Synthdata ✓
@SynthdataCo
Succeed in Financial Markets with Predictive Intelligence on Equities, Crypto and Commodities | Bittensor SN50
7 449Followers
19Following
1 016Posts total
746.9KViews on collected posts
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@SynthdataCo @opentensor how can the average user use this data? any products being built for alpha holders? prediction bots?
450 views · 3 likes
· 0 reposts · 1 replies
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Synth Ultra is now live. Ultra-low-latency forecasting for HFT systems has been achieved on Bittensor @opentensor
33K views · 149 likes
· 20 reposts · 9 replies
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@SynthdataCo @Kalshi @binance Obviously an indication of market manipulation more than anything else
2.6K views · 53 likes
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7/ That changes the information flow.
From:
Binance → Model → Kalshi
To:
Proprietary information → HFT forecast → Kalshi → Future Binance price
If traders can predict the next BTC move, their capital pushes Kalshi before that move appears on Binance.
7.1K views · 18 likes
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8/
This gives us a different way to think about Kalshi. There is no single model setting its BTC price.
Kalshi is becoming a market-based ensemble of short-horizon forecasting models.
And now, a venue for Bitcoin price discovery itself.
Full Synth Research report
6.8K views · 34 likes
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5/ And this predictive relationship is getting much stronger.
For Binance's immediate next 0–2 second move, correlation with the preceding Kalshi move increased from:
0.036 in January → 0.173 in August.
By June it had already reached 0.145. https://t.co/4asqrmuV9G
8.4K views · 19 likes
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6/ Why might this be happening?
Our view is that the participants setting Kalshi prices are becoming more sophisticated.
HFT firms don't just react to the current BTC price.
They forecast where BTC will trade in 5, 10 or 30 seconds using order books, cross-exchange flows,
10.1K views · 25 likes
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4/ The key point here is the ordering.
The Kalshi move happens first. The Binance return is measured afterwards, and information about Bitcoin's next move is already being expressed in the Kalshi price.
8.8K views · 15 likes
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3/ We measured changes in the Kalshi YES price over 2 seconds, then looked at what happened to BTC on Binance over the following 10 seconds.
The relationship is clear:
Kalshi moves down → BTC tends to move down.
Kalshi moves up → BTC tends to move up.
Larger Kalshi moves http
12.4K views · 27 likes
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2/ Conventional wisdom says the information flow should be:
@binance → Trading models → @Kalshi
Bitcoin moves on spot exchanges. Traders then observe the move, update the probability of BTC finishing above or below the Kalshi threshold, and Kalshi reprices.
But the data
16.6K views · 26 likes
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1/ Synth's HFT team has found that @Kalshi’s 15-min BTC market is beginning to predict subsequent BTC moves on @binance and all other CEXs.
And strength of this predictive relationship has grown materially throughout 2026 (see image).
@Kalshi is becoming the venue for BTC http
640.2K views · 520 likes
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Face aux comptes de taille comparable
13 posts des 90 derniers jours, à côté de la tranche de under 10K abonnés. en dessous de ses pairs à la fois en portée et en engagement.
Vues médianes8 429ce compte4 175médiane pour under 10K
Portée, %113.16%ce compte250.82%médiane pour under 10K
Engagement, %0.30%ce compte1.41%médiane pour under 10K
| Indicateur | Ce compte | Médiane pour under 10K | Rapport |
|---|---|---|---|
| Vues médianes par post | 8 429 | 4 175 | 2.02× |
| Portée (vues ÷ abonnés) | 113.16% | 2.5× audience | 0.45× |
| Taux d'engagement | 0.30% | 1.41% | 0.21× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
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
640.2K18 Aug
16.6K
12.4K
8.8K
10.1K
8.4K
6.8K
7.1K
2.6K
33K26 Aug
450
342
7927 Aug
Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.11%18 Aug
0.18%
0.24%
0.18%
0.30%
0.24%
0.54%
0.27%
2.02%
0.54%26 Aug
0.89%
0.88%
1.27%27 Aug
Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.
What the audience does
Likes46.7%893 in total
Reposts4.4%85 in total
Replies4.0%76 in total
Quotes4.8%91 in total
Bookmarks40.1%768 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
Followers by day
10 Sep
Daily snapshots since 10 Sep 2026; the dashed line is the starting count.
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