Case Study: Ein quantitatives Trading-Team aus Frankfurt revolutioniert seine Backtesting-Infrastruktur mit HolySheep AI — Latenzreduzierung um 57%, Kosten senkung um 84%.
客户案例：量化对冲基金的转型之路
Das Team bestand aus vier quantitativen Entwicklern, die täglich mit der Analyse historischer K-Line-Daten (Candlestick-Charts) arbeiteten. Ihre bisherige Infrastruktur basierte auf einer Kombination aus PostgreSQL für die Datenspeicherung, einem selbstentwickelten Python-Backtesting-Framework und der OpenAI API für Sentiment-Analysen der Nachrichtenlage.
业务痛点分析
- API-Latenz: Durchschnittlich 420ms pro Anfrage, Spitzenzeiten bis 800ms — inakzeptabel für Echtzeit-Backtesting mit 10.000+ Strategien
- Kostenexplosion: Monatliche Rechnung von $4.200 für 500.000 Token-Verarbeitungen pro Tag
- Datenintegration: Keine einheitliche Lösung für K-Line-Daten von Binance, OKX und Bybit
- 回测精度: Gleitende Durchschnitte und RSI-Indikatoren lieferten inkonsistente Ergebnisse zwischen Test- und Produktivumgebung
Warum HolySheep AI?
Nach einer sechswöchigen Evaluierungsphase entschied sich das Team für HolySheep AI aufgrund dreier entscheidender Faktoren:
- Latenz: Garantiert unter 50ms —实测 38ms im Durchschnitt
- Preis: $0.42/MTok für DeepSeek V3.2 vs. $8/MTok bei GPT-4.1
- Zahlungsmethoden: WeChat Pay und Alipay ermöglichen nahtlose Abrechnung für asiatische Märkte
具体迁移步骤
Schritt 1: Base-URL-Austausch
# Alte Konfiguration (NICHT VERWENDEN)
BASE_URL = "https://api.openai.com/v1" # ❌ VERALTET
Neue Konfiguration mit HolySheep AI
import os
HolySheep AI API Configuration
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1" # ✅ OFFIZIELLER ENDPOINT
Konfigurationsklasse für HolySheep
class HolySheepConfig:
"""Konfiguration für HolySheep AI API - K-Line Backtesting Framework"""
def __init__(self, api_key: str = None):
self.api_key = api_key or HOLYSHEEP_API_KEY
self.base_url = HOLYSHEEP_BASE_URL
self.timeout = 30 # Sekunden
self.max_retries = 3
self.default_model = "deepseek-chat" # Kosteneffizient für Backtesting
@property
def headers(self) -> dict:
return {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
def get_endpoint(self, model: str) -> str:
"""Generiert den vollständigen API-Endpunkt"""
return f"{self.base_url}/chat/completions"
Singleton-Instanz
config = HolySheepConfig()
print(f"HolySheep Base URL: {config.base_url}")
Output: HolySheep Base URL: https://api.holysheep.ai/v1
Schritt 2: API-Key-Rotation für不同的策略模型
import hashlib
from typing import Dict, List, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
@dataclass
class StrategyModel:
"""Modellkonfiguration für verschiedene Trading-Strategien"""
name: str
model: str
cost_per_1k_tokens: float
use_case: str
priority: int # 1 = höchste Priorität
class HolySheepModelRouter:
"""
Intelligentes Routing für K-Line-Backtesting mit HolySheep AI.
Wählt basierend auf Strategie-Typ und Budget das optimale Modell.
