#CryptoTrading #Web3 #TradingVolume #BlockchainAnalytics #PEPE
Trading volumes are one of the most important factors in market analysis, as they reflect real liquidity and trend strength. However, traditional analysis methods often overlook the influence of algorithmic trading, on-chain data, and the manipulative mechanisms of market makers. In this material, we will explore an authorial model of adaptive trading volume analysis that integrates a fractal approach, algorithmic filters, and blockchain analytics.
Example: PEPE (PEPE) became one of the most popular meme coins, demonstrating significant fluctuations in trading volumes. Its volatility is often linked to the activity of large players who create an illusion of liquidity.
Fractal Volume Analysis. One of the key aspects is the analysis of anomalous liquidity clusters. This allows tracking not only ordinary changes in volumes but also hidden activity of large players:
🔹 Detection of anomalous spikes: Using historical patterns to identify "atypical" changes in liquidity.
🔹 Connection to the order book structure: Analysis of market depth and hidden limit orders that affect price movement.
🔹 Volatility projection: Correlation between clustered volumes and potential price impulses.
Example: In April 2025, PEPE showed a significant spike in trading volumes, coinciding with the activity of large players. Analyzing liquidity clusters allowed the detection of hidden accumulations before the price increase.
Algorithmic Filters for Filtering Manipulations.
In modern markets, market makers and algorithms often create an illusion of liquidity. Our model presupposes three main filters:
🔸 Noise filter: Identifying false breakouts through volume structure in various timeframes.
🔸 Anomaly test: Analyzing sharp changes in volumes using on-chain data (large transfers, withdrawals).
🔸 Deep adaptation: Using neural networks to learn from anomalous trading patterns.
Example: PEPE often exhibits sharp changes in volumes that may be related to manipulations. Using algorithmic filters allows for filtering out "noise" spikes and identifying real entry points.
Web3 and On-Chain Data in Trading Volume Analysis:
Integrating blockchain analytics allows for increased forecasting accuracy. For example:
✅ **Capital Movement Monitoring:** Tracking fund flows on exchanges as a liquidity indicator.
✅ **NFT Guarantees and Smart Contracts:** Adaptive stop-losses based on on-chain data that reduce manipulative influence.
✅ **Distributed Trading Analyst:** Using decentralized networks to verify volumes.
Example: PEPE is actively used in the Web3 environment, and its on-chain data shows significant transfers between wallets that may signal future price changes.
The described approach allows not only to react to market changes but also to anticipate them through deeper analysis of volumes, algorithmic filters, and on-chain data. This opens up possibilities for new risk management methodologies that can be particularly useful for the Web3 environment.
This is just my own perspective. Will you correct me if I'm wrong..?
