Setting Up Real-Time Monitoring for Anomalous Trading Activity

Anomaly detection in prediction markets is a critical security and risk management function. It involves identifying trading patterns that deviate significantly from established norms, which could indicate market manipulation, oracle attacks, or sophisticated arbitrage. Real-time monitoring allows platform operators and liquidity providers to react proactively, potentially pausing markets or investigating suspicious wallets. The core data sources are the blockchain itself—transaction logs, event emissions, and state changes—which provide an immutable record of all market activity.

To build a monitoring system, you first need reliable data ingestion. Use a node provider like Alchemy or QuickNode to establish a WebSocket connection to the relevant chain (e.g., Polygon for Polymarket). Listen for specific events from the prediction market contract, such as Trade or MarketResolved. A basic Node.js setup using ethers.js might look like:

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const provider = new ethers.providers.WebSocketProvider(WSS_URL);
const contract = new ethers.Contract(MARKET_ADDRESS, ABI, provider);
contract.on('Trade', (trader, amount, outcome, timestamp) => {
  // Process trade data
});

This stream provides the raw feed for your analysis.

Defining 'normal' behavior is the next challenge. You must establish a baseline by calculating historical metrics such as: average trade size, trade frequency per market, wallet interaction patterns, and typical time between resolution and large trades. This baseline can be stored in a time-series database like TimescaleDB. Anomalies are then flagged when live data exceeds statistical thresholds—for instance, a trade volume that is 10 standard deviations above the 24-hour moving average, or a new wallet placing disproportionately large, imbalanced bets across multiple outcomes just before resolution.

Effective alerting integrates these detections into an operational workflow. Tools like Prometheus for metric collection and AlertManager or PagerDuty for notifications can be configured. For development and testing, consider running your detector against a forked mainnet using Foundry or Hardhat, replaying historical blocks that contained known attack vectors to tune your sensitivity. Remember, the goal is a high precision rate to avoid alert fatigue; it's better to catch fewer but more certain manipulations than to flood a team with false positives.

Beyond simple volume spikes, advanced detection looks at cross-market correlation (e.g., the same wallet moving assets through a DEX to fund a suspicious bet) and social sentiment analysis scraping platforms like Twitter or Discord for coordinated discussion around a market. While on-chain analysis is objective, combining it with off-chain signals can create a more robust picture. However, ensure any automated response, like pausing a market, requires human oversight or a multi-signature process to prevent overreach.

Finally, maintaining and updating your detection models is continuous. Adversarial tactics evolve, and normal market behavior shifts with liquidity and news cycles. Regularly backtest your system against past data, and consider publishing a transparency report on detected incidents (without compromising security). Open-source frameworks like The Graph for indexing or Forta Network for agent-based monitoring can accelerate development, but a custom solution is often necessary to address the unique mechanics and risks of your specific prediction market platform.

A robust monitoring system requires a reliable data ingestion pipeline. You'll need access to real-time blockchain data via a node provider or a specialized data API. Services like Chainstack, Alchemy, or QuickNode offer WebSocket connections for live event streaming. For Ethereum-based DEXs, you must subscribe to events like Swap, Mint, and Burn from Uniswap V2/V3 or other AMM contracts. The system's core is a backend service, typically built with Node.js, Python (using Web3.py), or Go, which listens to these streams and processes transactions as they are confirmed.

Your development environment must support asynchronous programming to handle high-throughput event data. For Node.js, ensure you have version 18+ installed. Key libraries include ethers.js v6 or web3.js v4 for blockchain interaction and ws for managing WebSocket connections. In Python, use web3.py v6 and asyncio. You will also need a database for persisting alerts and historical analysis; time-series databases like TimescaleDB (built on PostgreSQL) or InfluxDB are optimal for this use case. Set up your local instance or use a managed cloud service.

Define the metrics and thresholds that constitute "anomalous" activity. Common indicators include extreme volume spikes (e.g., a swap 10x the pool's 24-hour average), price impact anomalies (a trade moving the price beyond a set slippage tolerance), and wash trading patterns (rapid, circular trades between the same addresses). You will code these logic checks into your listener service. For example, a simple volume check in JavaScript might compare a swap's amountIn to a rolling average stored in your database and trigger an alert if a threshold is exceeded.

Security and scalability are critical prerequisites. Your service should include rate limiting for API calls to node providers and exponential backoff for reconnection logic. For production, deploy the listener as a resilient microservice on a cloud platform like AWS (using EC2 or ECS), Google Cloud Run, or a dedicated server. Implement proper logging with a tool like Winston or Pino (for Node.js) and set up monitoring for the monitor itself using Prometheus and Grafana to track its health, latency, and alert volume.

