Als DevOps-Ingenieur bei einer Krypto-Trading-Firma habe ich in den letzten drei Jahren über 200.000 US-Dollar an Verlusten durch unentdeckte API-Ausfälle erlebt. Ein einziger 4-Stunden-Ausfall der Binance-API kostete uns damals 47.000 Dollar in verpassten Arbitrage-Chancen. In diesem Tutorial zeige ich Ihnen, wie Sie ein robustes Monitoring-System bauen, das Sie in Echtzeit über API-Anomalien informiert – mit HolySheep AI als intelligentes Backend für Alarmierung und Vorhersage.
Warum API-Monitoring für Krypto-Börsen kritisch ist
Kryptowährungs-Börsen-APIs unterscheiden sich grundlegend von normalen REST-APIs:
- Millisekunden-Kritikalität: Bei Arbitrage-Strategien entscheiden 100ms über Profit oder Verlust
- Volatilitätsspitzen: Marktcrashs verursachen 10-fach höhere Anfragenraten
- Rate-Limiting: Binance erlaubt 1.200 Gewichte/Minute, Coinbase Pro 10 Anfragen/Sekunde
- Zustandslosigkeit: Eine fehlgeschlagene Order kann 500 Dollar in Gas-Gebühren kosten
Architektur des Überwachungssystems
Unser System besteht aus fünf Komponenten: Collector, Analyzer, Predictor, Alerter und Dashboard. HolySheep AI übernimmt die Rolle des Predictors – nutzt also GPT-4.1 oder Claude Sonnet 4.5, um Muster in Ihren API-Metriken zu erkennen und Anomalien vorherzusagen, bevor sie auftreten.
1. Basis-API-Collector implementieren
Der Collector sendet kontinuierlich Health-Checks an die Börsen-APIs und misst Latenz, Erfolgsquote und Antwortqualität. Hier ein vollständiges Python-Skript:
#!/usr/bin/env python3
"""
Crypto Exchange API Health Monitor
misst Latenz, Erfolgsrate und Rate-Limit-Status in Echtzeit
"""
import asyncio
import aiohttp
import time
import json
from dataclasses import dataclass, asdict
from typing import Dict, List, Optional
from datetime import datetime
import redis.asyncio as redis
@dataclass
class APIMetrics:
exchange: str
endpoint: str
timestamp: datetime
latency_ms: float
status_code: int
success: bool
error_message: Optional[str] = None
rate_limit_remaining: Optional[int] = None
class CryptoAPICollector:
def __init__(self, redis_url: str = "redis://localhost:6379"):
self.redis = redis.from_url(redis_url)
self.exchanges = {
"binance": {
"base_url": "https://api.binance.com",
"endpoints": ["/api/v3/ping", "/api/v3/time", "/api/v3/exchangeInfo"],
"weights_per_request": 1
},
"coinbase": {
"base_url": "https://api.exchange.coinbase.com",
"endpoints": ["/time", "/products/BTC-USD/book", "/products"],
"weights_per_request": 1
},
"kraken": {
"base_url": "https://api.kraken.com",
"endpoints": ["/0/public/Time", "/0/public/Assets", "/0/public/Ticker?pair=XBTUSD"],
"weights_per_request": 1
}
}
async def check_endpoint(
self,
session: aiohttp.ClientSession,
exchange: str,
endpoint: str
) -> APIMetrics:
base_url = self.exchanges[exchange]["base_url"]
url = f"{base_url}{endpoint}"
start = time.perf_counter()
try:
async with session.get(url, timeout=aiohttp.ClientTimeout(total=10)) as response:
latency = (time.perf_counter() - start) * 1000
remaining = response.headers.get("X-MBX-Used-Weight-1m",
response.headers.get("CB-ACCESS", "N/A"))
return APIMetrics(
exchange=exchange,
endpoint=endpoint,
timestamp=datetime.utcnow(),
latency_ms=round(latency, 2),
status_code=response.status,
success=response.status == 200,
rate_limit_remaining=int(remaining) if remaining.isdigit() else None
)
except aiohttp.ClientError as e:
latency = (time.perf_counter() - start) * 1000
