trading-daemon/daemon.py

import time
import logging
import datetime
import hashlib
import os
import requests
from src.exchanges.eix import EIXExchange
from src.exchanges.ls import LSExchange
from src.exchanges.deutsche_boerse import XetraExchange, FrankfurtExchange, QuotrixExchange
from src.exchanges.gettex import GettexExchange
from src.exchanges.stuttgart import StuttgartExchange
from src.exchanges.boersenag import (
    DUSAExchange, DUSBExchange, DUSCExchange, DUSDExchange,
    HAMAExchange, HAMBExchange, HANAExchange, HANBExchange
)
from src.database.questdb_client import DatabaseClient

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger("TradingDaemon")

DB_USER = os.getenv("DB_USER", "admin")
DB_PASSWORD = os.getenv("DB_PASSWORD", "quest")
DB_AUTH = (DB_USER, DB_PASSWORD) if DB_USER and DB_PASSWORD else None

def get_trade_hash(trade):
    """Erstellt einen eindeutigen Hash für einen Trade."""
    key = f"{trade.exchange}|{trade.isin}|{trade.timestamp.isoformat()}|{trade.price}|{trade.quantity}"
    return hashlib.md5(key.encode()).hexdigest()

def filter_new_trades_batch(db_url, exchange_name, trades, batch_size=1000):
    """Filtert neue Trades in Batches, um RAM zu sparen. Verwendet Batch-Queries statt einzelne Checks."""
    if not trades:
        return []
    
    new_trades = []
    total_batches = (len(trades) + batch_size - 1) // batch_size
    
    for batch_idx in range(0, len(trades), batch_size):
        batch = trades[batch_idx:batch_idx + batch_size]
        batch_num = (batch_idx // batch_size) + 1
        
        if batch_num % 10 == 0 or batch_num == 1:
            logger.info(f"Processing batch {batch_num}/{total_batches} ({len(batch)} trades)...")
        
        # Gruppiere Trades nach Tag für effizientere Queries
        trades_by_day = {}
        for trade in batch:
            day = trade.timestamp.replace(hour=0, minute=0, second=0, microsecond=0)
            if day not in trades_by_day:
                trades_by_day[day] = []
            trades_by_day[day].append(trade)
        
        # Prüfe jeden Tag separat
        for day, day_trades in trades_by_day.items():
            day_start_str = day.strftime('%Y-%m-%dT%H:%M:%S.000000Z')
            day_end = day + datetime.timedelta(days=1)
            day_end_str = day_end.strftime('%Y-%m-%dT%H:%M:%S.000000Z')
            
            # Hole alle existierenden Trades für diesen Tag
            query = f"""
            SELECT isin, timestamp, price, quantity 
            FROM trades 
            WHERE exchange = '{exchange_name}' 
            AND timestamp >= '{day_start_str}'
            AND timestamp < '{day_end_str}'
            """
            
            try:
                response = requests.get(f"{db_url}/exec", params={'query': query}, auth=DB_AUTH, timeout=30)
                if response.status_code == 200:
                    data = response.json()
                    existing_trades = set()
                    if data.get('dataset'):
                        for row in data['dataset']:
                            isin, ts, price, qty = row
                            # Normalisiere Timestamp für Vergleich
                            if isinstance(ts, str):
                                ts_dt = datetime.datetime.fromisoformat(ts.replace('Z', '+00:00'))
                            else:
                                ts_dt = datetime.datetime.fromtimestamp(ts / 1000000, tz=datetime.timezone.utc)
                            # Erstelle Vergleichs-Key (ohne Hash, direkter Vergleich)
                            key = (isin, ts_dt.isoformat(), float(price), float(qty))
                            existing_trades.add(key)
                    
                    # Prüfe welche Trades neu sind
                    for trade in day_trades:
                        trade_key = (trade.isin, trade.timestamp.isoformat(), float(trade.price), float(trade.quantity))
                        if trade_key not in existing_trades:
                            new_trades.append(trade)
                else:
                    # Bei Fehler: alle Trades als neu behandeln (sicherer)
                    logger.warning(f"Query failed for day {day}, treating all trades as new")
                    new_trades.extend(day_trades)
            except Exception as e:
                # Bei Fehler: alle Trades als neu behandeln (sicherer)
                logger.warning(f"Error checking trades for day {day}: {e}, treating all trades as new")
                new_trades.extend(day_trades)
        
        # Kleine Pause zwischen Batches, um DB nicht zu überlasten
        if batch_idx + batch_size < len(trades):
            time.sleep(0.05)
    
    return new_trades

def get_last_trade_timestamp(db_url, exchange_name):
    # QuestDB query: get the latest timestamp for a specific exchange
    query = f"trades where exchange = '{exchange_name}' latest by timestamp"
    try:
        # Using the /exec endpoint to get data
        response = requests.get(f"{db_url}/exec", params={'query': query}, auth=DB_AUTH)
        if response.status_code == 200:
            data = response.json()
            if data['dataset']:
                # QuestDB returns timestamp in micros since epoch by default in some views, or ISO
                # Let's assume the timestamp is in the dataset
                # ILP timestamps are stored as designated timestamps.
                ts_value = data['dataset'][0][0] # Adjust index based on column order
                if isinstance(ts_value, str):
                    return datetime.datetime.fromisoformat(ts_value.replace('Z', '+00:00'))
                else:
                    return datetime.datetime.fromtimestamp(ts_value / 1000000, tz=datetime.timezone.utc)
    except Exception as e:
        logger.debug(f"No existing data for {exchange_name} or DB unreachable: {e}")
    return datetime.datetime.min.replace(tzinfo=datetime.timezone.utc)

def run_task(historical=False):
    logger.info(f"Starting Trading Data Fetcher task (Historical: {historical})...")
    
