King

[Crypto老鹰 @laoyingkhq]

这件事说出来大概率没人相信，Claude通宵完成全量检索，把我收藏的Polymarket钱包、Github交易开源项目全部扫描解析。

第二天一早登录账户，原本200美元启动资金已经涨到3582美元。

这套模式不靠预判未来行情取胜，核心是AI筛选出11个具备稳定收益能力的大户钱包，提炼出四种重复兑现盈利的交易框架，同期市场多数用户持续亏损，只有这套逻辑稳定获利。

市面上大部分投资者挖掘套利机会的方向完全走偏，AI的分析逻辑更为全面精准。

Claude重点抓取四类关键信息维度：
- 超一万两千个链地址长期交易行为、持仓变化规律
- 各类开源交易代码完整盈亏历史记录
- 仓位随行情确定性动态调整，机会明确重仓，不确定性轻仓
- 市场套利窗口转瞬即逝，部分有效行情仅维持90秒

AI整合全部信息，生成专属自动化代理程序。

深夜完成部署上线，在我入睡时全自动执行下单。

全程合计94次成交，盈利订单占比87%，200美金本金最终做到3582美金。

Polymarket官网中文链接：https://t.co/XOIs6CMprG

没有玄学式预测，纯粹依靠数据信息差、数学模型捕捉短期套利机会。

整套部署流程操作简单，一小时就能全部落地。

代理搭建完成后24小时不间断运行，无需人工值守。

仅需要Claude账号、一台设备，一小时搭建时长，还开放24小时免费试用。

建议先收藏此文，周末完成智能代理部署，先用200美金小额试跑，依托实盘数据判断是否加仓。

想要一键跟单Polymarket大户，自用推荐PolyCop机器人：https://t.co/xrFNgicH8Q

#Polymarket

[SK @StepOneAi]

大多数人的努力程度，根本还没到需要拼天赋的地步，全是无效震荡。生活和交易一样，少做低胜率的纠结，多做高期望的执行。行动才是唯一的真实。

人生也是一场充满噪音的Time Series。与其每天在小周期里因为别人的看法而内耗，不如把周期拉长，坚定执行你的主策略。不要过度拟合别人的期待，做你自己。

[Robot James 🤖🏖 @therobotjames]

idiot edge

sometimes edges appear due to other trader's stupidity

i'll show you a crypto trade that is so silly you might not believe it

but i promise you it's real.

[QuantifiedStrategies.com @QuantifiedStrat]

Nasdaq (QQQ) Mean Reversion (Trading Strategy Analysis):

The Internal Bar Strength indicator (IBS) has worked remarkably well for over two decades. We can only guess why, but the stock market has been highly mean-reverting during this period, and this has, of course, given the IBS a nice tailwind.

* IBS= (Close-Low)/(High-Low);
* Buy= IBS<0.2 ;
* buyPrice=C ; //buy on the close
* Sell= IBS>0.8 ;
* sellPrice=C ; //sell on the close

We buy on close if IBS is lower than 0.2, and we sell any day later when the IBS closes above 0.8. An incredible simple IBS strategy and IBS trade!

The equity curve looks like the image shown below (compounded).

Can the strategy be improved or made different? If you have any suggestions, please comment 👇

[Jason @3PeaksTrading]

VVIX + VIX Term Structure - The Ultimate Volatility Trading System

🧵 Combining VVIX levels with VIX term structure creates a 3-dimensional view of volatility

This system has 75%+ win rate on volatility trades

Here's the complete framework and how I use VVIX in addition to VIX analysis to evaluate what volatility regime the market is in👇

[爱吃折耳根的Ace @_zheergen]

量化分析师 80% 的时间都在干这个：真实数据清洗全流程

有粉丝问我：有没有真正还原量化分析师日常工作的视频？不是只讲模型理论，而是把真实数据清洗、接口报错、对齐失败这些「巨坑」完整演示出来的。

我花了几天时间去找了这支来自 TechFin 的经典实操视频。

老师用 Python 在 Google Colab 上，完整估算苹果公司（AAPL）的 Fama-French 四因子风险模型（Market、Size、Value、Momentum），并提取因子暴露（Beta）和特异性风险（Alpha）。

视频真正的价值在于：它把量化工作中 80% 以上的时间都花在解决数据问题上，真实还原了各种常见坑点：

1️⃣ 因子数据获取与月度数据的选择理由
2️⃣ Yahoo Finance 接口失效后的 yfinance 修复
3️⃣ Timestamp 与 Period 索引不匹配导致 Merge 失败
4️⃣ 列名隐藏空格引发的 KeyError
5️⃣ 百分比与小数单位不一致
6️⃣ statsmodels OLS 默认不加截距项的问题

最终回归结果显示：苹果 Alpha 显著为正、Market Beta 约 1.2、HML 显著为负（符合成长股特征）、R² 约 28%，其余为特异性风险。

这支视频代码简洁、思路清晰，是学习金融计量与真实数据清洗非常值得参考的实操教程。对想了解「量化工作真实样子」的朋友，建议收藏。

[爱吃折耳根的Ace @_zheergen]

