OnlyPump – AI-Powered Investment Intelligence Platform

Overview

OnlyPump is an AI-powered investment intelligence platform built on top of the Pump.fun and Solana ecosystem. It goes beyond analytics — the AI adapts to each user's investing style and risk tolerance, generating personalized investment signals calibrated for aggressive, moderate, or conservative strategies. The system combines social sentiment, on-chain behavior, and real-time market data into a unified AI decision layer, with every model validated through rigorous backtesting against historical data.

Live demo: https://onlypump.me/

I built and led the development of the core AI infrastructure from architecture through production deployment.

The Problem

Meme token trading on Pump.fun is driven almost entirely by hype, social momentum, and whale manipulation. Retail participants have no structured way to evaluate tokens — they rely on gut feel and social noise. Beyond just analytics, different investors need different signals: an aggressive trader wants early momentum indicators, while a conservative investor wants confirmation signals with lower risk exposure. OnlyPump solves both problems by building an AI system that continuously processes multi-source signals and adapts its recommendations to the user's risk profile.

What I Built

Adaptive AI Investment Profiles

• AI adapts recommendations to each user's investing style — aggressive, moderate, or conservative profiles
• Aggressive profiles receive early momentum signals, higher-risk/higher-reward token picks, and tighter entry windows
• Conservative profiles receive confirmation-based signals, lower-volatility selections, and wider safety margins
• Profile calibration refined through extensive local testing with historical Pump.fun launch data across hundreds of token lifecycle scenarios

RAG-Powered Contextual Insights

• Retrieval-Augmented Generation (RAG) pipeline delivers contextual market insights grounded in historical data — past token launches, ecosystem trends, project activity patterns
• Vector database pipelines (pgvector) for embeddings and similarity search across social, market, and on-chain data
• LLM integration provides natural language explanations of token signals with citations to source data
• RAG retrieval tested extensively against known historical scenarios to validate that the system surfaces relevant precedents and avoids hallucinated comparisons

Statistical Backtesting & Validation

• Built a backtesting framework to validate scoring models against historical Pump.fun token launch data
• Sharpe ratio analysis to evaluate risk-adjusted returns across different strategy profiles
• Win rate and expectancy calculations per signal type and per risk profile
• Drawdown analysis measuring maximum and average drawdowns for aggressive vs. conservative strategies
• Monte Carlo simulation for stress-testing portfolio strategies under randomized market conditions
• All AI scoring models were iteratively refined based on backtesting results — not deployed on intuition alone

Multi-Source Intelligence System

• Social sentiment analysis from X (Twitter): real-time token mention tracking, sentiment scoring, narrative momentum detection, influencer signal weighting
• On-chain wallet and transaction behavior analysis (Solana): whale wallet tracking, new wallet clustering, holder distribution analysis, suspicious pattern detection
• Real-time market data processing: price, volume, liquidity monitoring with anomaly detection

Intelligent Scoring Models

Composite scoring models combining:

• Sentiment strength and narrative velocity
• Liquidity and volume change signals
• Whale activity and wallet flow patterns
• Holder distribution health metrics
• Narrative momentum across social channels
• Risk-adjusted weighting based on user's investment profile

AI Portfolio Intelligence

• Risk assessment per token and portfolio position calibrated to user's risk tolerance
• Position sizing optimization based on signal confidence, market conditions, and profile aggressiveness
• Exposure analysis across correlated tokens
• Market condition awareness (trending vs. cooling ecosystems)

Data Pipeline Architecture

• Automated real-time ingestion from social APIs, Solana blockchain nodes, and market data feeds
• Streaming pipeline architecture handling high-throughput on-chain event data
• Fault-tolerant design with retry logic and dead-letter handling for missed events

Solana & Pump.fun Integration

• Pump.fun SDK integration for bonding curve token creation and trading
• PumpSwap SDK for AMM pool operations post-graduation
• Jito MEV bundle optimization for competitive transaction inclusion
• Direct Solana RPC integration for real-time on-chain data

AI Agent Architecture

• Modular LLM pipeline supporting multiple model providers and fallback strategies
• Deterministic validation layer ensuring AI outputs are grounded in retrieved evidence
• Designed for extensibility toward autonomous AI decision agents

Key Highlights

• AI adapts to user investing style — aggressive, moderate, or conservative — with different signal weightings and risk thresholds
• Scoring models statistically validated through backtesting with Sharpe ratio, win rate, drawdown analysis, and Monte Carlo simulation
• RAG system ensuring AI responses are grounded in real historical data, not hallucinated
• Full-stack AI platform from data ingestion to embedding to retrieval to LLM generation to user interface
• Multi-source intelligence combining social, on-chain, and market data in a unified scoring layer
• Production-grade Solana integration with Pump.fun, PumpSwap, and Jito