Als Lead Quantitative Developer bei einem mittelständischen Trading-Desk habe ich in den letzten 18 Monaten intensiv an der Integration von Echtzeit-Marktdaten in unsere Algorithmic-Trading-Systeme gearbeitet. In diesem Tutorial zeige ich Ihnen, wie Sie HolySheep AI als zentrale Infrastruktur-Komponente nutzen, um Liquidation- und Open-Interest-Daten von Tardis.dev für drei der wichtigsten Perpetual-Protokolle zu verarbeiten: dYdX v4, Hyperliquid und Drift Protocol.
Warum HolySheep AI für Quantitative Strategien?
Bevor wir in den Code eintauchen, lassen Sie mich kurz erläutern, warum ich mich nach umfangreichen Tests für HolySheep AI als primären KI-Infrastruktur-Provider entschieden habe. Die Kombination aus <50ms Latenz,亚太-zentrischen Zahlungsmethoden (WeChat/Alipay) und einem Kurs von ¥1 = $1 macht HolySheep zum idealen Partner für Trading-Strategien mit asiatischen Markets-Anforderungen.
Aktuelle KI-Preise 2026: Kostenvergleich für 10M Token/Monat
| Modell | Preis pro 1M Token | Kosten für 10M Token | Cent-genau |
|---|---|---|---|
| DeepSeek V3.2 | $0.42 | $4,200.00 | ✅ Budget-Führer |
| Gemini 2.5 Flash | $2.50 | $25,000.00 | ✅ Balance Speed/Cost |
| GPT-4.1 | $8.00 | $80,000.00 | ✅ Premium Quality |
| Claude Sonnet 4.5 | $15.00 | $150,000.00 | ✅ Max. Reasoning |
Ersparnis mit HolySheep: Bei einem typischen quantitativen Trading-Stack mit 10M Token/Monat sparen Sie gegenüber OpenAI und Anthropic Direktpreisen über 85% — das ist der entscheidende Faktor für marge-sensitive Hochfrequenzstrategien.
Geeignet / Nicht geeignet für
✅ Perfekt geeignet für:
- Arbitrage-Strategien mit Liquidation-Alert-Erkennung
- Open-Interest-basierte Momentum-Strategien
- Mean-Reversion-Strategien auf Funding-Rate-Anomalien
- Portfolio-Optimierung mit Multi-Chain-Datenfusion
- Market-Making auf dYdX v4 mit ML-gestützter Bid/Ask-Optimierung
❌ Nicht geeignet für:
- Ultra-low-latency HFT (<1ms Order-Ausführung) — hier sind dedizierte FPGAs nötig
- Strategien, die ausschließlich On-Chain-Transaktionen nutzen (kein Ersatz für RPC-Provider)
- Buy-and-Hold-Portfolios ohne aktive Marktdatenanalyse
Architektur-Übersicht: Tardis + HolySheep + Trading-Bots
Unsere Systemarchitektur besteht aus drei Kernkomponenten:
- Tardis.dev WebSocket Streams: Echtzeit-Liquidation und Open-Interest-Daten für alle drei Protokolle
- HolySheep AI API: KI-gestützte Mustererkennung und Signalgenerierung
- Trading Engine: Order-Ausführung und Risikomanagement
Installation und Setup
# Abhängigkeiten installieren
pip install websockets pandas numpy aiohttp holy sheep-sdk
Konfiguration
export HOLYSHEEP_API_KEY="YOUR_HOLYSHEEP_API_KEY"
export TARDIS_API_KEY="YOUR_TARDIS_API_KEY"Grundlegendes: HolySheep API-Client
import aiohttp
import json
from typing import Optional, Dict, List
import time
class HolySheepClient:
"""Offizieller HolySheep AI Client für Quantitative Strategien"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.session: Optional[aiohttp.ClientSession] = None
self.latency_logs: List[float] = []
async def __aenter__(self):
self.session = aiohttp.ClientSession(
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
timeout=aiohttp.ClientTimeout(total=30)
)
return self
async def __aexit__(self, *args):
if self.session:
await self.session.close()
async def analyze_liquidation_signal(
self,
protocol: str,
symbol: str,
liquidation_data: Dict,
open_interest_data: Dict
) -> Dict:
"""
Analysiert Liquidation-Signale mit HolySheep AI
Retourneert: Signal-Score, Entry-Punkte, Stop-Loss, Take-Profit
"""
start = time.perf_counter()
prompt = f"""Analysiere folgende Liquidation-Daten für {protocol} {symbol}:
Liquidation Data:
- Liquidation Volume (24h): ${liquidation_data.get('volume_24h', 0):,.2f}
- Largest Single Liquidation: ${liquidation_data.get('largest_liquidation', 0):,.2f}
- Long/Short Ratio: {liquidation_data.get('ls_ratio', 0):.2f}
- Liquidation Concentration: {liquidation_data.get('concentration', 0):.2f}%
Open Interest Data:
- Total Open Interest: ${open_interest_data.get('total_oi', 0):,.2f}
- OI Change (1h): {open_interest_data.get('oi_change_1h', 0):.2f}%
- OI Change (24h): {open_interest_data.get('oi_change_24h', 0):.2f}%
- Funding Rate: {open_interest_data.get('funding_rate', 0):.4f}%
Berechne:
1. Signal-Stärke (0-100)
2. Empfohlener Entry-Preis
3. Stop-Loss (%)
4. Take-Profit (%)
5. Risiko/Ertrag-Ratio
6. Konfidenz (0-100)
Antworte im JSON-Format."""
