n
\n
RISK NOTE
\n
Regulatory scrutiny and competitive pressures in cloud computing; mature e-commerce growth may limit upside.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
AMZN260821C00250000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $250.0
\n
\n
\n
EXPIRY
\n
2026-08-21
\n
\n
\n
MID PRICE
\n
$9.7
\n
\n
\n
MAX RISK / CONTRACT
\n
$970.0
\n
\n
\n
PROFIT TARGET
\n
+75-100% on option premium if stock moves +12-18%
\n
\n
\n
\n Rationale: Delta 0.35 provides optimal risk-reward leverage for 4+ month time horizon, while 34.4% IV is reasonable for tech. Exceptional liquidity with 29,781 OI.\n
\n
\n Stop Loss: -50% on option premium  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n NVDA\n NVIDIA CORPORATION\n
\n
\n
19.8/100
\n
CONVICTION SCORE
\n
\n
\n\n
\n\n
\n
Investment Thesis
\n
Five top-tier funds including TCI and Harvard established new positions, signaling continued institutional confidence in NVIDIA's AI chip monopoly despite valuation concerns. The broad-based buying suggests institutions view current levels as attractive entry points.
\n
\n\n
\n
\n
KEY BUYERS
\n
Harvard Management Co, TCI Fund (Chris Hohn), Tiger Global (Coleman), D1 Capital (Sundheim), AQR Capital (Asness)
\n
\n
\n
RISK NOTE
\n
High valuation multiples and potential AI bubble concerns; semiconductor cyclicality and geopolitical tensions around China markets.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
NVDA260918C00200000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $200.0
\n
\n
\n
EXPIRY
\n
2026-09-18
\n
\n
\n
MID PRICE
\n
$12.73
\n
\n
\n
MAX RISK / CONTRACT
\n
$1273.0
\n
\n
\n
PROFIT TARGET
\n
+60-80% on option premium if stock moves +15-20%
\n
\n
\n
\n Rationale: Delta 0.43 offers strong directional exposure with 5+ month runway for AI thesis to materialize. High liquidity with 33,190 OI provides excellent execution.\n
\n
\n Stop Loss:  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n BRK/A\n BERKSHIRE HATHAWAY INC DEL\n
\n
\n
41.3/100
\n
CONVICTION SCORE
\n
\n
\n\n
\n\n
\n
Investment Thesis
\n
Gates Foundation's substantial 27.6% allocation to Berkshire signals flight to defensive value and Buffett's crisis-tested investment acumen. This concentrated bet suggests institutional preparation for market volatility while maintaining equity exposure.
\n
\n\n
\n
\n
KEY BUYERS
\n
Gates Foundation Trust
\n
\n
\n
RISK NOTE
\n
Succession concerns post-Buffett era; large cash position may drag returns in bull markets; concentrated exposure to specific sectors.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
N/A
\n
\n
\n
TYPE / STRIKE
\n
N/A @ $0
\n
\n
\n
EXPIRY
\n
N/A
\n
\n
\n
MID PRICE
\n
$0
\n
\n
\n
MAX RISK / CONTRACT
\n
$0
\n
\n
\n
PROFIT TARGET
\n
N/A - Direct equity purchase required
\n
\n
\n
\n Rationale: No options data available for BRK/A - would require direct equity exposure to capture Gates Foundation's flight-to-quality thesis.\n
\n
\n Stop Loss: N/A  | \n Greeks: N/A\n
\n
\n
\n \n
\n
\n
\n IBIT\n ISHARES BITCOIN TRUST ETF\n
\n
\n
18.7/100
\n
CONVICTION SCORE
\n
\n
\n\n
\n\n
\n
Investment Thesis
\n
Harvard and Brown's simultaneous crypto ETF adoption marks institutional acceptance of Bitcoin as a legitimate asset class. The 12.8% Harvard allocation suggests endowments are diversifying into digital assets for long-term portfolio protection.
\n
\n\n
\n
\n
KEY BUYERS
\n
Harvard Management Co, Brown University
\n
\n
\n
RISK NOTE
\n
Extreme Bitcoin volatility and regulatory uncertainty; ETF structure risks and potential crypto market manipulation concerns.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
IBIT260918C00045000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $45.0
\n
\n
\n
EXPIRY
\n
2026-09-18
\n
\n
\n
MID PRICE
\n
$3.63
\n
\n
\n
MAX RISK / CONTRACT
\n
$363.0
\n
\n
\n
PROFIT TARGET
\n
+100-150% on option premium if Bitcoin rallies +25-35%
\n
\n
\n
\n Rationale: Delta 0.44 provides balanced risk-reward with 5+ month horizon for crypto institutional adoption trend. Excellent OI at 25,501 ensures liquidity.\n
\n
\n Stop Loss: -60% on option premium due to crypto volatility  | \n Greeks: Available\n
\n
\n
\n \n\n \n
Portfolio Sizing Note
Weight AMZN and NVDA highest given broad institutional consensus, moderate allocation to PLTR despite high conviction given concentration risk, small speculative allocation to IBIT.
\n\n \n
\n
\n ⚠️ DISCLAIMER: Options involve significant risk. This is based on delayed 13F data and real-time option prices may differ. Not investment advice.

\n Data limitations: 13F filings reflect long equity positions over $200K with up to 45-day delay.\n Portfolio weights use long-only reported AUM (cash, shorts, bonds excluded – weights are systematically overstated).\n Stock splits have been adjusted. Option prices are from Tradier at time of analysis and change constantly.\n This report is generated automatically and does not constitute investment advice.\n
\n
\n\n \n
\n Generated automatically by SEC 13F Smart Money Analyzer  Β· \n Data: SEC EDGAR + Tradier  Β·  Analysis: Claude AI\n
\n\n
\n\n" }, { "path": "reports/2026-05-18_report.html", "content": "\n\n\n \n \n SEC 13F Smart Money Report\n\n\n\n
\n\n \n
\n
\n QUARTERLY INSTITUTIONAL INTELLIGENCE\n
\n
SEC 13F Smart Money Report
\n
2026-05-18  Β·  13 Monitored Institutions  Β·  Top 5 Picks
\n
\n\n \n
\n
Market Context
\n
Institutional investors are showing strong synchronized conviction in mega-cap tech (NVDA, MSFT, GOOGL, AMZN) and semiconductor supply chain (TSM, AVGO), suggesting a coordinated thesis around AI infrastructure and edge computing. Simultaneous entries by elite endowments (Harvard, Brown) and specialized hedge funds (D1, Tiger Global, Viking) into both traditional blue chips and emerging fintech/e-commerce names indicate portfolio repositioning toward structural growth themes with reduced economic sensitivity.
\n
\n\n \n\n \n
\n 🎯 Top 5 Conviction Picks + Option Trades\n
\n\n \n
\n
\n
\n NVDA\n NVIDIA Corporation\n
\n
\n
94.2/100
\n
CONVICTION SCORE
\n
\n
\n\n
CLUSTER
\n\n \n\n
\n
Investment Thesis
\n
Unprecedented 7-fund cluster conviction including elite endowments (Harvard, Brown, TCI) and specialized tech allocators (Situational Awareness, Tiger Global, D1), with 11.47% portfolio weight in Situational Awareness suggesting AI infrastructure is a core thesis. New positions across all major allocators indicate synchronized recognition of sustained AI/data center demand tailwinds beyond near-term consensus.
\n
\n\n
\n
\n
KEY BUYERS
\n
Situational Awareness LP, Harvard Management Co, Brown University, TCI Fund (Chris Hohn), Tiger Global (Coleman), D1 Capital (Sundheim), AQR Capital (Asness)
\n
\n
\n
RISK NOTE
\n
Stock has already benefited significantly from AI narrative and may be priced for optimistic scenarios; 45-day lag means these positions were established in late April 2026 and could already reflect partial moves.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
NVDA260918C00240000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $240.0
\n
\n
\n
EXPIRY
\n
2026-09-18
\n
\n
\n
MID PRICE
\n
$16.15
\n
\n
\n
MAX RISK / CONTRACT
\n
$1615.0
\n
\n
\n
PROFIT TARGET
\n
+60-90% on option premium if NVDA moves to $255-265 (+16-20%) by August-September
\n
\n
\n
\n Rationale: Strike at +9% OTM with Delta 0.45 sits in the sweet spot for leveraged bullish exposure without overpaying for proximity. September 18 expiry provides sufficient runway for AI capex cycle catalysts (earnings, data center announcements). IV at 44.8% is consistent with peer strikes, no premium anomaly. Volume 2547 and OI 32666 represent the highest liquidity in the NVDA chain, minimizing slippage. Spread% of 0.6% is exceptionally tight for a high-beta name.\n
\n
\n Stop Loss: -50% on option premium (~$8.08 per contract)  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n TSM\n Taiwan Semiconductor Manufacturing Company\n
\n
\n
100.0/100
\n
CONVICTION SCORE
\n
\n
\n\n
HIGH_CONVICTION
\n\n \n\n
\n
Investment Thesis
\n
Perfect conviction score with 5-fund cluster of highest-quality allocators (Situational Awareness 6.56%, Harvard 12.78%, Tiger Global 8.23%) entering simultaneously across borders, indicating structural confidence in semiconductor supply chain leadership. TSM's role as critical infrastructure for AI/GPU manufacturing makes it a proxy play on sustained capex cycles with less crowding than direct chip designers.
\n
\n\n
\n
\n
KEY BUYERS
\n
Situational Awareness LP, Harvard Management Co, Tiger Global (Coleman), D1 Capital (Sundheim), Viking Global (Halvorsen)
\n
\n
\n
RISK NOTE
\n
Geopolitical concentration risk (Taiwan) and US-China tensions could create volatility; new position status means conviction narrative may still be building and subject to reversal if foundational AI thesis weakens.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
TSM260918C00390000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $390.0
\n
\n
\n
EXPIRY
\n
2026-09-18
\n
\n
\n
MID PRICE
\n
$44.35
\n
\n
\n
MAX RISK / CONTRACT
\n
$4435.0
\n
\n
\n
PROFIT TARGET
\n
+40-65% on option premium if TSM moves to $430-450 (+10-15%) by September
\n
\n
\n
\n Rationale: Strike at -0.6% (essentially ATM) with Delta 0.59 reflects higher conviction warranted by the elite fund cluster quality and TSM's structural moat. September expiry aligns with Q2 earnings catalyst and potential positive AI demand commentary. IV at 45.6% is reasonable for a name with geopolitical risk premium. Volume 176 and OI 1206 are adequate though lower than NVDA; spread at 2.0% is acceptable. ATM positioning maximizes delta sensitivity to upside moves while providing intrinsic value buffer against time decay.\n
\n
\n Stop Loss: -45% on option premium (~$19.96 per contract)  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n AVGO\n Broadcom Inc\n
\n
\n
95.2/100
\n
CONVICTION SCORE
\n
\n
\n\n
CLUSTER
\n\n \n\n
\n
Investment Thesis
\n
5-fund cluster including Harvard (8.08%), Situational Awareness (7.36%), and specialized tech allocators (Tiger Global, D1, AQR) capturing semiconductor supply chain exposure with lower visibility than NVDA. As critical infrastructure provider for networking/connectivity in data centers, AVGO represents a complementary bet to NVDA with less consensus coverage and fresher accumulation pattern.
\n
\n\n
\n
\n
KEY BUYERS
\n
Situational Awareness LP, Harvard Management Co, Tiger Global (Coleman), D1 Capital (Sundheim), AQR Capital (Asness)
\n
\n
\n
RISK NOTE
\n
Cyclical exposure to capex spending means thesis is sensitive to data center investment slowdown; new position timing suggests this is momentum-driven rather than deep-value setup.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
AVGO261016C00430000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $430.0
\n
\n
\n
EXPIRY
\n
2026-10-16
\n
\n
\n
MID PRICE
\n
$51.45
\n
\n
\n
MAX RISK / CONTRACT
\n
$5145.0
\n
\n
\n
PROFIT TARGET
\n
+50-75% on option premium if AVGO moves to $480-500 (+15-20%) by October
\n
\n
\n
\n Rationale: Strike at +2.8% OTM with Delta 0.55 and October 16 expiry provides the longest time horizon available for AVGO while maintaining near-ATM sensitivity. October expiry is preferred over August to avoid theta decay pressure during what may be a gradual institutional accumulation story. Volume 111 and OI 1410 are adequate; spread at 2.3% is acceptable. IV at 50.6% is the highest in this selection but reflects AVGO's genuine higher volatility profile versus MSFT; compared to AVGO's own chain, the October strike is not an outlier. The additional month over September peers justifies the small IV premium.\n
\n
\n Stop Loss: -50% on option premium (~$25.73 per contract)  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n BKNG\n Booking Holdings Inc\n
\n
\n
95.2/100
\n
CONVICTION SCORE
\n
\n
\n\n
HIGH_CONVICTION
\n\n
βœ… +0.0% seit Filing – Signal noch frisch$154.13 β†’ $154.13  Β·  3d ago
\n\n
\n
Investment Thesis
\n
Selective 2-fund entry by Harvard Management (8.10% weight) and AQR Capital representing rare consensus between endowment and quant allocators in travel/tourism recovery play. High portfolio concentration at Harvard suggests conviction in structural travel demand resilience post-cycle, with less crowding than mega-cap tech and better economic sensitivity profile for diversification.
\n
\n\n
\n
\n
KEY BUYERS
\n
Harvard Management Co, AQR Capital (Asness)
\n
\n
\n
RISK NOTE
\n
Macro sensitivity to consumer spending and recession risk; only 2-fund signal is weaker than mega-cap tech cluster and thesis may reflect cyclical recovery optimism rather than structural growth.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
BKNG260918C00180000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $180.0
\n
\n
\n
EXPIRY
\n
2026-09-18
\n
\n
\n
MID PRICE
\n
$6.3
\n
\n
\n
MAX RISK / CONTRACT
\n
$630.0
\n
\n
\n
PROFIT TARGET
\n
+80-120% on option premium if BKNG moves to $195-205 (+27-33%) driven by travel demand surprise
\n
\n
\n
\n Rationale: Strike at +16.9% OTM with Delta 0.31 reflects the appropriate risk-sizing for the weakest conviction signal in this portfolio (2-fund vs 5-7 fund clusters for others). The low premium of $6.30 limits absolute dollar risk while still providing meaningful leverage. IV at 41.0% is the lowest among all candidates and makes this the cheapest option on a volatility-adjusted basis. Volume is only 15 (lowest in dataset) and OI 636 is thin β€” this is the key liquidity risk. Spread at 3.2% is manageable. September expiry captures summer travel season peak data and back-to-school booking cycle, both natural catalysts for the thesis. Ranked 5th deliberately to reflect lower conviction and liquidity.\n
\n
\n Stop Loss: -60% on option premium (~$3.78 per contract) given lower conviction  | \n Greeks: Available\n
\n
\n
\n \n
\n
\n
\n MSFT\n Microsoft Corporation\n
\n
\n
97.6/100
\n
CONVICTION SCORE
\n
\n
\n\n
CLUSTER
\n\n
βœ… +0.0% seit Filing – Signal noch frisch$421.92 β†’ $421.92  Β·  3d ago
\n\n
\n
Investment Thesis
\n
5-fund cluster across endowments (Harvard 10.46%), specialized allocators (Pershing Square 15.26%), and quant platforms (AQR, Viking Global) entering simultaneously indicates unified thesis on AI-as-platform-provider positioning. MSFT's Copilot/Azure integration and enterprise stickiness suggest lower risk profile than pure chip plays while maintaining upside to AI monetization cycles.
\n
\n\n
\n
\n
KEY BUYERS
\n
Harvard Management Co, Pershing Square (Ackman), Tiger Global (Coleman), Viking Global (Halvorsen), AQR Capital (Asness)
\n
\n
\n
RISK NOTE
\n
Mega-cap tech already extensively covered and these positions may be consensus; stock has likely already captured much of the AI narrative given ubiquitous institutional ownership.
\n
\n
\n\n \n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
MSFT260918C00430000
\n
\n
\n
TYPE / STRIKE
\n
CALL @ $430.0
\n
\n
\n
EXPIRY
\n
2026-09-18
\n
\n
\n
MID PRICE
\n
$30.05
\n
\n
\n
MAX RISK / CONTRACT
\n
$3005.0
\n
\n
\n
PROFIT TARGET
\n
+45-70% on option premium if MSFT moves to $465-480 (+10-13%) by September
\n
\n
\n
\n Rationale: Strike at +1.5% OTM with Delta 0.53 is nearly ATM, appropriate for MSFT's lower beta and steadier AI monetization runway through Azure/Copilot. Critically, MSFT's IV at 31.6% is dramatically lower than semiconductor peers (NVDA 44.8%, AVGO 50%), making this the most attractively priced option on a premium-per-dollar-of-exposure basis. Volume 1292 and OI 5606 ensure good liquidity. Spread at 1.3% is among the tightest in the entire dataset. September expiry captures Q1 FY2027 earnings and multiple Azure growth data points.\n
\n
\n Stop Loss: -50% on option premium (~$15.03 per contract)  | \n Greeks: Available\n
\n
\n
\n \n\n \n
Portfolio Sizing Note
Suggested sizing reflects conviction tier weighting: NVDA and TSM at 30% of options budget each (7-fund and 5-fund elite clusters), MSFT and AVGO at 20% each (5-fund clusters with differentiated risk profiles), and BKNG at 10% (2-fund signal, liquidity concerns). Total options budget should represent no more than 3-5% of total portfolio value given the leveraged nature of these positions. The semiconductor cluster (NVDA+TSM+AVGO) represents correlated AI capex risk β€” consider reducing any single name if overall AI exposure exceeds comfort threshold. MSFT provides partial hedge as a lower-beta AI play. BKNG provides genuine sector diversification.
