Analyze Form D Exempt Offerings using Python

   

On this page:

• Quick Start
• Download all Exempt Offerings from Form D
• Loading the Form D Exempt Offerings
• Exempt Offerings Over Time
  • Offerings by Year
  • Offerings by Quarter
  • Offerings by Month
• Top 20 Largest Offerings by Amount Offered
  • Aggregated Annual Offering Amounts by Year
• Types of Securities Offered
• Industry Groups
  • Offerings per Industry Group over Time
  • Types of Pooled Investment Funds
• Issuer Size: Revenue Range and Net Asset Values

This page provides a step-by-step guide on analyzing Form D exempt offerings using Python and the Form D API. Form D filings are submitted by companies that raise capital through private placements under Regulation D, allowing them to sell securities without full SEC registration.

What You Will Learn

• Use the Form D API to retrieve structured data from offerings filed with the SEC betbween 2008 and 2024.
• Understand offering trends over time by visualizing the number of offerings per year, quarter and month.
• Find the top 20 largest offerings by amount raised
• Determine the types of securities offered
• And more...

The example demonstrates how to retrieve Form D exempt offerings using the .get_data(search_params) method of the FormDApi class from the sec-api Python package. The script queries the database for offerings with a total offering amount of $1,000,000 or more, sorts them by the most recent filings, and returns the 10 most recent results.

!pip install sec-api

import json
from sec_api import FormDApi

formDApi = FormDApi("YOUR_API_KEY")

search_params = {
    "query": "offeringData.offeringSalesAmounts.totalOfferingAmount:[1000000 TO *]",
    "from": "0", # starting position of the result set
    "size": "10", # number of results to be returned. max: 50
    "sort": [{"filedAt": {"order": "desc"}}],
}

response = formDApi.get_data(search_params)
offerings = response["offerings"]

print(json.dumps(offerings[0], indent=2))

{
  "schemaVersion": "X0708",
  "submissionType": "D",
  "testOrLive": "LIVE",
  "primaryIssuer": {
    "cik": "0002057923",
    "entityName": "U.S. Venture Partners Select Fund I-A, L.P.",
    "issuerAddress": {
      "street1": "1460 EL CAMINO REAL, SUITE 100",
      "city": "MENLO PARK",
      "stateOrCountry": "CA",
      "stateOrCountryDescription": "CALIFORNIA",
      "zipCode": "94025"
    },
    "issuerPhoneNumber": "650-854-9080",
    "jurisdictionOfInc": "DELAWARE",
    "issuerPreviousNameList": [
      {
        "value": "None"
      }
    ],
    "edgarPreviousNameList": [
      {
        "value": "None"
      }
    ],
    "entityType": "Limited Partnership",
    "yearOfInc": {
      "withinFiveYears": true,
      "value": "2025"
    }
  },
  "relatedPersonsList": {
    "relatedPersonInfo": [
      {
        "relatedPersonName": {
          "firstName": "Richard",
          "middleName": "W",
          "lastName": "Lewis"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      },
      {
        "relatedPersonName": {
          "firstName": "Jonathan",
          "middleName": "D",
          "lastName": "Root"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      },
      {
        "relatedPersonName": {
          "firstName": "Casey",
          "lastName": "Tansey"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      },
      {
        "relatedPersonName": {
          "firstName": "Dafina",
          "lastName": "Toncheva"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      }
    ]
  },
  "offeringData": {
    "industryGroup": {
      "industryGroupType": "Pooled Investment Fund",
      "investmentFundInfo": {
        "investmentFundType": "Venture Capital Fund",
        "is40Act": false
      }
    },
    "issuerSize": {
      "revenueRange": "Not Applicable"
    },
    "federalExemptionsExclusions": {
      "item": [
        "06b",
        "3C",
        "3C.1"
      ]
    },
    "typeOfFiling": {
      "newOrAmendment": {
        "isAmendment": false
      },
      "dateOfFirstSale": {
        "yetToOccur": true
      }
    },
    "durationOfOffering": {
      "moreThanOneYear": false
    },
    "typesOfSecuritiesOffered": {
      "isPooledInvestmentFundType": true
    },
    "businessCombinationTransaction": {
      "isBusinessCombinationTransaction": false
    },
    "minimumInvestmentAccepted": 0,
    "salesCompensationList": {},
    "offeringSalesAmounts": {
      "totalOfferingAmount": 10000000,
      "totalAmountSold": 0,
      "totalRemaining": 10000000
    },
    "investors": {
      "hasNonAccreditedInvestors": false,
      "totalNumberAlreadyInvested": 0
    },
    "salesCommissionsFindersFees": {
      "salesCommissions": {
        "dollarAmount": 0
      },
      "findersFees": {
        "dollarAmount": 0
      }
    },
    "useOfProceeds": {
      "grossProceedsUsed": {
        "dollarAmount": 0
      }
    },
    "signatureBlock": {
      "authorizedRepresentative": false,
      "signature": [
        {
          "issuerName": "U.S. Venture Partners Select Fund I-A, L.P.",
          "signatureName": "/s/ Dale Holladay",
          "nameOfSigner": "Dale Holladay",
          "signatureTitle": "Chief Financial Officer",
          "signatureDate": "2025-02-25"
        }
      ]
    }
  },
  "accessionNo": "0002057923-25-000001",
  "filedAt": "2025-02-25T17:26:03-05:00",
  "id": "32055cb99476602d44b381101ebf9fa1"
}

