updated

Dockerfile.analytics

@@ -0,0 +1,10 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["python", "-m", "src.analytics.worker"]

daemon.py

@@ -6,7 +6,6 @@ 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,
@@ -28,6 +27,8 @@ 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'))
@@ -44,8 +45,14 @@ 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}), 
+        (eix, {'limit': None if historical else 5}), # Default limit 5 for safety if no historical
         (ls, {'include_yesterday': historical})
     ]
     
@@ -84,14 +91,6 @@ 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.")
     
@@ -112,12 +111,10 @@ 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:
@@ -125,7 +122,6 @@ 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

@@ -251,6 +251,40 @@
                     <div class="h-80"><canvas id="continentChart"></canvas></div>
                 </div>
             </div>
+            
+            <!-- Neue Statistiken Sektion -->
+            <div class="mt-10 mb-6">
+                <div class="flex items-center justify-between mb-6">
+                    <h2 class="text-2xl font-bold text-slate-200">Erweiterte Statistiken</h2>
+                    <select id="statisticsPeriod" class="input-glass px-4 py-2" onchange="loadStatistics()">
+                        <option value="7">7 Tage</option>
+                        <option value="30">30 Tage</option>
+                        <option value="42">42 Tage</option>
+                        <option value="69">69 Tage</option>
+                        <option value="180">180 Tage</option>
+                        <option value="365">365 Tage</option>
+                    </select>
+                </div>
+                
+                <!-- Moving Average Graph -->
+                <div class="glass p-8 mb-8">
+                    <h3 class="text-lg font-bold mb-6 text-slate-300">Moving Average: Tradezahlen & Volumen je Exchange</h3>
+                    <div class="h-96"><canvas id="movingAverageChart"></canvas></div>
+                </div>
+                
+                <!-- Volumen-Änderungen -->
+                <div class="glass p-8 mb-8">
+                    <h3 class="text-lg font-bold mb-6 text-slate-300">Tradingvolumen & Anzahl Änderungen</h3>
+                    <div class="h-96"><canvas id="volumeChangesChart"></canvas></div>
+                </div>
+                
+                <!-- Stock Trends -->
+                <div class="glass p-8">
+                    <h3 class="text-lg font-bold mb-6 text-slate-300">Trendanalyse: Häufig gehandelte Aktien</h3>
+                    <div class="h-96"><canvas id="stockTrendsChart"></canvas></div>
+                    <div id="stockTrendsTable" class="mt-6 overflow-x-auto"></div>
+                </div>
+            </div>
         </div>
 
         <!-- REPORT BUILDER VIEW -->
@@ -268,10 +302,6 @@
                         <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>
@@ -534,6 +564,10 @@
             document.getElementById('statIsins').innerText = store.metadata.length.toLocaleString();
             renderDashboardCharts();
             fillMetadataTable();
+            // Lade Statistiken neu wenn Dashboard aktualisiert wird
+            if (window.activeView === 'dashboard') {
+                loadStatistics();
+            }
         }
 
         function setChartType(type) { currentChartType = type; renderAnalyticsReport(); }
@@ -589,36 +623,18 @@
                         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: `${name} (Vol)`,
-                                data: volData,
+                                data: labels.map(l => {
+                                    const row = data.find(r => r[0] === l && r[1] === name);
+                                    return row ? row[3] : 0; // value_volume is index 3
+                                }),
                                 backgroundColor: `hsla(${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)`,
@@ -886,22 +902,6 @@
             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 => `
@@ -931,7 +931,355 @@
             rows.forEach(r => r.style.display = r.innerText.toLowerCase().includes(q.toLowerCase()) ? '' : 'none');
         }
 
