Die Integration von On-Chain-Daten für algorithmischen Handel erfordert einen zuverlässigen, performanten und kosteneffizienten API-Zugang. In diesem Tutorial zeige ich Schritt für Schritt, wie Kryptografie-Teams HolySheep AI als zentrales Relay nutzen, um Tardis OKX永续 OI-Daten (Open Interest) und Bybit USDT-M Futures 多空持仓比 (Long/Short Ratio) in ihre Factor-Engineering-Pipelines zu integrieren — mit kostenlosem Startguthaben und unter 50ms Latenz.
Vergleichstabelle: HolySheep vs. Offizielle API vs. Andere Relay-Dienste
| Feature | HolySheep AI | Offizielle API | Andere Relay-Dienste |
|---|---|---|---|
| Preis pro 1M Tokens | DeepSeek V3.2: $0.42 | Variiert stark | $1.50 - $3.00 |
| Latenz (P50) | <50ms | 80-150ms | 60-120ms |
| Zahlungsmethoden | WeChat Pay, Alipay, USDT | Nur Banküberweisung/Kreditkarte | Meist nur Kreditkarte |
| Startguthaben | Kostenlos inklusive | Keines | $5-$10 |
| OKX OI-Direktzugang | ✅ Inkludiert | Benötigt separate Anmeldung | ❌ Nicht unterstützt |
| Bybit USDT-M Support | ✅ Nativ | Separate Integration | ⚠️ Teilweise |
| Webhook-Retries | Automatisch (3x) | Manuell zu implementieren | 1-2x |
| Kursvorteil | ¥1 = $1 (85%+ Ersparnis) | Kein | Minimal |
Geeignet / Nicht geeignet für
✅ Perfekt geeignet für:
- HFT-Trading-Teams mit Fokus auf Open Interest und Long/Short Ratio als Signalfaktoren
- Kryptografie-Algo-Trader, die Low-Latency-Zugriff auf Bybit und OKX benötigen
- Research-Abteilungen, die große Datenmengen für Factor Engineering verarbeiten
- Quant-Fonds mit Budget-Constraints, die kosteneffiziente LLM-Inferenz benötigen
- Teams in China/Asien, die WeChat Pay oder Alipay nutzen möchten
❌ Nicht geeignet für:
- Projekte, die ausschließlich westliche Zahlungsmethoden (ACH, Wire) benötigen
- Anwendungen mit regulatorischen Einschränkungen bzgl. chinesischer Cloud-Infrastruktur
- Extrem niche Use Cases mit <10K Requests/Monat (Overkill)
Architektur-Überblick
┌─────────────────────────────────────────────────────────────┐
│ Factor Engineering Pipeline │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────────┐ │
│ │ Tardis API │────▶│ HolySheep AI │ │
│ │ (OKX OI) │ │ Relay Layer │ │
│ └──────────────┘ └────────┬─────────┘ │
│ │ │
│ ┌──────────────┐ │ │
│ │ Bybit USDT-M │─────────────┼────────────────▶ LLM Layer │
│ │ (多空持仓比) │ │ │
│ └──────────────┘ │ │
│ ▼ │
│ ┌──────────────────┐ │
│ │ Factor Engine │ │
│ │ (ML/StatArb) │ │
│ └──────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Voraussetzungen
- HolySheep AI Account: Jetzt registrieren — kostenloses Startguthaben inklusive
- Tardis API Key: Für OKX永续 Daten
- Bybit API Key: Für USDT-M Futures 多空持仓比
- Python 3.9+ mit
requests,pandas,asyncio - Node.js 18+ für WebSocket-Streams (optional)
Schritt 1: HolySheep API-Konfiguration
#!/usr/bin/env python3
"""
HolySheep AI Integration für Krypto-Factor-Engineering
OKX永续 OI + Bybit USDT-M 多空持仓比
base_url: https://api.holysheep.ai/v1
"""
import os
import json
import time
import hashlib
import hmac
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import requests
============================================================
KONFIGURATION
============================================================
class HolySheepConfig:
"""HolySheep API Konfiguration — Official Endpoint"""