Downloading the complete Form D exempt offerings dataset may take up to 20 minutes and requires approximately 2.6GB of disk space. The final dataset includes over 600,000 records spanning from 2008 to 2024 and is saved in JSONL format as form-d-dataset.jsonl.

import os
import random
import sys
import time
from concurrent.futures import ThreadPoolExecutor

YEARS = range(2024, 2007, -1) # from 2024 to 2008
TEMP_FILE_TEMPLATE = "./temp_file_form_d_{}.jsonl"
TARGET_FILE = "./form-d-dataset.jsonl"


def process_year(year):
    backoff_time = random.randint(10, 800) / 1000
    print(f"Starting year {year} with backoff time {backoff_time:,}s")
    sys.stdout.flush()
    time.sleep(backoff_time)

    tmp_filename = TEMP_FILE_TEMPLATE.format(year)
    tmp_file = open(tmp_filename, "w")

    for month in range(12, 0, -1):
        search_from = 0
        month_counter = 0

        while True:
            query = f"filedAt:[{year}-{month:02d}-01 TO {year}-{month:02d}-31]"
            search_params = {
                "query": query,
                "from": search_from,
                "size": "50",
                "sort": [{"filedAt": {"order": "desc"}}],
            }

            response = None
            try:
                response = formDApi.get_data(search_params)
            except Exception as e:
                print(f"{year}-{month:02d} error: {e}. Retrying...")
                sys.stdout.flush()
                continue

            if response == None or len(response["offerings"]) == 0:
                break

            if response["total"]["value"] >= 10000:
                print(
                    f"WARNING: {year}-{month:02d} has more than 10,000 Form D filings."
                )
                sys.stdout.flush()

            search_from += 50
            month_counter += len(response["offerings"])
            jsonl_data = "\n".join(
                [json.dumps(entry) for entry in response["offerings"]]
            )
            tmp_file.write(jsonl_data + "\n")

        print(f"Finished loading {month_counter} Form D for {year}-{month:02d}")
        sys.stdout.flush()

    tmp_file.close()

    return year


if not os.path.exists(TARGET_FILE):
    with ThreadPoolExecutor(max_workers=4) as pool:
        processed_years = list(pool.map(process_year, YEARS))
    print("Finished processing all years.", processed_years)

    # merge the temporary files into one final file
    with open(TARGET_FILE, "a") as outfile:
        for year in YEARS:
            temp_file = TEMP_FILE_TEMPLATE.format(year)
            if os.path.exists(temp_file):
                with open(temp_file, "r") as infile:
                    outfile.write(infile.read())
else:
    print("File already exists. Skipping download.")

File already exists. Skipping download.

import pandas as pd
import numpy as np

def process_chunk(df_chunk):
    return pd.json_normalize(df_chunk.to_dict(orient="records"))


df_chunks = [
    process_chunk(chunk)
    for chunk in pd.read_json(TARGET_FILE, lines=True, chunksize=100000)
]

structured_data = pd.concat(df_chunks, ignore_index=True)

print(structured_data.shape)

(673962, 71)

structured_data["filedAt"] = pd.to_datetime(structured_data["filedAt"], utc=True)
structured_data["filedAt"] = structured_data["filedAt"].dt.tz_convert("US/Eastern")
structured_data = structured_data.sort_values("filedAt", ascending=True).reset_index(
    drop=True
)
structured_data["year"] = structured_data["filedAt"].dt.year
structured_data["month"] = structured_data["filedAt"].dt.month
structured_data["qtr"] = structured_data["month"].apply(lambda x: (x - 1) // 3 + 1)
structured_data["dayOfWeek"] = structured_data["filedAt"].dt.day_name()
# filedAtClass: preMarket (4:00AM-9:30AM), regularMarket (9:30AM-4:00PM), afterMarket (4:00PM-8:00PM)
structured_data["filedAtClass"] = structured_data["filedAt"].apply(
    lambda x: (
        "preMarket"
        if x.hour < 9 or (x.hour == 9 and x.minute < 30)
        else (
            "regularMarket"
            if x.hour < 16
            else "afterMarket" if x.hour < 20 else "other"
        )
    )
)

structured_data.head()

	schemaVersion	submissionType	testOrLive	accessionNo	filedAt	id	issuerList	primaryIssuer.cik	primaryIssuer.entityName	primaryIssuer.issuerAddress.street1	...	offeringData.typesOfSecuritiesOffered.isMineralPropertyType	primaryIssuer.yearOfInc.yetToBeFormed	offeringData.salesCompensationList.over100RecipientFlag	relatedPersonsList.over100PersonsFlag	issuerList.over100IssuerFlag	year	month	qtr	dayOfWeek	filedAtClass
0	X0101	D	LIVE	0001445388-08-000001	2008-09-17 10:14:41-04:00	fe0a1228846ceb5480b82521df779de7	NaN	0001445388	Cause Global Impact Fund, LP	1266 EAST MAIN STREET	...	NaN	NaN	NaN	NaN	NaN	2008	9	3	Wednesday	regularMarket
1	X0101	D	LIVE	0001348938-08-000003	2008-09-18 17:57:02-04:00	43e701d16ff1097e33db9a31a3f7218d	NaN	0001348938	Living Independently Group, Inc.	767 THIRD AVE	...	NaN	NaN	NaN	NaN	NaN	2008	9	3	Thursday	afterMarket
2	X0101	D	LIVE	0001445444-08-000001	2008-09-19 13:18:32-04:00	2ead4c7e5bc390b8dae552ae6b832c30	NaN	0001445444	new york atv - sportsman club llc	240 WILLOW DRIVE	...	NaN	NaN	NaN	NaN	NaN	2008	9	3	Friday	regularMarket
3	X0101	D	LIVE	0001354488-08-001636	2008-09-19 18:21:59-04:00	b3d0f624fc2992121605e7ed54fd0c17	NaN	0001445579	Fox Life Settlements 2008-F, LLC	2129 N. JOSEY LANE	...	NaN	NaN	NaN	NaN	NaN	2008	9	3	Friday	afterMarket
4	X0101	D	LIVE	0001445773-08-000002	2008-09-22 17:15:22-04:00	fe1f4adb26cf65f5e059d2f4e6c70bdb	NaN	0001445773	Viking Financial Services Corp	4 NICKERSON STREET	...	NaN	NaN	NaN	NaN	NaN	2008	9	3	Monday	afterMarket

