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Over the past few months, I've been closely following the toolkit of successful market participants, and one of them—a trader under the pseudonym Tyler Durden with an audience of over 200,000 followers on X—shared his working methodology. He uses six specific prompts for neural networks that help him structure his analysis and make more informed decisions. These commands don't provide ready-made buy signals, but they turn AI into a powerful assistant for deep analysis of the situation. Let's break down what these prompts are and how they can be applied in practice.

Risk/Reward

The first prompt forces the neural network to analyze a specific trade through the lens of the potential loss-to-profit ratio. As output, the trader receives a suggested entry point, a stop-loss level, and a target profit-taking level. This approach helps to understand in advance whether the expected return justifies the risk taken and avoids entering trades with an unfavorable ratio. This is the basics, which many, unfortunately, ignore.

Apply the risk/reward framework to [my trading setup]. Calculate the optimal entry point, stop-loss level, and target levels.

Macro Overview

This prompt shifts the focus from the chart of a specific asset to the overall economic picture. The neural network is asked to assess how key macro factors—interest rates, inflation, and the strength of the dollar—affect the price. This is especially useful when market movement is driven not by technical signals, but by central bank decisions and general economic sentiment.

Use macro analysis to evaluate [asset]. Assess how interest rates, inflation, and the strength of the dollar influence the price direction.

Liquidity Map

This prompt is aimed at finding zones where liquidity is concentrated—clusters of retail traders' stop orders and large institutional players' orders. The idea is that price often gravitates toward levels with high volumes. Understanding these zones helps predict where the market might move in search of liquidity and avoid getting caught by your own stop-loss.

Create a liquidity map for [asset]. Identify where clusters of stops and institutional orders are most likely concentrated.

Correlation Matrix

This prompt analyzes how closely related the assets in a portfolio are. If several positions move in sync, the portfolio only appears diversified but actually carries concentrated risk: in a market reversal, they all decline simultaneously. The neural network helps uncover such hidden connections and assess real, rather than apparent, diversification.

Use correlation analysis on [my portfolio]. Identify hidden risk concentration between assets.

On-Chain Signals

This prompt uses blockchain data—public transaction history and wallet behavior. The neural network is asked to find signs of accumulation (when large holders increase their positions) or distribution (when they offload assets). Such patterns sometimes precede price movements and serve as an additional signal for technical analysis.

Apply on-chain analysis to [bitcoin/crypto asset]. Identify accumulation or distribution patterns based on wallet behavior.

Portfolio Stress Test

The last prompt tests the portfolio's resilience to adverse scenarios. The neural network models potential drawdowns—for example, a sharp market decline—and shows which positions would suffer the most. This helps to assess the maximum possible loss in advance and understand which assets make the portfolio most vulnerable.

Use stress testing to evaluate [my portfolio]. Simulate drawdown scenarios and identify the weakest positions.

My professional opinion: These prompts are not a magic bullet, but rather a system of checklists that disciplines the trader. They help not to miss key analysis points, but the final decision always remains with the human. The neural network can make mistakes, and I strongly recommend double-checking all its conclusions before opening a position. The market does not forgive blind trust in algorithms.