Place-Order-Trading-Bot/analyze_ny_session.py

#!/usr/bin/env python3
"""
🔍 NY Session Fine-Tuning Analyse
Was wäre wenn wir NY Session Threshold erhöhen?
"""

import sqlite3
import pandas as pd

conn = sqlite3.connect('trading_bot.db')

print('=' * 80)
print('🔍 NY SESSION DETAILLIERTE ANALYSE')
print('=' * 80)
print()

# 1. Basis-Performance NY vs Asian
print('1️⃣ NY vs. ASIAN SESSION VERGLEICH')
print('-' * 80)

comparison = pd.read_sql_query('''
    SELECT
        session,
        COUNT(*) as trades,
        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
        SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
        ROUND(AVG(confidence), 1) as avg_conf,
        ROUND(SUM(net_profit), 2) as total_profit,
        ROUND(AVG(net_profit), 2) as avg_profit,
        ROUND(AVG(CASE WHEN net_profit > 0 THEN net_profit END), 2) as avg_win,
        ROUND(AVG(CASE WHEN net_profit < 0 THEN net_profit END), 2) as avg_loss
    FROM trades
    WHERE session IN ('ny', 'asian')
    GROUP BY session
''', conn)

comparison['win_rate'] = (comparison['wins'] / comparison['trades'] * 100).round(1)

print(comparison.to_string(index=False))
print()

# 2. NY Session nach Confidence-Bands
print('=' * 80)
print('2️⃣ NY SESSION: PERFORMANCE NACH CONFIDENCE')
print('-' * 80)

ny_confidence = pd.read_sql_query('''
    SELECT
        CASE
            WHEN confidence >= 99 THEN '99-100%'
            WHEN confidence >= 98 THEN '98-99%'
            WHEN confidence >= 97 THEN '97-98%'
            WHEN confidence >= 96 THEN '96-97%'
            WHEN confidence >= 95 THEN '95-96%'
            ELSE '<95%'
        END as conf_range,
        COUNT(*) as trades,
        SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
        ROUND(AVG(confidence), 1) as avg_conf,
        ROUND(SUM(net_profit), 2) as total_profit,
        ROUND(AVG(net_profit), 2) as avg_profit
    FROM trades
    WHERE session = 'ny'
    GROUP BY conf_range
    ORDER BY avg_conf DESC
''', conn)

ny_confidence['win_rate'] = (ny_confidence['wins'] / ny_confidence['trades'] * 100).round(1)

print(ny_confidence.to_string(index=False))
print()

# 3. Alle NY Trades im Detail
print('=' * 80)
print('3️⃣ ALLE NY TRADES (chronologisch)')
print('-' * 80)

ny_trades = pd.read_sql_query('''
    SELECT
        DATE(entry_time) as date,
        TIME(entry_time) as time,
        ROUND(confidence, 1) as conf,
        quality,
        volume,
        ROUND(net_profit, 2) as profit,
        CASE WHEN net_profit > 0 THEN 'WIN' ELSE 'LOSS' END as result
    FROM trades
    WHERE session = 'ny'
    ORDER BY entry_time
''', conn)

print(ny_trades.to_string(index=False))
print()

# 4. Simulationen
print('=' * 80)
print('4️⃣ SIMULATION: WAS WÄRE WENN...')
print('-' * 80)
print()

scenarios = []

# Aktuell
current = pd.read_sql_query('''
    SELECT COUNT(*) as trades, SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
           ROUND(SUM(net_profit), 2) as profit
    FROM trades WHERE session = 'ny'
''', conn)
scenarios.append({
    'scenario': 'AKTUELL (alle NY)',
    'trades': current['trades'][0],
    'wins': current['wins'][0],
    'profit': current['profit'][0]
})