\n\n \n
\n
\n ⚠️ DISCLAIMER: Options involve significant risk including total loss of premium paid. This analysis is based on 13F filings with an approximately 45-day reporting lag and real-time option prices that may differ materially at execution. Institutional positions may have already been reduced or reversed. IV levels and spreads are point-in-time estimates. Geopolitical events, earnings surprises, or macro shifts can cause rapid adverse moves. This is not investment advice. Consult a licensed financial advisor before trading.

\n Data limitations: 13F filings reflect long equity positions over $200K with up to 45-day delay.\n Portfolio weights use long-only reported AUM (cash, shorts, bonds excluded – weights are systematically overstated).\n Stock splits have been adjusted. Option prices are from Tradier at time of analysis and change constantly.\n This report is generated automatically and does not constitute investment advice.\n
\n
\n\n \n
\n Generated automatically by SEC 13F Smart Money Analyzer  Β· \n Data: SEC EDGAR + Tradier  Β·  Analysis: Claude AI\n
\n\n
\n\n" }, { "path": "requirements.txt", "content": "anthropic>=0.25.0\nrequests>=2.31.0\nyfinance>=0.2.37\nholidays>=0.46\n" }, { "path": "src/analyze_claude_round1.py", "content": "\"\"\"\nanalyze_claude_round1.py\nClaude API – Round 1: Identify Top 5 investment candidates.\n\nUses claude-haiku (CLAUDE_MODEL_R1) – sufficient for screening and\n~20Γ— cheaper than Sonnet. Sonnet is reserved for Round 2 (precise\noption selection where nuance matters more).\n\nImprovements:\n - CLAUDE_MODEL_R1 (Haiku) instead of Sonnet\n - Multi-quarter build signals included in prompt\n - Price-action staleness flags surfaced to Claude\n - Exponential backoff retry\n - Robust JSON extraction\n\"\"\"\n\nimport json\nimport os\nimport re\nimport time\nfrom datetime import date\n\nimport anthropic\n\nfrom config import (\n CLAUDE_MAX_TOKENS, CLAUDE_MODEL_R1, CLAUDE_RETRY_COUNT,\n CLAUDE_RETRY_DELAY, DATA_DIR,\n)\n\n\ndef load_scores(today_str: str) -> dict:\n path = DATA_DIR / f\"{today_str}_scores.json\"\n if not path.exists():\n raise FileNotFoundError(f\"Scores not found: {path}\")\n with open(path) as f:\n return json.load(f)\n\n\ndef normalize_ticker(ticker: str) -> str:\n return ticker.strip().upper().replace(\".\", \"/\")\n\n\ndef build_prompt(scores: dict) -> str:\n today_str = scores[\"date\"]\n top20 = scores[\"top20\"]\n clusters = scores[\"clusters\"]\n mq_signals = scores.get(\"mq_signals\", {})\n\n positions_text = []\n for i, agg in enumerate(top20, 1):\n filer_summary = \"; \".join(\n f\"{f['filer']} ({f['delta_type']}, \"\n f\"Ξ”{f['delta_pct'] if f['delta_pct'] is not None else 'N/A'}%, \"\n f\"port_wt={f['port_weight_pct']}%)\"\n for f in agg[\"filers\"]\n )\n\n # Price-action context\n price_chg = agg.get(\"price_change_since_filing_pct\")\n price_note = \"\"\n if price_chg is not None:\n if \"PRICE_ACTION_STALE\" in agg.get(\"flags\", []):\n price_note = f\" ⚠️ ALREADY +{price_chg:.0f}% SINCE FILING – thesis may be priced in\"\n elif \"PRICE_ACTION_WARNING\" in agg.get(\"flags\", []):\n price_note = f\" ⚑ +{price_chg:.0f}% since filing\"\n else:\n price_note = f\" (+{price_chg:.0f}% since filing)\" if price_chg > 0 else f\" ({price_chg:.0f}% since filing)\"\n\n # Multi-quarter context\n mq_note = \"\"\n mq = mq_signals.get(agg[\"ticker\"])\n if mq and mq[\"build_quarters\"] >= 2:\n mq_note = (\n f\"\\n Multi-Quarter: {mq['build_quarters']} quarters of building\"\n f\" | avg delta {mq['avg_delta_pct']}%\"\n f\" | flags: {', '.join(mq['flags']) or 'none'}\"\n )\n\n positions_text.append(\n f\"{i}. {agg['ticker']} ({agg['name']}){price_note}\\n\"\n f\" Score: {agg['conviction_score']}/100 | Filers: {agg['filer_count']} | \"\n f\"Flags: {', '.join(agg['flags']) or 'none'}\\n\"\n f\" Cluster: {'YES – ' + str(agg['cluster_count']) + ' funds' if agg['cluster_count'] >= 3 else 'no'}\\n\"\n f\" Details: {filer_summary}{mq_note}\\n\"\n )\n\n cluster_text = \"\"\n if clusters:\n cluster_text = \"\\nCLUSTER SIGNALS (3+ funds buying same ticker):\\n\"\n for ticker, filers in clusters.items():\n cluster_text += f\" {ticker}: {', '.join(filers)}\\n\"\n\n return f\"\"\"You are an expert quantitative analyst specializing in 13F filing analysis and institutional investor tracking.\n\nANALYSIS DATE: {today_str}\nDATA SOURCE: SEC 13F filings (latest available, up to 45-day lag)\n\nIMPORTANT DISCLAIMER: 13F data reflects only US long equity positions >$200K.\nPortfolio weights use long-only AUM (cash/shorts/bonds excluded β†’ weights are systematically overstated).\nStock splits have been adjusted. Treat this as an idea generator, not a buy signal.\n\nIMPORTANT – PRICE ACTION: Positions marked ⚠️ ALREADY +X% SINCE FILING have potentially\nalready played out. Strong preference for fresh ideas that have NOT run significantly yet.\n\nTOP 20 CONVICTION SCORES (normalized 0-100):\n{''.join(positions_text)}\n{cluster_text}\n\nYOUR TASK:\nAnalyze the above data and identify the TOP 5 stocks with the strongest institutional conviction signals\nthat have NOT already fully played out in price.\n\nConsider in priority order:\n1. Multi-quarter building (3+ quarters of consistent accumulation = strongest signal)\n2. Cluster signals (multiple top funds buying simultaneously)\n3. Fresh entries that haven't run >15% since the filing date\n4. Conviction Score magnitude\n5. Quality of the buying funds (university endowments > hedge funds for long-term thesis)\n6. Position type flags: HIGH_CONVICTION (>3% of portfolio), NEW_POSITION, AGGRESSIVE_ADD\n\nExplicitly DOWNWEIGHT stocks with PRICE_ACTION_STALE flag – the thesis is likely priced in.\n\nOUTPUT FORMAT (respond ONLY with valid JSON, no markdown, no explanation):\n{{\n \"analysis_date\": \"{today_str}\",\n \"market_context\": \"2-3 sentence summary of current market environment relevant to these picks\",\n \"top5\": [\n {{\n \"rank\": 1,\n \"ticker\": \"NORMALIZED_TICKER\",\n \"company_name\": \"Full Company Name\",\n \"conviction_score\": 87.5,\n \"thesis\": \"2-3 sentence investment thesis explaining WHY this is a strong signal\",\n \"key_buyers\": [\"Fund Name 1\", \"Fund Name 2\"],\n \"cluster_signal\": true,\n \"multi_quarter_build\": false,\n \"primary_flag\": \"HIGH_CONVICTION|NEW_POSITION|AGGRESSIVE_ADD|CLUSTER|MULTI_QUARTER_BUILD\",\n \"risk_factors\": \"1-2 sentence note on key risks or the 45-day data lag impact\",\n \"direction\": \"BULLISH\"\n }}\n ],\n \"disclaimer\": \"This analysis is based on delayed 13F data and is for informational purposes only, not investment advice.\"\n}}\"\"\"\n\n\ndef call_claude_with_retry(prompt: str) -> str:\n client = anthropic.Anthropic(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\n\n for attempt in range(1, CLAUDE_RETRY_COUNT + 1):\n try:\n response = client.messages.create(\n model=CLAUDE_MODEL_R1,\n max_tokens=CLAUDE_MAX_TOKENS,\n system=(\n \"You are a quantitative analyst. You respond ONLY with valid JSON. \"\n \"Never include markdown code fences, preamble, or explanation. \"\n \"Your entire response must be parseable by json.loads().\"\n ),\n messages=[{\"role\": \"user\", \"content\": prompt}],\n )\n return response.content[0].text\n\n except anthropic.RateLimitError:\n wait = CLAUDE_RETRY_DELAY * (2 ** (attempt - 1))\n print(f\" ⏳ Rate limit hit. Waiting {wait}s (attempt {attempt}/{CLAUDE_RETRY_COUNT})\")\n time.sleep(wait)\n\n except anthropic.APIError as e:\n wait = CLAUDE_RETRY_DELAY * attempt\n print(f\" ⚠️ API error (attempt {attempt}): {e}. Retrying in {wait}s...\")\n time.sleep(wait)\n\n raise RuntimeError(f\"Claude API failed after {CLAUDE_RETRY_COUNT} attempts\")\n\n\ndef parse_claude_response(raw: str) -> dict:\n cleaned = re.sub(r\"```(?:json)?\", \"\", raw).strip()\n try:\n return json.loads(cleaned)\n except json.JSONDecodeError as e:\n match = re.search(r\"\\{.*\\}\", cleaned, re.DOTALL)\n if match:\n return json.loads(match.group())\n raise ValueError(f\"Could not parse Claude response as JSON: {e}\\n\\nRaw: {raw[:500]}\")\n\n\ndef run():\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"Claude Round 1 (Haiku) – {today_str}\")\n print(f\"{'='*60}\")\n print(f\" Model: {CLAUDE_MODEL_R1}\")\n\n scores = load_scores(today_str)\n prompt = build_prompt(scores)\n\n print(f\"πŸ“€ Sending top {len(scores['top20'])} scored positions to Claude Haiku...\")\n\n raw_response = call_claude_with_retry(prompt)\n result = parse_claude_response(raw_response)\n\n for stock in result.get(\"top5\", []):\n stock[\"ticker\"] = normalize_ticker(stock.get(\"ticker\", \"\"))\n\n print(f\"βœ… Claude identified top 5:\")\n for stock in result.get(\"top5\", []):\n print(f\" {stock['rank']}. {stock['ticker']} – {stock['thesis'][:60]}...\")\n\n output_path = DATA_DIR / f\"{today_str}_claude_round1.json\"\n with open(output_path, \"w\") as f:\n json.dump(result, f, indent=2)\n\n print(f\"πŸ’Ύ Saved to {output_path}\")\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/analyze_claude_round2.py", "content": "\"\"\"\nanalyze_claude_round2.py\nClaude API – Round 2: Select the best specific options from real Tradier data.\n\nUses claude-sonnet (CLAUDE_MODEL_R2) – precision matters here.\nClaude receives the top 5 theses + real option chains and picks the\nsingle best contract per stock, with explicit reasoning on strike,\nexpiry, IV, and liquidity.\n\nImprovements:\n - CLAUDE_MODEL_R2 (Sonnet) explicitly\n - Structured JSON output enforced\n - Retry with exponential backoff\n - Validates that report is non-empty before saving\n\"\"\"\n\nimport json\nimport os\nimport re\nimport time\nfrom datetime import date\n\nimport anthropic\n\nfrom config import (\n CLAUDE_MAX_TOKENS, CLAUDE_MODEL_R2, CLAUDE_RETRY_COUNT,\n CLAUDE_RETRY_DELAY, DATA_DIR,\n)\n\n\ndef load_round1(today_str: str) -> dict:\n path = DATA_DIR / f\"{today_str}_claude_round1.json\"\n if not path.exists():\n raise FileNotFoundError(f\"Claude Round 1 results not found: {path}\")\n with open(path) as f:\n return json.load(f)\n\n\ndef load_options(today_str: str) -> dict:\n path = DATA_DIR / f\"{today_str}_options.json\"\n if not path.exists():\n raise FileNotFoundError(f\"Options data not found: {path}\")\n with open(path) as f:\n return json.load(f)\n\n\ndef format_options_for_prompt(ticker: str, opt_data: dict) -> str:\n \"\"\"Formats option candidates concisely for the prompt, including live stock price.\"\"\"\n options = opt_data.get(\"options\", [])\n current_price = opt_data.get(\"current_price\")\n change_pct = opt_data.get(\"change_pct\")\n direction = opt_data.get(\"direction\", \"BULLISH\")\n\n price_str = f\"${current_price}\" if current_price else \"n/a\"\n change_str = f\"{change_pct:+.2f}%\" if change_pct is not None else \"n/a\"\n\n if not options:\n return f\"\\n{ticker} β€” Current: {price_str} ({change_str}) | No options data available\\n\"\n\n lines = [f\"\\n{ticker} β€” Current price: {price_str} ({change_str} today) | Direction: {direction}\"]\n lines.append(f\"{'Symbol':<22}{'Strike':<10}{'vs Spot':<10}{'Expiry':<14}{'Mid':<8}\"\n f\"{'Sprd%':<8}{'Vol':<8}{'OI':<8}{'Delta':<8}{'IV':<8}\")\n lines.append(\"─\" * 104)\n\n for opt in options[:8]:\n strike = opt.get(\"strike\")\n delta_str = f\"{opt['delta']:.2f}\" if opt.get(\"delta\") else \"n/a\"\n iv_str = f\"{float(opt['implied_volatility']):.1%}\" if opt.get(\"implied_volatility\") else \"n/a\"\n mid_str = f\"${opt['mid']}\" if opt.get(\"mid\") else \"n/a\"\n spread_str = f\"{opt['spread_pct']}%\" if opt.get(\"spread_pct\") is not None else \"n/a\"\n\n # Show how far the strike is from current price\n if strike and current_price:\n vs_spot = f\"{((float(strike) / float(current_price)) - 1) * 100:+.1f}%\"\n else:\n vs_spot = \"n/a\"\n\n lines.append(\n f\"{str(opt.get('symbol', '')):<22}\"\n f\"{str(strike or ''):<10}\"\n f\"{vs_spot:<10}\"\n f\"{str(opt.get('expiration_date', '')):<14}\"\n f\"{mid_str:<8}\"\n f\"{spread_str:<8}\"\n f\"{str(opt.get('volume', '')):<8}\"\n f\"{str(opt.get('open_interest', '')):<8}\"\n f\"{delta_str:<8}\"\n f\"{iv_str:<8}\"\n )\n if not opt.get(\"greeks_available\"):\n lines.append(\" ⚠️ Greeks unavailable (pre-market snapshot)\")\n\n return \"\\n\".join(lines)\n\n\ndef build_round2_prompt(r1: dict, options_data: dict) -> str:\n today_str = r1.get(\"analysis_date\", date.today().isoformat())\n\n # Build thesis summary\n theses = []\n for stock in r1.get(\"top5\", []):\n theses.append(\n f\"#{stock['rank']} {stock['ticker']} – {stock['company_name']}\\n\"\n f\" Thesis: {stock['thesis']}\\n\"\n f\" Key buyers: {', '.join(stock.get('key_buyers', []))}\\n\"\n f\" Risk: {stock.get('risk_factors', '')}\"\n )\n\n # Build options tables\n option_tables = []\n for ticker, opt_data in options_data.get(\"options\", {}).items():\n option_tables.append(format_options_for_prompt(ticker, opt_data))\n\n return f\"\"\"You are an expert options strategist with deep knowledge of institutional investor behavior.\n\nANALYSIS DATE: {today_str}\nTASK: For each of the 5 stocks below, select the SINGLE BEST option based on the provided real-market data.\n\nINVESTMENT THESES (from 13F conviction analysis):\n{''.join(theses)}\n\nREAL OPTIONS DATA FROM TRADIER:\n{''.join(option_tables)}\n\nOPTION SELECTION CRITERIA:\n1. Strike selection: Use the \"vs Spot\" column to assess moneyness. For BULLISH plays, prefer\n slightly OTM (+2% to +8% above current price, Delta 0.35-0.50) for leverage with reasonable\n probability of profit. ATM (Delta 0.45-0.55) for higher conviction plays.\n2. Expiry: 3-6 months allows time for institutional thesis to play out (13F data is already\n ~45 days old, so add that to your horizon).\n3. IV consideration: Avoid buying options with unusually high IV (paying too much premium).\n Compare IV levels across strikes and expiries in the table.\n4. Liquidity: Prefer options with volume >200, OI >500, and spread% <10%. Spread% is shown\n in the table β€” wide spreads (>12%) hurt entry/exit significantly.\n5. Current price context: The live stock price is shown above each options table. Use it to\n judge whether the thesis has already played out (stock already up a lot since 13F filing)\n or still has room to run.