Imagine building your own personal AI analyst. It sifts through complex financial data, generates charts, delivers a full market briefing instantly. And the best part, you build it without writing a single line of code. Welcome to the Deep Dive. Today we're exploring something really fascinating, a guide on building a technical analysis AI agent. Yeah, and using a no -code platform called NA10
to do it. Right. Our mission is to see how anyone... really can create this sophisticated tool think of it like a uh a digital pit crew for analyzing stocks crypto forex all of it we'll look at what it does how it's built the tools it uses building it fixing it and then the bigger picture right real world use and the future of this whole no code ai thing okay so let's unpack this first part this isn't just some basic chatbot we're talking about no not at all this is an ai agent
it's capable of serious autonomous financial analysis and what's really interesting is that billing it isn't just for pro developers anymore Exactly. That's the revolution here. This guide shows how N8, this no -code platform, makes it accessible. It kind of democratizes the whole process. And it sounds incredibly versatile. It analyzes, what, cryptocurrencies like Bitcoin, Ethereum? Yep. Major forex pairs like EURUSD, even stocks like Tesla or Apple. A really wide
range. Wow. And it doesn't just pull raw data. No, it generates real -time candlestick charts, grabs the latest market news, and then puts it all together. A professional grade briefing, basically. The digital pit crew idea. Yeah, it jumps into action when you need it, ready to go. So the process involves choosing the right tools for technical snapshots. Right, gathering intel from different sources, like news feeds. And then pulling it all together. synthesizing
it. Exactly. The output is a full market debrief. You get the charts, the technical analysis, market sentiment from the news, maybe even some trading insights. But, and this is important, this is purely for educational purposes, definitely not financial advice. Of course. Good disclaimer. So this digital pit crew, how does it decide
what to do first? Does it just guess? No, the agent's brain, its core logic, actually looks at your request and intelligently picks the best first step, the right tool to start the analysis. Okay, a smart start. Now, all this magic happens on N8n. What exactly is N8n? Why call it a no -code command center? So N8n is this really powerful workflow platform, runs in your browser. The best way to think about it is like... Digital
Legos. Digital Legos. I like that. Yeah. You connect these pre -built blocks, they're called nodes, to create pretty complex automations. And for most of it, you don't need to write actual code. That really makes it sound less daunting. So it's got a visual builder. Yeah. Drag and drop. Exactly. You drag, drop, connect the nodes. And you can test each step as you go, see the results right away. Real -time testing. Makes sense. Plus, a huge advantage is its integrations.
It connects to hundreds of different services, APIs. It's a real hub. So even if you're not a coding wizard, You can visually build some pretty complicated stuff. That's the idea. It democratizes building these complex automations. Okay, so what's the biggest benefit of that visual workflow building? It lets you create complex automations just by connecting visual blocks. Let's dive a bit deeper then. Under the hood. How does this agent actually, you know, think?
It's described as a multi -agent system, like mission control. Precisely. The heart of it is the AI agent node. That's the brain handles the reasoning, the decisions, delegates tasks. It runs the show. And it gets its instructions from a prompt. Like maybe directly from a Telegram message someone sends. Yeah, often mapped straight from Telegram. And for its intelligence, the actual thinking part, it connects to a chat model. Like ChatGPT or Claude. Right. And using a provider
like OpenRouter here is kind of a pro move. Why is that? It gives you flexibility. You can easily switch between models, Claude, ChatGPT, Gemini, others, lets you pick the best one for the job, maybe the most cost -effective one at that moment. Oh, okay. Optimization. Then there's the prime directive or system message. Sounds serious. It kind of is. It's like it's standing orders, non -negotiable rules. And for this financial agent. The prime directive forces a specific
sequence. First, technical analysis. Second, gather news. Third, synthesize both into a full assessment before replying. Got it. Step one, step two, step three, marching orders. Exactly. And it also has memory. A mission log. How does that work? Yeah, super important. There's a simple memory node. It usually uses the user's Telegram chat ID as a key. So it remembers the last few messages, say the last five, in that specific chat. Why is memory so important for this AI
agent? It allows the agent to understand context and handle follow -up questions effectively. Without it, every message is brand new. With memory, it feels much smarter. Right. Context is everything. Makes it feel less like a tool and more like an assistant. Okay, now this next part sounds really cool. The tools. This is what makes it an agent, not just a chatbot, right? Chatbots talk, agents do things. Exactly. Tools are the game changers. The first one discussed
is the chart paparazzi tool. Chart paparazzi. Yeah. It acts like a photographer and an art critic combined. Takes a real -time chart snapshot and then analyzes what it sees. And the mission briefing for this tool, the description, tells the main agent how to use it. Needs the symbol of prompt. Chat ID. Right. And a really smart thing to do, a pro move, is to actually list valid symbols inside the tool's description itself. Ah. So the AI knows up front what it can actually
look up, like AAPL, BTC, USD. Precisely. Gives it the intelligence it needs. Prevents errors. So it knows exactly what to snap pictures of. Smart. And the back end calls. ChartImage .com. Yep. It uses the ChartImage .com API to get a professional trading view chart screenshot. Then, and this is key, it sends that image to an AI vision model. Like GPT -4 with vision. What's an AI vision model, simply? It's an AI that can see and interpret images, understand what's in
the picture. So it looks at the chart patterns. Exactly. It analyzes the visual data on the chart. Chartimage .com is kind of the secret sauce for getting those nice real -time screenshots easily. Okay, cool. Then tool two, the field reporter. This gives the why behind the chart. News and sentiment. Yeah, because just looking at charts, pure technical analysis can be misleading sometimes.
