Crowdsourced Strategy Research
The infrastructure exists. The data is public. But finding profitable strategies for Solana memecoins requires more minds, more experiments, and more compute than any single trader can run alone.
Fathom is an open strategy research platform — built on nautilus_trader's architecture, adapted for the trenches. Contribute strategies. Backtest on real data. Get paid for what works.
Built on Nautilus Trader
We didn't build a trading engine from scratch. We adapted one of the most battle-tested open-source platforms in existence.
A high-performance algorithmic trading platform and event-driven backtester. Rust core, Python API. Used by institutional quant teams.
Nautilus architecture adapted for Solana memecoins. Event-driven engine, multi-factor scoring, 7-source discovery pipeline. Python-only for accessibility.
From Nautilus
Built for the trenches
Why Open Research
Data is expensive
Helius RPC, DexScreener, pump.fun — rate limits everywhere. Meaningful backtests need weeks of continuous collection across thousands of tokens. One person's free tier doesn't cut it.
Strategy space is massive
Holder analysis, momentum, mean reversion, social signals, deployer forensics — the combinations are infinite. No single trader can explore them all.
The edge compounds
A strategy that works for one person works better when refined by many. Shared backtests, shared data, shared learnings. Collective intelligence beats solo grinding.
“If it really works, why share it?”
Because finding what works requires more experiments than any single person can run. The infrastructure is here. The strategies are the hard part. That's what we're crowdsourcing.
Token Feed
Live Solana tokens scored by fathom's multi-factor model. Every token links to its source. Updates every 30s.
Why On-Chain Filtering Works
Thousands of tokens launch on Solana every day. Most are noise. The alpha isn't in finding tokens — it's in knowing which ones to ignore.
Multi-source discovery
7 data sources feeding one pipeline — pump.fun, DexScreener, GeckoTerminal, Helius, Jupiter. Tokens surface before CT catches them.
Holder forensics
On-chain holder distribution, dev wallet activity, sniper detection, top-10 concentration. The data that separates real projects from exit scams.
Liquidity validation
Fake billion-dollar mcaps with $10K liquidity? Ratio analysis catches them instantly. Real depth vs paper valuations.
Conviction scoring
5 signal categories weighted into a single score. Not binary trade/skip — graduated conviction with dynamic position sizing.
Speed bots will always be faster. MEV will always front-run. But being first doesn't matter if you're buying a token that dumps to zero.
The edge is intelligence — knowing what to skip before you ever place a trade.
What gets filtered
Golden: $1.07B mcap, $12K liquidity = 89,166:1 ratio
If 5 wallets own 90% of supply, they dump on retail
HgaD5ZSN launched 7 fake tokens in 24 hours
Real Results
347+ mints through the default scoring model across multiple collection runs. Early. Promising. Needs more minds on it.
// NOTABLE PICKS
High-Conviction Captures
Tokens the scoring model flagged with high conviction at graduation — tracked from bond to peak.
High buy ratio, strong holder distribution, massive volume surge post-graduation
Clean on-chain metrics, low top-10 concentration, sustained momentum
Highest transaction count in dataset — massive organic activity signal
Strong liquidity health, clean holder profile, steady accumulation pattern
Performance measured from graduation to 24h peak. Past performance does not guarantee future results.
Scam detection
Wallet HgaD5ZSN... deployed 7 fake tokens with billion-dollar mcaps and ~$10K liquidity. All caught.
Start exploring
Clone. Collect data. Test a hypothesis. Share what you find.