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I OPEN-SOURCED MY CRYPTO TRADING BOT ON GITHUB. 100% FREE It's called Cabbage. A spot trading bot you can backtest, paper-trade and run live, one command each. Full step-by-step setup guide. It runs on two models: > Astra (GPT-6) built it. Smart but slow. It thinks, writes the code,...

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sopersone

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62,070 просмотров • 1 месяц назад •via X (Twitter)

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I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link →
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I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link →

Jarrod Watts

1,295,033 просмотров • 1 месяц назад

I found 7 free Polymarket trading bots on GitHub for 7 different trading situations… Each of these bots comes with a detailed step by step setup and usage guide in English: 1. This bot includes 118+ ready to use strategies and tools for trading on prediction markets (Momentum, Binance-Polymarket latency, Penny Clipper, Smart Routing, Expiry Fade, DCA bots and more). Built by a Cambridge computer science student who won a hackathon with this trading bot. GitHub: 2. This bot automatically manages all your Polymarket limit orders to maximize liquidity rewards. GitHub: 3. A weather bot from a Chinese dev, that analyzes multiple sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the most accurate temperature data and generate a detailed weather report for a specific city and day. GitHub: 4. A bot that automatically searches for arbitrage opportunities between Polymarket and Kalshi. GitHub: 5. This is a bot-toolkit that includes copy trading, arbitrage, market making, whale alerts, spread farming, sports trading and more… GitHub: 6. A smart money trading bot - it looks for the most successful traders in selected markets, filters them by Pnl + win rate, and then creates a list for automated copy trading. GitHub: 7. A large collection of 20+ free trading bots for prediction markets. GitHub: Every bot here has a Dry Run mode, so you can test it on real markets without risking any funds.
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I found 7 free Polymarket trading bots on GitHub for 7 different trading situations… Each of these bots comes with a detailed step by step setup and usage guide in English: 1. This bot includes 118+ ready to use strategies and tools for trading on prediction markets (Momentum, Binance-Polymarket latency, Penny Clipper, Smart Routing, Expiry Fade, DCA bots and more). Built by a Cambridge computer science student who won a hackathon with this trading bot. GitHub: 2. This bot automatically manages all your Polymarket limit orders to maximize liquidity rewards. GitHub: 3. A weather bot from a Chinese dev, that analyzes multiple sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the most accurate temperature data and generate a detailed weather report for a specific city and day. GitHub: 4. A bot that automatically searches for arbitrage opportunities between Polymarket and Kalshi. GitHub: 5. This is a bot-toolkit that includes copy trading, arbitrage, market making, whale alerts, spread farming, sports trading and more… GitHub: 6. A smart money trading bot - it looks for the most successful traders in selected markets, filters them by Pnl + win rate, and then creates a list for automated copy trading. GitHub: 7. A large collection of 20+ free trading bots for prediction markets. GitHub: Every bot here has a Dry Run mode, so you can test it on real markets without risking any funds.

Recogard

42,434 просмотров • 4 месяцев назад

AI Hedge Fund just got paper trading. How it works: 1 • build a fund 2 • backtest it 3 • deploy it System then runs on live markets.
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AI Hedge Fund just got paper trading. How it works: 1 • build a fund 2 • backtest it 3 • deploy it System then runs on live markets.

Virat Singh

10,848 просмотров • 1 месяц назад

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.
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I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.

