A Pons sniper bot is easy to describe:
Detect a new token and buy it quickly.
Building one that is actually reliable is a different problem.
The interesting part isn't the final transaction. It's everything that happens before and after it:
event detection → token validation → pool validation → filtering → risk → execution → confirmation → reconciliation
I've been building the infrastructure underneath Pons trading applications on Robinhood Chain, starting with a TypeScript SDK and an onchain indexing layer.
This article walks through how I'd structure a Pons sniper system on top of that infrastructure.
The architecture
A useful starting point looks like this:
Robinhood Chain
│
▼
Pons Contracts
│
▼
TokenLaunched Events
│
▼
Event Detection
│
▼
Token / Pool Validation
│
▼
Filtering
│
▼
Risk Decision
│
▼
Execution
│
▼
Confirmation
│
▼
Reconciliation
The important design decision is to keep these components separate.
A sniper shouldn't have RPC calls, strategy logic, transaction submission, and position tracking mixed together in one large loop.
That becomes difficult to test and even harder to operate.
1. Start with reliable launch detection
The first requirement is knowing when something has actually launched.
For Pons, the natural starting point is the onchain TokenLaunched event.
Instead of repeatedly polling contract state, the system can consume launch events and turn them into normalized application data.
For example:
type TokenLaunch = {
version: "v1" | "v2";
factory: string;
token: string;
deployer: string;
pool?: string;
pairToken?: string;
blockNumber: bigint;
transactionHash: string;
logIndex: number;
};
This gives the rest of the system a clean event to work with.
The sniper doesn't need to know how the event was decoded.
It only needs to receive:
NewTokenLaunch
That separation becomes important later when the same infrastructure is reused by scanners, analytics, copy trading, or other automation.
2. Validate the token
A launch event shouldn't automatically become a trade.
The next step is validation.
Depending on the application, that can include:
- Is the token address valid?
- Is the contract actually deployed?
- Does the token have the expected metadata?
- Is the deployer known?
- Is the launch associated with a valid Pons factory?
- Is the token already indexed?
- Has this launch already been processed?
This is also where duplicate protection matters.
Onchain systems can produce retries, reconnects, overlapping queries, or repeated processing.
A simple idempotency key can prevent the same launch from entering the trading pipeline twice:
factory + transactionHash + logIndex
The principle is simple:
seeing the same event twice should not create two trades.
3. Validate the pool
Finding a token isn't enough.
A trading system needs to understand where liquidity actually exists.
The validation stage should establish things such as:
Token
↓
Pair
↓
Pool
↓
Liquidity / state
The exact checks depend on the trading strategy.
For example, a strategy might reject a launch when the pool isn't available yet, when the available liquidity is too small, or when the pool doesn't match the expected configuration.
This stage should happen before the execution layer.
Otherwise, the execution code ends up becoming responsible for figuring out whether a trade should exist in the first place.
That's a bad separation of concerns.
4. Add a filtering layer
Once the token and pool are validated, the system needs a decision layer.
I prefer keeping this separate from execution.
Something like:
const decision = strategy.evaluate({
token,
pool,
launch,
marketState,
});
if (!decision.shouldTrade) {
return;
}
This makes the strategy replaceable.
One user might want a very aggressive launch strategy.
Another might care about liquidity.
Another might want wallet-based signals.
Another might combine launch information with market activity.
The infrastructure shouldn't dictate the strategy.
It should provide the data and execution primitives required by the strategy.
5. Risk should be a separate component
A strategy saying:
BUY
doesn't necessarily mean:
BUY 100% OF AVAILABLE CAPITAL
Risk management should sit between the strategy decision and execution.
For example:
Strategy
↓
BUY
↓
Risk Engine
↓
Approved size
↓
Execution
The risk layer can enforce application-specific rules around:
- maximum position size
- maximum capital allocation
- exposure limits
- duplicate positions
- transaction limits
- failure handling
- emergency shutdown conditions
This separation also makes it much easier to test.
You can test the strategy independently from the wallet.
You can test the risk engine without submitting transactions.
And you can test execution without changing the strategy.
6. Execution is its own problem
This is where many simple bot architectures become fragile.
Detecting a launch quickly doesn't guarantee that the transaction will execute correctly.