-        window.onload = async () => { await fetchData(); syncStateFromUrl(); setInterval(fetchData, 30000); };
+        async function loadStatistics() {
+            const days = document.getElementById('statisticsPeriod').value;
+            await Promise.all([
+                loadMovingAverage(days),
+                loadVolumeChanges(days),
+                loadStockTrends(days)
+            ]);
+        }
+
+        async function loadMovingAverage(days) {
+            try {
+                const res = await fetch(`${API}/statistics/moving-average?days=${days}`).then(r => r.json());
+                const data = res.dataset || [];
+                const columns = res.columns || [];
+                
+                if (!data.length) {
+                    console.log('No moving average data available');
+                    return;
+                }
+                
+                const ctx = document.getElementById('movingAverageChart').getContext('2d');
+                
+                if (charts.movingAverage) charts.movingAverage.destroy();
+                
+                // Finde Spaltenindizes
+                const dateIdx = columns.findIndex(c => c.name === 'date' || c.name === 'timestamp');
+                const exchangeIdx = columns.findIndex(c => c.name === 'exchange');
+                const countIdx = columns.findIndex(c => c.name === 'trade_count');
+                const volumeIdx = columns.findIndex(c => c.name === 'volume');
+                const maCountIdx = columns.findIndex(c => c.name === 'ma_count');
+                const maVolumeIdx = columns.findIndex(c => c.name === 'ma_volume');
+                
+                // Gruppiere nach Exchange
+                const exchanges = [...new Set(data.map(r => r[exchangeIdx]))];
+                const dates = [...new Set(data.map(r => r[dateIdx]))].sort();
+                
+                const datasets = [];
+                const colors = ['#38bdf8', '#f43f5e', '#10b981', '#fbbf24', '#8b5cf6'];
+                
+                // Trade Count Datasets
+                exchanges.forEach((exchange, idx) => {
+                    datasets.push({
+                        label: `${exchange} - Trade Count`,
+                        data: dates.map(d => {
+                            const row = data.find(r => r[dateIdx] === d && r[exchangeIdx] === exchange);
+                            return row ? (row[countIdx] || 0) : 0;
+                        }),
+                        borderColor: colors[idx % colors.length],
+                        backgroundColor: colors[idx % colors.length] + '33',
+                        borderWidth: 2,
+                        yAxisID: 'y',
+                        tension: 0.3
+                    });
+                });
+                
+                // Moving Average Datasets
+                exchanges.forEach((exchange, idx) => {
+                    datasets.push({
+                        label: `${exchange} - MA Count`,
+                        data: dates.map(d => {
+                            const row = data.find(r => r[dateIdx] === d && r[exchangeIdx] === exchange);
+                            return row ? (row[maCountIdx] || 0) : 0;
+                        }),
+                        borderColor: colors[idx % colors.length],
+                        backgroundColor: 'transparent',
+                        borderWidth: 2,
+                        borderDash: [5, 5],
+                        yAxisID: 'y',
+                        tension: 0.3
+                    });
+                });
+                
+                // Volume Datasets
+                exchanges.forEach((exchange, idx) => {
+                    datasets.push({
+                        label: `${exchange} - Volume`,
+                        data: dates.map(d => {
+                            const row = data.find(r => r[dateIdx] === d && r[exchangeIdx] === exchange);
+                            return row ? (row[volumeIdx] || 0) : 0;
+                        }),
+                        borderColor: colors[(idx + 2) % colors.length],
+                        backgroundColor: colors[(idx + 2) % colors.length] + '33',
+                        borderWidth: 2,
+                        yAxisID: 'y1',
+                        tension: 0.3
+                    });
+                });
+                
+                charts.movingAverage = new Chart(ctx, {
+                    type: 'line',
+                    data: {
+                        labels: dates.map(d => new Date(d).toLocaleDateString()),
+                        datasets: datasets
+                    },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        interaction: { mode: 'index', intersect: false },
+                        scales: {
+                            y: {
+                                type: 'linear',
+                                display: true,
+                                position: 'left',
+                                title: { display: true, text: 'Trade Count', color: '#94a3b8' },
+                                grid: { color: 'rgba(255,255,255,0.05)' },
+                                ticks: { color: '#64748b' }
+                            },
+                            y1: {
+                                type: 'linear',
+                                display: true,
+                                position: 'right',
+                                title: { display: true, text: 'Volume (€)', color: '#94a3b8' },
