Early Warning System on Monad using GoldRush - PART 1

Rajdeep Singha

Content Writer

Learn how to build an early warning system on Monad using GoldRush API - Part 1

The decentralized finance landscape is entering a new phase as high-performance execution environments move from theory to mainnet reality. At the forefront is Monad, a Layer-1 blockchain capable of 10,000 transactions per second, powered by parallel execution, MonadBFT consensus, and optimized state access via MonadDb. But this speed comes with a new class of risk. With block times compressed to 400 milliseconds, traditional block-polling and reactive indexing fall apart. In a system where finality happens in the blink of an eye, malicious liquidity drains—especially the subtle “soft rug pull”—can unfold before off-chain systems even notice. To operate safely inside Monad’s execution pipeline, developers must abandon polling entirely and adopt a streaming-first, sub-100ms detection architecture built for real-time on-chain intelligence.

The Velocity of Risk in the Monad Ecosystem

The technical ambition of Monad is to scale the Ethereum Virtual Machine (EVM) without compromising on decentralization or tooling compatibility. By decoupling transaction execution from the consensus process, Monad allows nodes to agree on transaction ordering before the full state impact is computed. This asynchronous execution model, while revolutionary for throughput, narrows the reaction window for security tools. On Ethereum, a 12-second block time provides ample opportunity for a monitoring bot to detect a large withdrawal and alert users before the next block. On Monad, 30 blocks could have been finalized in that same timeframe.

The primary threat in this high-speed arena is the liquidity drain. While "hard rug pulls" involve a sudden and absolute withdrawal of all pool funds, the "soft rug" or "slow fade" is more sophisticated. In a soft rug pull, developers gradually exfiltrate liquidity or dump tokens in increments that avoid triggering simplistic volatility alerts. These events are often legally ambiguous, as the perpetrators can attribute the decline to market forces or technical setbacks, even as they quietly abandon the project. Detecting these patterns requires a data source that is not just fast, but rich in contextual metadata, delivered via a persistent connection that minimizes transport latency.

Architectural Comparison: Polling vs. Streaming

The standard approach to OnChain data acquisition involves periodic requests to an RPC provider using the eth_getLogs or eth_call methods. This "pull" model introduces a latent floor composed of the polling interval and the network round-trip time.

The GoldRush Streaming API leverages GraphQL subscriptions over WebSockets to provide real-time updates on blockchain events. For developers building on Monad, this means moving from a reactive state to a synchronous one. As soon as a RemoveLiquidity event is processed by the Monad consensus layer, the GoldRush indexers propagate a structured update to all subscribed clients, often arriving at the developer's terminal before the transaction is even visible on traditional block explorers.

The Anatomy of the Soft Rug Pull

To build an effective early warning system, one must understand the tactical variations of liquidity drains. Scammers exploit the psychological and technical lags of the market to maximize their exfiltration before detection.

The Spectrum of Deceit

The distinction between hard and soft rug pulls is defined by intent, legality, and velocity. Hard rug pulls are frequently premeditated acts of fraud where malicious code, such as "infinite mint" functions or "honeypot" sell-restrictions, is embedded directly into the smart contract. The exit is swift; developers withdraw 100% of the valuable asset (e.g., WMON or USDC) from the pool, leaving investors with worthless tokens.

Soft rug pulls are more insidious because they leverage legitimate functions. Developers may initially appear committed to the project, building community trust and attracting significant liquidity. Once a threshold of capital is reached, the "slow bleed" begins. They may sell small percentages of their holdings at regular intervals or remove liquidity in 5-10% chunks. This sustained selling pressure erodes value over time, and because the developers retain a presence on social media—at least initially—investors are slow to react.

Identification of Key Metrics

The updatePairs stream in the GoldRush API is the specific tool designed to counter these tactics. It provides a real-time feed of price, liquidity, volume, and market cap for tracked token pairs on Monad DEXs like OctoSwap and Uniswap V4.

