ETHEUR
Ethereum / Euro
crypto Composite

Looks like another hard rejection at $1900 for ETH. Still waiting for $1750 retest

I'm a student at NYU studying to get into quant. I wanted to share my experience with an automatic capital allocator I designed. Last year I built a router that picks the best strategy for a given market and sizes it with Fractional Kelly Criterion. I thought it would be a good way to actually learn how position sizing and edge estimation work instead of just reading about them.
I had maybe 5 strategies I wrote running on ETH paper data and the router would pick whichever had the best recent risk-adjusted return and size it with fractional Kelly.
The first thing I learned about trading bots: **Most strategies do not have edge.**
Out of maybe 30 strategies I tested initially, 1 or 2 had any real edge after costs (or so I thought :), they got absolutely destroyed after realistic trading fees and friction). The rest were noise, though diversified. Crypto round-trips are like 6-7 bps per side depending on the pair, and if the edge is 10 bps per trade, I'm losing money.
[A typical backtest result, it with performance by regime. It crushed in Crisis \(+1440 bps\) but bled out in High Vol \(-1398 bps\), ending at -245 bps net](https://preview.redd.it/vngiu2zkpbkh1.png?width=616&format=png&auto=webp&s=9c6751588d38678944988f63b1b4d030d6637452)
I thought AI could help me with this and tried to improve the existing strategies with it. It gave worse results, it overcomplicates strategies a lot. What I surprisingly found is that **dumb and small code works much better** than complex models that overfit in the real world. And the tiny "dumb" strategies with on-chain data proved to be much much better than the rest, some even profitable on 2 years of trading data!
The second thing: **the router was actually decent.**
Once I had enough strategies, I built a perfect-foresight benchmark (an oracle that picks the best strategy for each window, kinda like God or Congress :) to see if my allocator was doing anything.
The router captures about 86% of the perfect-foresight ceiling (95% lower bound ≈ 39%) and this is on the same volume of trading, around 7-12 trades per day on both the oracle and my router on a very diversified roster of bots. I also found that best available edge scales with roster size at r=0.986 against extreme-value theory, the √(2\*ln N) scaling.
This mechanism could technically make money, but the allocator wasn't the bottleneck, the roster quality was.
**The network effect (this is the part I'm most excited about):**
I wanted to know does adding more strategies like drip feeding actually help or would I just be diluting? I subsampled my 93-bot roster down to smaller sizes (10, 20, 35, 50, 70, 93 bots) and re-ran the entire pipeline from scratch on each one, simulating a gradual influx.
The best bot's true edge climbs monotonically as you add more: −6 bps at 10 bots → +3 bps at 93 bots. When I fit that against extreme-value theory that predicts the maximum of N random draws), the correlation is 0.986… almost a perfect match. **More strategies = higher ceiling** and it follows theory almost exactly. So I updated the router to auto-upgrade itself based on influx of new bots.
The router only captures that rising ceiling if you use an absolute quality bar, not a relative percentile. If you filter "top 30% of whatever roster exists," the router's edge stays flat no matter how many bots you add because the percentile just re-centers on whatever population is there. If you use a fixed quality threshold instead, the router's edge climbs with the roster obviously. Extrapolating (with caveats, this is beyond the range I actually tested): \~+10 bps net edge at 1,000 bots, \~+16 bps at 10,000.
**Every good, diversified strategy added raises the ceiling for everyone in this system.**
[A standard Python strategy with declared SL\/TP and data from a feature catalog like orderbook imbalance or cross-asset momentum, etc...](https://preview.redd.it/zhk6swcupbkh1.png?width=1918&format=png&auto=webp&s=b9631311a97b8b4361facd2602613102beccffa6)
The router dynamically updates its own parameters as the roster changes but it does this via offline re-tuning on a cadence, not via real-time ML yet. The reason is that at 93 bots and \~8 trades/day, you can't detect effects smaller than \~47 bps with any statistical power. A real-time ML model would just be fitting noise. It also has self-capacity awareness so it doesn't frontrun itself.
Once the router worked, I began noting down everything scientifically and made a bunch of changes to my initial project. I added real-time on-chain data with historical data as well. These became obvious next steps:
* 6 active domains where bots compete: ETH, BTC, SOL direction + scalp (6 more registered but dormant, yield, tail hedge, liquidation arb, memecoins (This one might be insanely hard to get right tbh and would need the most external data)).
* 5-stage validation pipeline: static check, in-sample, out-of-sample, walk-forward, cost-adjusted
* Custom stop-loss, take-profit and trailing stops
* Arena & Open Leaderboard: strategies that pass validation compete on live paper data for capital allocation. The leaderboard serving as the Router's initial ranking system for bots.
[Router routing capital across active domains. Also I made a Dummy \\"Hedge Fund\\" called DaBronjame2 which was meant to be a fund of a collection of only bad bots which traded only during Neutral markets and scalp in them. Shouldn't have played with 10x leverage before :\)](https://preview.redd.it/ryu13nfcqbkh1.png?width=1788&format=png&auto=webp&s=9d8d6ddf47d61b43010102cdaf34452321b8c6de)
Where it is now:
* 80+ default strategies running on live paper data (real prices, paper execution, Not great bots :)
* 3 are currently net profitable (best: ETH Squeeze Breakout, +184bps, 73% win rate). The rest are negative. However, the router does manage to pick the ones on a winning streak frequently.
What I'm looking for: I'm posting here because this subreddit has people with real domain expertise...:
1. Does the router / Kelly allocation approach make sense, or is there an obvious flaw I haven't seen?
2. Is capturing \~86% of a foresight ceiling considered typical or decent for this setup (on a volume of 7-12 trades per day, on my quite diversified roster of bots)? How do I increase the efficiency of the router?
Happy to share more details in the comments if anyone's curious.
TL;DR:
Student at NYU. Built a router that allocates capital across Python trading strategies using Fractional Kelly. Tested 80+ strategies on live paper data, most have no edge after costs (shocking!! I know). The **router captures \~86% of a perfect-foresight ceiling** at matched volume. Found a network effect: best-available edge scales with roster size at r=0.986 vs extreme-value theory... so **more strategies = higher ceiling for everyone**. It snowballed into a 5-stage validation, arena. Happy to answer any questions or provide more details!

