trading-daemon/dashboard/server.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import requests
import os
import logging
from typing import Optional, Dict, Any

logger = logging.getLogger(__name__)

app = FastAPI(title="Trading Dashboard API")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# Serve static files
app.mount("/static", StaticFiles(directory="dashboard/public"), name="static")

@app.get("/")
async def read_index():
    return FileResponse('dashboard/public/index.html')

# QuestDB Konfiguration
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"

# Hilfsfunktionen
def query_questdb(query: str, timeout: int = 10) -> Optional[Dict[str, Any]]:
    """Zentrale QuestDB-Abfrage-Funktion"""
    try:
        response = requests.get(f"{DB_URL}/exec", params={'query': query}, auth=DB_AUTH, timeout=timeout)
        if response.status_code == 200:
            return response.json()
        else:
            raise HTTPException(status_code=response.status_code, detail=f"QuestDB error: {response.text}")
    except requests.exceptions.Timeout:
        raise HTTPException(status_code=504, detail="QuestDB query timeout")
    except requests.exceptions.RequestException as e:
        raise HTTPException(status_code=500, detail=f"QuestDB connection error: {str(e)}")

def format_questdb_response(data: Dict[str, Any]) -> Dict[str, Any]:
    """Einheitliche Formatierung der QuestDB-Antworten"""
    if not data:
        return {'columns': [], 'dataset': []}
    return data

# API Endpunkte
@app.get("/api/trades")
async def get_trades(isin: str = None, days: int = 7):
    """
    Gibt aggregierte Analyse aller Trades zurück (nicht einzelne Trades).
    Nutzt vorberechnete Daten aus analytics_exchange_daily.
    """
    if isin:
        # Für spezifische ISIN: hole aus trades Tabelle
        query = f"""
        select 
            date_trunc('day', timestamp) as date,
            count(*) as trade_count,
            sum(price * quantity) as volume,
            avg(price) as avg_price
        from trades
        where isin = '{isin}'
        and timestamp > dateadd('d', -{days}, now())
        group by date
        order by date desc
        """
    else:
        # Aggregierte Daten aus analytics_exchange_daily
        query = f"""
        select 
            timestamp as date,
            exchange,
            trade_count,
            volume
        from analytics_exchange_daily
        where timestamp >= dateadd('d', -{days}, now())
        order by date desc, exchange asc
        """
    
    data = query_questdb(query)
    
    # Wenn analytics_exchange_daily leer ist, gib leere Daten zurück
    # Der Analytics Worker sollte die Daten berechnen
    if not data or not data.get('dataset') or len(data.get('dataset', [])) == 0:
        logger.warning(f"analytics_exchange_daily is empty. Analytics worker should calculate this data.")
        return {'columns': [{'name': 'date'}, {'name': 'exchange'}, {'name': 'trade_count'}, {'name': 'volume'}], 'dataset': []}
    
    return format_questdb_response(data)

@app.get("/api/metadata")
async def get_metadata():
    """Gibt alle Metadata zurück"""
    query = "select * from metadata"
    data = query_questdb(query)
    return format_questdb_response(data)

@app.get("/api/summary")
async def get_summary(days: int = None):
    """
    Gibt Zusammenfassung zurück. Nutzt analytics_daily_summary für total_trades.
    Optional: days Parameter für Zeitraum-basierte Zusammenfassung.
    """
    if days:
        # Zeitraum-basierte Zusammenfassung
        query = f"""
        select 
            sum(total_trades) as total_trades,
            sum(total_volume) as total_volume
        from analytics_daily_summary
        where timestamp >= dateadd('d', -{days}, now())
        """
    else:
        # Gesamtzahl aller Trades
        query = """
        select 
            sum(total_trades) as total_trades,
            sum(total_volume) as total_volume
        from analytics_daily_summary
        """
    
    data = query_questdb(query)
    
    # Wenn analytics_daily_summary leer ist, gib leere Daten zurück
    # Der Analytics Worker sollte die Daten berechnen
    if not data or not data.get('dataset') or not data['dataset']:
        logger.warning(f"analytics_daily_summary is empty. Analytics worker should calculate this data.")
        return {
            'columns': [
                {'name': 'continent'},
                {'name': 'trade_count'},
                {'name': 'total_volume'}
            ],
            'dataset': [['All', 0, 0.0]]
        }
    
    if data and data.get('dataset') and data['dataset']:
        total_trades = data['dataset'][0][0] if data['dataset'][0][0] else 0
        total_volume = data['dataset'][0][1] if data['dataset'][0][1] else 0.0
        
        # Formatiere für Kompatibilität
        return {
            'columns': [
                {'name': 'continent'},
                {'name': 'trade_count'},
                {'name': 'total_volume'}
            ],
            'dataset': [['All', total_trades, total_volume]]
        }
    
