acquisitionuniverse (Python)

Utilities for search funds, independent sponsors and corporate development teams who work from screened company lists. Score records, rank them, measure evidence gaps and generate a clean thesis brief.

Pure Python, standard library only, offline.

pip install acquisitionuniverse

Where it fits

A full-web screen hands you a long ranked file. Before you call anyone you want to answer three questions: what exactly did we ask for, which companies come first, and how much is the file really telling us?

This package answers those with small functions that work on dictionaries and DataFrame rows alike. The method behind the screens is described on the search fund target lists page.

1. Write the brief

from acquisitionuniverse import ThesisBrief

brief = ThesisBrief(
    name="Calibration labs, Midwest",
    vertical="Calibration and metrology",
    regions=["Ohio", "Michigan", "Indiana"],
    employees_min=8,
    employees_max=60,
    must_have=["ISO/IEC 17025 accreditation"],
    exclusions=["group-owned", "single-person shops"],
)

assert brief.validate() == []
print(brief.to_text())

to_json() gives the same content as a string for a ticket or a shared drive.

2. Rank the file

from acquisitionuniverse import rank

companies = [
    {"id": "C-01", "mandate_fit": 92, "outreach_suitability": 75, "transition_context": 60},
    {"id": "C-02", "mandate_fit": 97, "outreach_suitability": 90, "transition_context": 80, "group_owned": True},
    {"id": "C-03", "mandate_fit": 84, "outreach_suitability": 88, "transition_context": 40},
]

for c in rank(companies):
    print(c["id"], c["composite"])

C-02 has the best fit but belongs to a group, so its outreach component is zero and it falls behind C-01. That ordering is the point: you should call the independent first.

Custom weights:

rank(companies, weights={"mandate_fit": 0.6, "outreach_suitability": 0.3, "transition_context": 0.1})

3. Count the evidence

from acquisitionuniverse import SIGNALS, signal_coverage

record = {SIGNALS[0]: "founder led", SIGNALS[3]: "service company", SIGNALS[10]: None}
print(signal_coverage(record))   # {'visible': 2, 'checked': 3, 'ratio': 0.666...}

A value of None or the string "not visible" counts as silent. You see at a glance whether a high score rests on much evidence or little.

With pandas

import pandas as pd
from acquisitionuniverse import composite

df = pd.read_csv("screen.csv")
df["composite"] = df.apply(
    lambda r: composite({
        "mandate_fit": r.mandate_fit,
        "outreach_suitability": r.outreach_suitability,
        "transition_context": r.transition_context,
        "group_owned": r.group_owned == "yes",
    }),
    axis=1,
)
df.sort_values("composite", ascending=False).head(25).to_csv("call_list.csv", index=False)

Reference

Related products

Alpha Quantum also publishes an audience segmentation API for advertisers and a CV parser for hiring teams. Neither is needed here.

Questions

Is a database included? No. Bring your own screened records.

Why dictionaries? They match JSON, CSV rows and pandas without a conversion step.

Python versions? 3.7 and up. MIT license. info@alpha-quantum.com