"""
# Modellkonfigurationen (Stand 2026)
MODELS = {
"deepseek-chat": StrategyModel(
name="DeepSeek V3.2",
model="deepseek-chat",
cost_per_1k_tokens=0.42, # $0.42/MTok - BUDGET-SIEGER
use_case="Klassische Strategien, RSI/MACD-Analyse",
priority=1
),
"gpt-4.1": StrategyModel(
name="GPT-4.1",
model="gpt-4.1",
cost_per_1k_tokens=8.0, # $8/MTok - Premium für komplexe Muster
use_case="Deep Learning Pattern Recognition",
priority=2
),
"claude-sonnet": StrategyModel(
name="Claude Sonnet 4.5",
model="claude-sonnet",
cost_per_1k_tokens=15.0, # $15/MTok - Coding & Backtesting Engine
use_case="Framework-Entwicklung, Strategie-Backtesting",
priority=3
),
"gemini-flash": StrategyModel(
name="Gemini 2.5 Flash",
model="gemini-2.5-flash",
cost_per_1k_tokens=2.50, # $2.50/MTok - Schnelle Inferenz
use_case="Echtzeit-Sentiment, News-Analyse",
priority=1
)
}
def __init__(self, api_key: str, budget_limit: float = 1000.0):
self.api_key = api_key
self.budget_limit = budget_limit
self.current_spend = 0.0
self.request_count = 0
self.model_usage: Dict[str, int] = {}
def select_model(self, strategy_type: str, complexity: str = "medium") -> StrategyModel:
"""
Wählt das optimale Modell basierend auf Strategie und Budget.
Args:
strategy_type: "momentum", "mean_reversion", "breakout", "sentiment"
complexity: "low", "medium", "high"
"""
if strategy_type == "sentiment" and complexity == "low":
return self.MODELS["gemini-flash"]
if complexity == "high":
return self.MODELS["deepseek-chat"] # Bestes Preis-Leistungs-Verhältnis
# Budget-Check
if self.current_spend > self.budget_limit * 0.8:
print(f"⚠️ Budget-Alert: {self.current_spend:.2f}$ / {self.budget_limit:.2f}$")
return self.MODELS["deepseek-chat"] # Fallback zum günstigsten
return self.MODELS["deepseek-chat"]
def track_usage(self, model: str, tokens_used: int):
"""Verfolgt den Token-Verbrauch für Kostenanalyse"""
cost = (tokens_used / 1000) * self.MODELS[model].cost_per_1k_tokens
self.current_spend += cost
self.request_count += 1
self.model_usage[model] = self.model_usage.get(model, 0) + tokens_used
print(f"[{self.request_count}] {self.MODELS[model].name}: "
f"{tokens_used} tokens → ${cost:.4f} | "
f"Gesamt: ${self.current_spend:.2f}")
Beispiel: Router initialisieren
router = HolySheepModelRouter(
api_key="YOUR_HOLYSHEEP_API_KEY",
budget_limit=500.0 # $500/Monat Budget
)
Modell für verschiedene Strategien
strategy_models = {
"RSI_Overbought_Oversold": router.select_model("momentum", "low"),
"Bollinger_Breakout": router.select_model("breakout", "medium"),
"News_Sentiment": router.select_model("sentiment", "low"),
}
for name, model in strategy_models.items():
print(f"{name} → {model.name} (${model.cost_per_1k_tokens}/MTok)")
Schritt 3: Canary-Deployment für Strategie-Validierung
import asyncio
import aiohttp
from typing import List, Dict, Any, Tuple
from datetime import datetime
import json
class CanaryBacktester:
"""
Canary Deployment für Trading-Strategien.
Testet neue Strategien mit 5% des Kapitals, bevor Vollrollout.
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.canary_ratio = 0.05 # 5% für Canary
self.critical_ratio = 0.95 # 95% Erfolgsrate minimum
async def analyze_kline_with_holy_sheep(
self,
kline_data: Dict[str, Any],
strategy: str
) -> Dict[str, Any]:
"""
Sendet K-Line-Daten zur Analyse an HolySheep AI.
Nutzt DeepSeek V3.2 für kosteneffiziente Verarbeitung.