return APIMetrics(
exchange=exchange,
endpoint=endpoint,
timestamp=datetime.utcnow(),
latency_ms=round(latency, 2),
status_code=0,
success=False,
error_message=str(e)
)
async def collect_metrics(self) -> List[APIMetrics]:
"""Sammelt Metriken von allen Börsen parallel"""
async with aiohttp.ClientSession() as session:
tasks = []
for exchange, config in self.exchanges.items():
for endpoint in config["endpoints"]:
tasks.append(self.check_endpoint(session, exchange, endpoint))
results = await asyncio.gather(*tasks)
return [r for r in results if r is not None]
async def store_metrics(self, metrics: List[APIMetrics]):
"""Speichert Metriken in Redis für spätere Analyse"""
for m in metrics:
key = f"metrics:{m.exchange}:{int(m.timestamp.timestamp())}"
await self.redis.lpush(key, json.dumps(asdict(m)))
await self.redis.expire(key, 86400) # 24h TTL
async def run(self, interval: int = 30):
"""Hauptschleife: sammelt alle interval Sekunden Metriken"""
while True:
metrics = await self.collect_metrics()
await self.store_metrics(metrics)
print(f"[{datetime.now().isoformat()}] Gesammelt: {len(metrics)} Metriken")
await asyncio.sleep(interval)
if __name__ == "__main__":
collector = CryptoAPICollector()
asyncio.run(collector.run(interval=30))
2. Intelligente Anomalieerkennung mit HolySheep AI
Der Collector liefert Rohdaten. Jetzt brauchen wir einen Analyzer, der mit HolySheep AI Anomalien erkennt. Das Besondere: HolySheep bietet <50ms Latenz bei API-Aufrufen und unterstützt GPT-4.1 ($8/MToken), Claude Sonnet 4.5 ($15/MToken) und DeepSeek V3.2 ($0.42/MToken) – perfekt für kosteneffiziente Echtzeitanalyse.
#!/usr/bin/env python3
"""
Anomaly Detector mit HolySheep AI
Analysiert API-Metriken und erkennt Anomalien mit ML + LLM-Hilfe
"""
import asyncio
import json
import redis.asyncio as redis
from typing import Dict, List, Tuple
from datetime import datetime, timedelta
import statistics
HolySheep AI Configuration
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
class AnomalyDetector:
def __init__(self, holysheep_api_key: str, redis_url: str = "redis://localhost:6379"):
self.redis = redis.from_url(redis_url)
self.holysheep_key = holysheep_api_key
self.thresholds = {
"latency_p95_ms": 500, # 95th percentile Latenz
"error_rate": 0.05, # 5% Fehlerrate
"consecutive_failures": 3 # 3 aufeinanderfolgende Fehler
}
async def get_historical_metrics(self, exchange: str, minutes: int = 60) -> List[Dict]:
"""Lädt historische Metriken aus Redis"""
cutoff = datetime.utcnow() - timedelta(minutes=minutes)
cutoff_ts = int(cutoff.timestamp())
metrics = []
async for key in self.redis.scan_iter(f"metrics:{exchange}:*"):
ts = int(key.split(":")[-1])
if ts >= cutoff_ts:
data = await self.redis.lrange(key, 0, -1)
for item in data:
metrics.append(json.loads(item))
return metrics
async def calculate_baseline(self, metrics: List[Dict]) -> Dict:
"""Berechnet Baseline-Statistiken"""
if not metrics:
return {"latency_avg": 100, "latency_std": 20, "error_rate": 0.01}
latencies = [m["latency_ms"] for m in metrics if m["success"]]
errors = [1 for m in metrics if not m["success"]]
return {
"latency_avg": statistics.mean(latencies) if latencies else 100,
"latency_std": statistics.stdev(latencies) if len(latencies) > 1 else 20,
"latency_p95": sorted(latencies)[int(len(latencies) * 0.95)] if latencies else 200,
"error_rate": len(errors) / len(metrics) if metrics else 0
}
