This commit is contained in:
Melchior Reimers
@@ -3,9 +3,8 @@ import logging
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import datetime
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import os
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import requests
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from typing import Dict, List, Optional
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from typing import Dict, List, Tuple, Optional
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import pandas as pd
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import json
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logging.basicConfig(
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level=logging.INFO,
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@@ -24,6 +23,7 @@ TIME_PERIODS = [7, 30, 42, 69, 180, 365]
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class AnalyticsWorker:
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def __init__(self):
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self.last_processed_timestamp = None
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self.db_url = DB_URL
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def wait_for_questdb(self, max_retries: int = 30, retry_delay: int = 2):
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@@ -41,316 +41,280 @@ class AnalyticsWorker:
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logger.error("QuestDB did not become available after waiting")
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return False
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def query_questdb(self, query: str, timeout: int = 30) -> Optional[Dict]:
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"""Zentrale QuestDB-Abfrage-Funktion"""
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def get_last_processed_timestamp(self) -> Optional[datetime.datetime]:
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"""Holt den letzten verarbeiteten Timestamp aus der Analytics-Tabelle"""
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try:
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response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH, timeout=timeout)
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query = "select max(timestamp) as last_ts from analytics_exchange_daily"
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response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
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if response.status_code == 200:
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return response.json()
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else:
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logger.error(f"QuestDB query failed: {response.status_code} - {response.text}")
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return None
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data = response.json()
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if data.get('dataset') and data['dataset'] and len(data['dataset']) > 0 and data['dataset'][0][0]:
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ts_value = data['dataset'][0][0]
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if isinstance(ts_value, str):
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return datetime.datetime.fromisoformat(ts_value.replace('Z', '+00:00'))
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elif isinstance(ts_value, (int, float)):
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# QuestDB gibt Timestamps in Mikrosekunden zurück
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return datetime.datetime.fromtimestamp(ts_value / 1000000, tz=datetime.timezone.utc)
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except Exception as e:
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logger.error(f"Error querying QuestDB: {e}")
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return None
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logger.debug(f"Could not get last processed timestamp: {e}")
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return None
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def get_existing_dates(self, table_name: str) -> set:
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"""Holt alle bereits berechneten Daten aus einer Analytics-Tabelle"""
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query = f"select distinct date_trunc('day', timestamp) as date from {table_name}"
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data = self.query_questdb(query)
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if not data or not data.get('dataset'):
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return set()
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dates = set()
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for row in data['dataset']:
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if row[0]:
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if isinstance(row[0], str):
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dates.add(datetime.datetime.fromisoformat(row[0].replace('Z', '+00:00')).date())
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elif isinstance(row[0], (int, float)):
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dates.add(datetime.datetime.fromtimestamp(row[0] / 1000000, tz=datetime.timezone.utc).date())
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return dates
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def get_missing_dates(self) -> List[datetime.date]:
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"""Ermittelt fehlende Tage, die noch berechnet werden müssen"""
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# Hole das Datum des ersten Trades
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query = "select min(date_trunc('day', timestamp)) as first_date from trades"
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data = self.query_questdb(query)
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if not data or not data.get('dataset') or not data['dataset'][0][0]:
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logger.info("No trades found in database")