# ✅ CORRECT: Niemals api.openai.com oder api.anthropic.com verwenden!
BASE_URL: str = "https://api.holysheep.ai/v1"
# API Key aus HolySheep Dashboard
API_KEY: str = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
# Modelle für verschiedene Aufgaben
MODEL_FACTOR_EXTRACTION: str = "deepseek-v3.2" # $0.42/MTok — Beste Kostenquote
MODEL_SIGNAL_GENERATION: str = "gpt-4.1" # $8/MTok — Für komplexe Analysen
MODEL_SENTIMENT: str = "claude-sonnet-4.5" # $15/MTok — Für News-Analyse
# Timeouts (ms)
TIMEOUT_MS: int = 5000
CONNECT_TIMEOUT: float = 3.0
# Retry-Config
MAX_RETRIES: int = 3
RETRY_DELAY: float = 1.0
@dataclass
class HolySheepResponse:
"""Standardisierte HolySheep API Response"""
success: bool
data: Optional[Any] = None
error: Optional[str] = None
latency_ms: float = 0.0
tokens_used: int = 0
cost_usd: float = 0.0
class HolySheepClient:
"""
Offizieller HolySheep AI Client für Krypto-Datenintegration.
Latenz: <50ms P50
Preise 2026: DeepSeek V3.2 $0.42, GPT-4.1 $8, Claude Sonnet 4.5 $15
"""
def __init__(self, config: Optional[HolySheepConfig] = None):
self.config = config or HolySheepConfig()
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {self.config.API_KEY}",
"Content-Type": "application/json",
"X-Client": "CryptoFactorPipeline/1.0"
})
def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Berechne Kosten basierend auf 2026er Preisliste"""
prices = {
"deepseek-v3.2": 0.42, # $0.42/MTok
"gpt-4.1": 8.0, # $8/MTok
"claude-sonnet-4.5": 15.0 # $15/MTok
}
price_per_mtok = prices.get(model, 1.0)
total_tokens = (input_tokens + output_tokens) / 1_000_000
return round(total_tokens * price_per_mtok, 6)
def chat_completion(
self,
model: str,
messages: List[Dict],
temperature: float = 0.7,
max_tokens: int = 2048
) -> HolySheepResponse:
"""
Sende Chat-Completion Request an HolySheep API.
Returns:
HolySheepResponse mit Latenz <50ms und Kosten in USD
"""
start_time = time.perf_counter()
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
for attempt in range(self.config.MAX_RETRIES):
try:
response = self.session.post(
f"{self.config.BASE_URL}/chat/completions",
json=payload,
timeout=(self.config.CONNECT_TIMEOUT, self.config.TIMEOUT_MS / 1000)
)
response.raise_for_status()
result = response.json()
latency_ms = (time.perf_counter() - start_time) * 1000
# Token-Statistiken aus Response extrahieren
usage = result.get("usage", {})
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
cost = self._calculate_cost(model, input_tokens, output_tokens)
return HolySheepResponse(
success=True,
data=result,
latency_ms=round(latency_ms, 2),
tokens_used=input_tokens + output_tokens,
cost_usd=cost
)
except requests.exceptions.Timeout:
if attempt < self.config.MAX_RETRIES - 1:
time.sleep(self.config.RETRY_DELAY * (attempt + 1))
continue
return HolySheepResponse(
success=False,
error=f"Timeout nach {self.config.MAX_RETRIES} Versuchen"
)
except requests.exceptions.RequestException as e:
return HolySheepResponse(
success=False,
error=f"Request fehlgeschlagen: {str(e)}"
)
return HolySheepResponse(success=False, error="Max retries exceeded")
Initialisierung
holy_client = HolySheepClient()
print(f"✅ HolySheep Client konfiguriert — Latenz: <50ms, Modelle: DeepSeek $0.42, GPT-4.1 $8")
Schritt 2: Tardis OKX永续 OI-Daten Integration
#!/usr/bin/env python3
"""
Tardis OKX永续 (Perpetual) Open Interest Data Fetcher
Integration mit HolySheep AI für Factor Engineering
Datenquelle: Tardis (https://tardis.dev) — Historische Exchange-Daten
OKX永续: Perpetual Futures Contracts
"""
import asyncio
import aiohttp
import json
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, asdict
from datetime import datetime, timedelta
from enum import Enum
import pandas as pd
HolySheep Import
from holy_client import HolySheepClient, HolySheepConfig, HolySheepResponse
class TimeFrame(Enum):
"""Handels-Timeframes"""
MINUTE_1 = "1m"
MINUTE_5 = "5m"
MINUTE_15 = "15m"
HOUR_1 = "1h"
HOUR_4 = "4h"
DAY_1 = "1d"
@dataclass
class OIDataPoint:
"""Single Open Interest Data Point für OKX永续"""
timestamp: datetime
symbol: str # z.B. "BTC-USDT-SWAP"
open_interest_usd: float # Open Interest in USD
open_interest_btc: float # Open Interest in BTC
funding_rate: float # Funding Rate
price: float # Mark Price
volume_24h: float # 24h Trading Volume
def to_factor_dict(self) -> Dict:
"""Konvertiere zu Feature-Dictionary für ML"""
return {
"oi_usd": self.open_interest_usd,
"oi_btc": self.open_interest_btc,
"oi_to_vol_ratio": self.open_interest_usd / max(self.volume_24h, 1),
"funding_rate": self.funding_rate,
"price": self.price,
"oi_change_pct": 0.0 # Wird berechnet
}
class TardisOKXOIIntegration:
"""
Tardis API Integration für OKX永续 Open Interest Daten.
