I don't think Binance was ever at risk of insolvency over a $6B hole. Comparing it to FTX is a retarded take. FTX was a fraud built on commingled customer funds, and its brand was damaged badly, arguably beyond repair. Binance got hacked. That's a security failure, not fraud.
If it had come to that, Binance would have raised enough to make customers whole, and investors would have lined up for a piece of the world's largest crypto exchange. No doubt.
Anyone telling you otherwise is pushing their own narrative. You don't need to be a genius to see it.
Seeing a lot of people confidently calling the @jason bet wildly mispriced. Curious what models they’re using to reach that conclusion.
Quant retweeted
No human will ever write code better than LLMs. Accept this fact, stop pretending, and move on.
I've been using Opus 5.5 for 12+ hours a day since it came out.
I can confidently tell from firsthand experience that they nerfed it since the last few hours.
Jfc.
Why use Opus 5.5 with medium effort when you can use GPT 6 Astra with ultra effort at 10x the cost, 10x the slowness, and half the intelligence?
For those who don’t understand why I say this, win rate only really means something alongside the magnitude of your wins and losses.
If I make $1 on every positive trade but lose $100 on every negative trade, a 92% win rate still means I’m losing money despite the very high win rate.
On the contrary, if I lose $1 on every negative trade but make $100 on every winning trade, a 10% win rate still means I’m profitable.
Without any edge, the magnitude of your losing trades is expected to offset the winning trades in proportion to how often each happens, plus a little more lost to spreads, fees, slippage and/or adverse selection.
Unfortunately, this isn’t something you get to choose. You can’t just play with the asymmetry between your take profits and stop losses and fabricate positive expectancy out of nowhere.
If you want a very high win rate, it’s as simple as taking profit very tightly while setting your stop losses very loosely. You’ll win very often, but when you lose, you’ll lose much more.
On the contrary, you can set your take profits very wide and your stop losses very tightly. You’ll have a much lower win rate, but your wins will be much larger than your individual losses.
You should always remain skeptical of the opinions of a turkey.
Quant retweeted
🤡 Payaso tengo 9 años haciendo trading.... Y creeme dices una de las payasadas más grande que existe. Si claro con un 10% de win rate puedes ser rentable. Suerte con eso
Replying to @BaseLayer365
You clearly have no idea how trading works, and you’re publicly letting your followers know.
You can have a 92% win rate and still lose money.
You can have a 10% win rate and still be profitable.
Win rate tells you absolutely nothing about how good or bad a trader is.
86% win rate on my trades since 2024 on Hyperliquid
Last 30D performance is 92%
Data from @hypurrdash
This must hit pretty hard if you know nothing about the markets.
I’m here to tell you that you can easily achieve a ~90% win rate if you just set your stop losses 10 times as far away from your take profits, and you’ll likely still lose money.
You can trick the math however you like, but there’s no substitute for edge if you want to make money trading. Don't fall for this BS.
Replying to @AkaniQuant
You can absolutely manufacture your win rate by adjusting TP/SL asymmetry. That does not create edge. Expectancy is the only thing that matters. +5 bps TP with -50 bps SL can print a 90% hit rate and still be a loser after costs. What counts is net keep per trade. Trading R:R is trading guru BS.
Quant retweeted
“If you can find traders that don’t want to trade but need to trade, those are great people to trade
against.”
@quant_xbt joins the Insilico Terminal Podcast to talk about building a crypto HFT business, finding
repeatable edges, and adapting as competition catches up.
We covered running HFT in JavaScript, trading against forced flows, exchange risk, and why even automated
strategies still need human judgment.
00:00 Intro and Early Crypto Trading
05:55 Finding the First Arbitrage Edge
11:31 Running HFT in JavaScript
19:17 Finding Edge Across Exchanges
25:19 Consistent Returns and Hidden Risks
33:53 Research, Automation and In-House Data
38:14 Quant vs. Discretionary Trading
45:29 Volatility, Forced Trading and Finding Edge
51:33 Bitcoin Conviction and Why Altcoins Fail
Thanks @Sokio8D for having me on the podcast!
“If you can find traders that don’t want to trade but need to trade, those are great people to trade
against.”
@quant_xbt joins the Insilico Terminal Podcast to talk about building a crypto HFT business, finding
repeatable edges, and adapting as competition catches up.
We covered running HFT in JavaScript, trading against forced flows, exchange risk, and why even automated
strategies still need human judgment.
00:00 Intro and Early Crypto Trading
05:55 Finding the First Arbitrage Edge
11:31 Running HFT in JavaScript
19:17 Finding Edge Across Exchanges
25:19 Consistent Returns and Hidden Risks
33:53 Research, Automation and In-House Data
38:14 Quant vs. Discretionary Trading
45:29 Volatility, Forced Trading and Finding Edge
51:33 Bitcoin Conviction and Why Altcoins Fail














