核心结论：一站式 Lösung für Volatilitätsstrategie-Backtesting
Die Analyse von Deribit-Optionsketten erfordert eine zuverlässige Dateninfrastruktur, die historische Greeks, implizite Volatilität, Trades und Orderbuchdaten nahtlos speichert. Jetzt registrieren und von unter 50ms Latenz sowie 85% Kostenersparnis gegenüber alternativen APIs profitieren. Dieser Leitfaden zeigt die vollständige Architektur von der Datenakquise bis zur Backtesting-Pipeline.
| Kriterium | HolySheep AI | Deribit Official API | CoinAPI | CCXT Pro |
|---|---|---|---|---|
| Preis pro 1M Token | $0.42 (DeepSeek V3.2) | €50-500/Monat | $79-599/Monat | $30-300/Monat |
| Latenz | <50ms ★★★★★ | 100-300ms | 200-500ms | 150-400ms |
| Zahlungsmethoden | WeChat, Alipay, USDT | Nur Kreditkarte | Kreditkarte, PayPal | Krypto, Kreditkarte |
| Modellabdeckung | GPT-4.1, Claude 4.5, Gemini 2.5, DeepSeek | N/A (nur Rohdaten) | Begrenzt | Begrenzt |
| Geeignet für | Volatility Traders, Quant-Teams | Direkte Nutzung | Multi-Asset | Retail-Trader |
| kostenlose Credits | ✓ Ja | ✗ Nein | ✗ Nein | ✗ Nein |
Geeignet / Nicht geeignet für
✓ Ideal für:
- Volatilitäts-Strategieentwickler — Greeks und IV-Historie für Optionspricings
- Quant-Trading-Teams — Full-Stäck-Datenpipelines mit <50ms Latenz
- HFT-Firmen — Echtzeit-Orderbuch-Capture für Arbitrage-Strategien
- Research-Abteilungen — Historische Daten für Backtesting und Modellvalidierung
✗ Nicht optimal für:
- Einsteiger ohne Programmiererfahrung (erfordert API-Integration)
- Langfristige Storage-only-Projekte ohne Echtzeitanforderungen
Preise und ROI-Analyse 2026
Die Kostenstruktur von HolySheep bietet deutliche Vorteile für datenintensive Anwendungen:
| Modell | Preis pro 1M Tok | Ersparnis vs. OpenAI | Use Case |
|---|---|---|---|
| DeepSeek V3.2 | $0.42 | 91% | Datenverarbeitung, Greeks-Berechnung |
| Gemini 2.5 Flash | $2.50 | 75% | Strategy-Backtesting |
| GPT-4.1 | $8.00 | 20% | Komplexe Analyse |
| Claude Sonnet 4.5 | $15.00 | 25% | Research, Berichte |
ROI-Beispiel: Ein Team mit 10M monatlichen Tokens spart mit HolySheep gegenüber der offiziellen Deribit-API ca. €3.000-8.000 pro Monat bei gleichzeitig besserer Latenz.
Warum HolySheep wählen?
- Kursvorteil: ¥1 = $1 bedeutet 85%+ Ersparnis für chinesische und internationale Nutzer
- Flexible Zahlung: WeChat Pay, Alipay, USDT — keine westlichen Kreditkarten nötig
- Performance: <50ms Latenz für Echtzeit-Datenpipelines kritisch für Volatilitäts-Strategien
- Startguthaben: Kostenlose Credits für sofortige Tests ohne Initialkosten
- Multi-Modell: GPT-4.1, Claude 4.5, Gemini 2.5 Flash und DeepSeek V3.2 in einer Plattform
Architektur der Deribit-Datenerfassung
Systemübersicht
Die vollständige Pipeline zur Speicherung von Optionsdaten für Backtesting besteht aus vier Hauptkomponenten:
- WebSocket-Stream: Echtzeit-Orderbuch und Trades von Deribit
- REST-Polling: Greeks und IV-Daten für alle Strikes
- Daten-Transformations-Layer: Normalisierung mit HolySheep AI
- Time-Series-DB: InfluxDB/TimescaleDB für effiziente Queries
Vollständige Implementierung
1. WebSocket-Datenakquise für Orderbuch und Trades
#!/usr/bin/env python3
"""
Deribit WebSocket Client für Orderbuch- und Trade-Capture
Speichert in TimescaleDB für Backtesting
"""
import websockets
import asyncio
import json
import psycopg2
from datetime import datetime
from typing import Dict, List
HolySheep AI für Datenanreicherung
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
DERIBIT_WS = "wss://test.deribit.com/ws/v2"
DB_CONFIG = {
"host": "localhost",
"database": "deribit_options",
"user": "quant_user",
"password": "secure_password"
}
class DeribitDataCapture:
def __init__(self, instrument: str = "BTC-PERPETUAL"):
self.instrument = instrument
self.orderbook_cache = {}
self.trade_buffer = []
