# Insider Copytrade System -- Implementation Plan

## Description

A personal system that monitors SEC EDGAR Form 4 filings in real-time, filters for high-quality insider buying signals, alerts via Slack, and optionally executes trades automatically through Alpaca's paper or live trading API.

The system is fully self-hosted, uses only free/public data sources, and requires no third-party data subscriptions.

---

## Background

Company insiders (executives, directors, >10% shareholders) must file SEC Form 4 within 2 business days of any trade. This is public data via SEC EDGAR. The signal value of insider *buying* is academically documented -- executives buying their own stock with personal capital is a meaningful vote of confidence, particularly when:

- Multiple insiders buy simultaneously (cluster signal)
- The trade is unplanned (not a 10b5-1 scheduled plan)
- The company is small/mid-cap (less institutional arbitrage)

The edge vs. political trade copying: 2-day disclosure lag vs. 45 days, and the signal is company-specific rather than sector-level.

**Key risk:** This signal is publicly known and tracked. The edge is in filtering quality and execution speed, not data exclusivity. Large-cap Form 4 signals are arbitraged quickly. Focus on small/mid-cap, clustered, unplanned buys.

---

## System Outline

```
SEC EDGAR RSS Feed (poll every 10 min)
        |
   [Ingestion Layer]
        |
   Parse Form 4 XML
        |
   [Filter Engine]
    - Buy only (flag = A)
    - Exclude 10b5-1 plans
    - Min transaction size
    - Role weighting
    - Cluster detection
        |
   SQLite Database
        |
   ┌────────────┬──────────────┐
   |            |              |
[Backtester] [Slack Alert]  [Alpaca API]
             (manual)      (paper/live)
```

---

## Actionables

### Phase 1 -- Data Ingestion

**Goal:** Reliably pull and parse Form 4 filings as they appear.

**Tasks:**

1. Set up project structure
```
insider-copytrade/
  ingestion/
    edgar_poller.py      # polls EDGAR RSS
    form4_parser.py      # parses XML -> structured dict
  db/
    schema.sql
    db.py                # SQLite interface
  signals/
    filter_engine.py     # applies signal filters
    cluster_detector.py
  alerts/
    slack_alert.py
  broker/
    alpaca_client.py
  backtest/
    backtest.py
  config.py
  main.py
```

2. Poll EDGAR RSS for Form 4 filings every 10 minutes:
```
https://www.sec.gov/cgi-bin/browse-edgar?action=getcurrent&type=4&dateb=&owner=include&count=40&search_text=&action=getcurrent
```
SEC also provides a structured latest filings feed:
```
https://efts.sec.gov/LATEST/search-index?q=&forms=4
```

3. For each new filing, fetch and parse the XML document. Key fields to extract:
   - `issuerTradingSymbol` (ticker)
   - `rptOwnerName`, `officerTitle` (insider name + role)
   - `transactionDate`
   - `transactionAcquiredDisposedCode` (A = buy, D = sell)
   - `transactionShares`, `transactionPricePerShare`
   - `transactionTotalValue` (compute if not present)
   - `footnotes` (check for "10b5-1" mention)
   - `sharesOwnedFollowingTransaction`

4. Store raw filing XML + parsed fields. Track `accessionNumber` as dedup key.

**SQLite schema:**
```sql
CREATE TABLE filings (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    accession_number TEXT UNIQUE,
    ticker TEXT,
    cik TEXT,
    insider_name TEXT,
    role TEXT,
    transaction_date TEXT,
    filed_date TEXT,
    shares REAL,
    price REAL,
    total_value REAL,
    flag TEXT,           -- A or D
    is_10b51 INTEGER,    -- 0 or 1
    post_tx_shares REAL,
    created_at TEXT
);

CREATE TABLE signals (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    ticker TEXT,
    trigger_date TEXT,
    cluster_size INTEGER,
    total_cluster_value REAL,
    score REAL,
    alerted INTEGER DEFAULT 0,
    executed INTEGER DEFAULT 0,
    created_at TEXT
);
```

---

### Phase 2 -- Filter Engine

**Goal:** Reduce noise to actionable signals only.

**Filters to apply (in order):**

| Filter | Logic |
|---|---|
| Buy only | `flag == 'A'` |
| Exclude 10b5-1 | Scan footnotes for "10b5-1", "Rule 10b5", "adopted a plan" |
| Min transaction value | `total_value >= 50000` (configurable) |
| Exclude derivative transactions | Options exercises are weaker signal than open market purchases |
| Role weighting | CEO/CFO/President = high; Director = medium; 10% owner = context-dependent |
| Cluster detection | 2+ insiders buying same ticker within 30 days = elevated signal |

**Scoring formula (simple v1):**
```python
score = base_role_weight * log(total_value) * cluster_multiplier
# cluster_multiplier = 1.0 + (0.5 * (cluster_size - 1))
```

Expose all thresholds in `config.py` for easy tuning during backtesting.