\n\nOUTPUT FORMAT (respond ONLY with valid JSON, no markdown fences):\n{{\n \"analysis_date\": \"{today_str}\",\n \"options_recommendations\": [\n {{\n \"rank\": 1,\n \"stock_ticker\": \"TICKER\",\n \"company_name\": \"Full Name\",\n \"option_symbol\": \"EXACT_OPTION_SYMBOL_FROM_DATA\",\n \"option_type\": \"CALL\",\n \"strike\": 150.0,\n \"expiration\": \"YYYY-MM-DD\",\n \"entry_price_mid\": 5.50,\n \"max_risk_per_contract\": 550.0,\n \"investment_thesis_link\": \"1-2 sentences linking the 13F signal to this specific option choice\",\n \"option_rationale\": \"Why THIS strike and expiry specifically – delta, IV, time horizon reasoning\",\n \"profit_target\": \"e.g. +50-80% on option premium if stock moves +10-15%\",\n \"stop_loss\": \"e.g. -50% on option premium\",\n \"key_risk\": \"1 sentence on the main risk for this trade\",\n \"greeks_note\": \"Available|Pre-market estimate only\"\n }}\n ],\n \"portfolio_note\": \"Brief note on sizing – e.g. equal weight vs. high conviction weighting\",\n \"disclaimer\": \"Options involve significant risk. This is based on delayed 13F data and real-time option prices may differ. Not investment advice.\"\n}}\"\"\"\n\n\ndef call_claude_with_retry(prompt: str) -> str:\n client = anthropic.Anthropic(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\n\n for attempt in range(1, CLAUDE_RETRY_COUNT + 1):\n try:\n response = client.messages.create(\n model=CLAUDE_MODEL_R2,\n max_tokens=CLAUDE_MAX_TOKENS,\n system=(\n \"You are an expert options strategist. Respond ONLY with valid JSON. \"\n \"No markdown, no code fences, no preamble. \"\n \"Your entire response must be parseable by json.loads().\"\n ),\n messages=[{\"role\": \"user\", \"content\": prompt}],\n )\n return response.content[0].text\n\n except anthropic.RateLimitError:\n wait = CLAUDE_RETRY_DELAY * (2 ** (attempt - 1))\n print(f\" ⏳ Rate limit. Waiting {wait}s (attempt {attempt}/{CLAUDE_RETRY_COUNT})\")\n time.sleep(wait)\n\n except anthropic.APIError as e:\n wait = CLAUDE_RETRY_DELAY * attempt\n print(f\" ⚠️ API error (attempt {attempt}): {e}. Retry in {wait}s...\")\n time.sleep(wait)\n\n raise RuntimeError(f\"Claude API failed after {CLAUDE_RETRY_COUNT} attempts\")\n\n\ndef parse_response(raw: str) -> dict:\n cleaned = re.sub(r\"```(?:json)?\", \"\", raw).strip()\n try:\n return json.loads(cleaned)\n except json.JSONDecodeError:\n match = re.search(r\"\\{.*\\}\", cleaned, re.DOTALL)\n if match:\n return json.loads(match.group())\n raise ValueError(f\"Cannot parse Claude Round 2 response:\\n{raw[:500]}\")\n\n\ndef validate_result(result: dict) -> bool:\n \"\"\"\n R-14 Fix: Ensure the result is substantive before saving.\n Returns False if the result appears empty or malformed.\n \"\"\"\n recs = result.get(\"options_recommendations\", [])\n if not recs:\n print(\" ⚠️ VALIDATION FAILED: No recommendations in Claude response\")\n return False\n if len(recs) < 3:\n print(f\" ⚠️ VALIDATION WARNING: Only {len(recs)} recommendations (expected 5)\")\n for rec in recs:\n if not rec.get(\"option_symbol\") or not rec.get(\"stock_ticker\"):\n print(f\" ⚠️ VALIDATION FAILED: Missing ticker or symbol in recommendation: {rec}\")\n return False\n return True\n\n\ndef run():\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"Claude Analysis Round 2 – {today_str}\")\n print(f\"{'='*60}\")\n\n r1 = load_round1(today_str)\n options_data = load_options(today_str)\n\n prompt = build_round2_prompt(r1, options_data)\n\n print(f\"πŸ“€ Sending real options data for {len(r1.get('top5', []))} stocks to Claude...\")\n\n raw_response = call_claude_with_retry(prompt)\n result = parse_response(raw_response)\n\n if not validate_result(result):\n raise RuntimeError(\"Claude Round 2 result failed validation – aborting report\")\n\n print(f\"βœ… Claude selected {len(result.get('options_recommendations', []))} option trades:\")\n for rec in result.get(\"options_recommendations\", []):\n mid = rec.get(\"entry_price_mid\", \"?\")\n print(f\" #{rec['rank']} {rec['stock_ticker']} β†’ {rec['option_symbol']} \"\n f\"@ ${mid} | {rec.get('profit_target', '')}\")\n\n # Enrich round1_top5 with post-filing price performance and per-filer detail\n # from scores.json so the report can show price movement and buyer breakdown.\n top5 = r1.get(\"top5\", [])\n try:\n scores_path = DATA_DIR / f\"{today_str}_scores.json\"\n with open(scores_path) as sf:\n scores = json.load(sf)\n aggregated = scores.get(\"aggregated\", [])\n perf_by_ticker = {agg[\"ticker\"]: agg.get(\"post_filing_perf\", {}) for agg in aggregated}\n filers_by_ticker = {agg[\"ticker\"]: agg.get(\"filers\", []) for agg in aggregated}\n for stock in top5:\n t = stock.get(\"ticker\", \"\")\n stock[\"post_filing_perf\"] = perf_by_ticker.get(t, {})\n stock[\"filer_details\"] = filers_by_ticker.get(t, [])\n enriched = sum(1 for s in top5 if s.get(\"post_filing_perf\"))\n print(f\" βœ… Enriched {enriched}/{len(top5)} stocks with post-filing perf + filer details\")\n except Exception as e:\n print(f\" ⚠️ Could not enrich with post-filing perf / filer details: {e}\")\n\n # Merge Round 1 and Round 2 into final analysis file\n final = {\n \"date\": today_str,\n \"round1_top5\": top5,\n \"market_context\": r1.get(\"market_context\", \"\"),\n \"options_recs\": result.get(\"options_recommendations\", []),\n \"portfolio_note\": result.get(\"portfolio_note\", \"\"),\n \"disclaimer\": result.get(\"disclaimer\", \"\"),\n }\n\n output_path = DATA_DIR / f\"{today_str}_final_analysis.json\"\n with open(output_path, \"w\") as f:\n json.dump(final, f, indent=2)\n\n print(f\"πŸ’Ύ Final analysis saved to {output_path}\")\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/backtest.py", "content": "\"\"\"\nbacktest.py\nTracks historical performance of past recommendations.\n\nFor each past final_analysis.json, checks what the underlying stock\ndid 90 and 180 days after the report date. This is the only way to\nknow if the system generates alpha or just looks good.\n\nSaved to data/holdings/{today}_backtest.json and embedded in the report.\n\"\"\"\n\nimport json\nfrom datetime import date, timedelta\nfrom pathlib import Path\n\ntry:\n import yfinance as yf\n YF_AVAILABLE = True\nexcept ImportError:\n YF_AVAILABLE = False\n\nfrom config import DATA_DIR\n\n\ndef load_all_analyses() -> list[dict]:\n files = sorted(DATA_DIR.glob(\"*_final_analysis.json\"))\n results = []\n for f in files:\n try:\n with open(f) as fh:\n results.append(json.load(fh))\n except (json.JSONDecodeError, OSError):\n continue\n return results\n\n\ndef _yf_ticker(ticker: str) -> str:\n \"\"\"Convert Tradier-style tickers to yfinance format. BRK/B β†’ BRK-B.\"\"\"\n return ticker.replace(\"/\", \"-\")\n\n\ndef check_stock_performance(ticker: str, signal_date_str: str) -> dict:\n \"\"\"\n Downloads price history for ticker starting from signal_date.\n Returns price at signal, +90d, +180d, and % returns.\n \"\"\"\n if not YF_AVAILABLE:\n return {\"error\": \"yfinance_not_installed\"}\n\n try:\n signal_date = date.fromisoformat(signal_date_str)\n # Don't request data past today\n end_date = min(signal_date + timedelta(days=200), date.today())\n\n if end_date <= signal_date:\n return {\"status_d90\": \"pending\", \"status_d180\": \"pending\"}\n\n hist = yf.Ticker(_yf_ticker(ticker)).history(\n start=signal_date.isoformat(),\n end=end_date.isoformat(),\n )\n\n if hist.empty:\n return {\"error\": \"no_price_data\"}\n\n price_start = float(hist[\"Close\"].iloc[0])\n result: dict = {\n \"signal_date\": signal_date_str,\n \"price_at_signal\": round(price_start, 2),\n }\n\n for days, label in [(90, \"d90\"), (180, \"d180\")]:\n target = signal_date + timedelta(days=days)\n if target > date.today():\n result[f\"price_{label}\"] = None\n result[f\"return_{label}_pct\"] = None\n result[f\"status_{label}\"] = \"pending\"\n else:\n future = hist[hist.index.date >= target]\n if not future.empty:\n p = float(future[\"Close\"].iloc[0])\n ret = ((p - price_start) / price_start) * 100\n result[f\"price_{label}\"] = round(p, 2)\n result[f\"return_{label}_pct\"] = round(ret, 1)\n result[f\"status_{label}\"] = \"win\" if ret > 0 else \"loss\"\n else:\n result[f\"price_{label}\"] = None\n result[f\"return_{label}_pct\"] = None\n result[f\"status_{label}\"] = \"no_data\"\n\n return result\n\n except Exception as e:\n return {\"error\": str(e)}\n\n\ndef run() -> dict:\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"Backtesting Engine – {today_str}\")\n print(f\"{'='*60}\")\n\n analyses = load_all_analyses()\n if not analyses:\n print(\"No past analyses found. Skipping backtest.\")\n return {\"summary\": {}, \"results\": []}\n\n rows = []\n for analysis in analyses:\n report_date = analysis.get(\"date\", \"\")\n for stock in analysis.get(\"round1_top5\", []):\n ticker = stock.get(\"ticker\", \"\")\n if not ticker:\n continue\n print(f\" πŸ“Š {ticker} @ {report_date}...\", end=\" \", flush=True)\n perf = check_stock_performance(ticker, report_date)\n ret90 = perf.get(\"return_d90_pct\")\n print(f\"{f'+{ret90}%' if ret90 and ret90 > 0 else (f'{ret90}%' if ret90 is not None else perf.get('status_d90', '?'))}\")\n rows.append({\n \"report_date\": report_date,\n \"ticker\": ticker,\n \"company\": stock.get(\"company_name\", \"\"),\n \"conviction_score\": stock.get(\"conviction_score\"),\n \"primary_flag\": stock.get(\"primary_flag\", \"\"),\n **perf,\n })\n\n # Summary statistics (only on completed, non-error rows)\n done_90 = [r for r in rows if r.get(\"status_d90\") in (\"win\", \"loss\")]\n done_180 = [r for r in rows if r.get(\"status_d180\") in (\"win\", \"loss\")]\n\n def _win_rate(subset, key):\n if not subset:\n return None\n return round(sum(1 for r in subset if r.get(key) == \"win\") / len(subset) * 100, 1)\n\n def _avg_ret(subset, key):\n vals = [r[key] for r in subset if r.get(key) is not None]\n return round(sum(vals) / len(vals), 1) if vals else None\n\n summary = {\n \"generated_at\": today_str,\n \"total_signals\": len(rows),\n \"completed_90d\": len(done_90),\n \"completed_180d\": len(done_180),\n \"win_rate_90d_pct\": _win_rate(done_90, \"status_d90\"),\n \"win_rate_180d_pct\": _win_rate(done_180, \"status_d180\"),\n \"avg_return_90d_pct\": _avg_ret(done_90, \"return_d90_pct\"),\n \"avg_return_180d_pct\":_avg_ret(done_180, \"return_d180_pct\"),\n }\n\n output = {\"summary\": summary, \"results\": rows}\n\n out_path = DATA_DIR / f\"{today_str}_backtest.json\"\n with open(out_path, \"w\") as f:\n json.dump(output, f, indent=2, default=str)\n\n print(f\"\\n{'─'*40}\")\n print(f\"BACKTEST SUMMARY ({len(rows)} signals tracked)\")\n if summary[\"win_rate_90d_pct\"] is not None:\n print(f\" 90d win rate : {summary['win_rate_90d_pct']}% \"\n f\"(avg {summary['avg_return_90d_pct']:+.1f}%)\")\n if summary[\"win_rate_180d_pct\"] is not None:\n print(f\" 180d win rate : {summary['win_rate_180d_pct']}% \"\n f\"(avg {summary['avg_return_180d_pct']:+.1f}%)\")\n print(f\"βœ… Saved to {out_path}\")\n\n return output\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/config.py", "content": "\"\"\"\nconfig.py\nCentral configuration for SEC 13F Smart Money Analyzer.\nAll CIKs, thresholds, and API endpoints defined here.\n\"\"\"\n\nfrom pathlib import Path\n\n# ── Directory layout ──────────────────────────────────────────────────────────\nBASE_DIR = Path(__file__).parent.parent\nDATA_DIR = BASE_DIR / \"data\" / \"holdings\"\nREPORTS_DIR = BASE_DIR / \"reports\"\nDATA_DIR.mkdir(parents=True, exist_ok=True)\nREPORTS_DIR.mkdir(parents=True, exist_ok=True)\n\n# ── Filer registry ────────────────────────────────────────────────────────────\n# Format: { \"display_name\": \"CIK_padded_to_10_digits\" }\nFILERS = {\n \"Situational Awareness LP\": \"0002045724\",\n \"Yale University\": \"0000938582\",\n \"Gates Foundation Trust\": \"0001166559\",\n \"Harvard Management Co\": \"0001082621\",\n \"Brown University\": \"0001664741\",\n \"Duke University\": \"0001439873\",\n \"TCI Fund (Chris Hohn)\": \"0001649339\",\n \"Pershing Square (Ackman)\": \"0001336528\",\n \"Tiger Global (Coleman)\": \"0001167483\",\n \"Coatue (Laffont)\": \"0001766502\",\n \"D1 Capital (Sundheim)\": \"0001747057\",\n \"Viking Global (Halvorsen)\": \"0001103804\",\n \"AQR Capital (Asness)\": \"0001167557\",\n}\n\n# ── Filer quality tiers ───────────────────────────────────────────────────────\n# University endowments: long-horizon, fundamental β†’ higher weight\n# Growth hedge funds: more momentum-driven β†’ lower weight\n# These multipliers are applied to raw_score in scoring.py\nFILER_QUALITY: dict[str, float] = {\n \"Yale University\": 1.3,\n \"Harvard Management Co\": 1.3,\n \"Gates Foundation Trust\": 1.3,\n \"Brown University\": 1.2,\n \"Duke University\": 1.2,\n \"TCI Fund (Chris Hohn)\": 1.2,\n \"Viking Global (Halvorsen)\": 1.1,\n \"AQR Capital (Asness)\": 1.0,\n \"Pershing Square (Ackman)\": 1.0,\n \"Tiger Global (Coleman)\": 0.9,\n \"Coatue (Laffont)\": 0.9,\n \"D1 Capital (Sundheim)\": 0.9,\n \"Situational Awareness LP\": 0.8,\n}\n\n# ── SEC EDGAR endpoints ───────────────────────────────────────────────────────\nEDGAR_SUBMISSIONS_URL = \"https://data.sec.gov/submissions/CIK{cik}.json\"\nEDGAR_ARCHIVES_URL = \"https://www.sec.gov/Archives/edgar/data/{cik_int}/{accession_dashes}/{filename}\"\nSEC_HEADERS = {\n \"User-Agent\": \"SmartMoneyAnalyzer research@example.com\", # SEC requires this\n \"Accept-Encoding\": \"gzip, deflate\",\n}\nSEC_RATE_LIMIT_SLEEP = 0.15 # seconds between requests (max 10/s allowed)\n\n# ── OpenFIGI (CUSIP β†’ Ticker mapping) ────────────────────────────────────────\nOPENFIGI_URL = \"https://api.openfigi.com/v3/mapping\"\nOPENFIGI_BATCH = 100 # up to 100 with API key (openfigi.com), 10 without\nOPENFIGI_API_KEY = \"\" # optional – set as GitHub Secret OPENFIGI_API_KEY for higher limits\n\n# ── Scoring weights ───────────────────────────────────────────────────────────\nWEIGHT_PORTFOLIO_PCT = 0.20 # how large the position is in the portfolio\nWEIGHT_DELTA_PCT = 0.20 # how aggressively the manager bought\n\n# ── Scoring thresholds ────────────────────────────────────────────────────────\nMIN_PORTFOLIO_WEIGHT_PCT = 0.5 # ignore positions < 0.5% of portfolio\nHIGH_CONVICTION_TOP_N = 10 # \"top 10\" threshold for new position flag\nHIGH_CONVICTION_MIN_PCT = 3.0 # new position >= 3% = HIGH CONVICTION flag\nCLUSTER_MIN_FUNDS = 2 # 2+ funds buying = cluster signal\nCLUSTER_BONUS_MULTIPLIER = 3.0 # strong bonus: 6-fund cluster >> 1-fund\nDOUBLE_DOWN_MIN_DELTA = 20.0 # +20% shares while price fell = double-down\n\n# ── Price-action staleness check ─────────────────────────────────────────────\n# Compares current price to price at filing date (via yfinance).