A practical architecture for indexing Pons launch events, building persistent onchain state, and creating the data layer behind scanners, copy trading, and trading automation.
Most trading applications don't actually start with a trading strategy.
They start with data.
A scanner needs to know which tokens launched.
A copy-trading system needs to know what wallets are doing.
A sniper needs to detect events quickly and turn them into actionable state.
Analytics systems need historical data they can query repeatedly.
That means the application layer is only as reliable as the data infrastructure underneath it.
For my Robinhood Chain work, I'm building that infrastructure starting with Pons.
The architecture is:
Robinhood Chain
↓
Pons Contracts
↓
Onchain Events
↓
Pons SDK
↓
Historical Indexer
↓
Persistent State
↓
Scanner / Copy Trading / Sniper / Analytics
This article focuses on the indexer layer.
Why build an indexer?
You can query the blockchain directly whenever you need data.
For a small script, that's fine.
For a trading application, it quickly becomes inconvenient.
Imagine a scanner that wants to answer:
- Which tokens launched recently?
- When did they launch?
- Which factory/version created them?
- Who deployed them?
- Which pool was created?
- What block and transaction contained the launch?
- Has this launch already been processed?
- What happened during a previous indexing run?
Repeatedly scanning the RPC for all of this is inefficient and makes application logic responsible for too much blockchain-specific work.
An indexer creates a boundary:
Blockchain
↓
Indexer
↓
Queryable application state
The rest of the system can work with normalized records instead of raw RPC responses.
Start with events, not transactions
For Pons, launch activity can be represented through the TokenLaunched event.
That makes the event log a useful source of truth for discovering launches.
Conceptually:
TokenLaunched
↓
decode event
↓
normalize fields
↓
validate record
↓
persist record
A normalized launch might contain fields such as:
type PonsLaunch = {
version: "v1" | "v2";
factory: string;
token: string;
deployer: string;
dexFactory: string;
pairToken: string;
pool: string;
dexId: bigint;
launchConfigId: bigint;
positionId: bigint;
restrictionsEndBlock: bigint;
initialBuyAmount: bigint;
blockNumber: bigint;
transactionHash: string;
transactionIndex: number;
logIndex: number;
};
The important part is that the application doesn't need to know how those values were extracted from an RPC log.
That's the SDK's job.
Reuse the SDK
One mistake I want to avoid is creating a second implementation of the blockchain logic inside the indexer.
The Pons SDK already handles the lower-level pieces:
RPC
↓
Contract
↓
Event Log
↓
Decoder
↓
Typed SDK Result
The indexer should sit above that.
Pons SDK
↓
Indexer
↓
Database
This gives the system a cleaner separation of responsibilities.
The SDK understands Pons contracts and events.
The indexer understands historical synchronization and persistence.
The application understands what to do with the resulting state.
Historical indexing
The first useful version of the indexer doesn't need to be a complicated distributed system.
A local SQLite database is enough for an MVP.
The basic flow is:
start block
↓
fetch event logs
↓
decode TokenLaunched
↓
normalize events
↓
write to database
↓
advance cursor
↓
repeat
For example:
Block 8,900,000
↓
Block 8,901,000
↓
Block 8,902,000
↓
Block 8,903,000
↓
...
The important detail is chunking.
Trying to query an enormous block range in one RPC request isn't a reliable indexing strategy.
A chunked indexer can control the size of every request and continue from the last successful position.
The database is part of the system
For a local MVP, the schema can remain simple.
A launches table might store:
launches
--------
id
version
factory
token
deployer
dex_factory
pair_token
pool
dex_id
launch_config_id
position_id
restrictions_end_block
initial_buy_amount
block_number
transaction_hash
transaction_index
log_index
created_at
The blockchain quantities should remain exact.
For example, token amounts and block numbers shouldn't casually be converted to JavaScript floating-point numbers.
Use appropriate integer representations and preserve the original precision.
This matters later when those values become inputs to trading or risk systems.
Idempotency matters
An indexer will eventually process the same block twice.
Maybe a process restarts.
Maybe a range overlaps.
Maybe a deployment is replayed intentionally.
That shouldn't create duplicate records.
A useful uniqueness constraint is:
(transaction_hash, log_index)
The combination identifies a particular event log.
So processing the same event again becomes harmless.
Conceptually:
First run:
TX A + log 45 → INSERT
Second run:
TX A + log 45 → already exists
This is one of those small implementation details that becomes extremely important once an indexer runs continuously.
Cursor-based synchronization
The indexer also needs to know where it stopped.
A simple indexer_state table can contain:
indexer_state
-------------
name
last_processed_block
updated_at
Then a restart becomes:
last_processed_block
↓
next chunk
↓
continue indexing
Instead of starting from the beginning every time.
