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AI Agents For Trading (Free and Opensource) — Transcript · YTtoTranscript

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AI Agents For Trading (Free and Opensource)

Moon Dev · 2,081 words · 10 min read · EN

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Below is the complete, readable transcript of AI Agents For Trading (Free and Opensource) by Moon Dev on YouTube. Read the full text, copy any part you need, or generate a transcript for any video with our free tool.

All right, so I open sourced my AI agent for trading here and all you have to do is go to the link to get it. You can just hit star and fork fork if you want the code. Anyways, I wanted to go over this the trading functionalities within this package. There are a ton of agents,

20 different 20 plus different live agents, trading agent, strategy agent, risk agent, copy agent, whale agent, sentiment agent, listing arb agent, etc., etc. I'm a quanto. I focus mostly on algo trading, but of course I will be building all types of different agents here. I think this is obviously a new frontier. I mean, I don't have to tell

you that we're going to keep building here and then MCPs are here now and MCPs may lead into something interesting with chat GPT the app store. We'll see if that hits. If not, I think agents are still the way. We're going to have AI doing stuff for us. I think we can all agree on that.

So, trading, why wouldn't they? Let's go ahead and work on this and go through here. I want to go to the agent section. It's kind of a little rundown of the code. If you're on the read me, go read through the read me. You can understand what we've built. Then go to the source.

Everything will be in the source src. You can play with all the models here. You know, you have Claude, Deepseek, Gemini, Grock, O Lama, if you want to run local models, OpenAI, of course, XAI, and this will explain the whole model factory. Everything is written out, so it's super easy to run. Go to the SRC here. And I

wanted to go to agents today to talk about those trading agents. So, let's go ahead and learn about them from scratch, dude. Let's learn about them. Trading agents.md is going to be where I'm reading from here now. Table of contents here you can see. Anyways, Mundav's trading system uses multi-agent architecture where specialized AI agents

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work independently or together to analyze markets, generate signals, manage risk, and execute trades. So, agent categories, core trading, market intelligence, token discovery. Okay, so we have multiple agents and you saw where to get those, right? It's in the agents folder. We we're in it right now. Trading strategy and risk, execute trades and manage portfolio, market

intelligence, sentiment, whale funding, liquidation chart, provide market context, and then token discovery like the sniper, Solana, and copybot. Sniper and copybot. I wonder what this does. Buy new opportunities. Of course, none of this is financial advice. I'm not financial advisor. Please take this seriously. Pause this video right now. read that whole thing or screenshot it

if you're on your phone. So core trading agents here, these are the mains agents that make trading decisions and execute them. So the trading agent, trading agent py, the LLM decision maker. So this is pretty basic. This is where we first got started. Just a decision maker based off an LLM. So if

we go there, that should be called the trading agent. The trading agent. Okay, let's go ahead and put her to the side. And you can see here we have a prompt. Your Munda's AI trading assistant analyze the provided market data and strategy signals to make a trading decision. So, I gave it a bunch of

information plus the data and you can see everything.

Let's close this for now. You could see the prompts here. They're all here. You can go through this code on your own time.

What it does is pure AI analysis of market data using claude or whatever. No hard-coded rules. LLM decides everything. Manages portfolio allocation across all tokens. analyzes technical indicators and it can incorporate strategy signals from other agents. How it works? Collect market data. So it collects market data and then it has the LLM analyze them.

Handles exits first always to manage risk. LLM decides portfolio allocation and then execute entries. So key methods analyze market data. LM analysis allocate portfolio LM decides capital distribution allocate allocations executes handles exits configuration here I was using haiku at the moment AI model max position percentage what I want to do here is I want to say hey

at trading agent Since we just implemented X AI, their Grock model the four, I want you to do the same thing with um this trading agent.

Also make sure that the uh switchability with the models is at the top of the code with parameters. Thank you. Okay, so that's the trading agent. It's just like super simple like hey you decide AI and when I tested it didn't do too great. So uh there needs to be more than that. So that led me to the

strategy agent. So, strategy agent, the rulebased strategist loads custom strategies from our strategies custom folder. Each strategy generates buy, sell, or nothing. AI validates all signals before execution. Can work independently or feed signals to the trading agent. So, I had a conversation with this guy here. His name is Ernest Chan. You can go to the

podcast here. It should be live. And he does something. >> Please, please, please stop using chat GPT like Google. Disappear for a month and do this. >> He does something. >> Welcome to corrective AI. >> Anyways, corrective AI is hey, give the AI the strategy and then or yeah, give the AI the signal from the strategy and

it gets to decide if it wants to run it or not. So opposed to like predicting price or something like that. Everybody wants to predict price with AI. That may not be the way. I can't tell you yes or no. But you can test that all yourself. So here I wanted to figure out.