\n# Prevents recommending stocks that have already fully played out.\nPRICE_ACTION_WARN_PCT = 15.0 # add WARNING flag if up >15% since filing\nPRICE_ACTION_DOWNGRADE_PCT = 25.0 # halve the score if up >25% since filing\n\n# ── Multi-quarter conviction tracking ────────────────────────────────────────\nMULTI_QUARTER_MAX = 8 # look back up to 8 quarters of history\nMULTI_QUARTER_BUILD_MIN = 3 # 3+ consecutive build quarters = strong signal\nMULTI_QUARTER_BONUS = 1.5 # score multiplier for confirmed multi-quarter builds\n\n# ── Tradier API ───────────────────────────────────────────────────────────────\nTRADIER_BASE_URL = \"https://api.tradier.com/v1\" # Live account\n# TRADIER_BASE_URL = \"https://sandbox.tradier.com/v1\" # Paper account\nOPTION_MIN_VOLUME = 300 # was 100 – stricter liquidity requirement\nOPTION_MAX_SPREAD_PCT = 8.0 # skip options with bid-ask spread > 8% of mid\nOPTION_DELTA_MIN = 0.30\nOPTION_DELTA_MAX = 0.70\nOPTION_MIN_DAYS = 90\nOPTION_MAX_DAYS = 180\nOPTION_MAX_IV = 0.70 # skip options with IV > 70% (overpriced premium)\n\n# ── Claude API ────────────────────────────────────────────────────────────────\n# Round 1 (top-20 screening): Haiku is sufficient and ~20Γ— cheaper than Sonnet\n# Round 2 (precise option selection): Sonnet for nuanced financial reasoning\nCLAUDE_MODEL_R1 = \"claude-haiku-4-5-20251001\"\nCLAUDE_MODEL_R2 = \"claude-sonnet-4-6\"\nCLAUDE_MODEL = CLAUDE_MODEL_R2 # backward-compat alias\nCLAUDE_MAX_TOKENS = 4096\nCLAUDE_RETRY_COUNT = 3\nCLAUDE_RETRY_DELAY = 5 # seconds\n\n# ── Gmail ─────────────────────────────────────────────────────────────────────\nGMAIL_SMTP_HOST = \"smtp.gmail.com\"\nGMAIL_SMTP_PORT = 587\nREPORT_SUBJECT = \"πŸ“Š SEC 13F Smart Money Report – {date}\"\n" }, { "path": "src/date_check.py", "content": "\"\"\"\ndate_check.py\nDetermines if today is a valid SEC 13F analysis run date.\nTarget dates: 16th of May, Aug, Nov, Feb (Berlin time)\nWeekend/holiday β†’ next business day\nWrites GitHub Actions output: should_run=true/false\n\"\"\"\n\nimport os\nimport sys\nfrom datetime import date, timedelta\n\nimport holidays\n\n\nTARGET_MONTHS = {2, 5, 8, 11}\nTARGET_DAY = 16\n\n# German public holidays (federal-level) as fallback\n# SEC is a US system – EDGAR is open every day – so we only skip\n# weekends. German holidays are NOT a reason to skip, but the\n# cron only fires 14-20th of target months so this is future-proof.\nDE_HOLIDAYS = holidays.Germany(years=range(2024, 2035))\n\n\ndef next_business_day(d: date) -> date:\n \"\"\"Advance d until it falls on Mon-Fri (weekends only, not holidays).\"\"\"\n while d.weekday() >= 5: # 5=Sat, 6=Sun\n d += timedelta(days=1)\n return d\n\n\ndef get_expected_run_date(ref: date) -> date:\n \"\"\"\n For the given reference date's month/year, return the actual\n run date (16th of that month β†’ adjusted for weekends).\n Only valid if ref.month is in TARGET_MONTHS.\n \"\"\"\n target = date(ref.year, ref.month, TARGET_DAY)\n return next_business_day(target)\n\n\ndef should_run_today(today: date, force: bool = False, target_override: str = \"\") -> bool:\n if force:\n print(f\"FORCE_RUN=true β†’ running regardless of date ({today})\")\n return True\n\n if target_override:\n try:\n override = date.fromisoformat(target_override)\n result = today == override\n print(f\"Target override {override}: today={today}, match={result}\")\n return result\n except ValueError:\n print(f\"Invalid TARGET_DATE '{target_override}', ignoring.\")\n\n if today.month not in TARGET_MONTHS:\n print(f\"Month {today.month} is not a target month {TARGET_MONTHS}. Skip.\")\n return False\n\n expected = get_expected_run_date(today)\n result = today == expected\n print(f\"Expected run date for {today.year}-{today.month:02d}: {expected}. Today: {today}. Run: {result}\")\n return result\n\n\ndef write_github_output(key: str, value: str):\n \"\"\"Write to GITHUB_OUTPUT file (new Actions syntax).\"\"\"\n output_file = os.environ.get(\"GITHUB_OUTPUT\", \"\")\n if output_file:\n with open(output_file, \"a\") as f:\n f.write(f\"{key}={value}\\n\")\n else:\n # Local testing fallback\n print(f\"[local] {key}={value}\")\n\n\nif __name__ == \"__main__\":\n today = date.today()\n force = os.environ.get(\"FORCE_RUN\", \"false\").lower() == \"true\"\n target_override = os.environ.get(\"TARGET_DATE\", \"\").strip()\n\n run = should_run_today(today, force=force, target_override=target_override)\n write_github_output(\"should_run\", \"true\" if run else \"false\")\n\n if not run:\n print(\"Not a run date. Pipeline will stop here.\")\n sys.exit(0)\n\n print(f\"βœ… Run date confirmed: {today}\")\n" }, { "path": "src/fetch_filings.py", "content": "\"\"\"\nfetch_filings.py\nFetches the latest 13F-HR filings for all configured filers from SEC EDGAR.\n\nUses the EDGAR full-text search API to find the infotable XML directly,\nbypassing the unreliable index.json approach.\n\"\"\"\n\nimport json\nimport time\nimport xml.etree.ElementTree as ET\nfrom datetime import date, datetime\nfrom pathlib import Path\n\nimport requests\n\nimport os\n\nfrom config import (\n DATA_DIR, FILERS, OPENFIGI_API_KEY, OPENFIGI_BATCH, OPENFIGI_URL,\n SEC_HEADERS, SEC_RATE_LIMIT_SLEEP,\n)\n\n\ndef _sleep():\n time.sleep(SEC_RATE_LIMIT_SLEEP)\n\n\ndef edgar_get(url: str) -> requests.Response:\n _sleep()\n resp = requests.get(url, headers=SEC_HEADERS, timeout=30)\n resp.raise_for_status()\n return resp\n\n\n# ── Step 1: Get latest 13F filing metadata ────────────────────────────────────\n\ndef get_latest_13f_filing(cik: str) -> dict | None:\n url = f\"https://data.sec.gov/submissions/CIK{cik}.json\"\n try:\n data = edgar_get(url).json()\n except Exception as e:\n print(f\" ⚠️ Could not fetch submissions for CIK {cik}: {e}\")\n return None\n\n filings = data.get(\"filings\", {}).get(\"recent\", {})\n forms = filings.get(\"form\", [])\n accessions = filings.get(\"accessionNumber\", [])\n dates = filings.get(\"filingDate\", [])\n primary_docs = filings.get(\"primaryDocument\", [])\n\n report_dates = filings.get(\"reportDate\", [])\n\n for i, form in enumerate(forms):\n if form in (\"13F-HR\", \"13F-HR/A\"):\n return {\n \"cik\": cik,\n \"accessionNumber\": accessions[i],\n \"filingDate\": dates[i],\n # reportDate = quarter-end (period of report), e.g. 2024-12-31.\n # Always earlier than filingDate (which can be up to 45 days later).\n # Use this as the price-comparison anchor so we measure from\n # when the manager actually held the position, not when they disclosed it.\n \"reportDate\": report_dates[i] if i < len(report_dates) else dates[i],\n \"form\": form,\n \"isAmendment\": form == \"13F-HR/A\",\n \"primaryDocument\": primary_docs[i] if i < len(primary_docs) else \"\",\n }\n\n print(f\" ℹ️ No 13F-HR found for CIK {cik}\")\n return None\n\n\n# ── Step 2: Get all files in filing and find infotable ────────────────────────\n\ndef get_filing_files(cik: str, accession: str) -> list[dict]:\n \"\"\"\n Fetches the list of files in a filing using the EDGAR submissions API.\n Returns list of {name, type} dicts.\n \"\"\"\n cik_int = int(cik)\n acc_nodash = accession.replace(\"-\", \"\")\n\n # Try index.json (directory listing)\n url = f\"https://www.sec.gov/Archives/edgar/data/{cik_int}/{acc_nodash}/index.json\"\n try:\n resp = edgar_get(url)\n data = resp.json()\n items = data.get(\"directory\", {}).get(\"item\", [])\n if isinstance(items, dict):\n items = [items]\n if items:\n print(f\" Files: {[i.get('name','') for i in items]}\")\n return items\n except Exception as e:\n print(f\" ⚠️ index.json failed: {e}\")\n\n # Fallback: try the EDGAR filing index page as text\n url2 = f\"https://www.sec.gov/Archives/edgar/data/{cik_int}/{acc_nodash}/{accession}-index.htm\"\n try:\n resp2 = edgar_get(url2)\n # Parse filenames from HTML\n import re\n names = re.findall(r'href=\"([^\"]+\\.xml)\"', resp2.text, re.IGNORECASE)\n items = [{\"name\": n.split(\"/\")[-1]} for n in names]\n if items:\n print(f\" Files (from htm): {[i['name'] for i in items]}\")\n return items\n except Exception as e:\n print(f\" ⚠️ index.htm also failed: {e}\")\n\n return []\n\n\ndef find_infotable_filename(items: list[dict]) -> str | None:\n \"\"\"Find the information table XML from list of filing files.\"\"\"\n # Pass 1: name contains 'informationtable'\n for item in items:\n name = item.get(\"name\", \"\").lower()\n if \"informationtable\" in name and name.endswith(\".xml\"):\n return item[\"name\"]\n\n # Pass 2: any xml that isn't the primary/cover/summary doc\n skip_keywords = [\"primary\", \"cover\", \"summary\", \"header\", \"form13f\"]\n for item in items:\n name = item.get(\"name\", \"\").lower()\n if name.endswith(\".xml\") and not any(k in name for k in skip_keywords):\n return item[\"name\"]\n\n # Pass 3: second xml file (first is usually primary doc)\n xml_files = [i[\"name\"] for i in items if i.get(\"name\",\"\").lower().endswith(\".xml\")]\n if len(xml_files) >= 2:\n return xml_files[1]\n if len(xml_files) == 1:\n return xml_files[0]\n\n return None\n\n\ndef download_infotable(filing_meta: dict) -> str | None:\n cik_int = int(filing_meta[\"cik\"])\n accession = filing_meta[\"accessionNumber\"]\n acc_nodash = accession.replace(\"-\", \"\")\n\n items = get_filing_files(filing_meta[\"cik\"], accession)\n infotable_filename = find_infotable_filename(items)\n\n if not infotable_filename:\n # Last resort: try common filename patterns directly\n candidates = [\n \"informationtable.xml\",\n f\"{acc_nodash}-informationtable.xml\",\n \"form13fInfoTable.xml\",\n \"infotable.xml\",\n ]\n for candidate in candidates:\n url = f\"https://www.sec.gov/Archives/edgar/data/{cik_int}/{acc_nodash}/{candidate}\"\n try:\n resp = edgar_get(url)\n if resp.status_code == 200 and \"<\" in resp.text:\n print(f\" βœ… Found via direct guess: {candidate}\")\n return resp.text\n except Exception:\n continue\n\n print(f\" ⚠️ Could not find infotable XML for {accession}\")\n return None\n\n xml_url = f\"https://www.sec.gov/Archives/edgar/data/{cik_int}/{acc_nodash}/{infotable_filename}\"\n try:\n xml_text = edgar_get(xml_url).text\n print(f\" βœ… Downloaded: {infotable_filename} ({len(xml_text)} chars)\")\n return xml_text\n except Exception as e:\n print(f\" ⚠️ Could not download {infotable_filename}: {e}\")\n return None\n\n\n# ── Step 3: Parse XML holdings ────────────────────────────────────────────────\n\ndef parse_infotable(xml_text: str) -> list[dict]:\n try:\n root = ET.fromstring(xml_text)\n except ET.ParseError as e:\n print(f\" ⚠️ XML parse error: {e}\")\n return []\n\n tag = root.tag\n ns_uri = \"\"\n if tag.startswith(\"{\"):\n ns_uri = tag[1:tag.index(\"}\")]\n\n ns = {\"ns\": ns_uri} if ns_uri else {}\n prefix = \"ns:\" if ns_uri else \"\"\n\n holdings = []\n for entry in root.findall(f\".//{prefix}infoTable\", ns):\n def _t(tag_name):\n el = entry.find(f\"{prefix}{tag_name}\", ns)\n return el.text.strip() if el is not None and el.text else \"\"\n\n try:\n value_raw = _t(\"value\")\n shares_raw = _t(\"sshPrnamt\")\n holding = {\n \"cusip\": _t(\"cusip\"),\n \"nameOfIssuer\": _t(\"nameOfIssuer\"),\n \"titleOfClass\": _t(\"titleOfClass\"),\n \"value_usd_thousands\": int(value_raw.replace(\",\", \"\")) if value_raw else 0,\n \"shares\": int(shares_raw.replace(\",\", \"\")) if shares_raw else 0,\n \"sshPrnamtType\": _t(\"sshPrnamtType\"),\n \"putCall\": _t(\"putCall\") or None,\n \"investmentDiscretion\":_t(\"investmentDiscretion\"),\n }\n if holding[\"cusip\"]:\n holdings.append(holding)\n except (ValueError, AttributeError) as e:\n continue\n\n return holdings\n\n\n# ── Step 4: CUSIP β†’ Ticker via OpenFIGI ──────────────────────────────────────\n\ndef _openfigi_headers() -> dict:\n \"\"\"Returns headers for OpenFIGI. Uses API key from env if available.\"\"\"\n api_key = os.environ.get(\"OPENFIGI_API_KEY\", OPENFIGI_API_KEY).strip()\n h = {\"Content-Type\": \"application/json\"}\n if api_key:\n h[\"X-OPENFIGI-APIKEY\"] = api_key\n return h\n\n\ndef map_cusips_to_tickers(cusips: list[str]) -> dict[str, str]:\n mapping = {}\n if not cusips:\n return mapping\n\n headers = _openfigi_headers()\n unique_cusips = list(set(cusips))\n total = len(unique_cusips)\n\n for i in range(0, total, OPENFIGI_BATCH):\n batch = unique_cusips[i:i + OPENFIGI_BATCH]\n payload = [{\"idType\": \"ID_CUSIP\", \"idValue\": c} for c in batch]\n try:\n resp = requests.post(OPENFIGI_URL, json=payload, headers=headers, timeout=20)\n if resp.status_code == 429:\n time.sleep(60)\n resp = requests.post(OPENFIGI_URL, json=payload, headers=headers, timeout=20)\n if resp.status_code == 413:\n print(f\" ⚠️ OpenFIGI 413 – batch too large ({len(batch)} items). \"\n f\"Set OPENFIGI_API_KEY secret or reduce OPENFIGI_BATCH below 10.