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BornToWin
BornToWin

Posted on Originally published at guskarls.substack.com

Building a Robinhood Chain Trading Bot with TypeScript

#typescript #trading #blockchain #web3

How to build a modular trading engine for launch detection, token screening, strategy signals, risk controls, execution, and reconciliation.

Robinhood Chain is developing a new type of trading environment.

There are Stock Tokens, DEXs, launchpads, and a growing number of newly created tokens.

Pons currently lists more than 167,000 launched tokens and thousands of tokens that have graduated from its launch process.

That creates an obvious engineering opportunity:

Automate the process of finding, evaluating, and trading opportunities on Robinhood Chain.

But I would not build a bot as one large script.

Instead, I'd build a reusable trading engine.

Blockchain Events
       ↓
Token Detection
       ↓
Token Screening
       ↓
Strategy
       ↓
Risk Engine
       ↓
Execution
       ↓
Position Management
       ↓
Reconciliation
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The strategy can change.

The underlying engine stays the same.

Why Build an Engine Instead of a Script?

A simple trading script might look like:

if (newToken) {
  buy(newToken);
}
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That is easy to write.

It is also difficult to operate safely.

A real trading system needs to answer:

What token was detected?

Why did the strategy select it?

What risk checks passed?

How much capital can be used?

Was the transaction submitted?

Did it succeed?

What position was created?

Does local state match onchain state?
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Those questions require architecture.

The Stack

For an EVM-compatible Robinhood Chain application, a practical stack is:

TypeScript
Node.js
viem
Solidity
Foundry
PostgreSQL
Redis
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Robinhood's documentation describes Robinhood Chain as EVM-compatible and supports familiar Ethereum tooling.

A simple project structure:

robinhood-trading-bot/

src/
  chain/
  market/
  strategies/
  risk/
  execution/
  portfolio/
  reconciliation/
  monitoring/

tests/

contracts/

config/
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The goal is separation.

1. Connect to Robinhood Chain

Start with a public client.

import { createPublicClient, http } from "viem";

const client = createPublicClient({
  transport: http(process.env.RPC_URL),
});
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For production, the RPC URL should come from configuration rather than being hard-coded.

You also want the application to validate:

Chain ID
RPC connectivity
Latest block
Network configuration
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For mainnet, Robinhood Chain uses chain ID 4663; its testnet uses 46630.

2. Detect New Opportunities

The first module is the detector.

Conceptually:

RPC
 ↓
Blockchain Events
 ↓
Event Decoder
 ↓
Token Candidate
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A normalized event:

interface LaunchEvent {
  token: `0x${string}`;
  creator: `0x${string}`;
  blockNumber: bigint;
  transactionHash: `0x${string}`;
  timestamp: number;
}
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The detector shouldn't make trading decisions.

It only answers:

“Something happened.”

That makes it reusable for:

New launches
Pool creation
Graduations
Large trades
Liquidity changes
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3. Token Screening

Next comes token intelligence.

Launch Event
      ↓
Token Screening
      ↓
Candidate
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A screening result might contain:

interface TokenScreen {
  token: `0x${string}`;
  liquidity: bigint;
  creator: `0x${string}`;
  holderCount: number;
  tradingEnabled: boolean;
  score: number;
}
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Possible checks:

Contract exists
Trading enabled
Liquidity available
Expected token configuration
Unexpected permissions
Concentration
Strategy-specific requirements
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The purpose isn't to claim a token is “safe.”

It is to eliminate candidates that fail your predefined requirements.

4. Strategy Interface

This is where the architecture becomes powerful.

Instead of hard-coding one strategy:

if (newToken) {
  buy();
}
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define a strategy interface.

interface StrategyContext {
  token: `0x${string}`;
  price: bigint;
  liquidity: bigint;
  blockNumber: bigint;
  timestamp: number;
}

interface TradeIntent {
  token: `0x${string}`;
  side: "BUY" | "SELL";
  amount: bigint;
  reason: string;
}

interface Strategy {
  evaluate(
    context: StrategyContext,
  ): TradeIntent | null;
}
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Now several strategies can use the same engine.

5. Launch Strategy

A simple launch strategy might require:

Liquidity > minimum
AND
token passes screening
AND
strategy conditions pass
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Then:

class LaunchStrategy implements Strategy {
  evaluate(ctx: StrategyContext): TradeIntent | null {
    if (ctx.liquidity < MIN_LIQUIDITY) {
      return null;
    }

    return {
      token: ctx.token,
      side: "BUY",
      amount: ENTRY_SIZE,
      reason: "Launch criteria satisfied",
    };
  }
}
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Notice that this produces a trade intent.

It does not execute anything.

6. Momentum Strategy

The same engine can support momentum.