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
prompt = f"""
Analysiere folgende K-Line-Daten für eine {strategy}-Strategie:
Symbol: {kline_data.get('symbol', 'BTCUSDT')}
Zeitraum: {kline_data.get('interval', '1h')}
Eröffnung: {kline_data.get('open', 0)}
Hoch: {kline_data.get('high', 0)}
Tief: {kline_data.get('low', 0)}
Schluss: {kline_data.get('close', 0)}
Volumen: {kline_data.get('volume', 0)}
Berechne:
1. RSI (Relative Strength Index)
2. MACD (Moving Average Convergence Divergence)
3. Bollinger Bands Position
4. Trading-Signal (BUY/SELL/HOLD)
"""
payload = {
"model": "deepseek-chat",
"messages": [
{"role": "system", "content": "Du bist ein erfahrener Krypto-Trading-Analyst."},
{"role": "user", "content": prompt}
],
"temperature": 0.3,
"max_tokens": 500
}
async with aiohttp.ClientSession() as session:
start_time = datetime.now()
try:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=10)
) as response:
latency_ms = (datetime.now() - start_time).total_seconds() * 1000
if response.status == 200:
result = await response.json()
return {
"success": True,
"analysis": result['choices'][0]['message']['content'],
"latency_ms": latency_ms,
"tokens_used": result.get('usage', {}).get('total_tokens', 0)
}
else:
error_text = await response.text()
return {
"success": False,
"error": f"HTTP {response.status}: {error_text}",
"latency_ms": latency_ms
}
except asyncio.TimeoutError:
return {
"success": False,
"error": "Timeout nach 10 Sekunden",
"latency_ms": 10000
}
except Exception as e:
return {
"success": False,
"error": str(e),
"latency_ms": 0
}
async def run_canary_backtest(
self,
kline_batch: List[Dict],
strategy: str,
iterations: int = 100
) -> Tuple[float, Dict]:
"""
Führt Canary-Backtesting durch.
Gibt Erfolgsrate und detaillierte Metriken zurück.
"""
results = {
"total": iterations,
"successful": 0,
"failed": 0,
"avg_latency_ms": 0,
"total_cost": 0.0,
"signals": {"BUY": 0, "SELL": 0, "HOLD": 0}
}
total_latency = 0
for i in range(min(iterations, len(kline_batch))):
kline = kline_batch[i % len(kline_batch)]
result = await self.analyze_kline_with_holy_sheep(kline, strategy)
if result["success"]:
results["successful"] += 1
total_latency += result["latency_ms"]
results["total_cost"] += (result["tokens_used"] / 1000) * 0.42 # DeepSeek Preis
# Parse Signal aus Analyse
analysis = result["analysis"].upper()
if "BUY" in analysis:
results["signals"]["BUY"] += 1
elif "SELL" in analysis:
results["signals"]["SELL"] += 1
else:
results["signals"]["HOLD"] += 1
else:
results["failed"] += 1
print(f"⚠️ Iteration {i}: {result['error']}")
results["avg_latency_ms"] = total_latency / max(results["successful"], 1)
results["success_rate"] = results["successful"] / results["total"]
# Entscheidung über Vollrollout
if results["success_rate"] >= self.critical_ratio:
results["recommendation"] = "PRODUCTION"
else:
results["recommendation"] = "REJECT"
return results["success_rate"], results
asyncio main execution
async def main():
tester = CanaryBacktester(api_key="YOUR_HOLYSHEEP_API_KEY")
# Mock K-Line-Daten (typische Binance API Struktur)
sample_klines = [
{
"symbol": "BTCUSDT",
"interval": "1h",
"open": 67500.0,
"high": 67800.0,
"low": 67200.0,
"close": 67650.0,
"volume": 1250.5
},
{
"symbol": "ETHUSDT",
"interval": "1h",
"open": 3450.0,
"high": 3480.0,
"low": 3420.0,
"close": 3465.0,
"volume": 45000.0
}
] * 50 # 100 Iterationen
success_rate, metrics = await tester.run_canary_backtest(
sample_klines,
strategy="RSI_Momentum",
iterations=100
)
print(f"\n{'='*50}")
print(f"CANARY BACKTEST ERGEBNISSE")
print(f"{'='*50}")
print(f"Erfolgsrate: {success_rate*100:.1f}%")
print(f"Durchschnittliche Latenz: {metrics['avg_latency_ms']:.1f}ms")
print(f"Gesamtkosten: ${metrics['total_cost']:.4f}")
print(f"Signale: BUY={metrics['signals']['BUY']}, "
f"SELL={metrics['signals']['SELL']}, "
f"HOLD={metrics['signals']['HOLD']}")
print(f"Empfehlung: {metrics['recommendation']}")
print(f"{'='*50}")
if __name__ == "__main__":
asyncio.run(main())
30-Tage-Metriken nach der Migration
| Metrik | Vorher | Nachher | Verbesserung |
|---|---|---|---|
| API-Latenz | 420ms | 38ms | ↓ 91% |
| Monatliche Kosten | $4,200 | $680 | ↓ 84% |
| Backtesting-Durchsatz | 50 Strategien/Tag | 500 Strategien/Tag | ↑ 900% |
| Token-Kosten (DeepSeek) | $8/MTok | $0.42/MTok | ↓ 95% |
| ROI (Return on Investment) | — | 718% | Überragend |
Tardis K-Line 回测框架：完整架构
框架概述
Tardis (Time And Relative Dimension In Space) ist ein selbstentwickeltes Backtesting-Framework, das historische K-Line-Daten mit KI-gestützter Signalgenerierung kombiniert. Der Name symbolisiert die Fähigkeit, durch die "Zeitdimension" der historischen Daten zu reisen.
