![qwinsi0x's tweet photo. every profitable trading desk on the planet runs on one number most retail traders never once calculate. it's not a secret indicator. it's expected value, and how it actually works is not what you think.
most people think expected value is "the average." it's sharper than that. it's what a bet is worth on average, weighted by the probability of each outcome.
E[X] = Σ (each outcome × its probability)
start simple. a fair die:
E[X] = 1(1/6) + 2(1/6) + ... + 6(1/6) = 3.5
the die can never land on 3.5. that's the whole point. expected value isn't the result you'll see next. it's the number your results converge to as the sample grows. one trade tells you nothing. ten thousand tell you everything.
now put it on a real position. 40% chance to make $300, 60% chance to lose $150.
E = (0.40 × 300) + (0.60 × −150) = +$30
win rate under half, expectation positive, prints over distance. this is why desks take trades that lose more often than they win, and why chasing win rate quietly bankrupts retail.
then the property a real book runs on: expectation is linear.
E[aX + bY] = a·E[X] + b·E[Y]
it holds whether or not the positions are correlated. that's the unlock. the expected value of an entire portfolio is just the sum of its parts. you never have to model the tangled joint outcome, you add the pieces.
this is why a desk can stack hundreds of tiny edges and know the total in advance. a bet that wins with probability p is worth exactly p. line up n of them and your expected haul is n·p. no simulations. the math hands it over.
and it doesn't stop at dice and coins. anything continuous uses the same idea, the sum just becomes an integral:
E[X] = ∫ x·f(x) dx
same concept, heavier notation. sum over outcomes becomes integrate over the density.
expected value is the floor the entire building stands on. variance measures the spread around it. the law of large numbers guarantees you actually reach it. every quant tool you've heard of starts at this one number.
almost nobody runs it before they click. that, not the formula, was always the edge.](https://pbs.twimg.com/media/HPC2Zz4XwAAWebe.jpg)
Olivia
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5 GitHub repositories you should know if you’re building trading bots.
1. Freqtrade
A powerful open-source crypto trading bot built with Python.
It comes with backtesting, strategy optimization, WebUI, Telegram control and more.
https://t.co/DFxpq4o0iE
2. Hummingbot
An open-source framework for building and deploying automated trading strategies.
Great for experimenting with market-making and other automated trading strategies.
https://t.co/BQ8RNV07JD
3. Jesse
An advanced Python framework for crypto trading and strategy development.
It focuses heavily on strategy research, backtesting and systematic trading.
https://t.co/r1JPyiuzY5
4. QuantConnect Lean
A powerful open-source algorithmic trading engine built with Python and C#.
Useful for researching, backtesting and building systematic trading strategies.
https://t.co/3fq3q2OHWE
5. Lumibot
A Python framework for building, backtesting and running automated trading strategies.
Supports crypto, stocks and other assets, making it useful for experimenting with different strategies.
https://t.co/2g2FEXuS8e
Bookmark this for the week.
These 5 repos are worth exploring if you’re building or experimenting with trading bots.
SpaceXAI just dropped the best 1-hour course on Grok Bot agents: from 1 prompt to a full 24/7 system
2:15 - Build your first Grok Bot
6:52 - Give every Bot a role
16:51 - Make Bots work 24/7
31:50 - Run agents in parallel
52:18 - full autonomous system
Free, the best thing on Grok Bot agents I've come across.
Watch it, then read the full Grok Bot breakdown below
@LegacySiu Santi Cazorla
this is most dangerous thing on internet today
build AI hedge fund using "GROK BOTS" that hunts alpha 24/7 (Full Guide)
if I had this a year ago, I would've built my hedge fund in a week instead of a year
bookmark before someone takes it down
Some guy made a quant trading system that uses AI, real-time data processing, and risk management.
Then open sourced it for free in Python.
Here it is:
量化新玩法：让算法自己挖掘K线形态
强烈推荐给想用遗传算法自动挖掘 K 线形态的朋友。
这支约 14 分钟的视频，讲了一个很有意思的方向：不再靠人主观定义“锤头线”“吞没形态”，而是让算法从大量历史 K 线中，自己寻找可能有效的交易规律。
它把 OHLC 数据拆解成机器能理解的规则，通过遗传算法不断组合、筛选，再结合蒙特卡洛置换和 Walk-Forward 验证，判断这些形态到底是真规律还是历史巧合。
为什么值得看？
因为它把传统“看图找规律”变成了一套完整的量化流程：
1️⃣ 因为它把传统“看图找规律”变成了一套完整的量化流程：
2️⃣ 用 Martin Ratio 评估策略质量，不只看收益，还考虑回撤风险
3️⃣ 加入相关性惩罚，避免挖出一堆重复形态
4️⃣ 通过随���置换验证，判断发现的规律是否真的有效
核心流程：OHLC 数据 → 形态编码 → 遗传进化 → 多形态组合 → 严格验证
最终来看，它展示的不只是一个 K 线策略，而是一套自动化 Alpha 挖掘方法：
让机器从数据中寻找非线性规律，同时用严格验证降低过拟合风险。
建议收藏，配合代码(需要说一下)一起研究。
15 coded quant trading strategies.