5 rows × 76 columns

print(structured_data.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 673962 entries, 0 to 673961
Data columns (total 76 columns):
 #   Column                                                                        Non-Null Count   Dtype                     
---  ------                                                                        --------------   -----                     
 0   schemaVersion                                                                 673962 non-null  object                    
 1   submissionType                                                                673962 non-null  object                    
 2   testOrLive                                                                    673962 non-null  object                    
 3   accessionNo                                                                   673962 non-null  object                    
 4   filedAt                                                                       673962 non-null  datetime64[ns, US/Eastern]
 5   id                                                                            673962 non-null  object                    
 6   issuerList                                                                    0 non-null       float64                   
 7   primaryIssuer.cik                                                             673962 non-null  object                    
 8   primaryIssuer.entityName                                                      673962 non-null  object                    
 9   primaryIssuer.issuerAddress.street1                                           673962 non-null  object                    
 10  primaryIssuer.issuerAddress.city                                              673962 non-null  object                    
 11  primaryIssuer.issuerAddress.stateOrCountry                                    673958 non-null  object                    
 12  primaryIssuer.issuerAddress.stateOrCountryDescription                         641363 non-null  object                    
 13  primaryIssuer.issuerAddress.zipCode                                           673836 non-null  object                    
 14  primaryIssuer.issuerPhoneNumber                                               673953 non-null  object                    
 15  primaryIssuer.jurisdictionOfInc                                               673260 non-null  object                    
 16  primaryIssuer.issuerPreviousNameList                                          612987 non-null  object                    
 17  primaryIssuer.edgarPreviousNameList                                           658658 non-null  object                    
 18  primaryIssuer.entityType                                                      673962 non-null  object                    
 19  primaryIssuer.yearOfInc.overFiveYears                                         185008 non-null  object                    
 20  relatedPersonsList.relatedPersonInfo                                          673962 non-null  object                    
 21  offeringData.industryGroup.industryGroupType                                  673962 non-null  object                    
 22  offeringData.issuerSize.revenueRange                                          433179 non-null  object                    
 23  offeringData.federalExemptionsExclusions.item                                 673962 non-null  object                    
 24  offeringData.typeOfFiling.newOrAmendment.isAmendment                          673962 non-null  bool                      
 25  offeringData.typeOfFiling.dateOfFirstSale.value                               579646 non-null  object                    
 26  offeringData.durationOfOffering.moreThanOneYear                               673962 non-null  bool                      
 27  offeringData.typesOfSecuritiesOffered.isDebtType                              52078 non-null   object                    
 28  offeringData.typesOfSecuritiesOffered.isOptionToAcquireType                   45443 non-null   object                    
 29  offeringData.typesOfSecuritiesOffered.isOtherType                             73755 non-null   object                    
 30  offeringData.typesOfSecuritiesOffered.descriptionOfOtherType                  73755 non-null   object                    
 31  offeringData.businessCombinationTransaction.isBusinessCombinationTransaction  673962 non-null  bool                      
 32  offeringData.minimumInvestmentAccepted                                        673962 non-null  int64                     
 33  offeringData.offeringSalesAmounts.totalOfferingAmount                         673962 non-null  int64                     
 34  offeringData.offeringSalesAmounts.totalAmountSold                             673962 non-null  int64                     
 35  offeringData.offeringSalesAmounts.totalRemaining                              673962 non-null  int64                     
 36  offeringData.investors.hasNonAccreditedInvestors                              673962 non-null  bool                      
 37  offeringData.investors.totalNumberAlreadyInvested                             673962 non-null  int64                     
 38  offeringData.salesCommissionsFindersFees.salesCommissions.dollarAmount        673962 non-null  int64                     
 39  offeringData.salesCommissionsFindersFees.findersFees.dollarAmount             673962 non-null  int64                     
 40  offeringData.useOfProceeds.grossProceedsUsed.dollarAmount                     673962 non-null  int64                     
 41  offeringData.useOfProceeds.grossProceedsUsed.isEstimate                       173076 non-null  object                    
 42  offeringData.signatureBlock.authorizedRepresentative                          663003 non-null  object                    
 43  offeringData.signatureBlock.signature                                         673962 non-null  object                    
 44  primaryIssuer.issuerAddress.street2                                           362271 non-null  object                    
 45  offeringData.typeOfFiling.dateOfFirstSale.yetToOccur                          94271 non-null   object                    
 46  offeringData.typesOfSecuritiesOffered.isEquityType                            351392 non-null  object                    
 47  primaryIssuer.yearOfInc.withinFiveYears                                       487041 non-null  object                    
 48  primaryIssuer.yearOfInc.value                                                 487046 non-null  object                    
 49  offeringData.typesOfSecuritiesOffered.isSecurityToBeAcquiredType              33004 non-null   object                    
 50  offeringData.industryGroup.investmentFundInfo.investmentFundType              352185 non-null  object                    
 51  offeringData.industryGroup.investmentFundInfo.is40Act                         352185 non-null  object                    
 52  offeringData.typeOfFiling.newOrAmendment.previousAccessionNumber              261449 non-null  object                    
 53  offeringData.typesOfSecuritiesOffered.isPooledInvestmentFundType              357613 non-null  object                    
 54  offeringData.useOfProceeds.clarificationOfResponse                            218401 non-null  object                    
 55  offeringData.investors.numberNonAccreditedInvestors                           50701 non-null   float64                   
 56  offeringData.issuerSize.aggregateNetAssetValueRange                           240783 non-null  object                    
 57  offeringData.salesCompensationList.recipient                                  151729 non-null  object                    
 58  offeringData.offeringSalesAmounts.clarificationOfResponse                     90326 non-null   object                    
 59  primaryIssuer.entityTypeOtherDesc                                             68545 non-null   object                    
 60  offeringData.salesCommissionsFindersFees.salesCommissions.isEstimate          101935 non-null  object                    
 61  issuerList.issuer                                                             6073 non-null    object                    
 62  offeringData.salesCommissionsFindersFees.clarificationOfResponse              71985 non-null   object                    
 63  offeringData.salesCommissionsFindersFees.findersFees.isEstimate               64687 non-null   object                    
 64  offeringData.typesOfSecuritiesOffered.isTenantInCommonType                    775 non-null     object                    
 65  offeringData.businessCombinationTransaction.clarificationOfResponse           7761 non-null    object                    
 66  offeringData.typesOfSecuritiesOffered.isMineralPropertyType                   1742 non-null    object                    
 67  primaryIssuer.yearOfInc.yetToBeFormed                                         1913 non-null    object                    
 68  offeringData.salesCompensationList.over100RecipientFlag                       91 non-null      object                    
 69  relatedPersonsList.over100PersonsFlag                                         1 non-null       object                    
 70  issuerList.over100IssuerFlag                                                  1 non-null       object                    
 71  year                                                                          673962 non-null  int32                     
 72  month                                                                         673962 non-null  int32                     
 73  qtr                                                                           673962 non-null  int64                     
 74  dayOfWeek                                                                     673962 non-null  object                    
 75  filedAtClass                                                                  673962 non-null  object                    
dtypes: bool(4), datetime64[ns, US/Eastern](1), float64(2), int32(2), int64(9), object(58)
memory usage: 367.6+ MB
None

