PHPMem v2.0.1
Version
1.6.45
Uptime
17 days 7 hours 8 minutes 22 seconds
Memory
Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB
Keys
Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0
Connections
Current
15 / 1 024 max
Total
220 934
Rejected
0
llm:40ccb53c3f28a414a96d8ede1589328e62a6615428deb2494a8db16ff6b33c80
Edit
Yes. The most natural segmentation is by **exchange, taken from the ticker suffix**, with company/ticker as the finer level beneath it.
**Why exchange is the natural grouping**
- The table has one row per stock per trading day, covering 152 companies and 152 tickers (2012-01-02 to 2025-08-04).
- 189,108 of the 422,729 rows (about 45%) have an exchange suffix such as `.T`, `.TW` or `.SS`. The other 233,621 rows (about 55%) have no suffix and look like US-listed tickers. That gives 19 groups in total (18 suffixes plus the US-style group).
- The groups differ a lot in size:
- The US-style group is the largest, with 83 tickers.
- Tokyo (`T`, 14 tickers), Taiwan (`TW`, 11) and Shanghai (`SS`, 13) come next.
- Many European and other exchanges have only 1–3 tickers each.
- The groups also differ in trading activity. Median daily volume is about 0.63M shares for US-style tickers, 1.79M for Tokyo and 9.12M for Shanghai. It is only about 1,400 for Frankfurt (`F`).
- The groups differ in time coverage too. Milan (`MI`) only starts on 2022-02-15, while the others begin in January 2012.
**Caveat for any cross-group comparison**
- Prices are in local currency, so levels are not comparable across groups. Median close is about 22 for US-style tickers, about 1,756 for Tokyo and about 33,788 for Korea (`KS`). The overall close ranges from about 0.009 to 300,000.
- Compare groups on returns, percentage volatility or indexed prices rather than raw price levels. Raw volume is also only roughly comparable across exchanges.
**Other groupings you could use**
- **Company/ticker** is the finest grain, and the gold tables already aggregate by company and by date (year, month, day, hour).
- **Time period** suits before-and-after comparisons, such as the AI-surge era versus earlier years.
- **Price or volume tiers** (for example, volume quantiles within a ticker) are possible, but they are derived groupings rather than natural ones.
I suggest exchange group for the top-level segmentation, company within it, and year for trend comparisons.