Pump-and-dump detection model development

Development of Pump-and-Dump Detection Model

Pump-and-dump schemes in crypto markets operate far faster than their traditional-finance counterparts: from the initial coordinated buying push to the final dump, the entire cycle can unfold in hours or even minutes. Because all on-chain data is completely public, this creates a unique detection opportunity — we can observe wallet movements, volume concentration, and transaction synchronization in real time, giving protocols and users a meaningful window to respond before losses occur.

At our studio, we build turnkey pump-and-dump detection systems tailored to your token, DEX, or lending protocol. Our service includes data pipeline setup, custom ML model training, and integration with your smart contracts or alert infrastructure. Whether you need a standalone monitoring dashboard or an on-chain risk oracle, contact us to discuss your specific requirements and get an estimate for your project within a few business days.

Task: build a system that detects a P&D scheme during the pump phase, before the dump, so it can warn users or auto-protect the protocol in time.

Anatomy of pump-and-dump scheme

Understanding the mechanics is critical for building the right feature set.

Accumulation phase: organizers gradually buy the token with small orders, trying not to move the price. Signs include growing unique holder addresses with a stagnant price, unusual buy volume during off-hours, and synchronized wallets receiving ETH from a single source.

Pump phase: coordinated buying, usually organized through Telegram or Discord, drives the price up 200–2000% in hours. Volume spikes 10–100x the rolling average. Social media sees a surge of template messages promoting the token.

Dump phase: organizers sell at the peak. Retail buyers attracted by the price rise enter the market and end up holding worthless bags. The price crashes back to pre-pump levels or lower.

Features for model

On-chain metrics

Volume anomaly score (VAS) = current_volume / rolling_avg_volume_30d. Values above 10 without fundamental news are a strong signal.

Holder concentration delta uses the Herfindahl-Hirschman Index (HHI):

HHI = Σ (balance_i / total_supply)² 

Rising HHI means the token is concentrating in fewer addresses — a classic accumulation pattern.

Transaction synchronization coefficient measures how synchronized independent addresses are when making buys in the same time window (±5 minutes). Organic growth shows a uniform distribution; a P&D shows a clear spike.

Wallet clustering uses a graph of address relationships. Addresses receiving ETH from the same source, buying with similar EOAs, or sharing transaction patterns are likely controlled by a single entity. If 60%+ of volume comes from one cluster, that is a strong signal.

Price-volume divergence: healthy growth shows volume rising gradually alongside price. A P&D scheme shows volume spiking first and then a sharp price move — or both synchronized without the normal ramp-up.

Cross-market metrics

DEX vs CEX price discrepancy: if the DEX price is significantly above the CEX price, this may indicate intentional DEX price manipulation.

Liquidity depth change: sharp LP removal before the pump reduces buy resistance — a classic preparation pattern.

New wallet ratio: the percentage of transactions from wallets created less than 7 days ago. A high ratio suggests fresh addresses created specifically for the scheme.

Social signals (optional)

Telegram and Discord monitoring for ticker mentions. A sudden spike in positive sentiment combined with template calls-to-action is a coordinated pump signal.

Detection system architecture

Data pipeline

1. Connect to Blockchain RPC (geth or erigon) via WebSocket
2. Stream Transfer and Swap events into Kafka or RabbitMQ
3. Feature extractor (Python) computes all on-chain metrics per 5-minute window
4. Push features to Redis (real-time) and PostgreSQL (historical, 90-day rolling)
5. ML model runs inference and outputs a probability score 0–1
6. Alert engine triggers notifications or oracle updates based on configured thresholds

Real-time blockchain connection via WebSocket:

from web3 import Web3, AsyncWeb3 import asyncio async def stream_swaps(token_address: str, callback): w3 = AsyncWeb3(AsyncWeb3.AsyncWebsocketProvider('wss://mainnet.infura.io/ws/v3/KEY')) transfer_filter = await w3.eth.filter({ 'address': token_address, 'topics': [Web3.keccak(text='Transfer(address,address,uint256)').hex()] }) while True: events = await transfer_filter.get_new_entries() for event in events: await callback(event) await asyncio.sleep(0.1) 