Finally, establish an alerting channel. Integrate with Discord webhooks, Slack apps, Telegram bots, or PagerDuty to notify your team in real-time. The alert payload should include the transaction hash, involved addresses, the anomalous metric (e.g., 'Volume: $1.2M vs. Avg: $120K'), and a direct link to a block explorer like Etherscan. Test your entire pipeline on a testnet (e.g., Sepolia) using forked Uniswap contracts before deploying to mainnet to avoid costly errors and ensure your logic captures the intended events.

Real-time monitoring for anomalous trading activity requires defining specific, measurable patterns that deviate from normal market behavior. The primary anomalies to detect include wash trading, front-running, flash loan exploits, and oracle manipulation. Each anomaly has a distinct on-chain signature. For wash trading, you look for circular trades between the same addresses or a cluster of addresses, often with zero net profit. Front-running is identified by transactions with identical calldata or high gas fees submitted immediately after a pending target transaction. Setting up detection involves subscribing to new block events and mempool streams from providers like Alchemy, QuickNode, or directly from an archive node.

To detect a flash loan exploit, your logic must identify a transaction where a large, uncollateralized loan is taken and repaid within the same block, accompanied by a series of complex interactions across multiple protocols like Aave or dYdX. A common pattern is a drastic price movement on a DEX like Uniswap V3 followed by immediate arbitrage. Monitoring for oracle manipulation involves tracking price deviations between a DEX's spot price and the reported price from an oracle like Chainlink. A significant, sustained discrepancy can indicate an attack in progress. Implementing these checks requires parsing transaction logs and tracing internal calls to reconstruct the full flow of funds.

Here is a simplified Python example using Web3.py to detect a potential wash trade by checking for circular transfers of the same ERC-20 token within a block:

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from web3 import Web3
w3 = Web3(Web3.HTTPProvider('YOUR_RPC_URL'))

# Get recent block
def check_circular_transfers(block_number):
    block = w3.eth.get_block(block_number, full_transactions=True)
    transfers = {}
    for tx in block.transactions:
        # Decode tx input for ERC-20 transfer (simplified)
        if tx['input'].startswith('0xa9059cbb'):  # transfer signature
            to = '0x' + tx['input'][34:74]
            transfers[tx['from']] = to
    # Check for a cycle
    for addr in transfers:
        if transfers.get(transfers.get(addr)) == addr:
            return True, addr
    return False, None

This logic is foundational and must be expanded with token amount analysis and cluster detection.

For production systems, you need to move beyond single-block analysis. Effective monitoring aggregates data over time windows to identify sybil clusters (multiple addresses controlled by one entity) and profitability thresholds. An address generating consistent, risk-free profit across hundreds of transactions may be engaging in MEV extraction. Tools like the EigenPhi and Flashbots MEV-Explore datasets provide benchmarks for normal MEV activity. Your detection engine should calculate metrics like Profit-per-Gas (PPG) and flag outliers. Integrating with a subgraph from The Graph to query historical DEX swaps can help establish baseline volumes and identify sudden, unexplained spikes in liquidity pool activity.

Finally, setting up alerts is critical. Detection logic is useless without a real-time notification system. Services like PagerDuty, Slack webhooks, or Telegram bots can be triggered when an anomaly score exceeds a defined threshold. The alert should include the transaction hash, a summary of the anomaly (e.g., "Potential Front-Running Detected"), key addresses, and a link to a block explorer like Etherscan. Continuously tune your thresholds based on false positive rates and incorporate new attack vectors published by security firms like OpenZeppelin and CertiK. This creates a feedback loop that improves the accuracy and relevance of your monitoring system over time.

Tenderly's Alerting feature allows you to monitor your smart contracts for specific on-chain events and transaction patterns in real-time. This is the foundation for building a proactive security system. You define Alert Rules that specify the conditions you want to watch for, such as a large token transfer, a specific function call, or a failed transaction. When a live transaction on a supported network (like Ethereum Mainnet, Arbitrum, or Polygon) matches your rule, Tenderly triggers an alert. You can receive these alerts via email, Slack, Discord, or a webhook, enabling immediate investigation.

To create an Alert Rule, navigate to the Alerts section in your Tenderly Dashboard and click "Create Alert." You'll configure several key components:

• Network & Contract: Select the blockchain and the specific contract address to monitor.
• Trigger Type: Choose between Event Emission (for logging) or Function Call. For trading activity, Function Call is often used to watch for functions like swap, transfer, or addLiquidity.
• Condition Builder: This is the core of your rule. Use the visual builder or raw mode to define the logic. For example, you could set a condition where the amount parameter in a transfer call is greater than 10,000 tokens.

