In the arsenal of a modern crypto trader, neural networks are becoming an indispensable tool. One well-known analyst with an audience of over 200,000 followers on X, operating under the pseudonym Tyler Durden, shared his selection of six prompts that he uses for daily market analysis and making trading decisions. Let's break down each of them in detail.
Risk/Reward
The first prompt is a classic framework for evaluating any trade. The neural network is tasked with calculating the optimal entry point, stop-loss level, and target profit-taking levels based on a given trading setup. This approach disciplines the trader and filters out trades with an unfavorable potential profit-to-risk ratio.
"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 macroeconomic picture. The neural network assesses how key factors—interest rates, inflation, and the strength of the dollar—affect the price. This is especially valuable when market movement is driven not by technical signals but by central bank decisions.
"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 institutional orders. The idea is that price often gravitates toward levels with significant volume. Understanding these zones helps predict where the market might move in search of liquidity and avoid prematurely triggering one's own stop-loss.
"Create a liquidity map for [the asset]. Identify where clusters of stops and institutional orders are most likely concentrated."
Correlation Matrix
An important tool for risk management. The prompt analyzes how closely the assets in a portfolio are related. If several positions move in sync, the portfolio only appears diversified but actually carries concentrated risk. The neural network helps uncover these hidden connections and assess true 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 looks for accumulation patterns (when large holders increase their positions) or distribution patterns (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 final 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 pre-assess the maximum possible loss 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 comment: Using neural networks to structure the trading process is a logical step forward. However, it's important to remember that AI is just a tool, not a crystal ball. It can make mistakes, and blindly relying on it is a sure path to losses. The key to success is combining these prompts with your own analysis and strict risk management. Only then can you turn a neural network from a trendy toy into a real assistant.