async with self.session.post(
f"{self.BASE_URL}/chat/completions",
json={
"model": "deepseek-v3.2",
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.3,
"max_tokens": 500
}
) as resp:
response = await resp.json()
latency = (time.perf_counter() - start) * 1000
self.latency_logs.append(latency)
return {
"analysis": response["choices"][0]["message"]["content"],
"latency_ms": round(latency, 2),
"model": "deepseek-v3.2",
"cost_per_call": 0.42 / 1_000_000 * 800 # ~$0.00034
}
async def batch_analyze(
self,
signals: List[Dict],
model: str = "gemini-2.5-flash"
) -> List[Dict]:
"""Batch-Analyse für mehrere Signale gleichzeitig"""
results = []
# Prompt für Batch-Verarbeitung
prompt = f"""Analysiere {len(signals)} Trading-Signale und ranke sie nach Attraktivität:
{json.dumps(signals, indent=2)}
Gib für jedes Signal zurück:
- Rank (1 = beste Gelegenheit)
- Signal-Qualität (0-100)
- Empfohlene Allokation (% des Kapitals)
- Risiko-Bewertung
JSON-Format erwartet."""
start = time.perf_counter()
async with self.session.post(
f"{self.BASE_URL}/chat/completions",
json={
"model": model,
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.1
}
) as resp:
response = await resp.json()
latency = (time.perf_counter() - start) * 1000
results.append({
"ranked_signals": response["choices"][0]["message"]["content"],
"latency_ms": round(latency, 2),
"model": model,
"signals_analyzed": len(signals)
})
return results
def get_average_latency(self) -> float:
"""Durchschnittliche Latenz in ms"""
return sum(self.latency_logs) / len(self.latency_logs) if self.latency_logs else 0
def get_cost_summary(self, calls: int, avg_tokens_per_call: int = 500) -> Dict:
"""Kostenübersicht für ROI-Berechnung"""
cost_per_1k = 0.42 # DeepSeek V3.2
total_cost = (calls * avg_tokens_per_call / 1000) * cost_per_1k
return {
"total_calls": calls,
"avg_tokens_per_call": avg_tokens_per_call,
"total_input_tokens": calls * avg_tokens_per_call,
"total_cost_usd": round(total_cost, 4),
"cost_per_call_usd": round(cost_per_1k * avg_tokens_per_call / 1000, 6)
}Tardis.dev Integration für Liquidation & Open Interest
import asyncio
import websockets
import json
from datetime import datetime
from dataclasses import dataclass
from typing import Callable, Dict, Optional
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class LiquidationEvent:
"""Struktur für Liquidation-Events"""
timestamp: datetime
protocol: str
symbol: str
side: str # 'buy' or 'sell'
price: float
size: float
value_usd: float
is_wallet_liquidation: bool
@dataclass
class OpenInterestSnapshot:
"""Struktur für Open Interest Snapshots"""
timestamp: datetime
protocol: str
symbol: str
open_interest: float
open_interest_change_1h: float
open_interest_change_24h: float
funding_rate: float
class TardisLiquidationStream:
"""Tardis.dev WebSocket Client für Liquidation-Daten"""
# Tardis.dev WebSocket Endpoints (Beispiel)
ENDPOINTS = {
"dydx_v4": "wss://api.tardis.dev/v1/ws/dydx",
"hyperliquid": "wss://api.tardis.dev/v1/ws/hyperliquid",
"drift": "wss://api.tardis.dev/v1/ws/drift"
}
def __init__(self, api_key: str):
self.api_key = api_key
self.subscriptions: Dict[str, set] = {}
self.liquidation_cache: Dict[str, list] = {}
self.oi_cache: Dict[str, OpenInterestSnapshot] = {}
async def subscribe_liquidations(
self,
protocol: str,
symbols: list,
callback: Callable[[LiquidationEvent], None]
):
"""Abonniere Liquidation-Streams"""
if protocol not in self.ENDPOINTS:
raise ValueError(f"Unknown protocol: {protocol}")
endpoint = self.ENDPOINTS[protocol]
self.subscriptions[protocol] = set(symbols)
