PENGUUSDT · recorded history

One-sided tape: sellers on PENGUUSDT

Taker buy ratio over the last 15 minutes has run at 0.38 or less: a persistently seller-heavy tape.

Read it in context

Read PENGUUSDT itself
Inspect PENGUUSDT now or at a past minute
Test it on PENGUUSDT, then plan it
A tested result leads to Plan a trade, which measures what trading it would have paid after costs.
Edit this test in Workbench

What happened in the next 24 hours?

Reaching +5% was about as common as without it; falling to −10% was less common than without it.

Other minutes are the same markets and dates without this condition. Each percentage is the share of measured minutes that reached that move from the starting price.

Price touched +5% or higher

When this held: 40.6% · 158 of 389 minutes
Other minutes: 41.3% · 14,890 of 36,091 minutes

Price touched −10% or lower

When this held: 0.8% · 3 of 389 minutes
Other minutes: 2.4% · 857 of 36,091 minutes

Price can touch both levels within the same 24 hours.

This counts how often price touched a level, not what trading it would have paid.

Sample and counting

Rates use the 389 of 399 matches with a full 24 hours after them. The 10 newer ones are left out, not counted as zero.

Every condition is measured over the same 24 hours, whatever the length of its own reading. Other windows and move sizes shows shorter and longer windows.

When it happened

About 13 a day, on 30 of 31 days: spread through the record rather than one event.

Other windows and move sizes

Matches that reached each move within each window. Counts first; the measured row is what each rate is over.
Move	1 hour	4 hours	24 hours	72 hours	7 days
Measured	399	398	389	366	307
Too recent	0	1	10	33	92
+0.1% or more	351 (88.0%)	378 (95.0%)	382 (98.2%)	362 (98.9%)	305 (99.3%)
+0.2% or more	321 (80.5%)	361 (90.7%)	376 (96.7%)	359 (98.1%)	302 (98.4%)
+0.5% or more	232 (58.1%)	318 (79.9%)	355 (91.3%)	348 (95.1%)	295 (96.1%)
+1% or more	115 (28.8%)	232 (58.3%)	323 (83.0%)	332 (90.7%)	288 (93.8%)
+2% or more	31 (7.8%)	129 (32.4%)	272 (69.9%)	300 (82.0%)	269 (87.6%)
+5% or more	2 (0.5%)	30 (7.5%)	158 (40.6%)	208 (56.8%)	202 (65.8%)
+10% or more	0 (0.0%)	1 (0.3%)	40 (10.3%)	124 (33.9%)	151 (49.2%)
+15% or more	0 (0.0%)	0 (0.0%)	12 (3.1%)	76 (20.8%)	121 (39.4%)
+20% or more	0 (0.0%)	0 (0.0%)	6 (1.5%)	41 (11.2%)	109 (35.5%)
+30% or more	0 (0.0%)	0 (0.0%)	0 (0.0%)	26 (7.1%)	90 (29.3%)
+50% or more	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	28 (9.1%)
−0.1% or lower	362 (90.7%)	383 (96.2%)	383 (98.5%)	363 (99.2%)	305 (99.3%)
−0.2% or lower	317 (79.4%)	362 (91.0%)	375 (96.4%)	359 (98.1%)	302 (98.4%)
−0.5% or lower	240 (60.2%)	320 (80.4%)	356 (91.5%)	348 (95.1%)	293 (95.4%)
−1% or lower	131 (32.8%)	237 (59.5%)	324 (83.3%)	330 (90.2%)	282 (91.9%)
−2% or lower	30 (7.5%)	113 (28.4%)	248 (63.8%)	280 (76.5%)	250 (81.4%)
−5% or lower	2 (0.5%)	9 (2.3%)	104 (26.7%)	197 (53.8%)	210 (68.4%)
−10% or lower	0 (0.0%)	0 (0.0%)	3 (0.8%)	67 (18.3%)	117 (38.1%)
−15% or lower	0 (0.0%)	0 (0.0%)	0 (0.0%)	6 (1.6%)	78 (25.4%)
−20% or lower	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	9 (2.9%)

Each window counts only matches old enough for it, so a longer window can show fewer.

