Loading video...

Video Failed to Load

Go Home

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,...

sopersone's profile picture

sopersone

4,842 subscribers

57,953 views • 2 days ago •via X (Twitter)

FinanceEducationScience & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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.
0:17

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

42,137 views • 3 months ago

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.
0:18

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 views • 2 months ago

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.
0:16

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

58,826 views • 2 months ago

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
0:18

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 views • 6 months ago

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.
0:12

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 views • 1 month ago

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.
0:15

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

41,301 views • 5 days ago

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.
0:11

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 views • 10 days ago

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.
0:10

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 views • 6 months ago

🚨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.
0:17

🚨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

20,853 views • 6 months ago

Cancel your $200/mo Ahrefs subscription 🤯 Claude Code can now run your SEO for you. Point it at your Search Console and it finds the wins, writes the fixes, and renders a live dashboard off your own data. All inside Claude Code. Perfect for DTC brands and agencies sitting on months of Search Console data nobody has time to read. Here's what it does: → Connects to your Search Console and GA4 through one guided setup that routes around Google's auth landmines → Finds the keywords sitting at positions 4 to 20 and scores them by the clicks you're leaving on the table → Ships the fix instead of naming it, with the rewritten title, the headings, and paste-ready content → Turns redirect chains, broken canonicals, and slow pages into dev tickets ranked by traffic at risk → Maps every query into hub-and-spoke clusters and flags where your own pages compete with each other → Drops a Monday report with week-over-week movement and exactly 3 priorities What you get: → 9 skills in one plugin, from the Google setup through to the Monday report → A live SEO dashboard with a 0 to 100 health score, rendered as one self-contained HTML file → Orphan pages and money-page link gaps, listed paste-ready → Content drafted from your own search data instead of a keyword tool's guesses Built 100% in Claude Code on your Search Console and GA4 data. 📌 Get the free plugin here:
0:15

Cancel your $200/mo Ahrefs subscription 🤯 Claude Code can now run your SEO for you. Point it at your Search Console and it finds the wins, writes the fixes, and renders a live dashboard off your own data. All inside Claude Code. Perfect for DTC brands and agencies sitting on months of Search Console data nobody has time to read. Here's what it does: → Connects to your Search Console and GA4 through one guided setup that routes around Google's auth landmines → Finds the keywords sitting at positions 4 to 20 and scores them by the clicks you're leaving on the table → Ships the fix instead of naming it, with the rewritten title, the headings, and paste-ready content → Turns redirect chains, broken canonicals, and slow pages into dev tickets ranked by traffic at risk → Maps every query into hub-and-spoke clusters and flags where your own pages compete with each other → Drops a Monday report with week-over-week movement and exactly 3 priorities What you get: → 9 skills in one plugin, from the Google setup through to the Monday report → A live SEO dashboard with a 0 to 100 health score, rendered as one self-contained HTML file → Orphan pages and money-page link gaps, listed paste-ready → Content drafted from your own search data instead of a keyword tool's guesses Built 100% in Claude Code on your Search Console and GA4 data. 📌 Get the free plugin here:

Mike Futia

22,780 views • 1 month ago

save these two free services. they are what i built my Polymarket strategies on, the ones paying me around $10,000 a month. what it looks like day to day: you ask any question about the market in plain words, Claude Code digs through the data and comes back with numbers. you become a quant researcher. 1 - poly_data one command and every Polymarket trade sits on your own disk. who bought, when, at what price, what size, and who was standing on the other side of it. my first sync pulled 3.1 million markets. you can pull apart the edge of any trader on Polymarket you like. first sync runs a few hours, leave it overnight, after that updates land in seconds. 2 - pendulumflow archive this one holds the book itself: what orders were sitting there, at what prices, what size, second by second, on every single market. 288 billion events recorded, and 2.3 billion more coming in every day. i used to pay $200 a month for this kind of data. here you just open it and take it, nothing to sign up for. the front page has ready made questions you copy straight into Claude Code. the project runs on donations, storing and serving this much data costs real money every month. so if you start using it, send the guy something, he is carrying all of it alone. these two will give you what only a handful of people on this market have.
0:19