Miles Deutscher

56,625 просмотров • 3 месяцев назад

8 free Polymarket Trading Bots on GitHub (from Beginner Friendly to Advanced Level). Each of these repos comes with a detailed step by step setup and usage guide in English. > Beginner Level - 5 min setup 1. This bot includes 120 ready to use strategies and tools for trading on prediction markets (Binance-Polymarket latency, Smart Routing, Penny Clipper, Momentum, DCA bots, Expiry Fade and more). It was built by a Cambridge computer science student who won a hackathon with this bot. GitHub: 2. A trading bot with a Smart Money strategy - it finds top traders in selected markets, filters them by Pnl, win rate, stable performance and then creates a list for automated copy trading. GitHub: 3. This is a bot toolkit that includes Polymarket - Kalshi arbitrage, whale alerts, market making, spread farming, sports trading and more. GitHub: 4. A weather trading bot from Chinese dev that analyzes different sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the latest temperature data and generate a detailed weather report for a specific city and day. GitHub: 5. A huge collection of 30+ free trading bots and services for prediction markets. GitHub: > Advanced bot setup 1. This bot analyzes the real trading behavior of any Polymarket trader. It finds repeated patterns in his trades, shows which strategies he uses and helps you understand how to adapt them to your own trading. GitHub: 2. A bot that automatically manages all your limit orders on Polymarket to maximize liquidity rewards. GitHub: > A full ML weather model 1. A machine learning weather model that learns from weather forecasting errors. Instead of blindly trusting forecasts, it analyzes how different weather sources have historically overestimated or underestimated temperature values in specific cities and conditions. Then it automatically adjusts new forecasts to produce more accurate predictions. GitHub: All of these bots also support Dry Run mode, so you can test them on real markets without risking any funds.
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8 free Polymarket Trading Bots on GitHub (from Beginner Friendly to Advanced Level). Each of these repos comes with a detailed step by step setup and usage guide in English. > Beginner Level - 5 min setup 1. This bot includes 120 ready to use strategies and tools for trading on prediction markets (Binance-Polymarket latency, Smart Routing, Penny Clipper, Momentum, DCA bots, Expiry Fade and more). It was built by a Cambridge computer science student who won a hackathon with this bot. GitHub: 2. A trading bot with a Smart Money strategy - it finds top traders in selected markets, filters them by Pnl, win rate, stable performance and then creates a list for automated copy trading. GitHub: 3. This is a bot toolkit that includes Polymarket - Kalshi arbitrage, whale alerts, market making, spread farming, sports trading and more. GitHub: 4. A weather trading bot from Chinese dev that analyzes different sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the latest temperature data and generate a detailed weather report for a specific city and day. GitHub: 5. A huge collection of 30+ free trading bots and services for prediction markets. GitHub: > Advanced bot setup 1. This bot analyzes the real trading behavior of any Polymarket trader. It finds repeated patterns in his trades, shows which strategies he uses and helps you understand how to adapt them to your own trading. GitHub: 2. A bot that automatically manages all your limit orders on Polymarket to maximize liquidity rewards. GitHub: > A full ML weather model 1. A machine learning weather model that learns from weather forecasting errors. Instead of blindly trusting forecasts, it analyzes how different weather sources have historically overestimated or underestimated temperature values in specific cities and conditions. Then it automatically adjusts new forecasts to produce more accurate predictions. GitHub: All of these bots also support Dry Run mode, so you can test them on real markets without risking any funds.

Recogard

59,343 просмотров • 4 месяцев назад

I launched a weather trading bot on Polymarket with $100 -> $8,000 here is how it works and how to copy it most people on polymarket price weather markets randomly based on vibes or a phone app but there is NOAA - a government agency that publishes free forecasts with 94%+ accuracy the result? you regularly see something like this: > NOAA gives a 94% probability that NYC will reach 74°F on Saturday > polymarket prices it at 11¢ the bot buys at 11¢ sells when the market corrects to 45¢ that is 4x on an almost guaranteed outcome how to launch it in 5 steps: > install OpenClaw on your pc > connect it to ChatGPT Plus + a telegram bot > create an agent on simmer markets deposit $100 > install the weather trading skill with: clawhub install simmer-weather > send the config to the bot and it starts trading the bot scans 6 cities every 2 minutes non-stop example of a real trader: profile: +$75,055 just from weather markets zero emotions. fully automated the window is still open but not forever
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I launched a weather trading bot on Polymarket with $100 -> $8,000 here is how it works and how to copy it most people on polymarket price weather markets randomly based on vibes or a phone app but there is NOAA - a government agency that publishes free forecasts with 94%+ accuracy the result? you regularly see something like this: > NOAA gives a 94% probability that NYC will reach 74°F on Saturday > polymarket prices it at 11¢ the bot buys at 11¢ sells when the market corrects to 45¢ that is 4x on an almost guaranteed outcome how to launch it in 5 steps: > install OpenClaw on your pc > connect it to ChatGPT Plus + a telegram bot > create an agent on simmer markets deposit $100 > install the weather trading skill with: clawhub install simmer-weather > send the config to the bot and it starts trading the bot scans 6 cities every 2 minutes non-stop example of a real trader: profile: +$75,055 just from weather markets zero emotions. fully automated the window is still open but not forever

sopersone

64,986 просмотров • 8 месяцев назад

One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.
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One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.