    # Wenn keine Daten vorhanden, gib leere Daten zurück
    return {
        'columns': [
            {'name': 'continent'},
            {'name': 'trade_count'},
            {'name': 'total_volume'}
        ],
        'dataset': [['All', 0, 0.0]]
    }

@app.get("/api/statistics/total-trades")
async def get_total_trades(days: int = None):
    """Gibt Gesamtzahl aller Trades zurück (aus analytics_daily_summary). Optional: days Parameter für Zeitraum."""
    if days:
        query = f"select sum(total_trades) as total from analytics_daily_summary where timestamp >= dateadd('d', -{days}, now())"
    else:
        query = "select sum(total_trades) as total from analytics_daily_summary"
    data = query_questdb(query)
    if data and data.get('dataset') and data['dataset']:
        total = data['dataset'][0][0] if data['dataset'][0][0] else 0
        return {'total_trades': total}
    return {'total_trades': 0}

@app.get("/api/custom-analytics")
async def get_custom_analytics(
    date_from: str,
    date_to: str,
    x_axis: str = "date",
    y_axis: str = "volume",
    group_by: str = "exchange",
    exchanges: str = None
):
    """
    Flexibler Analytics-Endpunkt für custom Graphen.
    Nutzt vorberechnete Daten aus analytics_custom für bessere Performance.
    
    Parameters:
    - date_from: Startdatum (YYYY-MM-DD)
    - date_to: Enddatum (YYYY-MM-DD)
    - x_axis: X-Achse (date, exchange, isin) - aktuell nur "date" unterstützt
    - y_axis: Y-Achse (volume, trade_count, avg_price)
    - group_by: Gruppierung (exchange, isin, date)
    - exchanges: Komma-separierte Liste von Exchanges (optional)
    """
    # Validiere Parameter
    valid_x_axis = ["date", "exchange", "isin"]
    valid_y_axis = ["volume", "trade_count", "avg_price"]
    valid_group_by = ["exchange", "isin", "date"]
    
    if x_axis not in valid_x_axis:
        raise HTTPException(status_code=400, detail=f"Invalid x_axis. Must be one of: {valid_x_axis}")
    if y_axis not in valid_y_axis:
        raise HTTPException(status_code=400, detail=f"Invalid y_axis. Must be one of: {valid_y_axis}")
    if group_by not in valid_group_by:
        raise HTTPException(status_code=400, detail=f"Invalid group_by. Must be one of: {valid_group_by}")
    
    # Für Custom Analytics: x_axis muss "date" sein (wird täglich vorberechnet)
    if x_axis != "date":
        # Für nicht-date x_axis: gib Fehler zurück, da dies nicht vorberechnet wird
        raise HTTPException(
            status_code=400, 
            detail="x_axis must be 'date' for pre-calculated analytics. Other x_axis values are not supported for performance reasons."
        )
        y_axis_map = {
            "volume": "sum(price * quantity)",
            "trade_count": "count(*)",
            "avg_price": "avg(price)"
        }
        x_axis_map = {
            "exchange": "exchange",
            "isin": "isin"
        }
        group_by_map = {
            "exchange": "exchange",
            "isin": "isin",
            "date": "date_trunc('day', timestamp)"
        }
        
        y_metric = y_axis_map[y_axis]
        x_label = x_axis_map[x_axis]
        group_by_field = group_by_map[group_by]
        
        query = f"""
        select 
            {x_label} as x_value,
            {group_by_field} as group_value,
            {y_metric} as y_value
        from trades
        where timestamp >= '{date_from}'
        and timestamp <= '{date_to}'
        """
        
        if exchanges:
            exchange_list = ",".join([f"'{e.strip()}'" for e in exchanges.split(",")])
            query += f" and exchange in ({exchange_list})"
        
        query += f" group by {x_label}, {group_by_field} order by {x_label} asc, {group_by_field} asc"
        
        data = query_questdb(query, timeout=15)
        return format_questdb_response(data)
    
    # Nutze vorberechnete Daten aus analytics_custom
    exchange_filter = "all"
    if exchanges:
        # Wenn mehrere Exchanges angegeben, müssen wir kombinieren
        # Für jetzt: nutze nur wenn ein Exchange angegeben ist
        exchange_list = [e.strip() for e in exchanges.split(",")]
        if len(exchange_list) == 1:
            exchange_filter = exchange_list[0]
        else:
            # Bei mehreren Exchanges: gib Fehler zurück, da dies nicht vorberechnet wird
            raise HTTPException(
                status_code=400,
                detail="Multiple exchanges are not supported for pre-calculated analytics. Please specify a single exchange or leave empty for all exchanges."
            )
    