+                                grid: { drawOnChartArea: false },
+                                ticks: { color: '#64748b' }
+                            },
+                            x: {
+                                grid: { display: false },
+                                ticks: { color: '#64748b' }
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: true,
+                                position: 'bottom',
+                                labels: { color: '#94a3b8', boxWidth: 12, usePointStyle: true, padding: 15 }
+                            },
+                            tooltip: {
+                                backgroundColor: '#1e293b',
+                                titleColor: '#38bdf8',
+                                bodyColor: '#e2e8f0',
+                                borderColor: 'rgba(255,255,255,0.1)',
+                                borderWidth: 1
+                            }
+                        }
+                    }
+                });
+            } catch (err) {
+                console.error('Error loading moving average:', err);
+            }
+        }
+
+        async function loadVolumeChanges(days) {
+            try {
+                const res = await fetch(`${API}/statistics/volume-changes?days=${days}`).then(r => r.json());
+                const data = res.dataset || [];
+                const columns = res.columns || [];
+                
+                if (!data.length) {
+                    console.log('No volume changes data available');
+                    return;
+                }
+                
+                const ctx = document.getElementById('volumeChangesChart').getContext('2d');
+                
+                if (charts.volumeChanges) charts.volumeChanges.destroy();
+                
+                // Finde Spaltenindizes
+                const exchangeIdx = columns.findIndex(c => c.name === 'exchange');
+                const countChangeIdx = columns.findIndex(c => c.name === 'count_change_pct');
+                const volumeChangeIdx = columns.findIndex(c => c.name === 'volume_change_pct');
+                const trendIdx = columns.findIndex(c => c.name === 'trend');
+                
+                const exchanges = data.map(r => r[exchangeIdx]);
+                const countChanges = data.map(r => r[countChangeIdx] || 0);
+                const volumeChanges = data.map(r => r[volumeChangeIdx] || 0);
+                
+                charts.volumeChanges = new Chart(ctx, {
+                    type: 'bar',
+                    data: {
+                        labels: exchanges,
+                        datasets: [
+                            {
+                                label: 'Anzahl Änderung (%)',
+                                data: countChanges,
+                                backgroundColor: '#38bdf866',
+                                borderColor: '#38bdf8',
+                                borderWidth: 2,
+                                yAxisID: 'y'
+                            },
+                            {
+                                label: 'Volumen Änderung (%)',
+                                data: volumeChanges,
+                                backgroundColor: '#fbbf2466',
+                                borderColor: '#fbbf24',
+                                borderWidth: 2,
+                                yAxisID: 'y'
+                            }
+                        ]
+                    },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            y: {
+                                type: 'linear',
+                                display: true,
+                                title: { display: true, text: 'Änderung (%)', color: '#94a3b8' },
+                                grid: { color: 'rgba(255,255,255,0.05)' },
+                                ticks: { color: '#64748b' }
+                            },
+                            x: {
+                                grid: { display: false },
+                                ticks: { color: '#64748b' }
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: true,
+                                position: 'top',
+                                labels: { color: '#94a3b8', boxWidth: 12, usePointStyle: true, padding: 15 }
+                            },
+                            tooltip: {
+                                backgroundColor: '#1e293b',
+                                titleColor: '#38bdf8',
+                                bodyColor: '#e2e8f0',
+                                borderColor: 'rgba(255,255,255,0.1)',
+                                borderWidth: 1,
+                                callbacks: {
+                                    afterLabel: (context) => {
+                                        const idx = context.dataIndex;
+                                        const trend = data[idx][trendIdx]; // trend
+                                        return `Trend: ${trend || 'N/A'}`;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                });
+            } catch (err) {
+                console.error('Error loading volume changes:', err);
+            }
+        }
+
+        async function loadStockTrends(days) {
+            try {
+                const res = await fetch(`${API}/statistics/stock-trends?days=${days}&limit=20`).then(r => r.json());
+                const data = res.dataset || [];
+                const columns = res.columns || [];
+                
+                if (!data.length) {