async def detect_anomalies(self, exchange: str) -> List[Dict]:
"""Erkennt Anomalien basierend auf Statistiken"""
metrics = await self.get_historical_metrics(exchange, minutes=30)
baseline = await self.calculate_baseline(metrics)
anomalies = []
for metric in metrics[-10:]: # Letzte 10 Metriken prüfen
# Latenz-Anomalie
if metric["latency_ms"] > baseline["latency_avg"] + 3 * baseline["latency_std"]:
anomalies.append({
"type": "high_latency",
"severity": "warning",
"exchange": exchange,
"endpoint": metric["endpoint"],
"current_value": metric["latency_ms"],
"baseline": baseline["latency_avg"],
"message": f"Latenz {metric['latency_ms']}ms überschreitet Baseline {baseline['latency_avg']:.1f}ms"
})
# Fehler-Anomalie
if not metric["success"]:
errors_key = f"consecutive_errors:{exchange}:{metric['endpoint']}"
error_count = await self.redis.incr(errors_key)
await self.redis.expire(errors_key, 300)
if error_count >= self.thresholds["consecutive_failures"]:
anomalies.append({
"type": "consecutive_errors",
"severity": "critical",
"exchange": exchange,
"endpoint": metric["endpoint"],
"error_count": error_count,
"message": f"{error_count} aufeinanderfolgende Fehler bei {metric['endpoint']}"
})
return anomalies
async def analyze_with_llm(self, anomalies: List[Dict], context: Dict) -> str:
"""Nutzt HolySheep AI für tiefere Anomalie-Analyse"""
prompt = f"""
Analysiere die folgenden API-Anomalien für Krypto-Börsen:
Anomalien:
{json.dumps(anomalies, indent=2)}
Kontext:
- Gesamte Metriken: {context.get('total_metrics', 0)}
- Analysezeitpunkt: {datetime.utcnow().isoformat()}
- Letzte Wartung: {context.get('last_maintenance', 'unbekannt')}
Erkläre:
1. Wahrscheinliche Ursache jeder Anomalie
2. Empfohlene sofortige Aktionen
3. Ob dies ein Muster ist oder isolierter Vorfall
Antworte auf Deutsch, max 200 Wörter.
"""
import aiohttp
async with aiohttp.ClientSession() as session:
async with session.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {self.holysheep_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4.1",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 300,
"temperature": 0.3
}
) as resp:
if resp.status == 200:
data = await resp.json()
return data["choices"][0]["message"]["content"]
else:
return f"LLM-Analyse fehlgeschlagen (Status {resp.status})"
async def run(self, interval: int = 60):
"""Hauptschleife: analysiert alle interval Sekunden"""
while True:
for exchange in ["binance", "coinbase", "kraken"]:
anomalies = await self.detect_anomalies(exchange)
if anomalies:
context = {
"total_metrics": await self.redis.dbsize(),
"last_maintenance": "vor 3 Tagen"
}
llm_analysis = await self.analyze_with_llm(anomalies, context)
# Speichere für Alerter
alert_key = f"alerts:{exchange}:{int(datetime.utcnow().timestamp())}"
await self.redis.set(alert_key, json.dumps({
"anomalies": anomalies,
"llm_analysis": llm_analysis,
"timestamp": datetime.utcnow().isoformat()
}), ex=3600)
print(f"[ALERT] {exchange}: {len(anomalies)} Anomalien erkannt")
await asyncio.sleep(interval)
if __name__ == "__main__":
import os
api_key = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
detector = AnomalyDetector(holysheep_api_key=api_key)
asyncio.run(detector.run(interval=60))
3. Alarmierungssystem mit Multi-Channel-Benachrichtigung
Erkannte Anomalien nützen nichts, wenn niemand sie sieht. Unser Alerter sendet Benachrichtigungen über Slack, Telegram, E-Mail und SMS – basierend auf der Schwere der Anomalie.
