Sharp “pumps” are sudden, fast spikes in price and trading volume. They can be driven by news/listings, liquidity rotation, a large buyer, or outright manipulation. For a trader, the key edge is time: the earlier you spot the impulse, the more time you have to check conditions (liquidity, spread, context) and make a decision before the move exhausts.
Screener (free): PUMP/DUMP
1) What a Pump Screener Is and Why It Matters
A pump screener is a real-time tool that scans markets (exchanges) and detects abnormal activity in coins: sudden shifts in price, volume, trade flow, and order book behavior, plus (when available) on-chain and social signals. Its job is not to “tell you to buy,” but to flag assets early where a strong impulse may be starting—so you can quickly validate the cause and assess entry/exit conditions.
Why it’s useful: it saves time, reduces manual monitoring, helps you catch the early phase of a move (instead of seeing “top gainers” with a delay), enforces a structured workflow, and provides a prioritized list of tickers to review.
2) What Data and Signals a Good Screener Uses
Reliable detection is built on multi-channel signals (any single metric can be noisy). Common inputs inсlude:
- Price and speed: short-window moves (e.g., +10–30% in 1–10 minutes), rate of change, and candle structure (smooth move vs. spikes/wicks).
- Volume and trades: volume spikes (e.g., 5×–20× vs. a moving average), rising trade count, changes in average trade size, and aggressiveness (market buys vs. market sells dominance).
- Order book and liquidity: bid/ask imbalance, depth, spread expansion, “walls” and disappearing orders, and slippage risk estimation.
- On-chain (when applicable): exchange deposits, large transfers (“whale” moves), and abnormal network activity (depending on available data).
- Social signals: mention growth, acceleration of discussion, and synchronized “noise” (sometimes organic, sometimes coordinated).
Why normalization matters: the screener compares current values to each coin’s own “baseline,” otherwise low-liquidity assets will constantly look “abnormal.”
3) How Scoring (Ranking) Works and How Noise Gets Filtered
Why scoring matters: you can get many alerts per day. A scoring model helps you prioritize signals that are stronger and higher quality and ignore random spikes.
Scores are typically built by normalizing metrics and combining them with weights:
- dP — price move strength/speed (instant volatility).
- dV — volume multiple vs. average.
- Trades — trade count growth and changes in average trade size.
- OB — order book imbalance index (one-side pressure).
- Liquidity/Spread — penalty for low depth and wide spreads (trap protection).
- OnChain and Social — boosts from confirming signals (if available).
Quality filters often inсlude minimum liquidity, a maximum spread threshold, a minimum volume threshold, wick/spike filtering, and excluding extremely small/risky assets depending on your strategy. The result is a ranked list where signals tend to be tradable and less “fake.”
4) Trader Workflow: From Alert to Trade (Step by Step)
An alert is the start of validation. A practical workflow looks like this:
- Assess execution first: open the ticker, check spread and order book depth (can you enter/exit without major slippage?), and review the tape (is pressure persistent?).
- Validate the context: is there news/listing/official announcement? Is the move happening on one exchange or multiple? Any signs of coordinated shilling in chats/social?
- Define risk upfront: set risk per trade (e.g., 0.5–1% of equity), account for fees and potential slippage, and define the “idea invalidation” point (stop rule/level).
- Choose an entry type: micro-momentum (fast entry when liquidity is good), breakout with volume confirmation, or limit entry on a retest / slightly above breakout.
- Manage and exit: predefine profit-taking (targets/partial exits/trailing), avoid holding without a plan, and react quickly if conditions deteriorate (spread widens, depth disappears).
- After the trade: log results (why you entered, which metrics triggered, execution conditions) and refine filters/weights.
5) Profit Strategies Using Pump Signals (and When They Fit)
- Momentum trading: follow the impulse while volume and trade flow support it; exit before the sharp pullback. Main risk: entering too late, near distribution.
- Breakout trading: enter when price clears a key level with volume confirmation; stop placement is usually clearer (below the breakout zone). Main risk: false breakouts, especially on thin books.
- Scalping: short-duration trades with quick targets; requires tight execution and careful accounting for fees and slippage.
- Mean reversion (counter-trend): bet on a pullback after an extreme move; should be used only with strict risk control, especially on margin/shorts.
6) Risks, Legal Constraints, and How to Tell “Healthy” Impulses from Manipulation
Key risks: pump-and-dump manipulation, low liquidity and slippage, instant reversals, technical delays (WebSocket/API rate limits), fees and taxes, and potential legal issues tied to market manipulation in some jurisdictions.
Signs of a healthier impulse: volume rises steadily (not just one burst), the move appears across multiple exchanges, there’s a verifiable official/news context, liquidity and spreads remain reasonable, and price action isn’t dominated by extreme wicks.
Signs of manipulation: volume spikes on an empty order book with a wide spread, many uniform tiny trades (activity spoofing), aggressive market hits from a narrow set of participants, synchronized “entry/exit” instructions in closed groups, and no confirmation from official sources or on-chain data.
7) Technical Implementation, Backtesting, and Monetization (If You Build a Screener Product)
Technically, a screener is a streaming systеm: data collection (exchange WebSocket APIs) → real-time processing (normalization, metrics, filters) → scoring → alerts (Telegram/push/web) plus historical storage (for analysis and validation). Common tooling includes CCXT/SDKs, Redis/Kafka (queues/resilience), ClickHouse/TimescaleDB (history), and optional ML/rule layers to reduce false positives. Latency is critical: alert delays can turn a good entry into a late one.
Validation: backtest on historical data while accounting for fees and slippage, then run paper trading in real time, analyze costs (spread, execution speed, liquidity impact), and refine filters/weights.
Monetization: freemium (basic alerts), a Pro subscription (faster alerts, advanced filters, alert history), an enterprise API (for algo traders), and white-label offerings (for partners). Ethics: don’t encourage manipulation, show clear risk warnings and disclaimers, and comply with exchange rules and local laws.
Disclaimer: this article is not financial advice. All investing involves risk of loss. Consult a licensed professional before making investment decisions.