+                    console.log('No stock trends data available');
+                    return;
+                }
+                
+                const ctx = document.getElementById('stockTrendsChart').getContext('2d');
+                const tableContainer = document.getElementById('stockTrendsTable');
+                
+                if (charts.stockTrends) charts.stockTrends.destroy();
+                
+                // Finde Spaltenindizes
+                const isinIdx = columns.findIndex(c => c.name === 'isin');
+                const volumeIdx = columns.findIndex(c => c.name === 'volume');
+                const countIdx = columns.findIndex(c => c.name === 'trade_count');
+                const countChangeIdx = columns.findIndex(c => c.name === 'count_change_pct');
+                const volumeChangeIdx = columns.findIndex(c => c.name === 'volume_change_pct');
+                
+                // Sortiere nach Volumen
+                const sorted = [...data].sort((a, b) => (b[volumeIdx] || 0) - (a[volumeIdx] || 0)).slice(0, 10);
+                
+                const isins = sorted.map(r => r[isinIdx]);
+                const volumes = sorted.map(r => r[volumeIdx] || 0);
+                const countChanges = sorted.map(r => r[countChangeIdx] || 0);
+                const volumeChanges = sorted.map(r => r[volumeChangeIdx] || 0);
+                
+                charts.stockTrends = new Chart(ctx, {
+                    type: 'bar',
+                    data: {
+                        labels: isins.map(i => i.substring(0, 12) + '...'),
+                        datasets: [
+                            {
+                                label: 'Volumen (€)',
+                                data: volumes,
+                                backgroundColor: '#10b98166',
+                                borderColor: '#10b981',
+                                borderWidth: 2,
+                                yAxisID: 'y'
+                            }
+                        ]
+                    },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            y: {
+                                type: 'linear',
+                                display: true,
+                                title: { display: true, text: 'Volumen (€)', color: '#94a3b8' },
+                                grid: { color: 'rgba(255,255,255,0.05)' },
+                                ticks: { color: '#64748b' }
+                            },
+                            x: {
+                                grid: { display: false },
+                                ticks: { color: '#64748b', maxRotation: 45, minRotation: 45 }
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: true,
+                                position: 'top',
+                                labels: { color: '#94a3b8', boxWidth: 12, usePointStyle: true, padding: 15 }
+                            },
+                            tooltip: {
+                                backgroundColor: '#1e293b',
+                                titleColor: '#38bdf8',
+                                bodyColor: '#e2e8f0',
+                                borderColor: 'rgba(255,255,255,0.1)',
+                                borderWidth: 1
+                            }
+                        }
+                    }
+                });
+                
+                // Erstelle Tabelle
+                tableContainer.innerHTML = `
+                    <table class="w-full text-left text-sm">
+                        <thead>
+                            <tr class="border-b border-white/10">
+                                <th class="p-3 text-slate-400 font-bold">ISIN</th>
+                                <th class="p-3 text-slate-400 font-bold">Trades</th>
+                                <th class="p-3 text-slate-400 font-bold">Volumen (€)</th>
+                                <th class="p-3 text-slate-400 font-bold">Anzahl Δ (%)</th>
+                                <th class="p-3 text-slate-400 font-bold">Volumen Δ (%)</th>
+                            </tr>
+                        </thead>
+                        <tbody>
+                            ${sorted.map(r => `
+                                <tr class="border-b border-white/5 hover:bg-white/5">
+                                    <td class="p-3 font-mono text-sky-400">${r[isinIdx]}</td>
+                                    <td class="p-3 text-slate-300">${(r[countIdx] || 0).toLocaleString()}</td>
+                                    <td class="p-3 text-slate-300">€${((r[volumeIdx] || 0) / 1e6).toFixed(2)}M</td>
+                                    <td class="p-3 ${(r[countChangeIdx] || 0) >= 0 ? 'text-green-400' : 'text-red-400'}">${((r[countChangeIdx] || 0)).toFixed(2)}%</td>
+                                    <td class="p-3 ${(r[volumeChangeIdx] || 0) >= 0 ? 'text-green-400' : 'text-red-400'}">${((r[volumeChangeIdx] || 0)).toFixed(2)}%</td>
+                                </tr>
+                            `).join('')}
+                        </tbody>
+                    </table>
+                `;
+            } catch (err) {
+                console.error('Error loading stock trends:', err);
+            }
+        }
+
+        window.onload = async () => { 
+            await fetchData(); 
+            syncStateFromUrl(); 
+            setInterval(fetchData, 30000);
+            // Lade Statistiken beim Start
+            setTimeout(() => loadStatistics(), 1000);
+        };
     </script>
 </body>
 