#!/usr/bin/env python3
"""
Multi-Channel Alerting System
Sendet Benachrichtigungen basierend auf Anomalie-Schweregrad
"""
import asyncio
import json
import smtplib
import httpx
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from datetime import datetime
from dataclasses import dataclass
from typing import List, Optional
import os
@dataclass
class Alert:
exchange: str
alert_type: str
severity: str # info, warning, critical
title: str
message: str
data: dict
timestamp: datetime
class AlertDispatcher:
def __init__(self):
# Slack Configuration
self.slack_webhook = os.environ.get("SLACK_WEBHOOK_URL", "")
self.slack_channel_critical = "#alerts-critical"
self.slack_channel_warning = "#alerts-warning"
# Telegram Configuration
self.telegram_bot_token = os.environ.get("TELEGRAM_BOT_TOKEN", "")
self.telegram_chat_id = os.environ.get("TELEGRAM_CHAT_ID", "")
# Email Configuration
self.smtp_server = os.environ.get("SMTP_SERVER", "smtp.gmail.com")
self.smtp_port = int(os.environ.get("SMTP_PORT", "587"))
self.smtp_user = os.environ.get("SMTP_USER", "")
self.smtp_password = os.environ.get("SMTP_PASSWORD", "")
self.alert_emails = os.environ.get("ALERT_EMAILS", "").split(",")
# PagerDuty für kritische Alerts
self.pagerduty_key = os.environ.get("PAGERDUTY_KEY", "")
# Severity-Ebenen
self.severity_config = {
"critical": {"slack": True, "telegram": True, "email": True, "pagerduty": True, "sms": True},
"warning": {"slack": True, "telegram": True, "email": False, "pagerduty": False, "sms": False},
"info": {"slack": True, "telegram": False, "email": False, "pagerduty": False, "sms": False}
}
async def send_slack(self, alert: Alert, channel: str) -> bool:
"""Sendet Slack-Benachrichtigung"""
if not self.slack_webhook:
return False
severity_emoji = {"critical": "🚨", "warning": "⚠️", "info": "ℹ️"}
payload = {
"channel": channel,
"username": "Crypto API Monitor",
"icon_emoji": severity_emoji.get(alert.severity, "ℹ️"),
"attachments": [{
"color": {"critical": "#ff0000", "warning": "#ffa500", "info": "#36a64f"}.get(alert.severity, "#36a64f"),
"title": f"{severity_emoji.get(alert.severity, 'ℹ️')} {alert.title}",
"text": alert.message,
"fields": [
{"title": "Börse", "value": alert.exchange, "short": True},
{"title": "Typ", "value": alert.alert_type, "short": True},
{"title": "Zeit", "value": alert.timestamp.strftime("%Y-%m-%d %H:%M:%S UTC"), "short": True}
],
"footer": "HolySheep AI Monitoring"
}]
}
async with httpx.AsyncClient() as client:
try:
response = await client.post(self.slack_webhook, json=payload, timeout=10)
return response.status_code == 200
except Exception as e:
print(f"Slack Fehler: {e}")
return False
async def send_telegram(self, alert: Alert) -> bool:
"""Sendet Telegram-Nachricht"""
if not self.telegram_bot_token or not self.telegram_chat_id:
return False
severity_text = {"critical": "🔴 KRITISCH", "warning": "🟠 WARNUNG", "info": "🔵 INFO"}
message = f"""
{severity_text.get(alert.severity, '🔵 INFO')}
📊 *{alert.title}*
{exchange}: {alert.exchange}
Type: {alert.alert_type}
Time: {alert.timestamp.strftime('%Y-%m-%d %H:%M:%S')} UTC
{alert.message}
_Generated by HolySheep AI Monitor_
"""
url = f"https://api.telegram.org/bot{self.telegram_bot_token}/sendMessage"
async with httpx.AsyncClient() as client:
try:
response = await client.post(url, json={
"chat_id": self.telegram_chat_id,
"text": message,
"parse_mode": "Markdown"
}, timeout=10)
return response.status_code == 200
except Exception as e:
print(f"Telegram Fehler: {e}")
return False
async def send_email(self, alert: Alert) -> bool:
"""Sendet E-Mail-Benachrichtigung"""
if not self.smtp_user or not self.alert_emails:
return False
msg = MIMEMultipart("alternative")
msg["Subject"] = f"[{alert.severity.upper()}] {alert.title}"
msg["From"] = self.smtp_user
html_content = f"""
{alert.title}
Börse
{alert.exchange} Typ
{alert.alert_type} Schwere
{alert.severity} Zeit
{alert.timestamp.isoformat()}
Details:
{alert.message}
Generated by HolySheep AI Crypto Monitor
"""
msg.attach(MIMEText(html_content, "html"))
try:
with smtplib.SMTP(self.smtp_server, self.smtp_port) as server:
server.starttls()
server.login(self.smtp_user, self.smtp_password)
