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Repository: pcctradinginc-alt/SEC-QUATERLY
Branch: main
Commit: 930ca58409d5
Files: 30
Total size: 8.7 MB
Directory structure:
gitextract_wakc4r5b/
├── .github/
│ └── workflows/
│ └── quarterly_run.yml
├── README.md
├── data/
│ └── holdings/
│ ├── 2026-04-08_claude_round1.json
│ ├── 2026-04-08_final_analysis.json
│ ├── 2026-04-08_holdings_parsed.json
│ ├── 2026-04-08_options.json
│ ├── 2026-04-08_raw_holdings.json
│ ├── 2026-04-08_scores.json
│ ├── 2026-05-18_backtest.json
│ ├── 2026-05-18_claude_round1.json
│ ├── 2026-05-18_final_analysis.json
│ ├── 2026-05-18_holdings_parsed.json
│ ├── 2026-05-18_options.json
│ ├── 2026-05-18_raw_holdings.json
│ └── 2026-05-18_scores.json
├── reports/
│ ├── 2026-04-08_report.html
│ └── 2026-05-18_report.html
├── requirements.txt
└── src/
├── analyze_claude_round1.py
├── analyze_claude_round2.py
├── backtest.py
├── config.py
├── date_check.py
├── fetch_filings.py
├── multi_quarter.py
├── notify.py
├── options_lookup.py
├── parse_13f.py
├── scoring.py
└── send_report.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/quarterly_run.yml
================================================
name: SEC 13F Quarterly Analysis
on:
schedule:
# Run daily at 14:00 UTC (= 15:00 CET / 16:00 CEST)
# The Python script itself decides if today is the right date
- cron: '0 14 14-20 2,5,8,11 *'
workflow_dispatch:
inputs:
force_run:
description: 'Force run regardless of date'
required: false
default: 'false'
target_date:
description: 'Override target date (YYYY-MM-DD)'
required: false
default: ''
jobs:
analyze:
runs-on: ubuntu-latest
timeout-minutes: 30
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python 3.11
uses: actions/setup-python@v5
with:
python-version: '3.11'
cache: 'pip'
- name: Install dependencies
run: pip install -r requirements.txt
- name: Check if today is a run date
id: date_check
env:
FORCE_RUN: ${{ github.event.inputs.force_run }}
TARGET_DATE: ${{ github.event.inputs.target_date }}
TZ: Europe/Berlin
run: |
python src/date_check.py
continue-on-error: false
- name: Fetch SEC 13F Filings
if: steps.date_check.outputs.should_run == 'true'
env:
TZ: Europe/Berlin
OPENFIGI_API_KEY: ${{ secrets.OPENFIGI_API_KEY }}
run: python src/fetch_filings.py
- name: Parse Holdings & Calculate Deltas
if: steps.date_check.outputs.should_run == 'true'
run: python src/parse_13f.py
- name: Calculate Conviction Scores
if: steps.date_check.outputs.should_run == 'true'
run: python src/scoring.py
- name: Claude Analysis Round 1 - Identify Top 5 (Haiku)
if: steps.date_check.outputs.should_run == 'true'
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
run: python src/analyze_claude_round1.py
- name: Fetch Options Data via Tradier
if: steps.date_check.outputs.should_run == 'true'
env:
TRADIER_API_KEY: ${{ secrets.TRADIER_API_KEY }}
run: python src/options_lookup.py
- name: Claude Analysis Round 2 - Select Best Options (Sonnet)
if: steps.date_check.outputs.should_run == 'true'
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
run: python src/analyze_claude_round2.py
- name: Backtest - Track Historical Signal Performance
if: steps.date_check.outputs.should_run == 'true'
run: python src/backtest.py
- name: Generate & Send Report via Gmail
if: steps.date_check.outputs.should_run == 'true'
env:
GMAIL_ADDRESS: ${{ secrets.GMAIL_ADDRESS }}
GMAIL_APP_PASSWORD: ${{ secrets.GMAIL_APP_PASSWORD }}
run: python src/send_report.py
- name: Commit updated holdings data
if: steps.date_check.outputs.should_run == 'true'
run: |
git config --local user.email "action@github.com"
git config --local user.name "GitHub Action"
git add data/holdings/ reports/
git diff --staged --quiet || git commit -m "📊 Quarterly 13F analysis $(date +%Y-%m-%d)"
git push
# ── Failure notification ──────────────────────────────────────────────────
# Runs only when the workflow fails AND the main pipeline was supposed to run.
# Uses the existing Gmail credentials so no extra services are needed.
- name: Send failure alert
if: failure() && steps.date_check.outputs.should_run == 'true'
env:
GMAIL_ADDRESS: ${{ secrets.GMAIL_ADDRESS }}
GMAIL_APP_PASSWORD: ${{ secrets.GMAIL_APP_PASSWORD }}
run: |
python src/notify.py \
"GitHub Actions – SEC 13F Pipeline" \
"Workflow run ${{ github.run_id }} failed. Check: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}"
================================================
FILE: README.md
================================================
# SEC 13F Smart Money Analyzer
Automated quarterly analysis of 13F filings from 13 top institutional investors.