Let's keep going through here. So it loads in some custom strategies. So that's pretty cool because now we can add custom strategies and then have AI decide from those strategies. See what I'm saying? And you see where this is going. Okay, step one, we just say, "Hey AI, just tell me what to do. Here's the

data. Let me know long, short, or nothing." And that's elementary. So let's go ahead and try a next step. Okay. Well, here's a strategy. And now it's triggered. Let me know if I should take it or not. It's interesting interesting ideas. So then we have the risk agent, the safety guardian. What it does, it's a

circuit breaker. Stops trading when limits hit. Monitors max loss or gain over configurable time frame. AI powered override decisions for edge cases. So it's like having a risk control plus AI helping out with it. Minimum balance protection runs first before running anything else. So, it's looking at max loss, max gain, and hey, maybe have an

AI say, "Oh, this is actually this is running fast. Let's not take profit." But then again, that might get you smoked. So, you can read through all of this here, but um I wanted to see the first couple. Dez the whale watcher monitors BTC open interest changes detects whale activity large OI moves AI analyzes if the moves

are significant announces. Let's get Dez going. Let's get Dez back up in here. Dude, get Dez back. Okay. And you got we got XAI here. Perfect. Let's see if it still runs. Beautiful. Beautiful. Beautiful. What is that? Oh, it's collecting data. Bird eye API key.

Okay. Okay. This is working, which is good. Okay. I'm not going to let it keep running. It's about to about to buy something probably. execute an entry. Okay, so we just have to tweak that in order to the EMVs to fix that. And then let's go ahead and say get add dot get

commit slash um I can do this later actually. Updates get push. Okay.

Let's scroll through here after I get Dez going, the whale agent. Let's go ahead and get her going. Where is she? Well, do you see it? I don't see it.

Whale agent.

agents.

Where'd she go? She's right there, dude. Right in your face. Sheesh. This is using Deep Seek here. Copy path. activate activivate Tflow and then Python run it.

Okay, it's running.

beautiful. Let's keep going through here and seeing some other other agents. We have funding agent. Okay, this one's just telling me announces via voice when thresholds hit. This is interesting actually. I might run this as well. Funding agent. Liquidation agent here. Luna the liquidation monitor tracks liquidation volume longs or shorts. Detects sudden

liquidation spikes. AI analy an analyzes if liquidation signals reversals. So, I started building these for voice because if I get them built that way, then it's super easy to be like, "Okay, we have all the signals here. Put in a trade on XYZ exchange." I want to look into that. Matter of fact,

please look through this entire GitHub and don't print any sensitive keys or anything, but I want you to explain the exchanges we have connected. So, I know right now this connects to at least Solana blockchain because I just saw that in the trading agent, but um yeah, look through the whole GitHub and yeah, give me an update on that

chart analysis agent. What it does generates candlestick charts with indicators, uses AI vision to analyze analyze chart patterns, supports multiple time frames. So you see what I'm doing here, right? I'm building out the all the base agents. And then if you want to try trading with this, you just combine the chart analysis agent with

the trading agent. Trading agent knows how to trade, right? So same with the liquidations. Oh, you want to try some liquidations? Okay, the liquidation agent has all the data. It's just giving it to me in voice right now. And then it can easily be tested. Chart analysis agent. Okay, cool, cool, cool. Token discovery agents. These are

interesting. These were back in when Solana was really going crazy. Watches for new Solana tokens in real time. Solana agent. The Solana token analyzer analyzes recent token launches using mundage criteria. MCAP minimum liquidity. So criteria is market cap. Okay. Liquidity token age. Copy by agent analyzes current copy portfolio positions. Okay, this was cool actually.

Damn, there's so many ideas. There's so many paths. How agents work together. So the main loop integration here, here's how you can orchestrate all agents together. So you can bring in the risk agent, the strategy agent, the trading agent, the sentiment agent, the whale agents, funding agent, liquidation agent. That's a ton of different ideas

that could work together however you see fit. Initialize all agents here. Track signals from intelligence agents. So always check risk first. So got the risk agent. Okay. Collect market intelligence. Strategy signals. Trading agent. Information flow. Quick start guide. Configure your system. Tokens to trade. Position size. I may have just made this for Solana, dude.

We'll see. Bird eye. Mundave. run individual agents trading agent strategy. Yeah, I think I probably did because yeah, that makes sense cuz I was going to do this all on chain. I don't know. We'll see. We'll see. I just asked that question. Per agent overrides best practices. Risk plus trading agent rule-based hybrid.

Always run risk first. Cost optimization monitoring. No trades executing. Okay. Yada yada. This is a lot of a lot of slop you added on to this dude. A lot of slop. Okay. Sick dude. Remember trading involves risk. Please read the this right here because that's super duper important. So, I feel like that's

a pretty good overview of how the trading agents work. You just go to source here and you have access to all of these agents here right off the bat. I've got a lot I want to update to this and I'm going to continue to do that and I'll continue to make videos as well just to show you step by

step along the way. So, you know, if you're interested in this GitHub here, then make sure to, you know, subscribe or follow or like or whatever you need to do so you see the next one. But I think that's about it for here.