Building a Pons Wallet Tracker on Robinhood Chain with TypeScript

A Pons wallet tracker is more than a page that displays wallet addresses.

A useful tracker needs to turn on-chain activity into structured trader data:

Robinhood Chain
      ↓
Blockchain Events
      ↓
Pons Event Indexer
      ↓
Wallet Registry
      ↓
Trade Decoder
      ↓
Position Engine
      ↓
Portfolio State
      ↓
Alerts / API / Dashboard
      ↓
Copy Trading

This article shows how I would structure a production-oriented Pons wallet tracker on Robinhood Chain, with TypeScript examples using viem.

The same architecture can be extended into a Pons wallet monitor, trader analytics dashboard, alerting system, or copy-trading bot.

What a Pons Wallet Tracker Actually Tracks

For each tracked wallet, the system should answer questions such as:

• What tokens did this wallet buy?
• What tokens did it sell?
• How much did it buy or sell?
• What positions does it currently hold?
• What is the estimated portfolio value?
• What was the wallet's historical activity?
• Which wallets are trading the same token?
• Did a tracked wallet just enter or exit a position?
• Should the activity trigger an alert or trading strategy?

The important architectural decision is to avoid building the dashboard directly on raw blockchain RPC calls.

Instead:

Blockchain
    ↓
Indexer
    ↓
Normalized Events
    ↓
Position State
    ↓
  API
    ↓
Frontend

The blockchain remains the source of truth, while the application database provides fast queries and derived state.

1. Connect to Robinhood Chain

Robinhood Chain uses chain ID 4663.

A basic viem client can be created like this:

import { createPublicClient, http } from "viem";

const robinhoodChain = {
  id: 4663,
  name: "Robinhood Chain",
  nativeCurrency: {
    name: "Ether",
    symbol: "ETH",
    decimals: 18,
  },
  rpcUrls: {
    default: {
      http: ["https://rpc.mainnet.chain.robinhood.com"],
    },
  },
} as const;

export const client = createPublicClient({
  chain: robinhoodChain,
  transport: http(),
});

I prefer keeping the blockchain client isolated from the application logic.

That gives the project a clean separation:

src/
├── chain/
│   ├── client.ts
│   ├── contracts.ts
│   └── events.ts
│
├── indexer/
├── wallets/
├── trades/
├── positions/
├── portfolio/
├── alerts/
└── api/

2. Start With the Pons Event Layer

The tracker should not continuously poll every wallet for its entire history.

Instead, index the protocol's relevant events and associate them with wallet addresses.

For the current Pons integration, the factory emits TokenLaunched, while each trading pool emits Swap.

A simplified launch event definition:

import { parseAbiItem } from "viem";

export const tokenLaunchedEvent = parseAbiItem(
  "event TokenLaunched(" +
  "address indexed token," +
  "address indexed deployer," +
  "address indexed dexFactory," +
  "address pairToken," +
  "address pool," +
  "uint256 dexId," +
  "uint256 launchConfigId," +
  "uint256 positionId," +
  "uint256 restrictionsEndBlock," +
  "uint256 initialBuyAmount" +
  ")"
);

Then query the factory:

const launches = await client.getLogs({
  address: PONS_FACTORY,
  event: tokenLaunchedEvent,
  fromBlock: START_BLOCK,
  toBlock: "latest",
});

Each launch gives us a token and its trading pool.

That means the indexer can maintain:

token
 ├── deployer
 ├── pool
 ├── pairedToken
 ├── supply
 ├── poolFee
 └── launch metadata

The wallet tracker can then subscribe to the pool activity associated with those tokens.

3. Track Wallets Explicitly

I would not make "all wallets" the first version of the system.

Create a wallet registry.

export type WalletRecord = {
  address: `0x${string}`;
  label?: string;
  group?: string;
  enabled: boolean;
  createdAt: Date;
};

A database table could look like:

tracked_wallets
-------------------------
id
address
label
group_name
enabled
created_at
updated_at

This gives the product useful features later:

Smart Money
Top Traders
Whales
Watchlist
Copy Trading
Custom Group

The same wallet can also belong to multiple analytical groups without changing the underlying address.