By subscribing to this stream, the Sentinel system can monitor for:

Sudden Liquidity Drops: Detecting a >20% reduction in the liquidity field within a single transaction or a very short window.
Divergence in Volume and Price: Situations where high volume_usd is accompanied by a consistent drop in quote_rate_usd, signaling large-scale selling by a dominant actor.
Swap Count Anomalies: A spike in the swap_counts over the last_5m interval, often indicating the start of a coordinated exit.

Napkin Math: The 20% Threshold Logic

A robust early warning system must be built on sound mathematical foundations. Most DEXs on Monad, including OctoSwap V2, utilize the constant product formula pioneered by Uniswap V2.

The relationship between the reserves of two tokens in a pool is defined by:

The sub-100ms advantage becomes critical here. If the scammer is removing liquidity across three transactions in three consecutive blocks (spanning 1.2 seconds), a streaming sentinel will catch the first withdrawal (e.g., a 10% drop) and allow the user to exit their position before the subsequent 10% withdrawals are finalized.

Entity Richness: The Data Advantage

The efficacy of a monitoring system is proportional to the "richness" of the entities it tracks. Raw transaction logs are "thin" because they require significant external context to be actionable.

Technical Implementation: The Monad Sentinel CLI

The following TypeScript implementation provides a developer-friendly CLI for real-time liquidity monitoring. It uses the GoldRush SDK to subscribe to Monad DEX pairs and triggers a Discord webhook when a 20% drain is detected.

Prerequisites and Dependencies

The system requires Node.js v16+ and a valid GoldRush API key.

Installation of essential libraries

npm install @covalenthq/client-sdk commander chalk ora discord-webhook-node
npm install dotenv

Now , let’s move towards our implementation

Monad_dex.js

import "dotenv/config";

import {
  GoldRushClient,
  StreamingChain
} from "@covalenthq/client-sdk";

import { Webhook, MessageBuilder } from "discord-webhook-node";
import { Command } from "commander";
import chalk from "chalk";
import ora from "ora";

/* =======================
   CONFIG
======================= */

const MONAD_CHAIN = StreamingChain.MONAD_MAINNET;
const DRAIN_THRESHOLD = 0.20; // 20%

const API_KEY = process.env.GOLDRUSH_API_KEY;
// const WEBHOOK_URL = process.env.DISCORD_WEBHOOK;

if (!API_KEY) {
  console.error(chalk.red("❌ Missing GOLDRUSH_API_KEY in .env"));
  process.exit(1);
}

/* =======================
   CLI
======================= */

const program = new Command();

program
  .name("monad-sentinel")
  .description("Liquidity Drain Early Warning System for Monad DEXs")
  .version("1.0.0")
  .requiredOption("-p, --pairs <addresses...>", "Monad pair addresses")
  .parse(process.argv);

const { pairs } = program.opts();

/* =======================
   INIT
======================= */

const client = new GoldRushClient(API_KEY);
const hook = WEBHOOK_URL ? new Webhook(WEBHOOK_URL) : null;

const liquidityCache = {};
const spinner = ora("Connecting to Monad streaming pipeline...").start();

/* =======================
   STREAM LOGIC
======================= */

function runSentinel() {
  try {
    client.StreamingService.subscribeToUpdatePairs(
      {
        chain_name: MONAD_CHAIN,
        pair_addresses: pairs
      },
      {
        next: (res) => {
          spinner.stop();
          
          // Debug: log raw response
          console.log(chalk.cyan("\n📦 Raw data received:"), JSON.stringify(res, null, 2));

          const data = res.updatePairs;
          const address = data.pair_address;
          const currentLiq = Number(data.liquidity);
          const ticker = data.base_token.contract_ticker_symbol;
          const quote = data.quote_token.contract_ticker_symbol;
          const timestamp = new Date().toLocaleTimeString();

          // First snapshot
          if (!liquidityCache[address]) {
            liquidityCache[address] = currentLiq;
            console.log(
              chalk.blue(
                `[${timestamp}] Monitoring ${ticker}/${quote} (${address.slice(0, 8)}...)`
              )
            );
            console.log(
              chalk.gray(`Initial Liquidity: $${currentLiq.toLocaleString()}`)
            );
            return;
          }

          const prev = liquidityCache[address];
          const drop = (prev - currentLiq) / prev;

