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Insider Trades: Track SEC Form 4 Insider Buying and Selling with Python

Know when insiders buy or sell their own company's stock. SEC Forms 3, 4, and 5 disclose every transaction by officers, directors, and major shareholders. EdgarTools parses these filings into structured Python objects with computed insights like net position change and trading plan detection.

from edgar import Company

snow = Company("SNOW")
filing = snow.get_filings(form=4).latest(1)
form4 = filing.obj()
form4

Form 4 insider trade parsed with Python edgartools showing Snowflake director sale

Three lines to see who traded, what they traded, and the net impact on their position.

See Snowflake's insider trading activity on edgar.tools — 186K+ filings pre-parsed →

Get the Transaction Summary

The get_ownership_summary() method returns a TransactionSummary with computed properties that answer the questions you actually care about.

summary = form4.get_ownership_summary()

summary.insider_name          # "Bruce I. Sachs"
summary.position              # "Director"
summary.primary_activity      # "Purchase", "Sale", "Option Exercise", etc.
summary.net_change            # 15000 (positive = bought, negative = sold)
summary.net_value             # 3260400.0 (net dollar value of trades)
summary.remaining_shares      # 36599
Property What it tells you
primary_activity One-word categorization: Purchase, Sale, Tax Withholding, Grant/Award, Option Exercise, Mixed
net_change Net shares bought minus sold -- the single most important number
net_value Net dollar value of all transactions
remaining_shares Insider's position after all transactions
transaction_types List of unique activity types in this filing
has_non_derivatives Whether any common stock was traded

Detect Automated Trading Plans

The has_10b5_1_plan property tells you whether trades were pre-scheduled under a Rule 10b5-1 plan. This matters because pre-scheduled sales are less informative than discretionary ones.

summary.has_10b5_1_plan       # True, False, or None

# True  = trade executed under a 10b5-1 plan (automated, less signal)
# False = footnotes exist but no plan mentioned (discretionary)
# None  = no footnotes available

EdgarTools detects this by scanning transaction footnotes for 10b5-1 references -- something that would require manual XML parsing otherwise.

Access Individual Transactions

For transaction-level detail, use the filtered DataFrame properties on the Form4 object itself.

form4.market_trades              # All open-market buys and sells
form4.common_stock_purchases     # Just the buys
form4.common_stock_sales         # Just the sells
form4.shares_traded              # Total shares across all market trades

The market_trades DataFrame includes these columns:

Column What it is
Date Transaction date
Security Security title
Shares Number of shares
Price Price per share
Remaining Shares owned after this transaction
AcquiredDisposed "A" (acquired) or "D" (disposed)
Code Transaction code (P = purchase, S = sale)

Track Option Exercises and Derivatives

Insider filings often include option exercises, RSU conversions, and other derivative transactions.

form4.option_exercises           # Transactions with exercise code
form4.derivative_trades          # All derivative transactions

The derivative table includes exercise price, expiration date, and underlying security information.

Convert to DataFrame

The to_dataframe() method gives you full control over output format.

Detailed view (one row per transaction)

df = form4.to_dataframe()

Summary view (one row per filing)

df = form4.to_dataframe(detailed=False)

This aggregates everything into a single row with computed columns like Net Change, Net Value, Primary Activity, and per-type breakdowns (Purchase Shares, Avg Purchase Price, Sale Shares, etc.). Useful for building datasets across thousands of filings.

Strip metadata

df = form4.to_dataframe(include_metadata=False)

Removes the filing-level columns (Date, Form, Issuer, Ticker, Insider, Position) when you only need transaction data.