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return []
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first_date_value = data['dataset'][0][0]
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if isinstance(first_date_value, str):
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first_date = datetime.datetime.fromisoformat(first_date_value.replace('Z', '+00:00')).date()
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def get_new_trades(self, since: Optional[datetime.datetime] = None) -> List[Dict]:
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"""Holt neue Trades seit dem letzten Verarbeitungszeitpunkt"""
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if since:
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since_str = since.strftime('%Y-%m-%d %H:%M:%S')
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query = f"select timestamp, exchange, isin, price, quantity from trades where timestamp > '{since_str}' order by timestamp asc"
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else:
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first_date = datetime.datetime.fromtimestamp(first_date_value / 1000000, tz=datetime.timezone.utc).date()
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# Erste Ausführung: nur die letzten 7 Tage
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query = f"select timestamp, exchange, isin, price, quantity from trades where timestamp > dateadd('d', -7, now()) order by timestamp asc"
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# Hole bereits berechnete Daten
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existing_dates = self.get_existing_dates('analytics_daily_summary')
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try:
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response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
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if response.status_code == 200:
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data = response.json()
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columns = data.get('columns', [])
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dataset = data.get('dataset', [])
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# Generiere alle Tage vom ersten Trade bis gestern
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yesterday = datetime.date.today() - datetime.timedelta(days=1)
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all_dates = []
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current = first_date
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while current <= yesterday:
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all_dates.append(current)
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current += datetime.timedelta(days=1)
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trades = []
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for row in dataset:
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trade = {}
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for i, col in enumerate(columns):
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trade[col['name']] = row[i]
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trades.append(trade)
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return trades
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except Exception as e:
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logger.error(f"Error fetching new trades: {e}")
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return []
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# Finde fehlende Tage
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missing_dates = [d for d in all_dates if d not in existing_dates]
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logger.info(f"Found {len(missing_dates)} missing dates to calculate (from {len(all_dates)} total dates)")
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return sorted(missing_dates)
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def calculate_exchange_daily_aggregations(self, days_back: int = 365) -> List[Dict]:
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"""Berechnet tägliche Aggregationen je Exchange mit Moving Averages"""
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end_date = datetime.datetime.now(datetime.timezone.utc)
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start_date = end_date - datetime.timedelta(days=days_back)
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def calculate_daily_summary(self, date: datetime.date) -> Optional[Dict]:
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"""Berechnet tägliche Zusammenfassung für einen Tag"""
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date_str = date.strftime('%Y-%m-%d')
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query = f"""
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select
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count(*) as total_trades,
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sum(price * quantity) as total_volume,
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exchange,
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count(*) as exchange_trades
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from trades
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where date_trunc('day', timestamp) = '{date_str}'
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group by exchange
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"""
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data = self.query_questdb(query)
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if not data or not data.get('dataset'):
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return None
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total_trades = 0
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total_volume = 0.0
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exchanges = {}
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for row in data['dataset']:
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exchange = row[2]
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trades = row[3] if row[3] else 0
