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every proprietary indicator on tradingview is hiding raw pine script code you can extract in two seconds i stopped discretionary trading after using ai to convert luxalgo indicators into automated python backtests in under ten minutes tradingview backtests repaint data to fake win rates but extracting the source code... show more

Moon Dev

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13,661 просмотров • 8 дней назад •via X (Twitter)

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Today we’re launching Quant, the first AI to build indicators & strategies like a TradingView wizard. Utilizing 5 years of open-source & proprietary development at LuxAlgo, Quant builds, reverse engineers, and auto-debugs any Pine Script indicator for TradingView imaginable at remarkable quality, every time. You can start using it for free today.

Today we’re launching Quant, the first AI to build indicators & strategies like a TradingView wizard. Utilizing 5 years of open-source & proprietary development at LuxAlgo, Quant builds, reverse engineers, and auto-debugs any Pine Script indicator for TradingView imaginable at remarkable quality, every time. You can start using it for free today.

LuxAlgo

670,541 просмотров • 5 месяцев назад

3. How to install them on TradingView 1. I'll DM you the link 2. Copy the script 3. Go to TradingView and Open Pine Editor 4. Create New → Indicator 5. Paste the code → Add to Chart 👉 Both indicators are open source so you can modify them. 👉 I'll be adding more soon.

3. How to install them on TradingView 1. I'll DM you the link 2. Copy the script 3. Go to TradingView and Open Pine Editor 4. Create New → Indicator 5. Paste the code → Add to Chart 👉 Both indicators are open source so you can modify them. 👉 I'll be adding more soon.

Trader Diego

10,419 просмотров • 3 месяцев назад

In under 2 seconds, copy any TradingView indicator or backtesting strategy and instantly translate it into a Repla algorithm with 99% accuracy. Let AI improve or edit it for you. Or do the opposite create your own indicator on Repla and translate it into Pine Script within seconds!

In under 2 seconds, copy any TradingView indicator or backtesting strategy and instantly translate it into a Repla algorithm with 99% accuracy. Let AI improve or edit it for you. Or do the opposite create your own indicator on Repla and translate it into Pine Script within seconds!

AXB Zero

40,563 просмотров • 5 месяцев назад

Claude can now code @TradingView indicators for you! I've always wanted an indicator with all my key intraday levels in one place, the way I wanted to see them. I won't stop until my entire system is coded. Learn how to start creating yours here👇

Claude can now code @TradingView indicators for you! I've always wanted an indicator with all my key intraday levels in one place, the way I wanted to see them. I won't stop until my entire system is coded. Learn how to start creating yours here👇

Trader Diego

293,552 просмотров • 4 месяцев назад

I built and automated a real quant trading strategy with Claude Code. I open-sourced the Claude Skill, TradingView indicator, & more. If you trade financial markets, I genuinely hope you don't skip this. 0:00 Intro 2:37 wtf is a quant? 4:45 How quants build models 6:17 Predicting volatility 8:12 The GARCH model 10:02 Calculating real positions 14:22 Installing the Claude Skill 20:51 Importing to TradingView

I built and automated a real quant trading strategy with Claude Code. I open-sourced the Claude Skill, TradingView indicator, & more. If you trade financial markets, I genuinely hope you don't skip this. 0:00 Intro 2:37 wtf is a quant? 4:45 How quants build models 6:17 Predicting volatility 8:12 The GARCH model 10:02 Calculating real positions 14:22 Installing the Claude Skill 20:51 Importing to TradingView

Miles Deutscher

75,787 просмотров • 17 дней назад

How to Create a Stock Dashboard Using TradingView Charts Without Any Coding Skills Even if you're starting from 0programming knowledge, you'll first need to install PyCharm (an IDE for Python programming). It is free for 30 days. TradingView offers a free widget that can be embedded into your custom webpage. Here's how to do it: 1. Configure Your TradingView Chart: - First, set up the TradingView chart exactly how you want it to appear. - Copy the embed code provided by TradingView for their free widget. 2. Use AI Assistance: - Find an AI assistant like ChatGPT. - Explain what kind of dashboard you want to create. For example: I want to create a stock dashboard for AAPL, TSLA, and NVDA. The left side should display stock tickers with columns for price, price change, 5-day change %, and 20-day change %. Each column should be sortable. When I click on a row, the TradingView chart on the right should automatically switch to display the corresponding stock. Please write this in Python using Flask, in a single file. + "EMBED CODE from Tradingview" This approach allows you to build a functional stock dashboard shown in the video. If you want a better-looking UI and more features, just keep communicating with the AI to gradually improve it.

