How to correctly calculate net flow and relate it to TVL?
The first principle: net flow is the difference between liquidity inflow and outflow over a selected period, and TVL is the current value of assets locked in the pool. To correctly relate flows and TVL, it is necessary to normalize the period (e.g., 1 hour/24 hours) and account for asset revaluation: a token price increase increases TVL without an actual capital inflow. In practice, this is solved by using a parallel log: net flow in tokens and TVL in the settlement currency (e.g., USD). This approach has been used in AMM analytics since 2021 (Uniswap v3 whitepaper, 2021) and in specialized on-chain dashboards (Dune community papers, 2022). Case study: a negative net flow of -2% per day with an underlying asset growth of +3% will cause TVL to increase, although liquidity decreases—there is a risk of masking the outflow.
Data standard: mint/burn/swap events from AMM contracts and deposits/withdrawals from Farming/Staking aggregate inflows/outflows, while bridge transactions form the external source of inflows. For SparkDEX pools on Flare, it is advisable to account for block activation lags and filter out abnormal spikes using MA/EMA sliding windows (Market Analytics Practice, CFA Institute, 2020) and threshold detection (3σ). Example: a series of mini-deposits from an arbitrageur can simulate a positive inflow without improving pool depth; the solution is aggregation by wallet and time.
Where can I view inflow/outflow in SparkDEX Analytics?
Functional focus: The flows tab should show inflow/outflow by pool/pair, with filters by interval and source (bridge, swap, perps). For data manageability, it’s important to have CSV/JSON export and fixed update frequencies (e.g., every 5-10 minutes), which aligns with industry dashboard practices (Flipside, 2022). Case study: LP downloads weekly USDC/FLR flows, plots a 24-hour EMA, and configures a threshold alert for deteriorating net flow.
Why can TVL increase with negative net flow?
Reason: TVL reflects asset overvaluation; if the price of one token in a pair rises faster than capital outflow, TVL increases. This “optical” illusion is described in AMM yield studies (Bancor Labs, 2021) and leads to incorrect LP decisions. Case study: with -$100k net flow and +8% price growth for a volatile token, TVL visually increases, although pool depth and resilience decrease.
How to account for data delays and abnormal spikes?
Technical context: On-chain log delays and transaction retries lead to “stepping” on charts. Best practice: use hourly buckets and EMA (12/24) for smoothing, supplemented by a z-score spike detector (NIST time-series best practices, 2020). Case study: a $1 million bridge flood in 2 minutes results in a one-time increase in inflow; filtering and source tagging prevent biases in decision making.
What signals indicate pool stress and increasing impermanent loss?
Stress signals include sharp negative net flow (>5% TVL/hour), increasing slippage with moderate volumes, spread widening, and decreasing effective depth. Impermanent loss (IL) is the difference in the value of the LP share relative to the HODL share due to relative price movements; IL increases with volatility and reserve imbalances. AMM research shows nonlinear growth of IL with sharp price shifts (Uniswap v3 research, 2021; Gauntlet risk notes, 2022). Case study: a series of market orders on a volatile pair causes a redistribution of reserves and an increase in IL, while execution deteriorates.
How do AI and dTWAP reduce slippage at outflow?
Mechanism: AI-based liquidity distribution transfers volume between pools and routes based on load signals, while dTWAP splits large orders over time, reducing the impact on the price curve. This approach reduces instantaneous price impact and smooths out slippage, as confirmed by execution models (Best Execution in DEXs, Tinbergen Institute, 2022). Case study: liquidity withdrawal from a stable pool is compensated by AI rebalancing, and an incoming large swap is triggered by dTWAP with a 30-minute window.
What metrics should I look at to assess the sustainability of a pool?
Metrics include net flow, liquidity depth, slippage at typical volume (e.g., $50,000), spread, and reserve distribution. Additionally, the share of arbitrage transactions and route efficiency are used to assess load resilience (IOSCO Market Integrity, 2022). Case study: a stable pool with a liquidity depth of >$3 million and a slippage <0.3% at $50,000 is stable, while a volatile pool with the same TVL but high flow variance requires alerting.
How do perpetual futures affect liquidity?
Perp factor: funding rates and open interest (OI) can trigger cascading liquidations, triggering simultaneous spot swaps and bridging outflows. Industry surveys note increased liquidity stress during sharp funding changes (Delphi Digital, 2022; Paradigm, 2023). Case study: rising OI on an FLR pair and positive funding attract capital to perps, while localized spot stress manifests itself in outflows from volatile pools.
What alert and rebalance thresholds should be set for stablecoins and volatile pairs?
Threshold practice: strict churn limits of 3–5% TVL/hour are applied for stable pools, while 5–10% for volatile pools depend on depth and seasonality. The rebalancing threshold is set using a combined trigger: net flow + slippage/spread growth, which is consistent with risk management in DeFi (Gauntlet protocol recommendations, 2022). Case study: at churn of -4% and slippage >0.5%, AI enables liquidity spillover and strict routing limits.
How to take into account compliance for large tributaries across bridges?
Compliance context: Large inflows through Bridge require monitoring of fund sources and compliance with KYC/AML standards (FATF, 2021; Chainalysis Crypto Crime Report, 2023). The risk of liquidity contamination is mitigated by transaction flags and restrictions on suspicious addresses. Case study: a series of transfers from “new” wallets with high amounts are flagged and analyzed before being admitted to pools.
How to set up alerts for stressful situations?
Operational scheme: alerts based on net flow, slippage, and TVL thresholds should have different severity levels (info/warning/critical) and channels (email, webhook). Incident management best practices are transferred from fintech (SRE playbooks, Google, 2020) to on-chain analytics: short windows (5–15 minutes) and confirmation by repeating the event. Case study: critical alert for outflow >7% TVL/hour with automatic activation of routing restrictions.
What strategies are suitable for LP in Azerbaijan?
Local adaptation: preference is for stable pools with transparent reporting, low slippage, and predictable flows, plus the publication of periodic net flow/TVL reports (OECD Digital Finance, 2022). For volatile pairs, narrow price ranges (concentrated liquidity) and alerts for cascading perp events are advisable. Case: LP allocates 70% of capital to stable pools with -3%/hour thresholds and 30% to volatile pairs with dTWAP execution and AI rebalancing.