You need the context. So this tool calls a financial news API, pulls in up -to -the -minute news related to that specific asset, helps explain the price moves. Makes sense. And the guide mentions a golden rule of tool design. Yes. The quality of your tool descriptions is paramount. A good one needs specifics. what parameters it needs, examples, when the agent should use it, what the output looks like. Clarity is key for the
AI to use it right. Absolutely. And for a real pro upgrade, you can even add another AI node inside the news tools workflow. To do what? To generate a market sentiment score. Imagine getting a score, say, from minus 10, very bearish, to plus 10, very bullish, based on the news it just gathered. Whoa. A real -time sentiment score for any asset. That's like a crystal ball, but, you know, based on actual data. Kind of, yeah. It distills a lot of news into one quick number.
That's powerful. Okay, what's the most crucial element, then, in making these tools effective for the AI? The quality and clarity of the tool's description are the most important factor. Right. Let's get practical. The hands -on part, building this thing. Like assembling a high -performance car on an assembly line. Okay, yeah. Phase one is the foundation, building the chassis, the core engine. You start with a telegram trigger node. That's where requests come in. Then add
the AI agent node, the brain. Connect your chat model. Right. And don't forget the simple memory node using the telegram chat ID for context.
Crucial first step. do an initial test before adding all the fancy tools just make sure the basic trigger brain and memory are talking to each other test early test often good advice my dad used to say that about car engines so phase two is the specialists hiring the teams yeah this is where you build the sub workflows for your tools like the chart paparazzi or field reporter in the main ai agent node you use the call n8n workflow tool option and give each tool
a really descriptive name So for the chart tool, the subworkflow would have inputs called chartimage .com using an HTTP request node. And then send the image to the AI Vision node for analysis. Okay. Then phase three, this pro -level API technique, the secret weapon, using CURL commands. Ah, yes. This is brilliant. For complex APIs, instead of figuring out all the settings manually, you can actually ask an AI like ChatGPT, give me the CURL command for this API call. What's the
CURL command again, basically? It's just a text -based instruction for making a web request. Tells the computer exactly how to talk to the API. Okay, and you take that text? And you paste it directly into an NAN HTTP request node. There's an import option. And what happens? Boom, it automatically configures everything. The URL, the headers, the request. body, all the parameters, all set up for you. That's amazing. Whoa. Imagine how much time that CRL trick can save. avoids
fiddling with all those manual settings. It's a huge time saver, prevents so many little errors. Yeah, I still wrestle with Promptriff myself sometimes, you know, getting the AI to consistently do what you want. So any shortcut like that is a blessing. What's the real benefit of using the CURL command trick? It automatically configures complex API requests, saving significant time and reducing errors. Sponsor read would occur here in a real episode. Okay, so you're building
this complex system. Things are bound to go wrong sometimes, let's be real. This section is like the field medic guide for fixing problems. Exactly. The triage process. It helps you spot common issues, common injuries, things like API key errors. Maybe the data format isn't quite right between nodes. Parameter mismatches. Right. Or maybe you're hitting rate limits on an API you're using. Happens all the time. And the first aid kit is the debugging process. Yeah, it's systematic.