---

### Phase 3 -- SQLite Storage

SQLite is sufficient for this workload (low write volume, single process). Use WAL mode for concurrent reads during backtesting:

```python
conn = sqlite3.connect('insider.db')
conn.execute('PRAGMA journal_mode=WAL')
```

Keep raw filing XML in a `/data/filings/` directory keyed by accession number. Parse on ingest, re-parse never needed.

---

### Phase 4 -- Slack Alerts

**Goal:** Get notified immediately when a signal fires, with enough context to decide manually.

1. Create a Slack app, get a webhook URL (takes 5 minutes)
2. Alert format:

```
INSIDER BUY SIGNAL
Ticker:   $ACME
Insider:  John Smith (CEO)
Date:     2025-05-01
Shares:   10,000 @ $14.50 = $145,000
Cluster:  3 insiders in last 14 days
Score:    8.4
10b5-1:   No
EDGAR:    https://www.sec.gov/cgi-bin/browse-edgar?...
```

3. Alert only on signals above configurable score threshold
4. Mark `alerted = 1` in DB after sending to avoid duplicates on re-poll

```python
import requests

def send_slack_alert(webhook_url, signal):
    requests.post(webhook_url, json={"text": format_signal(signal)})
```

---

### Phase 5 -- Backtesting

**Goal:** Validate filter parameters on historical data before going live.

**Data:**
- Historical Form 4 filings: download bulk XML from `https://www.sec.gov/dera/data/form-4-data`
- Price data: `yfinance` (free, sufficient for backtesting)

**Backtest logic:**
```python
# For each signal in historical data:
# - Entry: next market open after filed_date
# - Exit: N days later (configurable: 30/60/90/180)
# - Calculate return vs SPY over same period
# - Aggregate by role, cluster_size, market_cap bucket
```

**Use `vectorbt` for performance:**
```python
import vectorbt as vbt
# Build entry/exit signal matrices aligned to price data
# Run portfolio simulation with configurable position sizing
```

**Output metrics:**
- Annualized return vs SPY benchmark
- Win rate
- Avg return by holding period
- Avg return by role / cluster size
- Max drawdown
- Sharpe ratio

**Critical:** Test on post-2022 data specifically. Pre-2022 results are likely inflated -- the signal became widely tracked after Autopilot/media coverage.

**Parameter grid to test:**
```python
MIN_VALUE = [25_000, 50_000, 100_000]
HOLDING_DAYS = [30, 60, 90, 180]
CLUSTER_WINDOW = [14, 30]
MIN_CLUSTER_SIZE = [1, 2, 3]
ROLES = ['all', 'c-suite-only']
```

---

### Phase 6 -- Alpaca Integration

**Goal:** Optionally auto-execute signals. Start with paper trading.

**Paper trading base URL:** `https://paper-api.alpaca.markets`
**Live trading base URL:** `https://api.alpaca.markets`

Swap via config flag -- never hardcode.

```python
from alpaca_trade_api import REST

api = REST(
    key_id=config.ALPACA_KEY,
    secret_key=config.ALPACA_SECRET,
    base_url=config.ALPACA_BASE_URL  # paper or live
)

def execute_signal(ticker, portfolio_value, signal_score):
    # Fixed fractional sizing: 2% of portfolio per signal
    price = api.get_latest_trade(ticker).price
    allocation = portfolio_value * 0.02
    qty = int(allocation / price)
    if qty < 1:
        return
    api.submit_order(
        symbol=ticker,
        qty=qty,
        side='buy',
        type='market',
        time_in_force='day'
    )
```

Position sizing: start at 2% per signal, max 10% in any single ticker. Add a max open positions limit (e.g. 20) to cap exposure.

Exit logic (v1): time-based only (close after N days). Add trailing stop later.

---

## Build Order

| Step | Deliverable | Est. Time |
|---|---|---|
| 1 | EDGAR poller + Form 4 XML parser + SQLite storage | 1 day |
| 2 | Filter engine + cluster detector | 0.5 day |
| 3 | Slack alert | 1 hour |
| 4 | Historical data download + backtest | 1-2 days |
| 5 | Alpaca paper trading integration | 0.5 day |
| 6 | Run paper trading 4-8 weeks, monitor | -- |
| 7 | Switch to live with small capital | -- |

Do not proceed to Step 7 without meaningful paper trading history.

---

## Dependencies

```
requests
lxml
sqlite3 (stdlib)
yfinance
vectorbt
alpaca-trade-api
python-dotenv
```

All free. No paid APIs required.

---

## Config Template

```python
# config.py
EDGAR_POLL_INTERVAL = 600        # seconds
MIN_TRANSACTION_VALUE = 50_000
MIN_CLUSTER_SIZE = 1             # raise to 2 for higher quality
CLUSTER_WINDOW_DAYS = 30
HOLDING_PERIOD_DAYS = 90
POSITION_SIZE_PCT = 0.02         # 2% per signal
MAX_POSITIONS = 20
SCORE_ALERT_THRESHOLD = 5.0

SLACK_WEBHOOK_URL = ""
ALPACA_KEY = ""
ALPACA_SECRET = ""
ALPACA_BASE_URL = "https://paper-api.alpaca.markets"  # switch for live
```