4. Normalize Blockchain Activity

Never make the frontend understand raw Uniswap or Pons event structures.

Convert them into your own domain model.

type TradeSide = "BUY" | "SELL";

export type WalletTrade = {
  wallet: `0x${string}`;
  token: `0x${string}`;
  pool: `0x${string}`;

  side: TradeSide;

  tokenAmount: bigint;
  quoteAmount: bigint;

  txHash: `0x${string}`;
  blockNumber: bigint;
  logIndex: bigint;

  timestamp: Date;
};

Now every part of the application can work with:

WalletTrade

instead of knowing how a particular protocol event is encoded.

This becomes extremely useful when adding another execution venue later.

5. Determine Buy vs Sell

The pool event contains signed token amounts.

The tracker needs to know which token is the Pons launch token and which side represents the quote asset.

A simplified classifier:

function classifyTrade(
  token: `0x${string}`,
  pairToken: `0x${string}`,
  amount0: bigint,
  amount1: bigint,
): TradeSide {
  const tokenIsToken0 =
    token.toLowerCase() < pairToken.toLowerCase();

  const pairSigned = tokenIsToken0
    ? amount1
    : amount0;

  return pairSigned > 0n ? "BUY" : "SELL";
}

The important thing here is that token ordering must be handled explicitly.

Do not assume:

amount0 = token
amount1 = WETH

for every pool.

Pool ordering is deterministic, but it depends on token addresses.

6. Store Atomic Amounts as BigInt

This is one of the most important implementation details.

Do not convert blockchain token amounts to JavaScript number.

Use:

type TokenAmount = bigint;
type UsdCents = bigint;
type BasisPoints = bigint;

For example:

type Position = {
  wallet: `0x${string}`;
  token: `0x${string}`;

  quantity: TokenAmount;

  averageEntryPriceUsdCents: UsdCents;

  realizedPnlUsdCents: UsdCents;
  unrealizedPnlUsdCents: UsdCents;
};

A token quantity and a USD value are completely different units.

Avoid logic such as:

if (position.quantity > maxTradeUsd) {
  // incorrect
}

Instead:

if (notionalUsdCents > limits.maxTradeUsdCents) {
  // risk violation
}

Convert between units only at clearly defined boundaries.

7. Build the Position Engine

The raw trade history is not the portfolio.

The portfolio is derived from the sequence of trades and transfers.

For example:

BUY  10,000 TOKEN
BUY   5,000 TOKEN
SELL  3,000 TOKEN
------------------
NET  12,000 TOKEN

The position engine processes every event in order.

type PositionState = {
  quantity: bigint;
  totalCostUsdCents: bigint;
  realizedPnlUsdCents: bigint;
};

function applyBuy(
  position: PositionState,
  quantity: bigint,
  costUsdCents: bigint,
): PositionState {
  return {
    ...position,
    quantity: position.quantity + quantity,
    totalCostUsdCents:
      position.totalCostUsdCents + costUsdCents,
  };
}

Selling requires separating the quantity removed from the realized P&L calculation.

That means the position engine should preserve enough information to calculate cost basis rather than simply overwriting the current balance.

8. Track Token Transfers Too

Trades are not the only source of wallet balance changes.

A wallet can receive tokens from another address.

For example:

Wallet A
   ↓ transfer
Wallet B

There was no buy event for Wallet B.

If the tracker only watches swaps, it can show an incorrect position.

Therefore a production wallet tracker should optionally index ERC-20 Transfer events as well.

const transferEvent = parseAbiItem(
  "event Transfer(address indexed from, address indexed to, uint256 value)"
);

Then:

Swap
Transfer
Transfer
Swap
Transfer
   ↓
Position Engine

The system can distinguish:

BUY
SELL
TRANSFER_IN
TRANSFER_OUT

That produces much more accurate wallet state.