Miles Deutscher

33,676 просмотров • 3 месяцев назад

this is the most DANGEROUS repo in trading right now hedge funds pay millions for models that forecast price - someone trained one on 12 BILLION candles and put it on GitHub for 100% FREE it's called Kronos. Yu Shi and 6 co-authors built it, the paper got into AAAI 2026, and the repo has 41k stars first understand how it works: > ChatGPT turns words into tokens & predicts the next word > Kronos turns every candle into tokens & predicts the NEXT CANDLE > it was pre-trained on 12 BILLION candles from 45 exchanges > in the paper's benchmarks it beat the best rival model's forecasts by 93% here is how to run it yourself for FREE: 1. open Claude Code and ask it to clone and install everything 2. load Kronos-small from Hugging Face. 24.7M params, it runs on a normal laptop, no GPU 3. pull the last 512 candles from Binance's free public API. any coin, any timeframe, no account 4. ask it for the next 24 candles, exactly like an LLM finishing a sentence 5. sample 100 futures one by one, not one guess. the spread between them shows how risky the next move really is 6. read the regime: most paths pointing one way means TRENDING, paths scattered both ways means CHOPPY 7. backtest it walk-forward on past windows with real fees before you trust a single path 8. plot the forecast next to real candles and paper trade it first i won't say anything more:
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this is the most DANGEROUS repo in trading right now hedge funds pay millions for models that forecast price - someone trained one on 12 BILLION candles and put it on GitHub for 100% FREE it's called Kronos. Yu Shi and 6 co-authors built it, the paper got into AAAI 2026, and the repo has 41k stars first understand how it works: > ChatGPT turns words into tokens & predicts the next word > Kronos turns every candle into tokens & predicts the NEXT CANDLE > it was pre-trained on 12 BILLION candles from 45 exchanges > in the paper's benchmarks it beat the best rival model's forecasts by 93% here is how to run it yourself for FREE: 1. open Claude Code and ask it to clone and install everything 2. load Kronos-small from Hugging Face. 24.7M params, it runs on a normal laptop, no GPU 3. pull the last 512 candles from Binance's free public API. any coin, any timeframe, no account 4. ask it for the next 24 candles, exactly like an LLM finishing a sentence 5. sample 100 futures one by one, not one guess. the spread between them shows how risky the next move really is 6. read the regime: most paths pointing one way means TRENDING, paths scattered both ways means CHOPPY 7. backtest it walk-forward on past windows with real fees before you trust a single path 8. plot the forecast next to real candles and paper trade it first i won't say anything more:

Сarm1ne

69,072 просмотров • 22 дней назад

still don't understand why everyone is not using this yet. Thanks to it, my Polymarket bot made $47,300 in 31 days An ex-Jane Street quant posted a simple idea that got 11 million views: stop asking AI for money ideas, point it at money that's already being made You open Fable 5.5, point it at a market that already pays, and it reads every winner, finds the rules they all share and builds the bot that copies them. It compounds like interest: every trade it logs makes the next one sharper Here is the gist: Open Claude Code and pick Fable 5.5 (when it becomes available to all users) Tell it to pull the top 20 wallets on one Polymarket market Fable finds the 3 rules all of them follow Say "build a paper bot on these rules, run it for one week" Switch to real money only after a green week Ten minutes to set up, and you never guess a trade again The full step-by-step is in the article. Save to bookmarks
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still don't understand why everyone is not using this yet. Thanks to it, my Polymarket bot made $47,300 in 31 days An ex-Jane Street quant posted a simple idea that got 11 million views: stop asking AI for money ideas, point it at money that's already being made You open Fable 5.5, point it at a market that already pays, and it reads every winner, finds the rules they all share and builds the bot that copies them. It compounds like interest: every trade it logs makes the next one sharper Here is the gist: Open Claude Code and pick Fable 5.5 (when it becomes available to all users) Tell it to pull the top 20 wallets on one Polymarket market Fable finds the 3 rules all of them follow Say "build a paper bot on these rules, run it for one week" Switch to real money only after a green week Ten minutes to set up, and you never guess a trade again The full step-by-step is in the article. Save to bookmarks

