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Alpha Raid Command: Operational information for early detection. Who are we?
The Alpha Raid team is our software that specializes in identifying intruders before they implement their exit strategies.
We don’t trade on hype. We don’t follow influencers. We monitor the behavior.
What are we doing Our work is at the intersection of blockchain forensics, behavioral pattern recognition, and real-time monitoring.
- Detection of fraudulent wallets We are constantly monitoring the Solana ecosystem (and beyond) to identify wallets involved in fraudulent schemes such as siphoning and dumping funds, supply manipulation, and coordinated withdrawal fraud.
- Analysis of behavioral patterns Operators leave traces. Their online behavior repeats itself:
Structures of proxy wallets
Methods of increasing sales volume
Time frame between purchases and distribution
Coordination between different wallets
We use these frameworks in the detection logic.
3. Generating signals before exiting The goal is simple: to identify the tokens supported by the operator before the retail trade begins to gain momentum and before the operator exits.
Our scripts combine several signals:
Volume anomalies
Wallet clusterization
Time of transactions
Confirmation of the operator’s behavior
Only when the model is confirmed, and not at the first spike, do we consider this to be a valid signal.
Our approach: Two-factor confirmation Most warning systems are triggered at the first sign of noise. We don’t.
We are waiting for behavioral confirmation — the moment when the operator adopts a recognizable pattern that separates genuine accumulation from deceptive noise.
In practice:
Description of the signal type The first indicator of the volume anomaly. High activity, often artificial.
Behavioral pattern:
The operator performs a known sequence of actions (proxy structure, time, wallet behavior). Entry is only possible after both factors have been agreed. This filter eliminates false positives and focuses on settings with real potential for asymmetry.
Current status:
Testing phase Our software is currently under testing.
Even in test mode, the results exceed expectations. The logic of detection is to identify entry points at or near the lows — before entering the mass market and before the operators leave.
What’s next We are preparing to launch a Telegram channel that will provide: Real-time signals based on identifying behavioral patterns.
