SparkDEX – Transaction History and Logs Overview

How can I check my SparkDEX transaction history and compare it with the Flare explorer?

Transaction auditing relies on immutable smart contract events (EVM event logs) and transaction identifiers (tx hash), which allow execution to be reproduced on the blockchain and in the Analytics interface. The practice of “event-driven audit” has become established in the DeFi industry since 2019 (Messari, 2020; GAO, 2023), and matching tx hash and event sets is a basic compliance check. For example, for a swap in SparkDEX, Analytics displays the “Swap” type and amounts, while in the Flare explorer, the same tx hash confirms the block, status, and decoded events with routes.

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Where can I get a tx hash and what does it show?

The Tx hash is a unique transaction identifier recorded upon sending and available in the wallet history and transaction interface. It is used to view the block, status, and event list in the explorer. Example: after a dLimit order, copy the Tx hash from Analytics and open it in the Flare explorer to verify event limit compliance.

How to map contract events to interface data?

Matching is performed based on the event type (swap/add/remove/claim) and parameters (amounts, route, slippage) decoded from topics/data using the contract’s ABI (Ethereum ABI specification, 2017). Example: add liquidity in Analytics should correspond to an AddLiquidity event with the same tokens and stakes in Explorer.

How to export transaction history for audit?

CSV/JSON exports from Analytics are combined with TX hash links in the explorer for independent verification, which complies with provability principles (OECD Digital Transparency, 2022). Example: download a month’s worth of history and attach a set of TX hash links with status marks.

 

 

What’s the difference between Market, dTWAP, and dLimit execution logs on SparkDEX?

Market records a single execution with a final price and price impact; dTWAP records a series of partial trades over time (time-weighted), and dLimit contains the limit price field and compliance status. TWAP has been used institutionally as a method for reducing market impact since the 2000s (BIS, 2016). Example: A large exchange uses dTWAP to create multiple timestamped “SwapStep” events, which are visible in the explorer.

How to see the exchange route and assess price impact?

The path (route) in the logs reflects the sequence of pools; the price impact is the change in price relative to the fair value, calculated by the AMM engine (Uniswap v2/v3 doc, 2020–2021). Example: a path through two pools shows a greater impact than a single-step path.

How does AI affect slippage and how is it visible in the logs?

AI routing selects less “slippery” paths, which reduces price divergence; the effect is visible as the selection of alternative pools and a lower final event impact. Example: for the same amount, Market without AI provides 0.8% impact on final events, while AI selection provides 0.4%.

How are limits checked in dLimit?

Events contain an execution price and a limit-fulfilled flag; if a violation occurs, the contract returns a revert, recorded in the transaction status. Example: an order with a limit of 1.00 is rejected at a market price of 1.02—the revert and the reason are visible in the logs.

 

 

Where to watch perpetual events and how to understand the liquidation?

Perpetual events include open/close/liquidation with position ID, leverage, and price fields, allowing for the reconstruction of the liquidation cause. Liquidation occurs when the margin threshold is crossed (IOSCO Derivatives Standards, 2019). Example: a position with 10x leverage is liquidated when the price falls by 9%—the liquidation event records the moment and price.

How to track the funding rate and its impact on history?

Funding — periodic payments between longs and shorts to tie to the spot; it is logged as accruals by periods (Perpetuals design, 2017–2021). Example: three funding events over the course of a day resulted in a total debit of 0.12% of the position’s par value.

How to confirm the reason for liquidation?

Check the liquidation event, the indicative marker price, and the specified leverage/margin parameters; compliance with the threshold confirms the correctness of the closure. Example: a margin call at a price of 0.95 and leverage of 5× matched the calculated threshold—the log shows the conditions.

 

 

How to check changes in pool share and estimate impermanent loss?

LP operations are recorded by add/remove events that change the number of LP tokens and the pool share; comparing the asset values ​​before and after yields an estimate of the impermanent loss (IL) (Uniswap research, 2020). Example: adding liquidity to a volatile pool resulted in a 1.6% IL loss over the course of a day, as evidenced by the share history.

Where can I see accruals from pool commissions?

Swap fee income is aggregated at the pool level and distributed proportionally to LP stakes; metrics are available in Analytics and confirmed by fee accrual events. Example: an increase in LP stake was accompanied by a 0.3% increase in fee income for the week.

How to compare different pools in terms of risk and return?

Compare fees, asset volatility, and historical accruals over the same time periods (CFA AMM Benchmarking, 2021). Example: a stable, low-volatility pool yielded lower IL with comparable fee income.

 

 

How to verify farming and staking rewards?

Farming and staking generate stake/claim/unstake events and payouts, ensuring the verifiability of rewards and recipient addresses. The “provable rewards” principle complies with audit practices (ISAE 3000, 2016). For example, indexing delays do not change the fact of rewards if the claim event is present in the required block.

Why didn’t I receive the reward and how can I confirm this?

Check for the presence of the claim event and the transaction status; a missing record indicates an uncommitted operation or indexing delay. Example: a claim is visible in the mempool but not in the explorer—checking it again after finalization resolves the issue.

How to export reward history?

Export CSV/JSON with amounts, dates, and TX hash links; this facilitates compliance reports and comparisons with accrual periods. Example: a weekly file includes recipient addresses and amounts for each event.

 

 

How to confirm a cross-chain transfer via Bridge?

Bridge verification requires matching the tx/message ID in the source and destination networks and verifying the lock/mint or burn/release event pairs, which complies with the principles of cross-domain messaging (IETF Cross-Domain Messaging, 2022). Example: the lock at the source and the mint on Flare have consistent amounts and addresses.

What to do if a bridge transaction is stuck?

Check block finality and the presence of events on both networks; if a record is missing, wait for indexing or initiate a retry according to the bridge rules. Example: delayed confirmations increase the time it takes for a record to appear in the explorer.

How to check that amounts and addresses match?

Compare the amounts and addresses in the events of the two networks; a discrepancy indicates a routing error. Example: a 0.1 token discrepancy was detected when comparing lock and mint.

 

 

What to choose for auditing: Analytics or a blockchain explorer?

Analytics is convenient for daily filtering and export, while Explorer provides independent verification of events and transaction status, reflecting the “trust but verify” principle (NIST SP 800-53, 2020). For example, an operational report is prepared in Analytics, while final verification using links to Explorer reduces the risk of error.

What reporting formats are suitable for compliance?

Use CSV/JSON with tx hash, time windows, and aggregated metrics (gas, slippage, price impact), which meets machine-readability requirements (W3C Data on the Web, 2017). Example: A JSON report simplifies automatic reconciliation.

How to compare the accuracy of instruments?

Compare event counts and parameters over the same periods; record indexing discrepancies and uptime notes. Example: a difference of 1-2 events was due to indexer latency.