Feature extraction

@dataclass class TokenFeatures: token_address: str timestamp: float volume_anomaly_score: float new_wallet_ratio: float transaction_sync_score: float holder_hhi_delta: float liquidity_depth_change: float price_velocity: float def compute_sync_score( transactions: pd.DataFrame, window_seconds: int = 300 ) -> float: """How synchronized are independent addresses in buys""" tx_times = transactions['timestamp'].values unique_senders = transactions['from'].nunique() if unique_senders < 2: return 0.0 bins = np.arange(tx_times.min(), tx_times.max() + window_seconds, window_seconds) hist, _ = np.histogram(tx_times, bins=bins) if hist.mean() == 0: return 0.0 cv = hist.std() / hist.mean() return max(0, 1 - cv / 2) 

ML model

For P&D detection, XGBoost or LightGBM on tabular features work well. Both are interpretable via SHAP values, fast at inference time, and robust to missing data. Historical labeled datasets typically contain 300–1,000 confirmed P&D events sourced from on-chain analytics providers and community-labeled datasets.

import xgboost as xgb from sklearn.model_selection import TimeSeriesSplit tscv = TimeSeriesSplit(n_splits=5) model = xgb.XGBClassifier( n_estimators=500, max_depth=6, learning_rate=0.01, subsample=0.8, colsample_bytree=0.8, scale_pos_weight=neg_count / pos_count, eval_metric='aucpr', early_stopping_rounds=50 ) 

Evaluation metrics: precision-recall is more important than raw accuracy due to strong class imbalance. The goal is precision above 0.7 at recall above 0.6. False positives (false alarms) annoy users; false negatives (missed P&D events) cause serious reputation damage.

Alerting implementation

Thresholds and confidence levels

The system outputs a probability score rather than a binary label:

• Score above 0.8: high confidence, immediate alert
• Score 0.6 to 0.8: medium confidence, warning
• Score below 0.6: monitoring only, no alert sent

Integration with protocol

For protocols that need on-chain protection: a trading contract can read the risk score via an oracle. If the risk score is high, the protocol can apply increased slippage tolerance or pause a specific pool automatically.

Why Is Early Detection Critical for Protocol Security?

Catching a pump-and-dump during the accumulation or early pump phase — not after the dump — is what separates a useful system from an audit report. Our team brings 5+ years of experience building on-chain monitoring tools across 30+ DeFi and CEX projects, and we guarantee that the detection pipeline goes live fully integrated, with documented APIs and a 30-day support period after delivery.

What Makes Our Approach More Reliable Than Rule-Based Alerts?

Rule-based systems (for example, "alert if volume > 10x") are trivially bypassed by sophisticated organizers who split orders or use proxy wallets. Our ML model is trained on labeled historical P&D events, cross-validated with 5-fold time-series splits, and continuously monitored for concept drift. This proven approach adapts to evolving manipulation patterns in ways that static thresholds cannot.

What's Included

The turnkey delivery package includes everything needed to go from zero to a production-ready monitoring system:

• Feature engineering pipeline (Python, deployable on your infrastructure or cloud)
• Trained XGBoost or LightGBM model with SHAP explainability reports
• Real-time WebSocket connection to on-chain data sources
• Redis and PostgreSQL feature store with 90-day rolling history
• Alert engine with configurable probability thresholds and webhook notifications
• Integration with your smart contracts or front-end dashboard
• Technical documentation, API reference, and deployment guide
• 30-day post-launch support and model performance monitoring

Typical project investment ranges from 12,000 USD to 35,000 USD depending on token coverage (single token vs multi-pool) and the number of required integrations. Contact us with your use case and we will prepare a detailed estimate within 3 business days.

Limitations and disclaimers

A detection system does not eliminate pump-and-dump — it warns. Organizers adapt to detection algorithms over time through adversarial techniques. Model quality degrades and requires periodic retraining on fresh data to remain effective.

On the legal side, automatic trading blocks based on ML predictions carry legal risks depending on jurisdiction. The safer approach is to warn users rather than automatically restricting trades.

Development timeline

Data collection and labeling: 3–4 weeks. Model training and validation: 2–3 weeks. Infrastructure and alerting setup: 3–5 weeks. Testing and protocol integration: 2 weeks.

Total: 8–14 weeks depending on the scope and number of integrations required.