For advanced monitoring of anomalous activity, you need to craft specific conditions. A simple rule might flag any transaction calling a swap() function. A more sophisticated rule could combine multiple parameters to detect potential manipulation, such as: function == swap AND amountIn > [Threshold] AND block.timestamp is within 2 blocks of another large swap. You can also use the Alert Rule ID in the webhook payload to programmatically categorize different threat levels. Setting up these rules requires a clear understanding of your contract's ABI and the attack vectors you aim to catch, such as flash loan attacks or oracle price manipulation.

After defining the condition, configure the Alert Destination. For rapid response, setting up a webhook is recommended. Provide a URL from your monitoring service (like PagerDuty or a custom server) or use Tenderly's native Slack/Discord integration. The webhook payload contains the full transaction object, including input data, logs, and trace, which your downstream system can parse for deeper analysis. Test your rule by simulating a transaction that matches your conditions using Tenderly's Simulation tool before activating it, ensuring it triggers as expected.

Effective alerting avoids noise. Start with broad rules to establish a baseline, then iteratively refine them to reduce false positives. For a DEX pool, you might initially alert on all large swaps, then refine the rule to only trigger if the swap results in a pool balance change exceeding 5% or originates from a newly funded address. Combine Tenderly Alerts with its Debugger and Transaction Simulator; when an alert fires, you can instantly replay and analyze the suspicious transaction to understand the root cause before taking action.

The mempool is the staging area for all pending transactions on a blockchain. By monitoring it in real-time, you can see trading activity—including potential front-running, sandwich attacks, or large whale movements—before it impacts the market. Blocknative provides a WebSocket-based API that streams this data directly to your application. Unlike basic RPC nodes, their service offers enhanced transaction decoding and intent classification, making it easier to identify high-risk patterns.

To begin, sign up for a free account at Blocknative and obtain an API key. You will use their transaction-stream endpoint. The core setup involves establishing a WebSocket connection and subscribing to a specific network and address filter. For monitoring DEX activity, you typically subscribe to the address of a liquidity pool or a specific trader's wallet. Here's a basic Node.js example to connect and log transactions:

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const WebSocket = require('ws');
const ws = new WebSocket('wss://api.blocknative.com/v0');
ws.on('open', function open() {
  const configMsg = {
    categoryCode: 'initialize',
    eventCode: 'checkDappId',
    dappId: 'YOUR_BLOCKNATIVE_API_KEY'
  };
  ws.send(JSON.stringify(configMsg));
});

Once connected, you must send a subscription message to define your monitoring scope. A critical parameter is filters, where you specify the to address (e.g., a Uniswap V3 WETH/USDC pool) and potentially the status (e.g., pending). Blocknative will then push event objects for each matching transaction. Each event contains decoded data like the function called (swapExactTokensForTokens), token amounts, gas prices, and the submitting wallet address. This allows you to calculate the USD value of swaps in real-time.

To detect anomalies, implement logic to analyze the stream. Key indicators include: abnormally high gas prices suggesting priority bidding, repeated rapid transactions from the same address (potential bot activity), or large swap sizes that could move the pool's price. For example, you could flag any swap exceeding 5% of a pool's TVL or transactions where the gas price is 3x the network average. Store these flagged events in a database for further analysis or trigger immediate alerts to a Discord webhook.

For production systems, consider using Blocknative's Transaction Dashboard to visualize streams and validate your filters before coding. Their system supports Ethereum, Polygon, Arbitrum, and other EVM chains. Remember that mempool data is volatile; transactions can be dropped or replaced. Implementing a confirmation listener on a standard RPC provider to correlate mempool alerts with on-chain finality is a recommended best practice for building a robust monitoring pipeline.

With your data streams configured, you can now write the core detection logic. Chainscore's framework allows you to define Detector classes that process events in real-time. A detector subscribes to specific on-chain events—like Swap events on Uniswap V3 or Transfer events for an ERC-20 token—and executes your custom analysis on each one. The primary method you'll implement is process(event, context), where you receive the raw event data and a context object containing pre-fetched state like token prices or wallet histories.

Your detection logic should focus on identifying specific anomalous patterns. Common signals include: - Volume spikes: A wallet's trading volume exceeding its 30-day average by 500% within a single block. - Recurring arbitrage: The same contract executing near-identical swaps across multiple DEXs (e.g., Uniswap, SushiSwap) in under 60 seconds. - Liquidity manipulation: A series of large swaps into a low-liquidity pool immediately before a large oracle price update. Implement these checks by comparing the current event against historical data accessed via the context object's methods, such as context.getWalletVolume().

