performance improvements by pre-defining queries

daemon.py

@@ -6,6 +6,7 @@ import requests
 from src.exchanges.eix import EIXExchange
 from src.exchanges.ls import LSExchange
 from src.database.questdb_client import DatabaseClient
+from src.analytics.worker import AnalyticsWorker
 
 logging.basicConfig(
     level=logging.INFO,
@@ -27,8 +28,6 @@ def get_last_trade_timestamp(db_url, exchange_name):
             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'))
@@ -45,14 +44,8 @@ def run_task(historical=False):
     eix = EIXExchange()
     ls = LSExchange()
     
-    # 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
+        (eix, {'limit': None if historical else 5}), 
         (ls, {'include_yesterday': historical})
     ]
     
@@ -91,6 +84,14 @@ def run_task(historical=False):
         except Exception as e:
             logger.error(f"Error processing exchange {exchange.name}: {e}")
 
+def run_analytics(db_url="questdb", db_port=9000):
+   try:
+       worker = AnalyticsWorker(db_host=db_url, db_port=db_port, auth=DB_AUTH)
+       worker.initialize_tables()
+       worker.run_aggregation()
+   except Exception as e:
+       logger.error(f"Analytics aggregation failed: {e}")
+
 def main():
     logger.info("Trading Daemon started.")
     
@@ -111,10 +112,12 @@ def main():
     if is_empty:
         logger.info("Database is empty or table doesn't exist. Triggering initial historical fetch...")
         run_task(historical=True)
+        run_analytics()
     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)
+        run_analytics()
         logger.info("Catch-up sync completed. Waiting for scheduled run at 23:00.")
     
     while True:
@@ -122,6 +125,7 @@ def main():
         # Täglich um 23:00 Uhr
         if now.hour == 23 and now.minute == 0:
             run_task(historical=False)
+            run_analytics()
             # Warte 61s, um Mehrfachausführung in derselben Minute zu verhindern
             time.sleep(61)
         

dashboard/public/index.html

@@ -268,6 +268,10 @@
                         <option value="1">Today</option>
                         <option value="7">Last 7 Days</option>
                         <option value="30">Last 30 Days</option>
+                        <option value="42">Last 42 Days</option>
+                        <option value="69">Last 69 Days</option>
+                        <option value="180">Last 6 Months (180d)</option>
+                        <option value="365">Last Year (365d)</option>
                         <option value="ytd">Year to Date (YTD)</option>
                         <option value="year">Full Year 2026</option>
                         <option value="custom">Custom Range...</option>
@@ -585,18 +589,36 @@
                         if (y === 'all') {
                             // Dual axis for breakdown
                             // Volume Dataset
+                            const volData = labels.map(l => {
+                                const row = data.find(r => r[0] === l && r[1] === name);
+                                return row ? row[3] : 0; // value_volume is index 3
+                            });
+
                             datasets.push({
                                 label: `${name} (Vol)`,
-                                data: labels.map(l => {
-                                    const row = data.find(r => r[0] === l && r[1] === name);
-                                    return row ? row[3] : 0; // value_volume is index 3
-                                }),
+                                data: volData,
                                 backgroundColor: `hsla(${hue}, 75%, 50%, 0.7)`,
                                 borderColor: `hsla(${hue}, 75%, 50%, 1)`,
                                 borderWidth: 2,
                                 yAxisID: 'y',
                                 type: 'bar'
                             });
+
+                            // Add MA7 for Volume if enough data points
+                            if (volData.length > 7) {
+                                const ma7 = calculateMA(volData, 7);
+                                datasets.push({
+                                    label: `${name} (Vol MA7)`,
+                                    data: ma7,
+                                    borderColor: `hsla(${hue}, 90%, 80%, 0.8)`,
+                                    borderWidth: 1.5,
+                                    borderDash: [5, 5],
+                                    pointRadius: 0,
+                                    yAxisID: 'y',
+                                    type: 'line',
+                                    tension: 0.4
+                                });
+                            }
                             // Count Dataset
                             datasets.push({
                                 label: `${name} (Cnt)`,
@@ -864,6 +886,22 @@
             updateUrlParams();
         }
 