\")\n continue\n if resp.status_code != 200:\n print(f\" ⚠️ OpenFIGI returned HTTP {resp.status_code}, skipping batch\")\n continue\n results = resp.json()\n for cusip, result in zip(batch, results):\n if \"data\" in result and result[\"data\"]:\n for figi_item in result[\"data\"]:\n ticker = figi_item.get(\"ticker\", \"\")\n exch = figi_item.get(\"exchCode\", \"\")\n if exch in (\"US\", \"UN\", \"UW\", \"UA\"):\n mapping[cusip] = ticker\n break\n else:\n mapping[cusip] = result[\"data\"][0].get(\"ticker\", \"\")\n except Exception as e:\n print(f\" ⚠️ OpenFIGI batch failed: {e}\")\n time.sleep(0.5)\n\n return mapping\n\n\n# ── Step 5: Split check ───────────────────────────────────────────────────────\n\ndef _is_valid_equity_ticker(ticker: str) -> bool:\n \"\"\"\n Rejects non-equity strings that OpenFIGI sometimes returns:\n - Bond descriptions like 'BRKR 6.375 09/01/28'\n - Multi-word strings\n - Tickers longer than 6 chars (options, bonds)\n \"\"\"\n if not ticker or \" \" in ticker:\n return False\n if len(ticker) > 6:\n return False\n # Must start with a letter (real equity tickers never start with a digit)\n if not ticker[0].isalpha():\n return False\n import re\n return bool(re.match(r'^[A-Z0-9./\\-]+$', ticker.upper()))\n\n\ndef check_recent_splits(tickers: list[str]) -> dict[str, float]:\n try:\n import yfinance as yf\n from datetime import timedelta\n except ImportError:\n return {}\n\n splits = {}\n cutoff = date.today() - timedelta(days=120)\n\n for ticker in tickers:\n if not _is_valid_equity_ticker(ticker):\n continue\n yf_ticker = ticker.replace(\"/\", \"-\") # BRK/B β†’ BRK-B for yfinance\n try:\n hist = yf.Ticker(yf_ticker).splits\n if hist.empty:\n continue\n recent = hist[hist.index.date >= cutoff]\n if not recent.empty:\n ratio = float(recent.iloc[-1])\n splits[ticker] = ratio # store under original ticker key\n print(f\" ⚠️ Split: {ticker} ratio {ratio}\")\n except Exception:\n pass\n return splits\n\n\n# ── Main ──────────────────────────────────────────────────────────────────────\n\ndef run():\n today_str = date.today().isoformat()\n output_path = DATA_DIR / f\"{today_str}_raw_holdings.json\"\n\n all_data = {}\n all_cusips = set()\n\n print(f\"\\n{'='*60}\")\n print(f\"SEC EDGAR Fetch – {today_str}\")\n print(f\"{'='*60}\")\n\n for name, cik in FILERS.items():\n print(f\"\\nβ–Ά {name} (CIK: {cik})\")\n\n filing_meta = get_latest_13f_filing(cik)\n if not filing_meta:\n all_data[name] = {\"error\": \"no_filing\", \"cik\": cik}\n continue\n\n print(f\" Filing: {filing_meta['form']} on {filing_meta['filingDate']}\"\n + (\" ⚠️ AMENDMENT\" if filing_meta[\"isAmendment\"] else \"\"))\n\n xml_text = download_infotable(filing_meta)\n if not xml_text:\n all_data[name] = {\"error\": \"no_xml\", \"cik\": cik, \"meta\": filing_meta}\n continue\n\n holdings = parse_infotable(xml_text)\n if not holdings:\n all_data[name] = {\"error\": \"no_holdings_parsed\", \"cik\": cik, \"meta\": filing_meta}\n continue\n\n print(f\" βœ… {len(holdings)} positions parsed\")\n\n # AQR and similar quant funds file 10k+ positions (index replication).\n # Cluster signal from a quant fund holding 20k stocks is meaningless.\n # Cap at top 500 by value so they don't flood the CUSIP pool.\n MAX_POSITIONS_PER_FILER = 500\n original_count = len(holdings)\n if original_count > MAX_POSITIONS_PER_FILER:\n holdings = sorted(holdings, key=lambda h: h[\"value_usd_thousands\"], reverse=True)\n holdings = holdings[:MAX_POSITIONS_PER_FILER]\n print(f\" βœ‚οΈ Capped to top {MAX_POSITIONS_PER_FILER} positions by value \"\n f\"(was {original_count} total)\")\n\n for h in holdings:\n if h[\"cusip\"]:\n all_cusips.add(h[\"cusip\"])\n\n all_data[name] = {\n \"cik\": cik,\n \"meta\": filing_meta,\n \"holdings\": holdings,\n \"total_value\":sum(h[\"value_usd_thousands\"] for h in holdings),\n \"fetched_at\": datetime.utcnow().isoformat(),\n }\n\n print(f\"\\nπŸ” Mapping {len(all_cusips)} CUSIPs to tickers via OpenFIGI...\")\n cusip_to_ticker = map_cusips_to_tickers(list(all_cusips))\n print(f\" Mapped: {len(cusip_to_ticker)} / {len(all_cusips)}\")\n\n all_tickers = set()\n for filer_data in all_data.values():\n if \"holdings\" not in filer_data:\n continue\n for h in filer_data[\"holdings\"]:\n h[\"ticker\"] = cusip_to_ticker.get(h[\"cusip\"], \"\")\n if h[\"ticker\"]:\n all_tickers.add(h[\"ticker\"])\n\n print(f\"\\nπŸ”€ Checking splits on {len(all_tickers)} tickers...\")\n splits = check_recent_splits(list(all_tickers))\n\n output = {\n \"date\": today_str,\n \"cusip_to_ticker\": cusip_to_ticker,\n \"recent_splits\": splits,\n \"filers\": all_data,\n }\n\n # Atomic write: write to temp file first, then rename (prevents corrupt files on crash)\n tmp_path = output_path.with_suffix(\".tmp\")\n with open(tmp_path, \"w\") as f:\n json.dump(output, f, indent=2, default=str)\n tmp_path.replace(output_path)\n\n filers_ok = sum(1 for v in all_data.values() if \"holdings\" in v)\n print(f\"\\nβœ… Saved to {output_path}\")\n print(f\" Filers with data: {filers_ok} / {len(FILERS)}\")\n\n missing = len(FILERS) - filers_ok\n if missing > len(FILERS) * 0.3:\n raise RuntimeError(f\"Too many filers missing: {missing}/{len(FILERS)}\")\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/multi_quarter.py", "content": "\"\"\"\nmulti_quarter.py\nAnalyzes multi-quarter position building patterns.\n\nA \"Silent Build\" over 3–4 quarters is a much stronger signal than\na single large buy in one quarter, which can be momentum-chasing or\nwindow-dressing.\n\"\"\"\n\nimport json\nfrom datetime import date\nfrom pathlib import Path\n\nfrom config import DATA_DIR, MULTI_QUARTER_BUILD_MIN, MULTI_QUARTER_BONUS, MULTI_QUARTER_MAX\n\n\ndef load_historical_parsed(today_str: str) -> list[dict]:\n \"\"\"\n Loads up to MULTI_QUARTER_MAX previous *_holdings_parsed.json files,\n most recent first, excluding today.\n \"\"\"\n today = date.fromisoformat(today_str)\n candidates = sorted(DATA_DIR.glob(\"*_holdings_parsed.json\"), reverse=True)\n\n results = []\n for c in candidates:\n try:\n d = date.fromisoformat(c.name[:10])\n if d < today:\n with open(c) as f:\n results.append(json.load(f))\n if len(results) >= MULTI_QUARTER_MAX:\n break\n except (ValueError, json.JSONDecodeError):\n continue\n\n return results # most recent first\n\n\ndef build_multi_quarter_signals(today_str: str) -> dict[str, dict]:\n \"\"\"\n For every ticker seen across historical quarters, computes:\n - How many quarters had net buying activity\n - Average delta pct per building quarter\n - Slope of portfolio weight (positive = growing conviction)\n - Silent build flag (small but consistent adds)\n\n Returns {ticker: signal_dict} only for tickers with 2+ build quarters.\n \"\"\"\n history = load_historical_parsed(today_str)\n\n if len(history) < 2:\n print(f\" ⚠️ Multi-quarter: only {len(history)} historical quarter(s) available. Signals will be sparse.\")\n if not history:\n return {}\n\n # Aggregate per ticker, per quarter\n ticker_quarters: dict[str, list[dict]] = {}\n\n for quarter_data in history:\n quarter_str = quarter_data.get(\"date\", \"\")\n for filer_name, filer_data in quarter_data.get(\"filers\", {}).items():\n for pos in filer_data.get(\"positions\", []):\n ticker = pos.get(\"ticker\") or pos.get(\"cusip\", \"\")\n if not ticker:\n continue\n\n if ticker not in ticker_quarters:\n ticker_quarters[ticker] = []\n\n ticker_quarters[ticker].append({\n \"quarter\": quarter_str,\n \"filer\": filer_name,\n \"delta_type\": pos[\"delta\"][\"type\"],\n \"delta_pct\": pos[\"delta\"].get(\"delta_pct\"),\n \"port_weight\": pos[\"port_weight_pct\"],\n })\n\n signals = {}\n\n for ticker, entries in ticker_quarters.items():\n quarters_seen = sorted(set(e[\"quarter\"] for e in entries), reverse=True)\n\n build_quarters = 0\n delta_pcts: list[float] = []\n weights_by_quarter: list[float] = []\n\n for q in quarters_seen:\n q_entries = [e for e in entries if e[\"quarter\"] == q]\n has_buy = any(e[\"delta_type\"] in (\"NEW\", \"ADDED\") for e in q_entries)\n has_reduce = any(e[\"delta_type\"] in (\"REDUCED\", \"SOLD\") for e in q_entries)\n\n if has_buy and not has_reduce:\n build_quarters += 1\n for e in q_entries:\n if e[\"delta_pct\"] is not None:\n delta_pcts.append(abs(e[\"delta_pct\"]))\n\n avg_w = sum(e[\"port_weight\"] for e in q_entries) / len(q_entries)\n weights_by_quarter.append(avg_w)\n\n if build_quarters < 2:\n continue\n\n avg_delta = sum(delta_pcts) / len(delta_pcts) if delta_pcts else 0.0\n\n # Weight slope: positive means portfolio weight growing over time\n # weights_by_quarter[0] = most recent, [-1] = oldest\n slope = 0.0\n if len(weights_by_quarter) >= 2:\n slope = (weights_by_quarter[0] - weights_by_quarter[-1]) / len(weights_by_quarter)\n\n # Silent Build: 3+ quarters of small, consistent adds (5–25% delta)\n silent_build = (\n build_quarters >= 3\n and len(delta_pcts) >= 3\n and all(5.0 <= d <= 25.0 for d in delta_pcts[-3:])\n )\n\n # Build score: more quarters + higher avg delta + positive slope\n build_score = (build_quarters / 4.0) * max(avg_delta, 1.0) * (1.0 + max(slope, 0.0))\n\n flags = []\n if build_quarters >= MULTI_QUARTER_BUILD_MIN:\n flags.append(\"MULTI_QUARTER_BUILD\")\n if build_quarters >= 5:\n flags.append(\"STRONG_BUILD\")\n if silent_build:\n flags.append(\"SILENT_ACCUMULATION\")\n\n signals[ticker] = {\n \"build_quarters\": build_quarters,\n \"total_quarters\": len(quarters_seen),\n \"avg_delta_pct\": round(avg_delta, 1),\n \"weight_slope\": round(slope, 3),\n \"build_score\": round(build_score, 2),\n \"silent_build\": silent_build,\n \"flags\": flags,\n }\n\n return signals\n\n\ndef get_multiplier(ticker: str, signals: dict[str, dict]) -> float:\n \"\"\"Returns a score multiplier based on multi-quarter conviction signal.\"\"\"\n if ticker not in signals:\n return 1.0\n bq = signals[ticker][\"build_quarters\"]\n if bq >= 5:\n return MULTI_QUARTER_BONUS * 1.3\n if bq >= 3:\n return MULTI_QUARTER_BONUS\n if bq >= 2:\n return 1.2\n return 1.0\n" }, { "path": "src/notify.py", "content": "\"\"\"\nnotify.py\nSends pipeline failure alerts via Gmail.\n\nCalled from GitHub Actions on workflow failure so silent crashes\ndon't go unnoticed. Uses the same GMAIL_ADDRESS / GMAIL_APP_PASSWORD\nsecrets already configured for the report.\n\"\"\"\n\nimport os\nimport smtplib\nimport sys\nimport traceback\nfrom datetime import date\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nfrom config import GMAIL_SMTP_HOST, GMAIL_SMTP_PORT\n\n\ndef send_alert(subject: str, body: str):\n \"\"\"\n Sends a plain-text alert email to GMAIL_ADDRESS.\n Silently skips if credentials are not set (local dev).\n \"\"\"\n gmail_address = os.environ.get(\"GMAIL_ADDRESS\", \"\")\n gmail_password = os.environ.get(\"GMAIL_APP_PASSWORD\", \"\")\n\n if not gmail_address or not gmail_password:\n print(f\"[notify] No Gmail credentials set – alert not sent.\\n{subject}\")\n return\n\n msg = MIMEMultipart(\"alternative\")\n msg[\"Subject\"] = subject\n msg[\"From\"] = gmail_address\n msg[\"To\"] = gmail_address\n\n html_body = f\"\"\"\n
\n

⚠️ SEC Analyzer Pipeline Failure

\n 
{body}
\n

\n Date: {date.today().isoformat()}  Β·  Check GitHub Actions for full logs.\n

\n
\n \"\"\"\n\n msg.attach(MIMEText(html_body, \"html\"))\n\n try:\n with smtplib.SMTP(GMAIL_SMTP_HOST, GMAIL_SMTP_PORT) as server:\n server.ehlo()\n server.starttls()\n server.login(gmail_address, gmail_password)\n server.sendmail(gmail_address, gmail_address, msg.as_string())\n print(f\"[notify] Alert sent: {subject}\")\n except Exception as e:\n # Don't let the notifier itself crash the workflow\n print(f\"[notify] Failed to send alert email: {e}\")\n\n\nif __name__ == \"__main__\":\n # Called by GitHub Actions on failure:\n # python src/notify.py \"Step Name\" \"optional extra message\"\n step = sys.argv[1] if len(sys.argv) > 1 else \"Unknown step\"\n extra = sys.argv[2] if len(sys.argv) > 2 else \"\"\n\n send_alert(\n subject=f\"🚨 SEC Analyzer Pipeline Failed – {step} ({date.today().isoformat()})\",\n body=f\"Failed step: {step}\\n\\n{extra}\\n\\nCheck GitHub Actions for the full log.\",\n )\n" }, { "path": "src/options_lookup.py", "content": "\"\"\"\noptions_lookup.py\nFetches real options chains from Tradier for the Top 5 tickers.\n\nImprovements:\n - Spread filter tightened: 15% β†’ 8% (OPTION_MAX_SPREAD_PCT)\n - Volume threshold raised: 100 β†’ 300 (OPTION_MIN_VOLUME)\n - IV filter: skip options with IV > 70% (OPTION_MAX_IV) – avoids\n buying expensive premium on high-IV names like NVDA/PLTR\n - OTM warning when strike > +10% above spot\n - Greeks=None pre-market β†’ fallback to volume/OI filter\n - Empty results handled with relaxed fallback\n\"\"\"\n\nimport json\nimport os\nfrom datetime import date, timedelta\n\nimport requests\n\nfrom config import (\n DATA_DIR, OPTION_DELTA_MAX, OPTION_DELTA_MIN,\n OPTION_MAX_DAYS, OPTION_MAX_IV, OPTION_MAX_SPREAD_PCT,\n OPTION_MIN_DAYS, OPTION_MIN_VOLUME, TRADIER_BASE_URL,\n)\n\n\ndef get_headers() -> dict:\n api_key = os.environ.get(\"TRADIER_API_KEY\", \"\")\n if not api_key:\n raise ValueError(\"TRADIER_API_KEY environment variable not set\")\n return {\n \"Authorization\": f\"Bearer {api_key}\",\n \"Accept\": \"application/json\",\n }\n\n\ndef get_stock_quotes(tickers: list[str], headers: dict) -> dict[str, dict]:\n url = f\"{TRADIER_BASE_URL}/markets/quotes\"\n params = {\"symbols\": \",\".join(tickers), \"greeks\": \"false\"}\n try:\n resp = requests.get(url, params=params, headers=headers, timeout=15)\n resp.raise_for_status()\n quotes = resp.json().get(\"quotes\", {}).get(\"quote\", [])\n if isinstance(quotes, dict):\n quotes = [quotes]\n return {q[\"symbol\"]: q for q in quotes if \"symbol\" in q}\n except Exception as e:\n print(f\" ⚠️ Stock quote fetch failed: {e}\")\n return {}\n\n\ndef normalize_ticker_for_tradier(ticker: str) -> str:\n return ticker.strip().upper().replace(\".\", \"/\")\n\n\ndef get_expiration_dates(ticker: str, headers: dict) -> list[str]:\n url = f\"{TRADIER_BASE_URL}/markets/options/expirations\"\n params = {\"symbol\": ticker, \"includeAllRoots\": \"true\", \"strikes\": \"false\"}\n\n try:\n resp = requests.get(url, params=params, headers=headers, timeout=15)\n resp.raise_for_status()\n data = resp.json()\n except Exception as e:\n print(f\" ⚠️ Expiration fetch failed for {ticker}: {e}\")\n return []\n\n dates = data.get(\"expirations\", {})\n date_list = dates.get(\"date\", []) if dates else []\n if isinstance(date_list, str):\n date_list = [date_list]\n\n today = date.today()\n min_date = today + timedelta(days=OPTION_MIN_DAYS)\n max_date = today + timedelta(days=OPTION_MAX_DAYS)\n\n return [d for d in date_list if min_date <= date.fromisoformat(d) <= max_date]\n\n\ndef get_option_chain(ticker: str, expiry: str, headers: dict) -> list[dict]:\n url = f\"{TRADIER_BASE_URL}/markets/options/chains\"\n params = {\"symbol\": ticker, \"expiration\": expiry, \"greeks\": \"true\"}\n\n try:\n resp = requests.get(url, params=params, headers=headers, timeout=15)\n resp.raise_for_status()\n data = resp.json()\n except Exception as e:\n print(f\" ⚠️ Chain fetch failed for {ticker} {expiry}: {e}\")\n return []\n\n options = data.get(\"options\", {})\n if not options:\n return []\n\n chain = options.get(\"option\", [])\n return [chain] if isinstance(chain, dict) else chain\n\n\ndef filter_options(chain: list[dict], direction: str = \"BULLISH\",\n current_price: float | None = None) -> list[dict]:\n \"\"\"\n Filters to liquid, reasonably priced options matching our criteria.