9. Wallet Activity Model

A useful normalized activity record might look like:

type WalletActivity =
  | {
      type: "TRADE";
      wallet: `0x${string}`;
      token: `0x${string}`;
      side: TradeSide;
      tokenAmount: bigint;
      quoteAmount: bigint;
      txHash: `0x${string}`;
    }
  | {
      type: "TRANSFER";
      wallet: `0x${string}`;
      token: `0x${string}`;
      direction: "IN" | "OUT";
      amount: bigint;
      counterparty: `0x${string}`;
      txHash: `0x${string}`;
    };

The frontend can now simply render:

09:42  BUY          MEMESTOCK
09:37  SELL         TOKEN X
09:21  BUY          TOKEN Y
08:56  TRANSFER IN  WETH
08:41  BUY          MEMESTOCK

without understanding blockchain event encoding.

10. Make the Indexer Restartable

A real indexer cannot assume that the process will run forever.

Store a persistent cursor.

type IndexerState = {
  chainId: number;
  contract: `0x${string}`;
  lastProcessedBlock: bigint;
};

Processing becomes:

Saved block
    ↓
Fetch next block range
    ↓
Decode events
    ↓
Persist events
    ↓
Update positions
    ↓
Save cursor

If the service crashes:

Block 9,000,000
        ↓
       crash
        ↓
restart
        ↓
resume from saved cursor

This is much safer than starting from latest and hoping the service has not missed anything.

11. Add Idempotency

Blockchain indexing should be safe to run more than once.

A practical event identifier is:

type EventId = {
  txHash: `0x${string}`;
  logIndex: bigint;
};

Create a database uniqueness constraint:

UNIQUE(tx_hash, log_index)

Then the indexer can safely retry a block range.

Block 100
Block 101
Block 102
      ↓
processing fails
      ↓
retry 101–102
      ↓
duplicate events ignored

This is especially important when public RPC providers timeout or when a worker is restarted.

12. Handle RPC Backfills in Chunks

Do not assume this will work reliably:

getLogs({
  fromBlock: "earliest",
  toBlock: "latest",
});

For a growing blockchain dataset, large log ranges can become expensive or timeout.

Use bounded ranges:

const CHUNK_SIZE = 10_000n;

for (
  let from = startBlock;
  from <= latestBlock;
  from += CHUNK_SIZE
) {
  const to =
    from + CHUNK_SIZE - 1n > latestBlock
      ? latestBlock
      : from + CHUNK_SIZE - 1n;

  const logs = await client.getLogs({
    address: PONS_FACTORY,
    event: tokenLaunchedEvent,
    fromBlock: from,
    toBlock: to,
  });

  await processLogs(logs);
}

A production implementation should make the chunk size configurable.

13. Store Wallet State Separately From History

A common mistake is calculating everything directly from the event table every time the API is called.

Instead, maintain both:

wallet_events
        ↓
position_state
        ↓
portfolio_state

Example:

wallet_positions
-------------------------
wallet
token
quantity
cost_basis
realized_pnl
updated_at

And:

wallet_portfolios
-------------------------
wallet
total_value
realized_pnl
unrealized_pnl
updated_at

Historical events remain immutable.

Current state is derived and updated.

This makes dashboard requests much faster.

14. Add Price Snapshots

A wallet tracker also needs a valuation layer.

Store price observations separately:

type TokenPriceSnapshot = {
  token: `0x${string}`;
  priceQuoteAtomic: bigint;
  timestamp: Date;
  blockNumber: bigint;
};

Then portfolio value becomes approximately:

token quantity
      ×
current token price
      ↓
position value

For multiple tokens:

Position A → $42,000
Position B → $18,200
Position C → $11,700
WETH       → $ 8,400
--------------------
Portfolio  → $80,300

Do not treat displayed portfolio value as an execution guarantee.

