The End Of Data Cleaning

Ask any professional trader building on Solana today, and you’ll hear a similar story.
They spend more time preparing data than actually trading.

Every block contains thousands of raw events: swaps, transfers, contract calls.
Turning those into something actionable requires endless work: resolving wallet addresses, mapping token mints, reconstructing multi-hop swaps, and guessing at intent.

By the time the data is usable, the opportunity it pointed to has already passed.

More than just a side inconvenience, this is the main bottleneck.

Opportunities in trading disappear fast.

• An arbitrage spread between two pools may exist for a single block.

• A shift in liquidity can change the pricing landscape in under a second.

• A coordinated move across wallets might signal momentum that closes before a slow bot reacts.

When traders spend seconds or even minutes cleaning data, they’re competing in a race that’s already finished.

This is the problem LYS Core was built to solve.
Instead of dumping raw logs, Core processes every block as it finalizes and emits structured streams that are already clean and contextualized.

Wallets are normalized. Tokens are resolved.
Multi-step flows are reconstructed into a single, coherent event.

Context comes attached, so that your bots never have to guess whether a move represents a swap, a rebalance or an accumulation.

What used to require hours of manual effort arrives in milliseconds as a machine-ready signal.

If you’re a trader, shifting to a structured stream can dramatically optimize your entire workflow.

• Before: Writing endless scripts to clean and stitch together data, with fragile pipelines that break when protocols update.

• After: Subscribing to a structured stream and focusing on what matters—the strategy itself.

Traders shouldn’t be data janitors.
Traders should trade.

They can iterate on new strategies, refine models, and test hypotheses without the overhead of constantly rebuilding their data pipelines.

Take an arbitrage desk monitoring multiple pools.

With raw feeds, developers spend time normalizing swaps from each DEX, decoding token decimals, and writing scripts to spot opportunities. By the time the signals are clear, spreads have closed: bye-bye gains.

With structured streams: the opportunity surfaces automatically, within ~14 ms of block finality, already contextualized as a spread event. Traders can pull the trigger immediately and execute with minimal delay.

The difference is measured directly in your PnL, by looking at captured vs. missed opportunities.

Structured streams are more than a mere technical upgrade.

They’re a tool for traders to win back their own time.
Hours once wasted cleaning data can now be invested in building better strategies.

That compounds: every new model, every incremental improvement, builds on top of a cleaner foundation.

This is why we call them alpha engines: apart from saving time, structured streams create an environment where alpha is amplified, not eroded by overhead.

Trading should be about competing on insight and execution, not about who can write the most resilient data-cleaning script.

For traders on Solana, this isn’t just a nice-to-have feature.
It’s the difference between operating with a clear picture of the market and working with stale, incomplete information.

In a market where milliseconds define outcomes, clarity at block speed is the only real edge.