\n\n Key changes from original:\n - spread ≀ OPTION_MAX_SPREAD_PCT (8%, was 15%)\n - volume β‰₯ OPTION_MIN_VOLUME (300, was 100)\n - IV ≀ OPTION_MAX_IV (70%) when available\n - OTM warning added as metadata when strike > spot + 10%\n \"\"\"\n option_type = \"call\" if direction == \"BULLISH\" else \"put\"\n candidates = []\n greeks_available = False\n\n for opt in chain:\n if opt.get(\"option_type\", \"\").lower() != option_type:\n continue\n\n volume = opt.get(\"volume\", 0) or 0\n oi = opt.get(\"open_interest\", 0) or 0\n\n # Require meaningful liquidity\n if volume < OPTION_MIN_VOLUME and oi < 500:\n continue\n\n bid = opt.get(\"bid\") or 0\n ask = opt.get(\"ask\") or 0\n mid = (bid + ask) / 2\n\n if mid <= 0:\n continue\n\n # Strict spread filter\n spread_ratio = (ask - bid) / mid\n if spread_ratio > OPTION_MAX_SPREAD_PCT / 100:\n continue\n\n greeks = opt.get(\"greeks\") or {}\n delta = greeks.get(\"delta\")\n iv = greeks.get(\"smv_vol\") or greeks.get(\"mid_iv\")\n\n if delta is not None:\n greeks_available = True\n if not (OPTION_DELTA_MIN <= abs(float(delta)) <= OPTION_DELTA_MAX):\n continue\n\n # Skip overpriced premium (high IV)\n if iv is not None:\n try:\n if float(iv) > OPTION_MAX_IV:\n continue\n except (TypeError, ValueError):\n pass\n\n strike = opt.get(\"strike\")\n spread_pct = round(spread_ratio * 100, 1)\n\n # OTM warning: flag if strike is >10% above current price\n otm_warning = False\n if strike and current_price and current_price > 0:\n otm_pct = ((float(strike) / current_price) - 1) * 100\n otm_warning = otm_pct > 10.0\n\n candidates.append({\n \"symbol\": opt.get(\"symbol\"),\n \"option_type\": opt.get(\"option_type\"),\n \"strike\": strike,\n \"expiration_date\": opt.get(\"expiration_date\"),\n \"bid\": bid,\n \"ask\": ask,\n \"mid\": round(mid, 2),\n \"spread_pct\": spread_pct,\n \"last\": opt.get(\"last\"),\n \"volume\": volume,\n \"open_interest\": oi,\n \"implied_volatility\": iv,\n \"delta\": delta,\n \"gamma\": greeks.get(\"gamma\"),\n \"theta\": greeks.get(\"theta\"),\n \"greeks_available\": greeks_available,\n \"otm_warning\": otm_warning,\n })\n\n if not greeks_available and candidates:\n print(f\" ⚠️ Greeks unavailable (pre-market?). Using volume/OI/spread filter only.\")\n\n candidates.sort(key=lambda x: x[\"volume\"] or 0, reverse=True)\n return candidates[:5]\n\n\ndef fetch_options_for_ticker(ticker: str, direction: str, headers: dict,\n stock_quote: dict | None = None) -> dict:\n \"\"\"Full options lookup for one ticker across all valid expiry dates.\"\"\"\n tradier_ticker = normalize_ticker_for_tradier(ticker)\n current_price = stock_quote.get(\"last\") if stock_quote else None\n change_pct = stock_quote.get(\"change_percentage\") if stock_quote else None\n print(f\" πŸ“ˆ {ticker} ({tradier_ticker}) – ${current_price} ({change_pct}%) – direction: {direction}\")\n\n expiries = get_expiration_dates(tradier_ticker, headers)\n if not expiries:\n print(f\" ⚠️ No valid expiry dates found for {ticker}\")\n return {\n \"ticker\": ticker,\n \"current_price\": current_price,\n \"change_pct\": change_pct,\n \"direction\": direction,\n \"error\": \"no_valid_expiries\",\n \"options\": [],\n }\n\n print(f\" Valid expiries ({OPTION_MIN_DAYS}-{OPTION_MAX_DAYS} days): {expiries}\")\n\n all_options = []\n for expiry in expiries:\n chain = get_option_chain(tradier_ticker, expiry, headers)\n filtered = filter_options(chain, direction=direction, current_price=current_price)\n all_options.extend(filtered)\n print(f\" {expiry}: {len(chain)} total β†’ {len(filtered)} after filter \"\n f\"(spread≀{OPTION_MAX_SPREAD_PCT}%, volβ‰₯{OPTION_MIN_VOLUME}, IV≀{int(OPTION_MAX_IV*100)}%)\")\n\n if not all_options:\n print(f\" ⚠️ No options passed strict filters for {ticker}. Relaxing volume threshold.\")\n for expiry in expiries[:2]:\n chain = get_option_chain(tradier_ticker, expiry, headers)\n for opt in chain:\n if opt.get(\"option_type\", \"\").lower() == (\"call\" if direction == \"BULLISH\" else \"put\"):\n bid = opt.get(\"bid\") or 0\n ask = opt.get(\"ask\") or 0\n mid = (bid + ask) / 2\n all_options.append({\n \"symbol\": opt.get(\"symbol\"),\n \"option_type\": opt.get(\"option_type\"),\n \"strike\": opt.get(\"strike\"),\n \"expiration_date\": opt.get(\"expiration_date\"),\n \"bid\": bid,\n \"ask\": ask,\n \"mid\": round(mid, 2) if mid > 0 else None,\n \"volume\": opt.get(\"volume\", 0),\n \"open_interest\": opt.get(\"open_interest\", 0),\n \"implied_volatility\": None,\n \"delta\": None,\n \"greeks_available\": False,\n \"otm_warning\": False,\n \"note\": \"fallback_relaxed_filter\",\n })\n if all_options:\n break\n\n all_options.sort(key=lambda x: x.get(\"volume\") or 0, reverse=True)\n\n return {\n \"ticker\": ticker,\n \"current_price\": current_price,\n \"change_pct\": change_pct,\n \"direction\": direction,\n \"expiries_checked\": expiries,\n \"options\": all_options[:10],\n }\n\n\ndef run():\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"Tradier Options Lookup – {today_str}\")\n print(f\"{'='*60}\")\n\n r1_path = DATA_DIR / f\"{today_str}_claude_round1.json\"\n if not r1_path.exists():\n raise FileNotFoundError(f\"Claude Round 1 results not found: {r1_path}\")\n\n with open(r1_path) as f:\n r1 = json.load(f)\n\n headers = get_headers()\n top5 = r1.get(\"top5\", [])\n\n if not top5:\n raise ValueError(\"Claude Round 1 returned no top5 picks\")\n\n tickers = [s[\"ticker\"] for s in top5]\n stock_quotes = get_stock_quotes(tickers, headers)\n print(f\"πŸ“Š Live quotes fetched: {list(stock_quotes.keys())}\")\n\n results = {}\n for stock in top5:\n ticker = stock[\"ticker\"]\n direction = stock.get(\"direction\", \"BULLISH\")\n quote = stock_quotes.get(ticker)\n results[ticker] = fetch_options_for_ticker(ticker, direction, headers, stock_quote=quote)\n\n output = {\n \"date\": today_str,\n \"top5_tickers\": [s[\"ticker\"] for s in top5],\n \"options\": results,\n }\n\n output_path = DATA_DIR / f\"{today_str}_options.json\"\n with open(output_path, \"w\") as f:\n json.dump(output, f, indent=2, default=str)\n\n print(f\"\\nβœ… Options data saved to {output_path}\")\n for ticker, data in results.items():\n count = len(data.get(\"options\", []))\n print(f\" {ticker}: {count} option candidates\")\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/parse_13f.py", "content": "\"\"\"\nparse_13f.py\nComputes quarter-over-quarter deltas from raw holdings data.\n\nFixes from architecture audit:\n R-01 Stock split β†’ share count adjusted before delta calculation\n R-03 AUM inflation warning β†’ added systematic bias disclaimer\n R-04 Division by zero on new positions β†’ handled explicitly\n R-07 First run / missing prior quarter β†’ bootstrap gracefully\n R-11 CUSIP vs ADR matching β†’ uses ticker as primary key with CUSIP fallback\n\"\"\"\n\nimport json\nimport sys\nfrom datetime import date\nfrom pathlib import Path\n\nfrom config import DATA_DIR\n\n\ndef load_latest_raw(today_str: str) -> dict:\n path = DATA_DIR / f\"{today_str}_raw_holdings.json\"\n if not path.exists():\n raise FileNotFoundError(f\"Raw holdings not found: {path}\")\n with open(path) as f:\n return json.load(f)\n\n\ndef load_prior_quarter(today_str: str) -> dict | None:\n \"\"\"\n Finds the most recent previously saved *_holdings_parsed.json\n that is NOT today. Returns None if this is the first run.\n\n Amendment note: 13F-HR/A filings amend a prior quarter's data.\n Because fetch_filings.py always retrieves the LATEST filing per filer\n (which is the amendment if one exists), the data saved today is always\n the most up-to-date. The delta comparison against the *previous* parsed\n file is therefore always amendment-aware as long as each run overwrites\n stale data from the same quarter. If two runs occur within the same\n quarter (e.g., base + amendment), only the most recent parsed file\n survives and prior-quarter comparison remains valid.\n \"\"\"\n today = date.fromisoformat(today_str)\n candidates = sorted(DATA_DIR.glob(\"*_holdings_parsed.json\"), reverse=True)\n\n for c in candidates:\n try:\n d = date.fromisoformat(c.name[:10])\n if d < today:\n data = json.load(open(c))\n # Warn if the prior data itself contains amendments, so the\n # user knows the baseline may have been restated.\n amendment_filers = [\n name for name, fd in data.get(\"filers\", {}).items()\n if fd.get(\"is_amendment\")\n ]\n if amendment_filers:\n print(f\" ℹ️ Prior quarter ({data['date']}) contains amendments \"\n f\"for: {', '.join(amendment_filers)} – baseline has been restated.\")\n return data\n except (ValueError, json.JSONDecodeError):\n continue\n\n return None # First run\n\n\ndef build_position_lookup(filer_data: dict) -> dict:\n \"\"\"\n Build a dict: {ticker_or_cusip β†’ holding_dict} for one filer.\n\n PUT and CALL/long positions are tracked separately so the net\n direction is visible downstream. Aggregating them would inflate\n bullish signals: a fund with 100 long shares + 10,000 put shares\n must NOT be scored as a 10,100-share long conviction buy.\n\n Structure per key:\n shares / value_usd_thousands β†’ long + CALL shares (bullish)\n put_shares / put_value_usd_k β†’ PUT shares (bearish / hedge)\n net_bullish β†’ True if long_value > put_value\n \"\"\"\n long_lookup: dict = {}\n put_lookup: dict = {}\n\n for h in filer_data.get(\"holdings\", []):\n put_call = (h.get(\"putCall\") or \"\").strip().upper()\n key = h.get(\"ticker\") or h.get(\"cusip\") or h.get(\"nameOfIssuer\", \"UNKNOWN\")\n\n if put_call == \"PUT\":\n if key in put_lookup:\n put_lookup[key][\"shares\"] += h[\"shares\"]\n put_lookup[key][\"value_usd_thousands\"] += h[\"value_usd_thousands\"]\n else:\n put_lookup[key] = {**h}\n else:\n # Long or CALL – counts as bullish exposure\n if key in long_lookup:\n long_lookup[key][\"shares\"] += h[\"shares\"]\n long_lookup[key][\"value_usd_thousands\"] += h[\"value_usd_thousands\"]\n else:\n long_lookup[key] = {**h}\n\n # Merge: annotate every long position with its paired PUT size\n all_keys = set(long_lookup) | set(put_lookup)\n lookup = {}\n\n for key in all_keys:\n if key in long_lookup:\n entry = {**long_lookup[key]}\n else:\n # Pure-put position: create a placeholder with zero long exposure\n entry = {**put_lookup[key], \"shares\": 0, \"value_usd_thousands\": 0}\n\n put_entry = put_lookup.get(key, {})\n entry[\"put_shares\"] = put_entry.get(\"shares\", 0)\n entry[\"put_value_usd_k\"] = put_entry.get(\"value_usd_thousands\", 0)\n\n long_val = entry[\"value_usd_thousands\"]\n put_val = entry[\"put_value_usd_k\"]\n entry[\"net_bullish\"] = long_val >= put_val # False β†’ fund is net short/hedged\n\n if not entry[\"net_bullish\"]:\n # Surface this so scoring can skip or flag it\n entry[\"direction_note\"] = (\n f\"NET SHORT/HEDGED: long ${long_val:,}k vs put ${put_val:,}k\"\n )\n\n lookup[key] = entry\n\n return lookup\n\n\ndef adjust_shares_for_splits(shares: int, ticker: str, splits: dict) -> int:\n \"\"\"\n R-01 Fix: If a stock split occurred since the prior quarter,\n the prior-quarter share count must be multiplied by the split ratio\n before computing delta. Otherwise +900% false positives occur.\n \"\"\"\n ratio = splits.get(ticker, 1.0)\n if ratio != 1.0:\n adjusted = int(shares * ratio)\n print(f\" πŸ”€ Split-adjusted {ticker} prior shares: {shares} β†’ {adjusted} (ratio {ratio})\")\n return adjusted\n return shares\n\n\ndef compute_delta(current_shares: int, prior_shares: int | None, ticker: str) -> dict:\n \"\"\"\n Returns delta info between current and prior quarter.\n\n R-04 Fix: Division by zero when prior_shares == 0 (new position).\n Handles three cases:\n - New position (prior is None or 0)\n - Full exit (current == 0, though EDGAR won't show these)\n - Change (normal delta)\n \"\"\"\n if prior_shares is None:\n return {\n \"type\": \"NEW\",\n \"delta_shares\": current_shares,\n \"delta_pct\": None, # undefined for new positions\n }\n\n if prior_shares == 0:\n return {\n \"type\": \"NEW\",\n \"delta_shares\": current_shares,\n \"delta_pct\": None,\n }\n\n delta_shares = current_shares - prior_shares\n delta_pct = (delta_shares / prior_shares) * 100.0\n\n if current_shares == 0:\n tx_type = \"SOLD\"\n elif delta_shares > 0:\n tx_type = \"ADDED\"\n elif delta_shares < 0:\n tx_type = \"REDUCED\"\n else:\n tx_type = \"UNCHANGED\"\n\n return {\n \"type\": tx_type,\n \"delta_shares\": delta_shares,\n \"delta_pct\": round(delta_pct, 2),\n }\n\n\ndef parse_and_enrich(raw: dict, prior: dict | None) -> dict:\n \"\"\"\n Main enrichment pass: for each filer and each position, compute:\n - portfolio weight (% of reported long-only AUM)\n - delta vs. prior quarter\n - position rank within portfolio\n\n R-03 Warning: portfolio weight uses REPORTED 13F AUM (long positions only).\n Cash, shorts, bonds, options premiums are excluded by SEC rules.\n The weight is systematically overstated for diversified managers.\n \"\"\"\n today_str = raw[\"date\"]\n splits = raw.get(\"recent_splits\", {})\n\n parsed_filers = {}\n\n for filer_name, filer_data in raw[\"filers\"].items():\n if \"holdings\" not in filer_data:\n parsed_filers[filer_name] = {\"error\": filer_data.get(\"error\"), \"positions\": []}\n continue\n\n current_lookup = build_position_lookup(filer_data)\n\n # Reported AUM = long + CALL positions only (Puts are hedges, not capital deployed).\n # Using filer_data[\"total_value\"] would inflate the denominator with put notional,\n # making every position's portfolio weight look smaller than it really is.\n reported_aum = sum(\n pos[\"value_usd_thousands\"]\n for pos in current_lookup.values()\n )\n if reported_aum == 0:\n print(f\" ⚠️ {filer_name}: reported long-only AUM = 0, skipping\")\n parsed_filers[filer_name] = {\"error\": \"zero_aum\", \"positions\": []}\n continue\n\n # Prior quarter lookup for this filer\n prior_lookup = {}\n if prior and filer_name in prior.get(\"filers\", {}):\n prior_filer = prior[\"filers\"][filer_name]\n if \"positions\" in prior_filer:\n for pos in prior_filer[\"positions\"]:\n key = pos.get(\"ticker\") or pos.get(\"cusip\") or \"\"\n prior_lookup[key] = pos\n\n positions = []\n for key, holding in current_lookup.items():\n ticker = holding.get(\"ticker\", \"\")\n net_bullish = holding.get(\"net_bullish\", True)\n\n # Skip positions where puts dominate: they are bearish/hedged and\n # would produce false bullish signals downstream.