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The crypto community continues to actively integrate artificial intelligence into trading strategies. One well-known trader with an audience of over 200,000 followers on X shared a set of six prompts that, according to him, significantly enhance the effectiveness of market analysis. Let's break down each one from a practical applicability perspective.

Risk/Reward

This prompt forces the neural network to break down a specific trade into its components: potential loss and potential profit. As output, the trader receives a suggested entry point, a stop-loss level, and target levels for taking profit. This method allows for weeding out trades with an unfavorable risk-to-reward ratio in advance, which is a basic but often ignored rule of capital management.

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 assesses how key macro factors—interest rates, inflation, and the strength of the dollar—affect the price. This is especially useful during periods when market movement is determined not by technical signals, but by central bank decisions and the general economic sentiment.

Use macro analysis to evaluate [the 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 stop orders from retail traders and large orders from institutional players. The idea is that price often gravitates towards levels with high volumes. Understanding these zones helps predict where the market might move in search of liquidity and avoid having your own stop triggered.

Create a liquidity map for [the 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 would all decline simultaneously. The neural network helps identify 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—the public transaction history and wallet behavior. The neural network is asked to find signs of accumulation, when large holders are increasing their positions, or distribution, when they are offloading 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 patterns of accumulation or distribution based on wallet behavior.

Portfolio Stress Test

This 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]. Model drawdown scenarios and identify the weakest positions.

Analyst's comment. It's important to remember: working through neural networks does not guarantee profitability. AI can make mistakes. It is crucial to double-check everything and make decisions independently, considering all possible risks. These prompts are merely tools to speed up analysis, not a substitute for trading discipline and common sense.