    # Query für vorberechnete Daten
    query = f"""
    select 
        timestamp as x_value,
        group_value,
        y_value
    from analytics_custom
    where timestamp >= '{date_from}'
    and timestamp <= '{date_to}'
    and y_axis = '{y_axis}'
    and group_by = '{group_by}'
    and exchange_filter = '{exchange_filter}'
    order by timestamp asc, group_value asc
    """
    
    data = query_questdb(query, timeout=5)
    if not data or not data.get('dataset'):
        # Wenn keine vorberechneten Daten vorhanden, gib leere Daten zurück
        logger.warning(f"No pre-calculated data found in analytics_custom. Analytics worker should calculate this data.")
        return {
            'columns': [
                {'name': 'x_value'},
                {'name': 'group_value'},
                {'name': 'y_value'}
            ],
            'dataset': []
        }
    
    return format_questdb_response(data)

@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")
    
    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"
    
    data = query_questdb(query, timeout=5)
    
    # Wenn analytics_exchange_daily leer ist, gib leere Daten zurück
    # Der Analytics Worker sollte die Daten berechnen
    if not data or not data.get('dataset') or len(data.get('dataset', [])) == 0:
        logger.warning(f"analytics_exchange_daily is empty. Analytics worker should calculate this data.")
        return {
            'columns': [
                {'name': 'date'},
                {'name': 'exchange'},
                {'name': 'trade_count'},
                {'name': 'volume'},
                {'name': 'ma_count'},
                {'name': 'ma_volume'}
            ],
            'dataset': []
        }
    
    return format_questdb_response(data)

@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")
    
    # Hole die neuesten Daten für den angegebenen Zeitraum
    query = f"""
    select 
        exchange,
        trade_count,
        volume,
        count_change_pct,
        volume_change_pct,
        trend
    from analytics_volume_changes
    where period_days = {days}
    order by timestamp desc
    limit 20
    """
    
    data = query_questdb(query, timeout=5)
    
    # Wenn keine vorberechneten Daten vorhanden, gib leere Daten zurück
    # Der Analytics Worker sollte die Daten berechnen
    if not data or not data.get('dataset'):
        logger.warning(f"No pre-calculated volume changes found for {days} days. Analytics worker should calculate this data.")
        return {
            'columns': [
                {'name': 'exchange'},
                {'name': 'trade_count'},
                {'name': 'volume'},
                {'name': 'count_change_pct'},
                {'name': 'volume_change_pct'},
                {'name': 'trend'}
            ],
            'dataset': []
        }
    
    return format_questdb_response(data)

@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")
    
    query = f"""
    select 
        timestamp as date,
        isin,
        trade_count,
        volume,
        count_change_pct,
        volume_change_pct
    from analytics_stock_trends
    where period_days = {days}
    and timestamp >= dateadd('d', -{days}, now())
    order by volume desc
    limit {limit}
    """
    
    data = query_questdb(query, timeout=5)
    return format_questdb_response(data)

@app.get("/api/analytics")
async def get_analytics(
    metric: str = "volume", 
    group_by: str = "day", 
    sub_group_by: str = None,
    date_from: str = None, 
    date_to: str = None,
    isins: str = None,
    continents: str = None
):
    """Analytics Endpunkt für Report Builder"""
    composite_keys = ["exchange_continent", "exchange_sector"]
    needs_metadata = any([
        group_by in ["name", "continent", "sector"] + composite_keys,
        sub_group_by in ["name", "continent", "sector"] + composite_keys,
        continents is not None
    ])
    
    t_prefix = "t." if needs_metadata else ""
    m_prefix = "m." if needs_metadata else ""
    
    metrics_map = {
        "volume": f"sum({t_prefix}price * {t_prefix}quantity)",
        "count": f"count(*)",
        "avg_price": f"avg({t_prefix}price)"
    }
    
    groups_map = {
        "day": f"date_trunc('day', {t_prefix}timestamp)",
        "month": f"date_trunc('month', {t_prefix}timestamp)",
        "exchange": f"{t_prefix}exchange",
        "isin": f"{t_prefix}isin",
        "name": f"coalesce({m_prefix}name, {t_prefix}isin)" if needs_metadata else "isin",
        "continent": f"coalesce({m_prefix}continent, 'Unknown')" if needs_metadata else "'Unknown'",
        "sector": f"coalesce({m_prefix}sector, 'Unknown')" if needs_metadata else "'Unknown'",
        "exchange_continent": f"concat({t_prefix}exchange, ' - ', coalesce({m_prefix}continent, 'Unknown'))" if needs_metadata else "'Unknown'",
        "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"])
    
    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"
    
    data = query_questdb(query)
    return format_questdb_response(data)

@app.get("/api/metadata/search")
async def search_metadata(q: str):
    """Case-insensitive search for ISIN or Name"""
    query = f"select isin, name from metadata where isin ilike '%{q}%' or name ilike '%{q}%' limit 10"
    data = query_questdb(query)
    return format_questdb_response(data)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)