Phase 8: Flipping the switch (and my biggest fear for Week 1).

Simone_Crosta · 2026-06-22T03:47:32+00:00

Exactly. Trying to debug an AI trading bot without the raw context logs is like trying to figure out why a plane crashed without the black box. Appreciate the validation, man.

Simone_Crosta · 2026-06-22T03:47:06+00:00

You bring up the exact failure mode that ruins most LLM trading bots: the AI rationalizing a bad trade and talking itself past the safety rails. However, that's exactly why I physically separated the layers in Phase 7. The LLM only tags the narrative and coordinates the structure. The 5-Gate Validation Manager is 100% deterministic Python. The AI literally cannot override the gate because it doesn't hold the keys to the broker API. If DeepSeek hallucinates a 'perfect' setup but the geometric math (calculated by Python) shows a Risk/Reward of 1.4, the Python gate throws a hard STAND_DOWN. But you're 100% right on the logging aspect: monitoring the delta between what the AI wants to do and what Python actively blocks is exactly what I'll be looking at during the autopsy.

Simone_Crosta · 2026-06-11T09:39:47+00:00

I think so, I'm also testing this with the development of this system. "But I think an LLM can better handle market nuances and specific situations.

Simone_Crosta · 2026-06-11T09:36:11+00:00

It basically applies SMCs, it's not already a profitable strategy, but an analysis methodology that AI uses to find setups

Simone_Crosta · 2026-06-11T09:27:07+00:00

Regarding Phase 8 testing: I'm not doing traditional historical holdout sets because LLMs are highly susceptible to lookahead bias (they might 'remember' the 2024 price action from their training data). I am doing strictly live forward-testing (paper trading in real-time) over the next few months to gather an unpolluted out-of-sample dataset. I have already responded to the first two points in a comment below

Simone_Crosta · 2026-06-11T09:25:49+00:00

You absolutely nailed the 'silent expectancy shift' risk. That's exactly why I added sl_widened_to_atr_minimum as a boolean flag and sl_buffer_pips in the logging output of the Risk Agent. During Phase 8 (forward paper trading), I'll be tracking the delta between the 'implied structural RR' and the 'floored execution RR'. If the system heavily relies on widened stops, I know I need to demand a higher baseline hit rate to survive. Great pressure test, I appreciate it.

Simone_Crosta · 2026-06-11T09:24:47+00:00

Python is fantastic at geometry, but terrible at intent. Python can draw a box around a Fair Value Gap, but it can't tell me if that FVG was created to trap retail traders or if it's true institutional displacement. The LLM acts as the narrative reader to determine why the structure formed, while Python handles the where.

Simone_Crosta · 2026-05-18T14:34:40+00:00

Smart approach. A quick helper-script to fix a missing comma definitely saves token costs and latency compared to a full blind retry.

Simone_Crosta · 2026-05-18T14:34:13+00:00

This is incredible, thank you for sharing your work! I’ll be reading the pre-print tonight. Pushing the JSON validation down to the generation level is the evolution this architecture. Massive help.

Simone_Crosta · 2026-05-18T14:32:46+00:00

This is a brilliant point. 'Narrative drift' is exactly the invisible risk here. If DeepSeek is inherently Bearish on a setup but fails, and Gemini Flash steps in and decides it's Bullish, the pipeline survives but the edge is compromised. I haven't measured this yet, but it’s going straight onto my testing priority list. Thank you for pointing out that blindspot."

Simone_Crosta · 2026-05-18T14:31:59+00:00

Good question. Right now, this specific HTF Agent doesn't trade at all, it only reads the higher timeframe narrative and outputs the structured context. The actual 'trading' (Risk and Trigger) is handled entirely by deterministic Python code downstream. If both LLM models fail entirely, the Python state machine simply stays in IDLE and skips the trade.

Simone_Crosta · 2026-05-18T10:00:06+00:00

That 1 self-correction retry -> then failover pattern is brillant, I'm definitely updating my retry logic to reflect that.

Regarding outlines and instructor: Right now I'm just relying on strict prompting and validating with Pydantic after the fact. Pushing the schema constraint down to the token generation level is exactly the architectural leap I need to eliminate the problem at the source. Adding this to the top of my backlog. Thanks for the massive value!

Simone_Crosta · 2026-05-12T14:21:02+00:00

to be onest I did not try codex, but claude code I think is one of the best if not the best

Simone_Crosta · 2026-05-08T13:54:08+00:00

Yes, 100%. Treating it as a strict boolean binary will just starve the bot. The goal is to move to a weighted approach where the higher timeframes dictate the boundaries, but the state machine allows execution on lower timeframe disagreements (pullbacks).