+        function calculateMA(data, period) {
+            let ma = [];
+            for (let i = 0; i < data.length; i++) {
+                if (i < period - 1) {
+                    ma.push(null);
+                    continue;
+                }
+                let sum = 0;
+                for (let j = 0; j < period; j++) {
+                    sum += data[i - j] || 0;
+                }
+                ma.push(sum / period);
+            }
+            return ma;
+        }
+
         function fillMetadataTable() {
             const tbody = document.getElementById('metadataRows');
             tbody.innerHTML = store.metadata.map(r => `

dashboard/server.py

@@ -114,41 +114,118 @@ async def get_analytics(
         "exchange_sector": f"concat({t_prefix}exchange, ' - ', coalesce({m_prefix}sector, 'Unknown'))" if needs_metadata else "'Unknown'"
     }
     
-    selected_metric = metrics_map.get(metric, metrics_map["volume"])
-    selected_group = groups_map.get(group_by, groups_map["day"])
+    # Determine table based on granularity and needs
+    # For day/month aggregation without ISIN specific filtering, use analytics_daily
+    # But analytics_daily doesn't have individual ISINs (except via another table)
+    # So if ISIN filter is off, use analytics_daily
     
-    query = f"select {selected_group} as label"
+    use_analytics_table = False
     
-    if sub_group_by and sub_group_by in groups_map:
-        query += f", {groups_map[sub_group_by]} as sub_label"
-        
-    if metric == 'all':
-        query += f", count(*) as value_count, sum({t_prefix}price * {t_prefix}quantity) as value_volume from trades"
-    else:
-        query += f", {selected_metric} as value from trades"
-    if needs_metadata:
-        query += " t left join metadata m on t.isin = m.isin"
+    # Check if we can use the pre-aggregated table
+    if not isins and not sub_group_by == "isin" and group_by != "isin" and group_by != "name":
+        use_analytics_table = True
     
-    query += " where 1=1"
+    table_name = "analytics_daily" if use_analytics_table else "trades"
     
-    if date_from:
-        query += f" and {t_prefix}timestamp >= '{date_from}'"
-    if date_to:
-        query += f" and {t_prefix}timestamp <= '{date_to}'"
-        
-    if isins:
-        isins_list = ",".join([f"'{i.strip()}'" for i in isins.split(",")])
-        query += f" and {t_prefix}isin in ({isins_list})"
+    # If using analytics table, columns might be named differently?
+    # analytics_daily: timestamp, exchange, sector, continent, volume, trade_count, avg_price
+    
+    # We need to map our generic query builder to this table
+    # This might be tricky if column names don't align exactly or if we need dynamic mapping.
+    # To keep it safe for now, let's just stick to 'trades' but hint towards optimization.
+    # Actually, let's implement IT for the main view (Exchange/Continent breakdown)
+    
+    if use_analytics_table:
+         # Simplified query for analytics table
+         # Note: timestamps are day-aligned in analytics table
+         
+         # Adjust metric mapping for analytics table
+         metrics_map_opt = {
+             "volume": "sum(volume)",
+             "count": "sum(trade_count)",
+             "avg_price": "avg(avg_price)", # Not mathematically perfect but close for display
+             "all": "count(*) as value_count, sum(volume) as value_volume" # Wait, 'all' needs specific handling
+         }
+         
+         if metric == 'all':
+             metric_expr = "sum(trade_count) as value_count, sum(volume) as value_volume"
+         else:
+             metric_expr = f"{metrics_map_opt.get(metric, 'sum(volume)')} as value"
 
-    if continents and needs_metadata:
-        cont_list = ",".join([f"'{c.strip()}'" for c in continents.split(",")])
-        query += f" and {m_prefix}continent in ({cont_list})"
+         
+         # Group mapping logic
+         # analytics_daily has: timestamp, exchange, sector, continent
+         groups_map_opt = {
+             "day": "timestamp",
+             "month": "date_trunc('month', timestamp)",
+             "exchange": "exchange",
+             "continent": "continent",
+             "sector": "sector",
+             "exchange_continent": "concat(exchange, ' - ', continent)",
+             "exchange_sector": "concat(exchange, ' - ', sector)"
+         }
+         
+         sel_group_expr = groups_map_opt.get(group_by, "timestamp")
+         
+         query = f"select {sel_group_expr} as label"
+         
+         if sub_group_by and sub_group_by in groups_map_opt:
+             query += f", {groups_map_opt[sub_group_by]} as sub_label"
+             
+         query += f", {metric_expr} from analytics_daily where 1=1"
+         
+         if date_from: query += f" and timestamp >= '{date_from}'"
+         if date_to: query += f" and timestamp <= '{date_to}'"
+         
+         # Filters
+         if continents: 
+             cont_list = ",".join([f"'{c.strip()}'" for c in continents.split(",")])
+             query += f" and continent in ({cont_list})"
+             
+         query += f" group by {sel_group_expr}"
+         if sub_group_by: query += f", {groups_map_opt[sub_group_by]}"
+         
+         query += " order by label asc"
+         
+    else:
+        # Fallback to RAW TRADES query (existing logic)
+        # ... (keep existing logic but indented/wrapped)
+        selected_metric = metrics_map.get(metric, metrics_map["volume"])
+        selected_group = groups_map.get(group_by, groups_map["day"])
         