核心组件
┌─────────────────────────────────────────────────────────────────┐
│ TARDIS BACKTESTING FRAMEWORK │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Data │───▶│ Strategy │───▶│ Backtest │ │
│ │ Loader │ │ Engine │ │ Engine │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Binance API │ │ HolySheep │ │ Results │ │
│ │ OKX API │ │ AI Analysis │ │ Analyzer │ │
│ │ Bybit API │ │ (DeepSeek) │ │ & Report │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │
│ API Endpoint: https://api.holysheep.ai/v1 │
│ Model: deepseek-chat ($0.42/MTok) │
│ Latenz: <50ms garantiert │
│ │
└─────────────────────────────────────────────────────────────────┘
Vollständige Framework-Implementierung
"""
Tardis K-Line Backtesting Framework
Komplette Implementierung für historische Candlestick-Daten-Analyse
"""
import sqlite3
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
import hashlib
import hmac
import time
============================================
1. DATENLADERSCHICHT
============================================
@dataclass
class KlineData:
"""Standardisierte K-Line Datenstruktur"""
symbol: str
interval: str # 1m, 5m, 1h, 4h, 1d
open_time: int
close_time: int
open: float
high: float
low: float
close: float
volume: float
quote_volume: float
trades: int
is_closed: bool
class DataLoader:
"""
Lädt K-Line-Daten von Binance/OKX/Bybit in SQLite.
Unterstützt Batch-Downloads für Backtesting.
"""
BINANCE_API = "https://api.binance.com/api/v3"
OKX_API = "https://api.okx.com/api/v5"
BYBIT_API = "https://api.bybit.com/v5"
INTERVAL_MAP = {
"1m": "1m", "5m": "5m", "15m": "15m",
"1h": "1h", "4h": "4h", "1d": "1d"
}
def __init__(self, db_path: str = "kline_data.db"):
self.db_path = db_path
self._init_database()
def _init_database(self):
"""Initialisiert SQLite-Datenbank mit K-Line-Tabelle"""
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
CREATE TABLE IF NOT EXISTS klines (
id INTEGER PRIMARY KEY AUTOINCREMENT,
exchange TEXT NOT NULL,
symbol TEXT NOT NULL,
interval TEXT NOT NULL,
open_time INTEGER NOT NULL,
close_time INTEGER NOT NULL,
open REAL NOT NULL,
high REAL NOT NULL,
low REAL NOT NULL,
close REAL NOT NULL,
volume REAL NOT NULL,
quote_volume REAL NOT NULL,
trades INTEGER NOT NULL,
is_closed INTEGER NOT NULL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
UNIQUE(exchange, symbol, interval, open_time)
)
""")
conn.execute("""
CREATE INDEX IF NOT EXISTS idx_klines_lookup
ON klines(exchange, symbol, interval, open_time)
""")
conn.commit()
def load_binance_klines(
self,
symbol: str,
interval: str = "1h",
start_time: int = None,
end_time: int = None,
limit: int = 1000
) -> List[KlineData]:
"""Lädt K-Line-Daten von Binance"""
import requests
endpoint = f"{self.BINANCE_API}/klines"
params = {
"symbol": symbol.upper(),
"interval": self.INTERVAL_MAP.get(interval, interval),
"limit": min(limit, 1000)
}
if start_time:
params["startTime"] = start_time
if end_time:
params["endTime"] = end_time
response = requests.get(endpoint, params=params, timeout=30)
response.raise_for_status()
klines = []
with sqlite3.connect(self.db_path) as conn:
for k in response.json():
kline = KlineData(
symbol=symbol.upper(),
interval=interval,
open_time=k[0],
close_time=k[6],
open=float(k[1]),
high=float(k[2]),
low=float(k[3]),
close=float(k[4]),
volume=float(k[5]),
quote_volume=float(k[7]),
trades=int(k[8]),
is_closed=bool(k[9])
)
klines.append(kline)
# Direkt in DB speichern
conn.execute("""
INSERT OR REPLACE INTO klines
(exchange, symbol, interval, open_time, close_time,
open, high, low, close, volume, quote_volume, trades, is_closed)
VALUES ('binance', ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""", (
kline.symbol, kline.interval, kline.open_time, kline.close_time,
kline.open, kline.high, kline.low, kline.close,
kline.volume, kline.quote_volume, kline.trades, kline.is_closed
))
conn.commit()
return klines
def get_klines_dataframe(
self,
symbol: str,
interval: str,
start_time: int = None,
end_time: int = None,
exchange: str = "binance"
) -> pd.DataFrame:
"""Gibt K-Line-Daten als Pandas DataFrame zurück"""
with sqlite3.connect(self.db_path) as conn:
query = """
SELECT open_time, close_time, open, high, low, close,
volume, quote_volume, trades
FROM klines
WHERE exchange = ? AND symbol = ? AND interval = ?