dashboard/server.py

@@ -114,168 +114,46 @@ async def get_analytics(
         "exchange_sector": f"concat({t_prefix}exchange, ' - ', coalesce({m_prefix}sector, 'Unknown'))" if needs_metadata else "'Unknown'"
     }
     
-    # 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
+    selected_metric = metrics_map.get(metric, metrics_map["volume"])
+    selected_group = groups_map.get(group_by, groups_map["day"])
     
-    use_analytics_table = False
+    query = f"select {selected_group} as label"
     
-    # 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
-    
-    table_name = "analytics_daily" if use_analytics_table else "trades"
-    
-    # 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"
-
-         
-         # 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"
-         
+    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:
-        # 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"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"
+        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"
     
-    print(f"Executing Query: {query}")
     try:
         response = requests.get(f"http://{DB_HOST}:9000/exec", params={'query': query}, auth=DB_AUTH)
         if response.status_code == 200:
             return response.json()
-            
-        print(f"DEBUG: Query Failed: {response.text}")
-        
-        if use_analytics_table:
-            print("DEBUG: Analytics query failed, falling back to RAW trades query...")
-            
-            selected_metric = metrics_map.get(metric, metrics_map["volume"])
-            selected_group = groups_map.get(group_by, groups_map["day"])
-            
-            raw_query = f"select {selected_group} as label"
-            
-            if sub_group_by and sub_group_by in groups_map:
-                raw_query += f", {groups_map[sub_group_by]} as sub_label"
-                
-            if metric == 'all':
-                raw_query += f", count(*) as value_count, sum({t_prefix}price * {t_prefix}quantity) as value_volume from trades"
-            else:
-                raw_query += f", {selected_metric} as value from trades"
-                
-            if needs_metadata:
-                raw_query += " t left join metadata m on t.isin = m.isin"
-            
-            raw_query += " where 1=1"
-            
-            if date_from: raw_query += f" and {t_prefix}timestamp >= '{date_from}'"
-            if date_to: raw_query += f" and {t_prefix}timestamp <= '{date_to}'"
-            if isins:
-                isins_list = ",".join([f"'{i.strip()}'" for i in isins.split(",")])
-                raw_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(",")])
-                raw_query += f" and {m_prefix}continent in ({cont_list})"
-                
-            raw_query += f" group by {selected_group}"
-            if sub_group_by and sub_group_by in groups_map:
-                raw_query += f", {groups_map[sub_group_by]}"
-            
-            raw_query += " order by label asc"
-            
-            print(f"Executing Fallback Query: {raw_query}")
-            fb_response = requests.get(f"http://{DB_HOST}:9000/exec", params={'query': raw_query}, auth=DB_AUTH)
-            if fb_response.status_code == 200:
-                return fb_response.json()
-        
         throw_http_error(response)
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
@@ -292,6 +170,104 @@ async def search_metadata(q: str):
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
+@app.get("/api/statistics/moving-average")
+async def get_moving_average(days: int = 7, exchange: str = None):
+    """
+    Gibt Moving Average Daten für Tradezahlen und Volumen je Exchange zurück.
+    Unterstützte Zeiträume: 7, 30, 42, 69, 180, 365 Tage
+    """
+    if days not in [7, 30, 42, 69, 180, 365]:
+        raise HTTPException(status_code=400, detail="Invalid days parameter. Must be one of: 7, 30, 42, 69, 180, 365")
+    
+    # Hole Daten aus der vorberechneten analytics_exchange_daily Tabelle
+    query = f"""
+    select 
+        timestamp as date,
+        exchange,
+        trade_count,
+        volume,
+        ma{days}_count as ma_count,
+        ma{days}_volume as ma_volume
+    from analytics_exchange_daily
+    where timestamp >= dateadd('d', -{days}, now())
+    """
+    
+    if exchange:
+        query += f" and exchange = '{exchange}'"
+    
+    query += " order by date asc, exchange asc"
+    
+    try:
+        response = requests.get(f"http://{DB_HOST}:9000/exec", params={'query': query}, auth=DB_AUTH)
+        if response.status_code == 200:
+            return response.json()
+        throw_http_error(response)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/api/statistics/volume-changes")
+async def get_volume_changes(days: int = 7):
+    """
+    Gibt Änderungen in Volumen und Anzahl je Exchange zurück.
+    Unterstützte Zeiträume: 7, 30, 42, 69, 180, 365 Tage
+    """
+    if days not in [7, 30, 42, 69, 180, 365]:
+        raise HTTPException(status_code=400, detail="Invalid days parameter. Must be one of: 7, 30, 42, 69, 180, 365")
+    
+    query = f"""
+    select 
+        timestamp as date,
+        exchange,
+        trade_count,
+        volume,
+        count_change_pct,
+        volume_change_pct,
+        trend
+    from analytics_volume_changes
+    where period_days = {days}
+    order by date desc, exchange asc
+    """
+    
+    try:
+        response = requests.get(f"http://{DB_HOST}:9000/exec", params={'query': query}, auth=DB_AUTH)
+        if response.status_code == 200:
+            return response.json()
+        throw_http_error(response)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/api/statistics/stock-trends")
+async def get_stock_trends(days: int = 7, limit: int = 20):
+    """
+    Gibt Trendanalyse für häufig gehandelte Aktien zurück.
+    Unterstützte Zeiträume: 7, 30, 42, 69, 180, 365 Tage
+    """
+    if days not in [7, 30, 42, 69, 180, 365]:
+        raise HTTPException(status_code=400, detail="Invalid days parameter. Must be one of: 7, 30, 42, 69, 180, 365")
+    
+    # Hole Top-Aktien nach Volumen für den Zeitraum
+    query = f"""
+    select 
+        timestamp as date,
+        isin,
+        trade_count,
+        volume,
+        count_change_pct,
+        volume_change_pct
+    from analytics_stock_trends
+    where period_days = {days}
+    order by volume desc
+    limit {limit}
+    """
+    
+    try:
+        response = requests.get(f"http://{DB_HOST}:9000/exec", params={'query': query}, auth=DB_AUTH)
+        if response.status_code == 200:
+            return response.json()
+        throw_http_error(response)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
 def throw_http_error(res):
     raise HTTPException(status_code=res.status_code, detail=f"QuestDB error: {res.text}")
 

docker-compose.yml

@@ -60,5 +60,19 @@ services:
       - DB_PASSWORD=${DB_PASSWORD:-quest}
       - DB_HOST=questdb
 