The number is an estimated valuation based on the available market data.

15. Build Wallet Alerts

Once wallet activity is normalized, alerts become straightforward.

For example:

type WalletAlert =
  | {
      type: "BUY";
      wallet: `0x${string}`;
      token: `0x${string}`;
      notionalUsdCents: bigint;
    }
  | {
      type: "SELL";
      wallet: `0x${string}`;
      token: `0x${string}`;
      notionalUsdCents: bigint;
    }
  | {
      type: "POSITION_CHANGED";
      wallet: `0x${string}`;
      token: `0x${string}`;
    };

Then users can create rules such as:

Wallet X
    ↓
BUY
    ↓
Notional > $5,000
    ↓
Alert

or:

Wallet X
    ↓
SELL > 50% position
    ↓
Alert

Notifications can later be delivered through:

WebSocket
Telegram
Discord
Email
Webhook
Dashboard

16. API Layer

The tracker becomes significantly more useful when exposed through an API.

For example:

GET /wallets/{address}

GET /wallets/{address}/activity

GET /wallets/{address}/positions

GET /wallets/{address}/portfolio

GET /wallets/{address}/pnl

GET /wallets/{address}/alerts

A response could look like:

{
  "wallet": "0x1234...abcd",
  "positions": [
    {
      "token": "0xabcd...1234",
      "symbol": "MEMESTOCK",
      "quantity": "52000000000000000000000",
      "estimatedValueUsdCents": "4231000"
    }
  ],
  "realizedPnlUsdCents": "182000",
  "unrealizedPnlUsdCents": "731000"
}

The frontend does not need to know anything about RPC calls.

17. Real-Time Dashboard

The frontend can subscribe to new activity through WebSockets.

Blockchain Event
       ↓
Indexer
       ↓
Database
       ↓
Position Engine
       ↓
WebSocket
       ↓
Dashboard

When a tracked wallet buys a token:

BUY detected
      ↓
Trade persisted
      ↓
Position updated
      ↓
Portfolio recalculated
      ↓
Alert evaluated
      ↓
Dashboard updated

This makes the application feel real-time without forcing every browser client to query the blockchain directly.

18. From Wallet Tracker to Copy Trading

This is where the architecture becomes commercially interesting.

A wallet tracker can be the observation layer for a copy-trading system.

TRACKED WALLET
      ↓
TRADE DETECTED
      ↓
TRADE CLASSIFIER
      ↓
RISK ENGINE
      ↓
STRATEGY
      ↓
EXECUTION
      ↓
POSITION
      ↓
RECONCILIATION

The important part is that wallet tracking and trade execution should remain separate.

The tracker answers:

What happened?

The strategy answers:

Should I react?

The risk engine answers:

Am I allowed to react?

The execution engine answers:

How should the trade be submitted?

19. Example Copy-Trade Decision

Suppose a tracked wallet buys:

MEMESTOCK
$4,000

The tracker emits:

const signal = {
  wallet,
  token,
  side: "BUY",
  notionalUsdCents: 400_000n,
};

The copy-trading strategy could transform that into:

const request = {
  token: signal.token,
  side: "BUY",
  notionalUsdCents: 100_000n,
};

The risk layer then checks:

Maximum trade
Maximum position
Maximum portfolio weight
Maximum price impact
Maximum daily loss
Token allowlist
Wallet allowlist

Only after those checks pass does the execution layer receive the order request.