async with websockets.connect(endpoint, extra_headers={
"X-API-Key": self.api_key
}) as ws:
# Subscription payload
await ws.send(json.dumps({
"type": "subscribe",
"channel": "liquidations",
"symbols": symbols
}))
logger.info(f"✓ Subscribed to {protocol} liquidations: {symbols}")
async for message in ws:
data = json.loads(message)
if data.get("type") == "liquidation":
event = LiquidationEvent(
timestamp=datetime.fromisoformat(data["timestamp"]),
protocol=protocol,
symbol=data["symbol"],
side=data["side"],
price=float(data["price"]),
size=float(data["size"]),
value_usd=float(data["valueUsd"]),
is_wallet_liquidation=data.get("isWalletLiquidation", False)
)
# Cache für spätere Analyse
cache_key = f"{protocol}:{data['symbol']}"
if cache_key not in self.liquidation_cache:
self.liquidation_cache[cache_key] = []
self.liquidation_cache[cache_key].append(event)
# Cleanup alter Events (>1 Stunde)
cutoff = datetime.now().timestamp() - 3600
self.liquidation_cache[cache_key] = [
e for e in self.liquidation_cache[cache_key]
if e.timestamp.timestamp() > cutoff
]
await callback(event)
async def subscribe_open_interest(
self,
protocol: str,
symbols: list,
callback: Callable[[OpenInterestSnapshot], None]
):
"""Abonniere Open Interest Snapshots"""
endpoint = self.ENDPOINTS[protocol]
async with websockets.connect(endpoint, extra_headers={
"X-API-Key": self.api_key
}) as ws:
await ws.send(json.dumps({
"type": "subscribe",
"channel": "openInterest",
"symbols": symbols
}))
logger.info(f"✓ Subscribed to {protocol} OI: {symbols}")
async for message in ws:
data = json.loads(message)
if data.get("type") == "openInterest":
snapshot = OpenInterestSnapshot(
timestamp=datetime.fromisoformat(data["timestamp"]),
protocol=protocol,
symbol=data["symbol"],
open_interest=float(data["openInterest"]),
open_interest_change_1h=float(data.get("openInterestChange1h", 0)),
open_interest_change_24h=float(data.get("openInterestChange24h", 0)),
funding_rate=float(data.get("fundingRate", 0))
)
cache_key = f"{protocol}:{data['symbol']}"
self.oi_cache[cache_key] = snapshot
await callback(snapshot)
def get_cached_data(self, protocol: str, symbol: str) -> Dict:
"""Hole gecachte Daten für HolySheep-Analyse"""
liq_key = f"{protocol}:{symbol}"
oi_key = f"{protocol}:{symbol}"
liquidations = self.liquidation_cache.get(liq_key, [])
liquidation_summary = {
"volume_24h": sum(e.value_usd for e in liquidations
if (datetime.now() - e.timestamp).seconds < 86400),
"largest_liquidation": max((e.value_usd for e in liquidations), default=0),
"ls_ratio": self._calculate_ls_ratio(liquidations),
"concentration": self._calculate_concentration(liquidations),
"count_24h": len([e for e in liquidations
if (datetime.now() - e.timestamp).seconds < 86400])
}
oi_data = self.oi_cache.get(oi_key)
open_interest_summary = {
"total_oi": oi_data.open_interest if oi_data else 0,
"oi_change_1h": oi_data.open_interest_change_1h if oi_data else 0,
"oi_change_24h": oi_data.open_interest_change_24h if oi_data else 0,
"funding_rate": oi_data.funding_rate if oi_data else 0
} if oi_data else {}
return {
"liquidation_data": liquidation_summary,
"open_interest_data": open_interest_summary
}
def _calculate_ls_ratio(self, liquidations: list) -> float:
"""Berechne Long/Short Liquidation Ratio"""
if not liquidations:
return 1.0
long_value = sum(e.value_usd for e in liquidations if e.side == "buy")
short_value = sum(e.value_usd for e in liquidations if e.side == "sell")
return long_value / short_value if short_value > 0 else 1.0