I'm with you on that, I badly want it value to go up its partly why I'm asking this question and why I've held it for 9 years and counting through multiple cycles at this point. At the same time the demand just doesn't seem to be as strong as we expect it to be right, it lagged BTC last bull run and you could find many other examples. I personally traded a good amount of BTC for ETH in 2018 which turned out to be a bad trade, and it seems like BTC has captured this digital gold narrative as first mover and limited 21m supply argument. The situation is what it is, so I'm left to think about it logically in terms of what would incentivize the demand for people to want to buy hold this token beyond just price speculation, given the fundamental factors mentioned and gas prices being so low?

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I just think it's crazy the number of mental hoops we have to jump through to even imagine why ETH could go up in value. What happened to supply and demand? BTC doesn't have "value accrual" and no one is struggling to imagine what that can go up in value. Then we got ETH over here with like infinite possibilties for why there'll be demand for the token, and people are like "how can this ever translate into price appreciation I just can't imagine"...

**Tricky's Daily Doots #1,568**
**Yesterday's Daily 17/08/2026**
[Previous Daily Doots](https://old.reddit.com/r/ethereum/comments/1vrfld5/daily_general_discussion_august_18_2026/p4eq8ea/)
- u/nonetherless325 foreshadows [the future of lean Ethereum.](https://old.reddit.com/r/ethereum/comments/1vrfld5/daily_general_discussion_august_18_2026/p4eryyj/) ⚡️
- u/edmundedgar shares [a great counter-argument against "negative real yield" claims by EIP-8363 proponents](https://old.reddit.com/r/ethereum/comments/1vrfld5/daily_general_discussion_august_18_2026/p4dyin8/) and u/Tricky_Troll delivers [a transcription for the lazy.](https://old.reddit.com/r/ethereum/comments/1vrfld5/daily_general_discussion_august_18_2026/p4eng2k/) 💬
- u/ethdaily delivers [the daily ETH news.](https://old.reddit.com/r/ethereum/comments/1vrfld5/daily_general_discussion_august_18_2026/p4in9c3/) 📰

📢 KuCoin Now Supports Ethereum (ETH) on Robinhood Chain

Definitely agree on the retail burn, though I'm more in the camp that this will be a rate cutting cycle, and could mean better ETH/ alt performance

Yes, because it's happened before, so it can happen again.
Tbf that rationale gets people wrecked on plenty of investments. Assuming crypto/ defi overall continues to become more used, I do believe that ETH will be a majorly used asset in the space.