Runs automatically on **16 May / 16 Aug / 16 Nov / 16 Feb** (next business day if weekend).
Delivers a **Top 5 stock picks + specific option trades** report via Gmail.
---
## Monitored Institutions
| Institution | CIK |
|---|---|
| Situational Awareness LP | 0002045724 |
| Yale University | 0000938582 |
| Gates Foundation Trust | 0001166559 |
| Harvard Management Co | 0001082621 |
| Brown University | 0001664741 |
| Duke University | 0001439873 |
| TCI Fund (Chris Hohn) | 0001649339 |
| Pershing Square (Ackman) | 0001336528 |
| Tiger Global (Coleman) | 0001167483 |
| Coatue (Laffont) | 0001766502 |
| D1 Capital (Sundheim) | 0001747057 |
| Viking Global (Halvorsen) | 0001103804 |
| AQR Capital (Asness) | 0001167557 |
---
## Pipeline
```
GitHub Actions Trigger (14:00 UTC, 14th-20th of target months)
│
├─ date_check.py → Is today the right run date?
├─ fetch_filings.py → SEC EDGAR: fetch 13F XMLs for all 13 CIKs
│ + CUSIP→Ticker mapping (OpenFIGI)
│ + Stock split detection (yfinance)
├─ parse_13f.py → Delta vs. prior quarter, portfolio weights
├─ scoring.py → Conviction scores, clustering detection
├─ analyze_claude_round1.py → Claude: Top 5 stocks + investment theses
├─ options_lookup.py → Tradier: Real option chains for Top 5
├─ analyze_claude_round2.py → Claude: Select best specific option per stock
└─ send_report.py → HTML report → Gmail
```
---
## Setup
### 1. Fork / Clone this repo
```bash
git clone https://github.com/YOUR_USERNAME/sec-smart-money.git
cd sec-smart-money
```
### 2. Add GitHub Secrets
Go to **Settings → Secrets and variables → Actions → New repository secret**:
| Secret Name | Value |
|---|---|
| `ANTHROPIC_API_KEY` | Your Anthropic API key |
| `TRADIER_API_KEY` | Your Tradier API key (live or sandbox) |
| `GMAIL_ADDRESS` | Your Gmail address (e.g. `you@gmail.com`) |
| `GMAIL_APP_PASSWORD` | Gmail App Password (not your login password) |
**How to create a Gmail App Password:**
1. Go to [myaccount.google.com/security](https://myaccount.google.com/security)
2. Enable 2-Factor Authentication if not already
3. Search for "App passwords"
4. Create a new app password → select "Mail" and "Other (custom)"
5. Copy the 16-character password into the `GMAIL_APP_PASSWORD` secret
**Tradier API:**
- Live account: use `https://api.tradier.com/v1` (default in config.py)
- Paper account: change `TRADIER_BASE_URL` in `src/config.py` to `https://sandbox.tradier.com/v1`
### 3. Configure Tradier Account Type
Edit `src/config.py`:
```python
TRADIER_BASE_URL = "https://api.tradier.com/v1" # Live (default)
# TRADIER_BASE_URL = "https://sandbox.tradier.com/v1" # Paper
```
### 4. Test a Manual Run
Go to **Actions → SEC 13F Quarterly Analysis → Run workflow**
Set `force_run = true` to bypass the date check.
---
## Local Development
```bash
pip install -r requirements.txt
# Set environment variables
export ANTHROPIC_API_KEY="your_key"
export TRADIER_API_KEY="your_key"
export GMAIL_ADDRESS="you@gmail.com"
export GMAIL_APP_PASSWORD="your_app_password"
# Run individual steps
python src/fetch_filings.py
python src/parse_13f.py
python src/scoring.py
python src/analyze_claude_round1.py
python src/options_lookup.py
python src/analyze_claude_round2.py
python src/send_report.py
```
---
## Data Architecture
```
data/holdings/
YYYY-MM-DD_raw_holdings.json ← Raw SEC XML data + CUSIP mapping
YYYY-MM-DD_holdings_parsed.json ← Delta-enriched positions
YYYY-MM-DD_scores.json ← Conviction scores
YYYY-MM-DD_claude_round1.json ← Top 5 stocks from Claude
YYYY-MM-DD_options.json ← Tradier options data
YYYY-MM-DD_final_analysis.json ← Combined analysis
reports/
YYYY-MM-DD_report.html ← Final HTML report (committed to repo)
```
---
## Known Limitations (by design)
| Limitation | Impact | Mitigation |
|---|---|---|
| 13F data is 45 days old | Positions may have changed | Use as idea generator only |
| Long-only AUM denominator | Portfolio weights overstated | Disclaimer in report |
| No shorts/hedges visible | Incomplete picture | Noted in report |
| Stock splits adjusted | High accuracy, not perfect | yfinance split check |
| Options prices at analysis time | Change constantly | Always verify before trading |
---
## Conviction Score Formula
```
Raw Score = (0.40 × portfolio_weight%) + (0.30 × |delta%|)
Cluster Bonus: ×1.5 if 3+ funds buy same ticker
Normalized: min-max scaled to [0, 100]
```
**Flags:**
- `HIGH_CONVICTION`: New position ≥3% of portfolio
- `NEW_POSITION`: Not in prior quarter's filing
- `AGGRESSIVE_ADD`: Position increased >20% by share count
- `CLUSTER`: 3+ monitored funds buying same ticker simultaneously
- `TOP10_ENTRY`: New position entered directly in top 10 holdings
---
## Disclaimer
This tool is for educational and informational purposes only. 13F data is publicly
available but delayed. Nothing in this repository constitutes investment advice.