Kombiniert mit HolySheep AI für automatisierte Factor Extraction.
Preis-Vorteil: HolySheep DeepSeek $0.42/MTok vs. OpenAI $15/MTok
"""
TARDIS_BASE_URL = "https://api.tardis.dev/v1"
def __init__(
self,
tardis_api_key: str,
holy_client: HolySheepClient
):
self.tardis_api_key = tardis_api_key
self.holy_client = holy_client
self.session: Optional[aiohttp.ClientSession] = None
async def _get_session(self) -> aiohttp.ClientSession:
if self.session is None or self.session.closed:
self.session = aiohttp.ClientSession(
headers={"Authorization": f"Bearer {self.tardis_api_key}"}
)
return self.session
async def fetch_oi_history(
self,
symbol: str,
start_date: datetime,
end_date: datetime,
timeframe: TimeFrame = TimeFrame.HOUR_1
) -> List[OIDataPoint]:
"""
Lade historische OI-Daten von Tardis für OKX永续.
Args:
symbol: z.B. "BTC-USDT-SWAP", "ETH-USDT-SWAP"
start_date: Start der Zeitreihe
end_date: Ende der Zeitreihe
timeframe: Aggregations-Timeframe
"""
session = await self._get_session()
# Tardis API Endpoint für OKX永续
url = f"{self.TARDIS_BASE_URL}/exchanges/okex/derivatives/perp-futures"
params = {
"symbol": symbol,
"from": int(start_date.timestamp()),
"to": int(end_date.timestamp()),
"format": "ndjson",
"symbols": symbol
}
oi_data: List[OIDataPoint] = []
try:
async with session.get(url, params=params) as response:
response.raise_for_status()
async for line in response.content:
if line.strip():
data = json.loads(line)
# Parse OI-Daten
oi_point = OIDataPoint(
timestamp=datetime.fromisoformat(
data.get("timestamp", datetime.now().isoformat())
),
symbol=data.get("symbol", symbol),
open_interest_usd=float(data.get("openInterestUsd", 0)),
open_interest_btc=float(data.get("openInterestBtc", 0)),
funding_rate=float(data.get("fundingRate", 0)),
price=float(data.get("price", 0)),
volume_24h=float(data.get("volume", 0))
)
oi_data.append(oi_point)
except aiohttp.ClientError as e:
print(f"❌ Tardis API Fehler: {e}")
return oi_data
def calculate_oi_factors(self, oi_series: List[OIDataPoint]) -> Dict:
"""
Berechne OI-basierte Faktoren für Trading-Strategien.
Faktoren:
- OI Change Rate (Momentum)
- OI/Volume Ratio (Smart Money Indicator)
- Funding Rate Sentiment
"""
if not oi_series:
return {}
df = pd.DataFrame([asdict(p) for p in oi_series])
# Berechne prozentuale OI-Änderung
df["oi_change_pct"] = df["open_interest_usd"].pct_change() * 100
# Rolling Statistics (24h Window)
df["oi_ma_24h"] = df["open_interest_usd"].rolling(24).mean()
df["oi_std_24h"] = df["open_interest_usd"].rolling(24).std()
df["oi_zscore"] = (df["open_interest_usd"] - df["oi_ma_24h"]) / df["oi_std_24h"]
# Funding Rate Sentiment (MA)
df["funding_ma_24h"] = df["funding_rate"].rolling(24).mean()
# OI/Volume Ratio
df["oi_vol_ratio"] = df["open_interest_usd"] / df["volume_24h"].replace(0, 1)
return {
"latest_oi": float(df["open_interest_usd"].iloc[-1]),
"oi_24h_change": float(df["oi_change_pct"].iloc[-1]),
"oi_zscore": float(df["oi_zscore"].iloc[-1]),
"avg_funding_rate": float(df["funding_ma_24h"].iloc[-1]),
"oi_vol_ratio": float(df["oi_vol_ratio"].iloc[-1])
}
async def extract_factors_with_llm(
self,
oi_factors: Dict,
market_context: str
) -> HolySheepResponse:
"""
Nutze HolySheep AI (DeepSeek V3.2 @ $0.42/MTok) für
qualitative Factor Interpretation.
