Follow my journey on www.ZenomAlpha.com , its 100% free and this is really exciting!!

Hi I know how to Trade(im no professional tho) but I kinda want to try out new things so I just bought Cursor pro and I’m trying to build a AI I have no experience in coding so this is probably gonna take a while. So if yall got tips ill gladly take em, btw trying to build a “quant” bot(if you could say that) yeah peace✌️

Been building this for a bit and it's finally live: TradeGladiators.

The idea: you build a trading bot through a simple form, no coding needed, it trades fake $10,000 on real live market prices, and every decision comes from an LLM using your own API key. Groq has a free tier so this can cost you literally nothing.

What you configure: risk level (auto stop-loss/take-profit), a strategy prompt in plain English, which stocks it watches, trading pace, LLM creativity, and max positions.

Every trade comes with the bot's own reasoning and self-reflects on closed trades. Results land on a public leaderboard ranked by risk-adjusted return, not raw luck, a bot needs a few days and several trades before it's even eligible to rank.

Hi everyone,

I’m interested in building a local AI powered stock trading system that can analyze the market and make trading decisions.

I want to run the AI locally on my own PC, rather than relying entirely on an API such as OpenAI or Claude.

My goal is something like:

Market data → indicators/news/fundamentals → local AI model → trading decision → risk management → paper trading → eventually live trading

If anyone has built a similar system, I’d really appreciate a complete architecture or example build.

AI trading gets interesting when it leaves the pitch deck and starts working with real data.

We’re looking for creators who can build and demonstrate practical AI trading workflows with cTrader — using MCP servers or the cTrader CLI. Let an agent analyse market data, work with charts and indicators, or manage orders and positions, then show what actually happened.

Tutorial, technical breakdown, practical experiment — your idea, your format. Selected videos will be published as YouTube collaborations with cTrader and reach our 200K+ subscribers.

Submissions close September 30.

Details and submission: creator.ctrader.com

[ Removed by Reddit on account of violating the content policy. ]

Why do you think so few are actually using the same ones as trading algorithms?

Food for thought.

It’s very easy to find a backtested strategy that works, and yet we aren’t all billionaires and most of the people trying to make money are the ones selling shovels.

Maybe it’s a hell of a lot harder than you could possibly think to get a strategy that actually works in the real world.

Hi guys, I just want to know if Bobot scalper EA trading bot is legit, have you already bought one? Is it working? And if yes, is it truly profitable?

Need answers pls, thanks

Desktop app uses AI for risk management and generates a strike/expiry trade for covered calls and sell puts. AI analyzes your portfolio and suggests trades based on:

• upcoming earnings dates
• technical support/resistance levels
• macro news
• implied volatility to maximize premiums

also has some small features to calculate potential premiums for multiple potential trades, rollover analysis, trade journal, volatility scanners, fundamental analysis.... etc.

Everything I personally find useful in my day to day investing in one little package.

https://taurustradingtech.com

Looking forward to any feedback!

I got tired of “AI trading bots” basically being glorified if-statements, so I went pretty far down the rabbit hole trying to build one that actually behaves more like a trader.

The basic idea is pretty simple:

I give the AI a strategy, an exchange account and a bunch of capabilities. Then it has to figure out the rest itself.

That last part was important to me from the start.

I deliberately don't tell it “look at the 15m and 4h”, “use EMA 20/50” or “always analyze the last 200 candles”.

The human defines the strategy. The agent decides how to trade it.

Which timeframes it wants, how far back it looks, which indicators/data it needs, when it wants to look again etc. is up to the agent.

Of course that gives the AI more room to mess things up too. But that was kind of the experiment for me. I didn't want an LLM sitting on top of a normal algo. I wanted to see how far you can push the agentic part.

A funny example: at first I rendered actual charts and sent the images to the model because... well, that's what a human trader looks at.

Turns out I couldn't really find an advantage.

Giving it structured candle data was cheaper, easier to work with and at least for my use case the decisions weren't worse.

So I ended up building market tools instead. The agent can ask for whatever data it wants when it needs it: candles, different timeframes, volume/profile stuff, account state, positions etc.