async def connect(self):
"""WebSocket-Verbindung zu Deribit"""
async with websockets.connect(DERIBIT_WS) as ws:
# Subscribe zu Orderbuch
await ws.send(json.dumps({
"jsonrpc": "2.0",
"id": 1,
"method": "subscribe",
"params": {
"channels": [
f"book.{self.instrument}.none.10.100ms",
f"trades.{self.instrument}.100ms"
]
}
}))
await self._process_messages(ws)
async def _process_messages(self, ws):
"""Verarbeitet eingehende WebSocket-Nachrichten"""
async for msg in ws:
data = json.loads(msg)
if "params" in data:
channel = data["params"]["channel"]
payload = data["params"]["data"]
if "book" in channel:
await self._store_orderbook(payload)
elif "trades" in channel:
await self._store_trades(payload)
async def _store_orderbook(self, data: Dict):
"""Speichert Orderbuch-Level-2-Daten"""
conn = psycopg2.connect(**DB_CONFIG)
cursor = conn.cursor()
timestamp = datetime.utcnow()
# Bids (Kaufseite)
for level in data.get("bids", []):
cursor.execute("""
INSERT INTO orderbook_btc
(timestamp, price, amount, side, instrument)
VALUES (%s, %s, %s, 'bid', %s)
""", (timestamp, level[0], level[1], self.instrument))
# Asks (Verkaufsseite)
for level in data.get("asks", []):
cursor.execute("""
INSERT INTO orderbook_btc
(timestamp, price, amount, side, instrument)
VALUES (%s, %s, %s, 'ask', %s)
""", (timestamp, level[0], level[1], self.instrument))
conn.commit()
cursor.close()
conn.close()
# Cache für Greeks-Berechnung aktualisieren
self.orderbook_cache = data
async def _store_trades(self, data: Dict):
"""Speichert ausgeführte Trades"""
conn = psycopg2.connect(**DB_CONFIG)
cursor = conn.cursor()
trades = data if isinstance(data, list) else [data]
for trade in trades:
cursor.execute("""
INSERT INTO trades_btc
(timestamp, price, amount, direction, instrument_id)
VALUES (%s, %s, %s, %s, %s)
""", (
datetime.fromtimestamp(trade["timestamp"]/1000),
trade["price"],
trade["amount"],
trade["direction"],
self.instrument
))
conn.commit()
cursor.close()
conn.close()
Datenbank-Initialisierung
def init_database():
"""Erstellt TimescaleDB-Hypertable für effiziente Zeitabfragen"""
conn = psycopg2.connect(**DB_CONFIG)
cursor = conn.cursor()
# Orderbuch-Tabelle
cursor.execute("""
CREATE TABLE IF NOT EXISTS orderbook_btc (
id BIGSERIAL,
timestamp TIMESTAMPTZ NOT NULL,
price DECIMAL(18,2),
amount DECIMAL(18,8),
side VARCHAR(3),
instrument VARCHAR(50)
);
""")
# Trades-Tabelle
cursor.execute("""
CREATE TABLE IF NOT EXISTS trades_btc (
id BIGSERIAL,
timestamp TIMESTAMPTZ NOT NULL,
price DECIMAL(18,2),
amount DECIMAL(18,8),
direction VARCHAR(4),
instrument_id VARCHAR(50)
);
""")
# Konvertiere zu Hypertable für bessere Performance
cursor.execute("""
SELECT create_hypertable('orderbook_btc', 'timestamp',
if_not_exists => TRUE);
SELECT create_hypertable('trades_btc', 'timestamp',
if_not_exists => TRUE);
""")
conn.commit()
cursor.close()
conn.close()
print("✓ Datenbank initialisiert mit TimescaleDB Hypertable")
if __name__ == "__main__":
init_database()
capture = DeribitDataCapture("BTC-PERPETUAL")
asyncio.run(capture.connect())2. Greeks und IV-Historie采集
#!/usr/bin/env python3
"""
Deribit Greeks und IV Historical Collector
Speichert Delta, Gamma, Vega, Theta, Rho und implizite Volatilität
"""
import requests
import pandas as pd
import psycopg2
from datetime import datetime, timedelta
import time
import hashlib
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
DERIBIT_API = "https://test.deribit.com/api/v2"
DB_CONFIG = {
"host": "localhost",
"database": "deribit_options",
"user": "quant_user",
"password": "secure_password"
}
class GreeksCollector:
def __init__(self):
self.session = requests.Session()
self.session.headers.update({"Content-Type": "application/json"})
def get_all_options(self, currency: str = "BTC") -> list:
"""Holt alle aktiven Optionskontrakte"""
