Herramienta de precios automáticos Binance P2P

Auto-pricing starts with configuration, not a price race

An auto-pricing rule begins with an ad, its side, pair, selected payment methods, and transaction constraints. PituBot then evaluates suitable market observations and produces a candidate. That candidate is not automatically the final price: the ad's own floor, ceiling, and safety conditions still decide whether it is acceptable.

This distinction matters because a visible low or high price may belong to a different payment method, an unusable order range, or an inactive ad. Matching conditions before pricing helps prevent a strategy from treating every listing as direct competition.

• Price bounds remain merchant-owned
• Comparable offers should match the ad's practical conditions
• A candidate outside a bound should not be applied

Set floors, ceilings, and side-specific rules

For a BUY ad, a maximum price can express the highest acceptable acquisition rate. For a SELL ad, a minimum price can express the lowest acceptable selling rate. Each ad has its own strategy card because using the same target for both sides can ignore their different commercial purpose.

PituBot also shows an estimate as settings change. Treat it as a current scenario based on currently available data. The next eligible market observation can differ, so the estimate should be reviewed with the configured bounds instead of treated as a promise.

• BUY maximums and SELL minimums can differ
• Per-ad payment selection is part of the price rule
• Estimate first, save only after review

Use filters to make the market signal relevant

Payment method is usually the first filter to review. An offer that accepts a bank your ad does not accept may not be directly comparable. Fiat and crypto limits can further distinguish a small transaction from a high-value transaction. Plan-available merchant-only, inactive-ad, and excluded-merchant controls should be used deliberately, not simply switched on.

A narrower market is not always a better one. If filters are too strict, there may be no eligible offer and no price change. That is often safer than creating a price from an unrelated listing.

See how PituBot competitor filters define a relevant comparison set.

Illustrative auto-pricing example

Illustrative BUY example: the BUY boundary is 0.995 USD per USDT, the competitor price is 0.998 USD per USDT, and the maximum BUY price is 0.997 USD per USDT. PituBot evaluates the relevant active BUY offer, but it does not exceed the configured maximum. The figures are illustrative only.

If the only cheaper listing has an incompatible selected payment method or an order range that does not fit, it is excluded from the example. The merchant's configured boundary wins over a market reference that would place the ad outside the permitted range.

• Numbers are illustrative only
• The merchant chooses the bound first
• Relevant filters matter as much as the displayed price

When PituBot should not change a price

No change is appropriate when the bot is paused, no comparable active offer remains after filters, a candidate violates the ad boundary, or the final same-side account check cannot find a valid nearby price. A skipped update can show that the controls are working.

Review the current strategy after changing payment methods, transaction limits, or the business purpose of an ad. Keeping the old configuration while the offer itself changes is a common cause of poor market comparisons.

• Paused bot
• No eligible competitor
• Boundary reached or no safe final placement

Review the final candidate, not only the market reference

The market reference is an input, while the final candidate is the number that must satisfy the complete strategy. A valid reference can still lead to no change when it conflicts with the ad boundary, a selected limit, or final same-account positioning. This prevents an otherwise relevant competitor from overriding a merchant-controlled limit.

When a result is not what you expected, compare the configured maximum or minimum with the displayed estimate, then inspect the selected competitor conditions. That sequence is more reliable than broadening every filter or increasing a boundary simply to force an update.

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