server.sendmail(self.smtp_user, self.alert_emails, msg.as_string())
return True
except Exception as e:
print(f"Email Fehler: {e}")
return False
async def send_pagerduty(self, alert: Alert) -> bool:
"""Triggert PagerDuty Incident für kritische Alerts"""
if not self.pagerduty_key:
return False
payload = {
"routing_key": self.pagerduty_key,
"event_action": "trigger",
"payload": {
"summary": f"{alert.exchange}: {alert.title}",
"severity": "critical" if alert.severity == "critical" else "warning",
"source": "crypto-api-monitor",
"custom_details": {
"exchange": alert.exchange,
"alert_type": alert.alert_type,
"message": alert.message,
"data": alert.data
}
}
}
async with httpx.AsyncClient() as client:
try:
response = await client.post(
"https://events.pagerduty.com/v2/enqueue",
json=payload,
headers={"Content-Type": "application/json"},
timeout=10
)
return response.status_code == 202
except Exception as e:
print(f"PagerDuty Fehler: {e}")
return False
async def dispatch(self, alert: Alert) -> dict:
"""Dispatcht Alert an alle konfigurierten Kanäle"""
config = self.severity_config.get(alert.severity, self.severity_config["info"])
results = {}
if config.get("slack"):
channel = self.slack_channel_critical if alert.severity == "critical" else self.slack_channel_warning
results["slack"] = await self.send_slack(alert, channel)
if config.get("telegram"):
results["telegram"] = await self.send_telegram(alert)
if config.get("email"):
results["email"] = await self.send_email(alert)
if config.get("pagerduty"):
results["pagerduty"] = await self.send_pagerduty(alert)
return results
class AlertManager:
"""Orchestriert Alert-Erkennung und -Versand"""
def __init__(self):
self.dispatcher = AlertDispatcher()
async def process_alerts(self, redis_url: str = "redis://localhost:6379"):
"""Verarbeitet wartende Alerts aus Redis"""
import redis.asyncio as redis
r = redis.from_url(redis_url)
async for key in r.scan_iter("alerts:*"):
alert_data = await r.get(key)
if not alert_data:
continue
data = json.loads(alert_data)
anomalies = data.get("anomalies", [])
for anomaly in anomalies:
alert = Alert(
exchange=anomaly["exchange"],
alert_type=anomaly["type"],
severity=anomaly["severity"],
title=f"API-{anomaly['type'].replace('_', ' ').title()}",
message=f"{anomaly['message']}\n\nLLM-Analyse:\n{data.get('llm_analysis', 'N/A')}",
data=anomaly,
timestamp=datetime.utcnow()
)
results = await self.dispatcher.dispatch(alert)
print(f"Alert dispatched: {results}")
# Alert nach Verarbeitung löschen
await r.delete(key)
await r.aclose()
async def run(self, interval: int = 30):
"""Prüft alle interval Sekunden auf neue Alerts"""
while True:
await self.process_alerts()
await asyncio.sleep(interval)
if __name__ == "__main__":
manager = AlertManager()
asyncio.run(manager.run(interval=30))
4. HolySheep AI: Intelligente Vorhersage und Pattern-Erkennung
Das HolySheep AI-System bietet entscheidende Vorteile für Ihr Monitoring: Unterstützung für DeepSeek V3.2 ($0.42/MToken) ermöglicht kostengünstige kontinuierliche Analyse, während GPT-4.1 für komplexe Vorhersagen genutzt werden kann. Mit <50ms Latenz und Zahlungsoptionen über WeChat und Alipay ist HolySheep ideal für chinesische Krypto-Trader.
#!/usr/bin/env python3
"""
Predictive Analytics mit HolySheep AI
Nutzt historische Daten, um Ausfälle vorherzusagen
"""
import asyncio
import json
import redis.asyncio as redis
from datetime import datetime, timedelta
from collections import defaultdict
import statistics
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
class PredictiveMonitor:
"""Vorhersage von API-Ausfällen basierend auf historischen Mustern"""
def __init__(self, holysheep_api_key: str, redis_url: str = "redis://localhost:6379"):
self.redis = redis.from_url(redis_url)
self.api_key = holysheep_api_key
async def get_aggregated_metrics(self, exchange: str, hours: int = 24) -> dict:
"""Aggregiert Metriken über Zeitfenster"""
cutoff = datetime.utcnow() - timedelta(hours=hours)
cutoff_ts = int(cutoff.timestamp())
all_latencies = []
error_count = 0
total_count = 0
hourly_buckets = defaultdict(list)
async for key in self.redis.scan_iter(f"metrics:{exchange}:*"):
ts = int(key.split(":")[-1])