"""
params = [exchange, symbol.upper(), interval]
if start_time:
query += " AND open_time >= ?"
params.append(start_time)
if end_time:
query += " AND open_time <= ?"
params.append(end_time)
query += " ORDER BY open_time ASC"
df = pd.read_sql_query(query, conn, params=params)
# Konvertiere zu numerischen Werten
for col in ['open', 'high', 'low', 'close', 'volume', 'quote_volume']:
df[col] = pd.to_numeric(df[col], errors='coerce')
return df
============================================
2. TECHNISCHE INDIKATOREN
============================================
class TechnicalIndicators:
"""Berechnet technische Indikatoren für K-Line-Analyse"""
@staticmethod
def calculate_rsi(df: pd.DataFrame, period: int = 14) -> pd.Series:
"""Relative Strength Index"""
delta = df['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi
@staticmethod
def calculate_macd(
df: pd.DataFrame,
fast: int = 12,
slow: int = 26,
signal: int = 9
) -> Tuple[pd.Series, pd.Series, pd.Series]:
"""MACD, Signal-Linie und Histogramm"""
ema_fast = df['close'].ewm(span=fast, adjust=False).mean()
ema_slow = df['close'].ewm(span=slow, adjust=False).mean()
macd = ema_fast - ema_slow
signal_line = macd.ewm(span=signal, adjust=False).mean()
histogram = macd - signal_line
return macd, signal_line, histogram
@staticmethod
def calculate_bollinger_bands(
df: pd.DataFrame,
period: int = 20,
std_dev: float = 2.0
) -> Tuple[pd.Series, pd.Series, pd.Series]:
"""Bollinger Bands (Upper, Middle, Lower)"""
middle = df['close'].rolling(window=period).mean()
std = df['close'].rolling(window=period).std()
upper = middle + (std * std_dev)
lower = middle - (std * std_dev)
return upper, middle, lower
@staticmethod
def calculate_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
"""Average True Range"""
high_low = df['high'] - df['low']
high_close = np.abs(df['high'] - df['close'].shift())
low_close = np.abs(df['low'] - df['close'].shift())
true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
atr = true_range.rolling(window=period).mean()
return atr
============================================
3. HOLYSHEEP AI INTEGRATION
============================================
class HolySheepAnalyzer:
"""
KI-gestützte K-Line-Analyse mit HolySheep AI.
Nutzt DeepSeek V3.2 für kostengünstige Sentiment-Analyse.
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.model = "deepseek-chat" # $0.42/MTok - optimales Preis-Leistungs-Verhältnis
self.total_cost = 0.0
self.request_count = 0
def _make_request(self, messages: List[Dict]) -> Dict:
"""Interner HTTP-Request an HolySheep API"""
import requests
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": self.model,
"messages": messages,
"temperature": 0.3,
"max_tokens": 300
}
start_time = time.time()
response = requests.post(
f"{self.BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=15
)
latency_ms = (time.time() - start_time) * 1000
if response.status_code != 200:
raise Exception(f"HolySheep API Error: {response.status_code} - {response.text}")
result = response.json()
tokens_used = result.get('usage', {}).get('total_tokens', 0)
# Kostenberechnung (DeepSeek V3.2: $0.42/MTok)
cost = (tokens_used / 1000) * 0.42
self.total_cost += cost
self.request_count += 1
return {
"content": result['choices'][0]['message']['content'],
"tokens_used": tokens_used,
"cost": cost,
"latency_ms": latency_ms
}
def analyze_market_sentiment(
self,
symbol: str,
current_price: float,
rsi: float,
macd_histogram: float,
bollinger_position: float
) -> Dict:
"""
Analysiert Marktsentiment basierend auf technischen Indikatoren.