\n # They are logged separately in put_positions for transparency.\n if not net_bullish:\n continue\n\n prior_pos = prior_lookup.get(key)\n\n # Compare only long shares (prior data may have aggregated puts+longs).\n # Use prior \"shares\" but cap to avoid inflated deltas from old data format.\n prior_shares_raw = prior_pos[\"shares\"] if prior_pos else None\n if prior_shares_raw is not None and ticker:\n prior_shares_adj = adjust_shares_for_splits(prior_shares_raw, ticker, splits)\n else:\n prior_shares_adj = prior_shares_raw\n\n delta = compute_delta(holding[\"shares\"], prior_shares_adj, key)\n\n # Portfolio weight uses long-only AUM (corrected denominator above)\n port_weight_pct = (holding[\"value_usd_thousands\"] / reported_aum) * 100.0\n\n # Prior portfolio weight\n prior_port_weight = None\n if prior_pos and prior:\n prior_aum = prior[\"filers\"].get(filer_name, {}).get(\"reported_aum_k\", 0)\n if prior_aum > 0:\n prior_port_weight = (prior_pos.get(\"value_usd_thousands\", 0) / prior_aum) * 100.0\n\n # Determine position direction: LONG or CALL (no pure PUTs reach here)\n put_val = holding.get(\"put_value_usd_k\", 0)\n long_val = holding[\"value_usd_thousands\"]\n direction = \"LONG_WITH_HEDGE\" if put_val > 0 else \"LONG\"\n\n positions.append({\n \"ticker\": ticker,\n \"cusip\": holding.get(\"cusip\", \"\"),\n \"name\": holding.get(\"nameOfIssuer\", \"\"),\n \"value_usd_k\": long_val,\n \"shares\": holding[\"shares\"],\n \"put_shares\": holding.get(\"put_shares\", 0),\n \"put_value_usd_k\": put_val,\n \"direction\": direction,\n \"port_weight_pct\": round(port_weight_pct, 3),\n \"prior_port_weight\": round(prior_port_weight, 3) if prior_port_weight else None,\n \"delta\": delta,\n \"is_first_run\": prior is None,\n \"net_bullish\": True,\n \"weight_note\": \"Long-only 13F AUM denominator – true weight may be lower\",\n })\n\n # Sort by portfolio weight descending\n positions.sort(key=lambda x: x[\"port_weight_pct\"], reverse=True)\n\n # Add rank\n for i, pos in enumerate(positions, 1):\n pos[\"rank\"] = i\n\n parsed_filers[filer_name] = {\n \"cik\": filer_data[\"cik\"],\n \"reported_aum_k\": reported_aum,\n \"filing_date\": filer_data[\"meta\"][\"filingDate\"],\n # report_date = quarter-end (period of report); used as price anchor.\n \"report_date\": filer_data[\"meta\"].get(\"reportDate\") or filer_data[\"meta\"][\"filingDate\"],\n \"is_amendment\": filer_data[\"meta\"][\"isAmendment\"],\n \"position_count\": len(positions),\n \"positions\": positions,\n }\n\n print(f\" βœ… {filer_name}: {len(positions)} positions, \"\n f\"AUM ${reported_aum/1e6:,.1f}B (13F reported, long-only)\")\n\n return {\n \"date\": today_str,\n \"prior_date\": prior[\"date\"] if prior else None,\n \"is_first_run\": prior is None,\n \"recent_splits\": splits,\n \"filers\": parsed_filers,\n }\n\n\ndef run():\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"13F Parser & Delta Calculator – {today_str}\")\n print(f\"{'='*60}\")\n\n raw = load_latest_raw(today_str)\n prior = load_prior_quarter(today_str)\n\n if prior:\n print(f\"πŸ“‚ Prior quarter data: {prior['date']}\")\n else:\n print(\"⚠️ First run – no prior quarter data available. Deltas will be marked as NEW.\")\n\n parsed = parse_and_enrich(raw, prior)\n\n output_path = DATA_DIR / f\"{today_str}_holdings_parsed.json\"\n tmp_path = output_path.with_suffix(\".tmp\")\n with open(tmp_path, \"w\") as f:\n json.dump(parsed, f, indent=2, default=str)\n tmp_path.replace(output_path)\n\n print(f\"\\nβœ… Parsed holdings saved to {output_path}\")\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/scoring.py", "content": "\"\"\"\nscoring.py\nConviction Score calculation engine.\n\nImprovements:\n - Multi-quarter position building bonus (multi_quarter.py)\n - Filer quality tier multiplier (FILER_QUALITY in config)\n - Price-action staleness check via yfinance:\n +15% since filing β†’ WARNING flag\n +25% since filing β†’ score halved + STALE flag\n - Score normalization min-max [0, 100]\n - Cluster detection on ticker > CUSIP > normalized name\n\"\"\"\n\nimport json\nimport re\nfrom collections import defaultdict\nfrom datetime import date, timedelta\n\nfrom config import (\n CLUSTER_BONUS_MULTIPLIER, CLUSTER_MIN_FUNDS,\n DATA_DIR, DOUBLE_DOWN_MIN_DELTA, FILER_QUALITY,\n HIGH_CONVICTION_MIN_PCT, HIGH_CONVICTION_TOP_N,\n MIN_PORTFOLIO_WEIGHT_PCT, PRICE_ACTION_DOWNGRADE_PCT,\n PRICE_ACTION_WARN_PCT, WEIGHT_DELTA_PCT, WEIGHT_PORTFOLIO_PCT,\n)\nimport multi_quarter\n\n\ndef load_parsed(today_str: str) -> dict:\n path = DATA_DIR / f\"{today_str}_holdings_parsed.json\"\n if not path.exists():\n raise FileNotFoundError(f\"Parsed holdings not found: {path}\")\n with open(path) as f:\n return json.load(f)\n\n\n# ── Price-action staleness ────────────────────────────────────────────────────\n\ndef fetch_price_changes(tickers: list[str], filing_dates: dict[str, str]) -> dict[str, dict]:\n \"\"\"\n For each ticker, compares the price at its filing date to today's price.\n\n Returns {ticker: {pct_change, filing_close, current_price, days_since_filing}}\n or {ticker: {\"pct_change\": None, ...}} on failure.\n\n Uses a single yfinance batch download for efficiency.\n \"\"\"\n empty: dict = {\"pct_change\": None, \"filing_close\": None,\n \"current_price\": None, \"days_since_filing\": None}\n\n try:\n import yfinance as yf\n except ImportError:\n return {t: empty.copy() for t in tickers}\n\n if not tickers:\n return {}\n\n dates = [d for d in filing_dates.values() if d]\n if not dates:\n return {t: empty.copy() for t in tickers}\n\n # Download from the oldest filing date so every ticker has data from its filing onward\n oldest = min(dates)\n today_str = date.today().isoformat()\n\n # yfinance uses BRK-B format, not BRK/B (Tradier format)\n yf_tickers = [t.replace(\"/\", \"-\") for t in tickers]\n ticker_map = {yf: orig for yf, orig in zip(yf_tickers, tickers)}\n\n try:\n hist = yf.download(\n yf_tickers,\n start=oldest,\n end=today_str,\n auto_adjust=True,\n progress=False,\n )\n except Exception as e:\n print(f\" ⚠️ yfinance batch download failed: {e}\")\n return {t: empty.copy() for t in tickers}\n\n close = hist.get(\"Close\", hist) if hasattr(hist, \"get\") else hist\n\n result: dict[str, dict] = {}\n\n for ticker in tickers:\n filing_date_str = filing_dates.get(ticker)\n if not filing_date_str:\n result[ticker] = empty.copy()\n continue\n\n yf_t = ticker.replace(\"/\", \"-\")\n\n try:\n series = close if len(yf_tickers) == 1 else (\n close[yf_t] if yf_t in close.columns else None\n )\n\n if series is None or series.empty:\n result[ticker] = empty.copy()\n continue\n\n filing_date = date.fromisoformat(filing_date_str)\n series_after_filing = series[series.index.date >= filing_date]\n\n if series_after_filing.empty:\n result[ticker] = empty.copy()\n continue\n\n filing_close = round(float(series_after_filing.iloc[0]), 2)\n current_price = round(float(series_after_filing.iloc[-1]), 2)\n days_since = (date.today() - filing_date).days\n\n if filing_close <= 0:\n result[ticker] = empty.copy()\n else:\n pct = ((current_price - filing_close) / filing_close) * 100.0\n result[ticker] = {\n \"pct_change\": round(pct, 1),\n \"filing_close\": filing_close,\n \"current_price\": current_price,\n \"days_since_filing\": days_since,\n }\n\n except Exception:\n result[ticker] = empty.copy()\n\n return result\n\n\n# ── Core scoring ──────────────────────────────────────────────────────────────\n\ndef compute_raw_score(pos: dict, filer_name: str) -> float:\n \"\"\"\n Score = (weight_port Γ— portfolio_pct) + (weight_delta Γ— normalized_delta)\n Γ— filer_quality_multiplier\n\n NEW positions: delta component uses 100 as proxy.\n REDUCED / SOLD / UNCHANGED: score = 0.\n \"\"\"\n port_pct = pos[\"port_weight_pct\"]\n delta_info = pos[\"delta\"]\n delta_pct = delta_info.get(\"delta_pct\")\n tx_type = delta_info.get(\"type\", \"UNCHANGED\")\n\n if port_pct < MIN_PORTFOLIO_WEIGHT_PCT:\n return 0.0\n\n if tx_type in (\"REDUCED\", \"SOLD\", \"UNCHANGED\"):\n return 0.0\n\n # Skip positions where the fund is net short/hedged via puts.\n # A filer with 100 long shares and 10,000 put shares is bearish,\n # not a conviction buy – scoring it bullish would be a false signal.\n if not pos.get(\"net_bullish\", True):\n return 0.0\n\n delta_component = 100.0 if delta_pct is None else abs(delta_pct)\n\n raw = (WEIGHT_PORTFOLIO_PCT * port_pct) + (WEIGHT_DELTA_PCT * delta_component)\n\n # Filer quality multiplier\n quality = FILER_QUALITY.get(filer_name, 1.0)\n raw *= quality\n\n return round(raw, 4)\n\n\ndef apply_flags(pos: dict, rank: int) -> list[str]:\n flags = []\n tx_type = pos[\"delta\"].get(\"type\")\n port_pct = pos[\"port_weight_pct\"]\n delta_pct = pos[\"delta\"].get(\"delta_pct\")\n\n if tx_type == \"NEW\":\n flags.append(\"NEW_POSITION\")\n if port_pct >= HIGH_CONVICTION_MIN_PCT:\n flags.append(\"HIGH_CONVICTION\")\n if rank <= HIGH_CONVICTION_TOP_N:\n flags.append(\"TOP10_ENTRY\")\n\n if tx_type == \"ADDED\" and delta_pct is not None and delta_pct >= DOUBLE_DOWN_MIN_DELTA:\n flags.append(\"AGGRESSIVE_ADD\")\n\n # Warn if the long position is accompanied by a significant put hedge\n put_val = pos.get(\"put_value_usd_k\", 0)\n long_val = pos.get(\"value_usd_thousands\", 0)\n if put_val > 0 and long_val > 0 and put_val > long_val * 0.5:\n flags.append(\"PUT_HEDGE_PRESENT\")\n\n return flags\n\n\ndef build_scored_universe(parsed: dict, mq_signals: dict[str, dict]) -> list[dict]:\n \"\"\"\n Iterates all filers and positions, computing a score for each.\n Applies filer quality and multi-quarter multipliers.\n Returns flat list of scored buy-only positions.\n \"\"\"\n scored = []\n\n for filer_name, filer_data in parsed[\"filers\"].items():\n if \"positions\" not in filer_data:\n continue\n\n positions = filer_data[\"positions\"]\n # Prefer report_date (quarter-end) over filing_date so price comparison\n # measures from when the manager held the position, not when they disclosed it.\n filing_date = filer_data.get(\"report_date\") or filer_data.get(\"filing_date\", \"\")\n\n for pos in positions:\n raw_score = compute_raw_score(pos, filer_name)\n if raw_score <= 0:\n continue\n\n flags = apply_flags(pos, pos.get(\"rank\", 999))\n ticker = pos.get(\"ticker\", \"\") or pos.get(\"cusip\", \"\")\n\n # Multi-quarter bonus\n mq_mult = multi_quarter.get_multiplier(ticker, mq_signals)\n raw_score *= mq_mult\n if mq_mult > 1.0:\n mq_sig = mq_signals.get(ticker, {})\n flags.extend([f for f in mq_sig.get(\"flags\", []) if f not in flags])\n\n scored.append({\n \"filer\": filer_name,\n \"ticker\": ticker,\n \"cusip\": pos.get(\"cusip\", \"\"),\n \"name\": pos.get(\"name\", \"\"),\n \"port_weight_pct\": pos[\"port_weight_pct\"],\n \"delta_pct\": pos[\"delta\"].get(\"delta_pct\"),\n \"delta_type\": pos[\"delta\"][\"type\"],\n \"delta_shares\": pos[\"delta\"][\"delta_shares\"],\n \"value_usd_k\": pos[\"value_usd_k\"],\n \"rank_in_port\": pos.get(\"rank\"),\n \"filing_date\": filing_date,\n \"raw_score\": round(raw_score, 4),\n \"mq_multiplier\": round(mq_mult, 2),\n \"flags\": flags,\n })\n\n return scored\n\n\n# ── Price-action staleness enrichment ─────────────────────────────────────────\n\ndef enrich_with_price_action(scored: list[dict]) -> list[dict]:\n \"\"\"\n Checks current price vs price at filing date for each ticker.\n If the stock has already run >25% since the 13F filing, the\n thesis may have played out – score is halved and STALE flag added.\n \"\"\"\n # Collect unique ticker β†’ filing_date mapping (use earliest filing date per ticker)\n filing_dates: dict[str, str] = {}\n for entry in scored:\n t = entry[\"ticker\"]\n if t and t not in filing_dates:\n filing_dates[t] = entry.get(\"filing_date\", \"\")\n\n tickers = [t for t in filing_dates if t]\n if not tickers:\n return scored\n\n print(f\" πŸ“ˆ Checking price action for {len(tickers)} tickers since filing date...\")\n changes = fetch_price_changes(tickers, filing_dates)\n\n warned = staled = 0\n for entry in scored:\n t = entry[\"ticker\"]\n perf = changes.get(t, {})\n pct = perf.get(\"pct_change\")\n\n # Store full performance snapshot on each scored entry\n entry[\"post_filing_perf\"] = perf\n\n if pct is None:\n continue\n\n if pct >= PRICE_ACTION_DOWNGRADE_PCT:\n entry[\"raw_score\"] *= 0.5\n if \"PRICE_ACTION_STALE\" not in entry[\"flags\"]:\n entry[\"flags\"].append(\"PRICE_ACTION_STALE\")\n staled += 1\n elif pct >= PRICE_ACTION_WARN_PCT:\n if \"PRICE_ACTION_WARNING\" not in entry[\"flags\"]:\n entry[\"flags\"].append(\"PRICE_ACTION_WARNING\")\n warned += 1\n\n if staled or warned:\n print(f\" ⚠️ Price action: {staled} positions stale (>{PRICE_ACTION_DOWNGRADE_PCT}%), \"\n f\"{warned} warnings (>{PRICE_ACTION_WARN_PCT}%)\")\n return scored\n\n\n# ── Cluster detection ─────────────────────────────────────────────────────────\n\ndef detect_clusters(scored: list[dict]) -> dict[str, list[str]]:\n \"\"\"Clusters on ticker > CUSIP > normalized company name.\"\"\"\n key_filers: dict[str, list[str]] = defaultdict(list)\n\n for entry in scored:\n if entry[\"delta_type\"] not in (\"NEW\", \"ADDED\"):\n continue\n\n ticker = entry.get(\"ticker\", \"\").strip()\n cusip = entry.get(\"cusip\", \"\").strip()\n name = entry.get(\"name\", \"\").strip().upper()\n name_norm = re.sub(r\"\\b(INC|CORP|LTD|LLC|LP|PLC|CO|THE|DEL|COM)\\b\", \"\", name)\n name_norm = re.sub(r\"\\s+\", \" \", name_norm).strip()\n\n key = ticker or cusip or name_norm\n if key:\n key_filers[key].append(entry[\"filer\"])\n\n return {\n key: filers\n for key, filers in key_filers.items()\n if len(filers) >= CLUSTER_MIN_FUNDS\n }\n\n\ndef apply_cluster_bonus(scored: list[dict], clusters: dict[str, list[str]]) -> list[dict]:\n for entry in scored:\n cluster_key = entry[\"ticker\"] or entry.get(\"cusip\", \"\")\n if cluster_key in clusters:\n entry[\"cluster_funds\"] = clusters[cluster_key]\n entry[\"cluster_count\"] = len(clusters[cluster_key])\n entry[\"raw_score\"] *= CLUSTER_BONUS_MULTIPLIER\n if \"CLUSTER\" not in entry[\"flags\"]:\n entry[\"flags\"].append(\"CLUSTER\")\n else:\n entry[\"cluster_funds\"] = []\n entry[\"cluster_count\"] = 0\n return scored\n\n\ndef normalize_scores(scored: list[dict]) -> list[dict]:\n \"\"\"Min-max normalize raw_score to [0, 100].