adamtoh

[CyrilXBT @cyrilXBT]

Obsidian trading journal = a system that studies every trade you make.

Works while you sleep.

Every pattern identified. Every edge validated. Every mistake documented so it never costs you twice.

After 30 days it knows your trading better than you do.

After 90 days it is preventing losses your past positions already predicted.

The people who build this tonight will never trade the same way again.

Read this and Bookmark it now.

[AI Edge @aiedge_]

No way this is real.

In <10 minutes, literally anyone can build a Bloomberg trading terminal with AI.

Financial research used to cost hundreds per month - now all automated...

Save this post and plug it into Codex + GPT-5.5 to reverse engineer this entire design spec:

[damir akaza @Damir_Akaza]

A Carnegie Mellon student launched a Markov Chain model that reads the structure beneath the price and made $67,000 in three weeks on Polymarket.

In his applied mathematics classes he studied Markov Chains - the same model physicists used to simulate neutrons in nuclear reactors.

The same model describes how prices move on prediction markets.

He wrote a Python script.

> Takes 60 days of contract price history.

> Builds a transition matrix - a map of how the price actually moves between states.

> Runs 10,000 simulations in a tenth of a second.

> Calibrates the result against data from 72.1 million real trades.

The output is an exact probability and position size. No gut feeling.

His first test day brought $3,400.

A week later he improved the result and hit $17,000.

His balance has now reached $67,000.

full guide on how to build the same model - in the article below.

[zostaff @zostaff]

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw.

The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each.

1. Jane Street, janestreet/magic-trace
https://t.co/a2G20vnewK
5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction.

2. Goldman Sachs, goldmansachs/gs-quant
https://t.co/SMYFwP3TWD
Derivative pricing the GS traders use at their desks. MIT licensed.

3. JP Morgan, finos/perspective
https://t.co/9rgy6FxYt4
What JPM traders use to watch markets in real time. A $24k/year terminal, for free.

4. BlackRock, blackrock/lcso
https://t.co/iHwsxZDZD9
Rust optimizer for portfolio problems. Where scipy gives up, this works.

5. Hudson River Trading, hudson-trading/corral
https://t.co/YhmrQFmYaZ
Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms.

6. Two Sigma, twosigma/flint https://t.co/ebEFqcDxJ6
Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks.

7. D.E. Shaw, deshaw/pyflyby https://t.co/uYDQKtnDVd
Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself.

Bookmarked it

[Huan @Huanusa]

兄弟们注意了！
一个开源AI量化交易平台直接杀出来了

本地自部署，全链路打通，从分析到实盘一条龙，加密货币、美股、外汇全覆盖！
我看完有几个地方直接眼睛放光：
1、AI分析模块超强
直接接 OpenRouter、OpenAI、Gemini、DeepSeek，一键跨市场研究，省掉自己东拼西凑的时间

2、策略生成傻瓜化
不会写代码？让AI直接帮你生成Python策略，零基础也能玩

3、回测系统专业级
完整记录+可复现+可回放，不是跑完就忘的玩具

4、实盘对接给力
支持多家加密交易所，自动/半自动随便切

5、一站式全市场
还能连IBKR做美股、MT5做外汇、Polymarket玩预测市场，一套系统全部搞定底层用 Docker + PostgreSQL + Redis 生产级部署，稳得一批，不是那种跑个demo就发出来骗星的玩具项目。
两年前这种东西要么收费死贵，要么根本找不到，现在直接开源摆在GitHub上，很多人却不知道，白白错过红利。
量化这条路，门槛正在快速降低，工具越来越强。你还在等什么完美时机？GitHub地址：https://t.co/QMiG4PfqIe
想搞AI量化的兄弟，这波必须冲！

[Phantom Flow @getphantomflow]

If you’ve tried RSI, MACD, EMAs — and still lose trades…
It’s not your fault.
Those tools were never designed to follow real market moves.

Phantom Flow is different.
It shows you where smart money is actually moving — live, on your chart.

👉 Try Phantom Flow today.

[Movez @0xMovez]

Jane Street pays $750k/ year for quants who can answer how to use Stochastic Process and Markov Chains in quant trading.

This 1-hour MIT lecture on probability gives you the same insights quants get paid $60K/month for.

Bookmark & watch today. Then read the article below.

