Between Grace and Works, a piece of thought about inner duality.

I don't really know where I'm going with this one, just wanted to write about the topic. Wrote it in one go, thought it could resonate with a few of you here. So here's a little part of my soul, written at 5AM :)

----------

Have you ever been touched by grace? Beauty striking you, passing through your senses to tingle your brain. 

But if you ever had a glimpse of beauty, did you ever feel the call of works as well? Blood rushing through your veins, adrenaline keeping you awake, the urge to build and leave a part of you in the world.

I - The old words

Grace and works, contemplation and action. 

This is the duality I’ve been through for years. Two parts of my person I’ve tried to explore and explain. Should one fuel the other ? Should one take over the other ? Can the two co-exist ?

Over a duality, it used to be a tension.

One is about slowness. The other about speed. 

One is immaterial. The other purely material.

One is about writing slowly, letting the world infuse through your mind and feelings that themselves dictate your hand which guides tip of your pen on an ivory notebook. The other is about typing frenetically on a mechanical keyboard, filling a CRM database in a notion workspace.

I finally found words for it in Thomas d’Aquin (Summa Theologica) and Hannah Arendt (The Human Condition). They handed me the old terms: vita contemplativa and vita activa.

Vita contemplativa is the contemplative life: thoughts, prayer, philosophy, withdrawal from practical urgency.

Vita activa is the active life: work, actions, politics, transforming the world. 

Those terms take their root in Greek philosophy, especially Aristotle. For whom contemplation was above action since contemplation touches what’s eternal while action is temporary. This hierarchy lived through eras: Thomas d’Aquin still acknowledges contemplation over action: action has to be rooted in contemplation or it becomes ego or noise. But we start seeing a switch during Renaissance, where we see a hybrid ideal: the cultivated mind that acts. Contemplation as a preparation for action. Finally, the tables turn in the modern era. Action becomes superior. Action used to serve truth, now truth serves action.

II - The war

I was born in this action-first world. Work on yourself so you can produce more. And surprisingly, it’s probably through action that I was introduced to contemplation. Self-growth books led me to philosophy, philosophy led me to literature. The search for answers and solutions helped me find questions and problems.

Hermann Hesse’s books walked me through this tension and I grew by reading them multiple times, at different times of my life, with a different understanding. The duality displayed in the books: Steppenwolf: the wolf and the bourgeois, Narcisse and Goldmund: the thinker and the artist, Demian: light and dark. Probably shaped my young brain that read them very simply. Maybe I was a duality myself ? When humans can’t grasp the complexity of the world, they try to simplify it. So I did. 

On one side, I put the thinker: Slow life, philosophy, poetry, aimless walks and cigarettes

On the other side, the merchant: Fast life, business, self-growth, discipline and e-cigs.

The two were leading a war. How could the thinker waste time thinking about life, reading books and taking time finding its meaning while he should be building a business, growing his network and fixing his sleep schedule ? How could the merchant sacrifice humanity for fixed habits, time-blocked calendars and SaaS ?

So I lived through cycles. The thinker bloomed when the body was exhausted and the mind needed alignement. The merchant was productive when the body was replenished and the mind aligned thanks to the thinker’s work and he pushes it until the body can’t follow anymore and the mind doesn’t remember why he’s doing all this. I lived three years that way. Three years thinking the conflict would never stop, that excellence meant sacrifice and that I needed to kill one of my sides to let the other live.

That’s when I re-read Steppenwolf and understood I was wrong from the beginning. Harry’s tragedy was to try reducing himself to his duality. The goal was never to kill the wolf or the man, it was to embrace inner multiplicity. 

You can work hard and live softly, discipline yourself and keep your chaos, celebrate the beauty of life and celebrate a deal.

Those are not contradictions, they are your whole. Stop making one fight the other, let them dance together.

There are two ways to be rare: to go deeper than anyone into a single life, or to hold together lives that were never meant to meet.

III - Notes from the battlefield

Problem: while expertise is widely documented, how the hell am I supposed to hold together multiple lives at once ? 

So I did what the merchant does with undocumented problems: learn, build, measure and run experiments, and what the thinker does with any life: I kept the journal.

Eight years of dated entries, one summer of life scoring spreadsheets, a mathematical model of my energy cycles, a personal AI coach.