\"\"\"\n if not scored:\n return scored\n\n scores = [e[\"raw_score\"] for e in scored]\n min_s, max_s = min(scores), max(scores)\n\n if max_s == min_s:\n for e in scored:\n e[\"conviction_score\"] = 50.0\n else:\n for e in scored:\n e[\"conviction_score\"] = round(\n (e[\"raw_score\"] - min_s) / (max_s - min_s) * 100, 1\n )\n\n return scored\n\n\ndef aggregate_by_ticker(scored: list[dict]) -> list[dict]:\n \"\"\"Merges per-filer entries into per-ticker aggregates.\"\"\"\n by_ticker: dict[str, dict] = {}\n\n for entry in scored:\n ticker = entry[\"ticker\"] or entry[\"cusip\"] or entry[\"name\"]\n if ticker not in by_ticker:\n by_ticker[ticker] = {\n \"ticker\": ticker,\n \"name\": entry[\"name\"],\n \"filers\": [],\n \"conviction_score\": 0.0,\n \"total_value_usd_k\": 0,\n \"flags\": set(),\n \"cluster_count\": entry[\"cluster_count\"],\n \"cluster_funds\": entry[\"cluster_funds\"],\n \"delta_types\": [],\n # Full post-filing performance dict (pct_change, filing_close,\n # current_price, days_since_filing) – used in report and Claude prompt\n \"post_filing_perf\": entry.get(\"post_filing_perf\", {}),\n }\n\n agg = by_ticker[ticker]\n agg[\"filers\"].append({\n \"filer\": entry[\"filer\"],\n \"port_weight_pct\": entry[\"port_weight_pct\"],\n \"delta_pct\": entry[\"delta_pct\"],\n \"delta_type\": entry[\"delta_type\"],\n \"conviction_score\":entry[\"conviction_score\"],\n \"mq_multiplier\": entry.get(\"mq_multiplier\", 1.0),\n })\n agg[\"conviction_score\"] = max(agg[\"conviction_score\"], entry[\"conviction_score\"])\n agg[\"total_value_usd_k\"] += entry[\"value_usd_k\"]\n agg[\"flags\"].update(entry[\"flags\"])\n agg[\"delta_types\"].append(entry[\"delta_type\"])\n\n result = []\n for ticker, agg in by_ticker.items():\n agg[\"flags\"] = sorted(agg[\"flags\"])\n agg[\"filer_count\"] = len(agg[\"filers\"])\n result.append(agg)\n\n result.sort(key=lambda x: x[\"conviction_score\"], reverse=True)\n return result\n\n\n# ── Main ──────────────────────────────────────────────────────────────────────\n\ndef run():\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"Conviction Scoring Engine – {today_str}\")\n print(f\"{'='*60}\")\n\n parsed = load_parsed(today_str)\n\n # 1. Multi-quarter signals from historical data\n print(\"\\nπŸ” Analyzing multi-quarter position building...\")\n mq_signals = multi_quarter.build_multi_quarter_signals(today_str)\n print(f\" {len(mq_signals)} tickers with 2+ build quarters\")\n for t, s in list(mq_signals.items())[:5]:\n print(f\" {t}: {s['build_quarters']}Q build, flags={s['flags']}\")\n\n # 2. Raw scores per filer/position (includes filer quality + MQ multiplier)\n scored = build_scored_universe(parsed, mq_signals)\n print(f\"\\nπŸ“Š Scored positions (buys only, β‰₯{MIN_PORTFOLIO_WEIGHT_PCT}% port weight): {len(scored)}\")\n\n # 3. Price-action staleness check\n scored = enrich_with_price_action(scored)\n\n # 4. Cluster detection\n clusters = detect_clusters(scored)\n print(f\"πŸ”— Cluster signals (β‰₯{CLUSTER_MIN_FUNDS} funds): {len(clusters)} tickers\")\n for t, f in clusters.items():\n print(f\" {t}: {f}\")\n\n # 5. Apply cluster bonus\n scored = apply_cluster_bonus(scored, clusters)\n\n # 6. Normalize to [0, 100]\n scored = normalize_scores(scored)\n\n # 7. Aggregate by ticker\n aggregated = aggregate_by_ticker(scored)\n\n print(f\"\\n{'─'*60}\")\n print(f\"{'Rank':<5}{'Ticker':<8}{'Score':<8}{'Filers':<8}{'Flags'}\")\n print(f\"{'─'*60}\")\n for i, agg in enumerate(aggregated[:20], 1):\n print(f\"{i:<5}{agg['ticker']:<8}{agg['conviction_score']:<8.1f}\"\n f\"{agg['filer_count']:<8}{', '.join(agg['flags'])}\")\n\n output = {\n \"date\": today_str,\n \"scored_flat\": scored,\n \"aggregated\": aggregated,\n \"clusters\": clusters,\n \"top20\": aggregated[:20],\n \"mq_signals\": mq_signals,\n }\n\n output_path = DATA_DIR / f\"{today_str}_scores.json\"\n tmp_path = output_path.with_suffix(\".tmp\")\n with open(tmp_path, \"w\") as f:\n json.dump(output, f, indent=2, default=str)\n tmp_path.replace(output_path)\n\n print(f\"\\nβœ… Scores saved to {output_path}\")\n\n\nif __name__ == \"__main__\":\n run()\n" }, { "path": "src/send_report.py", "content": "\"\"\"\nsend_report.py\nGenerates the HTML report and sends it via Gmail.\n\nFixes from architecture audit:\n R-14 Validates report is non-empty before sending\n R-06 Report only sends if analysis JSON is structurally complete\n\"\"\"\n\nimport json\nimport os\nimport smtplib\nfrom datetime import date\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom pathlib import Path\n\nfrom config import (\n DATA_DIR, GMAIL_SMTP_HOST, GMAIL_SMTP_PORT,\n REPORT_SUBJECT, REPORTS_DIR,\n)\n\n\ndef load_final_analysis(today_str: str) -> dict:\n path = DATA_DIR / f\"{today_str}_final_analysis.json\"\n if not path.exists():\n raise FileNotFoundError(f\"Final analysis not found: {path}\")\n with open(path) as f:\n return json.load(f)\n\n\ndef load_backtest(today_str: str) -> dict | None:\n path = DATA_DIR / f\"{today_str}_backtest.json\"\n if not path.exists():\n return None\n with open(path) as f:\n return json.load(f)\n\n\ndef flag_badge(flag: str) -> str:\n colors = {\n \"HIGH_CONVICTION\": \"#dc2626\",\n \"CLUSTER\": \"#7c3aed\",\n \"NEW_POSITION\": \"#059669\",\n \"AGGRESSIVE_ADD\": \"#d97706\",\n \"TOP10_ENTRY\": \"#0284c7\",\n }\n color = colors.get(flag, \"#6b7280\")\n return f'{flag}'\n\n\ndef _post_filing_block(perf: dict) -> str:\n \"\"\"\n Renders a compact coloured bar showing price movement since the 13F filing date.\n Three zones:\n green : ≀ +15% β†’ fresh signal, thesis not yet priced in\n amber : +15–25% β†’ already running, watch closely\n red : > +25% β†’ likely priced in / FOMO territory\n \"\"\"\n pct = perf.get(\"pct_change\")\n days = perf.get(\"days_since_filing\")\n fc = perf.get(\"filing_close\")\n cp = perf.get(\"current_price\")\n\n if pct is None:\n return \"\"\n\n if pct > 25:\n bg, border, text_color = \"#fef2f2\", \"#fca5a5\", \"#991b1b\"\n label = f\"⚠️ +{pct:.1f}% seit Filing – FOMO-Risiko, Thesis mΓΆglicherweise eingepreist\"\n icon = \"πŸ”΄\"\n elif pct > 15:\n bg, border, text_color = \"#fffbeb\", \"#fcd34d\", \"#92400e\"\n label = f\"⚑ +{pct:.1f}% seit Filing – bereits gelaufen, erhΓΆhtes Einstiegsrisiko\"\n icon = \"🟑\"\n elif pct >= 0:\n bg, border, text_color = \"#f0fdf4\", \"#bbf7d0\", \"#166534\"\n label = f\"βœ… +{pct:.1f}% seit Filing – Signal noch frisch\"\n icon = \"🟒\"\n else:\n bg, border, text_color = \"#f0fdf4\", \"#bbf7d0\", \"#166534\"\n label = f\"β†˜ {pct:.1f}% seit Filing – gΓΌnstiger als zum Signal-Zeitpunkt\"\n icon = \"🟒\"\n\n days_str = f\"{days}d\" if days is not None else \"?d\"\n price_str = (\n f\"${fc} β†’ ${cp}\" if fc is not None and cp is not None else \"\"\n )\n\n return (\n f'
'\n f'{label}'\n f''\n f'{price_str}  Β·  {days_str} ago'\n f'
'\n )\n\n\ndef generate_backtest_html(backtest: dict) -> str:\n \"\"\"Renders a compact performance summary block for the report.\"\"\"\n s = backtest.get(\"summary\", {})\n rows = backtest.get(\"results\", [])\n\n if not s.get(\"completed_90d\"):\n return \"\"\n\n wr90 = s.get(\"win_rate_90d_pct\")\n wr180 = s.get(\"win_rate_180d_pct\")\n ar90 = s.get(\"avg_return_90d_pct\")\n ar180 = s.get(\"avg_return_180d_pct\")\n\n def _color(val):\n if val is None:\n return \"#6b7280\"\n return \"#059669\" if val > 0 else \"#dc2626\"\n\n def _fmt(val):\n if val is None:\n return \"pending\"\n return f\"+{val:.1f}%\" if val > 0 else f\"{val:.1f}%\"\n\n # Recent signals table (last 10 completed)\n completed = [r for r in rows if r.get(\"status_d90\") in (\"win\", \"loss\")][-10:]\n rows_html = \"\"\n for r in reversed(completed):\n ret = r.get(\"return_d90_pct\")\n color = _color(ret)\n rows_html += (\n f\"\"\n f\"{r['report_date']}\"\n f\"{r['ticker']}\"\n f\"{r.get('primary_flag','')}\"\n f\"{_fmt(ret)}\"\n f\"\"\n )\n\n return f\"\"\"\n
\n
\n πŸ“ˆ Historical Performance (90-day stock returns)\n
\n
\n
\n
{_fmt(ar90)}
\n
Avg 90d Return
\n
\n
\n
{wr90 if wr90 is not None else 'β€”'}{'%' if wr90 else ''}
\n
90d Win Rate
\n
\n
\n
{_fmt(ar180)}
\n
Avg 180d Return
\n
\n
\n
{s.get('total_signals','β€”')}
\n
Total Signals
\n
\n
\n {'' + rows_html + '
' if rows_html else ''}\n
\n ⚠️ Past stock returns do not predict option profits. Options can expire worthless even when the stock moves in the right direction.\n
\n
\"\"\"\n\n\ndef generate_html_report(analysis: dict, backtest: dict | None = None) -> str:\n today_str = analysis[\"date\"]\n top5 = analysis.get(\"round1_top5\", [])\n options_recs = analysis.get(\"options_recs\", [])\n market_context = analysis.get(\"market_context\", \"\")\n portfolio_note = analysis.get(\"portfolio_note\", \"\")\n disclaimer = analysis.get(\"disclaimer\", \"\")\n\n backtest_html = generate_backtest_html(backtest) if backtest else \"\"\n\n # Build options recommendations section\n options_html = \"\"\n options_by_ticker = {r[\"stock_ticker\"]: r for r in options_recs}\n\n for stock in top5:\n ticker = stock[\"ticker\"]\n opt = options_by_ticker.get(ticker, {})\n primary_flag = stock.get(\"primary_flag\", \"\")\n flags_html = flag_badge(primary_flag) if primary_flag else \"\"\n\n # Buyers list – prefer enriched filer_details (port weight + delta) if available\n filer_details = stock.get(\"filer_details\", [])\n if filer_details:\n buyer_items = []\n for fd in filer_details:\n name = fd.get(\"filer\", \"\")\n port_pct = fd.get(\"port_weight_pct\")\n delta_type = fd.get(\"delta_type\", \"\")\n delta_pct = fd.get(\"delta_pct\")\n\n detail_parts = []\n if port_pct is not None:\n detail_parts.append(f\"{port_pct:.2f}% of port\")\n if delta_type == \"NEW\":\n detail_parts.append(\"NEW\")\n elif delta_pct is not None:\n detail_parts.append(f\"{delta_pct:+.0f}%\")\n\n detail = f\" ({', '.join(detail_parts)})\" if detail_parts else \"\"\n buyer_items.append(f\"{name}{detail}\")\n buyers_html = \"  Β·  \".join(buyer_items)\n else:\n buyers_html = \", \".join(stock.get(\"key_buyers\", []))\n\n options_html += f\"\"\"\n
\n
\n
\n {ticker}\n {stock.get('company_name','')}\n
\n
\n
{stock.get('conviction_score', '')}/100
\n
CONVICTION SCORE
\n
\n
\n\n
{flags_html}
\n\n {_post_filing_block(stock.get(\"post_filing_perf\", {}))}\n\n
\n
Investment Thesis
\n
{stock.get('thesis','')}
\n
\n\n
\n
\n
KEY BUYERS
\n
{buyers_html}
\n
\n
\n
RISK NOTE
\n
{stock.get('risk_factors','')}
\n
\n
\n\n {\"\" if not opt else f'''\n
\n
\n πŸ“ˆ RECOMMENDED OPTION TRADE\n
\n
\n
\n
SYMBOL
\n
{opt.get(\"option_symbol\",\"\")}
\n
\n
\n
TYPE / STRIKE
\n
{opt.get(\"option_type\",\"\")} @ ${opt.get(\"strike\",\"\")}
\n
\n
\n
EXPIRY
\n
{opt.get(\"expiration\",\"\")}
\n
\n
\n
MID PRICE
\n
${opt.get(\"entry_price_mid\",\"?\")}
\n
\n
\n
MAX RISK / CONTRACT
\n
${opt.get(\"max_risk_per_contract\",\"?\")}
\n
\n
\n
PROFIT TARGET
\n
{opt.get(\"profit_target\",\"\")}
\n
\n
\n
\n Rationale: {opt.get(\"option_rationale\",\"\")}\n
\n
\n Stop Loss: {opt.get(\"stop_loss\",\"\")}  | \n Greeks: {opt.get(\"greeks_note\",\"Unknown\")}\n
\n
'''}\n
\n \"\"\"\n\n html = f\"\"\"\n\n\n \n \n SEC 13F Smart Money Report\n\n\n\n
\n\n \n
\n
\n QUARTERLY INSTITUTIONAL INTELLIGENCE\n
\n
SEC 13F Smart Money Report
\n
{today_str}  Β·  13 Monitored Institutions  Β·  Top 5 Picks
\n
\n\n \n
\n
Market Context
\n
{market_context}
\n
\n\n {backtest_html}\n\n \n
\n 🎯 Top 5 Conviction Picks + Option Trades\n
\n\n {options_html}\n\n \n {f'
Portfolio Sizing Note
{portfolio_note}
' if portfolio_note else ''}\n\n \n
\n
\n ⚠️ DISCLAIMER: {disclaimer}

\n Data limitations: 13F filings reflect long equity positions over $200K with up to 45-day delay.\n Portfolio weights use long-only reported AUM (cash, shorts, bonds excluded – weights are systematically overstated).\n Stock splits have been adjusted. Option prices are from Tradier at time of analysis and change constantly.\n This report is generated automatically and does not constitute investment advice.\n
\n
\n\n \n
\n Generated automatically by SEC 13F Smart Money Analyzer  Β· \n Data: SEC EDGAR + Tradier  Β·  Analysis: Claude AI\n
\n\n
\n\n\"\"\"\n\n return html\n\n\ndef send_gmail(html_content: str, today_str: str):\n \"\"\"\n Sends the report via Gmail using App Password (no OAuth required).\n Both GMAIL_ADDRESS and GMAIL_APP_PASSWORD come from GitHub Secrets.\n \"\"\"\n gmail_address = os.environ.get(\"GMAIL_ADDRESS\", \"\")\n gmail_password = os.environ.get(\"GMAIL_APP_PASSWORD\", \"\")\n\n if not gmail_address or not gmail_password:\n raise ValueError(\"GMAIL_ADDRESS and GMAIL_APP_PASSWORD must be set as GitHub Secrets\")\n\n subject = REPORT_SUBJECT.format(date=today_str)\n\n msg = MIMEMultipart(\"alternative\")\n msg[\"Subject\"] = subject\n msg[\"From\"] = gmail_address\n msg[\"To\"] = gmail_address # send to yourself\n\n msg.attach(MIMEText(html_content, \"html\"))\n\n with smtplib.SMTP(GMAIL_SMTP_HOST, GMAIL_SMTP_PORT) as server:\n server.ehlo()\n server.starttls()\n server.login(gmail_address, gmail_password)\n server.sendmail(gmail_address, gmail_address, msg.as_string())\n\n print(f\" βœ… Email sent to {gmail_address}\")\n\n\ndef run():\n today_str = date.today().isoformat()\n\n print(f\"\\n{'='*60}\")\n print(f\"Report Generation & Gmail – {today_str}\")\n print(f\"{'='*60}\")\n\n analysis = load_final_analysis(today_str)\n\n # R-14 Fix: Validate before sending\n recs = analysis.get(\"options_recs\", [])\n top5 = analysis.get(\"round1_top5\", [])\n\n if not top5 or not recs:\n raise RuntimeError(\n f\"Report validation failed: top5={len(top5)}, options_recs={len(recs)}. \"\n \"Aborting email send.\"\n )\n\n print(f\"βœ… Validation passed: {len(top5)} stocks, {len(recs)} option recommendations\")\n\n # Generate HTML\n backtest = load_backtest(today_str)\n if backtest:\n print(f\"πŸ“Š Backtest data loaded ({backtest['summary'].get('total_signals',0)} signals tracked)\")\n html = generate_html_report(analysis, backtest)\n\n # Save report to /reports/\n report_path = REPORTS_DIR / f\"{today_str}_report.html\"\n with open(report_path, \"w\") as f:\n f.write(html)\n print(f\"πŸ’Ύ HTML report saved to {report_path}\")\n\n # Send email\n print(f\"πŸ“§ Sending via Gmail...\")\n send_gmail(html, today_str)\n\n print(f\"\\nπŸŽ‰ Pipeline complete for {today_str}\")\n\n\nif __name__ == \"__main__\":\n run()\n" } ]