All in Python.
All free.
Get them here:
How to build your own algorithmic trading analyzer with Claude Code.
(bookmark this for later)
How to make a simple algorithmic trading strategy with a 472% return using Python.
A thread. 🧵
12 Python libraries for free market data everyone should know:
Learn a skill here for free.
Cybersecurity
https://t.co/8yvCVs1Z6l…
UI/UX Design
https://t.co/8yvCVs1Z6l…
AI Automation
https://t.co/PVKAvUdJ3w…
AI Video Creation
https://t.co/8yvCVs1Z6l…
Video Editing with CapCut
https://t.co/8yvCVs1Z6l…
Repost/bookmark and get to work!
HOW TO RUN AN AI MODEL FROM A USB FLASH DRIVE
ESTE TIPO USÓ CLAUDE PARA CONSTRUIR UN SISTEMA DE TRADING AUTOMATIZADO QUE GANÓ 847 $ DE LA NOCHE A LA MAÑANA
Todo empezó con un solo artículo sobre bots de Trading.
Claude lo leyó, seleccionó 7 repositorios de GitHub, los conectó en un pipeline funcional y desplegó el sistema de principio a fin.
A partir de ahí, todo funcionó de forma autónoma.
El sistema escaneó más de 412.000 operaciones, rastreó wallets de ballenas, detectó actividad de insiders y tomó decisiones en tiempo real.
Según él, cada pocos segundos analizaba datos, decidía si comprar, vender o pasar, y ejecutaba las operaciones automáticamente.
47 operaciones después, el sistema terminó la noche con 847 $ de ganancia, sin ninguna intervención manual.
LOCURA
Top 10 Algorithmic Trading Strategies (and how they work) 🧵
this is f*cking gold
How to build your first AI agent (Full guide)
if I had this a year ago, I would've shipped my first agent in a day instead of 2 weeks in the right hands, this changes everything:
نصيحة للمتداولين: لا تحصر نفسك في Finviz.. إليك 4 فلاتر ذهبية في TradingView 🚀
يستخدم الكثير من المتداولين فلاتر موقع Finviz الشهيرة، لكن الكثيرين لا يعلمون أن منصة TradingView توفر فلاتر (Screeners) احترافية تمنحك دقة عالية ورصداً مباشراً لحركة الأموال.
إليك أهم 4 فلاتر مجانية يمكن استخدامها يومياً وأسبوعياً لتطوير الأداء:
1) فلتر "أقوى الرابحين" (Best Winners)
هذا هو الفلتر الجوهري للبحث عن أقوى الأسهم والقادة الحقيقيين في السوق. يتميز برصد الزخم العالي والقوة النسبية والاتجاهات الواضحة. هذه هي الأسماء التي يمكن أن تصبح رابحة لعدة أسابيع متتالية إذا سمحت ظروف السوق بذلك.
🔗 https://t.co/xmc0A6jysX
2) فلتر "القوة اليومية" (Daily Strong)
فلتر يعطي "رد فعل السوق" الصافي. يُنصح بتشغيله يومياً لمعرفة أين تتدفق الأموال؛ فهو يوضح ما إذا كانت القوة تتوسع أم تجف — بدون آراء شخصية وبدون ضجيج.
🔗 https://t.co/hwe3JDGN1u
3) فلتر "البيع المكشوف" (Short Screener)
تزداد أهميته خاصة في الاتجاهات الهابطة. يرصد الأسهم المنهكة التي فقدت زخمها وظهرت عليها ردود فعل ضعيفة أو قمم فاشلة. المتداول المحترف لا يخمن صفقات البيع — بل يستعد لها.
🔗 https://t.co/ptju1cMTzN
4) فلتر "ما قبل الافتتاح" (Pre-Market)
لمراقبة الفجوات السعرية (Gap-ups)، وأحجام التداول غير العادية، والمحفزات الجديدة. ال��دف ليس المطاردة — بل المراقبة وإعداد خطط مهيكلة قبل أن تتدخل العواطف مع بداية الجلسة.