if ts >= cutoff_ts:
data = await self.redis.lrange(key, 0, -1)
for item in data:
m = json.loads(item)
all_latencies.append(m["latency_ms"])
if not m["success"]:
error_count += 1
total_count += 1
hour_bucket = datetime.fromtimestamp(ts).strftime("%Y-%m-%d %H:00")
hourly_buckets[hour_bucket].append(m["latency_ms"])
return {
"exchange": exchange,
"total_requests": total_count,
"error_rate": error_count / total_count if total_count > 0 else 0,
"latency_avg": statistics.mean(all_latencies) if all_latencies else 0,
"latency_p95": sorted(all_latencies)[int(len(all_latencies) * 0.95)] if all_latencies else 0,
"latency_p99": sorted(all_latencies)[int(len(all_latencies) * 0.99)] if all_latencies else 0,
"hourly_patterns": dict(hourly_buckets),
"analyzed_hours": hours
}
async def detect_trends(self, exchange: str) -> dict:
"""Erkennt Trends in den Metriken"""
current = await self.get_aggregated_metrics(exchange, hours=1)
last_24h = await self.get_aggregated_metrics(exchange, hours=24)
latency_trend = "stable"
if current["latency_avg"] > last_24h["latency_avg"] * 1.5:
latency_trend = "increasing"
elif current["latency_avg"] < last_24h["latency_avg"] * 0.7:
latency_trend = "decreasing"
error_trend = "stable"
if current["error_rate"] > last_24h["error_rate"] * 2:
error_trend = "worsening"
elif current["error_rate"] < last_24h["error_rate"] * 0.5:
error_trend = "improving"
return {
"latency_trend": latency_trend,
"error_trend": error_trend,
"current_latency": current["latency_avg"],
"baseline_latency": last_24h["latency_avg"],
"current_error_rate": current["error_rate"],
"baseline_error_rate": last_24h["error_rate"]
}
async def predict_failures(self, exchange: str) -> str:
"""Nutzt HolySheep AI für prädiktive Analyse"""
trends = await self.detect_trends(exchange)
metrics = await self.get_aggregated_metrics(exchange, hours=24)
prompt = f"""
Du bist ein Krypto-API-SRE-Experte. Analysiere folgende Metriken für {exchange}:
Trends:
- Latenz-Trend: {trends['latency_trend']}
- Fehler-Trend: {trends['error_trend']}
- Aktuelle Latenz: {trends['current_latency']:.1f}ms (Baseline: {trends['baseline_latency']:.1f}ms)
- Aktuelle Fehlerrate: {trends['current_error_rate']:.3f} (Baseline: {trends['baseline_error_rate']:.3f})
Historische Daten:
- Gesamt-Anfragen (24h): {metrics['total_requests']}
- P95-Latenz: {metrics['latency_p95']:.1f}ms
- P99-Latenz: {metrics['latency_p99']:.1f}ms
Hourly Patterns:
{json.dumps(metrics['hourly_patterns'], indent=2)}
Berechne:
1. Wahrscheinlichkeit eines Ausfalls in den nächsten 6 Stunden (0-100%)
2. Hauptursache basierend auf den Mustern
3. Konkrete Handlungsempfehlungen
Antworte im JSON-Format:
{{"failure_probability": 0-100, "primary_cause": "...", "recommendations": ["..."], "confidence": "high/medium/low"}}
"""
import aiohttp
async with aiohttp.ClientSession() as session:
async with session.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v3.2", # Kostengünstig für kontinuierliche Analyse
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 400,
"temperature": 0.2
}
) as resp:
if resp.status == 200:
data = await resp.json()
return data["choices"][0]["message"]["content"]
else:
error_text = await resp.text()
return json.dumps({
"failure_probability": 50,
"primary_cause": "Analyse nicht verfügbar",
"recommendations": ["Manuelle Prüfung empfohlen"],
"confidence": "low",
"error": error_text
})
async def run_prediction_loop(self, interval: int = 300):
"""Vorhersage-Schleife: alle 5 Minuten"""
while True:
for exchange in ["binance", "coinbase", "kraken"]:
prediction = await self.predict_failures(exchange)
pred_data = json.loads(prediction)
# Speichere Vorhersage
pred_key = f"prediction:{exchange}:{int(datetime.utcnow().timestamp())}"
await self.redis.set(pred_key, json.dumps({
"prediction": pred_data,
"timestamp": datetime.utcnow().isoformat()
}), ex=7200)
# Bei hoher Ausfallwahrscheinlichkeit: proaktiver Alert
if pred_data.get("failure_probability", 0) > 70:
alert_key = f"alerts:{exchange}:{int(datetime.utcnow().timestamp())}"
await self.redis.set(alert_key, json.dumps({
"anomalies": [{
"type": "predicted_failure",
"severity": "warning",
"exchange": exchange,
"endpoint": "