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PyQuant News šŸ engagement report

@pyquantnews - 164K followers on X

Measured over 7 original posts from a 30-day window, last computed on October 24, 2026.

Engagement

Per follower
0.018%
of 164K followers
Per impression
0.577%
5.0K views on a typical post
Reach
3.07%
of its followers see a post
Typical post
29
interactions (median)
Saved
0.676%
34 bookmarks on a typical post
Posting rate
1.83/day
active 30% of days
Peak time
12:00 UTC
Sunday

Early reading. We have captured 7 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.

A typical post picks up 29 interactions against 164K followers, an engagement rate of 0.018%. Posts are seen about 5.0K times each, and 0.577% of those impressions turn into an interaction. That is about 3.07% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 12:00 UTC, and Sunday is the busiest day of the week. Of the 7 posts sampled, 86% carry an image or video and 100% are part of a thread. The account's strongest tracked post pulled 223 interactions, about 7.7x its own typical post. Only 7 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

Measured over 7 original posts from a 30-day window, last computed on October 24, 2026.

Where this sits in the catalog

At 0.018%, PyQuant News šŸ sits above the 10th percentile of the 205,152 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.

p100.004%
p250.024%
p50 (median)0.143%
p750.678%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 205,152 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,894 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.024%
50th percentile0.143%
75th percentile0.678%
90th percentile2.55%
99th percentile82.4%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 12:00 UTC, and Sunday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 12:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 12:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Sunday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Sunday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video86% of posts+111%+108% to +115%34K
Outbound link0% of posts-41%-42% to -40%32K
Typical length--3%-3% to -2%54K
  • 86% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 0% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
  • Its average post runs 129 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below the same accounts' other posts.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 14, 20267.7x their median

    Myth: You need a computer science degree to get started with Python, NumPy, and pandas. Reality: You need these 8 YouTube videos:

    188295116K viewsView on X
  • Oct 21, 2026

    A *gold mine* for prompt engineering code. Grab the link on GitHub: https://t.co/WHdgeBOgTz

    327305.2K viewsView on X
  • Sep 16, 2026

    Look at this guy... Even though he made $400,000 a year, he stole Goldman Sachs' high-frequency trading code. Or did he? The FBI thought so (and arrested him). Here's the crazy story: https://t.co/zYIvgyw3Do

    3613115K viewsView on X
  • Oct 20, 2026

    Code to compute volatility 5 ways most people don't know (and 1 way most people do): • Parkinson • Yang-Zhang • Garman-Klass • Rogers-Satchell • Hodges-Tompkins • Standard Deviation Get the code (for free) here: https://t.co/z4TPXgcW0h

    266305.0K viewsView on X
  • Oct 18, 2026

    Bayes' Theorem cheatsheet (bookmark this one for later): https://t.co/2zfeK6Bv7n

    274115.2K viewsView on X
  • Oct 21, 2026

    A beginner's guide to gauging portfolio risk: https://t.co/sEquR6Ixk3

    233215.2K viewsView on X
  • Oct 18, 2026

    The bias-variance tradeoff (in a dead-simple picture): https://t.co/XkTDONUVs9

    232304.8K viewsView on X
  • Oct 23, 2026

    The best free course on the internet: Poker Theory and Analytics https://t.co/rg4aPLgnDV

    250205.0K viewsView on X
  • Oct 24, 2026

    Systematic trading is the domain of secretive hedge funds and banks. Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool). Use the same tools the professionals use. Here are 17 Python libraries that open the black box:

    160413.2K viewsView on X
  • Sep 21, 2026

    Build and backtest a pairs trading strategy with Python (with code): https://t.co/lc7c4ikL02

    42101.8K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 29 interactions against 164K followers, an engagement rate of 0.018%. Posts are seen about 5.0K times each, and 0.577% of those impressions turn into an interaction. That is about 3.07% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 12:00 UTC, and Sunday is the busiest day of the week. Of the 7 posts sampled, 86% carry an image or video and 100% are part of a thread. The account's strongest tracked post pulled 223 interactions, about 7.7x its own typical post. Only 7 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

What is PyQuant News šŸ's engagement rate on X?
PyQuant News šŸ (@pyquantnews) has an engagement rate of 0.018%, based on the median interactions across 7 original posts from the last 30 days against 163,917 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.018%, PyQuant News šŸ sits above the 10th percentile of the 205,152 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @pyquantnews have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @pyquantnews post?
Most posts go out around 12:00 UTC, and Sunday is its busiest day, at roughly 1.83 posts per day across the measured window.

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