20. Suggested Repository Structure

For a TypeScript implementation:

src/
├── chain/
│   ├── client.ts
│   ├── contracts.ts
│   └── events.ts
│
├── indexer/
│   ├── runner.ts
│   ├── backfill.ts
│   ├── cursor.ts
│   └── dedupe.ts
│
├── wallets/
│   ├── registry.ts
│   ├── activity.ts
│   └── labels.ts
│
├── trades/
│   ├── decoder.ts
│   ├── classifier.ts
│   └── normalizer.ts
│
├── positions/
│   ├── engine.ts
│   ├── costBasis.ts
│   └── reconciliation.ts
│
├── portfolio/
│   ├── valuation.ts
│   ├── pnl.ts
│   └── snapshots.ts
│
├── alerts/
│   ├── rules.ts
│   └── dispatcher.ts
│
├── api/
│   ├── wallets.ts
│   ├── positions.ts
│   └── portfolio.ts
│
└── app.ts

Database:

PostgreSQL
    ├── wallets
    ├── tokens
    ├── pools
    ├── events
    ├── trades
    ├── transfers
    ├── positions
    ├── portfolio_snapshots
    └── alerts

Redis
    ├── live wallet activity
    ├── WebSocket state
    └── short-lived caches

21. The Complete Architecture

Putting everything together:

              ROBINHOOD CHAIN
                     ↓
               PONS CONTRACTS
                     ↓
               EVENT INDEXER
                     ↓
          ┌──────────┴──────────┐
          ↓                     ↓
       TRADES                TRANSFERS
          ↓                     ↓
          └──────────┬──────────┘
                     ↓
              WALLET ACTIVITY
                     ↓
              POSITION ENGINE
                     ↓
              PORTFOLIO STATE
              ↙      ↓       ↘
           P&L     ALERTS    API
                     ↓
                STRATEGIES
                     ↓
               RISK ENGINE
                     ↓
             COPY TRADING
                     ↓
                EXECUTION

This structure is useful because each component has one responsibility.

The wallet tracker is not the trading bot.

It is the data and state layer that a trading bot can consume.

22. Important Production Details

A production implementation should also account for:

• RPC failures and retries
• bounded event backfills
• persistent block cursors
• duplicate event protection
• chain reorganizations
• token decimals
• integer-safe accounting
• missing price data
• wallet transfers
• position reconciliation
• stale portfolio snapshots
• database transaction boundaries
• WebSocket reconnects
• API authentication
• rate limiting
• monitoring and error reporting

Blockchain data pipelines should assume that individual infrastructure components can fail.

The goal is not to make the indexer run perfectly once.

The goal is to make it recover correctly.

Pons Wallet Tracker → Larger Trading Infrastructure

A Pons wallet tracker can become the foundation for a much larger application:

Pons Wallet Tracker
        ↓
Trader Analytics
        ↓
Wallet Alerts
        ↓
Token Scanner
        ↓
Trading Terminal
        ↓
Copy Trading
        ↓
Automated Execution

That is why I prefer building the tracker as a reusable backend instead of creating a dashboard that only displays addresses.

The same normalized wallet events can power several products.

Conclusion

A useful Pons wallet tracker on Robinhood Chain is fundamentally an event-processing and portfolio-state system.

The important pieces are:

On-chain events
      ↓
Reliable indexing
      ↓
Normalized activity
      ↓
Position accounting
      ↓
Portfolio state
      ↓
Alerts / API
      ↓
Trading strategies

The frontend is only the final layer.

The difficult engineering work is building the data pipeline underneath it: decoding protocol activity, maintaining wallet state, handling transfers, preserving exact token amounts, recovering from RPC failures, and keeping positions synchronized with the chain.

Once that foundation exists, the same infrastructure can support a Pons wallet monitor, trader analytics platform, alerting service, Pons trading terminal, or copy-trading system.

Building a Custom Pons Trading System?

I build custom Pons and Robinhood Chain trading infrastructure, including:

• Pons wallet trackers
• Trader-wallet monitoring
• Pons copy-trading systems
• Pons token scanners
• Pons launch monitors
• Pons trading terminals
• Wallet analytics dashboards
• Real-time alerting systems
• Market-data APIs
• Automated execution infrastructure

The architecture can be adapted around the exact workflow, wallet strategy, risk model, and execution requirements of the product.

The goal is not just to display blockchain data.

It is to turn that data into usable trading infrastructure.