For example:

Price acceleration
+
Volume
+
Liquidity
+
Recent activity
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A momentum strategy could expose:

class MomentumStrategy implements Strategy {
  evaluate(ctx: StrategyContext): TradeIntent | null {
    if (!momentumCondition(ctx)) {
      return null;
    }

    return {
      token: ctx.token,
      side: "BUY",
      amount: MOMENTUM_SIZE,
      reason: "Momentum threshold reached",
    };
  }
}
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Now:

LaunchStrategy
MomentumStrategy
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share:

Risk
Execution
Portfolio
Reconciliation
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7. Copy Trading as Another Signal Source

Copy trading can also become a strategy module.

Instead of:

Launch Event
 ↓
Strategy
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use:

Tracked Wallet
 ↓
Detected Trade
 ↓
Copy Signal
 ↓
Risk
 ↓
Execution
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Example:

interface WalletTrade {
  wallet: `0x${string}`;
  token: `0x${string}`;
  side: "BUY" | "SELL";
  amount: bigint;
  timestamp: number;
}
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Then normalize it into the same TradeIntent.

This is the advantage of a common strategy interface.

8. Risk Engine

Every strategy must go through risk.

Strategy
   ↓
Risk
   ↓
Approved / Rejected
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For example:

interface RiskContext {
  orderValue: bigint;
  currentExposure: bigint;
  maxOrderValue: bigint;
  maxExposure: bigint;
}
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Then:

function validateRisk(ctx: RiskContext) {
  if (ctx.orderValue > ctx.maxOrderValue) {
    throw new Error("Maximum order exceeded");
  }

  if (
    ctx.currentExposure + ctx.orderValue >
    ctx.maxExposure
  ) {
    throw new Error("Maximum exposure exceeded");
  }
}
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You can also enforce:

Maximum position
Maximum portfolio exposure
Maximum daily loss
Maximum slippage
Maximum number of active positions
Maximum gas cost
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The strategy generates the idea.

The risk engine controls the capital.

9. Global Risk

This becomes important when several strategies run at once.

Imagine:

Sniper     → 0.2 ETH
Momentum   → 0.3 ETH
Copy       → 0.4 ETH
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Each trade might independently pass.

But the portfolio could still exceed the maximum exposure.

Therefore:

Strategy Risk
      ↓
Portfolio Risk
      ↓
Execution
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The risk system should operate at both levels.

10. Execution Engine

After risk approves the trade:

Trade Intent
     ↓
Execution Planner
     ↓
Quote
     ↓
Slippage Check
     ↓
Transaction
     ↓
Robinhood Chain
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Keep this logic separate from the strategy.

A simple interface:

interface Executor {
  execute(
    intent: TradeIntent,
  ): Promise<ExecutionResult>;
}
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Result:

interface ExecutionResult {
  executionId: string;
  txHash?: `0x${string}`;
  status: "PENDING" | "SUCCESS" | "FAILED";
  executedAmount?: bigint;
}
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11. Transaction Simulation

Before sending capital, validate the transaction whenever the execution path supports simulation.

Check things such as:

Transaction succeeds
Expected output
Minimum output
Balance
Allowance
Gas estimate
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The objective is to catch predictable errors before broadcasting.

12. Slippage Controls

A strategy shouldn't blindly accept whatever execution price appears.

For example:

interface ExecutionPolicy {
  maxSlippageBps: number;
  maxGas: bigint;
}
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Then:

if (slippageBps > policy.maxSlippageBps) {
  throw new Error("Slippage exceeds policy");
}
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This is particularly important in low-liquidity markets.

A strategy can be profitable at one price and unprofitable after execution costs.

13. Order State

Don't store:

SUCCESS
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and call it finished.

Use a state machine:

CREATED
   ↓
RISK_CHECKED
   ↓
SUBMITTED
   ↓
PENDING
   ↓
CONFIRMED
   ↓
SETTLED
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Failure paths:

PENDING
 ├──→ FAILED
 └──→ CANCELLED
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Potential partial execution:

PENDING
   ↓
PARTIALLY_FILLED
   ↓
SETTLED
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This makes recovery much easier.

14. Idempotency

Now consider:

Submit transaction
       ↓
RPC timeout
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The timeout doesn't automatically tell you whether the transaction exists.

Never treat:

request failed
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as automatically equivalent to:

trade failed
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Use a unique execution ID.

const executionId = crypto.randomUUID();
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Store it before attempting execution.

Then the worker can recover after:

Timeout
Crash
Restart
Network failure
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without treating the retry as a new logical trade.

15. Position Management

After execution:

Execution
   ↓
Position
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A position model:

interface Position {
  token: `0x${string}`;
  quantity: bigint;
  averageEntryPrice: bigint;
  realizedPnl: bigint;
  unrealizedPnl: bigint;
}
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Now the exit strategy can work against actual portfolio state.

16. Exit Automation

Entry is only one half of a trading system.

A strategy can define:

Take Profit
Stop Loss
Trailing Stop
Time Exit
Partial Exit
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For example:

Entry
 ↓
+25% → partial exit
 ↓
+50% → another partial exit
 ↓
Trailing stop → close remainder
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These should be deterministic rules in the execution system.