unique_years = structured_data["year"].nunique()
unique_issuers = structured_data["primaryIssuer.cik"].nunique()
unique_filings = structured_data["accessionNo"].nunique()
min_year = structured_data["year"].min()
max_year = structured_data["year"].max()
max_year_full = max_year  # to avoid incomplete data for the current year
print("Loaded Form D filings")
print(f"Number of filings: {unique_filings:,}")
print(f"Number of years: {unique_years:,} ({min_year}-{max_year})")
print(f"Number of unique issuers: {unique_issuers:,}")

Loaded Form D filings
Number of filings: 673,962
Number of years: 17 (2008-2024)
Number of unique issuers: 312,008

import matplotlib.pyplot as plt
import matplotlib.style as style
import matplotlib.ticker as mtick
import seaborn as sns

style.use("default")

params = {
    "axes.labelsize": 8, "font.size": 8, "legend.fontsize": 8,
    "xtick.labelsize": 8, "ytick.labelsize": 8, "font.family": "sans-serif",
    "axes.spines.top": False, "axes.spines.right": False, "grid.color": "grey",
    "axes.grid": True, "axes.grid.axis": "y", "grid.alpha": 0.5, "grid.linestyle": ":",
}

plt.rcParams.update(params)

submissionTypeCount = structured_data["submissionType"].value_counts().to_frame()
submissionTypeCount.columns = ["count"]
submissionTypeCount["pct"] = (
    submissionTypeCount["count"] / submissionTypeCount["count"].sum() * 100
)
submissionTypeCount["pct"] = submissionTypeCount["pct"].round(1)
submissionTypeCount

	count	pct
submissionType
D	412513	61.2
D/A	261449	38.8

form_d = structured_data[structured_data["submissionType"] == "D"]
form_d_a = structured_data[structured_data["submissionType"] == "D/A"]

counts_d = form_d.groupby(["year"]).size().to_frame(name="count")
counts_d_a = form_d_a.groupby(["year"]).size().to_frame(name="count")

print(f"Form D counts from {min_year} to {max_year_full}.")
counts_d.T

Form D counts from 2008 to 2024.