+  analytics_worker:
+    build:
+      context: .
+      dockerfile: Dockerfile.analytics
+    container_name: analytics_worker
+    depends_on:
+      - questdb
+    restart: always
+    environment:
+      - PYTHONUNBUFFERED=1
+      - DB_USER=${DB_USER:-admin}
+      - DB_PASSWORD=${DB_PASSWORD:-quest}
+      - DB_HOST=questdb
+
 volumes:
   questdb_data:

src/analytics/__init__.py

@@ -0,0 +1 @@
+# Analytics module for trading data aggregation

src/analytics/worker.py

@@ -1,168 +1,446 @@
-import logging
-import requests
 import time
-from datetime import datetime, timedelta
+import logging
+import datetime
+import os
+import requests
+from typing import Dict, List, Tuple, Optional
+import pandas as pd
 
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
 logger = logging.getLogger("AnalyticsWorker")
 
+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
+DB_HOST = os.getenv("DB_HOST", "questdb")
+DB_URL = f"http://{DB_HOST}:9000"
+
+# Unterstützte Zeiträume
+TIME_PERIODS = [7, 30, 42, 69, 180, 365]
+
 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):
+    def __init__(self):
+        self.last_processed_timestamp = None
+        self.db_url = DB_URL
+        
+    def get_last_processed_timestamp(self) -> Optional[datetime.datetime]:
+        """Holt den letzten verarbeiteten Timestamp aus der Analytics-Tabelle"""
         try:
-            response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=self.auth)
+            query = "select max(timestamp) as last_ts from analytics_exchange_daily"
+            response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_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
+                data = response.json()
+                if data.get('dataset') and data['dataset'] and len(data['dataset']) > 0 and data['dataset'][0][0]:
+                    ts_value = data['dataset'][0][0]
+                    if isinstance(ts_value, str):
+                        return datetime.datetime.fromisoformat(ts_value.replace('Z', '+00:00'))
+                    elif isinstance(ts_value, (int, float)):
+                        # QuestDB gibt Timestamps in Mikrosekunden zurück
+                        return datetime.datetime.fromtimestamp(ts_value / 1000000, tz=datetime.timezone.utc)
         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"""
+            logger.debug(f"Could not get last processed timestamp: {e}")
+        return None
+    
+    def get_new_trades(self, since: Optional[datetime.datetime] = None) -> List[Dict]:
+        """Holt neue Trades seit dem letzten Verarbeitungszeitpunkt"""
+        if since:
+            since_str = since.strftime('%Y-%m-%d %H:%M:%S')
+            query = f"select timestamp, exchange, isin, price, quantity from trades where timestamp > '{since_str}' order by timestamp asc"
+        else:
+            # Erste Ausführung: nur die letzten 7 Tage
+            query = f"select timestamp, exchange, isin, price, quantity from trades where timestamp > dateadd('d', -7, now()) order by timestamp asc"
         
-        # 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)
+        try:
+            response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
+            if response.status_code == 200:
+                data = response.json()
+                columns = data.get('columns', [])
+                dataset = data.get('dataset', [])
+                
+                trades = []
+                for row in dataset:
+                    trade = {}
+                    for i, col in enumerate(columns):
+                        trade[col['name']] = row[i]
+                    trades.append(trade)
+                return trades
+        except Exception as e:
+            logger.error(f"Error fetching new trades: {e}")
+        return []
+    
+    def calculate_exchange_daily_aggregations(self, days_back: int = 365) -> List[Dict]:
+        """Berechnet tägliche Aggregationen je Exchange mit Moving Averages"""
+        end_date = datetime.datetime.now(datetime.timezone.utc)
+        start_date = end_date - datetime.timedelta(days=days_back)
         
         query = f"""
-        INSERT INTO analytics_daily
-        SELECT 
-            timestamp,
+        select 
+            date_trunc('day', timestamp) as date,
             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')
+            sum(price * quantity) as volume
+        from trades
+        where timestamp >= '{start_date.strftime('%Y-%m-%d')}'
+        group by date, exchange
+        order by date asc, exchange asc
         """
         