Here are a few things I learnt along the way:

  1. The body is the shared infrastructure: Merchant or thinker, they both share the same body. The body dictates who show up. If I master the body, I choose who show up and when. 
  2. Separate their hours, not their importance: The merchant lives the day when the coffee’s hot and the phone’s ringing, the thinker mostly the night when the lights are dim and the streets are quiet.
  3. Give each one his own tools: The merchant gets the notion and the databases, the thinker gets Apple notes and the paper notebook. I tried forcing one into the other’s tool, it never worked for me.
  4. Let the thinker live when happiness is at its peak: The happier I was, the less I was asking myself questions, that’s when I let work take over, and push reflection aside as there are no problems to treat. That’s precisely when I need to schedule the thinker.
  5. Learn the signals of death from both: Each side dies differently. Too much building and work turns to noise: I lose the why. Too much contemplating and the days go hollow: I lose the drive. The fix is always to pay the other man: when it's noise, take a walk and think. When it's hollow, build something small to get back on track.
  6. Find bridges between them: Build around your soul with an engineering approach, an essay like this one.

I opened this short piece of writing with two questions: « Have you ever been touched by grace ? » and « Have you ever felt the call of works ? »

It took me years to hear them as one question: What makes you live ?

This text is part of my answer.

contemplata aliis tradere

reddit.com
u/Akamirr — 1 day ago

Polymarket API is shit (?)

Been working a lot with Poly APIs to make my data analysis posts. Am I the only one finding it hard to work with ? Rate limits, offset limits make it hard to reconstruct historical performances of a trader. Having to work with 3 APIs or complete then with on-chain data was horrible.

reddit.com
u/Akamirr — 1 day ago

World Cup 2026 Polymarket data: most wallets back one favourite, but most money is spread across several

I took the 51,572 Polymarket wallets holding “Yes” positions on Polymarket World Cup 2026 winner market.

The interesting split: the crowd and the money are backing mostly the same favourites, but most wallets are simple one-team bets, but most of the capital is not.

I separated all the Yes-holder wallets into 4 categories and the two most interesting are: single-favourite backers with 19,624 wallets and favourite diversifiers with 15,143 wallets, but hold 56% of all money.

These diversifier wallets have a much larger ticket size: $57 vs $15 for single-favourite backers.

I then looked deeper inside the favourite-diversifier wallets. Most of them don’t diversify that widely**: 37%** hold 2 teams, 21% hold 3 teams, 13% hold 4 teams, 8% hold 5 teams.

But again, we see a divergence between the crowd and the money as the wider baskets carry the money: Wallets holding 6+ teams are only 21% of diversifiers, yet they represent 57% of the diversifier money

And as basket size increases, median ticket size rises sharply:

  • 2 teams: $24
  • 3 teams: $45
  • 4 teams: $63
  • 5 teams: $93
  • 6–9 teams: $154
  • 10–14 teams: $366

Also included the most popular team combo for each basket size (No big surprises, Spain and France being in almost every popular combos)

PS: Feel free to join my discord if you're interested in those analysis or the data I use, I'm trying to turn my data pipeline in an API anyone could use ! https://discord.gg/bvWxgQvnDG

u/Akamirr — 18 days ago

World Cup 2026 Polymarket data: most wallets back one favourite, but most money is spread across several

This time, I took the 51,572 Polymarket wallets holding “Yes” positions on Polymarket World Cup 2026 winner market.

The interesting split: the crowd and the money are backing mostly the same favourites, but they’re doing it in very different ways: Most wallets are simple one-team bets. But most of the capital is not.

Pretty much what we could have expected tho: most wallets are sport betting enjoyers or people who want to back their favorite on Poly.

I separated all the Yes-holder wallets into 4 categories and the two most interesting are:

- Single-favourite backers: 19,624 wallets (where we see the most wallets), but only 25% of total exposure

- Favourite diversifiers: 15,143 wallets, but hold 56% of all money (where we see the most money)

These diversifier wallets have a much larger ticket size: $57 vs $15 for single-favourite backers.

I then looked deeper inside the favourite-diversifier wallets. Most of them don’t diversify that widely:

  • 37% hold 2 teams
  • 21% hold 3 teams
  • 13% hold 4 teams
  • 8% hold 5 teams

But again, we see a divergence between the crowd and the money as the wider baskets carry the money:

  • Wallets holding 6+ teams are only 21% of diversifiers
  • Yet they represent 57% of the diversifier money

And as basket size increases, median ticket size rises sharply:

  • 2 teams: $24
  • 3 teams: $45
  • 4 teams: $63
  • 5 teams: $93
  • 6–9 teams: $154
  • 10–14 teams: $366

Also included the most popular team combo for each basket size, so you can have a look at the favorites !

PS: I've talked with a few of you in DM, if you're interested in those analysis or the data I use, join my discord here: https://discord.gg/bvWxgQvnDG

u/Akamirr — 20 days ago

He backed all 49 World Cup teams to win. He's up $75,991.