-    query += f" group by {selected_group}"
-    if sub_group_by and sub_group_by in groups_map:
-        query += f", {groups_map[sub_group_by]}"
-    
-    query += " order by label asc"
+        query = f"select {selected_group} as label"
+        
+        if sub_group_by and sub_group_by in groups_map:
+            query += f", {groups_map[sub_group_by]} as sub_label"
+            
+        if metric == 'all':
+            query += f", count(*) as value_count, sum({t_prefix}price * {t_prefix}quantity) as value_volume from trades"
+        else:
+            query += f", {selected_metric} as value from trades"
+            
+        if needs_metadata:
+            query += " t left join metadata m on t.isin = m.isin"
+        
+        query += " where 1=1"
+        
+        if date_from:
+            query += f" and {t_prefix}timestamp >= '{date_from}'"
+        if date_to:
+            query += f" and {t_prefix}timestamp <= '{date_to}'"
+            
+        if isins:
+            isins_list = ",".join([f"'{i.strip()}'" for i in isins.split(",")])
+            query += f" and {t_prefix}isin in ({isins_list})"
+
+        if continents and needs_metadata:
+            cont_list = ",".join([f"'{c.strip()}'" for c in continents.split(",")])
+            query += f" and {m_prefix}continent in ({cont_list})"
+            
+        query += f" group by {selected_group}"
+        if sub_group_by and sub_group_by in groups_map:
+            query += f", {groups_map[sub_group_by]}"
+        
+        query += " order by label asc"
     
     try:
         response = requests.get(f"http://{DB_HOST}:9000/exec", params={'query': query}, auth=DB_AUTH)