year	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
count	379	13770	17586	18170	18190	19841	22194	23030	23097	24589	27272	27502	28147	42506	41671	31868	32701

def plot_timeseries(ts, title):
    fig, ax = plt.subplots(figsize=(4, 2.5))
    ts["count"].plot(ax=ax, legend=False)
    ax.set_title(title)
    ax.set_xlabel("Year")
    ax.set_ylabel("Number of\nForm D Filings")
    ax.set_xticks(np.arange(min_year, max_year_full + 1, 1))
    ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
    ax.set_xlim(min_year - 1, max_year_full + 1)
    ax.grid(axis="x")
    ax.set_axisbelow(True)
    plt.xticks(rotation=90)

    for year in range(min_year, max_year_full + 1, 1):
        year_y_max = ts.loc[year, "count"]
        ax.vlines(year, 0, year_y_max, linestyles=":", colors="grey", alpha=0.5, lw=1)

    plt.tight_layout()
    plt.show()


plot_timeseries(
    counts_d,
    title=f"Form D Disclosures per Year ({min_year} - {max_year_full})",
)

plot_timeseries(
    counts_d_a,
    title=f"Form D/A Disclosures per Year ({min_year} - {max_year_full})",
)

counts_qtr_yr_piv = (form_d.groupby(["year", "qtr"]).size().unstack().fillna(0)).astype(
    int
)

print(f"Form D counts by quarter from {min_year} to {max_year_full}.")
counts_qtr_yr_piv.T

Form D counts by quarter from 2008 to 2024.

year	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
qtr
1	0	1547	4649	4848	4970	5031	5546	5786	6107	6405	6992	6761	7218	9303	12465	8320	7924
2	0	3886	4403	4460	4572	4568	5504	5780	5757	6047	6803	7025	5958	10513	10764	8095	8248
3	36	4126	4104	4506	4238	4942	5435	5679	5448	5834	6587	6889	6776	10498	9558	7735	8027
4	343	4211	4430	4356	4410	5300	5709	5785	5785	6303	6890	6827	8195	12192	8884	7718	8502

plt.figure(figsize=(6, 2))
sns.heatmap(
    counts_qtr_yr_piv.T,
    annot=True,
    fmt="d",
    cmap="magma",
    cbar_kws={"label": "Count"},
    mask=counts_qtr_yr_piv.T == 0,
    cbar=False,
    annot_kws={"fontsize": 6},
)
plt.grid(False)
plt.title(f"Form D Counts by Quarter ({min_year} - {max_year_full})")
plt.xlabel("Year")
plt.ylabel("Quarter")
plt.tight_layout()
plt.show()

counts_qtr_yr = counts_qtr_yr_piv.stack().reset_index(name="count")

fig, ax = plt.subplots(figsize=(6, 2.5))
counts_qtr_yr_piv.plot(kind="bar", ax=ax, legend=True)
ax.legend(title="Quarter", loc="upper right", bbox_to_anchor=(1.15, 1))
ax.set_title(f"Number of Form D Disclosures per Quarter\n({min_year}-{max_year_full})")
ax.set_xlabel("Year")
ax.set_ylabel("Number of\nForm D Filings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()

counts_month_yr_piv = (
    form_d.groupby(["year", "month"]).size().unstack().fillna(0)
).astype(int)

print(
    f"Descriptive statistics for Form D counts by month from 2009 to {max_year_full}."
)
month_stats = (
    counts_month_yr_piv.loc[2009:]
    .describe(percentiles=[0.025, 0.975])
    .round(0)
    .astype(int)
)
month_stats

Descriptive statistics for Form D counts by month from 2009 to 2024.

month	1	2	3	4	5	6	7	8	9	10	11	12
count	16	16	16	16	16	16	16	16	16	16	16	16
mean	2264	1873	2355	2180	2100	2119	2189	2129	1956	2207	2005	2381
std	839	754	857	766	702	647	649	669	611	706	632	814
min	174	218	1155	1346	1231	1309	1459	1289	1246	1384	1298	1481
2.5%	723	617	1349	1380	1266	1370	1466	1327	1249	1388	1322	1499
50%	2170	1846	2220	1946	1878	2049	2036	2003	1812	2030	1950	2207
97.5%	3676	3347	4274	3822	3495	3480	3486	3444	3216	3590	3356	4052
max	4106	3666	4693	3977	3624	3670	3562	3482	3454	3973	3685	4534

fig, ax = plt.subplots(figsize=(3.5, 3))

counts_month_yr_piv.loc[2009:].boxplot(
    ax=ax,
    grid=False,
    showfliers=False,
    flierprops=dict(marker="o", markersize=3),
    patch_artist=True,
    boxprops=dict(facecolor="white", color="tab:blue"),
    showmeans=True,
    meanline=True,
    meanprops={"color": "tab:blue", "linestyle": ":"},
    medianprops={"color": "black"},
    capprops={"color": "none"},
)

ax.set_title(f"Form D Filings by Month\n({min_year} - {max_year_full})")
ax.set_xlabel("")
ax.set_ylabel("Form D Count")
xticklabels = [pd.to_datetime(str(x), format="%m").strftime("%b") for x in range(1, 13)]
ax.set_xticklabels(xticklabels)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

counts_month_yr_piv_d_a = (
    form_d_a.groupby(["year", "month"]).size().unstack().fillna(0)
).astype(int)

print(
    f"Descriptive statistics for Form D/A counts by month from 2009 to {max_year_full}."
)
month_stats = (
    counts_month_yr_piv_d_a.loc[2009:]
    .describe(percentiles=[0.025, 0.975])
    .round(0)
    .astype(int)
)
month_stats

Descriptive statistics for Form D/A counts by month from 2009 to 2024.

