On Chain Performance Indicators for DeFi Protocols and User Groups

Introduction

Decentralized finance (DeFi) has moved beyond simple token swaps to become an ecosystem of complex protocols that offer lending, borrowing, staking, and insurance.
Because DeFi is built on public blockchains, every interaction is recorded on‑chain. This transparency turns the protocol and its users into a rich data source that can be mined for insights advanced DeFi analytics.
On‑chain performance indicators (OPIs) quantify how well a protocol operates and how its users behave. By examining these metrics, investors, developers, auditors, and regulators can assess risk, spot trends, and drive product improvements.
This article introduces the core OPIs for both protocols and user groups, explains how to compute them, and shows how cohort analysis can turn raw numbers into actionable knowledge.

Protocol‑Level Indicators

These metrics give a snapshot of a protocol’s financial health, user engagement, and technical efficiency. They are usually reported in dashboards such as DeFiLlama, Dune Analytics, or custom GraphQL endpoints.

User‑Group Indicators

User behavior in DeFi can be segmented into functional cohorts—depositors, borrowers, liquidity providers, and traders behavioral segmentation. Each group exhibits distinct on‑chain patterns.

Aggregating these metrics allows protocol operators to target incentives, adjust fee structures, or identify emerging user segments.

Cohort Analysis

Cohort analysis dissects user behavior over time by grouping users based on a common attribute, such as the block in which they first interacted with the protocol or the token they first deposited.
Typical cohort steps:

1. Define Cohort Criteria – e.g., users who made their first deposit in a given month.
2. Track Metrics Over Time – measure retention, lifetime value, or churn at monthly intervals.
3. Compare Across Cohorts – identify whether changes in incentives, upgrades, or market conditions influence user behavior.

Example: Loyalty of Liquidity Providers

Suppose a protocol introduces a new reward program in January. By creating a cohort of LPs who joined before January and another cohort that joined afterward, you can calculate:

• Retention Rate: % of LPs who remain active after 3, 6, 12 months.
• Average IL: Compare IL for the two cohorts to see if the new rewards mitigate impermanent loss.
• Fee Earned per LP: Determine if the incentive improves overall profitability.

The resulting insight might be that early LPs show higher retention, implying that onboarding processes and community support are critical.

Data Sources and Retrieval

On‑chain data is publicly available, but extracting meaningful metrics requires efficient querying:

When designing a data pipeline, consider:

• Batch vs. Streaming: Batch processing is easier for periodic reports; streaming is better for real‑time dashboards.
• Caching: Reuse query results for high‑frequency metrics to reduce RPC load.
• Normalization: Convert all token amounts to a common unit (e.g., wei to ETH, then to USD) to avoid inconsistent reporting.

Calculating Core Metrics

TVL

def calculate_tvl(token_balances, token_prices):
    tvl = 0
    for token, balance in token_balances.items():
        tvl += balance * token_prices[token]
    return tvl

BDR

bdr = total_borrowed_value / total_deposited_value

APY for Liquidity Providers

apy = (rewards_earned / capital_locked) * 100

Impermanent Loss (Simplified)

def impermanent_loss(price_initial, price_final, ratio_initial, ratio_final):
    value_initial = (price_initial * ratio_initial) + (price_initial / ratio_initial)
    value_final = (price_final * ratio_final) + (price_final / ratio_final)
    il = (value_initial - value_final) / value_initial
    return il

Gas Cost per Transaction

gas_cost = total_gas_used * gas_price
cost_per_tx = gas_cost / number_of_transactions

Use Cases

Risk Management

• Liquidity Shortfall Alerts: Monitor liquidity and slippage to trigger margin calls for traders.
• Borrowing Heat Map: High BDR and LTV combinations flag potential default risk. For deeper risk quantification, see our work on quantifying DeFi risk.

Product Development

• Fee Structure Optimization: Compare APY vs. slippage across fee tiers to find equilibrium.
• Feature Adoption: Track interaction counts of new protocol features over time.

Governance

• Voting Participation: Measure DAPAs to assess community engagement before proposals.
• Revenue Distribution: Use NPR to decide on treasury allocations.

Marketing

• Targeted Incentives: Identify under‑represented user cohorts and tailor rewards.
• Referral Tracking: Use on‑chain addresses to validate referral claims.

Tools and Platforms

Combining these tools can deliver end‑to‑end pipelines from raw blocks to interactive dashboards, and the mathematical underpinning of liquidity can be expanded through "modeling liquidity pools with mathematical metrics and on chain signals".

Challenges and Mitigation

Addressing these issues early prevents data drift and analytical inaccuracies.

Best Practices

1. Normalize Units – Always convert token amounts to a single base unit (e.g., wei) before aggregation.
2. Timestamp Consistency – Use block timestamps in UTC; avoid local time conversions.
3. Version Control – Keep scripts and queries under Git to track changes with protocol upgrades.
4. Metric Documentation – Maintain a living glossary of indicators, formulas, and data sources.
5. Data Governance – Define data ownership and privacy policies, especially when merging on‑chain with off‑chain data.
6. Automation – Schedule nightly ETL jobs; use CI/CD pipelines for deployments.

Visual Insight

After establishing the cohort framework, a visual dashboard can quickly reveal patterns. For example:

• A line chart showing retention of LPs over 12 months for each cohort.
• A heat map of TVL growth by month.
• A scatter plot of APY vs. IL for different liquidity pools.

These visualizations turn raw numbers into strategic insights that drive decision‑making.

Conclusion

On‑chain performance indicators provide a quantitative lens through which to evaluate DeFi protocols and their user groups. By dissecting metrics like TVL, APY, IL, and cohort‑specific behaviors, stakeholders can:

• Quantify risk and reward in real time.
• Identify high‑value user segments for targeted incentives.
• Adjust protocol parameters to balance liquidity, yield, and safety.
• Make data‑driven governance decisions.

The richness of on‑chain data, when combined with rigorous methodology and advanced analytics, turns a permissionless ecosystem into a measurable, optimizable environment. As DeFi continues to mature, mastering these indicators will become essential for developers, investors, auditors, and regulators alike.