response = self.session.get(
f"{DERIBIT_API}/public/get_book_summary_by_currency",
params={"currency": currency, "kind": "option"}
)
data = response.json()
return data.get("result", [])
def get_option_details(self, instrument_name: str) -> dict:
"""Holt detaillierte Greeks für ein einzelnes Instrument"""
response = self.session.get(
f"{DERIBIT_API}/public/get_book_summary_by_instrument",
params={"instrument_name": instrument_name}
)
return response.json().get("result", {})
def get_option_book(self, instrument_name: str) -> dict:
"""Holt Orderbuch mit Greeks"""
response = self.session.post(
f"{DERIBIT_API}/public/get_order_book",
json={"instrument_name": instrument_name}
)
result = response.json().get("result", {})
return {
"instrument_name": instrument_name,
"greeks": result.get("greeks", {}),
"underlying_price": result.get("underlying_price"),
"mark_price": result.get("mark_price"),
"best_bid_price": result.get("best_bid_price"),
"best_ask_price": result.get("best_ask_price"),
"timestamp": datetime.utcnow()
}
def calculate_iv_from_greeks(self, greeks: dict) -> float:
"""Berechnet IV aus vorhandenen Greeks-Daten oder schätzt"""
if "iv" in greeks and greeks["iv"]:
return float(greeks["iv"])
# Fallback: Schätzung basierend auf Markpreisen
# Nutze HolySheep AI für präzise Berechnung
return None
def enrich_with_ai(self, option_data: dict) -> dict:
"""
Nutzt HolySheep AI zur Datenanreicherung
Für komplexe Greeks-Berechnungen und IV-Smile-Analyse
"""
import openai
client = openai.OpenAI(
api_key=API_KEY,
base_url=BASE_URL
)
prompt = f"""
Analysiere folgende Optionsdaten für Volatilitätsstrategie:
Instrument: {option_data['instrument_name']}
Greeks: {option_data.get('greeks', {})}
Mark Price: {option_data.get('mark_price')}
Underlying: {option_data.get('underlying_price')}
Berechne und schätze:
1. Implizite Volatilität
2. Put-Call-Parity-Deviation
3. Risk-Reversal Signal
4. Strangle-Breakeven
"""
try:
response = client.chat.completions.create(
model="gpt-4.1",
messages=[
{"role": "system", "content": "Du bist ein quantitativer Optionsanalyst."},
{"role": "user", "content": prompt}
],
temperature=0.1
)
analysis = response.choices[0].message.content
return {"original": option_data, "ai_analysis": analysis}
except Exception as e:
print(f"AI enrichment failed: {e}")
return {"original": option_data, "ai_analysis": None}
def store_greeks_batch(self, options_data: list):
"""Batch-Insert für Performance"""
conn = psycopg2.connect(**DB_CONFIG)
cursor = conn.cursor()
records = []
for opt in options_data:
greeks = opt.get("greeks", {})
records.append((
opt.get("timestamp", datetime.utcnow()),
opt["instrument_name"],
greeks.get("delta"),
greeks.get("gamma"),
greeks.get("vega"),
greeks.get("theta"),
greeks.get("rho"),
float(opt.get("underlying_price", 0)),
float(opt.get("mark_price", 0)),
float(opt.get("best_bid_price", 0)),
float(opt.get("best_ask_price", 0)),
self.calculate_iv_from_greeks(greeks)
))
cursor.executemany("""
INSERT INTO greeks_history
(timestamp, instrument, delta, gamma, vega, theta, rho,
underlying_price, mark_price, bid, ask, iv)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
""", records)
conn.commit()
cursor.close()
conn.close()
print(f"✓ {len(records)} Greeks-Records gespeichert")
def continuous_collector(self, interval_seconds: int = 60):
"""Kontinuierliche Datensammlung im Hintergrund"""
print(f"Starte kontinuierliche Greeks-Sammlung (Intervall: {interval_seconds}s)")
while True:
try:
instruments = self.get_all_options()
print(f"Gefundene Instrumente: {len(instruments)}")
batch_data = []
for inst in instruments:
details = self.get_option_book(inst["instrument_name"])
enriched = self.enrich_with_ai(details)
batch_data.append(enriched["original"])
# Rate limiting für API
time.sleep(0.1)
self.store_greeks_batch(batch_data)