[WARNING: The data showcased on this analysis are not right]

Few minutes after I posted, u/old_flying_fart and u/xmot7 highlighted how surprising it was that this trader could have bought shares as low as 1.67c for some favorites. Agreed on the statement, so I dug into the data to understand what was wrong. And in fact, there were some subtleties in multi-outcomes market Polymarket data endpoint that I didn't take into account in the usual script I use that works for the binary outcomes markets.

Letting the post live so people can learn from my error.

Detailed what I did wrong in this comment under the post: https://www.reddit.com/r/PredictionMarkets/comments/1u4sdg8/comment/orfnwdb/

Keep being critics folk, that's how we spot mistakes !

u/Akamirr — 24 days ago

In-depth trader's stats: 1M$ PnL, 7 months, $65M volume, 5,187 resolved trades

One of the most active wallets on Polymarket:

  • 211 days of trading
  • $65M in closed volume
  • 5,187 resolved positions
  • N°128 in Poly global leaderboard

Here’s what the data actually shows.

Global stats:

  • Win rate: 60.5% on resolved trades
  • Realized PnL: ~$1M
  • ROI on closed volume: 1.6%
  • Profit factor: 1.09
  • Median bet: $2,600

Where the money comes from

Segment Positions Win rate PnL
League of Legends 1,819 49.0% +$1.47M
Counter-Strike 393 46.8% +$132K
NFL 467 89.5% +$73K
NBA 209 61.7% +$46K
NHL 508 53.9% -$28K
MLB 86 61.6% -$156K
Other 1,705 69.8% -$506K

An interesting paradox:

NFL: 89.5% win rate, +$73K.
LoL: 49.0% win rate, +$1.47M.

As usual, volume, entry price, and sizing matter more than win rate as metrics.

Trading's stye:

Entry prices cluster around 0.50–0.70, with 1,584 positions in that bucket.

Best edges are at:

  • 0.30–0.50 entries: $239K PnL
  • 0.70–0.90 entries: $571.7K PnL

He plays medium bets with a median bet of $2,600 but has some massive single bets like a $343K bet, showing huge convictions.

Sizing and risk

Average losing position: $13,745.
Average winning position: $10,855.

The losses are larger than the wins, so the win rate has to carry the PnL.

Longest win streak: 292.
Longest losing run: 44.

The streaks are pretty category-driven.

Open book

Current truly open positions:

  • 74 positions
  • $303K exposure
  • Only $17K underwater

The book is rather clean.

Btw, if you loved this analysis, join my discord ! https://discord.gg/bvWxgQvnDG I'm also working on a smart money API and gonna release all those traders' stats in my API soon so you can do your own analysis.

u/Akamirr — 28 days ago

This Polymarket wallet was coin-flipping for 6 month. Then it hit 94% winrate. A trader's stats deep-dive.

This Polymarket wallet spent 6 months losing, then went 94% win rate.

A quick breakdown:

Aug 2025 → Feb 2026

  • 486 trades
  • 49.6% win rate
  • -$96k

Basically coin-flipping, no edge, a normal trader playing big sums.

Feb 2026 → now

  • 626 trades
  • 62% win rate
  • +$991k

Those february stats needs to be taken carefully since he has $591k in open losses. He probably doesn't close his positions to keep his realized PnL high.

His peak window was in May with a 94% win rate on 50 consecutive trades all without farming near-resolution markets as only 4 trades were made in the 0.90c-0.99c buckets.

He’s an esports specialist, having outstanding results trading on league of legends markets.

Curious if anyone heard anything about the evolution of this trader or has any theory of what happened ?

u/Akamirr — 1 month ago

From 36% WR, -$50k PnL to 84% WR, 900K PnL: Deep7 comeback (And my V.4 trader's page proposition)

Here's my attempt for a V.4 version of my Polymarket trader's analysis !

Shit used to be unreadable, so much data everywhere so I decided to make something a bit funnier, way easier to read to prioritize clarity over mass metrics.

When I first saw this trader with a 100/100 score given by my smart money API, I was kind of disturbed: bro had so many losses and huge drawdowns lmao, I even started to reconsider my trader's score formula.

But then I realized that he went from a very bad trader behavior to insane stats (actually our score considers performance over time and weight it by recency, which explains why he scores greatly haha).

And that's why I love doing those stats: a winrate or a PnL are all aggregated, it doesn't show granularity. How is a trader's behaving ? How is he evolving over time ? Where is he good ? When ?