# >= 97%
sim97 = pd.read_sql_query('''
    SELECT COUNT(*) as trades, SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
           ROUND(SUM(net_profit), 2) as profit
    FROM trades WHERE session = 'ny' AND confidence >= 97
''', conn)
scenarios.append({
    'scenario': 'NY >= 97% Conf',
    'trades': sim97['trades'][0],
    'wins': sim97['wins'][0],
    'profit': sim97['profit'][0]
})

# >= 98%
sim98 = pd.read_sql_query('''
    SELECT COUNT(*) as trades, SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
           ROUND(SUM(net_profit), 2) as profit
    FROM trades WHERE session = 'ny' AND confidence >= 98
''', conn)
scenarios.append({
    'scenario': 'NY >= 98% Conf',
    'trades': sim98['trades'][0],
    'wins': sim98['wins'][0],
    'profit': sim98['profit'][0]
})

# >= 99%
sim99 = pd.read_sql_query('''
    SELECT COUNT(*) as trades, SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
           ROUND(SUM(net_profit), 2) as profit
    FROM trades WHERE session = 'ny' AND confidence >= 99
''', conn)
scenarios.append({
    'scenario': 'NY >= 99% Conf',
    'trades': sim99['trades'][0],
    'wins': sim99['wins'][0],
    'profit': sim99['profit'][0]
})

# NY blockiert
scenarios.append({
    'scenario': 'NY BLOCKIERT',
    'trades': 0,
    'wins': 0,
    'profit': 0.0
})

# DataFrame
sims = pd.DataFrame(scenarios)
sims['win_rate'] = (sims['wins'] / sims['trades'] * 100).round(1)
sims.loc[sims['trades'] == 0, 'win_rate'] = 0
sims['avg_profit'] = (sims['profit'] / sims['trades']).round(2)
sims.loc[sims['trades'] == 0, 'avg_profit'] = 0

print(sims.to_string(index=False))
print()

# 5. Empfehlung
print('=' * 80)
print('5️⃣ EMPFEHLUNG')
print('-' * 80)
print()

print('Basierend auf den Daten:')
print()
print('Option 1: AKTUELL BEHALTEN (alle NY Trades)')
print(f'  Trades: 28')
print(f'  Profit: $1,489')
print(f'  Win-Rate: 46.4%')
print(f'  Pro: Mehr Trades, profitabel')
print(f'  Con: Niedrige Win-Rate, mehr Stress')
print()

print('Option 2: NY >= 98% Confidence')
print(f'  Trades: {sim98["trades"][0]}')
print(f'  Profit: ${sim98["profit"][0]}')
print(f'  Win-Rate: {(sim98["wins"][0]/sim98["trades"][0]*100):.1f}%' if sim98["trades"][0] > 0 else '  Win-Rate: N/A')
print(f'  Pro: Höhere Win-Rate, bessere Qualität')
print(f'  Con: Weniger Trades')
print()

print('Option 3: NY BLOCKIEREN')
print(f'  Trades: 0')
print(f'  Profit: $0')
print(f'  Pro: Focus auf Asian (97.8% WR!), weniger Drawdown')
print(f'  Con: -$1,489 Profit verzichtet')
print()

# Asian Info
asian = pd.read_sql_query('''
    SELECT COUNT(*) as trades, ROUND(SUM(net_profit), 2) as profit
    FROM trades WHERE session = 'asian'
''', conn)

print(f'KONTEXT: Asian Session bringt ${asian["profit"][0]} bei 97.8% WR')
print(f'NY ist nur {(1489/asian["profit"][0]*100):.1f}% vom Asian Profit')
print()

conn.close()

print('=' * 80)
print('FAZIT')
print('=' * 80)
print()
print('1. NY Session ist PROFITABEL aber VOLATIL (46.4% WR)')
print('2. Asian Session ist DOMINANT (97.8% WR, $6,943 Profit)')
print('3. NY Threshold auf 98%+ würde Win-Rate verbessern')
print('4. Oder: Focus auf Asian, NY blockieren (weniger Stress)')
print()