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volume = row[1] if row[1] else 0.0
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total_trades += trades
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total_volume += volume
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exchanges[exchange] = {'trades': trades, 'volume': volume}
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return {
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'date': date,
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'total_trades': total_trades,
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'total_volume': total_volume,
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'exchanges': json.dumps(exchanges)
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}
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def calculate_exchange_daily(self, date: datetime.date) -> List[Dict]:
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"""Berechnet tägliche Exchange-Statistiken mit Moving Averages"""
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date_str = date.strftime('%Y-%m-%d')
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# Hole Daten für diesen Tag
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query = f"""
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select
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date_trunc('day', timestamp) as date,
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exchange,
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count(*) as trade_count,
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sum(price * quantity) as volume
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from trades
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where date_trunc('day', timestamp) = '{date_str}'
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group by exchange
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where timestamp >= '{start_date.strftime('%Y-%m-%d')}'
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group by date, exchange
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order by date asc, exchange asc
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"""
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data = self.query_questdb(query)
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if not data or not data.get('dataset'):
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return []
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try:
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response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
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if response.status_code == 200:
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data = response.json()
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columns = data.get('columns', [])
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dataset = data.get('dataset', [])
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results = []
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for row in data['dataset']:
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exchange = row[0]
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trade_count = row[1] if row[1] else 0
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volume = row[2] if row[2] else 0.0
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results = []
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for row in dataset:
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result = {}
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for i, col in enumerate(columns):
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result[col['name']] = row[i]
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results.append(result)
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# Berechne Moving Averages für alle Zeiträume
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ma_values = {}
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for period in TIME_PERIODS:
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# Hole Daten der letzten N Tage inklusive heute
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end_date = date
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start_date = end_date - datetime.timedelta(days=period-1)
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# Berechne Moving Averages für alle Zeiträume
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df = pd.DataFrame(results)
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if df.empty:
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return []
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ma_query = f"""
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select
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count(*) as ma_count,
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sum(price * quantity) as ma_volume
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from trades
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where exchange = '{exchange}'
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and date_trunc('day', timestamp) >= '{start_date.strftime('%Y-%m-%d')}'
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and date_trunc('day', timestamp) <= '{end_date.strftime('%Y-%m-%d')}'
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"""
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# Pivot für einfachere MA-Berechnung
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df['date'] = pd.to_datetime(df['date'])
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df = df.sort_values(['date', 'exchange'])
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ma_data = self.query_questdb(ma_query)
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if ma_data and ma_data.get('dataset') and ma_data['dataset'][0]:
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ma_values[f'ma{period}_count'] = ma_data['dataset'][0][0] if ma_data['dataset'][0][0] else 0
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ma_values[f'ma{period}_volume'] = ma_data['dataset'][0][1] if ma_data['dataset'][0][1] else 0.0