-        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")
+        try:
+            response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
+            if response.status_code == 200:
+                data = response.json()
+                columns = data.get('columns', [])
+                dataset = data.get('dataset', [])
+                
+                results = []
+                for row in dataset:
+                    result = {}
+                    for i, col in enumerate(columns):
+                        result[col['name']] = row[i]
+                    results.append(result)
+                
+                # Berechne Moving Averages für alle Zeiträume
+                df = pd.DataFrame(results)
+                if df.empty:
+                    return []
+                
+                # Pivot für einfachere MA-Berechnung
+                df['date'] = pd.to_datetime(df['date'])
+                df = df.sort_values(['date', 'exchange'])
+                
+                # Berechne MA für jeden Zeitraum
+                for period in TIME_PERIODS:
+                    df[f'ma{period}_count'] = df.groupby('exchange')['trade_count'].transform(
+                        lambda x: x.rolling(window=period, min_periods=1).mean()
+                    )
+                    df[f'ma{period}_volume'] = df.groupby('exchange')['volume'].transform(
+                        lambda x: x.rolling(window=period, min_periods=1).mean()
+                    )
+                
+                # Konvertiere zurück zu Dict-Liste
+                return df.to_dict('records')
+        except Exception as e:
+            logger.error(f"Error calculating exchange daily aggregations: {e}")
+        return []
+    
+    def calculate_stock_trends(self, days: int = 365) -> List[Dict]:
+        """Berechnet Trenddaten je ISIN mit Änderungsprozenten"""
+        end_date = datetime.datetime.now(datetime.timezone.utc)
+        start_date = end_date - datetime.timedelta(days=days)
         
-        
-        # 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,
+        # Aktuelle Periode
+        query_current = f"""
+        select 
+            date_trunc('day', timestamp) as date,
             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
+            sum(price * quantity) as volume
+        from trades
+        where timestamp >= '{start_date.strftime('%Y-%m-%d')}'
+        group by date, isin
+        order by date asc, isin asc
         """
         