The cursor should only advance after the corresponding database transaction succeeds.
That gives us an important invariant:
Never record progress for data that wasn't successfully persisted.
Transactionality
Consider this failure:
Block range fetched
↓
50 launches decoded
↓
30 launches written
↓
process crashes
↓
cursor already advanced
Now the indexer believes the entire range was processed even though it wasn't.
That's a data-loss bug.
A safer sequence is:
BEGIN TRANSACTION
insert launches
update cursor
COMMIT
If something fails:
ROLLBACK
The next run can safely retry the same range.
This is much more important than adding fancy infrastructure early.
Deterministic ordering
Blockchain data should also be processed deterministically.
Within a block, multiple transactions can exist.
Within a transaction, multiple logs can exist.
So the indexer needs a consistent ordering such as:
blockNumber
↓
transactionIndex
↓
logIndex
That makes replay behavior predictable.
It also makes debugging substantially easier.
When something looks wrong, you should be able to point to a precise onchain location:
block
transaction
log
rather than simply saying that an event was detected "around that time."
Real onchain verification
The Pons SDK foundation has already been connected to the Robinhood Chain mainnet RPC.
One useful reference launch is a real Pons V1 launch at:
Block:
8963150
Transaction:
0x1f54f25fec2d963dcb338ecb8b46a6eb123198a5c7a746d34cb2dbe78d074af8
The TokenLaunched event can be decoded into structured information including the token, deployer, factory, pool, position ID, and launch metadata.
This is important for the project because the indexer shouldn't be built around invented fixtures and then assumed to work on mainnet.
The test data should eventually include real event fixtures as well as synthetic cases for failure handling.
Testing the indexer
The interesting tests aren't just:
"does it insert a row?"
The more important questions are:
What happens when the same range runs twice?
Expected:
run 1 → records inserted
run 2 → no duplicates
What happens when ranges overlap?
For example:
run 1 → blocks 1000–2000
run 2 → blocks 1500–2500
The overlapping events should remain unique.
What happens when persistence fails?
The cursor shouldn't move past data that wasn't committed.
What happens after a restart?
The indexer should continue from its persisted state.
What happens with V1 and V2?
The indexer should normalize both versions into the application-level model without forcing every downstream consumer to understand contract-specific differences.
These properties matter more than simply getting one successful indexing run.
Why this matters for a trading system
The indexer isn't the trading strategy.
That's intentional.
It's infrastructure.
Once launch data is persistent and queryable, multiple applications can consume it.
For example:
┌──→ Token Scanner
│
Pons Indexer ────┼──→ Sniper
│
├──→ Analytics
│
├──→ Copy Trading
│
└──→ Monitoring
The same underlying data doesn't need to be rediscovered independently by every application.
That's the advantage of building the infrastructure layer first.
From indexer to real-time systems
Historical indexing is only one side of the problem.
Trading applications eventually need real-time updates.
That creates another path:
Pons Events
↓
Event Stream
↓
State Update
↓
Application
The historical indexer can establish the initial state.
A real-time event stream can then keep that state current.
Conceptually:
Historical Sync
+
Real-Time Events
↓
Current Onchain State
That becomes a much stronger foundation for scanners, copy trading, and automated execution.
What I am building next
The broader Robinhood Chain project is becoming a reusable trading infrastructure stack rather than a collection of isolated bots.
The direction is:
Pons Contracts
↓
Pons SDK
↓
Indexer
↓
Onchain State
↓
Wallet Intelligence
↓
Applications
├── Scanner
├── Copy Trading
├── Sniper
├── Bundler
└── Analytics
↓
Risk + Execution
Each layer should solve one problem well.
That makes it possible to build different trading applications without rebuilding the blockchain integration every time.
The bigger lesson
A trading bot is often presented as:
signal → buy → sell
In production, the actual system looks more like:
contracts
→ events
→ indexing
→ state
→ detection
→ filtering
→ risk
→ execution
→ confirmation
→ reconciliation
→ monitoring
The strategy is only one component.
For Robinhood Chain, I'm starting with Pons because it provides a concrete ecosystem to build against, but the goal is broader:
reusable trading infrastructure for applications running on Robinhood Chain.
The indexer is one of the pieces that makes that possible.
Building something similar?
I'm interested in building custom Robinhood Chain infrastructure and trading systems, including:
- Pons SDKs and contract integrations
- onchain indexers
- token scanners
- wallet intelligence
- copy-trading systems
- sniper systems
- bundlers
- trading analytics
- automated execution infrastructure
The focus is the same throughout: reliable systems built around real onchain data, not just a strategy wrapped in a script.
GitHub
The project is being developed as open-source Robinhood Chain trading infrastructure, starting with Pons.