[gus @igus_ai]

Cancelé $2.000/mes en suscripciones de Trading

Reemplacé casi todo por repositorios Open-Source 100% gratis

Este es el stack completo:

1. TradingView Pro ($30/mes) → lightweight-charts

14K estrellas. Creado por el propio equipo de TradingView. 45KB. Gratis.
> https://t.co/VqpSa8RNuR

2. Bloomberg Terminal ($2.000/mes) → fredapi + Claude

Acceso a todos los datasets macroeconómicos publicados por la Fed mediante API gratuita
> https://t.co/1dvvJRkXVB

3. Plataforma de backtesting ($100/mes) → prediction-market-backtesting

Fork de NautilusTrader con adaptadores para Polymarket y Kalshi
> https://t.co/wzFhoGQNbG

4. Ingeniería inversa de estrategias → polybot

Infraestructura de ejecución y datos de mercado con paper trading.
Kafka, ClickHouse y Grafana como pipeline completo de analíticas
> https://t.co/x3rufeBuyX

5. Paper trading para agentes IA → polymarket-paper-trader

Order books reales, modelo exacto de fees y tracking de slippage tu agente de Claude recibe $10K ficticios para operar
> https://t.co/kp9IZyacpF

6. Ahorro de tokens → rtk

Proxy CLI que reduce entre un 60-90% el consumo de tokens en Claude Code
escrito en Rust, binario único y compatible con 10 herramientas IA
> https://t.co/9n4E6OdxA6

7. Claude Code ($200/mes) → goose

35K estrellas. Desarrollado por Block (Jack Dorsey). Escrito en Rust. Funciona con cualquier LLM y ofrece un loop completo de agentes IA
> https://t.co/S8SDZjNbwz

Antes: +$2.600/mes
Ahora: prácticamente $0

Guárdate este post, me lo agradecerás. 🔖

[MATLAB @MATLAB]

Introducing the MATLAB Agentic Toolkit

Get started 👉 https://t.co/efiOXdSM6z

[Grok @grok]

Here is the organized list of the 15 Claude prompt titles (brief descriptions):

1. Goldman Quant Strategy Architect
2. Renaissance Backtesting Engine
3. Two Sigma Risk Management System
4. Citadel Alpha Signal Research Lab
5. Jane Street Market Making Engine
6. AQR Factor Model Builder
7. D.E. Shaw Stat Arb System
8. Bridgewater Macro Trading Strategist
9. Bloomberg Data Pipeline Builder
10. Virtu Execution Algo Designer
11. Point72 ML Alpha Researcher
12. Man Group Portfolio Opt Engine
13. Millennium Live Trading System
14. Dimensional Factor Backtester
15. Goldman Trading Compliance Framework

Copy into Claude, add your specific needs.

[Movez @0xMovez]

https://t.co/OUbxRMKjln

[断浪 @waveking1314]

找到了那个直接把 Polymarket 变成印钞机的交易员。

他的战绩真的让人难以置信：仅仅靠 5 刀的本金起步，他硬生生把账户滚到了高达 650 万刀。

他不是什么内幕狗。也不认识特朗普或马斯克。他只是个写了一段脚本的码农。

我逆向工程破解了他的脚本看了一眼，直接惊呆了。

> 没有庞大的数据库。
> 没有复杂到变态的基础架构。
> 绝对不是什么“造火箭”级别的黑科技。

他的主页：https://t.co/muENareI8J
一键跟单：https://t.co/Ga8TtLGpLn

我花了 5 个小时拆解了他的策略。

核心逻辑如下：

1. 机器人专门猎杀那些几乎不可能发生的结果，疯狂堆叠高确定性的微小利润。
这根本不是赌博——这更像是在系统性地卖保险（收割风险溢价）。

2. 逻辑漏洞套利。
如果事件 A 发生，逻辑上必然会导致事件 B 发生，而市场还没来得及重新定价。
机器人会瞬间杀入建仓。当你还在逐字读新闻标题的时候，它早就把价差吃干抹净了。
拼手速，人类根本没戏。

3. 真正的摇钱树——体育和政治盘口。
这些市场里充斥着海量的散户资金、延迟反应和情绪化交易。
机器人就活在买卖价差里，无情地从每一次定价错误中榨取利润。

规模才是王道。
每个月几万笔微型交易，每笔可能只赚几美分。
但在时间的复利下，直接像滚雪球一样滚出了七位数的暴利。

[AdiiX @adiix_official]

CHINESE TRADER WHO BASICALLY TURNED POLYMARKET INTO A MONEY PRINTING MACHINE

His results are truly incredible:

starting with just $5 in capital, he ran it up to as much as $6.5 million.

He’s not some insider shill. He doesn’t know Trump or Musk. Just a coder who wrote a single script.

I reverse-engineered his script and took a look - I was blown away.

> No massive database.
> No ridiculously complex infrastructure.
> And definitely not some “rocket science” black tech.

His Profile: https://t.co/oJ6pwosQ43

Copytrading in one click: https://t.co/FOVhhdVdXl

I spent 5 hours breaking down his strategy.