-        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")
+        try:
+            response = requests.get(f"{self.db_url}/exec", params={'query': query_current}, auth=DB_AUTH)
+            if response.status_code == 200:
+                data = response.json()
+                columns = data.get('columns', [])
+                dataset = data.get('dataset', [])
+                
+                results = []
+                for row in dataset:
+                    result = {}
+                    for i, col in enumerate(columns):
+                        result[col['name']] = row[i]
+                    results.append(result)
+                
+                if not results:
+                    return []
+                
+                df = pd.DataFrame(results)
+                df['date'] = pd.to_datetime(df['date'])
+                
+                # Aggregiere je ISIN über den gesamten Zeitraum
+                df_agg = df.groupby('isin').agg({
+                    'trade_count': 'sum',
+                    'volume': 'sum'
+                }).reset_index()
+                
+                # Berechne Änderungen: Vergleich mit vorheriger Periode
+                # Für jede ISIN: aktueller Zeitraum vs. vorheriger Zeitraum
+                trends = []
+                for isin in df_agg['isin'].unique():
+                    isin_data = df[df['isin'] == isin].sort_values('date')
+                    
+                    # Teile in zwei Hälften für Vergleich
+                    mid_point = len(isin_data) // 2
+                    if mid_point > 0:
+                        first_half = isin_data.iloc[:mid_point]
+                        second_half = isin_data.iloc[mid_point:]
+                        
+                        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()
+                        
+                        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
+                    else:
+                        count_change = 0
+                        volume_change = 0
+                    
+                    total_count = isin_data['trade_count'].sum()
+                    total_volume = isin_data['volume'].sum()
+                    
+                    trends.append({
+                        'isin': isin,
+                        'date': isin_data['date'].max(),
+                        'trade_count': total_count,
+                        'volume': total_volume,
+                        'count_change_pct': count_change,
+                        'volume_change_pct': volume_change
+                    })
+                
+                return trends
+        except Exception as e:
+            logger.error(f"Error calculating stock trends: {e}")
+        return []
+    
+    def calculate_volume_changes(self, days: int = 365) -> List[Dict]:
+        """Berechnet Volumen- und Anzahl-Änderungen je Exchange"""
+        end_date = datetime.datetime.now(datetime.timezone.utc)
+        start_date = end_date - datetime.timedelta(days=days)
+        
+        query = f"""
+        select 
+            date_trunc('day', timestamp) as date,
+            exchange,
+            count(*) as trade_count,
+            sum(price * quantity) as volume
+        from trades
+        where timestamp >= '{start_date.strftime('%Y-%m-%d')}'
+        group by date, exchange
+        order by date asc, exchange asc
+        """
+        
+        try:
+            response = requests.get(f"{self.db_url}/exec", params={'query': query}, auth=DB_AUTH)
+            if response.status_code == 200:
+                data = response.json()
+                columns = data.get('columns', [])
+                dataset = data.get('dataset', [])
+                
+                results = []
+                for row in dataset:
+                    result = {}
+                    for i, col in enumerate(columns):
+                        result[col['name']] = row[i]
+                    results.append(result)
+                
+                if not results:
+                    return []
+                
+                df = pd.DataFrame(results)
+                df['date'] = pd.to_datetime(df['date'])
+                df = df.sort_values(['date', 'exchange'])
+                
+                # Berechne Änderungen je Exchange
+                changes = []
+                for exchange in df['exchange'].unique():
+                    exchange_data = df[df['exchange'] == exchange].sort_values('date')
+                    
+                    # 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:]
+                        
+                        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()
+                        
+                        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
+                        
+                        # 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"
+                    
+                    total_count = exchange_data['trade_count'].sum()
+                    total_volume = exchange_data['volume'].sum()
+                    
+                    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 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"""
+        if not data:
+            return
+        
+        lines = []
+        for row in data:
+            try:
+                # Konvertiere Datum zu Timestamp
+                if 'date' in row:
+                    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)
+                else:
+                    timestamp_ns = int(datetime.datetime.now(datetime.timezone.utc).timestamp() * 1e9)
+                
+                # Baue ILP-Line auf
+                tags = []
+                fields = []
+                
+                # Exchange als Tag
+                if 'exchange' in row and row['exchange']:
+                    exchange = str(row['exchange']).replace(' ', '\\ ').replace(',', '\\,')
+                    tags.append(f"exchange={exchange}")
+                
+                # ISIN als Tag
+                if 'isin' in row and row['isin']:
+                    isin = str(row['isin']).replace(' ', '\\ ').replace(',', '\\,')
+                    tags.append(f"isin={isin}")
+                
+                # Trend als Tag
+                if 'trend' in row and row['trend']:
+                    trend = str(row['trend']).replace(' ', '\\ ').replace(',', '\\,')
+                    tags.append(f"trend={trend}")
+                
+                # Numerische Felder
+                for key, value in row.items():
+                    if key in ['date', 'exchange', 'isin', 'trend']:
+                        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}"')
+                
+                if tags and fields:
+                    line = f"{table_name},{','.join(tags)} {','.join(fields)} {timestamp_ns}"
+                    lines.append(line)
+            except Exception as e:
+                logger.error(f"Error formatting row for {table_name}: {e}, row: {row}")
+                continue
+        
+        if not lines:
+            return
+        
+        payload = "\n".join(lines) + "\n"
+        
+        try:
+            response = requests.post(
+                f"{self.db_url}/write",
+                data=payload,
+                params={'precision': 'ns'},
+                auth=DB_AUTH
+            )
+            if response.status_code not in [200, 204]:
+                logger.error(f"Error saving to {table_name}: {response.text}")
+            else:
+                logger.info(f"Saved {len(lines)} rows to {table_name}")
+        except Exception as e:
+            logger.error(f"Error connecting to QuestDB: {e}")
+    
+    def process_all_analytics(self):
+        """Verarbeitet alle Analytics für alle Zeiträume"""
+        logger.info("Starting analytics processing...")
+        
+        # 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)
+        
+        # 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)
+        
+        # 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("Analytics processing completed.")
+    
+    def run(self):
+        """Hauptschleife des Workers"""
+        logger.info("Analytics Worker started.")
+        
+        # Initiale Verarbeitung
+        self.process_all_analytics()
+        self.last_processed_timestamp = datetime.datetime.now(datetime.timezone.utc)
+        
+        # Polling-Schleife
+        while True:
+            try:
+                # Prüfe auf neue Trades
+                last_ts = self.get_last_processed_timestamp()
+                new_trades = self.get_new_trades(since=last_ts)
+                
+                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.")
+                
+                # Warte 30 Sekunden vor nächster Prüfung
+                time.sleep(30)
+            except Exception as e:
+                logger.error(f"Error in worker loop: {e}")
+                time.sleep(60)  # Längere Pause bei Fehler
 
-    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"
+def main():
+    worker = AnalyticsWorker()
+    worker.run()
 
 if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
-    worker = AnalyticsWorker(db_host="localhost", auth=("admin", "quest"))
-    worker.initialize_tables()
-    worker.run_aggregation()
+    main()

systemd/analytics-worker.service

@@ -0,0 +1,18 @@
+[Unit]
+Description=Trading Analytics Worker
+After=network.target questdb.service
+
+[Service]
+Type=simple
+User=melchiorreimers
+WorkingDirectory=/Users/melchiorreimers/Documents/trading_daemon
+Environment="PYTHONUNBUFFERED=1"
+Environment="DB_USER=admin"
+Environment="DB_PASSWORD=quest"
+Environment="DB_HOST=localhost"
+ExecStart=/usr/bin/python3 -m src.analytics.worker
+Restart=always
+RestartSec=10
+
+[Install]
+WantedBy=multi-user.target