Initial Ownership (Form 3)

When an insider first joins a company, they file a Form 3 disclosing what they already own. EdgarTools parses these into the same object hierarchy.

filing = Company("HROW").get_filings(form=3).latest(1)
form3 = filing.obj()
summary = form3.get_ownership_summary()    # Returns InitialOwnershipSummary

summary.total_shares          # Total non-derivative shares owned
summary.has_derivatives       # True if they hold options/warrants
summary.holdings              # List of SecurityHolding objects

Form 3 initial beneficial ownership parsed with Python edgartools showing Harrow insider holdings

Each SecurityHolding in the list has:

Property What it is
security_title Name of the security
shares Number of shares or units
direct_ownership True if directly owned
ownership_description "Direct" or "Indirect (reason)"
is_derivative Whether this is a derivative holding
exercise_price Exercise price (derivatives only)
expiration_date Expiration date (derivatives only)

Look Up a Specific Insider

from edgar import Company

apple = Company("AAPL")

# All insider filings (Forms 3, 4, 5)
filings = apple.get_filings(form=[3, 4, 5])

# Just Form 4s
form4_filings = apple.get_filings(form=4)

# Latest transaction
latest = form4_filings.latest(1).obj()
print(f"{latest.insider_name} ({latest.position}): {latest.get_ownership_summary().primary_activity}")

Common Analysis Patterns

Find large purchases

from edgar import get_filings

filings = get_filings(form=4)
for f in filings[:20]:
    form4 = f.obj()
    if form4:
        summary = form4.get_ownership_summary()
        if summary.net_change > 10000:
            print(f"{summary.insider_name} bought {summary.net_change:,} shares of {summary.issuer}")

Filter out automated sales

summary = form4.get_ownership_summary()
if summary.has_10b5_1_plan is False:
    # Discretionary trade -- potentially more informative
    print(f"{summary.primary_activity}: {summary.net_change:,} shares")

Build a dataset across filings

import pandas as pd

filings = Company("AAPL").get_filings(form=4)
rows = []
for f in filings[:50]:
    form4 = f.obj()
    if form4:
        rows.append(form4.to_dataframe(detailed=False))

df = pd.concat(rows, ignore_index=True)

See this on edgar.tools

The code above parses individual Form 4 filings. edgar.tools connects 186K+ insider filings and 802K+ transactions into a searchable intelligence layer with sentiment analysis.

Includes net buy/sell sentiment, executive profiles, and cross-filing linkages to 8-K material events. Free tier available. Pricing →

Metadata Quick Reference

Property Returns Example
form Form type "4"
reporting_period Transaction date "2024-01-18"
insider_name Reporting insider "Bruce I. Sachs"
position Role at company "Director"
issuer.name Company name "VERTEX PHARMACEUTICALS INC"
issuer.ticker Ticker symbol "VRTX"
issuer.cik Company CIK "875320"
no_securities No securities owned False
remarks Filing remarks ""
shares_traded Total shares in market trades 15000

Methods Quick Reference

Call Returns What it does
form4.get_ownership_summary() TransactionSummary or InitialOwnershipSummary Computed summary with net change, activity type, 10b5-1 detection
form4.get_transaction_activities() list[TransactionActivity] All transactions as structured objects
form4.to_dataframe() DataFrame Full transaction data, one row per trade
form4.to_dataframe(detailed=False) DataFrame Single summary row with aggregated metrics
form4.market_trades DataFrame Open-market buys and sells only
form4.common_stock_purchases DataFrame Filtered to acquisitions
form4.common_stock_sales DataFrame Filtered to dispositions
form4.option_exercises DataFrame Option exercise transactions
form4.derivative_trades DataHolder All derivative transactions
form4.extract_form3_holdings() list[SecurityHolding] Holdings from Form 3 filings
form4.to_html() str HTML representation

Things to Know

Form 3 vs 4 vs 5. Form 3 is initial ownership (when someone becomes an insider). Form 4 is changes (buys, sells, grants). Form 5 is an annual catch-up for anything not reported on Form 4. Most analysis focuses on Form 4.

Transaction codes. P = open-market purchase, S = open-market sale, M = option exercise, A = grant/award, F = tax withholding, G = gift, C = conversion. The primary_activity property translates these for you.

Footnotes contain critical context. Prices, share counts, and 10b5-1 plan disclosures often live in footnotes, not the transaction table. EdgarTools resolves footnote references automatically.

Derivative transactions are complex. Option exercises often pair with a same-day sale. The derivative_trades property keeps these separate from common stock transactions.

Amended filings (3/A, 4/A, 5/A). EdgarTools handles amendments transparently -- they parse identically to the original form types.