"""
messages = [
{
"role": "system",
"content": """Du bist ein erfahrener Krypto-Marktanalyst.
Analysiere die technischen Indikatoren und gib eine klare Empfehlung.
Format: JSON mit keys 'signal' (BUY/SELL/HOLD), 'confidence' (0-100),
'reason' (Kurze Begründung)."""
},
{
"role": "user",
"content": f"""
Analysiere {symbol}:
- Preis: ${current_price}
- RSI (14): {rsi:.2f}
- MACD Histogram: {macd_histogram:.4f}
- Bollinger Position: {bollinger_position:.2%}
Beurteile die Marktlage und gib ein klares Signal.
"""
}
]
result = self._make_request(messages)
# Parse JSON aus Response
import json
import re
content = result['content']
json_match = re.search(r'\{[^}]+\}', content, re.DOTALL)
if json_match:
return json.loads(json_match.group())
else:
return {
"signal": "HOLD",
"confidence": 50,
"reason": "Analyse fehlgeschlagen, halte Position"
}
def generate_backtest_report(
self,
strategy_name: str,
results: Dict
) -> str:
"""Generiert einen zusammenfassenden Report für Backtesting-Ergebnisse"""
messages = [
{
"role": "system",
"content": "Du bist ein Finanzanalyst. Erstelle prägnante Backtest-Berichte."
},
{
"role": "user",
"content": f"""
Erstelle einen Backtest-Bericht für die Strategie '{strategy_name}':
- Gesamtrendite: {results.get('total_return', 0):.2f}%
- Sharpe Ratio: {results.get('sharpe_ratio', 0):.2f}
- Max Drawdown: {results.get('max_drawdown', 0):.2f}%
- Win Rate: {results.get('win_rate', 0):.2f}%
- Trade Count: {results.get('trade_count', 0)}
Gib eine Bewertung der Strategie-Performance.
"""
}
]
result = self._make_request(messages)
return result['content']
============================================
4. BACKTESTING ENGINE
============================================
@dataclass
class Trade:
"""Repräsentiert einen einzelnen Trade"""
entry_time: int
entry_price: float
exit_time: int
exit_price: float
side: str # LONG oder SHORT
pnl: float
pnl_percent: float
class BacktestEngine:
"""
Führt Backtesting von Trading-Strategien durch.
Unterstützt Long/Short Positionen und simulierte Ausführung.
"""
def __init__(
self,
initial_capital: float = 10000.0,
commission: float = 0.001, # 0.1%
slippage: float = 0.0005 # 0.05%
):
self.initial_capital = initial_capital
self.commission = commission
self.slippage = slippage
self.capital = initial_capital
self.position = 0.0
self.position_side = None
self.trades: List[Trade] = []
self.equity_curve = []
def reset(self):
"""Setzt den Backtester auf初始状态 zurück"""
self.capital = self.initial_capital
self.position = 0.0
self.position_side = None
self.trades = []
self.equity_curve = []
def open_long(self, price: float, size: float, timestamp: int):
"""Eröffnet eine Long-Position"""
cost = price * size
commission_cost = cost * self.commission
slippage_cost = cost * self.slippage
total_cost = cost + commission_cost + slippage_cost
if total_cost > self.capital:
return False
self.capital -= total_cost
self.position = size
self.position_side = "LONG"
self.entry_price = price
self.entry_time = timestamp
return True
def close_long(self, price: float, timestamp: int):
"""Schließt eine Long-Position"""
if self.position <= 0 or self.position_side != "LONG":
return False
revenue = self.position * price
commission_cost = revenue * self.commission
slippage_cost = revenue * self.slippage
net_revenue = revenue - commission_cost - slippage_cost
pnl = net