Options trading involves significant risk of loss. Always conduct your own research
before making any investment decisions.
================================================
FILE: data/holdings/2026-04-08_claude_round1.json
================================================
{
"analysis_date": "2026-04-08",
"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.",
"top5": [
{
"rank": 1,
"ticker": "PLTR",
"company_name": "PALANTIR TECHNOLOGIES INC",
"conviction_score": 100.0,
"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.",
"key_buyers": [
"TCI Fund (Chris Hohn)"
],
"cluster_signal": false,
"primary_flag": "HIGH_CONVICTION",
"risk_factors": "Extreme concentration risk given 66% portfolio weight; government contract dependency and competitive AI landscape present execution risks.",
"direction": "BULLISH"
},
{
"rank": 2,
"ticker": "AMZN",
"company_name": "AMAZON COM INC",
"conviction_score": 21.0,
"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.",
"key_buyers": [
"Harvard Management Co",
"Pershing Square (Ackman)",
"Tiger Global (Coleman)",
"D1 Capital (Sundheim)",
"Viking Global (Halvorsen)",
"AQR Capital (Asness)"
],
"cluster_signal": true,
"primary_flag": "CLUSTER",
"risk_factors": "Regulatory scrutiny and competitive pressures in cloud computing; mature e-commerce growth may limit upside.",
"direction": "BULLISH"
},
{
"rank": 3,
"ticker": "NVDA",
"company_name": "NVIDIA CORPORATION",
"conviction_score": 19.8,
"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.",
"key_buyers": [
"Harvard Management Co",
"TCI Fund (Chris Hohn)",
"Tiger Global (Coleman)",
"D1 Capital (Sundheim)",
"AQR Capital (Asness)"
],
"cluster_signal": true,
"primary_flag": "CLUSTER",
"risk_factors": "High valuation multiples and potential AI bubble concerns; semiconductor cyclicality and geopolitical tensions around China markets.",
"direction": "BULLISH"
},
{
"rank": 4,
"ticker": "BRK/A",
"company_name": "BERKSHIRE HATHAWAY INC DEL",
"conviction_score": 41.3,
"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.",
"key_buyers": [
"Gates Foundation Trust"
],
"cluster_signal": false,
"primary_flag": "HIGH_CONVICTION",
"risk_factors": "Succession concerns post-Buffett era; large cash position may drag returns in bull markets; concentrated exposure to specific sectors.",
"direction": "BULLISH"
},
{
"rank": 5,
"ticker": "IBIT",
"company_name": "ISHARES BITCOIN TRUST ETF",
"conviction_score": 18.7,
"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.",
"key_buyers": [
"Harvard Management Co",
"Brown University"
],
"cluster_signal": true,
"primary_flag": "CLUSTER",
"risk_factors": "Extreme Bitcoin volatility and regulatory uncertainty; ETF structure risks and potential crypto market manipulation concerns.",
"direction": "BULLISH"
}
],
"disclaimer": "This analysis is based on delayed 13F data and is for informational purposes only, not investment advice."
