u/Akamirr

Playing around with my smart money API. Asked it to make an analysis of some of the best smart traders profiles and fell on this profile that has such crazy stats. He's almost only on sport markets and has a pretty recent wallet (started trading 11th of April).

He doesn't have such an big edge, but the confidence is quite high due to his frequency of trades and wins

I have a smart money API that tells me where smart money is positioned, but tbh I always wondered if this data was even useful.

So here is the question I asked myself:

Once we detect smart money positioning at a time T, how does the price actually moves at T + 14d ?

📈Data:

To isolate one data, I’ve focused on bearish signals: markets where smart money thinks the probability is overpriced.

• Took historical bearish signals snapshots from our API (317 snapshots across 70 distinct markets)
• Compared the price of the market between the snapshot and the price at +14d
• Plotted confidence on the y-axis (confidence is a score we give on how confident we are on the smart traders positions), 14-day price drift on the x-axis

👉 The key results:

Confidence	Markets lowered	Price lowered at +14d	Price stayed flat at +14d	Price increased at +14d
>= 0.60	8/9 (89%)	33/45 (73%)	3/45 (7%)	9/45 (20%)
< 0.60	17/62 (27%)	62/272 (23%)	149/272 (55%)	61/272 (22%)

🔍 Interpretation:

The key point is that “smart money thinks the market is overpriced” is not enough.

You still need to ask: who is behind the flow, how broad is it, how concentrated is it, is the market probability sane, and does the signal have enough confidence to matter ?

In this sample, that filter matters:

  - Below 0.60 confidence: 17/62 markets moved lower after 14d

  - Above 0.60 confidence: 8/9 markets moved lower after 14d

So the bearish label gives direction and a confidence score tells you whether that direction is worth taking seriously.

Btw you should not even trust me, 9 high confidence markets is still a low sample to take good conclusions. What I want to do is raise awareness about smart money, conduct robust analysis or you'll get fooled by shitty promises on X or reddit.

👉 If you want more details about the confidence score, check our docs https://docs.radion.app/concepts/confidence-scoring#confidence-scoring

Working on a smart wallet data API and I just fell in love with playing around, doing data-visualisation on it. Thought it would be interesting to share !

Took 5,000 smart traders, mapped them across two dimensions: estimated edge (per-trade skill) and confidence score (statistical reliability based on activity).

The result: these two metrics move in opposite directions (correlation r = −0.55)

The higher the confidence score is, the lower the edge seems to be. Seems logical as outstanding edge often come from some lucky trades.

What I read from the quadrant: 

🟡 WHALES (top-left: high confidence, low edge)
High confidence because they trade a lot. But median win rate: 69%, the lowest of an archetype. These wallets account for 61% of total volume. Obvious but clearly showcases again that volume ≠ skill. 

🟣 SWEET SPOT (top-right: high confidence AND high edge)
Only 15% of traders (n = 746) clear both thresholds. This is the actionable zone. Snipers live here: 21 traders, 90% win rate, high edge.

⚫️ NOISE (bottom-left: low confidence, low edge)
35% of traders. Fewer than 10 effective positions. Their win rates look strong precisely because small samples produce extreme numbers. This signal is not really exploitable tbh, maybe I should remove them from our data

🔴 RAW UNPROVEN EDGE (bottom-right: high edge, low confidence)
High edge on paper, but median volume under $500. These wallets had 3–9 good trades. More luck than skill !