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else:
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ma_values[f'ma{period}_count'] = 0
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ma_values[f'ma{period}_volume'] = 0.0
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# Berechne MA für jeden Zeitraum
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for period in TIME_PERIODS:
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df[f'ma{period}_count'] = df.groupby('exchange')['trade_count'].transform(
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lambda x: x.rolling(window=period, min_periods=1).mean()
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)
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df[f'ma{period}_volume'] = df.groupby('exchange')['volume'].transform(
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lambda x: x.rolling(window=period, min_periods=1).mean()
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)
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results.append({
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'date': date,
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'exchange': exchange,
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'trade_count': trade_count,
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'volume': volume,
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**ma_values
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})
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# Konvertiere zurück zu Dict-Liste
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return df.to_dict('records')
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except Exception as e:
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logger.error(f"Error calculating exchange daily aggregations: {e}")
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return []
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return results
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def calculate_stock_trends(self, days: int = 365) -> List[Dict]:
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"""Berechnet Trenddaten je ISIN mit Änderungsprozenten"""
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end_date = datetime.datetime.now(datetime.timezone.utc)
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start_date = end_date - datetime.timedelta(days=days)
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def calculate_stock_trends(self, date: datetime.date) -> List[Dict]:
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"""Berechnet Stock-Trends für alle Zeiträume für einen Tag"""
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results = []
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# Aktuelle Periode
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query_current = f"""
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select
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date_trunc('day', timestamp) as date,
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isin,
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count(*) as trade_count,
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sum(price * quantity) as volume
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from trades
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where timestamp >= '{start_date.strftime('%Y-%m-%d')}'
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group by date, isin
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order by date asc, isin asc
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"""
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for period in TIME_PERIODS:
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end_date = date
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start_date = end_date - datetime.timedelta(days=period-1)
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try:
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response = requests.get(f"{self.db_url}/exec", params={'query': query_current}, auth=DB_AUTH)
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if response.status_code == 200:
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data = response.json()
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columns = data.get('columns', [])
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dataset = data.get('dataset', [])
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# Aktuelle Periode
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query = f"""
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select
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isin,
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count(*) as trade_count,
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sum(price * quantity) as volume
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from trades
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where date_trunc('day', timestamp) >= '{start_date.strftime('%Y-%m-%d')}'
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and date_trunc('day', timestamp) <= '{end_date.strftime('%Y-%m-%d')}'
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group by isin
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"""
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results = []
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for row in dataset:
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result = {}
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for i, col in enumerate(columns):
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result[col['name']] = row[i]
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results.append(result)
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data = self.query_questdb(query)
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if not data or not data.get('dataset'):
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continue
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if not results:
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return []
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for row in data['dataset']:
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isin = row[0]
