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This dashboard system consists of two main components that need to be executed sequentially:

# Install dependencies
pip install web3 pandas numpy matplotlib seaborn plotly streamlit sqlalchemy

# Run the data extraction script
python ip_staking.py

This script will:

• Connect to Story Mainnet
• Extract staking events (Deposit, Withdraw, Redelegate)
• Process and transform the data
• Store results in a SQLite database (staking.db)

# Run the Streamlit dashboard
python run_app.py

This will:

• Start a Streamlit web server
• Set up localtunnel for external access
• Launch the dashboard in your default browser
• Display metrics and visualizations based on the staking data

Access the dashboard at the localtunnel URL displayed in the console, or at http://localhost:8501 if accessing locally.

The IP Staking Token Flow Dashboard system is designed with a three-tier architecture:

1. Data Extraction Layer

   • Connects to Story Mainnet via RPC endpoint
   • Retrieves blockchain events (Deposit, Withdraw, Redelegate) from the IPTokenStaking contract
   • Processes raw event data into structured formats

2. Data Storage Layer

   • Stores processed event data in a relational database
   • Enables efficient querying and aggregation for metrics calculation
   • Maintains historical data for trend analysis

3. Visualization Layer

   • Queries the database to calculate key metrics
   • Renders interactive dashboard components
   • Presents insights through charts and key performance indicators

┌─────────────────────┐     ┌─────────────────────┐     ┌─────────────────────┐
│                     │     │                     │     │                     │
│  Data Extraction    │     │   Data Storage      │     │   Visualization     │
│                     │     │                     │     │                     │
│  - RPC Connection   │ ──> │  - SQLite Database  │ ──> │  - Streamlit App    │
│  - Event Processing │     │  - Data Tables      │     │  - Plotly Charts    │
│  - Data Cleaning    │     │  - SQL Queries      │     │  - Metrics Display  │
│                     │     │                     │     │                     │
└─────────────────────┘     └─────────────────────┘     └─────────────────────┘

• Web3.py: Python library for interacting with Ethereum-compatible blockchains
• Pandas: Data manipulation and transformation library
• Python datetime: For timestamp processing and conversions

• SQLite: Lightweight relational database for data storage
• SQLAlchemy: SQL toolkit and ORM for database operations

• Pandas: Advanced data analysis and manipulation
• NumPy: Numerical operations and calculations

• Streamlit: Web application framework for creating interactive dashboards
• Plotly: Interactive, publication-quality graphs and charts
• Localtunnel: Exposes local server to the internet for demonstration purposes

• Incremental Updates: Replace full historical scans with incremental event fetching using fromBlock=lastBlock+1 pattern
• Event Subscription: Implement WebSocket connections for real-time event monitoring instead of polling
• Error Handling: Add robust error recovery mechanisms with automatic retries and checkpointing
• Event Signature Monitoring: Watch for contract upgrades that might change event signatures

• Database Scaling: Migrate from SQLite to PostgreSQL or DuckDB for higher concurrency and volume
• Data Partitioning: Implement time-based partitioning for efficient querying of recent vs. historical data
• Read Replicas: Set up read replicas to separate analytics queries from real-time data ingestion
• Backups: Implement automated backup strategies with point-in-time recovery capabilities

• Orchestration: Replace manual execution with Apache Airflow or GitHub Actions for scheduled pipeline runs
• Data Quality Checks: Implement automated validation of event data integrity and consistency
• Alerting: Add monitoring for pipeline failures or unusual patterns in the data
• Logging: Enhanced logging for easier debugging and audit purposes

• Hosted Solution: Deploy dashboard on Streamlit Cloud or similar service for public accessibility
• Authentication: Add user authentication for sensitive metrics or admin features
• Caching: Implement query result caching to improve dashboard performance
• API Endpoints: Create REST API endpoints for programmatic access to key metrics

• Query Optimization: Create materialized views for frequently accessed metrics to reduce calculation overhead
• Index Tuning: Add specialized indexes based on common query patterns
• Batch Processing: Group transactions for more efficient database operations
• Resource Scaling: Implement auto-scaling based on user traffic and data processing needs

Note on Unbonding Period: The metrics in this dashboard does not account for the 14-day unbonding period. When a withdrawal is initiated, the tokens are immediately counted as unstaked in our metrics, though they remain locked in the protocol during the unbonding period before becoming transferable to the delegator.

The data model in staking.db consists of four primary tables derived from blockchain events:

This data model allows for efficient analysis of token flows, staking patterns, and validator performance across the Story Mainnet IP Staking ecosystem.