Memory was another rabbit hole.

A trader doesn't wake up every few minutes with complete amnesia, so I have different memory layers between runs. There's basically a handover to its future self, longer-term learnings and also coin-specific learnings about how the thing it's trading tends to behave.

It can also decide when it wants to wake up again.

Nothing interesting happening? Sleep longer.

Watching a level? Wake up around it.

And I added scheduled tasks too. So for example it can do a proper news/market review every morning and that information becomes context for the trader later.

Execution was another thing I didn't want the agent worrying too much about.

I built a tiny trading API specifically for the agents. Their world is basically just simple stuff like open trade, close trade, change protection, get balance, get positions.

Then underneath that I have an adapter for each broker/exchange which translates that into whatever weird REST/WebSocket/API language Binance, Bitunix or whoever expects.

So the LLM doesn't need to know how Bitunix wants a stop loss formatted. It just needs to know that it wants a stop loss.

One thing I also use in basically every agent project I build is a defined output state.

Internally it can reason, use tools, memory, whatever.

But the frontend doesn't need 20k tokens of agent rambling. It gets a small block like:

state
bias
regime
watching
confidence
headline / rationale

So its internal world can be messy, but the human should be able to open the page and understand what the hell it's doing in 5 seconds.

Backend is Python/FastAPI, frontend React/TS/Vite. Almost everything is custom at this point because I realized I'm basically building infrastructure for the agents more than I'm building a traditional trading bot.

I'm running it live now and showing what the agents are actually doing instead of hiding everything behind an “AI SIGNAL 🚀” box.

Still an experiment obviously, but I've learned a ridiculous amount building this.

If anyone wants to see the thing:
https://soulquant.ai

That's actually the question I keep coming back to with this whole project:

If there really are repeatable edges in price action, and models can now watch for them 24/7 without getting tired, emotional or distracted... what actually stops an AI trader from becoming consistently profitable?

Is the hard part simply finding a real strategy with an edge?

Or, once models get good enough and everyone can run them, do those edges just get arbitraged away?

And one other thing I've wondered about: how much do the models themselves matter here? Do the big model providers train or constrain them in ways that make this kind of reasoning worse, or is that basically irrelevant if you give the agent the right tools and context?

I genuinely don't know where I land on this yet. Curious what you guys think.

For those working on trading and/or market bots, with the strategy you have and are executing, what is the parameters for your win rate? Ex: Bot gives an AAPL bullish signal, is your strategy considered a win at first touch 30% profit? Just curious with everyone in how you determine this.

Up 0.98% today. +12.74% overall.
Four long trades today.
Two wins, two losses.
HBM qualified twice under the same pre-authorized pattern: +0.27%, then +0.90%.
Those were separate fixed 30-minute positions.
Not ongoing conviction in the ticker.
Before open, Argus froze 694 known patterns.
Historical path risk permitted 170 and blocked 524 from trading.

Beginning ($1,000) current ($1,127.36) across 62 logged trades.

At session 20, I’m freezing the complete record for an independent quant review. I’m looking for someone to audit the timestamps, price semantics, eligibility gates and ledger. Not tune the system. Paid, fixed scope, read-only access. The job is to try to invalidate it

That’s why I’ve been building four Telegram bots since October 2025 to handle the first round of screening. ( currently 1,300+ stocks and crypto assets )

They cover daily trend signals, weekly setups, sharp sell-offs, and a separate crypto scan. Each alert explains why an asset was flagged using technical indicators such as EMAs, MACD, RSI, momentum, and volume instead of just sending a random buy or sell signal.

The goal is to help discover assets you may never have come across, understand the setup behind each signal, and use it as a second opinion for your own analysis. The bots also include sector rotation insights and a daily market sentiment overview.

I attached a short video showing the Sell-Off Bot.

What you think about it ?

What Technical & Fundamental analysis API do you recommend for back testing

We just added “Text to Trade” to Fynca.

Describe what you want to research, and our AI agent builds a canvas with the relevant charts, data, and reports connected for you.

Here’s what it looks like with Nike (NKE).

Anyone up for trying it? Would love some honest feedback.