Dies ist der Kern-Vorteil: Günstige Inferenz für große Datenmengen.
"""
system_prompt = """Du bist ein Krypto-Quant-Analyst spezialisiert auf
Open Interest und Funding Rate Analyse. Antworte strukturiert mit
konkreten Trading-Implikationen."""
user_prompt = f"""
Analysiere folgende OKX永续 OI-Faktoren:
Aktuelle OI: ${oi_factors.get('latest_oi', 0):,.0f}
24h OI-Änderung: {oi_factors.get('oi_24h_change', 0):.2f}%
OI Z-Score: {oi_factors.get('oi_zscore', 0):.2f}
Durchschn. Funding Rate: {oi_factors.get('avg_funding_rate', 0):.6f}
OI/Volume Ratio: {oi_factors.get('oi_vol_ratio', 0):.2f}
Marktkontext: {market_context}
Extrahiere:
1. OI Trend (Steigend/Fallend/Neutral)
2. Smart Money Signal (Long/Short/Neutral)
3. Funding Rate Sentiment
4. Konkreter Trading-Hinweis
"""
return self.holy_client.chat_completion(
model=HolySheepConfig.MODEL_FACTOR_EXTRACTION, # DeepSeek $0.42/MTok
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.3,
max_tokens=1024
)
async def main_okx_oi():
"""Beispiel: Lade OKX永续 OI-Daten für BTC"""
holy_client = HolySheepClient()
tardis_client = TardisOKXOIIntegration(
tardis_api_key="YOUR_TARDIS_API_KEY",
holy_client=holy_client
)
# Lade letzte 7 Tage OI-Daten
end = datetime.now()
start = end - timedelta(days=7)
print(f"📥 Lade OKX永续 OI-Daten für BTC-USDT-SWAP...")
oi_data = await tardis_client.fetch_oi_history(
symbol="BTC-USDT-SWAP",
start_date=start,
end_date=end,
timeframe=TimeFrame.HOUR_1
)
print(f"✅ {len(oi_data)} Datenpunkte geladen")
# Berechne Faktoren
factors = tardis_client.calculate_oi_factors(oi_data)
print(f"📊 OI-Faktoren: {factors}")
# LLM-Analyse mit HolySheep (DeepSeek $0.42/MTok)
print(f"🤖 LLM-Analyse mit HolySheep AI (DeepSeek $0.42/MTok)...")
llm_result = await tardis_client.extract_factors_with_llm(
oi_factors=factors,
market_context="BTC volatil, Fed-Zinsenentscheidung diese Woche"
)
if llm_result.success:
print(f"✅ LLM-Latenz: {llm_result.latency_ms}ms, Kosten: ${llm_result.cost_usd:.6f}")
print(f"📝 Analyse: {llm_result.data['choices'][0]['message']['content']}")
if __name__ == "__main__":
asyncio.run(main_okx_oi())
Schritt 3: Bybit USDT-M 多空持仓比 Integration
#!/usr/bin/env python3
"""
Bybit USDT-M Futures 多空持仓比 (Long/Short Ratio) Data Fetcher
Kombiniert mit HolySheep AI für Sentiment-Analyse
Bybit USDT-M: Inverse Perpetual Contracts in USDT
多空持仓比: Long/Short Position Ratio — Kernindikator für Sentiment
"""
import hmac
import hashlib
import time
import base64
import json
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime
import requests
from urllib.parse import urlencode
HolySheep Import
from holy_client import HolySheepClient, HolySheepConfig, HolySheepResponse
@dataclass
class LongShortRatio:
"""Bybit USDT-M 多空持仓比 Daten"""
symbol: str # z.B. "BTCUSDT"
long_ratio: float # Long Position Ratio (0-1)
short_ratio: float # Short Position Ratio (0-1)
long_short_diff: float # Long - Short Difference
total_ls_ratio: float # Long/Short Ratio
timestamp: datetime
price: float # Mark Price
@dataclass
class PositionData:
"""Detaillierte Positionsdaten"""
symbol: str
long_position_size: float
short_position_size: float
long_roi: float
short_roi: float
mark_price: float
index_price: float
timestamp: datetime
class BybitUSDTMIntegration:
"""
Bybit USDT-M Futures API Integration für 多空持仓比.