          // 🚨 Liquidity Drain
          if (drop >= DRAIN_THRESHOLD) {
            const title = `🚨 LIQUIDITY DRAIN ALERT: ${ticker}`;
            const desc = `Liquidity dropped ${(drop * 100).toFixed(2)}% on Monad`;

            console.log(chalk.red.bold(`\n${title}`));
            console.log(chalk.red(desc));
            console.log(
              chalk.yellow(
                `Old: $${prev.toLocaleString()} → New: $${currentLiq.toLocaleString()}\n`
              )
            );

            if (hook) {
              const embed = new MessageBuilder()
                .setTitle(title)
                .setColor(15158332)
                .setDescription(desc)
                .addField("Pair", address, true)
                .addField(
                  "Liquidity Removed",
                  `$${(prev - currentLiq).toLocaleString()}`,
                  true
                )
                .addField("Network", "Monad Mainnet", true)
                .setTimestamp();

              hook.send(embed);
            }
          }

          // 🟢 Liquidity Added
          if (drop < 0) {
            console.log(
              chalk.green(
                `[${timestamp}] +${Math.abs(drop * 100).toFixed(2)}% liquidity added to ${ticker}`
              )
            );
          }

          liquidityCache[address] = currentLiq;
        },

        error: (err) => {
          spinner.fail("❌ Streaming interrupted");
          console.error(chalk.red(err));
        },

        complete: () => {
          console.log(chalk.gray("Stream closed by server"));
        }
      }
    );

    spinner.succeed(
      chalk.green("✅ Connected — monitoring Monad liquidity events")
    );
  } catch (err) {
    spinner.fail("Failed to start sentinel");
    console.error(err);
  }
}

runSentinel();

Now , we run : node Monad_dex.js , we will get :

Flowchart: Data-to-Signal Pipeline

Monad Validator: Finalizes a block containing a RemoveLiquidity transaction.
GoldRush Indexer: Decodes the log event and updates the pair's state metrics.
GraphQL WebSocket: Pushes the structured updatePairs object to the Sentinel.
CLI Comparator: Calculates the delta between the current and previous liquidity values.
Alert Dispatcher: If the delta > 20%, a Discord notification is sent.

This entire flow occurs within milliseconds of the block finality, ensuring the user has the maximal possible time to react to the exfiltration.

Deep Dive: Monad's DEX Landscape

The choice of which pairs to monitor is as important as the speed of the monitor itself. The Monad ecosystem features a blend of established protocols and native innovations.

Top Monad DEXs for Monitoring

Uniswap V4 is a particularly interesting target for the Sentinel because its "Hooks" allow for custom liquidity logic that could be used to create complex drain mechanisms. Conversely, Kuru’s Central Limit Order Book (CLOB) architecture requires a different monitoring heuristic, focusing on the depth of the bid-ask spread rather than $xy=k$ liquidity.

Conclusion: The New Standard for Onchain Safety

The migration of high-frequency trading principles to the EVM via Monad necessitates a corresponding evolution in security infrastructure. The "Sentinel" approach—leveraging the GoldRush Streaming API for sub-100ms detection—is no longer a luxury for the professional trader; it is the baseline requirement for navigating high-performance blockchains.

By shifting from a world of latent polling to real-time streaming, developers can bridge the gap between human reaction times and machine-speed exploits. The "soft rug pull" thrives in the shadows of data delay and market ambiguity. In the light of a sub-100ms early warning system, these maneuvers become visible as they happen, providing users with the critical window needed to protect their capital in the fastest environment the EVM has ever seen.

As the Monad mainnet matures and liquidity deepens across protocols like OctoSwap, Uniswap, and Kuru, the systems that can interpret streaming data will define the winners of the next DeFi cycle. The Sentinel is the first step in that journey, providing a developer-friendly, high-performance foundation for a safer decentralized future on Monad

Building a Sniper Bot on Monad using GoldRush (Part 1/ 4) — Build The Token Launch Radar

Building a Real-Time Hyperliquid Liquidation Dashboard using GoldRush - Part 4