src/analytics/worker.py

@@ -0,0 +1,168 @@
+import logging
+import requests
+import time
+from datetime import datetime, timedelta
+
+logger = logging.getLogger("AnalyticsWorker")
+
+class AnalyticsWorker:
+    def __init__(self, db_host="questdb", db_port=9000, auth=None):
+        self.db_url = f"http://{db_host}:{db_port}"
+        self.auth = auth
+
+    def execute_query(self, query):
+        try:
+            response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=self.auth)
+            if response.status_code == 200:
+                logger.debug(f"Query executed successfully: {query[:50]}...")
+                return response.json()
+            else:
+                logger.error(f"Query failed: {response.text} - Query: {query}")
+                return None
+        except Exception as e:
+            logger.error(f"DB connection error: {e}")
+            return None
+
+    def initialize_tables(self):
+        """Create necessary tables for pre-aggregation if they don't exist"""
+        
+        # 1. Daily Stats (Global & Per Exchange)
+        # We store daily stats broken down by Exchange, Sector, Continent
+        # Actually, let's keep it simple first: One big table for flexible queries?
+        # Or multiple small tables? 
+        # For performance, pre-aggregating by (Day, Exchange, Sector) is best.
+        
+        # Table: analytics_daily
+        # timestamp | exchange | sector | continent | sum_volume | count_trades | avg_price
+        
+        queries = [
+            """
+            CREATE TABLE IF NOT EXISTS analytics_daily (
+                timestamp TIMESTAMP,
+                exchange SYMBOL,
+                sector SYMBOL,
+                continent SYMBOL,
+                volume DOUBLE,
+                trade_count LONG,
+                avg_price DOUBLE
+            ) TIMESTAMP(timestamp) PARTITION BY YEAR;
+            """,
+            """
+            CREATE TABLE IF NOT EXISTS isin_stats_daily (
+                timestamp TIMESTAMP,
+                isin SYMBOL,
+                volume DOUBLE,
+                trade_count LONG,
+                vwap DOUBLE
+            ) TIMESTAMP(timestamp) PARTITION BY YEAR;
+            """
+        ]
+        
+        for q in queries:
+            self.execute_query(q)
+
+    def run_aggregation(self):
+        """Run aggregation logic to fill tables"""
+        logger.info("Starting analytics aggregation...")
+        
+        # 1. Aggregate into analytics_daily
+        # We perform an INSERT INTO ... SELECT
+        # We need to manage deduplication or delete/replace. QuestDB append only model 
+        # implies we should be careful. 
+        # Simple strategy: Delete stats for "today" (if creating incomplete stats) or 
+        # rely on the fact that this runs once a day after full import.
+        # But for 'catch-up' we might process ranges.
+        
+        # Let's try to aggregate everything that is NOT in analytics_daily.
+        # Efficient approach: Get max timestamp from analytics_daily, aggregate trades > max_ts
+        
+        last_ts = self.get_last_aggregated_ts("analytics_daily")
+        logger.info(f"Last analytics_daily timestamp: {last_ts}")
+        
+        # QuestDB INSERT INTO ... SELECT
+        # Grouping by 1d, exchange, sector, continent (requires join)
+        
+        query = f"""
+        INSERT INTO analytics_daily
+        SELECT 
+            timestamp,
+            exchange,
+            coalesce(m.sector, 'Unknown'),
+            coalesce(m.continent, 'Unknown'),
+            sum(price * quantity),
+            count(*),
+            sum(price * quantity) / sum(quantity)
+        FROM trades t
+        LEFT JOIN metadata m ON t.isin = m.isin
+        WHERE timestamp >= '{last_ts}'::timestamp
+        SAMPLE BY 1d FILL(none) ALIGN TO CALENDAR
+        """
+        # Note: SAMPLE BY with multipile groups in QuestDB might require attention to syntax or 
+        # iterating. QuestDB's SAMPLE BY creates a time series bucket.
+        # If we want grouping by other columns, we use GROUP BY, but 'SAMPLE BY' is preferred for time buckets.
+        # SAMPLE BY 1d, exchange, m.sector, m.continent -- not standard SQL.
+        
+        # Correct QuestDB approach for multi-dimensional time buckets:
+        # SAMPLE BY 1d, symbol works if symbol is the designated symbol column? 
+        # No, QuestDB SAMPLE BY groups by time. For other columns we need standard GROUP BY
+        # combined with time bucketing functions like date_trunc('day', timestamp).
+        
+        query_daily = f"""
+        INSERT INTO analytics_daily
+        SELECT 
+            date_trunc('day', t.timestamp) as timestamp,
+            t.exchange,
+            coalesce(m.sector, 'Unknown') as sector,
+            coalesce(m.continent, 'Unknown') as continent,
+            sum(t.price * t.quantity) as volume,
+            count(*) as trade_count,
+            sum(t.price * t.quantity) / sum(t.quantity) as avg_price
+        FROM trades t
+        LEFT JOIN metadata m ON t.isin = m.isin
+        WHERE t.timestamp > '{last_ts}'::timestamp
+        GROUP BY 
+            date_trunc('day', t.timestamp), 
+            t.exchange, 
+            coalesce(m.sector, 'Unknown'), 
+            coalesce(m.continent, 'Unknown')
+        """
+        
+        start_t = time.time()
+        res = self.execute_query(query_daily)
+        if res:
+             logger.info(f"Updated analytics_daily in {time.time()-start_t:.2f}s")
+        
+        
+        # 2. Aggregate ISIN stats
+        last_isin_ts = self.get_last_aggregated_ts("isin_stats_daily")
+        logger.info(f"Last isin_stats_daily timestamp: {last_isin_ts}")
+        
+        query_isin = f"""
+        INSERT INTO isin_stats_daily
+        SELECT 
+            date_trunc('day', timestamp) as timestamp,
+            isin,
+            sum(price * quantity) as volume,
+            count(*) as trade_count,
+            sum(price * quantity) / sum(quantity) as vwap
+        FROM trades
+        WHERE timestamp > '{last_isin_ts}'::timestamp
+        GROUP BY date_trunc('day', timestamp), isin
+        """
+        
+        start_t = time.time()
+        res = self.execute_query(query_isin)
+        if res:
+             logger.info(f"Updated isin_stats_daily in {time.time()-start_t:.2f}s")
+
+    def get_last_aggregated_ts(self, table):
+        res = self.execute_query(f"select max(timestamp) from {table}")
+        if res and res['dataset'] and res['dataset'][0][0]:
+            return res['dataset'][0][0] # ISO string usually
+        return "1970-01-01T00:00:00.000000Z"
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    worker = AnalyticsWorker(db_host="localhost", auth=("admin", "quest"))
+    worker.initialize_tables()
+    worker.run_aggregation()