month	1	2	3	4	5	6	7	8	9	10	11	12
count	16	16	16	16	16	16	16	16	16	16	16	16
mean	1495	1453	2580	1404	1208	1200	1290	1113	1101	1264	1034	1194
std	542	404	533	423	399	411	392	366	328	354	306	401
min	44	172	2028	510	449	413	642	487	492	546	467	495
2.5%	284	563	2057	627	502	498	681	544	533	604	527	546
50%	1680	1502	2409	1424	1234	1298	1338	1161	1183	1280	1060	1212
97.5%	2065	1923	3750	2041	1836	1797	1835	1771	1556	1749	1449	1788
max	2088	1988	3942	2086	1884	1830	1899	1836	1580	1842	1484	1866

fig, ax = plt.subplots(figsize=(3.5, 3))

counts_month_yr_piv_d_a.loc[2009:].boxplot(
    ax=ax,
    grid=False,
    showfliers=False,
    flierprops=dict(marker="o", markersize=3),
    patch_artist=True,
    boxprops=dict(facecolor="white", color="tab:blue"),
    showmeans=True,
    meanline=True,
    meanprops={"color": "tab:blue", "linestyle": ":"},
    medianprops={"color": "black"},
    capprops={"color": "none"},
)

ax.set_title(f"Form D/A Filings by Month\n({min_year} - {max_year_full})")
ax.set_xlabel("")
ax.set_ylabel("Form D/A Count")
xticklabels = [pd.to_datetime(str(x), format="%m").strftime("%b") for x in range(1, 13)]
ax.set_xticklabels(xticklabels)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

pd.options.display.float_format = "{:,.0f}".format

col = "offeringData.offeringSalesAmounts.totalOfferingAmount"

top_20 = (
    form_d.sort_values(by=col, ascending=False)
    .head(20)
    .sort_values(by=col, ascending=True)
    .reset_index(drop=True)
)
top_20["year"] = top_20["filedAt"].dt.year
# cut names to 50 characters
top_20["primaryIssuer.entityName"] = top_20["primaryIssuer.entityName"].str.slice(0, 50)

years = top_20["filedAt"].dt.year.to_list()

top_20.plot(
    kind="barh",
    x="primaryIssuer.entityName",
    y=col,
    figsize=(6, 3.5),
)

ax = plt.gca()
for p, year in zip(ax.patches, years):
    ax.annotate(
        year,
        (p.get_x() + p.get_width(), p.get_y()),
        xytext=(0, 0),
        textcoords="offset points",
        fontsize=7,
    )

ax.xaxis.set_major_formatter(
    mtick.FuncFormatter(lambda x, p: format(int(x) / 1000000000, ",.0f"))
)
ax.grid(True)
ax.set_axisbelow(True)
ax.grid(color="gray", linestyle="dashed", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.grid(False), ax.legend().remove()
plt.title(f"Top 20 Largest Exempt Offerings by Total Proceeds\n({min_year} - {max_year})")
plt.xlabel("Total Proceeds (USD, in billions)")
plt.ylabel("")
plt.tight_layout()
plt.show()

print(f"Top 20 Largest Offerings by Total Offering Amounts (in $ billions, {min_year} - {max_year})")
top_20["offeringAmount"] = top_20[col] / 1_000_000_000
top_20[["primaryIssuer.entityName", "offeringAmount", "year"]].sort_values(
    by="offeringAmount", ascending=False
).reset_index(drop=True)

Top 20 Largest Offerings by Total Offering Amounts (in $ billions, 2008 - 2024)

	primaryIssuer.entityName	offeringAmount	year
0	1B USD WORLDWIDE VENTURE CAPITAL FUND	1,000	2017
1	Royal Bank of Amexem Trust	999	2021
2	Grace & Trust S.a.r.l.	950	2018
3	Curtis Gregory Smith Junior ACH Thrift Fund 05...	900	2021
4	JAAME AMUN RE EL	900	2011
5	Shaykamaxum Republic Tribal Government	900	2021
6	Crusade of Connor Stephens, LLC	640	2018
7	Reginald Cupid Noble ACH Investment Thrift Fun...	541	2021
8	MEL FREE EL	500	2016
9	JOHN ADAMS SMART EL	500	2016
10	PERI AL HEDRO CARTA DEY	500	2015
11	One World Bancorp, Inc.	500	2022
12	HELENE HOLLOMAN CHIN EL	500	2016
13	One World Bancorp, Inc.	500	2024
14	ESTATE OF HAZINE CHERMON CHERY	400	2018
15	BYRON DEVON LYNCH IV ESTATE	400	2020
16	Mcclain Zachary Christian	400	2022
17	ZACHARY CHRISTIAN MCCLAIN	400	2024
18	T&B Imports International Inc	200	2021
19	National Investment & Infrastructure Fund	160	2021

total = form_d[["year", col]].copy()
total.fillna(0, inplace=True)
total = total.apply(pd.to_numeric, errors="coerce").fillna(0)
total

	year	offeringData.offeringSalesAmounts.totalOfferingAmount
0	2008	300000000
1	2008	11498347
2	2008	500000
3	2008	3300000
4	2008	10000000
...	...	...
673956	2024	26000
673958	2024	-1
673959	2024	2500000
673960	2024	2000000
673961	2024	5000000