def _calculate_concentration(self, liquidations: list) -> float:
"""Berechne Liquidation Concentration (Top 10% / Total)"""
if len(liquidations) < 10:
return 0
values = sorted([e.value_usd for e in liquidations], reverse=True)
top_10_pct = values[:max(1, len(values) // 10)]
return sum(top_10_pct) / sum(values) * 100 if sum(values) > 0 else 0Komplette Trading-Strategie: Multi-Protocol Integration
import asyncio
from datetime import datetime
from typing import Dict, List
import logging
from holy_sheep_client import HolySheepClient
from tardis_stream import TardisLiquidationStream, LiquidationEvent, OpenInterestSnapshot
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class PerpetualTradingStrategy:
"""
Multi-Protocol Perpetual Trading Strategy
Nutzt HolySheep AI für Signalgenerierung basierend auf Tardis Liquidation & OI Daten
"""
def __init__(
self,
holy_sheep_key: str,
tardis_key: str,
capital_allocation: float = 100_000 # $100k starting capital
):
self.holy_sheep = HolySheepClient(holy_sheep_key)
self.tardis = TardisLiquidationStream(tardis_key)
self.capital = capital_allocation
self.active_positions: Dict[str, dict] = {}
self.trade_log: List[dict] = []
# Protokoll-Konfiguration
self.protocols = ["dydx_v4", "hyperliquid", "drift"]
self.symbols_per_protocol = {
"dydx_v4": ["BTC-USD", "ETH-USD", "SOL-USD"],
"hyperliquid": ["BTC", "ETH", "SOL"],
"drift": ["BTC-PERP", "ETH-PERP", "SOL-PERP"]
}
# Risk Management
self.max_position_size = 0.1 # 10% des Kapitals pro Position
self.max_leverage = 10
self.stop_loss_pct = 0.02 # 2%
self.take_profit_pct = 0.05 # 5%
# Performance Metrics
self.total_pnl = 0
self.total_trades = 0
self.winning_trades = 0
async def run(self):
"""Hauptschleife: Starte alle Streams parallel"""
async with self.holy_sheep:
# Starte Streams für alle Protokolle
tasks = []
for protocol in self.protocols:
symbols = self.symbols_per_protocol[protocol]
# Liquidation Stream
tasks.append(self._liquidation_loop(protocol, symbols))
# Open Interest Stream
tasks.append(self._oi_loop(protocol, symbols))
# Starte alle Tasks
await asyncio.gather(*tasks)
async def _liquidation_loop(self, protocol: str, symbols: list):
"""Liquidation Event Handler"""
async def on_liquidation(event: LiquidationEvent):
logger.info(
f"📊 {protocol} {event.symbol}: "
f"{event.side.upper()} ${event.value_usd:,.2f} @ ${event.price:,.2f}"
)
# Hole gecachte Daten für Analyse
cached = self.tardis.get_cached_data(protocol, event.symbol)
# Analysiere mit HolySheep AI
signal = await self.holy_sheep.analyze_liquidation_signal(
protocol=protocol,
symbol=event.symbol,
liquidation_data=cached["liquidation_data"],
open_interest_data=cached["open_interest_data"]
)
logger.info(
f"🧠 HolySheep Signal (Latenz: {signal['latency_ms']:.2f}ms, "
f"Kosten: ${signal['cost_per_call']:.6f})"
)
# Parse Signal und evtl. Trade ausführen
await self._process_signal(protocol, event.symbol, signal)
await self.tardis.subscribe_liquidations(protocol, symbols, on_liquidation)
async def _oi_loop(self, protocol: str, symbols: list):
"""Open Interest Update Handler"""
async def on_oi_update(snapshot: OpenInterestSnapshot):
logger.info(
f"📈 {protocol} {snapshot.symbol}: "
f"OI=${snapshot.open_interest:,.2f} "
f"(1h: {snapshot.open_interest_change_1h:+.2f}%, "
f"24h: {snapshot.open_interest_change_24h:+.2f}%, "
f"Funding: {snapshot.funding_rate:.4f}%)"
)
# Prüfe auf ungewöhnliche OI-Änderungen
if abs(snapshot.open_interest_change_1h) > 10:
logger.warning(
f"⚠️ Signifikante OI-Änderung bei {protocol} {snapshot.symbol}!"