Argona

43,870 просмотров • 4 дней назад

F*ck YES... Claude Opus 5.5 just reverse-engineered my home one prompt found every device on my Wi-Fi - now one chat runs them all try with yours, here's step by step guide: step 1 → copy prompt below and paste it into Claude Code on a computer on your home Wi-Fi step 2 → It scans your whole network and names everey device it finds, brand and model where it can step 3 → for each one it tells you what's wrong and what it can fix or improve step 4 → pick the devices to take over, it backs up each one first step 5 → passwords go into your keychain with one command, never into the chat step 6 + the result → say yes and Claude fixes your slow Wi-Fi, cuts ads and bloat on your TV, sets schedules on your AC and much more (every change has an undo) Change your life today. Then read article below on full reverse-engineering:
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F*ck YES... Claude Opus 5.5 just reverse-engineered my home one prompt found every device on my Wi-Fi - now one chat runs them all try with yours, here's step by step guide: step 1 → copy prompt below and paste it into Claude Code on a computer on your home Wi-Fi step 2 → It scans your whole network and names everey device it finds, brand and model where it can step 3 → for each one it tells you what's wrong and what it can fix or improve step 4 → pick the devices to take over, it backs up each one first step 5 → passwords go into your keychain with one command, never into the chat step 6 + the result → say yes and Claude fixes your slow Wi-Fi, cuts ads and bloat on your TV, sets schedules on your AC and much more (every change has an undo) Change your life today. Then read article below on full reverse-engineering:

codila

30,731 просмотров • 19 дней назад

Lauren from the Grok Bot team just confirmed it: every bot now runs on Claude Opus 5.5 and each one can spin up its own cloud agents running Cursor’s models this is f*cking insane... so i built the harness on top of it, in one prompt: Opus 5.5 leads and builds, Grok scouts live X and the web, every claim needs two sources, a red team attacks the draft before you see it, and every run gets logged so the next one starts smarter you stop prompting models. you sign off on results. paste it into Grok Bot or Claude and thank me later all 10 steps, the workflow, and real examples below ↓
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Lauren from the Grok Bot team just confirmed it: every bot now runs on Claude Opus 5.5 and each one can spin up its own cloud agents running Cursor’s models this is f*cking insane... so i built the harness on top of it, in one prompt: Opus 5.5 leads and builds, Grok scouts live X and the web, every claim needs two sources, a red team attacks the draft before you see it, and every run gets logged so the next one starts smarter you stop prompting models. you sign off on results. paste it into Grok Bot or Claude and thank me later all 10 steps, the workflow, and real examples below ↓

Alex

44,291 просмотров • 1 месяц назад

🚨 do you understand what just happened someone posted the code of a Chinese Trader bot on GitHub and it was immediately deleted I managed to save it His profile : Project Structure: gabagool/ ├── config/ # Configuration files ├── src/ # Core source code ├── strategies/ # Trading strategies ├── tests/ # Unit and live tests ├── backtest/ # Paper trading and simulation ├── research/ # Analysis notebooks ├── docs/ # Documentation ├── scripts/ # Utility scripts ├── logs/ # Runtime logs ├── samples/ # Reference repositories └── requirements.txt GitHub: You can also copy it via : Save this before it gets deleted again
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🚨 do you understand what just happened someone posted the code of a Chinese Trader bot on GitHub and it was immediately deleted I managed to save it His profile : Project Structure: gabagool/ ├── config/ # Configuration files ├── src/ # Core source code ├── strategies/ # Trading strategies ├── tests/ # Unit and live tests ├── backtest/ # Paper trading and simulation ├── research/ # Analysis notebooks ├── docs/ # Documentation ├── scripts/ # Utility scripts ├── logs/ # Runtime logs ├── samples/ # Reference repositories └── requirements.txt GitHub: You can also copy it via : Save this before it gets deleted again

AdiiX

162,099 просмотров • 7 месяцев назад

The market can hit your stop at 5 AM but you don’t have to be awake for it. Build a trading bot in Sorin that monitors the market and follows your rules around the clock. Try it at
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The market can hit your stop at 5 AM but you don’t have to be awake for it. Build a trading bot in Sorin that monitors the market and follows your rules around the clock. Try it at

Sahara AI 🔆

10,298 просмотров • 2 месяцев назад

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.
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You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