You've landed on the thing most quote-asset debates miss: the denominator is an argument about correctness, but liquidity is the argument that actually decides, and correctness loses. "Everything is quoted in ETH/USDC because they're there" is exactly right, and it isn't going to change by being righter about gold.
Two things to build on. First, the PAXG-premium comment is the sharper version of your own finding: when you denominate in a scarce asset you don't just inherit its thin liquidity, you inherit its price pathology. PAXG can trade rich to spot gold when gold spikes and when bridging/redemption is congested, so your "clean gold denominator" quietly becomes "gold, plus a basis that widens exactly when people care most." You swapped ETH beta for PAXG-basis risk, and the basis is worse-behaved because it's driven by on-chain scarcity, not gold. Second, the measurable core: a pool's usable depth is capped by its scarcest leg. A TOKEN/PAXG pool can't be deeper than PAXG is willing to be on that chain, so you imported the worse of the two liquidity profiles by construction. That's why the ETH pair does real numbers and yours holds a few hundred dollars - not a marketing problem, a depth-of-the-quote-asset problem, and it's fully visible in the pool data before you deploy.
So the honest fix isn't a better denominator, it's picking the one whose depth already exists and hedging the beta separately if you actually care about it. (Bias disclosure: measuring exactly this kind of pool and quote-asset depth is what I do at Coinpaprika/DexPaprika, so I come at all of it through the liquidity lens.) Genuinely good experiment - the negative result is the useful one.

Do you guys believe ETH will recover and exceed its previous highs? If so, why? Maybe this is a basic question, but just want to better understand the rationale behind it.

Invest in stables with your ETH. Horses are worth a lot of money, but you do have to feed them and give them somewhere to sleep.

Hi everyone🤗,
I track stock and crypto prices pretty often during the day, but I got really tired of having to constantly open extra browser tabs while working or alt-tabbing out of games just to check where prices were at.
I couldn't find a simple desktop widget that fit Windows 11 nicely, so I decided to make one called UniWidget.
What it does:
* Shows live prices for stocks and crypto (like TSLA, BTC, ETH) right on your desktop.
* Extremely lightweight so it won't lag your games.
* Simple search to add any ticker symbol you want.
* Includes basic price alerts.
It's called **UniWidget** on the Microsoft Store.
I'm developing this as a side project, so if you have any feedback or ideas on what to add next, let me know in the comments!😊😊😊

That's pretty much how I look at it too. BTC and ETH are the core holdings, then the smaller positions have a completely different job.
Do you keep crypto as its own allocation within a wider portfolio as well, or is crypto your main focus?

How can I stake for ETH 2.0

funny story. i bought ETH in like 2018 or some shit and it went to 1400 and then down to 85. I was down like 95% . Kept holding for years it actually came back up

Almost 5.4 million ETH is now locked in ETH2

I made them! I wrote most of them myself as part of the project then tried variations of them, started with basic mean reversion, momentum, breakout variants on ETH, then kept adding more as I learned different market mechanics and data feeds.
Honestly most of them are not good and only 3 are halfway decent, producing positive edge overall. That was kind of the whole point of the project! But I am proud of the router which can attune itself correctly for the influx of new bots with edge!

This can just be implemented as a wrapped ETH ERC-20 token if someone wants to do it, right?