)
await self.tardis.subscribe_open_interest(protocol, symbols, on_oi_update)
async def _process_signal(self, protocol: str, symbol: str, signal: Dict):
"""Verarbeite HolySheep Signal und führe Trade aus"""
# Hier würde die eigentliche Order-Ausführung stattfinden
# (aus Platzgründen vereinfacht dargestellt)
self.total_trades += 1
logger.info(f"📝 Trade Logged: {protocol} {symbol}")
def _calculate_position_size(self, entry_price: float, stop_loss: float) -> float:
"""Berechne Positionsgröße basierend auf Risk Management"""
risk_amount = self.capital * self.max_position_size
price_risk = abs(entry_price - stop_loss)
if price_risk == 0:
return 0
position_value = risk_amount / price_risk * entry_price
position_value = min(position_value, self.capital * self.max_position_size)
return position_value
def get_performance_report(self) -> Dict:
"""Generiere Performance-Report"""
avg_latency = self.holy_sheep.get_average_latency()
cost_summary = self.holy_sheep.get_cost_summary(
calls=self.total_trades,
avg_tokens_per_call=500
)
win_rate = (
self.winning_trades / self.total_trades * 100
if self.total_trades > 0 else 0
)
return {
"total_pnl_usd": self.total_pnl,
"total_trades": self.total_trades,
"win_rate": f"{win_rate:.2f}%",
"avg_latency_ms": round(avg_latency, 2),
"holy_sheep_cost_usd": cost_summary["total_cost_usd"],
"roi_percent": round(self.total_pnl / self.capital * 100, 2)
}
async def main():
"""Hauptfunktion"""
holy_sheep_key = "YOUR_HOLYSHEEP_API_KEY"
tardis_key = "YOUR_TARDIS_API_KEY"
strategy = PerpetualTradingStrategy(
holy_sheep_key=holy_sheep_key,
tardis_key=tardis_key,
capital_allocation=100_000
)
try:
await strategy.run()
except KeyboardInterrupt:
logger.info("⏹️ Strategy gestoppt")
report = strategy.get_performance_report()
logger.info(f"📊 Performance Report: {report}")
if __name__ == "__main__":
asyncio.run(main())Backtesting-Framework
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Tuple
import json
class BacktestEngine:
"""
Backtesting-Engine für HolySheep-basierte Strategien
Nutzt historische Tardis-Daten
"""
def __init__(
self,
initial_capital: float = 100_000,
holy_sheep_key: str = None
):
self.initial_capital = initial_capital
self.capital = initial_capital
self.positions: List[dict] = []
self.trades: List[dict] = []
self.equity_curve: List[float] = [initial_capital]
self.dates: List[datetime] = [datetime.now()]
# HolySheep Client für Simulationen
self.holy_sheep = HolySheepClient(holy_sheep_key) if holy_sheep_key else None
async def run_backtest(
self,
historical_data: pd.DataFrame,
strategy_params: dict = None
) -> dict:
"""
Führe Backtest auf historischen Daten aus
historical_data muss enthalten:
- timestamp, symbol, liquidation_volume, largest_liquidation
- ls_ratio, concentration, open_interest, oi_change_1h, funding_rate
"""
if strategy_params is None:
strategy_params = {
"min_signal_strength": 70,
"max_positions": 3,
"position_size_pct": 0.1,
"stop_loss_pct": 0.02,
"take_profit_pct": 0.05
}
for idx, row in historical_data.iterrows():
timestamp = row['timestamp']
symbol = row['symbol']
# Simuliere HolySheep Analyse
signal_score = self._simulate_signal(row)
# Check für neue Trades
if signal_score >= strategy_params["min_signal_strength"]:
if len(self.positions) < strategy_params["max_positions"]:
await self._open_position(
symbol=symbol,
entry_price=row.get('close', 0),
signal_score=signal_score,
params=strategy_params
)
# Aktualisiere bestehende Positionen
self._update_positions(current_price=row.get('close', 0))
# Track Equity
current_equity = self.capital + self._calculate_portfolio_value(
current_price=row.get('close', 0)
)
self.equity_curve.append(current_equity)
self.dates.append(timestamp)
return self._generate_report()
def _simulate_signal(self, row: pd.Series) -> float:
"""
Simuliere HolySheep AI Signal-Score
In Produktion: echter API-Call
"""
# Vereinfachte Signalberechnung
factors = []
# Liquidation Volume Factor
if row.get('liquidation_volume', 0) > 1_000_000:
factors.append(20)
# Concentration Factor
if row.get('concentration', 0) > 50:
factors.append(25)
# OI Change Factor
if abs(row.get('oi_change_1h', 0)) > 5:
factors.append(20)
# Funding Rate Factor
funding = abs(row.get('funding_rate', 0))
if funding > 0.01:
factors.append(15)
# Long/Short Ratio Anomalie
ls_ratio = row.get('ls_ratio', 1)
if ls_ratio < 0.5 or ls_ratio > 2:
factors.append(20)
base_score = sum(factors)
noise = np.random.uniform(-10, 10)
return min(100, max(0, base_score + noise))
async def _open_position(
self,
symbol: str,
entry_price: float,
signal_score: float,
params: dict
):
"""Eröffne neue Position"""
position_value = self.capital * params["position_size_pct"]
position = {
"symbol": symbol,
"entry_price": entry_price,
"size": position_value / entry_price,
"stop_loss": entry_price * (1 - params["stop_loss_pct"]),
"take_profit": entry_price * (1 + params["take_profit_pct"]),
"signal_score": signal_score,
"entry_date": datetime.now(),
"pnl": 0
}
self.positions.append(position)
self.capital -= position_value
self.trades.append({
"type": "OPEN",
"symbol": symbol,
"price": entry_price,
"value": position_value,
"signal_score": signal_score,
"timestamp": datetime.now()
})
def _update_positions(self, current_price: float):
"""Aktualisiere offene Positionen"""
closed_positions = []
for pos in self.positions:
pos["pnl"] = (current_price - pos["entry_price"]) / pos["entry_price"]
# Check Stop-Loss / Take-Profit
if current_price <= pos["stop_loss"] or current_price >= pos["take_profit"]:
closed_positions.append(pos)
pnl_value = pos["pnl"] * (pos["entry_price"] * pos["size"])
self.capital += pos["entry_price"] * pos["size"] + pnl_value
self.trades.append({
"type": "CLOSE",
"symbol": pos["symbol"],
"price": current_price,
"pnl": pnl_value,
"pnl_pct": pos["pnl"],
"timestamp": datetime.now()
})
# Entferne geschlossene Positionen
for pos in closed_positions:
self.positions.remove(pos)
def _calculate_portfolio_value(self, current_price: float) -> float:
"""Berechne aktuellen Portfolio-Wert"""
position_value = 0
for pos in self.positions:
position_value += pos["size"] * current_price
return position_value
def _generate_report(self) -> dict:
"""Generiere Backtest-Report"""
closed_trades = [t for t in self.trades if t["type"] == "CLOSE"]
if closed_trades:
pnls = [t["pnl"] for t in closed_trades]
winning_trades = [p for p in pnls if p > 0]
metrics = {
"initial_capital": self.initial_capital,
"final_capital": self.capital + self._calculate_portfolio_value(
self.equity_curve[-1]
),
"total_return_pct": (
(self.capital - self.initial_capital) / self.initial_capital * 100
),
"total_trades": len(closed_trades),
"winning_trades": len(winning_trades),
"losing_trades": len(closed_trades) - len(winning_trades),
"win_rate_pct": len(winning_trades) / len(closed_trades) * 100,
"avg_win": np.mean(winning_trades) if winning_trades else 0,
"avg_loss": np.mean([p for p in pnls if p < 0]) if pnls else 0,
"max_drawdown_pct": self._calculate_max_drawdown(),
"sharpe_ratio": self._calculate_sharpe_ratio(),
"avg_holy_sheep_latency_ms": 42.5, # Simuliert
"estimated_holy_sheep_cost": len(self.trades) * 0.00034
}
else:
metrics = {
"initial_capital": self.initial_capital,
"final_capital": self.capital,
"total_return_pct": 0,
"total_trades": 0,
"message": "Keine Trades im Backtest-Zeitraum"
}
return metrics
def _calculate_max_drawdown(self) -> float:
"""Berechne Maximum Drawdown"""
equity = np.array(self.equity_curve)
running_max = np.maximum.accumulate(equity)
drawdown = (equity - running_max) / running_max * 100
return abs(np.min(drawdown))
def _calculate_sharpe_ratio(self) -> float:
"""Berechne Sharpe Ratio (annualisiert)"""
if len(self.equity_curve) < 2:
return 0
returns = np.diff(self.equity_curve) / self.equity_curve[:-1]
if np.std(returns) == 0:
return 0
sharpe = np.mean(returns) / np.std(returns) * np.sqrt
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