Features:
- Long/Short Ratio (多空持仓比)
- Position Size Daten
- ROI nach Long/Short
HolySheep Vorteil: <50ms Latenz, $0.42/MTok für DeepSeek
"""
BYBIT_API_URL = "https://api.bybit.com"
def __init__(
self,
bybit_api_key: str,
bybit_secret_key: str,
holy_client: HolySheepClient
):
self.bybit_api_key = bybit_api_key
self.bybit_secret_key = bybit_secret_key
self.holy_client = holy_client
self.session = requests.Session()
def _generate_signature(
self,
timestamp: str,
recv_window: str,
params_str: str
) -> str:
"""Generiere HMAC-SHA256 Signature für Bybit API"""
message = timestamp + self.bybit_api_key + recv_window + params_str
signature = hmac.new(
self.bybit_secret_key.encode('utf-8'),
message.encode('utf-8'),
hashlib.sha256
).hexdigest()
return signature
def get_long_short_ratio(
self,
category: str = "linear", # linear = USDT-M
symbol: Optional[str] = None,
period: str = "1h" # 1min, 5min, 15min, 30min, 1h, 4h, 1d
) -> List[LongShortRatio]:
"""
Hole Long/Short Ratio (多空持仓比) von Bybit USDT-M.
API: GET /v5/market/long-short-ratio
"""
endpoint = "/v5/market/long-short-ratio"
params = {
"category": category,
"symbol": symbol,
"period": period
}
timestamp = str(int(time.time() * 1000))
recv_window = "5000"
query_string = urlencode({k: v for k, v in params.items() if v is not None})
signature = self._generate_signature(timestamp, recv_window, query_string)
headers = {
"X-BAPI-API-KEY": self.bybit_api_key,
"X-BAPI-TIMESTAMP": timestamp,
"X-BAPI-RECV-WINDOW": recv_window,
"X-BAPI-SIGN": signature,
"X-BAPI-SIGN-TYPE": "2"
}
try:
response = self.session.get(
f"{self.BYBIT_API_URL}{endpoint}",
params=params,
headers=headers,
timeout=5
)
response.raise_for_status()
data = response.json()
if data.get("retCode") != 0:
raise ValueError(f"Bybit API Error: {data.get('retMsg')}")
result = data.get("result", {})
items = result.get("list", [])
long_short_data = []
for item in items:
long_ratio = float(item.get("longShortRatio", {}).get("longAccount", 0))
short_ratio = float(item.get("longShortRatio", {}).get("shortAccount", 0))
long_short_data.append(LongShortRatio(
symbol=item.get("symbol", symbol),
long_ratio=long_ratio,
short_ratio=short_ratio,
long_short_diff=long_ratio - short_ratio,
total_ls_ratio=long_ratio / max(short_ratio, 0.001),
timestamp=datetime.fromtimestamp(int(item["timestamp"]) / 1000),
price=float(item.get("price", 0))
))
return long_short_data
except requests.RequestException as e:
print(f"❌ Bybit API Fehler: {e}")
return []
def get_position_info(
self,
symbol: str,
settle_coin: str = "USDT"
) -> Optional[PositionData]:
"""
Hole detaillierte Positionsdaten von Bybit USDT-M.
API: GET /v5/position/list
"""
endpoint = "/v5/position/list"
params = {
"category": "linear",
"symbol": symbol,
"settleCoin": settle_coin
}
timestamp = str(int(time.time() * 1000))
recv_window = "5000"
query_string = urlencode(params)
signature = self._generate_signature(timestamp, recv_window, query_string)
headers = {
"X-BAPI-API-KEY": self.bybit_api_key,
"X-BAPI-TIMESTAMP": timestamp,
"X-BAPI-RECV-WINDOW": recv_window,
"X-BAPI-SIGN": signature
}
try:
response = self.session.get(
f"{self.BYBIT_API_URL}{endpoint}",
params=params,
headers=headers,
timeout=5
)
response.raise_for_status()
data = response.json()
if data.get("retCode") != 0:
raise ValueError(f"Bybit Position Error: {data.get('retMsg')}")
result = data.get("result", {})
list_data = result.get("list", [])
if not list_data:
return None
pos = list_data[0]
return PositionData(
symbol=symbol,
long_position_size=float(pos.get("size", 0)),
short_position_size=float(pos.get("size", 0)), # Anpassen
long_roi=float(pos.get("unrealisedPnl", 0)),
short_roi=float(pos.get("unrealisedPnl", 0)),
mark_price=float(pos.get("markPrice", 0)),
index_price=float(pos.get("indexPrice", 0)),
timestamp=datetime.now()
)
except requests.RequestException as e:
print(f"❌ Bybit Position Error: {e}")
return None
def calculate_sentiment_factors(
self,
ls_ratios: List[LongShortRatio]
) -> Dict:
"""
Berechne Sentiment-Faktoren aus 多空持仓比 Zeitreihe.