save these two free services. they are what i built my Polymarket strategies on, the ones paying me around $10,000 a month. what it looks like day to day: you ask any question about the market in plain words, Claude Code digs through the data and comes back with numbers. you become a quant researcher. 1 - poly_data one command and every Polymarket trade sits on your own disk. who bought, when, at what price, what size, and who was standing on the other side of it. my first sync pulled 3.1 million markets. you can pull apart the edge of any trader on Polymarket you like. first sync runs a few hours, leave it overnight, after that updates land in seconds. 2 - pendulumflow archive this one holds the book itself: what orders were sitting there, at what prices, what size, second by second, on every single market. 288 billion events recorded, and 2.3 billion more coming in every day. i used to pay $200 a month for this kind of data. here you just open it and take it, nothing to sign up for. the front page has ready made questions you copy straight into Claude Code. the project runs on donations, storing and serving this much data costs real money every month. so if you start using it, send the guy something, he is carrying all of it alone. these two will give you what only a handful of people on this market have.

may.crypto {🦅}

25,908 views • 18 days ago

+$8,500 IN 30 SECONDS. THIS FUCKING GENIUS BUILT HIS OWN TRADING BOT WITH AI 🤯 He didn’t spend months learning how to code. He used an AI tutorial to build a bot that analyzes the market, finds setups, and executes trades based on his strategy. Now that tutorial is basically helping him automate his trading. Here’s how he did it ↓ → Picked one simple trading strategy → Used AI to turn the rules into a trading bot → Added entry + exit conditions → Backtested the strategy → Automated the execution → Let the bot scan for setups The key part: He didn’t ask AI to magically “make money.” He gave it a clear strategy and used AI to turn those rules into an automated system. One trade reportedly made him +$8,500 in around 30 seconds. Obviously, this doesn’t mean every trade will look like that. But the idea is powerful: You don’t necessarily need to become a programmer to start building your own trading tools. AI can help turn a trading idea into something you can actually test and automate. Here’s the full strategy + how he built the bot
0:18

+$8,500 IN 30 SECONDS. THIS FUCKING GENIUS BUILT HIS OWN TRADING BOT WITH AI 🤯 He didn’t spend months learning how to code. He used an AI tutorial to build a bot that analyzes the market, finds setups, and executes trades based on his strategy. Now that tutorial is basically helping him automate his trading. Here’s how he did it ↓ → Picked one simple trading strategy → Used AI to turn the rules into a trading bot → Added entry + exit conditions → Backtested the strategy → Automated the execution → Let the bot scan for setups The key part: He didn’t ask AI to magically “make money.” He gave it a clear strategy and used AI to turn those rules into an automated system. One trade reportedly made him +$8,500 in around 30 seconds. Obviously, this doesn’t mean every trade will look like that. But the idea is powerful: You don’t necessarily need to become a programmer to start building your own trading tools. AI can help turn a trading idea into something you can actually test and automate. Here’s the full strategy + how he built the bot

Niche trader

89,003 views • 20 days ago

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.
0:16

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here's what it built on its own: > Monitoring agent running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

0xMarioNawfal

80,450 views • 6 months ago

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.
0:16

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.

Discover

14,679 views • 6 months ago

Stop manually making flowcharts. Try this GitHub skill with Claude Code. I found an open-source tool that turns a plain description of a software system into a clean, interactive diagram. It's called Archify, and it has more than 66,000 stars on GitHub. You add it as a skill to an AI coding agent like Claude Code, Codex, Cursor, or OpenCode. Then you describe the system in the chat in one line. Archify turns that line into a single HTML file you can open, click through, and send to anyone. If you point your agent at an existing codebase, it reads the code and Archify maps how the pieces connect. I liked five things about it. 1. It makes five kinds of diagrams, including system maps and step-by-step workflows. 2. You can click any piece and trace what connects to it. 3. It checks the layout before it hands the diagram over, so arrows don't pile up and labels don't overlap. 4. It has dark and light themes and exports to PNG, SVG, or a short video. 5. You keep editing in plain chat, with requests like "add Redis" or "highlight the rollback path." You don't need a codebase to use it. A description in the chat is enough.
0:12