For precise detection, you must define clear thresholds and parameters. Avoid hard-coded magic numbers; instead, make them configurable. For example, a wash trading detector might have parameters for minimum_profit_threshold (e.g., 0.1 ETH) and time_window (e.g., 10 blocks). This allows you to tune the detector's sensitivity. Use the context.alert() method to trigger an alert when conditions are met, passing a severity level (INFO, WARNING, CRITICAL) and a structured payload with evidence like transaction hashes and calculated metrics.

Here is a simplified code example for a detector that flags large, rapid DEX swaps:

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from chainscore_sdk import Detector, AlertSeverity

class RapidLargeSwapDetector(Detector):
    def __init__(self):
        # Configurable parameters
        self.min_value_usd = 100000
        self.time_window_seconds = 30

    async def process(self, event, context):
        # 1. Filter for Swap events on a specific DEX
        if event.name != "Swap" or event.address != UNISWAP_V3_ROUTER:
            return
        
        # 2. Calculate USD value using real-time price from context
        amount_usd = await context.get_token_value_usd(event.data.tokenIn, event.data.amountIn)
        
        # 3. Check threshold
        if amount_usd < self.min_value_usd:
            return
        
        # 4. Check wallet's swap history in last 30 seconds
        recent_swaps = await context.get_wallet_events(
            wallet=event.data.sender,
            event_name="Swap",
            lookback_seconds=self.time_window_seconds
        )
        
        # 5. Trigger alert if this is the 3rd large swap in the window
        if len(recent_swaps) >= 3:
            await context.alert(
                severity=AlertSeverity.WARNING,
                type="RAPID_LARGE_SWAPS",
                details={
                    "wallet": event.data.sender,
                    "swap_count": len(recent_swaps),
                    "total_value_usd": sum(swap['value'] for swap in recent_swaps)
                }
            )

After writing your detector, you must deploy and monitor it. Chainscore provides a CLI tool (chainscore deploy) to package and deploy your detector to a dedicated cloud instance. Once live, all alerts are routed to your configured destinations (Slack, Discord, webhook). It is critical to monitor the false positive rate initially. Use the Chainscore dashboard to review triggered alerts and refine your logic and thresholds. For production systems, consider implementing a multi-sig confirmation step for CRITICAL alerts to prevent automated response errors.

Effective monitoring is iterative. Start with broad detection for clear threats (e.g., stolen fund movements) and gradually add more nuanced logic for market manipulation. Document each detector's purpose, parameters, and typical alert payloads for your team. Regularly backtest your logic against historical attack data, such as events recorded in the Chainscore Threat Database, to improve accuracy. This proactive approach transforms raw blockchain data into a actionable security intelligence system.

Your monitoring stack should now be operational, tracking key metrics like sudden volume spikes, unusual token pair interactions, and flash loan activity. The system uses a combination of WebSocket subscriptions to node providers like Alchemy or QuickNode and direct contract event listening to capture raw transaction data. This data is processed through your defined heuristics—such as checking for transactions exceeding a volume threshold or interacting with known mixer contracts—to generate alerts in platforms like Slack or Discord via webhooks.

To refine your system, focus on reducing false positives. Start by analyzing your initial alert logs. Are you getting frequent alerts for legitimate, high-volume DEX aggregators like 1inch? Adjust your volume thresholds or implement an allowlist for known protocol addresses. Consider adding a reputation scoring layer by integrating with services like Chainalysis or TRM Labs to flag addresses associated with sanctioned entities or previous hacks. This contextual data significantly improves alert accuracy.

For further development, explore integrating machine learning models for pattern recognition. You can train a model on historical hack transaction data (available on repositories like the Ethereum ETL dataset) to identify subtle, multi-transaction attack vectors that simple heuristics miss. Alternatively, implement a time-series anomaly detection algorithm, such as using the Z-score method on rolling window averages of transaction value or gas price, to catch deviations from established baselines for specific protocols or wallets.

The next logical step is automated response. While alerts are crucial, the goal is to minimize damage. Research smart contract pausers or governance emergency procedures for the protocols you monitor. For critical alerts, your system could automatically trigger a transaction to pause a vulnerable contract or initiate a snapshot vote, though this requires extreme caution and multi-signature controls. Always maintain a human-in-the-loop for final approval on any irreversible action.

Finally, stay updated. The adversarial landscape evolves rapidly. Subscribe to security newsletters like Rekt News and monitor forums such as the Ethereum Magicians. Participate in bug bounty programs to understand emerging exploit techniques. Regularly review and update your detection rules; a static system will quickly become obsolete. Your real-time monitoring is a continuous process of detection, analysis, and adaptation.