PyQuant News 🐍 ✓
@pyquantnews · Free algo trading videos → · joined 20 Apr 2015
Where finance practitioners get started with Python for quant finance, algorithmic trading, and data analysis | Tweets & threads with free Python code & tools.
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Against accounts of the same size
29 posts from the last 90 days, next to the 100K–1M follower range. below its peers on both reach and engagement.
| Metric | This account | Median for 100K–1M | Ratio |
|---|---|---|---|
| Median views per post | 403 | 6 582 | 0.06× |
| Reach (views ÷ followers) | 0.25% | 1.66% | 0.15× |
| Engagement rate | 0.62% | 0.98% | 0.63× |
Others in this range → Compare with another account → How these benchmarks are built →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.98%.
What the audience does
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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