17. Reconciliation

This is where many trading bots become unreliable.

Your application can miss an event.

A worker can crash.

An RPC request can time out.

A database can become temporarily unavailable.

So periodically compare:

Onchain State
      ↕
Internal State
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For example:

Read token balances
        ↓
Read relevant transactions
        ↓
Check positions
        ↓
Compare
        ↓
Repair
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The architecture:

             ROBINHOOD CHAIN
               /          \
              /            \
          Events             RPC
             ↓                ↓
        Event Worker    Reconciliation
              \              /
               \            /
                 State DB
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This pattern is one of the most important pieces of reliable blockchain trading infrastructure.

18. Monitoring

The bot should expose metrics.

launches_detected
tokens_screened
signals_generated

orders_submitted
orders_confirmed
orders_failed

risk_rejections

execution_latency
gas_used
slippage

position_exposure
reconciliation_errors
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And useful alerts:

RPC unavailable
Execution failures increasing
Unexpected exposure
Position mismatch
Repeated risk rejection
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A bot should not be a black box.

19. Dashboard

The client needs to see what is happening.

For example:

┌───────────────────────────────────┐
│ Robinhood Chain Trading Engine    │
├───────────────────────────────────┤
│                                   │
│ Strategies                        │
│                                   │
│ Launch      RUNNING               │
│ Momentum    RUNNING               │
│ Copy        PAUSED                │
│ Arbitrage   RUNNING               │
│                                   │
│ Portfolio                         │
│ Exposure                          │
│ PnL                               │
│                                   │
│ Recent Signals                    │
│ Recent Executions                 │
│ Risk Events                       │
│                                   │
└───────────────────────────────────┘
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The dashboard is the interface.

The trading engine is the product.

20. Stock Token Arbitrage

The same engine can support Stock Token strategies.

Robinhood describes Stock Tokens as ERC-20 assets on Robinhood Chain with Chainlink price feeds and documents trading and other composable applications around them. (docs.robinhood.com)

An arbitrage strategy can compare:

Stock Token price
       ↓
Oracle/reference price
       ↓
DEX price
       ↓
Spread
       ↓
Gas + slippage
       ↓
Risk
       ↓
Execution
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The strategy changes.

The infrastructure doesn't.

21. One Engine, Multiple Strategies

This is the architecture I would ultimately aim for:

                    TRADING ENGINE
                          │
          ┌───────────────┼───────────────┐
          ↓               ↓               ↓
       LAUNCH          MOMENTUM          COPY
          │               │               │
          └───────────────┼───────────────┘
                          ↓
                 STOCK TOKEN ARB
                          ↓
                         RISK
                          ↓
                      EXECUTION
                          ↓
                      PORTFOLIO
                          ↓
                   RECONCILIATION
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The strategy is replaceable.

The financial infrastructure is reusable.

22. Why This Is More Valuable Than a Sniper Script

A simple sniper script answers:

“Can I automatically send a buy?”

A real trading system answers:

“Can I automatically detect, evaluate, size, execute, manage, and reconcile a trading opportunity?”

Those are very different problems.

The second one requires:

Market Data
Strategy
Risk
Execution
State
Reconciliation
Monitoring
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That's the engineering layer clients pay for.

23. Building the MVP

I would build the first version in this order:

1. Robinhood Chain connection
2. Event detector
3. Token screening
4. One strategy
5. Risk engine
6. Execution layer
7. Position management
8. Exit rules
9. Reconciliation
10. Monitoring
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Then add:

11. Copy trading
12. Momentum
13. Stock Token arbitrage
14. Portfolio automation
15. AI-generated signals
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This keeps the system modular.

Final Architecture

The finished platform becomes:

                    BLOCKCHAIN
                         │
                         ▼
                    DETECTION
                         │
                         ▼
                    ANALYSIS
                         │
                         ▼
                    STRATEGY
                         │
                         ▼
                       RISK
                         │
                         ▼
                    EXECUTION
                         │
                         ▼
                     POSITION
                         │
                         ▼
                       EXIT
                         │
                         ▼
                 RECONCILIATION
                         │
                         ▼
                    MONITORING
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And the strategies can be:

Launch Sniper
Momentum
Copy Trading
Stock Token Arbitrage
LP Automation
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The important distinction is that the bot is not the architecture.

The bot is the strategy layer sitting on top of the architecture.

That's the approach I would use for Robinhood Chain development.

The ecosystem is already showing demand for launch detection and automated trading: commercial tools advertise launch/graduation detection, contract screening, automated exits, copy trading and limit orders, while public projects are implementing Robinhood Chain token monitors and trading bots. (turn565048search0)

For a developer trying to attract clients, that means the strongest message isn't:

“I build sniper bots.”

It's:

“I build automated trading infrastructure on Robinhood Chain, and I can turn a specific strategy—sniping, momentum, copy trading, or Stock Token arbitrage—into a production-oriented system.”

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