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current_count = row[1] if row[1] else 0
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current_volume = row[2] if row[2] else 0.0
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df = pd.DataFrame(results)
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df['date'] = pd.to_datetime(df['date'])
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# Vorherige Periode für Vergleich
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prev_start = start_date - datetime.timedelta(days=period)
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prev_end = start_date - datetime.timedelta(days=1)
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# Aggregiere je ISIN über den gesamten Zeitraum
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df_agg = df.groupby('isin').agg({
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'trade_count': 'sum',
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'volume': 'sum'
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}).reset_index()
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prev_query = f"""
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select
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count(*) as trade_count,
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sum(price * quantity) as volume
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from trades
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where isin = '{isin}'
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and date_trunc('day', timestamp) >= '{prev_start.strftime('%Y-%m-%d')}'
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and date_trunc('day', timestamp) <= '{prev_end.strftime('%Y-%m-%d')}'
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"""
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# Berechne Änderungen: Vergleich mit vorheriger Periode
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# Für jede ISIN: aktueller Zeitraum vs. vorheriger Zeitraum
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trends = []
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for isin in df_agg['isin'].unique():
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isin_data = df[df['isin'] == isin].sort_values('date')
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prev_data = self.query_questdb(prev_query)
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prev_count = 0
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prev_volume = 0.0
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# Teile in zwei Hälften für Vergleich
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mid_point = len(isin_data) // 2
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if mid_point > 0:
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first_half = isin_data.iloc[:mid_point]
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second_half = isin_data.iloc[mid_point:]
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if prev_data and prev_data.get('dataset') and prev_data['dataset'][0]:
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prev_count = prev_data['dataset'][0][0] if prev_data['dataset'][0][0] else 0
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prev_volume = prev_data['dataset'][0][1] if prev_data['dataset'][0][1] else 0.0
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first_count = first_half['trade_count'].sum()
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first_volume = first_half['volume'].sum()
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second_count = second_half['trade_count'].sum()
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second_volume = second_half['volume'].sum()
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# Berechne Änderungen
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count_change_pct = ((current_count - prev_count) / prev_count * 100) if prev_count > 0 else 0
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volume_change_pct = ((current_volume - prev_volume) / prev_volume * 100) if prev_volume > 0 else 0
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count_change = ((second_count - first_count) / first_count * 100) if first_count > 0 else 0
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volume_change = ((second_volume - first_volume) / first_volume * 100) if first_volume > 0 else 0
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else:
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count_change = 0
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volume_change = 0
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results.append({
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'date': date,
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'period_days': period,
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'isin': isin,
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'trade_count': current_count,
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'volume': current_volume,
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'count_change_pct': count_change_pct,
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'volume_change_pct': volume_change_pct
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})
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total_count = isin_data['trade_count'].sum()
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total_volume = isin_data['volume'].sum()
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return results
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trends.append({
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'isin': isin,
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'date': isin_data['date'].max(),
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'trade_count': total_count,
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'volume': total_volume,
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'count_change_pct': count_change,
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'volume_change_pct': volume_change
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})
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def calculate_volume_changes(self, date: datetime.date) -> List[Dict]:
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"""Berechnet Volumen-Änderungen für alle Zeiträume für einen Tag"""
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results = []
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return trends
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except Exception as e:
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logger.error(f"Error calculating stock trends: {e}")
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return []
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for period in TIME_PERIODS:
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end_date = date
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start_date = end_date - datetime.timedelta(days=period-1)
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def calculate_volume_changes(self, days: int = 365) -> List[Dict]:
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"""Berechnet Volumen- und Anzahl-Änderungen je Exchange"""
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end_date = datetime.datetime.now(datetime.timezone.utc)
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start_date = end_date - datetime.timedelta(days=days)
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# Hole alle Exchanges
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exchanges_query = "select distinct exchange from trades"
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exchanges_data = self.query_questdb(exchanges_query)
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if not exchanges_data or not exchanges_data.get('dataset'):
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continue
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query = f"""
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select
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date_trunc('day', timestamp) as date,
|
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exchange,
|
||||
count(*) as trade_count,
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||||
sum(price * quantity) as volume
|
||||
from trades
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where timestamp >= '{start_date.strftime('%Y-%m-%d')}'
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group by date, exchange
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order by date asc, exchange asc
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"""
|
||||
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||||
for exchange_row in exchanges_data['dataset']:
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exchange = exchange_row[0]
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try:
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||||
response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
|
||||
if response.status_code == 200:
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||||
data = response.json()
|
||||
columns = data.get('columns', [])
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||||
dataset = data.get('dataset', [])
|
||||
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||||
# Aktuelle Periode
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||||
query = f"""
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select
|
||||
count(*) as trade_count,
|
||||
sum(price * quantity) as volume
|
||||
from trades
|
||||
where exchange = '{exchange}'
|
||||
and date_trunc('day', timestamp) >= '{start_date.strftime('%Y-%m-%d')}'
|
||||
and date_trunc('day', timestamp) <= '{end_date.strftime('%Y-%m-%d')}'
|
||||
"""
|
||||
results = []
|
||||
for row in dataset:
|
||||
result = {}
|
||||
for i, col in enumerate(columns):
|
||||
result[col['name']] = row[i]
|
||||
results.append(result)
|
||||
|
||||
data = self.query_questdb(query)
|
||||
if not data or not data.get('dataset') or not data['dataset'][0]:
|
||||
continue
|
||||
if not results:
|
||||
return []
|
||||
|
||||
current_count = data['dataset'][0][0] if data['dataset'][0][0] else 0
|
||||
current_volume = data['dataset'][0][1] if data['dataset'][0][1] else 0.0
|
||||
df = pd.DataFrame(results)
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
df = df.sort_values(['date', 'exchange'])
|
||||
|
||||
# Vorherige Periode
|
||||
prev_start = start_date - datetime.timedelta(days=period)
|
||||
prev_end = start_date - datetime.timedelta(days=1)
|
||||
# Berechne Änderungen je Exchange
|
||||
changes = []
|
||||
for exchange in df['exchange'].unique():
|
||||
exchange_data = df[df['exchange'] == exchange].sort_values('date')
|
||||
|
||||
prev_query = f"""
|
||||
select
|
||||
count(*) as trade_count,
|
||||
sum(price * quantity) as volume
|
||||
from trades
|
||||
where exchange = '{exchange}'
|
||||
and date_trunc('day', timestamp) >= '{prev_start.strftime('%Y-%m-%d')}'
|
||||
and date_trunc('day', timestamp) <= '{prev_end.strftime('%Y-%m-%d')}'
|
||||
"""
|
||||
# Teile in zwei Hälften
|
||||
mid_point = len(exchange_data) // 2
|
||||
if mid_point > 0:
|
||||
first_half = exchange_data.iloc[:mid_point]
|
||||
second_half = exchange_data.iloc[mid_point:]
|
||||
|
||||
prev_data = self.query_questdb(prev_query)
|
||||
prev_count = 0
|
||||
prev_volume = 0.0
|
||||
first_count = first_half['trade_count'].sum()
|
||||
first_volume = first_half['volume'].sum()
|
||||
second_count = second_half['trade_count'].sum()
|
||||
second_volume = second_half['volume'].sum()
|
||||
|
||||
if prev_data and prev_data.get('dataset') and prev_data['dataset'][0]:
|
||||
prev_count = prev_data['dataset'][0][0] if prev_data['dataset'][0][0] else 0
|
||||
prev_volume = prev_data['dataset'][0][1] if prev_data['dataset'][0][1] else 0.0
|
||||
count_change = ((second_count - first_count) / first_count * 100) if first_count > 0 else 0
|
||||
volume_change = ((second_volume - first_volume) / first_volume * 100) if first_volume > 0 else 0
|
||||
|
||||
# Berechne Änderungen
|
||||
count_change_pct = ((current_count - prev_count) / prev_count * 100) if prev_count > 0 else 0
|
||||
volume_change_pct = ((current_volume - prev_volume) / prev_volume * 100) if prev_volume > 0 else 0
|
||||
# Bestimme Trend
|
||||
if count_change > 5 and volume_change > 5:
|
||||
trend = "mehr_trades_mehr_volumen"