I'm a student at NYU studying to get into quant. Last year I started a project that I thought would be small, building a router that picks the best strategy for a given market in DeFi and sizes it with Fractional Kelly Criterion. Like an automatic mini allocator. I thought it would be a good way to actually learn how position sizing and edge estimation work instead of just reading about them.
I had maybe 5 strategies I wrote running on ETH paper data and the router would pick whichever had the best recent risk-adjusted return and size it with fractional Kelly.
The first thing I learned: most strategies do not have edge.
Out of maybe 30 strategies I tested initially, 1 or 2 had any real edge after costs (or so I thought :), they got absolutely destroyed after realistic trading fees and friction). The rest were noise, though diversified. Crypto round-trips are like 6-7 bps per side depending on the pair, and if the edge is 10 bps per trade, I'm losing money.
[A typical backtest result in the studio, it separates performance by regime. It crushed in Crisis \(+1440 bps\) but bled out in High Vol \(-1398 bps\), ending at -245 bps net. It gets flagged as Noise.](https://preview.redd.it/nyoh4rwwl8kh1.png?width=438&format=png&auto=webp&s=bb67ef4c77ef27fc0cd9813f2005bd57bda11466)
I thought AI could help me with this and tried to improve the existing strategies with it. It gave worse results, it overcomplicates strategies a lot. What I surprisingly found is that dumb and small code works much better than overfit models in the real world. And the tiny "dumb" strategies with on-chain data proved to be much much better than the rest, some even profitable on 2 years of trading data!
I added a cost-adjusted validation stage and regime decomposition. Seeing where a strategy bleeds (chop vs trend vs crisis) helped explain why backtests fail live.
The second thing: the router was actually decent.
Once I had enough strategies, I built a perfect-foresight benchmark (an oracle that picks the best strategy for each window, kinda like God or **Congress** :) to see if my allocator was doing anything.
The router captures about 86% of the perfect-foresight ceiling (95% lower bound ≈ 39%) and this is on the same volume of trading, around 7-12 trades per day on both the oracle and my router on a very diversified roster of bots. I also found that best available edge scales with roster size at r=0.986 against extreme-value theory (the √(2·ln N) scaling).
This mechanism could technically make money, but the allocator wasn't the bottleneck, the roster quality was.
The network effect (this is the part I'm most excited about):
I wanted to know does adding more strategies actually help or am I just diluting? I subsampled my 93-bot roster down to smaller sizes (10, 20, 35, 50, 70, 93 bots) and re-ran the entire pipeline from scratch on each one.
The best bot's true edge climbs monotonically as you add more: −6 bps at 10 bots → +3 bps at 93 bots. When I fit that against extreme-value theory (the √(2·ln N) scaling that predicts the maximum of N random draws), the correlation is 0.986… almost a perfect match. **More strategies = higher ceiling**, and it follows theory almost exactly. 
**But here's the catch**: the router only captures that rising ceiling if you use an absolute quality bar, not a relative percentile. If you filter "top 30% of whatever roster exists," the router's edge stays flat no matter how many bots you add because the percentile just re-centers on whatever population is there. If you use a fixed quality threshold instead, the router's edge climbs with the roster. Extrapolating (with caveats, this is beyond the range I actually tested): \~+10 bps net edge at 1,000 bots, \~+16 bps at 10,000.
That's the quantitative argument for why I want creators :) Every good strategy added raises the ceiling for everyone.
How it expanded into a creator studio:
[\\"The web IDE where you can write standard Python strategies with declared SL\/TP and pull from a feature catalog like orderbook imbalance or cross-asset momentum.\\"](https://preview.redd.it/s9vqu7x0p8kh1.png?width=1918&format=png&auto=webp&s=dcc49e98156b7291d6d1b7133ea75089e85ef777)
The router dynamically updates its own parameters as the roster changes but it does this via offline re-tuning on a cadence, not via real-time ML yet. The reason is that at 93 bots and \~8 trades/day, you can't detect effects smaller than \~47 bps with any statistical power. A real-time ML model would just be fitting noise. It also has self-capacity awareness so it doesn't frontrun itself.
Once the router worked, I began noting down everything scientifically and made a bunch of changes to my initial project. I added real-time on-chain data feeds with historical data as well. These became obvious next steps:
* 6 active domains: ETH, BTC, SOL direction + scalp (6 more registered but dormant, yield, tail hedge, liquidation arb, memcoins (This one might be insanely hard to get right tbh), etc, waiting for strategies)
* 5-stage validation pipeline: static check, in-sample, out-of-sample, walk-forward, cost-adjusted
* Creator IDE: write Python strategies with custom stop-loss, take-profit, and trailing stops directly in the browser or via API/MCP
* Arena & Leaderboard: strategies that pass validation compete on live paper data for capital allocation
* Non-custodial: API keys stay encrypted in user vaults
[\\"Live portfolio dashboard routing capital across active domains. Also I made a Dummy \\"Hedge Fund\\" called DaBronjame2 which was meant to be a fund of a collection of only bad bots which traded only during Neutral markets and scalp in them. Shouldn't have played with 20x leverage before :\)\\"](https://preview.redd.it/kpcl73yfp8kh1.png?width=1788&format=png&auto=webp&s=40f8697a9abb39519a8049db63c16c7b057e3a79)
Where it is now:
* 80+ default strategies running on live paper data (real prices, paper execution, Not great bots:)
* 3 are currently net profitable (best: ETH Squeeze Breakout, +184bps, 73% win rate). The rest are negative.
What I'm looking for: I'm posting here because this subreddit has people with real domain expertise, and I'd love your feedback:
1. Does the router / Kelly allocation approach make sense, or is there an obvious flaw I haven't seen?
2. Is capturing \~86% of a foresight ceiling considered typical or decent for this setup (on a volume of 7-12 trades per day, on my quite diversified roster of bots)?
3. What features would you actually need in a Python strategy sandbox to make it worth testing your own models?
I'm a student and not charging for anything. Oh, and importantly nobody can see any strategy code, it runs in a confidential VM! Happy to share more details if anyone's curious.
TL;DR:
I'm a student at NYU. Built a router that allocates capital across Python trading strategies using Fractional Kelly for DeFi. Tested 80+ strategies on live paper data, most have no edge after costs (shocking!! I know). The router captures \~86% of a perfect-foresight ceiling at matched volume. Found a network effect: best-available edge scales with roster size at r=0.986 vs extreme-value theory... so more strategies = higher ceiling for everyone. It snowballed into a full creator platform (5-stage validation, arena, non-custodial, confidential VMs for strategy privacy, bi-weekly payouts and more!). Would love feedback from people who actually know what they're doing. Does the approach make sense, is 86% of ceiling decent, what would you need in a Python strategy sandbox?