Stop manually making flowcharts. Try this GitHub skill with Claude Code. I found an open-source tool that turns a plain description of a software system into a clean, interactive diagram. It's called Archify, and it has more than 66,000 stars on GitHub. You add it as a skill to an AI coding agent like Claude Code, Codex, Cursor, or OpenCode. Then you describe the system in the chat in one line. Archify turns that line into a single HTML file you can open, click through, and send to anyone. If you point your agent at an existing codebase, it reads the code and Archify maps how the pieces connect. I liked five things about it. 1. It makes five kinds of diagrams, including system maps and step-by-step workflows. 2. You can click any piece and trace what connects to it. 3. It checks the layout before it hands the diagram over, so arrows don't pile up and labels don't overlap. 4. It has dark and light themes and exports to PNG, SVG, or a short video. 5. You keep editing in plain chat, with requests like "add Redis" or "highlight the rollback path." You don't need a codebase to use it. A description in the chat is enough.

Alex Veremeyenko

52,933 views • 7 days ago

CHINESE ENGINEERS JUST WROTE CLAUDE SCRIPT AND TURNED $6.02 INTO $3.3 MILLION ON POLYMARKET Nobody tells you about them and you still think this is a person placing bets manually I guess. Let me disappoint you, this is a fully automated script built by Chinese engineers 100%. This is true. They called it PHANTOM X. It runs completely through Claude. Their account here: Result: $6.02 -> $3,354,000. Win rate 71%. Biggest win: $179,000 (single bet). I’m copying their trades here: (Just added their wallet to TG bot 0xee613b3fc183ee44f9da9c05f53e2da107e3debf, it's so easy) How the bot works: -> It simultaneously tracks thousands of sports markets on Polymarket and Kalshi. -> Finds discrepancies between the platforms. -> Enters positions faster than any human could imo. Just three strategies in one: -- Pairs Trading: the bot sees YES on the Rockets at $0.62 while NO is at $0.41. Total = $1.03 instead of $1.00. That’s a 3% risk-free profit. It enters automatically within milliseconds. -- Sentiment AI: scans Twitter (X) and news in real time. If something big breaks, it recalculates the probability in 2 seconds before the market reacts. -- Calendar + Volatility: 15–20 minutes before the game, volatility spikes. The bot takes positions early and closes after the first major move. Why sports is perfect? Sports O/U markets have clear paired contracts that should total exactly $1.00, but constant deviations create reliable arbitrage. This is exactly how [sovereign2013] built $3.35M. > A human physically cannot monitor 50+ markets at once, react in milliseconds, stay awake 24/7, avoid emotions after losses, and run Z-scores on 60 bars of data. > The bot does all of this in parallel without breaks. Manual trading is dying. The automation era has arrived. Start learning Claude now. If you’re interested in writing your own bot on Polymarket: Comment the word "BOT" Like and repost this post Follow me (so I can message you easly) And within 24 hours I will send you a full manual on how to build a bot that can earn $2,900+/month. Also SAVE this info and article.
0:44