except Exception as e:
print(f"Fehler: {e}")
time.sleep(interval_seconds)
def init_greeks_table():
"""Erstellt Greeks-Hypertable"""
conn = psycopg2.connect(**DB_CONFIG)
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS greeks_history (
id BIGSERIAL,
timestamp TIMESTAMPTZ NOT NULL,
instrument VARCHAR(100),
delta DECIMAL(10,6),
gamma DECIMAL(10,6),
vega DECIMAL(10,4),
theta DECIMAL(10,4),
rho DECIMAL(10,4),
underlying_price DECIMAL(18,2),
mark_price DECIMAL(18,4),
bid DECIMAL(18,4),
ask DECIMAL(18,4),
iv DECIMAL(8,4),
PRIMARY KEY (timestamp, instrument, id)
);
""")
cursor.execute("""
SELECT create_hypertable('greeks_history', 'timestamp',
if_not_exists => TRUE);
""")
# Index für schnelle Greeks-Queries
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_greeks_instrument
ON greeks_history (instrument, timestamp DESC);
""")
conn.commit()
cursor.close()
conn.close()
print("✓ Greeks-Tabelle initialisiert")
if __name__ == "__main__":
init_greeks_table()
collector = GreeksCollector()
collector.continuous_collector(interval_seconds=60)3. Backtesting-Engine mit HolySheep AI
#!/usr/bin/env python3
"""
Volatility Strategy Backtester
Nutzt HolySheep AI für Strategieanalyse und Optimierung
"""
import psycopg2
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Tuple
import json
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
DB_CONFIG = {
"host": "localhost",
"database": "deribit_options",
"user": "quant_user",
"password": "secure_password"
}
class VolatilityBacktester:
def __init__(self):
self.strategy_results = []
def load_historical_data(
self,
instrument: str,
start: datetime,
end: datetime
) -> pd.DataFrame:
"""Lädt historische Daten für Backtesting"""
conn = psycopg2.connect(**DB_CONFIG)
query = """
SELECT
time_bucket('1 minute', g.timestamp) as bucket,
g.instrument,
AVG(g.delta) as avg_delta,
AVG(g.gamma) as avg_gamma,
AVG(g.vega) as avg_vega,
AVG(g.theta) as avg_theta,
AVG(g.iv) as avg_iv,
AVG(g.underlying_price) as underlying,
AVG(g.mark_price) as option_price,
COUNT(*) as sample_count
FROM greeks_history g
WHERE g.instrument = %s
AND g.timestamp BETWEEN %s AND %s
GROUP BY bucket, g.instrument
ORDER BY bucket;
"""
df = pd.read_sql_query(query, conn, params=[instrument, start, end])
conn.close()
return df
def calculate_volatility_signal(self, df: pd.DataFrame) -> pd.DataFrame:
"""Berechnet Volatilitätssignale für Strategie"""
# IV-Rank: aktuelle IV vs. historisches 30-Tage-Hoch/Tief
df["iv_percentile"] = df["avg_iv"].rank(pct=True)
# IV-HV-Spread: Implied vs. Historical Volatility
df["returns"] = df["underlying"].pct_change()
df["hv_20"] = df["returns"].rolling(20).std() * np.sqrt(365 * 24)
df["iv_hv_spread"] = df["avg_iv"] - df["hv_20"]
# Skew-Signal
df["iv_skew"] = df["avg_iv"].rolling(5).skew()
return df
def generate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
"""Generiert Trading-Signale basierend auf Volatilität"""
df["signal"] = 0
# Long Volatility: IV-Rank < 20% und negativer Spread
df.loc[(df["iv_percentile"] < 0.20) &
(df["iv_hv_spread"] < -0.05), "signal"] = 1
# Short Volatility: IV-Rank > 80% und positiver Spread
df.loc[(df["iv_percentile"] > 0.80) &
(df["iv_hv_spread"] > 0.05), "signal"] = -1
return df
def backtest_strategy(
self,
df: pd.DataFrame,
capital: float = 100000,
position_size: float = 0.1
) -> dict:
"""Führt Backtest durch"""
df = self.calculate_volatility_signal(df)
df = self.generate_signals(df)
df["position"] = df["signal"].shift(1) * capital * position_size
df["pnl"] = df["position"].shift(1) * df["returns"]
df["cumulative_pnl"] = df["pnl"].cumsum()
# Performance-Metriken
total_return = df["cumulative_pnl"].iloc[-1] if len(df) > 0 else 0
sharpe = df["pnl"].mean() / df["pnl"].std() * np.sqrt(365*24) if df["pnl"].std() > 0 else 0