How to Create a Stock Dashboard Using TradingView Charts Without Any Coding Skills Even if you're starting from 0programming knowledge, you'll first need to install PyCharm (an IDE for Python programming). It is free for 30 days. TradingView offers a free widget that can be embedded into your custom webpage. Here's how to do it: 1. Configure Your TradingView Chart: - First, set up the TradingView chart exactly how you want it to appear. - Copy the embed code provided by TradingView for their free widget. 2. Use AI Assistance: - Find an AI assistant like ChatGPT. - Explain what kind of dashboard you want to create. For example: I want to create a stock dashboard for AAPL, TSLA, and NVDA. The left side should display stock tickers with columns for price, price change, 5-day change %, and 20-day change %. Each column should be sortable. When I click on a row, the TradingView chart on the right should automatically switch to display the corresponding stock. Please write this in Python using Flask, in a single file. + "EMBED CODE from Tradingview" This approach allows you to build a functional stock dashboard shown in the video. If you want a better-looking UI and more features, just keep communicating with the AI to gradually improve it.

Will Hu

26,988 просмотров • 1 год назад

our AI Indicator feature is now in beta and rolling out gradually to users you can vibe-code the indicator you want using popular data sources like Yahoo Finance, Alpha Vantage, FRED API, and more. then plug it into your 24/7 AI agent to watch the market more access coming soon 🚀

our AI Indicator feature is now in beta and rolling out gradually to users you can vibe-code the indicator you want using popular data sources like Yahoo Finance, Alpha Vantage, FRED API, and more. then plug it into your 24/7 AI agent to watch the market more access coming soon 🚀

TradeOS

80,565 просмотров • 4 месяцев назад

This is genuinely one of the most powerful ways to use AI in your trading. I recently connected OpenClaw to the new TradingView MCP - and it's insane. You can literally turn your Claw agent into your personal trading assistant. Watch a live demo here:

This is genuinely one of the most powerful ways to use AI in your trading. I recently connected OpenClaw to the new TradingView MCP - and it's insane. You can literally turn your Claw agent into your personal trading assistant. Watch a live demo here:

Miles Deutscher

28,959 просмотров • 3 месяцев назад

CLAUDE READS YOUR CHARTS NOW most traders don't know this yet TradingView + Claude = your own hedge fund AI 10 minute setup. here's how: step 1 - install claude code → - runs in your terminal → no coding experience needed step 2 - connect tradingview → open TradingView desktop app → enable MCP server integration → claude now sees your live charts step 3 - ask it anything → 'scan my watchlist for RSI divergence' → 'write pine script for this pattern' → 'find the best setup for today' step 4 - let it trade → connect your exchange API → reads chart → writes bot → backtests → deploys → you watch citadel. jane street. two sigma. they've had this for years now you do too full guide → like + bookmark - you'll need this

CLAUDE READS YOUR CHARTS NOW most traders don't know this yet TradingView + Claude = your own hedge fund AI 10 minute setup. here's how: step 1 - install claude code → - runs in your terminal → no coding experience needed step 2 - connect tradingview → open TradingView desktop app → enable MCP server integration → claude now sees your live charts step 3 - ask it anything → 'scan my watchlist for RSI divergence' → 'write pine script for this pattern' → 'find the best setup for today' step 4 - let it trade → connect your exchange API → reads chart → writes bot → backtests → deploys → you watch citadel. jane street. two sigma. they've had this for years now you do too full guide → like + bookmark - you'll need this

AI Bulls

206,594 просмотров • 3 месяцев назад

The new Screenshot to Code from Vercel v0 is insane. I just took a screenshot of a pricing table and it was converted into code using AI in seconds! It even outputs the code and allows you to modify any specific parts.

The new Screenshot to Code from Vercel v0 is insane. I just took a screenshot of a pricing table and it was converted into code using AI in seconds! It even outputs the code and allows you to modify any specific parts.

