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.