🔬Feature Extraction for ML Models

Learn which data is extracted and why.

Alvatix's Machine Learning (ML) models are empowered by a refined feature extraction process, designed to distill complex and high-dimensional data into a comprehensive suite of predictive features. This process is critical for enhancing the accuracy and efficacy of our predictive analytics. Below, we provide a detailed overview of the expanded feature extraction framework:

On-Chain Wallet Activity Features

• Temporal Wallet Activity Analysis: Frequency of transactions per wallet is monitored to distinguish active trading patterns from dormant ones, revealing wallet activity trends.

• Transactional Volume Metrics: Size and value of transactions from each wallet are scrutinized to assess the impact of capital flows on the market.

• Smart Contract Interaction Metrics: Detailed data from EVM nodes are harnessed to extract nuanced features like contract executions, gas utilization, and interaction frequencies.

Whale Movement Indicators

• Quantitative Whale Activity Tracking: Significant transactions by large holders are enumerated to quantify their market influence and anticipate resultant trends.

• Pattern Recognition in Whale Behavior: Machine Learning algorithms recognize and learn from transaction patterns among whales, using this data to forecast market trends.

Exchange Data Features

• Liquidity and Market Depth Analysis: Depth of the order book is evaluated to infer market liquidity and foreshadow price movements.

• Volume Oscillation Patterns: Historical trade volumes are analyzed for identifying market activity patterns and trends.

• Sentiment Indexation from Trade Ratios: The balance of buy and sell orders provides insights into prevailing market sentiment.

Technical Analysis Derived Features

• Trend Following Metrics: Short-term and long-term moving averages are employed to trace and predict market trends.

• Market Volatility Features: Volatility is measured using indicators like Bollinger Bands to identify potential breakouts or consolidations.

• Momentum Indicators: Features from momentum indicators such as RSI indicate overbought or oversold market conditions.

Additional Feature Sets

• Network Congestion Metrics: Data on network congestion is used to predict its impact on transaction costs and timing.

• Inter-Wallet Transfer Behaviors: The flow of assets between wallets, particularly non-whale wallets, can reveal clusters of activity that may influence the market.

• Smart Contract Function Calls: Analysis of specific function calls within smart contracts can shed light on strategy shifts or dApp usage changes.

• Tokenomics Features: Features related to a token's economic model, including distribution, burns, and staking rewards, are critical for understanding price dynamics.

• Cross-Blockchain Analysis: Movement of assets across blockchains is tracked to understand liquidity shifts and their market effects.

• Regulatory Change Indicators: Market reactions to regulatory announcements and compliance changes are captured to predict their impact on trading behavior.

• Macro-Economic Indicators: Broader economic data are integrated to gauge their influence on market sentiment and crypto trends.

• Order Flow Imbalance Metrics: Discrepancies in buy and sell order flows are analyzed to anticipate price movements driven by demand-supply imbalances.

• Counterparty Risk Metrics: In DeFi, counterparty risk assessment in lending and borrowing activities provides insights into protocol health and market stability.

The feature extraction process at Alvatix underpins our ML models' ability to make informed predictions. By transforming raw, multi-source data into a strategically curated set of features, Alvatix offers users a nuanced view of the crypto market, fostering advanced trading decisions that leverage deep market understanding and foresight. This holistic approach ensures that Alvatix's models are not only reflective of the current market state but are also predictive of future dynamics, cementing our position as a leader in the field of cryptocurrency trading analytics.