}
================================================
FILE: data/holdings/2026-04-08_final_analysis.json
================================================
{
"date": "2026-04-08",
"round1_top5": [
{
"rank": 1,
"ticker": "PLTR",
"company_name": "PALANTIR TECHNOLOGIES INC",
"conviction_score": 100.0,
"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.",
"key_buyers": [
"TCI Fund (Chris Hohn)"
],
"cluster_signal": false,
"primary_flag": "HIGH_CONVICTION",
"risk_factors": "Extreme concentration risk given 66% portfolio weight; government contract dependency and competitive AI landscape present execution risks.",
"direction": "BULLISH"
},
{
"rank": 2,
"ticker": "AMZN",
"company_name": "AMAZON COM INC",
"conviction_score": 21.0,
"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.",
"key_buyers": [
"Harvard Management Co",
"Pershing Square (Ackman)",
"Tiger Global (Coleman)",
"D1 Capital (Sundheim)",
"Viking Global (Halvorsen)",
"AQR Capital (Asness)"
],
"cluster_signal": true,
"primary_flag": "CLUSTER",
"risk_factors": "Regulatory scrutiny and competitive pressures in cloud computing; mature e-commerce growth may limit upside.",
"direction": "BULLISH"
},
{
"rank": 3,
"ticker": "NVDA",
"company_name": "NVIDIA CORPORATION",
"conviction_score": 19.8,
"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.",
"key_buyers": [
"Harvard Management Co",
"TCI Fund (Chris Hohn)",
"Tiger Global (Coleman)",
"D1 Capital (Sundheim)",
"AQR Capital (Asness)"
],
"cluster_signal": true,
"primary_flag": "CLUSTER",
"risk_factors": "High valuation multiples and potential AI bubble concerns; semiconductor cyclicality and geopolitical tensions around China markets.",
"direction": "BULLISH"
},
{
"rank": 4,
"ticker": "BRK/A",
"company_name": "BERKSHIRE HATHAWAY INC DEL",
"conviction_score": 41.3,
"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.",
"key_buyers": [
"Gates Foundation Trust"
],
"cluster_signal": false,
"primary_flag": "HIGH_CONVICTION",
"risk_factors": "Succession concerns post-Buffett era; large cash position may drag returns in bull markets; concentrated exposure to specific sectors.",
"direction": "BULLISH"
},
{
"rank": 5,
"ticker": "IBIT",
"company_name": "ISHARES BITCOIN TRUST ETF",
"conviction_score": 18.7,
"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.",
"key_buyers": [
"Harvard Management Co",
"Brown University"
],
"cluster_signal": true,
"primary_flag": "CLUSTER",
"risk_factors": "Extreme Bitcoin volatility and regulatory uncertainty; ETF structure risks and potential crypto market manipulation concerns.",
"direction": "BULLISH"
}
],
"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.",
"options_recs": [
{
"rank": 1,
"stock_ticker": "AMZN",
"company_name": "Amazon Com Inc",
"option_symbol": "AMZN260821C00250000",
"option_type": "CALL",
"strike": 250.0,
"expiration": "2026-08-21",
"entry_price_mid": 9.7,
"max_risk_per_contract": 970.0,
"investment_thesis_link": "Six elite institutions' coordinated buying signals broad consensus on Amazon's AI cloud dominance, creating strong institutional momentum.",
"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.",
"profit_target": "+75-100% on option premium if stock moves +12-18%",
"stop_loss": "-50% on option premium",
"key_risk": "Regulatory scrutiny and intense cloud competition could limit upside momentum despite institutional support.",
"greeks_note": "Available"
},
{
"rank": 2,
"stock_ticker": "NVDA",
"company_name": "Nvidia Corporation",
"option_symbol": "NVDA260918C00200000",
"option_type": "CALL",
"strike": 200.0,
"expiration": "2026-09-18",
"entry_price_mid": 12.73,
"max_risk_per_contract": 1273.0,
"investment_thesis_link": "Five top-tier funds including TCI and Harvard view current levels as attractive entry points for AI chip monopoly play.",
"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.",
"profit_target": "+60-80% on option premium if stock moves +15-20%",
"Stop_loss": "-50% on option premium",
"key_risk": "High valuation multiples vulnerable to AI sentiment shifts and geopolitical semiconductor tensions.",
"greeks_note": "Available"
},
{
"rank": 3,
"stock_ticker": "PLTR",
"company_name": "Palantir Technologies Inc",
"option_symbol": "PLTR260821C00150000",
"option_type": "CALL",
"strike": 150.0,
"expiration": "2026-08-21",
"entry_price_mid": 17.18,
"max_risk_per_contract": 1718.0,
"investment_thesis_link": "TCI Fund's extraordinary 66% portfolio allocation demonstrates unprecedented conviction in Palantir's AI platform transformation potential.",
"option_rationale": "Delta 0.55 provides strong directional exposure matching highest conviction thesis. 4+ month expiry allows government/enterprise adoption cycles to develop.",
"profit_target": "+80-120% on option premium if stock moves +20-25%",
"stop_loss": "-50% on option premium",
"key_risk": "Extreme concentration risk from TCI's 66% allocation creates vulnerability to any execution disappointments.",
"greeks_note": "Available"
},
{
"rank": 4,
"stock_ticker": "IBIT",
"company_name": "iShares Bitcoin Trust ETF",
"option_symbol": "IBIT260918C00045000",
"option_type": "CALL",
"strike": 45.0,
"expiration": "2026-09-18",
"entry_price_mid": 3.63,
"max_risk_per_contract": 363.0,
"investment_thesis_link": "Harvard and Brown's simultaneous crypto ETF adoption marks institutional legitimization of Bitcoin as asset class diversifier.",
"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.",
"profit_target": "+100-150% on option premium if Bitcoin rallies +25-35%",
"stop_loss": "-60% on option premium due to crypto volatility",
"key_risk": "Extreme Bitcoin volatility and regulatory uncertainty could trigger sharp reversals despite institutional adoption.",
"greeks_note": "Available"
},
{
"rank": 5,
"stock_ticker": "BRK/A",
"company_name": "Berkshire Hathaway Inc Del",
"option_symbol": "N/A",
"option_type": "N/A",
"strike": 0,
"expiration": "N/A",
"entry_price_mid": 0,
"max_risk_per_contract": 0,
"investment_thesis_link": "Gates Foundation's 27.6% allocation signals defensive value play, but no options available for leveraged exposure.",
"option_rationale": "No options data available for BRK/A - would require direct equity exposure to capture Gates Foundation's flight-to-quality thesis.",
"profit_target": "N/A - Direct equity purchase required",
"stop_loss": "N/A",
"key_risk": "Unable to implement leveraged strategy without available options market.",
"greeks_note": "N/A"
}
],
"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.",
"disclaimer": "Options involve significant risk. This is based on delayed 13F data and real-time option prices may differ. Not investment advice."