Anyway. Hope you'll enjoy this new trader's card version.

As always, tell me how to improve the next trader's analysis so I can make it even better !

( And small ad, but if you like my work and are interested in my smart money API feel free to join my disc ! https://discord.gg/bvWxgQvnDG )

u/Akamirr — 1 month ago

$220k realized PnL over, 776 trades. Let's dissect this trader's stats.

👉 Krackensruster trader's stats panel

Here's a new trader analysis, with a few improvements since last version.

  • Enhanced the stats per Entry Price Bucket as I found the breakdown truly interesting: different entry price buckets can reflect different approaches.
  • Someone suggested to add news timestamps to view the correlation between trades and news. Did a few tries, but was hard to get an interesting timestamps - entries correlations. Will do another try on another traders and with other sources of news.
  • Reduced noise, removed useless sections or texts introduced by my AI pipeline to focus on the important informations.

Now, let's move to the insights:

  1. 84.3% Winrate on the 0.90c - 0.99c bucket, but negative PnL: He places a lot of small bets ($172 median size) on high probability markets but still has a negative PnL for this bucket. One $2,943 position bought at 0.914, swiped out the gains from his 43 other wins. Another concrete example that winrate should not be seen as a standalone indicator
  2. 37% Winrate on the 0.10c - 0.30c bucket, but +$28k PnL: The opposite to the previous insight. His positions reflect his convictions with an average $5,556 trade size on his wins, vs $3,362 on his losses.
  3. Trades as probabilities evolves, is not playing the outcomes: He traded on both side on 54 distinct markets. He plays on the probability as it evolves.
  4. His performances do not translate to other topics: While he maintains very good performances on Russian / Ukraine and Iran conflict, his performances are bad on US politics. Showcases how important it is to segment smart money based on the category they're specialised in.

Lmk how you think we can keep improving those analysis or what datas you would be interested to see so I can level up my game !

u/Akamirr — 1 month ago

I analysed a Polymarket wallet that made $806K on Iran escalation. This is my attempt at a deeper V2 analysis.

I got fair criticism on my previous high-winrate wallet posts: surfacing outliers is useful, but win rate alone is not enough. (Didn't answer to the comments yet btw, will do).

Understood that looking for high-winrate wallets as a first pass was not great as it was mostly surfacing traders who farmed near-end market resolutions, so I lowered this first winrate filter.

Here's my try for a deeper V2 audit on a Polymarket wallet that made $806K realized PnL mostly trading Iran / Middle East escalation.

Main checks:

  • 94 closed positions
  • 72.3% win rate
  • $806K realized PnL
  • 45% of PnL from entries below 30c
  • 0% of PnL from 90c-99c entries
  • only +$12K open PnL vs +$806K realized
  • top 5 bets = 59% of closed volume
  • 94 raw positions -> about 63 independent decisions

Cleaner than the previous high-winrate examples because it does not look like near-resolution farming or hidden open losses.

However, it's a very concentrated single-thesis wallet, so this is still not "proven repeatable alpha" but we already have something more interesting :)

Curious what other checks you would add for V3.

u/Akamirr — 1 month ago

Smart money survivorship bias visualized

Radion trader score by wallet age - 86,680 Polymarket wallets - score ≥ 0.30 classified as smart money

A quick graph showing the distribution of traders based on their profile's performance for different wallet ages.

I've been posting quite a lot about smart money on different subreddits and the survivorship bias came so many time in comment, so I wanted to try visualizing it in a dataviz.

Older Polymarket wallets look stronger, but that’s partly the point. Survivorship bias filters out weak wallets over time, leaving older cohorts with a higher concentration of high-score traders.

So here we go !

u/Akamirr — 2 months ago

A few interesting gaps between smart money positioning and crowd !

Small analysis on 3 markets where we see a huge gap between where smart money is positioned vs where the crowd is positioned.

Usually, we see that kind of huge gaps for long term markets highly influenced by news where smart traders can take a position at a low price, then wait for a peak due to crowd reaction to news so they can exit and take the profit.

That's precisely what we see here and it's usually on that kind of markets that we see the biggest concentration of high-confidence traders, with a good distribution (not concentrated on a single smart trader)

u/Akamirr — 2 months ago

Another 94.4% winrate (225W/15L closed positions), 22 days of trading identified

Last time I posted a profile with >94% winrate, I got roasted because the trader was farming near-resolution markets. And tbh it was a mistake on my side to present this trader.

So I come back with another profile I identified, which has an outstanding 94% winrate with an average entry price of 0.545¢.

What is interesting is that this trader focuses on holding to resolution, curious behavior considering his average entry price.