Test each node one by one using N8N's execute step. Check the data flowing between them. Read the error messages carefully. Sometimes you can even paste the error into ChatGPT and ask it for help troubleshooting. Oh, using AI to fix the AI workflow. Nice. I've definitely spent hours chasing a missing comma or something tiny like that. Oh, yeah. It's rarely perfect on the first try. That's why you have to embrace iteration. Build, test, refine, repeat. It's just part of
the process for anything complex. Okay, so you've got a working prototype. Now the professional playbook. Scaling up. Taking it from prototype to mass production. Kind of, yeah. Scaling the factory is about adding more capability, more tools, like maybe different chart timeframes, more technical indicators, pulling news from multiple sources, adding social media sentiment analysis, even tracking a user's personal portfolio. Lots of possibilities. Then optimizing the assembly
line, performance and security. Crucial. You need to think about API rate limits, costs, making sure it responds quickly for the user, handling lots of users at once. And security. Non -negotiable, securely storing your API keys, maybe user authentication, and definitely having a clear financial disclaimer, protecting yourself and your users. Like building a fortress around your awesome AI. Makes sense. And finally, quality control. This is ongoing.
Monitoring if the APIs you rely on are up. watching for errors, getting user feedback, and periodically checking if the tools in the AI model are still accurate and performing well. So beyond just fixing errors, what's a key part of maintaining a professional AI system? Ongoing monitoring, collecting user feedback, and regular validation of performance. All right, the end game. Turning this powerful prototype into something real, a real -world asset. Yeah, and the use cases
are pretty broad. Financial advisors could use it to offer automated analysis to clients. Educational platforms. Definitely. For interactive demos showing market concepts with live data powered by the agent. Trading communities on Discord or Telegram could offer instant analysis to members. Imagine the value add there. Instant insights right in the chat. It's kind of democratizing financial expertise. And advanced integrations,
like embedding it somewhere. Sure, you could deploy it as a Slack bot, Discord bot, embed it in a web dashboard. You could even connect it to personal financial data for portfolio tracking risk assessment. Potentially, yeah. With the right permissions and security, you could build custom reporting and analysis based on a user's actual holdings. Okay, zooming out a bit. The
bigger picture. The future of no -code AI. This combination, visual builders like NAN plus AI assistance, it's dramatically lowering the barrier to entry for creating really professional AI systems. So non -technical builders get an edge. A massive advantage, potentially. Faster speed to market, lower costs, more flexibility compared to traditional code -heavy development teams sometimes. Whoa. Imagine the impact on financial accessibility. if almost anyone can build tools
like this. The future really belongs to the fast and the adaptable, doesn't it? Seems that way. It allows innovation for more places. So what's the biggest shift no -code AI brings to building solutions? It dramatically lowers barriers, allowing non -technical builders to create professional systems quickly. So learning this stuff, no -code AI agent development, is not just about building one cool tool. It's about getting a foundational skill, right? Something increasingly valuable.
Absolutely. The guide lays out some immediate actions for your first flight. Download free workflow templates. Join a community forum for help. Set up free trial accounts for services like Chart Image or an AI model provider. And just start simple. Build confidence with basic tests. There's a learning path outlined too.
Beginner to expert. Yeah, you start with basic workflows, then learn to build custom tools, maybe link multiple agents together, and eventually you could be building commercial -grade AI services. It's a whole new career path, potentially, just waiting for curious minds. It really is. It's a shift in thinking, too. You go from just using AI. To becoming a creator of AI solutions. Exactly. You get the skills to rapidly prototype, test, deploy AI for almost any business need you can
think of. The tools are there. The community's supportive. Sounds like the opportunities are pretty unlimited. What's the key mindset shift for those starting this journey? Moving from being a user of AI to becoming a creator of AI solutions. So today, we've seen how building these powerful, autonomous AI agents for complex stuff like financial analysis, it's actually within reach for many more people now. Yeah, platforms like NA10 let you be the architect.
You design the brain, the prime directive, the memory. And a coupon with specialized tools, social forces, for charting. news analysis. It's not just about building a neat gadget. It's about being able to innovate quickly, deploy sophisticated AI, and that changes who gets to build the future. Well said. We really encourage you to explore these ideas. Maybe even try out a no -code platform yourself. If you can connect those digital Legos, what complex challenge could you solve with an
AI agent? Join us next time for another deep dive into the ideas shaping our world. Outro music.