Crowdwisdom360 Index Methodology

Crowdwisdom360’s Index Methodology combines machine learning, data-driven validation, and decentralized governance to create transparent, high-accuracy indices. Each index reflects a thematic or institutional narrative such as AI, DeFi, Memecoins or Market and serves as both a benchmarking tool and an investable model within the Crowdwisdom360 ecosystem.

1. Machine Learning–Based Asset Selection

The foundation of every index is a machine-learning (ML) model that continuously identifies top-performing cryptocurrencies across multiple datasets. The ML system evaluates each asset using a composite performance score derived from:

• Price & Performance: Historical and risk-adjusted returns
• Trading Volume: Variations in Trading Volume
• Internal Grade: Proprietary scoring based on relative performance
• Technical Data: Weekly RSI, MACD, Fibonacci retracement, and OBV trends
• Sentiment Data: Retail and influencer sentiment captured from social and platform metrics
• Influencer Data: Presence and win rate across top-ranked influencer portfolios

The result is a short-list of high-signal cryptocurrencies with strong fundamentals and consistent momentum

2. Cross-Index Validation

Once the candidate list is generated, each token undergoes cross-index validation to safety and reliability.

• Each crypto is checked to confirm it appears in at least one other indices in the market [Currently a list 45 Indices].
• Tokens that do not appear in other indices are dropped, avoiding redundancy or niche overexposure.
• This ensures that only shared, high-consensus assets form the final index composition, reinforcing reliability and category coherence.

3. Weighting Framework

The final eligible cryptocurrencies are assigned market-cap–based weights, ensuring balanced exposure while avoiding concentration risk.

• Weighting Rule: Market-cap weighted
• Maximum Allocation: 25% per cryptocurrency

This structure aligns each index with market reality while preserving diversification and stability.

4.Rebalancing

Crowdwisdom360 indices are rebalanced monthly, with mid-month adjustments triggered by significant weight distortions.

• Monthly Rebalance: Conducted at the start of every month based on updated ML rankings.
• Mid-Month Trigger: If any token’s live weight exceeds 35% due to a sharp price increase, we balance within the 25% cap.
• Data Refresh: All relevant data (price, sentiment, technicals, influencer metrics) is refreshed prior to each rebalance cycle.

This ensures that each index remains adaptive and risk-controlled while still capturing emerging market momentum.

5. Governance Integration [To be Implemented after Presale]

Crowdwisdom360 indices will be governed through a DAO-aligned structure where WISD token holders actively participate in decision-making.

WISD holders can propose and vote on:

• Inclusion or exclusion of tokens
• Adjustments to index weighting logic
• Creation of new thematic or institutional indices

Additionally:

• Index validators to be elected quarterly by DAO voting.
• Validators are ranked by past accuracy and track record in maintaining methodological integrity.

This governance framework ensures that each index evolves with the collective intelligence of its most active and credible contributors.

6. Benchmarking

Each index is measured against established baselines to evaluate alpha generation and comparative performance. These benchmarks allow users to assess index effectiveness and relative strength across timeframes and categories.

7. Output and Utility

Crowdwisdom360 indices play a central role across the ecosystem’s operational and analytical layers.

• Real-Time Alerts: Indices feed automated signals that notify users about composition changes or weight shifts.
• Rebalancing Portfolios: The same logic powers live, theme-based portfolios that update automatically.
• Performance Dashboards: Each index features a verified ROI history visible to all users for transparency.
• User Interaction: Indices can be cloned, tracked, or used as templates in Portfolio Contests and Forecast360 prediction markets.

This integration transforms indices from static benchmarks into living, data-driven investment engines within the InfoFi ecosystem.

Summary

The Crowdwisdom360 Index Methodology combines:

1. Machine learning driven selection
2. Cross-index validation
3. Market-cap based weighting
4. Rebalancing
5. DAO-based governance [To be Implemented after Presale]
6. Benchmark comparisons
7. Integrated utilities and alerts

Together, these steps ensure that every index remains objective, verifiable, and continuously optimized, serving as a transparent foundation for decision-making across Crowdwisdom360’s InfoFi architecture.