Distributions and daily counts

Upside reached · 24hThe most price moved up within 24 hours of each match: a high-water mark, never a close. 389 with a complete 24 hours.

Drawdown · 24hThe worst price moved against within 24 hours of each match: the deepest point, never the close. 389 with a complete 24 hours.

Price 30 minutes laterForward 30-minute returns across 399 matches: close to an even split, which is what a coin flip looks like. 0 too recent.

How to read a distribution

Matches per UTC day

1 Sep16 matches
2 Sep14 matches
3 Sep16 matches
4 Sep12 matches
5 Sep16 matches
6 Sep11 matches
7 Sep16 matches
8 Sep16 matches
9 Sep12 matches
10 Sep12 matches
11 Sep15 matches
12 Sep14 matches
13 Sep15 matches
14 Sep11 matches
15 Sep17 matches
16 Sep10 matches
17 Sep15 matches
18 Sep13 matches
19 Sep15 matches
20 Sep14 matches
21 Sep13 matches
22 Sep9 matches
23 Sep5 matches
24 Sep16 matches
25 Sep14 matches
26 Sep14 matches
27 Sep15 matches
28 Sep9 matches
29 Sep14 matches
30 Sep10 matches
1 Oct0 matches

How this is counted

Each rate counts only matches with the full window after them.
A minute where a reading was missing is left out and reported, not read as zero.
The condition picks the setup, never the outcome: a definition that filters on what followed is rejected (OUTCOME_IN_PREDICATE). Every match is measured, winners and losers alike.
"Other minutes" is every other eligible minute in the same market and window. It is a reference, not a control group.
"Reached +5%" uses the highest price inside the window (maximum favourable excursion, MFE); "fell to −10%" uses the lowest (maximum adverse excursion, MAE). Neither is the close.
Checked 43,201 eligible market-minutes (eligible_symbol_buckets) on PENGUUSDT, 2026-09-01 00:00 to 2026-10-01 00:00 UTC.

What are MFE and MAE?

Reproducibility key (q#080db2bd)

The key that re-runs this exact count. Same key, same bytes; when the record grows, the dataset revision changes.

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: c764b05a2cce2a39e38b40d35fcb2488ecc1bfda3dfbabbbd252d4ba3f3a9680
query hash: 080db2bd4dc1496f9b079b1ae238b9458feec9f4ce4f556b682b413861ec187f
scanned window: 2026-09-01T00:00:00.000Z to 2026-10-01T00:00:00.000Z (UTC)

Re-run over the API

The exact search definition behind this count. Send it to the key-authed research API (or the MCP research tool) and compare the bytes; a cached rerun is free.

{
  "schema_version": "research_query.v2",
  "normalization_version": "archive_normalization.v1",
  "feature_version": "feature_defs.v1",
  "target": "record_occurrences",
  "where": {
    "all": [
      [
        "feature.taker_buy_ratio_15m",
        "lte",
        0.38
      ],
      [
        "identity.symbol",
        "eq",
        "penguusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-09-01T00:00:00.000Z",
          "2026-10-01T00:00:00.000Z"
        ]
      ]
    ]
  },
  "sort": [
    "times.anchor_time",
    "desc"
  ],
  "page": {
    "limit": 50,
    "cursor": null
  }
}

curl -s https://app.edgedepth.com/api/v1/research/query \
  -H 'authorization: Bearer $EDGEDEPTH_API_KEY' \
  -H 'content-type: application/json' \
  -d '{"schema_version":"research_query.v2","normalization_version":"archive_normalization.v1","feature_version":"feature_defs.v1","target":"record_occurrences","where":{"all":[["feature.taker_buy_ratio_15m","lte",0.38],["identity.symbol","eq","penguusdt"],["times.anchor_time","between",["2026-09-01T00:00:00.000Z","2026-10-01T00:00:00.000Z"]]]},"sort":["times.anchor_time","desc"],"page":{"limit":50,"cursor":null}}'

One-sided tape: sellers on PENGUUSDT

Read it in context

Read PENGUUSDT itself

Test it on PENGUUSDT, then plan it

What happened in the next 24 hours?

Price touched +5% or higher

Price touched −10% or lower