}
================================================
FILE: data/holdings/2026-04-08_holdings_parsed.json
================================================
{
"date": "2026-04-08",
"prior_date": null,
"is_first_run": true,
"recent_splits": {},
"filers": {
"Situational Awareness LP": {
"cik": "0002045724",
"reported_aum_k": 5516758345,
"filing_date": "2026-02-11",
"is_amendment": false,
"position_count": 25,
"positions": [
{
"ticker": "",
"cusip": "21873S108",
"name": "COREWEAVE INC",
"value_usd_k": 1211162272,
"shares": 0,
"putCall": null,
"port_weight_pct": 21.954,
"prior_port_weight": null,
"delta": {
"type": "NEW",
"delta_shares": 0,
"delta_pct": null
},
"is_first_run": true,
"weight_note": "Long-only 13F AUM denominator \u2013 true weight may be lower",
"rank": 1
},
{
"ticker": "",
"cusip": "093712107",
"name": "BLOOM ENERGY CORP",
"value_usd_k": 911000117,
"shares": 0,
"putCall": null,
"port_weight_pct": 16.513,
"prior_port_weight": null,
"delta": {
"type": "NEW",
"delta_shares": 0,
"delta_pct": null
},
"is_first_run": true,
"weight_note": "Long-only 13F AUM denominator \u2013 true weight may be lower",
"rank": 2
},
{
"ticker": "",
"cusip": "458140100",
"name": "INTEL CORP",
"value_usd_k": 746760097,
"shares": 0,
"putCall": null,
"port_weight_pct": 13.536,
"prior_port_weight": null,
"delta": {
"type": "NEW",
"delta_shares": 0,
"delta_pct": null
},
"is_first_run": true,
"weight_note": "Long-only 13F AUM denominator \u2013 true weight may be lower",
"rank": 3
},
{
"ticker": "",
"cusip": "55024U109",
"name": "LUMENTUM HLDGS INC",
"value_usd_k": 478577256,
"shares": 0,
"putCall": null,
"port_weight_pct": 8.675,
"prior_port_weight": null,
"delta": {
"type": "NEW",
"delta_shares": 0,
"delta_pct": null
},
"is_first_run": true,
"weight_note": "Long-only 13F AUM denominator \u2013 true weight may be lower",
"rank": 4
},
{
"ticker": "",
"cusip": "21874A106",
"name": "CORE SCIENTIFIC INC NEW",
"value_usd_k": 418694320,
"shares": 0,
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},
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},
{
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},
{
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},
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"name": "NEWMONT CORP",
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{
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"name": "LOCKHEED MARTIN CORP",
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"w
gitextract_wakc4r5b/
├── .github/
│ └── workflows/
│ └── quarterly_run.yml
├── README.md
├── data/
│ └── holdings/
│ ├── 2026-04-08_claude_round1.json
│ ├── 2026-04-08_final_analysis.json
│ ├── 2026-04-08_holdings_parsed.json
│ ├── 2026-04-08_options.json
│ ├── 2026-04-08_raw_holdings.json
│ ├── 2026-04-08_scores.json
│ ├── 2026-05-18_backtest.json
│ ├── 2026-05-18_claude_round1.json
│ ├── 2026-05-18_final_analysis.json
│ ├── 2026-05-18_holdings_parsed.json
│ ├── 2026-05-18_options.json
│ ├── 2026-05-18_raw_holdings.json
│ └── 2026-05-18_scores.json
├── reports/
│ ├── 2026-04-08_report.html
│ └── 2026-05-18_report.html
├── requirements.txt
└── src/
├── analyze_claude_round1.py
├── analyze_claude_round2.py
├── backtest.py
├── config.py
├── date_check.py
├── fetch_filings.py
├── multi_quarter.py
├── notify.py
├── options_lookup.py
├── parse_13f.py
├── scoring.py
└── send_report.py
SYMBOL INDEX (72 symbols across 11 files) FILE: src/analyze_claude_round1.py function load_scores (line 31) | def load_scores(today_str: str) -> dict: function normalize_ticker (line 39) | def normalize_ticker(ticker: str) -> str: function build_prompt (line 43) | def build_prompt(scores: dict) -> str: function call_claude_with_retry (line 146) | def call_claude_with_retry(prompt: str) -> str: function parse_claude_response (line 176) | def parse_claude_response(raw: str) -> dict: function run (line 187) | def run(): FILE: src/analyze_claude_round2.py function load_round1 (line 31) | def load_round1(today_str: str) -> dict: function load_options (line 39) | def load_options(today_str: str) -> dict: function format_options_for_prompt (line 47) | def format_options_for_prompt(ticker: str, opt_data: dict) -> str: function build_round2_prompt (line 96) | def build_round2_prompt(r1: dict, options_data: dict) -> str: function call_claude_with_retry (line 166) | def call_claude_with_retry(prompt: str) -> str: function parse_response (line 196) | def parse_response(raw: str) -> dict: function validate_result (line 207) | def validate_result(result: dict) -> bool: function run (line 225) | def run(): FILE: src/backtest.py function load_all_analyses (line 25) | def load_all_analyses() -> list[dict]: function _yf_ticker (line 37) | def _yf_ticker(ticker: str) -> str: function check_stock_performance (line 42) | def check_stock_performance(ticker: str, signal_date_str: str) -> dict: function run (line 97) | def run() -> dict: FILE: src/date_check.py function next_business_day (line 26) | def next_business_day(d: date) -> date: function get_expected_run_date (line 33) | def get_expected_run_date(ref: date) -> date: function should_run_today (line 43) | def should_run_today(today: date, force: bool = False, target_override: ... function write_github_output (line 67) | def write_github_output(key: str, value: str): FILE: src/fetch_filings.py function _sleep (line 25) | def _sleep(): function edgar_get (line 29) | def edgar_get(url: str) -> requests.Response: function get_latest_13f_filing (line 38) | def get_latest_13f_filing(cik: str) -> dict | None: function get_filing_files (line 76) | def get_filing_files(cik: str, accession: str) -> list[dict]: function find_infotable_filename (line 115) | def find_infotable_filename(items: list[dict]) -> str | None: function download_infotable (line 140) | def download_infotable(filing_meta: dict) -> str | None: function parse_infotable (line 181) | def parse_infotable(xml_text: str) -> list[dict]: function _openfigi_headers (line 225) | def _openfigi_headers() -> dict: function map_cusips_to_tickers (line 234) | def map_cusips_to_tickers(cusips: list[str]) -> dict[str, str]: function _is_valid_equity_ticker (line 278) | def _is_valid_equity_ticker(ticker: str) -> bool: function check_recent_splits (line 296) | def check_recent_splits(tickers: list[str]) -> dict[str, float]: function run (line 326) | def run(): FILE: src/multi_quarter.py function load_historical_parsed (line 17) | def load_historical_parsed(today_str: str) -> list[dict]: function build_multi_quarter_signals (line 40) | def build_multi_quarter_signals(today_str: str) -> dict[str, dict]: function get_multiplier (line 144) | def get_multiplier(ticker: str, signals: dict[str, dict]) -> float: FILE: src/notify.py function send_alert (line 21) | def send_alert(subject: str, body: str): FILE: src/options_lookup.py function get_headers (line 28) | def get_headers() -> dict: function get_stock_quotes (line 38) | def get_stock_quotes(tickers: list[str], headers: dict) -> dict[str, dict]: function normalize_ticker_for_tradier (line 53) | def normalize_ticker_for_tradier(ticker: str) -> str: function get_expiration_dates (line 57) | def get_expiration_dates(ticker: str, headers: dict) -> list[str]: function get_option_chain (line 81) | def get_option_chain(ticker: str, expiry: str, headers: dict) -> list[di... function filter_options (line 101) | def filter_options(chain: list[dict], direction: str = "BULLISH", function fetch_options_for_ticker (line 192) | def fetch_options_for_ticker(ticker: str, direction: str, headers: dict, function run (line 262) | def run(): FILE: src/parse_13f.py function load_latest_raw (line 21) | def load_latest_raw(today_str: str) -> dict: function load_prior_quarter (line 29) | def load_prior_quarter(today_str: str) -> dict | None: function build_position_lookup (line 67) | def build_position_lookup(filer_data: dict) -> dict: function adjust_shares_for_splits (line 132) | def adjust_shares_for_splits(shares: int, ticker: str, splits: dict) -> ... function compute_delta (line 146) | def compute_delta(current_shares: int, prior_shares: int | None, ticker:... function parse_and_enrich (line 189) | def parse_and_enrich(raw: dict, prior: dict | None) -> dict: function run (line 318) | def run(): FILE: src/scoring.py function load_parsed (line 30) | def load_parsed(today_str: str) -> dict: function fetch_price_changes (line 40) | def fetch_price_changes(tickers: list[str], filing_dates: dict[str, str]... function compute_raw_score (line 135) | def compute_raw_score(pos: dict, filer_name: str) -> float: function apply_flags (line 171) | def apply_flags(pos: dict, rank: int) -> list[str]: function build_scored_universe (line 196) | def build_scored_universe(parsed: dict, mq_signals: dict[str, dict]) -> ... function enrich_with_price_action (line 250) | def enrich_with_price_action(scored: list[dict]) -> list[dict]: function detect_clusters (line 300) | def detect_clusters(scored: list[dict]) -> dict[str, list[str]]: function apply_cluster_bonus (line 325) | def apply_cluster_bonus(scored: list[dict], clusters: dict[str, list[str... function normalize_scores (line 340) | def normalize_scores(scored: list[dict]) -> list[dict]: function aggregate_by_ticker (line 360) | def aggregate_by_ticker(scored: list[dict]) -> list[dict]: function run (line 408) | def run(): FILE: src/send_report.py function load_final_analysis (line 24) | def load_final_analysis(today_str: str) -> dict: function load_backtest (line 32) | def load_backtest(today_str: str) -> dict | None: function flag_badge (line 40) | def flag_badge(flag: str) -> str: function _post_filing_block (line 52) | def _post_filing_block(perf: dict) -> str: function generate_backtest_html (line 101) | def generate_backtest_html(backtest: dict) -> str: function generate_html_report (line 169) | def generate_html_report(analysis: dict, backtest: dict | None = None) -... function send_gmail (line 350) | def send_gmail(html_content: str, today_str: str): function run (line 379) | def run():
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Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (10,271K chars).
[
{
"path": ".github/workflows/quarterly_run.yml",
"chars": 3943,
"preview": "name: SEC 13F Quarterly Analysis\n\non:\n schedule:\n # Run daily at 14:00 UTC (= 15:00 CET / 16:00 CEST)\n # The Pyth"
},
{
"path": "README.md",
"chars": 5292,
"preview": "# SEC 13F Smart Money Analyzer\n\nAutomated quarterly analysis of 13F filings from 13 top institutional investors.\nRuns au"
},
{
"path": "data/holdings/2026-04-08_claude_round1.json",
"chars": 4521,
"preview": "{\n \"analysis_date\": \"2026-04-08\",\n \"market_context\": \"The market appears to be experiencing significant institutional "
},
{
"path": "data/holdings/2026-04-08_final_analysis.json",
"chars": 9363,
"preview": "{\n \"date\": \"2026-04-08\",\n \"round1_top5\": [\n {\n \"rank\": 1,\n \"ticker\": \"PLTR\",\n \"company_name\": \"PALAN"
},
{
"path": "data/holdings/2026-04-08_holdings_parsed.json",
"chars": 2074004,
"preview": "{\n \"date\": \"2026-04-08\",\n \"prior_date\": null,\n \"is_first_run\": true,\n \"recent_splits\": {},\n \"filers\": {\n \"Situat"
},
{
"path": "data/holdings/2026-04-08_options.json",
"chars": 19204,
"preview": "{\n \"date\": \"2026-04-08\",\n \"top5_tickers\": [\n \"PLTR\",\n \"AMZN\",\n \"NVDA\",\n \"BRK/A\",\n \"IBIT\"\n ],\n \"option"
},
{
"path": "data/holdings/2026-04-08_raw_holdings.json",
"chars": 5528159,
"preview": "{\n \"date\": \"2026-04-08\",\n \"cusip_to_ticker\": {\n \"89854H102\": \"TTEC\",\n \"316092790\": \"FQAL\",\n \"97717Y642\": \"NTS"
},
{
"path": "data/holdings/2026-04-08_scores.json",
"chars": 238599,
"preview": "{\n \"date\": \"2026-04-08\",\n \"scored_flat\": [\n {\n \"filer\": \"Situational Awareness LP\",\n \"ticker\": \"\",\n "
},
{
"path": "data/holdings/2026-05-18_backtest.json",
"chars": 3630,
"preview": "{\n \"summary\": {\n \"generated_at\": \"2026-05-18\",\n \"total_signals\": 10,\n \"completed_90d\": 0,\n \"completed_180d\""
},
{
"path": "data/holdings/2026-05-18_claude_round1.json",
"chars": 6448,
"preview": "{\n \"analysis_date\": \"2026-05-18\",\n \"market_context\": \"Institutional investors are showing strong synchronized convicti"
},
{
