Feat(observer): Detect artificially noisy stalled publisher prices (#81)

* feat(observer): improve stall detection check

* fix(observer): revert changes made for local debugging

* fix(observer): fix pyright errors

* fix(observer): fix ci and pr comments

* chore(observer): remove unneeded log

* fix(observer): fix exact stall, add test

* bump version

Author: tejasbadadare

.gitignore

@@ -6,4 +6,5 @@ __pycache__/
 .envrc
 .coverage
 
-.env
+.env
+.vscode/

.python-version

@@ -0,0 +1 @@
+3.10

README.md

@@ -6,9 +6,13 @@ Observe Pyth on-chain price feeds and run sanity checks on the data.
 
 Container images are available at https://github.com/pyth-network/pyth-observer/pkgs/container/pyth-observer
 
-To run Observer locally, make sure you have a recent version of [Poetry](https://python-poetry.org) installed and run:
+To run Observer locally, you will need: 
+- Python 3.10 ([pyenv](https://github.com/pyenv/pyenv) is a nice way to manage Python installs, and once installed will automatically set the version to 3.10 for this project dir via the `.python-version` file).
+- [Poetry](https://python-poetry.org), which handles package and virtualenv management. 
 
+Install dependencies and run the service:
 ```sh
+$ poetry env use $(which python) # point Poetry to the pyenv python shim
 $ poetry install
 $ poetry run pyth-observer
 ```

poetry.lock

@@ -4,7 +4,7 @@ ignore_missing_imports = true
 
 [tool.poetry]
 name = "pyth-observer"
-version = "0.2.15"
+version = "0.3.0"
 description = "Alerts and stuff"
 authors = []
 readme = "README.md"
@@ -27,6 +27,7 @@ throttler = "1.2.1"
 types-pyyaml = "^6.0.12"
 types-pytz = "^2022.4.0.0"
 python-dotenv = "^1.0.1"
+numpy = "^2.1.3"
 
 
 [tool.poetry.group.dev.dependencies]

pyproject.toml

@@ -1,16 +1,34 @@
 import time
-from dataclasses import dataclass
+from collections import defaultdict, deque
+from dataclasses import asdict, dataclass
 from datetime import datetime
 from typing import Dict, Protocol, runtime_checkable
 from zoneinfo import ZoneInfo
 
+from loguru import logger
 from pythclient.calendar import is_market_open
 from pythclient.pythaccounts import PythPriceStatus
 from pythclient.solana import SolanaPublicKey
 
+
+@dataclass
+class PriceUpdate:
+    """Represents a single price with its timestamp (epoch seconds)."""
+
+    timestamp: int
+    price: float
+
+
 PUBLISHER_EXCLUSION_DISTANCE = 25
+PUBLISHER_CACHE_MAX_LEN = 30
+"""Roughly 30 mins of updates, since the check runs about once a minute"""
 
-PUBLISHER_CACHE = {}
+PUBLISHER_CACHE = defaultdict(lambda: deque(maxlen=PUBLISHER_CACHE_MAX_LEN))
+"""
+Cache that holds tuples of (price, timestamp) for publisher/feed combos as they stream in.
+Entries longer than `PUBLISHER_CACHE_MAX_LEN` are automatically pruned.
+Used by the PublisherStalledCheck to detect stalls in prices.
+"""
 
 
 @dataclass
@@ -240,6 +258,16 @@ def __init__(self, state: PublisherState, config: PublisherCheckConfig):
         self.__abandoned_time_limit: int = int(config["abandoned_time_limit"])
         self.__max_slot_distance: int = int(config["max_slot_distance"])
 
+        from pyth_observer.check.stall_detection import (  # noqa: deferred import to avoid circular import
+            StallDetector,
+        )
+
+        self.__detector = StallDetector(
+            stall_time_limit=self.__stall_time_limit,
+            noise_threshold=float(config["noise_threshold"]),
+            min_noise_samples=int(config["min_noise_samples"]),
+        )
+
     def state(self) -> PublisherState:
         return self.__state
 
@@ -254,36 +282,49 @@ def run(self) -> bool:
 
         distance = self.__state.latest_block_slot - self.__state.slot
 
+        # Pass for redemption rates because they are expected to be static for long periods
+        if self.__state.asset_type == "Crypto Redemption Rate":
+            return True
+
         #  Pass when publisher is offline because PublisherOfflineCheck will be triggered
         if distance >= self.__max_slot_distance:
             return True
 
-        publisher_key = (self.__state.publisher_name, self.__state.symbol)
         current_time = int(time.time())
-        previous_price, last_change_time = PUBLISHER_CACHE.get(
-            publisher_key, (None, None)
-        )
 
-        if previous_price is None or self.__state.price != previous_price:
-            PUBLISHER_CACHE[publisher_key] = (self.__state.price, current_time)
-            return True
+        publisher_key = (self.__state.publisher_name, self.__state.symbol)
+        updates = PUBLISHER_CACHE[publisher_key]
+
+        # Only cache new prices, let repeated prices grow stale.
+        # These will be caught as an exact stall in the detector.
+        is_repeated_price = updates and updates[-1].price == self.__state.price
+        cur_update = PriceUpdate(current_time, self.__state.price)
+        if not is_repeated_price:
+            PUBLISHER_CACHE[publisher_key].append(cur_update)
+
+        # Analyze for stalls
+        result = self.__detector.analyze_updates(list(updates), cur_update)
+        logger.debug(f"Stall detection result: {result}")
 
-        time_since_last_change = current_time - last_change_time
-        if time_since_last_change > self.__stall_time_limit:
-            if time_since_last_change > self.__abandoned_time_limit:
-                return True  # Abandon this check after the abandoned time limit
-            return False
+        self.__last_analysis = result  # For error logging
+
+        # If we've been stalled for too long, abandon this check
+        if result.is_stalled and result.duration > self.__abandoned_time_limit:
+            return True
 
-        return True
+        return not result.is_stalled
 
     def error_message(self) -> dict:
+        stall_duration = f"{self.__last_analysis.duration:.1f} seconds"
         return {
-            "msg": f"{self.__state.publisher_name} has been publishing the same price for too long.",
+            "msg": f"{self.__state.publisher_name} has been publishing the same price of {self.__state.symbol} for {stall_duration}",
             "type": "PublisherStalledCheck",
             "publisher": self.__state.publisher_name,
             "symbol": self.__state.symbol,
             "price": self.__state.price,
-            "stall_duration": f"{int(time.time()) - PUBLISHER_CACHE[(self.__state.publisher_name, self.__state.symbol)][1]} seconds",
+            "stall_type": self.__last_analysis.stall_type,
+            "stall_duration": stall_duration,
+            "analysis": asdict(self.__last_analysis),
         }