Smaug

Monitors SEC EDGAR Form 4 filings in near real-time, detects insider buy clusters, sends Slack alerts, and optionally executes trades via Alpaca. Copying the idea from insidercopytrading.com. Available at insidercopytradingcopy.com.

Architecture

EDGAR (Form 4 feed)
      |
      v
ingestion/edgar_poller.py    -- polls every 10 min, dedupes by accession
ingestion/sec_bulk_ingest.py -- bulk historical ingest via quarterly form.idx archives
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      v
ingestion/form4_parser.py    -- parses XML, detects 10b5-1 plans, extracts tx_code
      |
      v
db/models.py + db/db.py      -- SQLAlchemy ORM: filings, signals, price_cache tables
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      v
signals/filter_engine.py     -- buy-only, open-market (P) only, exclude 10b5-1,
signals/cluster_detector.py    min $50k, role-weighted scoring, as-of-date aware
      |
      +---> alerts/slack_alert.py   -- POST to Slack webhook when score >= threshold
      +---> broker/alpaca_client.py -- paper/live order (NOT FULLY IMPLEMENTED -- see Results)

backtest/backtest.py         -- per-signal return / alpha vs SPY
backtest/simulate.py         -- portfolio simulation with configurable transaction costs
backtest/plot.py             -- HP sweep heatmap + equity curve plots

Usage

pip install -r requirements.txt
cp .env.example .env  # fill in credentials

# Live polling (every 10 min)
python main.py run

# Bulk-ingest historical filings (2 years took ~3 days at SEC's 10 req/s rate limit)
python main.py backfill --years 2023 2024
python main.py backfill --year 2024 --quarter 1

# Per-signal backtest: win rate, alpha vs SPY
python main.py backtest

# Portfolio simulation with transaction cost modelling
python main.py simulate [options]

# Generate HP heatmap + equity curve plots (saves to plots/)
python main.py plot

Simulate options

Strategy:
  --holding-days N                  Days to hold each position (default: 7)
  --buy-delay N                     Days after signal to enter (default: 1)
  --position-size F                 Fraction of available cash per trade (default: 0.10)
  --min-score F                     Minimum signal score (default: 0.0)
  --min-cluster N                   Minimum cluster size (default: 1)
  --cap-tier large|mid|small|micro  Filter by market cap tier (default: all)
  --capital F                       Initial capital (default: 100000)

Transaction costs (Alpaca has zero commission, set --commission 0):
  --spread F            One-way bid-ask half-spread at entry and exit (default: 0.003)
  --slippage F          Entry slippage / market impact (default: 0.002)
  --commission F        Per-trade commission as fraction of notional (default: 0.001)

Round-trip = spread x 2 + slippage + commission x 2.

Cap tiers: large >$10B, mid $2-10B, small $300M-2B, micro <$300M. Market caps are fetched from yfinance on first use and cached in the DB.

Setup

Variable	Default	Description
SLACK_WEBHOOK_URL		Incoming webhook URL for alerts
ALPACA_KEY		Alpaca API key
ALPACA_SECRET		Alpaca API secret
ALPACA_BASE_URL	https://paper-api.alpaca.markets	Paper or live endpoint
DB_PATH	insider.db	SQLite database path

Key config (config.py)

Parameter	Default	Description
MIN_TRANSACTION_VALUE	$50,000	Ignore buys below this
MIN_CLUSTER_SIZE	1	Unique insiders before a signal fires
CLUSTER_WINDOW_DAYS	30	Rolling window for cluster counting
HOLDING_PERIOD_DAYS	90	Days held per position
POSITION_SIZE_PCT	2%	Fraction of portfolio per trade
SCORE_ALERT_THRESHOLD	5.0	Minimum score to trigger alert

Scoring

score = role_weight * log(total_value) * (1 + 0.5 * (cluster_size - 1))

Role weights: CEO 3.0, CFO/President 2.5, COO 2.0, Director 1.5, VP 1.2, 10% owner 1.0

No Hosted Version

Yeah, no. There is actually no hosted version available of Smaug. Bazinga! Read along to learn why. If you still want to run it yourself, see Usage.

Results

16,279 signals from 302k Form 4 filings (2020-2025).

Per-signal stats (pre-cost)

Hold	Avg return	Alpha vs SPY	Sharpe	Win rate
3d	+0.61%	+0.52%	~0.80	~53%
7d	+1.19%	+0.68%	~1.05	~54%
14d	+1.41%	+0.55%	~0.90	~54%
30d	+1.89%	+0.41%	~0.70	~54%

The signal exists. It just does not survive transaction costs.

Portfolio simulation (7d hold, 1d delay, 10% of cash per signal)

Alpaca charges $0 commission on US equities. Real costs are spread + slippage only. Cost estimates based on SEC small-cap liquidity research and Alpaca documentation. Simulated on 2020-2025 data, 7d hold, 1d entry delay, 10% of cash per signal:

Cap tier	Signals	RT cost	Ann. return	vs SPY
Large (>$10B)	4,098	~0.2%	+2.4%	-20.0%
Mid ($2-10B)	3,537	~0.5%	+0.9%	-15.1%
Small ($300M-2B)	3,871	~1.5%	see plot	see plot
Micro (<$300M)	5,048	~5% (if listed)	see plot	see plot

Note on micro-cap: Alpaca does not allow opening new positions in OTC/Pink Sheet stocks (close-only). Most micro-cap signals involve OTC-listed names that are simply not tradeable. For exchange-listed micro-caps, realistic round-trip costs are ~5% or more based on SEC spread data — the simulated alpha disappears entirely at that cost level.

About insidercopytrading.com

Their website advertises backtested returns that significantly outperform the market. Those numbers cannot be replicated in practice because the backtesting methodology omits the costs that matter most:

• Same-day entry at the closing price of the filing date — a price you cannot buy at as a retail trader.
• No spread or slippage — SEC data shows small/micro-cap bid-ask spreads of 0.5-2%+ each way.
• Survivorship bias — signals for stocks that later delisted or became untradeable are excluded from their results but would have been part of your portfolio.

Under realistic assumptions, the strategy underperforms SPY across all tested parameters. insidercopytrading.com advertises performance numbers that their own subscribers cannot reproduce. Their website is rather pretty though, and their subscription revenue is presumably real.

Alpaca integration exists in the codebase (broker/alpaca_client.py) but is not fully implemented or tested.

Modules

Path	Purpose
config.py	Thresholds and env-var loading
ingestion/edgar_poller.py	EDGAR Atom feed polling
ingestion/sec_bulk_ingest.py	Bulk historical ingest via form.idx
ingestion/form4_parser.py	Form 4 XML parser; 10b5-1 detection
db/models.py	SQLAlchemy ORM models (Filing, Signal, PriceCache, TickerMeta)
db/db.py	DB access layer
signals/filter_engine.py	Filing to signal pipeline
signals/cluster_detector.py	Cluster detection
alerts/slack_alert.py	Slack webhook
broker/alpaca_client.py	Alpaca order execution
backtest/backtest.py	Per-signal backtest
backtest/simulate.py	Portfolio simulator with cap-tier filtering
backtest/plot.py	Plot generator
main.py	CLI: run / backfill / backtest / simulate / plot

Requirements

Python 3.11+. See requirements.txt.