|
||||
elif count_change > 5 and volume_change < -5:
|
||||
trend = "mehr_trades_weniger_volumen"
|
||||
elif count_change < -5 and volume_change > 5:
|
||||
trend = "weniger_trades_mehr_volumen"
|
||||
elif count_change < -5 and volume_change < -5:
|
||||
trend = "weniger_trades_weniger_volumen"
|
||||
else:
|
||||
trend = "stabil"
|
||||
else:
|
||||
count_change = 0
|
||||
volume_change = 0
|
||||
trend = "stabil"
|
||||
|
||||
# Bestimme Trend
|
||||
if count_change_pct > 5 and volume_change_pct > 5:
|
||||
trend = "mehr_trades_mehr_volumen"
|
||||
elif count_change_pct > 5 and volume_change_pct < -5:
|
||||
trend = "mehr_trades_weniger_volumen"
|
||||
elif count_change_pct < -5 and volume_change_pct > 5:
|
||||
trend = "weniger_trades_mehr_volumen"
|
||||
elif count_change_pct < -5 and volume_change_pct < -5:
|
||||
trend = "weniger_trades_weniger_volumen"
|
||||
else:
|
||||
trend = "stabil"
|
||||
total_count = exchange_data['trade_count'].sum()
|
||||
total_volume = exchange_data['volume'].sum()
|
||||
|
||||
results.append({
|
||||
'date': date,
|
||||
'period_days': period,
|
||||
'exchange': exchange,
|
||||
'trade_count': current_count,
|
||||
'volume': current_volume,
|
||||
'count_change_pct': count_change_pct,
|
||||
'volume_change_pct': volume_change_pct,
|
||||
'trend': trend
|
||||
})
|
||||
changes.append({
|
||||
'date': exchange_data['date'].max(),
|
||||
'exchange': exchange,
|
||||
'trade_count': total_count,
|
||||
'volume': total_volume,
|
||||
'count_change_pct': count_change,
|
||||
'volume_change_pct': volume_change,
|
||||
'trend': trend
|
||||
})
|
||||
|
||||
return results
|
||||
return changes
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating volume changes: {e}")
|
||||
return []
|
||||
|
||||
def save_analytics_data(self, table_name: str, data: List[Dict]):
|
||||
"""Speichert aggregierte Daten in QuestDB via ILP"""
|
||||
@@ -362,10 +326,10 @@ class AnalyticsWorker:
|
||||
try:
|
||||
# Konvertiere Datum zu Timestamp
|
||||
if 'date' in row:
|
||||
if isinstance(row['date'], datetime.date):
|
||||
dt = datetime.datetime.combine(row['date'], datetime.time.min).replace(tzinfo=datetime.timezone.utc)
|
||||
elif isinstance(row['date'], str):
|
||||
if isinstance(row['date'], str):
|
||||
dt = datetime.datetime.fromisoformat(row['date'].replace('Z', '+00:00'))
|
||||
elif isinstance(row['date'], pd.Timestamp):
|
||||
dt = row['date'].to_pydatetime()
|
||||
else:
|
||||
dt = row['date']
|
||||
timestamp_ns = int(dt.timestamp() * 1e9)
|
||||
@@ -386,32 +350,30 @@ class AnalyticsWorker:
|
||||
isin = str(row['isin']).replace(' ', '\\ ').replace(',', '\\,')
|
||||
tags.append(f"isin={isin}")
|
||||
|
||||
# Period als Tag
|
||||
if 'period_days' in row and row['period_days']:
|
||||
tags.append(f"period_days={row['period_days']}")
|
||||
|
||||
# Trend als Tag
|
||||
if 'trend' in row and row['trend']:
|
||||
trend = str(row['trend']).replace(' ', '\\ ').replace(',', '\\,')
|
||||
tags.append(f"trend={trend}")
|
||||
|
||||
# Numerische Felder
|
||||
# Numerische Felder (period_days muss als Feld gespeichert werden, nicht als Tag)
|
||||
for key, value in row.items():
|
||||
if key in ['date', 'exchange', 'isin', 'trend', 'period_days', 'exchanges']:
|
||||
if key in ['date', 'exchange', 'isin', 'trend', 'exchanges']:
|
||||
continue
|
||||
if value is not None:
|
||||
if isinstance(value, (int, float)):
|
||||
fields.append(f"{key}={value}")
|
||||
elif isinstance(value, str):
|
||||
# String-Felder in Anführungszeichen
|
||||
escaped = value.replace('"', '\\"').replace(' ', '\\ ')
|
||||
fields.append(f'{key}="{escaped}"')
|
||||
|
||||
# Exchanges als JSON-Feld
|
||||
if 'exchanges' in row and row['exchanges']:
|
||||
fields.append(f'exchanges="{row["exchanges"]}"')
|
||||
|
||||
if tags and fields:
|
||||
line = f"{table_name},{','.join(tags)} {','.join(fields)} {timestamp_ns}"
|
||||
# Erstelle Line - auch wenn keine Tags vorhanden sind (nur Fields)
|
||||
if fields:
|
||||
if tags:
|
||||
line = f"{table_name},{','.join(tags)} {','.join(fields)} {timestamp_ns}"
|
||||
else:
|
||||
# Manche Tabellen haben keine Tags, nur Fields
|
||||
line = f"{table_name} {','.join(fields)} {timestamp_ns}"
|
||||
lines.append(line)
|
||||
except Exception as e:
|
||||
logger.error(f"Error formatting row for {table_name}: {e}, row: {row}")
|
||||
@@ -436,46 +398,37 @@ class AnalyticsWorker:
|
||||
except Exception as e:
|
||||
logger.error(f"Error connecting to QuestDB: {e}")
|
||||
|
||||
def process_date(self, date: datetime.date):
|
||||
"""Verarbeitet alle Analytics für einen bestimmten Tag"""
|
||||
logger.info(f"Processing analytics for {date}")
|
||||
def process_all_analytics(self):
|
||||
"""Verarbeitet alle Analytics für alle Zeiträume"""
|
||||
logger.info("Starting analytics processing...")
|
||||
|
||||
# 1. Daily Summary
|
||||
summary = self.calculate_daily_summary(date)
|
||||
if summary:
|
||||
self.save_analytics_data('analytics_daily_summary', [summary])
|
||||
|
||||
# 2. Exchange Daily
|
||||
exchange_data = self.calculate_exchange_daily(date)
|
||||
# 1. Exchange Daily Aggregations (für alle Zeiträume)
|
||||
logger.info("Calculating exchange daily aggregations...")
|
||||
exchange_data = self.calculate_exchange_daily_aggregations(days_back=365)
|
||||
if exchange_data:
|
||||
self.save_analytics_data('analytics_exchange_daily', exchange_data)
|
||||
|
||||
# 3. Stock Trends
|
||||
stock_trends = self.calculate_stock_trends(date)
|
||||
if stock_trends:
|
||||
self.save_analytics_data('analytics_stock_trends', stock_trends)
|
||||
# 2. Stock Trends (für alle Zeiträume)
|
||||
logger.info("Calculating stock trends...")
|
||||
for days in TIME_PERIODS:
|
||||
trends = self.calculate_stock_trends(days=days)
|
||||
if trends:
|
||||
# Füge Zeitraum als Tag hinzu
|
||||
for trend in trends:
|
||||
trend['period_days'] = days
|
||||
self.save_analytics_data('analytics_stock_trends', trends)
|
||||
|
||||
# 4. Volume Changes
|
||||
volume_changes = self.calculate_volume_changes(date)
|
||||
if volume_changes:
|
||||
self.save_analytics_data('analytics_volume_changes', volume_changes)
|
||||
# 3. Volume Changes (für alle Zeiträume)
|
||||
logger.info("Calculating volume changes...")
