Quick Start

Basic Usage

Get the earnings calendar for a specific date:

import pykabu_calendar as cal

# Get calendar for February 10, 2026
df = cal.get_calendar("2026-02-10")

print(df[["code", "name", "datetime", "inferred_datetime"]])

Output:

   code             name            datetime   inferred_datetime
0  1414   ショーボンドホールディングス 2026-02-10 15:30:00 2026-02-10 15:30:00
1  1446           キャンディル 2026-02-10 15:30:00 2026-02-10 15:30:00
2  167A  リョーサン菱洋ホールディングス 2026-02-10 16:00:00 2026-02-10 16:00:00
...

Selecting Sources

By default, all sources are used (SBI, Matsui, Tradersweb). For faster results without browser automation:

# Use only lightweight sources (no Playwright needed)
df = cal.get_calendar("2026-02-10", sources=["matsui", "tradersweb"])

# Use only SBI (requires Playwright)
df = cal.get_calendar("2026-02-10", sources=["sbi"])

# Check SBI-specific datetime
print(df[["code", "name", "datetime", "sbi_datetime"]])

Note: SBI requires Playwright and may be slightly slower than other sources.

Without Historical Inference

For faster results, disable historical inference:

# Faster, but less accurate
df = cal.get_calendar("2026-02-10", infer_from_history=False)

Viewing Past Datetimes

Each row includes historical earnings announcement times:

df = cal.get_calendar("2026-02-10")

# Check past earnings times for first company
row = df.iloc[0]
print(f"Company: {row['name']}")
print(f"Past announcements: {row['past_datetimes']}")

Output:

Company: ショーボンドホールディングス
Past announcements: [Timestamp('2025-11-10 15:30:00'), Timestamp('2025-08-12 15:30:00'), ...]

Candidate Datetimes

The candidate_datetimes column shows all possible times, ordered by confidence:

df = cal.get_calendar("2026-02-10")

# When inferred matches source data, it's listed first (high confidence)
print(df[["code", "name", "candidate_datetimes"]].head())

Export to CSV

df = cal.get_calendar("2026-02-10")

# Use the built-in export function (handles list columns)
cal.export_to_csv(df, "earnings_2026-02-10.csv")

# Or use pandas directly
df.to_csv("earnings.csv", index=False, encoding="utf-8-sig")

The utf-8-sig encoding ensures Excel and Google Sheets handle Japanese characters correctly.

Output Columns

Column	Description
`code`	Stock code (e.g., "7203")
`name`	Company name
`datetime`	Best guess announcement datetime
`candidate_datetimes`	List of candidate datetimes (most likely first)
`sbi_datetime`	Datetime from SBI
`matsui_datetime`	Datetime from Matsui
`tradersweb_datetime`	Datetime from Tradersweb
`inferred_datetime`	Datetime inferred from history
`past_datetimes`	List of past earnings datetimes

Using Individual Scrapers

You can also use individual scrapers directly:

import pykabu_calendar as cal

# Matsui (lightweight)
df_matsui = cal.get_matsui("2026-02-10")

# Tradersweb (lightweight)
df_tradersweb = cal.get_tradersweb("2026-02-10")

# SBI (requires Playwright)
df_sbi = cal.get_sbi("2026-02-10")