Abstract
A Python-based Telegram notification bot that sits downstream of the PartsRadar price-tracking pipeline, evaluating normalized price observations against user-configured alert rules and delivering a formatted message when a condition is met. The key design decision is keeping alerting fully decoupled from data collection so the two can evolve independently.
1. What This Is
The bot is the notification layer for the PartsRadar project set. Once the upstream data pipeline has normalized and tracked prices, this component reacts to each new observation by checking it against the user's configured alert conditions and, when one fires, pushes a product-and-price summary to the user via Telegram.
It was part of a 2026 project set and is closed-source. No standalone public repository was present in the reviewed GitHub account, so exact bot commands, the persistence schema, and the alert-rule syntax are not documented here.
2. How It Works
The workflow is a linear five-stage pipeline. Each stage has a single responsibility, which keeps the notification logic easy to swap or extend without touching the scraping or normalization layers.
| # | Stage | Input | Tool | Output |
|---|---|---|---|---|
| 01 | Receive observation | PartsRadar refresh event | Python | Normalized price record |
| 02 | Load alert condition | Tracked-product ID | Rule/state store | Configured threshold |
| 03 | Evaluate rule | New price + threshold | Python | Boolean: condition met |
| 04 | Build message | Product + price context | Python | Formatted alert text |
| 05 | Deliver notification | Alert text | Telegram / automation | User receives message |
3. Implementation Notes
3.1 Decoupled alerting
Alerting is a separate component from scraping and classification. The data pipeline runs on its own schedule; the bot only activates when a new observation lands. This means notification logic can be changed, paused, or extended without affecting data collection, and vice versa.
3.2 Normalization-first evaluation
Alerts fire on normalized price data, not raw retailer listings. The intent is that a user tracking a single product does not receive separate notifications for retailer-name variants of the same item. The trade-off is that the bot depends on the upstream normalization step being correct; a misclassified variant can suppress or duplicate an alert.
3.3 Rule and state handling
A small rule/state layer tracks which configured conditions should produce notifications for each tracked product. The exact schema is not public, but the design keeps the rule definition separate from the delivery mechanism so the same condition can drive multiple channels in the future.
4. Constraints
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Closed-source, no public repo
No standalone GitHub repository was found. Bot commands, persistence schema, and alert-rule syntax are undocumented, making external review or contribution impossible.
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Single delivery channel
Telegram is the only notification path. There is no email, SMS, or webhook fallback, so a Telegram outage or rate-limit silently drops alerts.
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No visible test coverage
As a closed component with no public CI, there is no way to verify alert-rule edge cases (e.g., price oscillation around a threshold, duplicate-suppression logic) from the outside.
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Project marked closed
The 2026 project set is marked CLOSED in the source PDF. The bot is not under active maintenance, so upstream API changes or Telegram bot-token rotation are unaddressed.
5. Next
- a. Publish a minimal open-source reference implementation of the alert-rule engine with a documented condition DSL.
- b. Add a second delivery channel (email or generic webhook) behind the same rule interface so a single condition can fan out to multiple targets.
- c. Document the persistence schema and add a small integration test suite covering threshold-boundary and duplicate-suppression cases.
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