🔗 https://t.co/7qmEe8buAP
الحقيقة التي يجب معرفتها:
الفلتر في حد ذاته لا يمنحك الأفضلية، بل معرفة كيفية قراءته هي ما يصنع الفارق.
الفلاتر هي مجرد أدوات، والخبرة والممارسة هما ما يحولان هذه الأدوات إلى سلاح فعال. هذه الأنماط تتكرر دائماً، وإتقان التعامل معها مهارة يمكن لأي متداول اكتسابها مع الوقت.
Rus bir doktorun hazırladığı sırt ve disk ağrılarını ameliyatsız olarak ortadan kaldıran özel egzersizler.
G��nde 15 dakikanızı ayırmanız yeterli olacaktır..
every profitable trading desk on the planet runs on one number most retail traders never once calculate. it's not a secret indicator. it's expected value, and how it actually works is not what you think.
most people think expected value is "the average." it's sharper than that. it's what a bet is worth on average, weighted by the probability of each outcome.
E[X] = Σ (each outcome × its probability)
start simple. a fair die:
E[X] = 1(1/6) + 2(1/6) + ... + 6(1/6) = 3.5
the die can never land on 3.5. that's the whole point. expected value isn't the result you'll see next. it's the number your results converge to as the sample grows. one trade tells you nothing. ten thousand tell you everything.
now put it on a real position. 40% chance to make $300, 60% chance to lose $150.
E = (0.40 × 300) + (0.60 × −150) = +$30
win rate under half, expectation positive, prints over distance. this is why desks take trades that lose more often than they win, and why chasing win rate quietly bankrupts retail.
then the property a real book runs on: expectation is linear.
E[aX + bY] = a·E[X] + b·E[Y]
it holds whether or not the positions are correlated. that's the unlock. the expected value of an entire portfolio is just the sum of its parts. you never have to model the tangled joint outcome, you add the pieces.
this is why a desk can stack hundreds of tiny edges and know the total in advance. a bet that wins with probability p is worth exactly p. line up n of them and your expected haul is n·p. no simulations. the math hands it over.
and it doesn't stop at dice and coins. anything continuous uses the same idea, the sum just becomes an integral:
E[X] = ∫ x·f(x) dx
same concept, heavier notation. sum over outcomes becomes integrate over the density.
expected value is the floor the entire building stands on. variance measures the spread around it. the law of large numbers guarantees you actually reach it. every quant tool you've heard of starts at this one number.
almost nobody runs it before they click. that, not the formula, was always the edge.
![qwinsi0x's tweet photo. every profitable trading desk on the planet runs on one number most retail traders never once calculate. it's not a secret indicator. it's expected value, and how it actually works is not what you think.
most people think expected value is "the average." it's sharper than that. it's what a bet is worth on average, weighted by the probability of each outcome.
E[X] = Σ (each outcome × its probability)
start simple. a fair die:
E[X] = 1(1/6) + 2(1/6) + ... + 6(1/6) = 3.5
the die can never land on 3.5. that's the whole point. expected value isn't the result you'll see next. it's the number your results converge to as the sample grows. one trade tells you nothing. ten thousand tell you everything.
now put it on a real position. 40% chance to make $300, 60% chance to lose $150.
E = (0.40 × 300) + (0.60 × −150) = +$30
win rate under half, expectation positive, prints over distance. this is why desks take trades that lose more often than they win, and why chasing win rate quietly bankrupts retail.
then the property a real book runs on: expectation is linear.
E[aX + bY] = a·E[X] + b·E[Y]
it holds whether or not the positions are correlated. that's the unlock. the expected value of an entire portfolio is just the sum of its parts. you never have to model the tangled joint outcome, you add the pieces.
this is why a desk can stack hundreds of tiny edges and know the total in advance. a bet that wins with probability p is worth exactly p. line up n of them and your expected haul is n·p. no simulations. the math hands it over.
and it doesn't stop at dice and coins. anything continuous uses the same idea, the sum just becomes an integral:
E[X] = ∫ x·f(x) dx
same concept, heavier notation. sum over outcomes becomes integrate over the density.
expected value is the floor the entire building stands on. variance measures the spread around it. the law of large numbers guarantees you actually reach it. every quant tool you've heard of starts at this one number.
almost nobody runs it before they click. that, not the formula, was always the edge.](https://pbs.twimg.com/media/HPC2Z02X0AAqsGl.jpg)
The secret to algorithmic trading was just laid out in a 27 page paper.
The complete mathematical framework showing why markets are not random...
And how top hedge funds exploit markets to get alpha.
Get it here:
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