这个量化金融开源库基本是行业标准了

QuantLib 是一个成熟的开源 C++ 量化金融库，专注于金融产品的建模、定价、交易和风险管理。

它覆盖了大量衍生品定价模型、利率曲线构建、期权定价、Greeks 计算、风险指标等功能，被银行、对冲基金、研究机构和监管机构广泛使用。

它主要解决的是衍生品定价、风险管理和金融建模问题，覆盖范围比较广：

1️⃣ 利率、股票、外汇、信用等各类衍生品定价
2️⃣ 完整的期限结构、波动率曲面和定价引擎
3️⃣ 支持蒙特卡洛、有限差分、树模型等多种数值方法
4️⃣ 风险指标计算（Greeks、VaR 等）
5️⃣ 有 Python 绑定和 Excel 插件，使用门槛相对可控

虽然学习曲线不算低，但它在银行、对冲基金和量化团队中使用非常广泛，很多生产环境都会直接或间接依赖它。

对做衍生品定价、风险管理，或者想系统学习量化金融建模的人来说，QuantLib 目前仍然是最值得深入的开源库之一。

[Brett Harrison @BrettHarrison]

HFT Market-Making Systems

I spent the first 13 years of my career designing algorithmic trading systems for large HFTs in options, futures, and ETFs (intl/US equities, commodities, fixed income, volatility). Here’s a summary of each component, note the role AI/ML actually plays.

Market data. A market-making system requires realtime orderbook deltas and trades, not just for the symbol that’s being quoted but also for every related instrument used in computing the theoretical fair value of the instrument. Every exchange has a different protocol specification and format, from JSON over websocket (slowest) to FIX (common) to fixed-width binary (fastest). The lowest latency systems will ingest market data on a different thread or process to avoid interfering with the core trading logic of the system.

Fair Value. Successful market making depends on a precise understanding of the theoretic fair value of an instrument, which is the indifference point between buying and selling the instrument modulo fees and other costs. The complexity spans from a several-parameter model based on one or two instruments to highly multivariate calculations across a basket of different symbols. This component is usually the firm’s most guarded intellectual property.

Order placement. Accurately predicting the price of an instrument is only half of the core logic of a trading system; deciding where and how to place orders is equally determinative of P&L. Maintaining balanced two-sided quotes, optimizing for queue position, minimizing traffic to comply with order rate limits, canceling to avoid adverse selection, and order sizing are all intricately determined by the strategy designer.

Exchange connectivity. Similar to the market data component, all trading systems must send orders and cancels using each exchange’s unique messaging protocol. JSON, FIX, and binary formats are all used, with the last most common among traditional exchanges where latency and throughput are optimized. The connectivity layer can be embedded within the order placement process or segregated as its own gateway process.

Offline training. Training is not part of realtime transformation of ticks to trades, but it is one of most important differentiators between trading firms. All parameters that define fair value calculation and order placement are set via offline compute tasks that are often guided by machine learning. A significant amount of the GPUs that large HFTs buy or rent are directed to model discovery and parameter optimization.

A handful of HFTs already use specialized GPUs for the linear algebra behind valuation, but the practice remains rare, confined to strategies where predictive power matters more than shaving the last nanoseconds off latency. As purpose-built inference silicon such as Google’s TPU and OpenAI’s Jalapeño becomes widely available, more HFTs will fold online ML and inference into their systems’ trading loops.

[Lira @CryptoLira]

@suitman @whalecalls

[King @king_man5548]

@waleswoosh Who are you talking about?

[zek @zekramu]

in 6 months all of tech twitter will be talking about pi/omp supremacy

[King @king_man5548]

@crypto8645 @RunnerXBT Plasma one website shows it is under waitlist. How do I access it? Do they have an app?

[King @king_man5548]

@CoachAbedii @RunnerXBT @Plasma Plasma one shows join the waitlist. Are they live?

[CBB @Cbb0fe]

If you’re living in Dubai, this is a thread for you:

2 years ago, I started putting some effort into improving my quality of life.

Here are some of the best changes I’ve made so far:

1. Purify your air.

I installed multiple Atem X Air Purifier units from IQAir at home and in my gym.

[Yam Peleg @Yampeleg]

MiniMax-M2.7 woww

[sysls @systematicls]

It is crazy to me that in this day and age you can create enterprise value just by having impeccable tastes in design and implementation and having the ability to articulate these tastes.

Implementation risk is now a function of eloquence.

[Matias | Moby 🐳 @mattdorta]

Want to see if people are panic selling Dubai real estate?

I built a site that tracks every price drop across 16,000+ luxury listings in real-time.

➡️ https://t.co/DbMTJzcMZP

[Mikko Ohtamaa @moo9000]

It was funny to watch Claude Cowork scroll the Twitter feed up and down and try to figure out how the website works:

https://t.co/0hKfugpxTc

However when the task in the hand can be converted to a repeatable skill and when the skill can be made to rely on APIs (even if automatically reverse engineered by LLM), then it works much better.

[Degen Ape Trader @DegenApe99]

GPT-5.2 > Opus-4.5 > GPT-5.1 > Sonnet-4.5 > GLM-4.7 > Kimi-2.5

GPT-5.2 is the best as main brain of @openclaw since that model can fix code right away, without needing to call another coding agent.

Opus make many "quick win" mistakes when working alone. Usually it needs to pair with Sonnet (for context exploring) and another Opus (For code review + audit) as a coding team.

[Luca Netz 🐧✳️ @LucaNetz]

I’m looking for a crypto card. Which one is the best one?