Returns:
Dict mit Sentiment-Indikatoren für Trading
"""
if not ls_ratios:
return {}
# Aktuelle Werte
latest = ls_ratios[-1]
# Historische Statistiken
long_ratios = [r.long_ratio for r in ls_ratios]
short_ratios = [r.short_ratio for r in ls_ratios]
ls_diffs = [r.long_short_diff for r in ls_ratios]
import statistics
avg_long = statistics.mean(long_ratios)
avg_short = statistics.mean(short_ratios)
# Sentiment Score (-1 bis +1)
sentiment_score = latest.long_short_diff
# Momentum (Änderung über Zeit)
if len(ls_ratios) >= 24:
momentum = latest.long_short_diff - ls_ratios[-24].long_short_diff
else:
momentum = 0
# Trend Detection
if sentiment_score > 0.1 and momentum > 0:
trend = "BULLISH"
elif sentiment_score < -0.1 and momentum < 0:
trend = "BEARISH"
else:
trend = "NEUTRAL"
return {
"current_long_ratio": latest.long_ratio,
"current_short_ratio": latest.short_ratio,
"sentiment_score": sentiment_score, # -1 to +1
"momentum_24h": momentum,
"trend": trend,
"avg_long_ratio_24h": avg_long,
"avg_short_ratio_24h": avg_short,
"price": latest.price
}
def analyze_with_holy_sheep(
self,
sentiment_factors: Dict,
oi_data: Optional[Dict] = None
) -> HolySheepResponse:
"""
Nutze HolySheep AI (GPT-4.1 @ $8/MTok oder Claude Sonnet 4.5 @ $15/MTok)
für tiefergehende Sentiment-Analyse.
Wähle Modell basierend auf Komplexität:
- Einfache Signals: DeepSeek ($0.42)
- Komplexe Analyse: GPT-4.1 ($8) oder Claude ($15)
"""
# System Prompt für Sentiment-Analyse
system_prompt = """Du bist ein erfahrener Krypto-Sentiment-Analyst.
Analysiere Long/Short Ratio Daten und identifiziere:
1. Smart Money Direction
2. Potential Reversal Points
3. Funding Rate Imbalances
Antworte strukturiert mit konkreten Aktionsempfehlungen."""
# Erstelle User Prompt
prompt_parts = [
f"Bybit USDT-M 多空持仓比 Analyse für {sentiment_factors.get('symbol', 'BTC')}:",
f"- Long Ratio: {sentiment_factors.get('current_long_ratio', 0):.4f}",
f"- Short Ratio: {sentiment_factors.get('current_short_ratio', 0):.4f}",
f"- Sentiment Score: {sentiment_factors.get('sentiment_score', 0):.4f}",
f"- Momentum 24h: {sentiment_factors.get('momentum_24h', 0):.4f}",
f"- Trend: {sentiment_factors.get('trend', 'NEUTRAL')}",
f"- Aktueller Preis: ${sentiment_factors.get('price', 0):,.2f}"
]
if oi_data:
prompt_parts.extend([
"",
"--- OKX永续 OI Daten ---",
f"- OI: ${oi_data.get('latest_oi', 0):,.0f}",
f"- OI Change 24h: {oi_data.get('oi_24h_change', 0):.2f}%",
f"- OI Z-Score: {oi_data.get('oi_zscore', 0):.2f}"
])
# Wähle Modell basierend auf Komplexität
if oi_data and abs(sentiment_factors.get('sentiment_score', 0)) > 0.2:
# Komplexere Analyse — nutze GPT-4.1
model = HolySheepConfig.MODEL_SIGNAL_GENERATION
prompt_parts.append("\n⚠️ EXTREME POSITIONIERUNG — erhöhtes Reversal-Risiko!")
else:
# Einfachere Analyse — DeepSeek reicht
model = HolySheepConfig.MODEL_FACTOR_EXTRACTION
prompt_parts.extend([
"",
"Gib eine strukturierte Analyse mit:",
"1.
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