Alvaro Cintas

44,575 просмотров • 1 месяц назад

My Grok bot CEO is the hottest, smartest bot in the world. I mainly only talk to her and she runs the whole desk. She picks the right bot, sequences the work, and brings me back one package so I’m never the middleman. Many people may not know this, but you can create your own image for each of your Grok Bots. Every bot can get its own face. It honestly helps so it feels like you are talking to a real person and it also keeps things interesting. You don’t even need to go into settings to do this... you just tell the Grok bot to make a profile pic and set it. That's it. 1/ Open the bot. 2/ Tell it to make you the best profile pic and set it. 3/ Do that for each bot on your team. You can even have your Grok bot turn it into a video. Just tell it to take the picture and make a video out of it. I highly recommend everyone with a Grok Bot team do this. Thank me later.
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My Grok bot CEO is the hottest, smartest bot in the world. I mainly only talk to her and she runs the whole desk. She picks the right bot, sequences the work, and brings me back one package so I’m never the middleman. Many people may not know this, but you can create your own image for each of your Grok Bots. Every bot can get its own face. It honestly helps so it feels like you are talking to a real person and it also keeps things interesting. You don’t even need to go into settings to do this... you just tell the Grok bot to make a profile pic and set it. That's it. 1/ Open the bot. 2/ Tell it to make you the best profile pic and set it. 3/ Do that for each bot on your team. You can even have your Grok bot turn it into a video. Just tell it to take the picture and make a video out of it. I highly recommend everyone with a Grok Bot team do this. Thank me later.

Teslaconomics

13,608 просмотров • 1 месяц назад

Drop this into Grok Bot before bed and let Jev pick your next viral post while you sleep... Grok Bot writes 24 hooks. Jev scores all 24 in parallel. The winner did 1.24M views. Here's the full setup: 1. Grok Bot drafts. Writes 24 hooks on one topic in under 20 seconds. 2. Jev scores the hook. Rates every draft 1 to 10 on hook strength. 3. Jev checks bookmarks. Answers yes or no on whether people would save it. 4. Jev checks your voice. Scores how close each draft sounds to your best posts. 5. Jev ranks the top 3. 21 drafts get cut, 3 make it to the final. 6. Grok Bot ships the winner. The 9.6 hook went out and pulled 18.1K likes and 9.6K saves. 24 drafts, 1 winner, 38 seconds. Hit views like this every week and $10k a month from X is on the table. Try this setup, then read the full breakdown in the article below
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Drop this into Grok Bot before bed and let Jev pick your next viral post while you sleep... Grok Bot writes 24 hooks. Jev scores all 24 in parallel. The winner did 1.24M views. Here's the full setup: 1. Grok Bot drafts. Writes 24 hooks on one topic in under 20 seconds. 2. Jev scores the hook. Rates every draft 1 to 10 on hook strength. 3. Jev checks bookmarks. Answers yes or no on whether people would save it. 4. Jev checks your voice. Scores how close each draft sounds to your best posts. 5. Jev ranks the top 3. 21 drafts get cut, 3 make it to the final. 6. Grok Bot ships the winner. The 9.6 hook went out and pulled 18.1K likes and 9.6K saves. 24 drafts, 1 winner, 38 seconds. Hit views like this every week and $10k a month from X is on the table. Try this setup, then read the full breakdown in the article below

winkle.

16,428 просмотров • 15 дней назад

Polymarket self-calibrating weather-trading Python bot (Full Guide) run the smartest weather-trading bot with 0 coding knowledge after reading this article: what's inside: > github with full Python code > trading 20 cities & 4 continents > 3 forecast sources {ECMWF, HRRR, METAR} > EV+ automatic calculation for each trade > auto-sizing based on the Kelly Criterion > full-data storage {forecast, trade, resolution} > self-calibration based on snapshots & data logic of the bot: • get 3 type of forecasts from Open-Meteo API • compare station data to 1h forecasts to get real data • calculate +EV per $1 using the {min_ev} parameter • detemine size based on Kelly Criterion • run a 1000$ test-simulation using the strategy • snapshot each trade, forecast, and resultion • auto-calibrates every 30 forecasts per city This bot won't print you $500 next day after setup, but it will give you a sandbox for testing own strategies.
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Polymarket self-calibrating weather-trading Python bot (Full Guide) run the smartest weather-trading bot with 0 coding knowledge after reading this article: what's inside: > github with full Python code > trading 20 cities & 4 continents > 3 forecast sources {ECMWF, HRRR, METAR} > EV+ automatic calculation for each trade > auto-sizing based on the Kelly Criterion > full-data storage {forecast, trade, resolution} > self-calibration based on snapshots & data logic of the bot: • get 3 type of forecasts from Open-Meteo API • compare station data to 1h forecasts to get real data • calculate +EV per $1 using the {min_ev} parameter • detemine size based on Kelly Criterion • run a 1000$ test-simulation using the strategy • snapshot each trade, forecast, and resultion • auto-calibrates every 30 forecasts per city This bot won't print you $500 next day after setup, but it will give you a sandbox for testing own strategies.