max_drawdown = df["cumulative_pnl"].cummax().sub(df["cumulative_pnl"]).max()
win_rate = (df["pnl"] > 0).sum() / (df["pnl"] != 0).sum() if (df["pnl"] != 0).any() else 0
return {
"total_return": total_return,
"sharpe_ratio": sharpe,
"max_drawdown": max_drawdown,
"win_rate": win_rate,
"total_trades": (df["signal"].diff() != 0).sum(),
"final_capital": capital + total_return
}
def optimize_with_ai(self, historical_results: List[dict]) -> dict:
"""
Nutzt HolySheep AI zur Strategieoptimierung
Generiert neue Strategieparameter basierend auf Ergebnissen
"""
import openai
client = openai.OpenAI(
api_key=API_KEY,
base_url=BASE_URL
)
prompt = f"""
Optimiere folgende Volatilitäts-Strategie basierend auf Backtesting-Ergebnissen:
Historische Results:
{json.dumps(historical_results, indent=2)}
Analysiere die Ergebnisse und schlage optimale Parameter vor:
1. IV-Rank Thresholds
2. IV-HV-Spread Filter
3. Position-Sizing
4. Risk-Management
5. Entry/Exit-Kriterien
Antworte im JSON-Format mit optimierten Parametern.
"""
try:
response = client.chat.completions.create(
model="deepseek-v3.2", # $0.42/MTok - günstigste Option
messages=[
{"role": "system", "content": "Du bist ein quantitativer Strategieoptimierer."},
{"role": "user", "content": prompt}
],
temperature=0.2
)
optimization = response.choices[0].message.content
return json.loads(optimization)
except Exception as e:
print(f"AI optimization error: {e}")
return {}
def run_full_backtest(
self,
instruments: List[str],
start_date: datetime,
end_date: datetime
) -> pd.DataFrame:
"""Führt vollständigen Backtest für mehrere Instrumente durch"""
all_results = []
for instrument in instruments:
print(f"Backtesting: {instrument}")
df = self.load_historical_data(instrument, start_date, end_date)
if len(df) > 100:
results = self.backtest_strategy(df)
results["instrument"] = instrument
all_results.append(results)
return pd.DataFrame(all_results)
def create_sample_data():
"""Erstellt Beispieldaten für Testing"""
conn = psycopg2.connect(**DB_CONFIG)
cursor = conn.cursor()
# Sample Greeks-Daten für 24 Stunden
base_time = datetime.utcnow() - timedelta(hours=24)
sample_records = []
for i in range(1440): # 1 Minute Intervalle
ts = base_time + timedelta(minutes=i)
underlying = 45000 + np.random.randn() * 500
for strike in [44000, 45000, 46000]:
sample_records.append((
ts,
f"BTC-{strike}-{(ts + timedelta(days=7)).strftime('%d%b%y').upper()}",
np.random.uniform(-0.5, 0.5), # Delta
np.random.uniform(0.001, 0.01), # Gamma
np.random.uniform(0.1, 0.5), # Vega
np.random.uniform(-0.05, -0.01), # Theta
np.random.uniform(-0.1, 0.1), # Rho
underlying,
underlying * np.random.uniform(0.95, 1.05),
underlying * 0.98,
underlying * 1.02,
np.random.uniform(0.3, 0.8) # IV
))
cursor.executemany("""
INSERT INTO greeks_history
(timestamp, instrument, delta, gamma, vega, theta, rho,
underlying_price, mark_price, bid, ask, iv)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
""", sample_records)
conn.commit()
cursor.close()
conn.close()
print(f"✓ {len(sample_records)} Sample-Records erstellt")
if __name__ == "__main__":
# Demo mit Beispieldaten
create_sample_data()
backtester = VolatilityBacktester()
instruments = ["BTC-44000-08MAY25", "BTC-45000-08MAY25", "BTC-46000-08MAY25"]
end = datetime.utcnow()
start = end - timedelta(hours=24)
results_df = backtester.run_full_backtest(instruments, start, end)
print("\n=== Backtesting Results ===")
print(results_df)
# AI-Optimierung
if len(results_df) > 0:
print("\n=== AI-Optimierung ===")
optimized = backtester.optimize_with_ai(results_df.to_dict("records"))
print(json.dumps(optimized, indent=2))Häufige Fehler und Lösungen
Fehler 1: WebSocket-Disconnect bei hohem Volumen
Problem: Die WebSocket-Verbindung zu Deribit trennt bei starkem Nachrichtenaufkommen (z.B. bei Volatilitätsspitzen).
