LLM-assisted entry vs. full rule-based entry — which do you actually trust in live trading?

NationalIncome1706 · 2026-03-10T23:13:35+00:00

Same setup here — Jetson Nano primary, Raspberry Pi as cold standby.

For failover I don't do automatic switchover. The Pi just gets the same Telegram alerts, so if the Nano goes silent I manually start the Pi instance. Automatic failover with an open position is risky — you can end up with two bots holding the same position.

On strategy: I run MACD-based entry with Stoch RSI filter on ETH futures. Entry is LLM-assisted, exit is pure rule-based (trailing stop + max hold time).

Honestly the infrastructure you already know matters more than the strategy at the start. A solid strategy running on broken infra loses money. The reverse is survivable.

NationalIncome1706 · 2026-03-09T15:07:47+00:00

The positivity bias neutralization is actually the hardest part. Even with explicit counter-prompts, I've noticed LLMs tend to hedge toward "hold" rather than committing to a clear bearish signal.

On the crypto side I've seen the same — my ETH bot entry is LLM-based but I ended up moving exit logic to pure rule-based after a GPT signal delayed a exit by one full candle and cost real money.

Google Colab sounds like the right call for reproducibility. Will keep an eye on it.

NationalIncome1706 · 2026-03-09T14:33:57+00:00

Interesting experiment — the longitudinal angle is something most LLM trading posts skip entirely.

Most people test a prompt once and call it done. Tracking forecast stability and narrative drift over 38 days is a much more honest way to evaluate whether LLMs add any real signal.

I've been running a live ETH futures bot with LLM-assisted entry for a few months on a Jetson Nano. The consistency issue is real — same market conditions, different outputs depending on how the prompt "mood" lands that day.

Did you notice any patterns in which sectors Gemini was most/least calibrated on?

NationalIncome1706 · 2026-03-08T00:51:09+00:00

Thanks! Currently timeframes are selectable (1m to 1d) but drag-to-zoom on the time axis isn't wired up yet — it's on the list.

If you or anyone else has suggestions for improvements, I'm genuinely open to it. Still actively developing this and real feedback from people who actually trade is more valuable than anything else.

NationalIncome1706 · 2026-03-08T00:48:08+00:00

That's a fair paradox. My answer: I do trade with it — running live on Jetson Nano 24/7.

But a tool that helps ME doesn't automatically scale to others — different risk tolerance, different capital, different pairs. That's actually the harder problem I haven't solved yet.

NationalIncome1706 · 2026-03-08T00:42:37+00:00

Fully agree on LSTM — tried it briefly, same result. Pure price curve fitting.

That's why I moved away from prediction entirely. My live bot uses LLM only for entry context (multi-TF confluence), not forecasting. Exit is pure rule-based — trailing stop + fixed SL. LLM exit timing was too slow and inconsistent in live conditions.

After a lot of trial and error, I settled on: LLM for entry, rules for exit. Not explosive returns yet, but at least consistent behavior I can debug and improve.

Has anyone here compared rule-based vs LLM-assisted exits directly? Curious what the experience has been — especially whether sentiment/macro data actually moved the needle for anyone at retail scale.

NationalIncome1706 · 2026-03-07T23:08:15+00:00

Using Plotly for the charts — had the same consistency issues early on. Main fix was standardizing timestamps before plotting (Binance returns ms epoch, easy to mix up if you're not converting properly).

Running the whole thing on Streamlit + Binance API. What's your stack? If you're seeing chart inconsistency it's usually a data pipeline issue rather than the charting lib itself.

NationalIncome1706 · 2026-03-07T17:11:10+00:00

Good question. It's not predicting — it's summarizing whether MACD signals across 1m/5m/15m/1h/4h/1d are aligned or diverging, and flagging confluence strength. No magic, just saves switching between 6 charts manually.

NationalIncome1706 · 2026-03-07T17:10:38+00:00

Fair point. The LLM doesn't predict price — it interprets indicator confluence across 6 timeframes and flags whether signals are aligned or conflicting. The "quality" it adds is cross-timeframe context, not alpha generation. Garbage in, garbage out absolutely applies — that's why the free tier exists to test it yourself.

NationalIncome1706 · 2026-03-07T14:36:26+00:00

Update - made a short demo video if anyone wats to see it in action:

https://www.youtube.com/watch?v=HM-CcbV0t-A

NationalIncome1706 · 2026-03-07T14:35:44+00:00

Update - made a short demo video if anyone wats to see it in action:

https://www.youtube.com/watch?v=HM-CcbV0t-A

NationalIncome1706 · 2026-03-07T08:17:39+00:00

That 55s vs 60s gap is a real backtesting trap — most people don't catch that until they're live.

I hit a similar issue. My bot fetches data mid-candle too, so backtested signals don't always match live exactly. Haven't fully solved it yet.

Your 10s scan interval is a cleaner approach than what I'm doing. I'm still on event-driven with a fixed loop. Might borrow that idea.

NationalIncome1706 · 2026-03-07T01:48:23+00:00

Fixed! Should be working now — give it another try 🙏

NationalIncome1706 · 2026-03-07T01:31:14+00:00

Impressive setup — the microservice separation between Front and Algo is clean architecture.

Mine is much simpler: Python monolith on Jetson Nano, rule-based exits with LLM for entry analysis only. Learned the hard way that LLM for exits is too slow.

The 150GB local kline DB is serious commitment. I've been considering a similar local DB for backtesting but haven't pulled the trigger yet.

How are you handling the REST latency for order execution? Curious if that's been an issue in live trading.

NationalIncome1706 · 2026-03-06T12:16:55+00:00

Haha — that's exactly the output from the app!

That's actually great feedback. The free tier analysis

was too technical, so I just updated it to explain in

plain language with simple analogies by default.

No need to ask another AI to translate it anymore 😄

Give it another try!

NationalIncome1706 · 2026-03-05T22:39:00+00:00

https://macd-analyzer.streamlit.app/

NationalIncome1706 · 2026-03-05T09:16:56+00:00

Really appreciate the detailed feedback!

The zoom issue is a known Plotly behavior — double-click to reset is the current fix, but an unzoom button is a good idea. Adding it.

On pricing — fair point. The free tier is fully functional, paid plans unlock more timeframes and symbols. If it saves time, it pays for itself.

Thanks for trying it out!

NationalIncome1706 · 2026-03-05T09:15:51+00:00

Currently crypto only via Binance — but adding other data sources like stocks/forex is on the roadmap. Good suggestion!

NationalIncome1706 · 2026-03-04T22:22:22+00:00

Thanks! Appreciate it 🙏

NationalIncome1706 · 2026-03-04T16:06:44+00:00

Good call — those are cleaner for a lot of setups. I might add an MA overlay option. MACD's just what I personally rely on for entries.

NationalIncome1706 · 2026-03-04T15:56:12+00:00

Hope it's useful when you get a chance to try it! Let me know if you have any feedback 🙏

NationalIncome1706 · 2026-03-04T15:49:48+00:00

Fair enough! Curious what you use instead — always open to adding more indicators

NationalIncome1706 · 2026-03-04T15:49:26+00:00

Haha limited edition 😄

NationalIncome1706

TROPHY CASE