    # Initialize exchanges
    eix = EIXExchange()
    ls = LSExchange()
    
    # Neue Deutsche Börse Exchanges
    xetra = XetraExchange()
    frankfurt = FrankfurtExchange()
    quotrix = QuotrixExchange()
    gettex = GettexExchange()
    stuttgart = StuttgartExchange()
    
    # Börsenag Exchanges (Düsseldorf, Hamburg, Hannover)
    dusa = DUSAExchange()
    dusb = DUSBExchange()
    dusc = DUSCExchange()
    dusd = DUSDExchange()
    hama = HAMAExchange()
    hamb = HAMBExchange()
    hana = HANAExchange()
    hanb = HANBExchange()
    
    # Pass last_ts to fetcher to allow smart filtering
    # daemon.py runs daily, so we want to fetch everything since DB state
    # BUT we need to be careful: eix.py's fetch_latest_trades needs 'since_date' argument
    # We can't pass it here directly in the tuple easily because last_ts is calculated inside the loop.
    
    # We will modify the loop below to handle args dynamically
    exchanges_to_process = [
        (eix, {'limit': None if historical else 5}), # Default limit 5 for safety if no historical
        (ls, {'include_yesterday': historical}),
        # Deutsche Börse Exchanges
        (xetra, {'include_yesterday': historical}),
        (frankfurt, {'include_yesterday': historical}),
        (quotrix, {'include_yesterday': historical}),
        (gettex, {'include_yesterday': historical}),
        (stuttgart, {'include_yesterday': historical}),
        # Börsenag Exchanges (Düsseldorf, Hamburg, Hannover)
        (dusa, {'include_yesterday': historical}),
        (dusb, {'include_yesterday': historical}),
        (dusc, {'include_yesterday': historical}),
        (dusd, {'include_yesterday': historical}),
        (hama, {'include_yesterday': historical}),
        (hamb, {'include_yesterday': historical}),
        (hana, {'include_yesterday': historical}),
        (hanb, {'include_yesterday': historical}),
    ]
    
    db = DatabaseClient(host="questdb", user=DB_USER, password=DB_PASSWORD)

    for exchange, args in exchanges_to_process:
        try:
            db_url = "http://questdb:9000"
            last_ts = get_last_trade_timestamp(db_url, exchange.name)
            
            logger.info(f"Fetching data from {exchange.name} (Last trade: {last_ts})...")
            
            # Special handling for EIX to support smart filtering
            call_args = args.copy()
            if exchange.name == "EIX" and not historical:
                 call_args['since_date'] = last_ts.replace(tzinfo=datetime.timezone.utc)
                 # Remove limit if we are filtering by date to ensure we get everything
                 if 'limit' in call_args:
                     call_args.pop('limit')
            
            trades = exchange.fetch_latest_trades(**call_args)
            
            if not trades:
                logger.info(f"No trades fetched from {exchange.name}.")
                continue
            
            # Hash-basierte Deduplizierung - Batch-Verarbeitung um RAM zu sparen
            logger.info(f"Filtering {len(trades)} trades for duplicates (batch processing)...")
            new_trades = filter_new_trades_batch(db_url, exchange.name, trades, batch_size=500)
            
            logger.info(f"Found {len(trades)} total trades, {len(new_trades)} are new.")
            
            if new_trades:
                # Sort trades by timestamp before saving (QuestDB likes this)
                new_trades.sort(key=lambda x: x.timestamp)
                db.save_trades(new_trades)
                logger.info(f"Stored {len(new_trades)} new trades in QuestDB.")
        except Exception as e:
            logger.error(f"Error processing exchange {exchange.name}: {e}")

def main():
    logger.info("Trading Daemon started.")
    
    # 1. Startup Check: Ist die DB leer?
    db_url = "http://questdb:9000"
    is_empty = True
    try:
        # Prüfe ob bereits Trades in der Tabelle sind
        response = requests.get(f"{db_url}/exec", params={'query': 'select count(*) from trades'}, auth=DB_AUTH)
        if response.status_code == 200:
            data = response.json()
            if data['dataset'] and data['dataset'][0][0] > 0:
                is_empty = False
    except Exception:
        # Falls Tabelle noch nicht existiert oder DB nicht erreichbar ist
        is_empty = True

    if is_empty:
        logger.info("Database is empty or table doesn't exist. Triggering initial historical fetch...")
        run_task(historical=True)
    else:
        logger.info("Found existing data in database. Triggering catch-up sync...")
        # Run a normal task to fetch any missing data since the last run
        run_task(historical=False)
        logger.info("Catch-up sync completed. Waiting for scheduled run at 23:00.")
    
    while True:
        now = datetime.datetime.now()
        # Täglich um 23:00 Uhr
        if now.hour == 23 and now.minute == 0:
            run_task(historical=False)
            # Warte 61s, um Mehrfachausführung in derselben Minute zu verhindern
            time.sleep(61)
        
        # Check alle 30 Sekunden
        time.sleep(30)

if __name__ == "__main__":
    main()