412513 rows × 2 columns

total_by_year = total.groupby("year").sum()
total_by_year.plot(kind="bar", figsize=(4, 3))
plt.title(f"Annual Form D Offering Amounts per Year\n({min_year} - {max_year})")
plt.ylabel("Offering Amount\n(USD, in billions)"); plt.xlabel("")
ax = plt.gca()
ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x) / 1000000000, ",.0f")))
ax.get_legend().remove(), ax.grid(), ax.set_axisbelow(True)
ax.grid(True); ax.set_axisbelow(True); ax.grid(color="gray", linestyle="dashed", alpha=0.3)
ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False); ax.xaxis.grid(False)
plt.setp(ax.get_xticklabels(), fontsize=7); plt.tight_layout(); plt.show()

typesOfSecuritiesOfferedCols = [col for col in form_d.columns if "typesOfSecuritiesOffered" in col]
typesOfSecuritiesOfferedCols.remove("offeringData.typesOfSecuritiesOffered.descriptionOfOtherType")
typesOfSecuritiesOffered = form_d[["year", *typesOfSecuritiesOfferedCols]]
typesOfSecuritiesOffered = typesOfSecuritiesOffered.groupby("year").count()
typesOfSecuritiesOffered.columns = [col.split(".")[-1] for col in typesOfSecuritiesOffered.columns]

print("Types of Securities Offered by Year")
typesOfSecuritiesOffered

Types of Securities Offered by Year

	isDebtType	isOptionToAcquireType	isOtherType	isEquityType	isSecurityToBeAcquiredType	isPooledInvestmentFundType	isTenantInCommonType	isMineralPropertyType
year
2008	51	50	46	239	25	64	3	3
2009	1848	1936	1798	8632	1092	3174	33	119
2010	2226	2510	2167	11616	1527	4034	22	152
2011	2299	2349	2298	12211	1522	4227	51	149
2012	2377	2217	2267	12076	1530	4461	31	146
2013	2671	2160	2203	12890	1522	5272	27	134
2014	3021	2314	2314	14555	1667	5924	28	175
2015	3126	2258	2454	14796	1586	6653	44	82
2016	2972	2260	2240	14966	1689	6867	58	36
2017	2801	2348	2481	16130	1727	7742	37	44
2018	2987	2358	3044	17119	1800	9285	44	60
2019	2975	2390	2733	17425	1908	9516	37	60
2020	2721	2415	2821	16698	1945	11564	45	42
2021	2856	2631	3612	22616	2005	21294	57	42
2022	2380	2327	3611	21479	1781	21342	46	65
2023	2158	2240	3039	17865	1621	15122	24	52
2024	1990	2211	3022	18035	1662	16366	32	57

fig, ax = plt.subplots(figsize=(6, 3))

typesOfSecuritiesOffered.loc[2009:].plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Offerings by Type of Security")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Offerings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
ax.legend(title="Type of Security", bbox_to_anchor=(1.02, 1))
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

industryGroupType = form_d["offeringData.industryGroup.industryGroupType"].value_counts().to_frame()
industryGroupType.index.name = "industryGroupType"
industryGroupType["count"] = industryGroupType["count"].apply(lambda x: f"{x:,}")
industryGroupType

	count
industryGroupType
Pooled Investment Fund	149,718
Other	57,952
Other Technology	56,359
Commercial	19,627
Other Health Care	15,923
Other Real Estate	14,889
Residential	12,196
Biotechnology	11,133
REITS and Finance	8,839
Investing	8,283
Oil and Gas	7,906
Manufacturing	6,325
Other Banking and Financial Services	6,160
Retailing	4,529
Other Energy	4,308
Pharmaceuticals	4,004
Business Services	3,546
Restaurants	3,128
Computers	2,990
Commercial Banking	2,629
Insurance	2,575
Telecommunications	2,111
Agriculture	1,961
Hospitals and Physicians	1,027
Construction	995
Lodging and Conventions	591
Energy Conservation	590
Environmental Services	516
Other Travel	375
Investment Banking	353
Tourism and Travel Services	293
Electric Utilities	258
Health Insurance	169
Coal Mining	132
Airlines and Airports	123

topIndustryGroupTypes = industryGroupType.head(10).index.to_list()
topIndustryGroupTypes.remove("Other")

# create piv table for industry group type, year, and count
form_d_top_10_others = form_d.copy()
form_d_top_10_others.loc[
    ~form_d_top_10_others["offeringData.industryGroup.industryGroupType"].isin(
        topIndustryGroupTypes
    ),
    "offeringData.industryGroup.industryGroupType",
] = "Other"
industryGroupTypePiv = (
    form_d_top_10_others.groupby(
        ["offeringData.industryGroup.industryGroupType", "year"]
    )
    .size()
    .unstack()
    .fillna(0)
)
industryGroupTypePiv.index.name = "industryGroupType"
industryGroupTypePiv["total"] = industryGroupTypePiv.sum(axis=1)
industryGroupTypePiv = industryGroupTypePiv.reindex(
    index=topIndustryGroupTypes + ["Other"]
)

print("Offerings by Top 10 Industry Groups per Year")
industryGroupTypePiv.apply(lambda x: x.apply(lambda y: f"{y:,}"), axis=1)