CHINESE ENGINEERS JUST WROTE CLAUDE SCRIPT AND TURNED $6.02 INTO $3.3 MILLION ON POLYMARKET Nobody tells you about them and you still think this is a person placing bets manually I guess. Let me disappoint you, this is a fully automated script built by Chinese engineers 100%. This is true. They called it PHANTOM X. It runs completely through Claude. Their account here: Result: $6.02 -> $3,354,000. Win rate 71%. Biggest win: $179,000 (single bet). I’m copying their trades here: (Just added their wallet to TG bot 0xee613b3fc183ee44f9da9c05f53e2da107e3debf, it's so easy) How the bot works: -> It simultaneously tracks thousands of sports markets on Polymarket and Kalshi. -> Finds discrepancies between the platforms. -> Enters positions faster than any human could imo. Just three strategies in one: -- Pairs Trading: the bot sees YES on the Rockets at $0.62 while NO is at $0.41. Total = $1.03 instead of $1.00. That’s a 3% risk-free profit. It enters automatically within milliseconds. -- Sentiment AI: scans Twitter (X) and news in real time. If something big breaks, it recalculates the probability in 2 seconds before the market reacts. -- Calendar + Volatility: 15–20 minutes before the game, volatility spikes. The bot takes positions early and closes after the first major move. Why sports is perfect? Sports O/U markets have clear paired contracts that should total exactly $1.00, but constant deviations create reliable arbitrage. This is exactly how [sovereign2013] built $3.35M. > A human physically cannot monitor 50+ markets at once, react in milliseconds, stay awake 24/7, avoid emotions after losses, and run Z-scores on 60 bars of data. > The bot does all of this in parallel without breaks. Manual trading is dying. The automation era has arrived. Start learning Claude now. If you’re interested in writing your own bot on Polymarket: Comment the word "BOT" Like and repost this post Follow me (so I can message you easly) And within 24 hours I will send you a full manual on how to build a bot that can earn $2,900+/month. Also SAVE this info and article.

slash1s

16,145 views • 5 months ago

Made $100K+ by building a simulation that predicts SPX price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.
0:18

Made $100K+ by building a simulation that predicts SPX price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.

Himanshu Kumar

76,361 views • 6 months ago

a $400k research analyst at a hedge fund gets through maybe 15 papers a month a Grok Bot read 3,900 of them last night and pulled out 6 with a tradable rule inside analyst's actual job was never insight, it was filtering - deciding which of ~200 new quant papers a week deserves a backtest that filter used to cost a salary. now it's an api key and one instruction: keep only papers with an executable rule guy running it is 26, no finance degree. built it because arxiv publishes an rss feed nobody in retail bothers to read overnight it pulls every new q-fin paper, strips anything without a testable signal, pushes survivors straight into a backtest 11 weeks. 4,100 papers scraped. 9 came out of sample alive 9 out of 4,100 isn't the bot failing, that ratio is what published quant research looks like once you demand it hold on data it has never seen what kills the other 4,091 is deflated sharpe. run 4,000 variants and something always looks brilliant purely by luck desks have known this since 2014. Bailey's paper is free, sitting on the same arxiv the bot scrapes every night one of the 9 is live on 3 exchanges right now. 0.9 sharpe, nothing cinematic, just real papers were public the whole time and so was the math that filters them. gap was never access, it's that nobody had 4,000 hours to read Grok Bot has 4,000 hours. it spent them last night while you slept
0:12

a $400k research analyst at a hedge fund gets through maybe 15 papers a month a Grok Bot read 3,900 of them last night and pulled out 6 with a tradable rule inside analyst's actual job was never insight, it was filtering - deciding which of ~200 new quant papers a week deserves a backtest that filter used to cost a salary. now it's an api key and one instruction: keep only papers with an executable rule guy running it is 26, no finance degree. built it because arxiv publishes an rss feed nobody in retail bothers to read overnight it pulls every new q-fin paper, strips anything without a testable signal, pushes survivors straight into a backtest 11 weeks. 4,100 papers scraped. 9 came out of sample alive 9 out of 4,100 isn't the bot failing, that ratio is what published quant research looks like once you demand it hold on data it has never seen what kills the other 4,091 is deflated sharpe. run 4,000 variants and something always looks brilliant purely by luck desks have known this since 2014. Bailey's paper is free, sitting on the same arxiv the bot scrapes every night one of the 9 is live on 3 exchanges right now. 0.9 sharpe, nothing cinematic, just real papers were public the whole time and so was the math that filters them. gap was never access, it's that nobody had 4,000 hours to read Grok Bot has 4,000 hours. it spent them last night while you slept

Livsun

13,674 views • 1 month ago

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
0:17

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

738,832 views • 6 months ago