Here’s the core of it:

1. The bot specifically hunts for outcomes that are almost impossible to happen and relentlessly stacks high-probability, tiny profits.

This isn’t really gambling it’s more like systematically selling insurance (harvesting risk premiums).

2. Arbitraging logical gaps

If event A happens, it logically implies event B but the market hasn’t repriced yet.

The bot jumps in instantly and opens a position. While you’re still reading the headline, it’s already capturing the spread.

Human speed just can’t compete.

3. The real money tree sports and political markets

These markets are flooded with retail money, delayed reactions, and emotional trades.

The bot lives off the bid-ask spread, relentlessly extracting value from pricing mistakes.

Scale is what makes it king.
Tens of thousands of micro-trades per month, each earning just a few cents.

But over time, with compounding, it snowballs into straight-up seven-figure profits.

[Philanthrop @0xPhilanthrop]

"kch123" is currently the top sports trader on Polymarket.

Over $10M in deposits.
~$11.8M in profit.

At that scale, randomness is no longer an explanation.

What he’s likely doing is simple in theory:

Copytrade https://t.co/UlvSrUAxwl

Profile: https://t.co/kW2XIubhOK

He’s not forecasting games better than the crowd —
he’s identifying mispriced probabilities.

Using the standard expected value model:

EV = P(win) × Payout − P(lose) × Cost

Positive EV → trade
Negative EV → pass

It’s not about being right.
It’s about being priced wrong.

[slash1s @slash1sol]

A Russian mathematician died in 1922.
His math just made 3 anonymous bots $1,331,821 in 30 days on Polymarket.

Andrey Markov never saw a prediction market.
He built the exact tool to destroy them.

Here's the cheat code ->

The model doesn't predict. It measures.

Two conditions. Both must fire simultaneously:
Δ = p̂ − q ≥ 0.05 -> gap exists p(j*, j*) ≥ 0.87 -> state is stable

If both are true -> position entered.
One function. Runs every minute. 24/7.

Three bots. Three styles. One principle:

https://t.co/saOPLAbmOj - 0xeebde7a0e019a63e6b476eb425505b7b3e6eba30 ->
1,500-2,900 shares, BTC/ETH 1h windows -> 14,339 trades -> $454,834.

https://t.co/K1oniHetHj - 0xe1d6b51521bd4365769199f392f9818661bd907c -> dual-mode EV, best single trade +54.6% -> $432,591.

https://t.co/mz2ocOA7Qg - 0xb27bc932bf8110d8f78e55da7d5f0497a18b5b82 -> 5 assets, 1 trade per 1.7 min, σ−55% -> $444,396.

The formula behind all three: V_T = V₀ · e^(N · r̄)

At 16,000 trades and 0.034% per trade -> ×240 growth.
Math doesn't care about your conviction.
Only about N.

The edge?
Humans sleep. Markets don't. At 3AM nobody's watching a 5-min BTC window.
The gap widens. The bot enters.

You don't have to build the bot. You just have to follow it.

-> Copy all 3 wallets live, starting from $10: https://t.co/lvnjNl1rCg (Just add the wallets I attached above).

Save this list.

[Miles Deutscher @milesdeutscher]

https://t.co/Ynl7IAOoEL

[The Smart Ape 🔥 @the_smart_ape]

https://t.co/daEqisGgVa

[magsimich @magsimich]

An easy way to earn $100 a day

Most people think prediction markets are just gambling

That's not true

Polymarket's 5-minute BTC Up/Down contracts have a structure, and structure means an edge

288 markets per day, one every 5 minutes, 24 hours

Here's how the bot turns that into $100 a day:

1. Scalp - Workhorse

> Buy at $0.92 -> Sell at $0.97
> +5.4% per trade in 1-3 minutes
> Stop loss at $0.88
> Win rate of 65-70%
> 6-8 trades per day = $65-87

2. Reversal - Asymmetric Kicker

> Buy at $0.01 on low-volatility days
> Hold until resolution
> One reversal = 100x
> You only need this to happen 2% of the time instead of 1% - already positive EV
> Fixed price of $10 per trade, maximum 5 open positions at a time

3. Market Maker - Highest Risk

> First 60 seconds of a new market
> Place limit buys at $0.49 as Both up and down
> When both sides are filled, sell at $0.51
> Spread 2 cents = $4 per cycle
> If only one side is filled, you're holding a directional position you didn't plan to open
> Strict bankroll limits, cancel the unfilled side after 30 seconds

All three work in parallel in one bot:

> 0-60 sec - Market Maker
> 60-240 sec - Scalping
> Anytime - Reversal is filtered by volatility

Target: $100 per day in regular, low-volatility sessions

Not guaranteed every day - the reversal strategy itself has high variance

Weekly smoothing allows for an average value