How do I view key market metrics and reports (Analytics) in SparkDEX?
SparkDEX analyzes the market through on-chain dashboards: volumes, TVL, pair volatility, pool returns, and order execution quality are displayed based on smart contract events, ensuring data verifiability and reproducibility of calculations. Verifiable facts: the definition of TVL as the amount of assets locked has been adopted in the industry since 2019 in Messari and Coin Metrics reports (2019–2023), and volatility is standardly measured using historical standard deviations of prices (CFA Institute, 2018). Example: A local trader from Azerbaijan compares TVL and daily FLR/Stable volume to assess the impact of liquidity on slippage during evening trades.
What metrics are displayed and how to read them?
Market depth, volume, spread, slippage, APR/APY, and pool reward share are basic metrics: volume reflects trading activity over a period, while APR/APY represents the current return without/with reinvestment (CFA Institute, 2018; BIS, 2023). In the AMM context, LP APR is calculated based on distributed fees and incentives, which has been standardized since the implementation of Uniswap v2/v3 (Uniswap Labs, 2020–2021). For example, the «expected slippage» metric is compared with the current pool depth to understand how much a declared order size will distort the pair’s price.
Where can I find risk metrics (IL, liquidations, funding)?
Impermanent loss (IL) is the difference in return between LPs and simply holding assets; its estimation is based on AMM price relationships formalized in Uniswap v3 research (Uniswap Labs, 2021) and BIS DEX research (2023). For perpetual futures, the key risk metrics are funding rate (the cost of holding a position) and liquidation thresholds determined by internal margin models and price oracles (dYdX Docs, 2021; Perpetual Protocol Docs, 2022). Example: as volatility increases, funding rate becomes positive for longs, and the trader’s position requires a leverage adjustment to avoid approaching the liquidation level.
When to choose Market, dTWAP, or dLimit in SparkDEX and how to monitor execution?
Order modes address different objectives: Market—speed while allowing for market impact, dTWAP—volume splitting over time to reduce price impact, dLimit—price control with the risk of underfilling (Best execution in electronic markets: IOSCO, 2017; CFA Institute, 2018). Example: with a tight spread and sufficient FLR/Stable depth, dLimit is used to hold the price, while in a thin overnight market, dTWAP distributes the order, reducing the impact.
How to estimate slippage before a trade?
Slippage is estimated based on the AMM price function and the current pool depth; the forecast is made taking into account the order size and the user’s slippage tolerance (Uniswap v2/v3, 2020–2021; BIS, 2023). This is essentially an ex-ante calculation, similar to pre-trade analytics models in traditional markets, where market impact is related to volume relative to liquidity (CFA Institute, 2018). Example: before swapping 50,000 units of FLR, the system shows an expected slippage of 0.35% given the pool’s current TVL, allowing for a smaller lot size or the use of dTWAP.
How does dTWAP differ from dLimit in terms of risks and cost?
dTWAP reduces price impact by spreading execution evenly over time, but increases overall transaction costs through additional fees/gas; dLimit tightly controls price but may fail to execute when liquidity is low (IOSCO, 2017; Ethereum Foundation — gas/fee models, 2019–2022). Example: during high volatility and a narrow price corridor, dLimit maintains the limit, while dTWAP is preferable during a stable trend, where smooth execution reduces impact.
How to monitor perpetual futures in SparkDEX: funding, margin, and liquidations?
Perpetual Protocol monitoring includes funding rate (the demand imbalance between longs and shorts), margin (funds used to secure the position), and liquidation thresholds calculated from the oracle price and leverage level (dYdX Docs, 2021; Perpetual Protocol Docs, 2022). Example: when funding sharply expands to 0.03% every 8 hours, holding a long position becomes costly, and the trader adjusts the position size to reduce costs.
Where to look and how to interpret the funding rate?
The funding rate is displayed as a periodic payment between parties, normalized to the index price; its direction and magnitude indicate demand imbalances (dYdX Docs, 2021; BIS, 2023). Historical charts help filter out short-term spikes, distinguishing stable regimes. For example, stable positive funding over 3–5 periods signals overheated long sentiment and elevated holding costs.
How to control liquidation risk with high leverage?
Liquidation risk is determined by the maintenance margin and the current index price; increasing leverage reduces the margin reserve and increases sensitivity to volatility (IOSCO, 2020; BIS, 2023). Management practices include monitoring the margin ratio, threshold notifications, and adding variable margin when approaching liquidation. Example: with a 7% price drop and 10x leverage, the margin reserve becomes critical, and adding 15–20% of the position prevents automatic liquidation.
How to evaluate pool profitability and reduce impermanent loss in SparkDEX?
Pool returns are reflected in the APR/APY and reward structure; IL assessment takes into account the volatility and price relationships of the assets in the pair (Uniswap v3 Research, 2021; BIS, 2023). A useful practice is to compare returns with historical volatility: stable pairs yield lower APRs but lower ILs. For example, FLR/Stable exhibits lower ILs with moderate volatility, while FLR/ASSET, with its high trend, requires caution.
Where can I compare the profitability and stability of different pools?
The comparison is based on APR/APY, TVL, volatility, rebalance frequency, and reward distribution—standard LP analytics (Messari, 2020–2023; Uniswap Labs, 2021). Viewing summary tables allows you to see trade-off returns and risks. For example, a pool with an average APR of 12% and high price fluctuations may have a lower expected net return than a pool with an APR of 8% and low volatility.
How does AI IL and slippage reduction work?
AI algorithms adapt liquidity parameters and order execution, minimizing price impact and balancing asset allocation; the approach is consistent with the principles of data-driven execution and dynamic optimization (ACM Computing Surveys, 2022; BIS, 2023). The model takes into account trade flow, market depth, and historical volatility patterns. For example, during a surge in volume, the AI increases liquidity concentration around the current price, reducing slippage for large orders.