Alvaro Cintas

378,020 просмотров • 2 лет назад

Everyone is talking about how crazy this new Claude computer use is, It can compile some indicators on TradingView, meanwhile i shared this 2 months ago, in my premium, Hands MCP, Screenshot MCP, optimized on Cuda GPU, We've been using it to autonomously code strategies, Not to compile some indicators, What is your edge if everyone is using it? Not even the speed is better, Mine was arguably faster

Everyone is talking about how crazy this new Claude computer use is, It can compile some indicators on TradingView, meanwhile i shared this 2 months ago, in my premium, Hands MCP, Screenshot MCP, optimized on Cuda GPU, We've been using it to autonomously code strategies, Not to compile some indicators, What is your edge if everyone is using it? Not even the speed is better, Mine was arguably faster

ZenomTrader

13,121 просмотров • 4 месяцев назад

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,010 просмотров • 5 месяцев назад

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

Faheem Ullah

21,390 просмотров • 9 месяцев назад

I created a real-time Voice RAG Agent, powered by Gemma 3. - It lets you to talk to you docs in a voice you prefer - You can also clone your voice in just 5 seconds The code is open-source, and here's a step-by-step guide:

I created a real-time Voice RAG Agent, powered by Gemma 3. - It lets you to talk to you docs in a voice you prefer - You can also clone your voice in just 5 seconds The code is open-source, and here's a step-by-step guide:

Akshay 🚀

152,586 просмотров • 1 год назад

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 просмотров • 1 месяц назад

What if xAI's Grok had a voice? 👀 Using the new ElevenLabs Conversational AI SDK + Replit ⠕ agent, I built a real-time chatbot that you can speak to powered by Grok all in under 15 minutes! Demo and code below.

What if xAI's Grok had a voice? 👀 Using the new ElevenLabs Conversational AI SDK + Replit ⠕ agent, I built a real-time chatbot that you can speak to powered by Grok all in under 15 minutes! Demo and code below.

Ammaar Reshi

117,276 просмотров • 1 год назад

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

Matt Dancho (Business Science)

12,736 просмотров • 1 год назад

You're Still Manually Trading in 2026. That's Why You're Broke. Everyone who's making money in markets right now built their own OpenClaw AI bots months ago. You're still staring at charts. I've made the exact step-by-step guide to build your own AI trading bots + indicators. No coding. Free. Giving it Free for 24 hours, To get it: 1. Comment "OpenClaw" 2. Like and Retweet 3. Follow me Himanshu Kumar (so i can DM you) While you're paying $200/month for some random guy's signals that don't work, people with zero coding skills are building their own bots in 30 minutes using ChatGPT. You don't need to code. You don't need experience. You just need to stop doing what's clearly not working. > Turn any TradingView indicator into a full strategy > AI indicators for scalping and day trading > Backtest everything before risking a single dollar > Works for forex, crypto, stocks Stop buying courses. Stop paying for signals. Stop watching others make money. Full video tutorial attached. This is the only guide you need. You Must Follow me Himanshu Kumar, so i can send you DM. Save this post to make sure you don't miss this one.

You're Still Manually Trading in 2026. That's Why You're Broke. Everyone who's making money in markets right now built their own OpenClaw AI bots months ago. You're still staring at charts. I've made the exact step-by-step guide to build your own AI trading bots + indicators. No coding. Free. Giving it Free for 24 hours, To get it: 1. Comment "OpenClaw" 2. Like and Retweet 3. Follow me Himanshu Kumar (so i can DM you) While you're paying $200/month for some random guy's signals that don't work, people with zero coding skills are building their own bots in 30 minutes using ChatGPT. You don't need to code. You don't need experience. You just need to stop doing what's clearly not working. > Turn any TradingView indicator into a full strategy > AI indicators for scalping and day trading > Backtest everything before risking a single dollar > Works for forex, crypto, stocks Stop buying courses. Stop paying for signals. Stop watching others make money. Full video tutorial attached. This is the only guide you need. You Must Follow me Himanshu Kumar, so i can send you DM. Save this post to make sure you don't miss this one.

Himanshu Kumar

54,575 просмотров • 4 месяцев назад

I generated over $100,000 in just 3 months using my proprietary CBC strategy. After extensive testing and refinement, I’ve now converted it into a free TradingView indicator that provides clear buy and sell signals—eliminating guesswork from your trading. This system has been consistently delivering strong results over months of live testing. Interested? — Comment “CBC”, and I’ll send it to you directly. — Like this post — Follow me Theo (Make sure you’re following so I can message you.)

I generated over $100,000 in just 3 months using my proprietary CBC strategy. After extensive testing and refinement, I’ve now converted it into a free TradingView indicator that provides clear buy and sell signals—eliminating guesswork from your trading. This system has been consistently delivering strong results over months of live testing. Interested? — Comment “CBC”, and I’ll send it to you directly. — Like this post — Follow me Theo (Make sure you’re following so I can message you.)

Theo

19,561 просмотров • 3 месяцев назад

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 просмотров • 1 год назад