Offerings by Top 10 Industry Groups per Year

year	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	total
industryGroupType
Pooled Investment Fund	68	3,407	4,182	4,304	4,455	5,232	5,844	6,453	6,621	7,487	9,102	9,413	11,355	20,930	20,673	14,500	15,692	149,718
Other Technology	57	1,999	2,579	2,925	2,992	3,210	3,682	3,876	3,778	3,749	4,140	3,879	3,618	4,741	4,260	3,588	3,286	56,359
Commercial	14	279	429	498	563	694	796	1,038	1,201	1,378	1,489	1,737	1,511	2,128	2,386	1,660	1,826	19,627
Other Health Care	23	696	912	920	916	926	1,020	1,068	1,089	1,106	1,095	1,139	1,030	1,185	956	1,011	831	15,923
Other Real Estate	16	317	390	483	547	614	811	874	842	984	1,094	1,282	1,095	1,415	1,521	1,193	1,411	14,889
Residential	10	191	254	284	398	517	593	749	838	830	837	983	868	1,393	1,463	1,074	914	12,196
Biotechnology	11	536	687	638	622	606	661	713	706	746	750	726	783	823	734	729	662	11,133
REITS and Finance	3	93	161	211	346	403	545	508	620	767	693	631	725	718	988	774	653	8,839
Investing	11	367	476	491	394	421	482	460	458	469	507	512	572	794	673	507	689	8,283
Other	166	5,885	7,516	7,416	6,957	7,218	7,760	7,291	6,944	7,073	7,565	7,200	6,590	8,379	8,017	6,832	6,737	115,546

fig, ax = plt.subplots(figsize=(6, 3))

industryGroupTypePiv.T.loc[2009:2024, :].plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Offerings by Industry Group Type")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Offerings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
handles, labels = ax.get_legend_handles_labels()
ax.legend(
    reversed(handles),
    reversed(labels),
    title="Industry Group Type",
    bbox_to_anchor=(1.02, 1),
    labelspacing=0.15,
)
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

form_d_pooledInvestmentFunds = form_d[
    form_d["offeringData.industryGroup.industryGroupType"] == "Pooled Investment Fund"
]
pooledFundTypes = (
    form_d_pooledInvestmentFunds[
        "offeringData.industryGroup.investmentFundInfo.investmentFundType"
    ]
    .value_counts()
    .to_frame()
)
pooledFundTypes["count"] = pooledFundTypes["count"].apply(lambda x: f"{x:,}")
pooledFundTypes.index.name = "Pooled Investment Fund Type"

print("Offerings per Type of Pooled Investment Fund (2008 - 2024)")
pooledFundTypes

Offerings per Type of Pooled Investment Fund (2008 - 2024)

	count
Pooled Investment Fund Type
Venture Capital Fund	42,098
Other Investment Fund	39,549
Private Equity Fund	39,214
Hedge Fund	28,857

pooledFundTypesPiv = (
    form_d_pooledInvestmentFunds.groupby(
        ["offeringData.industryGroup.investmentFundInfo.investmentFundType", "year"]
    )
    .size()
    .unstack()
    .fillna(0)
)
pooledFundTypesPiv.index.name = "Pooled Investment Fund Type"
pooledFundTypesPiv["total"] = pooledFundTypesPiv.sum(axis=1)
pooledFundTypesPiv = pooledFundTypesPiv.sort_values(by="total", ascending=False)

print("Offerings by Pooled Investment Fund Type per Year")
pooledFundTypesPiv.apply(lambda x: x.apply(lambda y: f"{y:,}"), axis=1)

Offerings by Pooled Investment Fund Type per Year

year	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	total
Pooled Investment Fund Type
Venture Capital Fund	5	131	232	328	365	474	682	936	896	925	1,266	2,252	3,300	8,631	9,295	6,003	6,377	42,098
Other Investment Fund	14	803	968	974	1,055	1,257	1,648	1,987	2,088	2,541	3,172	2,722	3,261	5,458	5,001	3,197	3,403	39,549
Private Equity Fund	7	527	840	1,034	1,083	1,390	1,582	1,706	1,936	2,256	2,794	2,948	3,194	4,827	4,736	3,922	4,432	39,214
Hedge Fund	42	1,946	2,142	1,968	1,952	2,111	1,932	1,824	1,701	1,765	1,870	1,491	1,600	2,014	1,641	1,378	1,480	28,857

fig, ax = plt.subplots(figsize=(4, 3))

pooledFundTypesPiv.T.loc[2009:2024, :].plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Offerings by Type of Pooled Investment Fund")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Offerings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
handles, labels = ax.get_legend_handles_labels()
ax.legend(
    reversed(handles),
    reversed(labels),
    title="Fund Type",
    labelspacing=0.15,
)
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()

revenueRange = form_d["offeringData.issuerSize.revenueRange"].value_counts().to_frame()
revenueRange["count"] = revenueRange["count"].apply(lambda x: f"{x:,}")
revenueRange.index.name = "Revenue Range"
print("Issuer Size by Revenue Range")
revenueRange

Issuer Size by Revenue Range

	count
Revenue Range
Decline to Disclose	254,194
No Revenues	39,942
$1-$1,000,000	21,831
Not Applicable	9,899
$1,000,001-$5,000,000	8,643
$5,000,001-$25,000,000	5,307
$25,000,001-$100,000,000	2,228
Over $100,000,000	2,063

netAssetRange = form_d["offeringData.issuerSize.aggregateNetAssetValueRange"].value_counts().to_frame()
netAssetRange["count"] = netAssetRange["count"].apply(lambda x: f"{x:,}")
netAssetRange.index.name = "Net Asset Range"
print("Issuer Size by Net Asset Range")
netAssetRange

Issuer Size by Net Asset Range

	count
Net Asset Range
Decline to Disclose	60,605
$1-$5,000,000	2,294
$5,000,001-$25,000,000	1,397
No Aggregate Net Asset Value	1,281
Not Applicable	1,120
Over $100,000,000	773
$25,000,001-$50,000,000	501
$50,000,001-$100,000,000	435