The execution layer needs to deal with things like:
- transaction construction
- gas
- nonce management
- RPC failures
- transaction submission
- confirmation
- reverted transactions
- retries
- timeouts
The important distinction is:
a transaction being submitted is not the same thing as a trade being confirmed.
The system should maintain explicit state.
For example:
DETECTED
↓
VALIDATED
↓
APPROVED
↓
SUBMITTED
↓
CONFIRMED
And there should also be failure paths:
SUBMITTED
↓
FAILED
or:
SUBMITTED
↓
TIMEOUT
↓
RECONCILE
This makes the system observable instead of relying on log messages like:
"probably bought"
7. Reconciliation closes the loop
A trading system shouldn't assume its internal state is correct forever.
After execution, the system needs to reconcile its assumptions against the chain.
For example:
Internal state
│
▼
Transaction hash
│
▼
Onchain confirmation
│
▼
Actual token balance
│
▼
Actual position state
If something doesn't match, the system should detect the discrepancy.
This becomes especially important when RPC requests fail, processes restart, or transactions take longer than expected.
A reliable trading system should be able to restart without losing track of what happened.
8. The indexer is more important than it looks
This is one reason I built the Pons indexer before trying to build the complete sniper.
The indexer provides a persistent representation of what happened onchain.
Instead of making the sniper responsible for reconstructing history, the architecture becomes:
Robinhood Chain
↓
Pons Events
↓
Indexer
↓
Normalized State
↓
Trading Applications
Now multiple applications can use the same data layer.
For example:
┌── Scanner
│
Pons Indexer ───────┼── Copy Trading
│
├── Sniper
│
├── Analytics
│
└── Wallet Intelligence
Build the infrastructure once.
Build different applications on top of it.
9. Testing matters more than speed
A sniper system is latency-sensitive, but making it fast before making it correct is usually the wrong order.
For the Pons SDK foundation I've been building, the current test suite includes:
- 30/30 unit tests passing
- 3/3 live integration tests passing
- real Robinhood Chain event data
-
TokenLauncheddecoding - contract reads
- historical event queries
That gives the application layer something much more useful than a collection of untested RPC calls.
It gives it a known interface.
The next step is proving the complete detection and decision pipeline under realistic conditions.
10. Don't put everything inside one bot
A tempting implementation looks like this:
while (true) {
detect();
validate();
decide();
trade();
checkBalance();
}
It works for a prototype.
It becomes painful when the system grows.
A more maintainable architecture is:
Event Source
↓
Event Processor
↓
State / Indexer
↓
Strategy
↓
Risk
↓
Execution
↓
Reconciliation
Each component has one responsibility.
That makes it possible to replace the strategy without rewriting the indexer.
It also makes it possible to reuse the same execution layer for a scanner, copy-trading system, or another automated trading application.
From SDK to trading applications
This is the broader direction I'm working toward on Robinhood Chain.
The Pons SDK provides the low-level contract and event layer.
The indexer turns onchain events into usable state.
Then applications can be built on top:
Pons SDK
↓
Indexer
↓
Normalized State
↓
Wallet Intelligence
↓
┌──────────────┬──────────────┬──────────────┐
│ Scanner │ Copy Trading │ Sniper │
├──────────────┼──────────────┼──────────────┤
│ Bundler │ Analytics │ Automation │
└──────────────┴──────────────┴──────────────┘
That's more interesting to me than building a single-purpose bot.
The same infrastructure can support multiple trading systems.
What I'd build next
The remaining pieces of a complete Pons sniper include:
- Real-time launch monitoring
- Token and pool validation
- Strategy-specific filtering
- Risk management
- Transaction execution
- Confirmation handling
- Position reconciliation
- Monitoring and alerting
- Failure recovery
- Simulation and testing against historical launches
The important part is building these as separate layers rather than turning everything into one script.
Final thought
A Pons sniper bot isn't really a “bot.”
It's a small trading system.
The trigger is only one part of it.
The difficult engineering is making sure the system can reliably go from:
event → state → decision → transaction → confirmation → reconciled position
without losing track of what happened.
That's also why I'm building the infrastructure layer first.
The same foundation can eventually support scanners, copy trading, snipers, bundlers, wallet intelligence, and other Robinhood Chain trading applications.
If you're building a trading application on Robinhood Chain and need custom infrastructure, automation, wallet intelligence, scanners, copy trading, or execution systems, I build these systems around the requirements of the strategy and application.
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