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Achieve Life Sciences, Inc.: diluted weighted-average shares

Diluted weighted-average shares for Achieve Life Sciences, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Achieve Life Sciences, Inc. financial histories

What this measure means

Weighted-average shares used for diluted earnings per share. Potential shares are included under the applicable dilution rules, not simply added to outstanding shares.

Exact concept: us-gaap:WeightedAverageNumberOfDilutedSharesOutstanding. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2020-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Reading these values

This selected numerical history matches Basic weighted-average shares for the same reporting intervals and original units. The accounting definitions remain distinct. Equal values do not establish that the concepts are interchangeable or explain why they match; filing dates and accessions may differ. Compare the definitions and source filings before combining them.

Context from the filing

For 2023–2025, Achieve reports equal basic and diluted net loss per share and describes excluding potentially issuable shares because they are anti-dilutive. Shares and per-share amounts are exempt from the thousands heading. The note’s excluded-instrument total uses inconsistent million-shares wording alongside individual counts; that narrative total is not the weighted-average denominator and is not substituted here. The selected EPS and weighted-average figures remain as reported. Read the source filing.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-3143,594,652shares2026-03-2410-K · 0001193125-26-120690
2024-01-012024-12-3132,071,146shares2026-03-2410-K · 0001193125-26-120690
2023-01-012023-12-3119,827,534shares2026-03-2410-K · 0001193125-26-120690
2022-01-012022-12-3110,593,034shares2025-03-1110-K · 0000950170-25-036831
2021-01-012021-12-318,119,836shares2024-03-2810-K · 0000950170-24-038049
2020-01-012020-12-312,718,909shares2023-03-1610-K · 0001564590-23-003800

Related financial histories

Inspect the source

Entity
Achieve Life Sciences, Inc. / CIK 0000949858
Captured
2026-09-20T05:10:47.545Z
SEC response SHA-256
99bc74bd7159630b925b01f21dc60b0899467b369bb5a1524cbbfa411e86b911

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000949858.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))