# Lösung: Auto-Reconnect mit Exponential Backoff
import asyncio
import websockets
from collections import deque
class ResilientWebSocket:
def __init__(self, url: str, max_retries: int = 5):
self.url = url
self.max_retries = max_retries
self.reconnect_delay = 1
self.message_queue = deque(maxlen=10000)
self._running = False
async def connect_with_retry(self):
"""Verbindung mit automatischem Reconnect"""
self._running = True
retries = 0
while self._running and retries < self.max_retries:
try:
async with websockets.connect(self.url) as ws:
print(f"✓ Verbunden mit Deribit")
retries = 0
self.reconnect_delay = 1
while self._running:
try:
message = await asyncio.wait_for(
ws.recv(),
timeout=30.0
)
self.message_queue.append(message)
except asyncio.TimeoutError:
# Heartbeat-Check
await ws.ping()
except (websockets.ConnectionClosed,
ConnectionError,
OSError) as e:
print(f"⚠ Verbindung verloren: {e}")
retries += 1
wait_time = min(self.reconnect_delay * (2 ** retries), 60)
print(f"Reconnect in {wait_time}s (Versuch {retries}/{self.max_retries})")
await asyncio.sleep(wait_time)
async def process_messages(self, handler):
"""Message-Handler im separaten Task"""
while True:
if self.message_queue:
msg = self.message_queue.popleft()
await handler(msg)
else:
await asyncio.sleep(0.001) # CPU-sparendes WartenFehler 2: Greeks-Berechnung bei fehlenden Marktdaten
Problem: IV und Greeks zeigen NULL, wenn das Orderbuch nicht liquide genug ist.
# Lösung: Fallback-IV-Berechnung mit historischen Daten
def estimate_iv_fallback(
instrument: str,
days_history: int = 30
) -> float:
"""
Schätzt IV basierend auf historischen Volatilitätsmustern
wenn aktuelle Marktdaten nicht verfügbar
"""
conn = psycopg2.connect(**DB_CONFIG)
# Hole historisches IV-Profil für ähnliche Strikes/Maturities
query = """
WITH similar_instruments AS (
SELECT DISTINCT instrument
FROM greeks_history
WHERE instrument LIKE %s
AND timestamp > NOW() - INTERVAL '%s days'
)
SELECT
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY iv) as median_iv,
PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY iv) as q1_iv,
PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY iv) as q3_iv
FROM greeks_history
WHERE instrument IN (SELECT instrument FROM similar_instruments)
AND timestamp > NOW() - INTERVAL '%s days';
"""
cursor = conn.cursor()
cursor.execute(query, (f"%{instrument.split('-')[1]}%",
days_history, days_history))
result = cursor.fetchone()
cursor.close()
conn.close()
if result and result[0]:
return float(result[0]) # Median-IV als Schätzwert
# Final Fallback: ATMF-IV basierend auf Underlying-Volatility
return 0.5 # 50% annualized volatility als Conservative EstimateFehler 3: Time-Zone-Konflikte bei Backtesting
Problem: Timestamps stimmen nicht