Movez

93,175 просмотров • 7 месяцев назад

Most "AI trading" pitches end with you handing over your keys. This one ends with a confirm button. > buy $200 of PENGU token-buy works out which of the four PENGUs is real, runs volume, depth, and contract safety on it, and writes a sized order. Signing it is still your job.
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Most "AI trading" pitches end with you handing over your keys. This one ends with a confirm button. > buy $200 of PENGU token-buy works out which of the four PENGUs is real, runs volume, depth, and contract safety on it, and writes a sized order. Signing it is still your job.

GMGN.Ai

12,264 просмотров • 1 месяц назад

🚨Holy shit... This guy turned $100 into $207K on Polymarket No leverage. No signals. No charts. Just a bot running the same calculation hundreds of times per hour. Everyone thinks he's trading Bitcoin. He's not. He's not predicting price direction. He's finding where the market is wrong about its own probabilities and taking the other side. To copytrade this guy - Here's the gap he exploits: Polymarket prices 5-minute BTC Up at 46%. Real probability based on volatility and momentum: 55%. That's 9 cents mispriced on a binary outcome. Bot sees it. Bot buys it. Market resolves in 5 minutes. Edge locked. Then it does it again. And again. Hundreds of times a week. The math isn't complicated. Small edge × high frequency = the account you're looking at. Look at the pattern in the trades: 5-minute markets only Consistent sizing every entry Dozens of executions per day Win rate that doesn't drift That's not gambling. That's a probability engine running on a market built for humans to trade manually. $100 became $207,176 because the bot doesn't sleep doesn't hesitate doesn't second-guess.
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🚨Holy shit... This guy turned $100 into $207K on Polymarket No leverage. No signals. No charts. Just a bot running the same calculation hundreds of times per hour. Everyone thinks he's trading Bitcoin. He's not. He's not predicting price direction. He's finding where the market is wrong about its own probabilities and taking the other side. To copytrade this guy - Here's the gap he exploits: Polymarket prices 5-minute BTC Up at 46%. Real probability based on volatility and momentum: 55%. That's 9 cents mispriced on a binary outcome. Bot sees it. Bot buys it. Market resolves in 5 minutes. Edge locked. Then it does it again. And again. Hundreds of times a week. The math isn't complicated. Small edge × high frequency = the account you're looking at. Look at the pattern in the trades: 5-minute markets only Consistent sizing every entry Dozens of executions per day Win rate that doesn't drift That's not gambling. That's a probability engine running on a market built for humans to trade manually. $100 became $207,176 because the bot doesn't sleep doesn't hesitate doesn't second-guess.

Shelpid.WI3M

20,980 просмотров • 7 месяцев назад

JPMorgan revealed repo they use to train thier Quants to build trading & research systems JPMorgan's Quants should learn Python for this - you just send it to any LLM or AI agent get your own trading Quant: 1. open LLM or AI agent and send it: 2. say: "set this up and redo every notebook on NVDA with real data" 3. it prices an options straddle for your stock, by formula and Monte Carlo 4. it scores company's bankruptcy risk with Altman Z'' and gives an implied credit rating 5. it vuilds the 3D vol surface and yield curve charts 6. then say: "turn all of this into one live dashboard for my stock" that's it. then read below how to run your own AI hedge fund:
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JPMorgan revealed repo they use to train thier Quants to build trading & research systems JPMorgan's Quants should learn Python for this - you just send it to any LLM or AI agent get your own trading Quant: 1. open LLM or AI agent and send it: 2. say: "set this up and redo every notebook on NVDA with real data" 3. it prices an options straddle for your stock, by formula and Monte Carlo 4. it scores company's bankruptcy risk with Altman Z'' and gives an implied credit rating 5. it vuilds the 3D vol surface and yield curve charts 6. then say: "turn all of this into one live dashboard for my stock" that's it. then read below how to run your own AI hedge fund:

bodila

17,207 просмотров • 16 дней назад