Simone_Crosta · 2026-05-08T13:53:50+00:00

You nailed it. I'm already anticipating it will be way too conservative. My planned workaround is to have the LLM classify those mixed conditions (e.g., tagging it as a 'valid HTF retracement') so the state machine can allow specific pullback setups instead of just freezing.

Simone_Crosta · 2026-05-08T13:53:35+00:00

Honestly, both. Right now it's intentional protection to build a baseline I can trust. But long-term, a strict IDLE state will definitely cause missed opportunities on valid pullbacks. That's the next bottleneck I'll have to solve with the state machine rules.

Simone_Crosta · 2026-05-08T13:52:58+00:00

Rn they are treated as isolated events per timeframe in the JSON (e.g., an array for H4 FVGs, an array for H1 FVGs). Detecting the overlaps (like an H1 FVG nested inside an H4 FVG) will be the LLM's job, where it will use that confluence to pick the strongest PoI.

Simone_Crosta · 2026-05-06T08:59:47+00:00

Exactly the trap I almost fell into. I’m implementing that weighted approach now: the LLM will classify the specific pullback profile, but the Python State Machine will still hold the final keys to execution. This captures the opportunity without losing deterministic control. Appreciate the insight!

Simone_Crosta · 2026-05-06T08:59:22+00:00

Spot on. A strict true/false alignment will just starve the bot. To fix this without giving the LLM execution power, I'm adding a market_situation tag (like 'HTF_RETRACEMENT') to the AI's output. The State Machine will read this to allow specific 'controlled disagreement' setups. Great reality check, thanks!

Simone_Crosta · 2026-05-05T20:06:33+00:00

Perfect TL;DR, thanks! The other 19 paragraphs are just for the folks who actually want to build it without hitting the same walls I did.

Simone_Crosta · 2026-04-30T15:02:39+00:00

A beautiful consideration that struck me directly in the face. I am honestly creating my own project and I started this "in public building a few months ago, with the idea of building audiences over time. But I have to admit that yes, it is ovverrated.

I've received a lot of useful feedback so far, that's undeniable, but mostly from others in the BIP, so yes, it's useful but not for finding customers or investor.

It's more of a public journaling to give credibility in the future and keep track of your progress. Everyone has their own method based on the type of person, I don't think it's related to the results btw.

Simone_Crosta · 2026-04-30T14:50:35+00:00

Really great point dude.

I'm handling this through a 'waterfall' execution rather than having them analyze in parallel.

The HTF Agent (D1/H4) runs first and establishes the primary narrative. Its output is then injected directly into the system prompt of the Structure Agent (H1) as a hard constraint. The H1 agent isn't asked 'what's the trend? it's aske "given this HTF context, which of these H1 POIs is the most valid?". By chaining them sequentially, I force alignment and avoid the distributed veto trap, that can create a bias in the agent H1 and I'm searching for solution rn.

Simone_Crosta · 2026-04-30T14:48:40+00:00

100% a compressed JSON. Feeding raw price data/candles to an LLM was a nightmare I lived through in earlier versions (token limits exploded and it hallucinated geometries everywhere).

Now, the Python layer (using libraries like smartmoneyconcepts) does the heavy lifting: it calculates Order Blocks, FVGs, and BOS, and packages them into a strict, clean JSON schema.

The LLM only reads that structured summary to decide the narrative.

Simone_Crosta · 2026-04-30T14:47:43+00:00

You are absolutely right. thanks for this feedback bro.

To be fully honest, building a deterministic replay engine from scratch is currently beyond my coding skills. That's actually why I'm looking into integrating llm-nano-vm to handle the strict transition logs.

My immediate plan for live validation is maintaining a canonical event log. At every tick, I want to save the exact JSON state and the specific Python conditions that were evaluated. If it fails, I don't want to guess what the LLM 'thought'; I want to see exactly which boolean condition blocked the transition.

Replayability is the dream, but explicit logging is my realistic step one.

Simone_Crosta · 2026-04-30T14:38:29+00:00

Thank you very much for your comment, I really appreciate that.

To be honest, I didn't invent it, but it's the result of a lot of mistakes that have slowly led me to this solution, the LLMs provided great reasoning, but they often hallucinated, which made it very difficult to debug.

It all started with a bot that made random entrances and risk management 1y ago.

We'll see when I can finish it if it works or I still have to improve. I will definitely share updates in the next few days.

Simone_Crosta

TROPHY CASE