Findings

The chart shows a clear, stepwise growth pattern in total staked over time. There are several periods of rapid inflow—most notably in late February, early-March, late-April and early May—where staking amounts jump sharply, likely driven by key protocol events or campaigns. Between these spikes, the growth is steady but slower, and there are no major prolonged declines. Overall, this suggests strong user confidence, with periodic boosts from specific catalysts.

Why track this metric?

Tracking Total Staked Over Time reveals overall trust and engagement trends in the network. It helps identify periods of growth or concern, shows the impact of protocol changes or incentives, and provides a clear signal of the network’s security and health trajectory

Findings

The dataset shows a few days with very large staking inflows—most notably on March 5 (~189M IP), April 25 (~61M), and February 21 (~30M). The biggest single-day unstake happened on April 14 (~5M IP). Most days are net positive, but there are 16 days where more was unstaked than staked; the largest single-day net outflow is around –3M IP. Overall, inflows far outweigh outflows, with net staking strongly positive on most days and only occasional, relatively modest withdrawals.

Why track this metric?

Tracking daily stake and unstake helps the network quickly spot changes in user confidence, assess the impact of events or incentives, and detect unusual outflows that could signal emerging risks.

Findings

The number of active delegators grew rapidly from just 1 at launch to over 500 by the end of the period. The biggest single-day jump (+178) happened on March 3, and the largest daily decrease (–10) was minor by comparison. Overall, the trend is strongly upward, indicating a healthy and expanding staking community.

Why track this metric?

Tracking daily active delegators helps measure user engagement and decentralization, showing whether more unique participants are contributing to the network’s security.

Findings

A total of 587 new delegators joined during the period, with the largest influx (178 new delegators) on March 3. No days recorded zero new delegators, and 37 newcomers joined in the most recent week, indicating continued growth and steady onboarding.

Why track this metric?

Tracking daily new delegators highlights onboarding momentum and the effectiveness of outreach or incentive campaigns.

Findings

The vast majority of tokens (over 93%) are staked in the FLEXIBLE tier, while the MEDIUM tier accounts for just over 6%. SHORT and LONG lockups combined represent less than half a percent. This distribution shows a strong user preference for liquidity and flexibility over long-term commitment.

Why track this metric? Tracking stake duration distribution helps protocols understand user risk appetite, adjust incentive structures, and monitor the stickiness of staked capital

Findings

The top validator alone controls just over 10% of total stake. The top 10 validators combined hold about 45%, while the remaining validators (“Others”) collectively account for over 55% of the staked tokens. This suggests a reasonably decentralized stake distribution, though the top entities still hold significant influence.

Why track this metric? Tracking validator stake share helps monitor decentralization and security risks. High concentration may increase the risk of collusion or single points of failure, while a more even spread indicates stronger, more resilient network security

Story Network shows impressive early growth and engagement: total staked has climbed rapidly, active and new delegators are steadily increasing, and stake distribution across validators remains reasonably decentralized. The majority of capital is currently held in flexible staking, indicating a user base that values liquidity and low exit barriers. Most days see positive net inflows, with only minor, isolated outflows—evidence of strong confidence and network health.

Advice to team

• Build on Positive Momentum: The continued growth in staked capital and delegator participation is a signal to keep reinforcing community trust and expanding outreach.
• Address Flexibility Bias: With most stake in flexible terms, consider adjusting incentives to encourage longer lockups. This will help improve network security and align with long-term growth.
• Monitor Validator Distribution: Although decentralization is good, keep an eye on top validator concentration. Proactively encourage stake migration to smaller validators to prevent centralization risks.
• React to Outflows: Even small periods of net outflow can be early warnings; continue real-time monitoring so you can quickly diagnose and respond to sudden changes in user sentiment.

Future Analytic Improvements

• Track Retention Cohorts: Analyze how long new delegators keep their stake and whether different cohorts behave differently.

• Incorporate Unbonding Period: Include the 14-day unbonding period to better align with the official numbers.

• Stake Ratio: Track total staked as a % of circulating supply to benchmark security against other chains.

• Validator Commission Rates: Analyze whether commission changes impact stake flow or delegator migration.

• Net Stake Flow by Segment: Break down stake/unstake by delegator type (new, returning, whales, etc.).

• On-Chain Governance Participation: Cross-reference delegators and validators with governance activity to understand true network engagement.

• Slashing & Performance Metrics: Include validator uptime, missed blocks, and slashing events for a full security health dashboard.