He's entirely focusing on football markets.

Curious if you guys have any hints on what kind of strategies he might be applying with the available data ?

u/Akamirr — 2 months ago

Shittt, so my trader confidence score works for real hehe

I'm working on smart money analysis product and we have a specific score we calculate for each traders to define whether we believe if the trader is great or not.

We had the formula but didn't have enough datapoints to make the first backtest on them yet. Happy to announce that it works haha.

At >50/100 score, >60% of the identified smart traders have made a positive PnL at +14D.

Special credits for the 37 traders at >80/100 score, where 70% of them made a positive PnL at +14D with a +$1.2k median PnL.

Feels good to finally be able to assess the efficiency of a homemade formula

u/Akamirr — 2 months ago
▲ 17 r/polymarketAnalysis+1 crossposts

Sampled 50k Polymarket traders on-chain to compare their stats with 45k smart traders

I’ve been curious about comparing stats from the average traders sample from Poly vs smart traders.

Getting the average trader was hard since most API pull from Polymarket were giving me active traders and not random traders, so I pulled wallets directly from Polymarket exchange contract events instead of starting from the API or leaderboard.

From 861,344 unique wallets that traded on-chain between Jan–Jun 2025, I sampled 50,367 active traders and compared them to 46,754 wallets classified as consistently profitable / “smart money.”

Here’s the median trader in each group:

Metric Random active traders Smart money
Win rate 33% 60% (+27.1pp)
Total volume $875 $17,246 (19.7x)
Avg position size $31 $150 (4.8x)
Markets traded 12 36 (3.0x)
Volume per market $70 $382 (5.4x)
Closed Positions count 13 28 (2.2x)

The win-rate gap is the obvious one: 33% vs 60%.

But the thing I found more interesting is the shape of the behavior.

Smart money doesn’t just win more. It also trades more markets, sizes much larger, and still puts way more volume into each market.

I would have guessed the best wallets were mostly specialists: fewer markets, deeper conviction, narrower scope.

But at least in this sample, they look broader and more concentrated at the same time.

More markets traded, bigger average position, more volume per market, higher hit rate.

That’s a very different game from the median active trader.

Methodology

I queried Polymarket’s CTFExchange and NegRiskCTFExchange contracts for OrderFilled events between January and June 2025.

That gave me a 861k wallet universe.

Then I randomly sampled wallets until I had 50,367 with at least one visible trade through the data API.

For each wallet, I pulled:

  • volume
  • position count
  • markets traded
  • average position size
  • closed-position win rate

Win rate here means:

% of closed positions where realizedPnl > 0

Open positions are excluded. Wallets with fewer than 2 closed positions are excluded from the win-rate distribution because otherwise the chart becomes mostly noise.

The smart money group comes from a homemade API that scores wallets based on long-term realized performance and consistency.

My read

The naive takeaway is “follow smart money.”

And tbh I don’t think that’s right.

Wallet tracking is messy. Big wallets can be wrong. One trader can split across multiple wallets. Bots and market makers are in the data. One actual decision can show up as many fills.

So raw activity is a bad signal.

The better question is probably:

When a wallet with a real history of being right sizes up, is that meaningful?

That seems much closer to the actual edge.

Not “wallet X bought this.”

More like:

  • has this wallet been right repeatedly?
  • across how many markets?
  • is this position large relative to its normal size?
  • is this one whale, or a cluster of independent good traders?
  • is this actual conviction or just noisy fill activity?

A few caveats

Win rate is not ROI. A 33% hit rate can still make money if the wins are much bigger than the losses.

The smart money set is selected by design, so yes, there is survivorship bias.

Closed positions only means open losses are not counted.

And wallets are not humans. Some are bots, market makers, or the same person split across addresses.

But still, the gap is hard to ignore, the median active trader in this sample looks nothing like the wallets that consistently make money !

u/Akamirr — 2 months ago

Wtf is this guy with 94.8% winrate on 445 markets, started trading 11th of April

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

u/Akamirr — 2 months ago

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

u/Akamirr — 2 months ago
▲ 72 r/PredictionSignal+1 crossposts

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 !

u/Akamirr — 1 month ago

https://preview.redd.it/o04vtjjurwyg1.png?width=1080&format=png&auto=webp&s=7697c0456b7e5b8c1859886bcd51b0dfb762b3a3

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 !

reddit.com
u/Akamirr — 2 months ago

Working on a smart wallet data API and I just fell in love with playing around with 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)

Smart wallets mapping quadrant

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 !

reddit.com
u/Akamirr — 2 months ago