"path": "data/holdings/2026-05-18_final_analysis.json",
"chars": 16568,
"preview": "{\n \"date\": \"2026-05-18\",\n \"round1_top5\": [\n {\n \"rank\": 1,\n \"ticker\": \"NVDA\",\n \"company_name\": \"NVIDI"
},
{
"path": "data/holdings/2026-05-18_holdings_parsed.json",
"chars": 423984,
"preview": "{\n \"date\": \"2026-05-18\",\n \"prior_date\": \"2026-04-08\",\n \"is_first_run\": false,\n \"recent_splits\": {\n \"BDX\": 1.272,\n"
},
{
"path": "data/holdings/2026-05-18_options.json",
"chars": 24514,
"preview": "{\n \"date\": \"2026-05-18\",\n \"top5_tickers\": [\n \"NVDA\",\n \"TSM\",\n \"AVGO\",\n \"BKNG\",\n \"MSFT\"\n ],\n \"options\""
},
{
"path": "data/holdings/2026-05-18_raw_holdings.json",
"chars": 277648,
"preview": "{\n \"date\": \"2026-05-18\",\n \"cusip_to_ticker\": {\n \"097023105\": \"BA\",\n \"099724106\": \"BWA\",\n \"456788108\": \"INFY\","
},
{
"path": "data/holdings/2026-05-18_scores.json",
"chars": 355286,
"preview": "{\n \"date\": \"2026-05-18\",\n \"scored_flat\": [\n {\n \"filer\": \"Situational Awareness LP\",\n \"ticker\": \"SMH\",\n "
},
{
"path": "reports/2026-04-08_report.html",
"chars": 25094,
"preview": "<!DOCTYPE html>\n<html>\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scal"
},
{
"path": "reports/2026-05-18_report.html",
"chars": 31400,
"preview": "<!DOCTYPE html>\n<html>\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scal"
},
{
"path": "requirements.txt",
"chars": 67,
"preview": "anthropic>=0.25.0\nrequests>=2.31.0\nyfinance>=0.2.37\nholidays>=0.46\n"
},
{
"path": "src/analyze_claude_round1.py",
"chars": 8159,
"preview": "\"\"\"\nanalyze_claude_round1.py\nClaude API – Round 1: Identify Top 5 investment candidates.\n\nUses claude-haiku (CLAUDE_MODE"
},
{
"path": "src/analyze_claude_round2.py",
"chars": 11476,
"preview": "\"\"\"\nanalyze_claude_round2.py\nClaude API – Round 2: Select the best specific options from real Tradier data.\n\nUses claude"
},
{
"path": "src/backtest.py",
"chars": 6034,
"preview": "\"\"\"\nbacktest.py\nTracks historical performance of past recommendations.\n\nFor each past final_analysis.json, checks what t"
},
{
"path": "src/config.py",
"chars": 5490,
"preview": "\"\"\"\nconfig.py\nCentral configuration for SEC 13F Smart Money Analyzer.\nAll CIKs, thresholds, and API endpoints defined he"
},
{
"path": "src/date_check.py",
"chars": 2895,
"preview": "\"\"\"\ndate_check.py\nDetermines if today is a valid SEC 13F analysis run date.\nTarget dates: 16th of May, Aug, Nov, Feb (Be"
},
{
"path": "src/fetch_filings.py",
"chars": 15023,
"preview": "\"\"\"\nfetch_filings.py\nFetches the latest 13F-HR filings for all configured filers from SEC EDGAR.\n\nUses the EDGAR full-te"
},
{
"path": "src/multi_quarter.py",
"chars": 5446,
"preview": "\"\"\"\nmulti_quarter.py\nAnalyzes multi-quarter position building patterns.\n\nA \"Silent Build\" over 3–4 quarters is a much st"
},
{
"path": "src/notify.py",
"chars": 2573,
"preview": "\"\"\"\nnotify.py\nSends pipeline failure alerts via Gmail.\n\nCalled from GitHub Actions on workflow failure so silent crashes"
},
{
"path": "src/options_lookup.py",
"chars": 11203,
"preview": "\"\"\"\noptions_lookup.py\nFetches real options chains from Tradier for the Top 5 tickers.\n\nImprovements:\n - Spread filter t"
},
{
"path": "src/parse_13f.py",
"chars": 13286,
"preview": "\"\"\"\nparse_13f.py\nComputes quarter-over-quarter deltas from raw holdings data.\n\nFixes from architecture audit:\n R-01 St"
},
{
"path": "src/scoring.py",
"chars": 16482,
"preview": "\"\"\"\nscoring.py\nConviction Score calculation engine.\n\nImprovements:\n - Multi-quarter position building bonus (multi_quar"
},
{
"path": "src/send_report.py",
"chars": 17674,
"preview": "\"\"\"\nsend_report.py\nGenerates the HTML report and sends it via Gmail.\n\nFixes from architecture audit:\n R-14 Validates r"
}
]
About this extraction
This page contains the full source code of the pcctradinginc-alt/SEC-QUATERLY GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (8.7 MB), approximately 2.3M tokens, and a symbol index with 72 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.