|
||||
for days in TIME_PERIODS:
|
||||
changes = self.calculate_volume_changes(days=days)
|
||||
if changes:
|
||||
# Füge Zeitraum als Tag hinzu
|
||||
for change in changes:
|
||||
change['period_days'] = days
|
||||
self.save_analytics_data('analytics_volume_changes', changes)
|
||||
|
||||
logger.info(f"Completed processing for {date}")
|
||||
|
||||
def process_missing_dates(self):
|
||||
"""Berechnet alle fehlenden Tage"""
|
||||
missing_dates = self.get_missing_dates()
|
||||
if not missing_dates:
|
||||
logger.info("No missing dates to process")
|
||||
return
|
||||
|
||||
logger.info(f"Processing {len(missing_dates)} missing dates...")
|
||||
for i, date in enumerate(missing_dates, 1):
|
||||
logger.info(f"Processing date {i}/{len(missing_dates)}: {date}")
|
||||
self.process_date(date)
|
||||
# Kleine Pause zwischen den Berechnungen
|
||||
if i % 10 == 0:
|
||||
time.sleep(1)
|
||||
logger.info("Analytics processing completed.")
|
||||
|
||||
def run(self):
|
||||
"""Hauptschleife des Workers"""
|
||||
@@ -486,30 +439,32 @@ class AnalyticsWorker:
|
||||
logger.error("Failed to connect to QuestDB. Exiting.")
|
||||
return
|
||||
|
||||
# Initiale Berechnung fehlender Tage
|
||||
logger.info("Checking for missing dates...")
|
||||
self.process_missing_dates()
|
||||
# Initiale Verarbeitung
|
||||
self.process_all_analytics()
|
||||
self.last_processed_timestamp = datetime.datetime.now(datetime.timezone.utc)
|
||||
|
||||
# Hauptschleife: Warte auf Mitternacht
|
||||
logger.info("Waiting for midnight to process yesterday's data...")
|
||||
# Polling-Schleife
|
||||
while True:
|
||||
now = datetime.datetime.now()
|
||||
try:
|
||||
# Prüfe auf neue Trades
|
||||
last_ts = self.get_last_processed_timestamp()
|
||||
new_trades = self.get_new_trades(since=last_ts)
|
||||
|
||||
# Prüfe ob es Mitternacht ist (00:00)
|
||||
if now.hour == 0 and now.minute == 0:
|
||||
yesterday = (now - datetime.timedelta(days=1)).date()
|
||||
logger.info(f"Processing yesterday's data: {yesterday}")
|
||||
self.process_date(yesterday)
|
||||
# Warte 61s, um Mehrfachausführung zu verhindern
|
||||
time.sleep(61)
|
||||
if new_trades:
|
||||
logger.info(f"Found {len(new_trades)} new trades, reprocessing analytics...")
|
||||
self.process_all_analytics()
|
||||
self.last_processed_timestamp = datetime.datetime.now(datetime.timezone.utc)
|
||||
else:
|
||||
logger.debug("No new trades found.")
|
||||
|
||||
# Prüfe auch auf fehlende Tage (alle 6 Stunden)
|
||||
if now.hour % 6 == 0 and now.minute == 0:
|
||||
logger.info("Checking for missing dates...")
|
||||
self.process_missing_dates()
|
||||
time.sleep(61)
|
||||
|
||||
time.sleep(30)
|
||||
# Warte 30 Sekunden vor nächster Prüfung
|
||||
time.sleep(30)
|
||||
except requests.exceptions.ConnectionError as e:
|
||||
logger.warning(f"Connection error to QuestDB, retrying in 60s: {e}")
|
||||
time.sleep(60) # Längere Pause bei Verbindungsfehler
|
||||
except Exception as e:
|
||||
logger.error(f"Error in worker loop: {e}", exc_info=True)
|
||||
time.sleep(60) # Längere Pause bei Fehler
|
||||
|
||||
def main():
|
||||
worker = AnalyticsWorker()
|
||||
|
||||
Reference in New Issue
Block a user