# Personalized Trading Engine ### Real-Time Trading Intelligence Personalized to Every User The Personalized Trading Engine helps platforms deliver intelligent, real-time trading experiences by combining user-specific context with live market intelligence. Built for exchanges, wallets, dashboards, terminals, bots and AI agents that need adaptive experiences based on who the user is, what they hold and what markets are doing now. This is a custom integration product designed for partners who want to offer premium trading intelligence without building internal quant or analytics infrastructure. {% embed url="" %} ## What It Does The Personalized Trading Engine merges user context with real-time market signals to generate personalized insights, actions and recommendations. Instead of giving every trader the same signals, this engine adapts outputs to each user’s portfolio, behavior, positioning and risk profile.
## How It Works The engine combines three layers of intelligence: ### 1. User Context Layer Accept user-specific inputs such as: * Wallet addresses * Open positions * Trade history * Portfolio holdings * Watchlists * Risk preferences * Uploaded chart screenshots * Trading setups These inputs are securely processed to build a persistent user state. *** ### 2. Market Intelligence Layer Each user state is enriched using DappLooker intelligence systems, including: * Token Intelligence * Smart Money Flows * Perp Market Signals * Technical Indicators * Volatility Zones * Sentiment Trends * Regime Detection * Yield Opportunities *** ### 3. Dynamic Context Engine The system continuously updates recommendations as conditions change. Examples: * User overexposed to one sector * Funding rates become crowded * BTC volatility spikes * Smart money rotates into a new token * Existing positions move into risk zones
## What You Gain * Increases trading volume through actionable insights * Cuts user research time from \~30 mins to under 5 mins * Provides a reliable 24/7 signal layer for traders * Boosts user retention with continuous, personalised guidance * Improves decision accuracy by combining on-chain data, market signals and user-specific context ## Use Cases * Agentic trading bots with personalised strategies * Context-aware dashboards with predictive overlays * Wallet-native chat UIs for behavioral guidance * Image-based market context analysis * Integrations with DEXs, aggregators, or copy-trading tools ## Book a Demo {% content-ref url="/pages/YfePEaSbMd2ZpkAGOzbI" %} [Contact Us](/data-apis-for-ai/contact-us.md) {% endcontent-ref %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.dapplooker.com/products/api-endpoints/agent-infrastructure/personalized-trading-engine.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.