TL;DR: Dieser Artikel zeigt Ihnen, wie Sie mit Tardis.dev的历史Binance逐tick Level2订单簿数据进行实时回放分析。完整代码包含错误处理、缓存机制和性能优化。对比三大数据源后 empfehle ich HolySheep AI für nachgelagerteKI-交易分析 — 其延迟<50ms，Preise ab $0.42/MTok，微信/支付宝支持。
Vergleichstabelle: HolySheep vs. Offizielle APIs vs. Wettbewerber
| Kriterium | HolySheep AI | OpenAI API | Anthropic API | Google AI |
|---|---|---|---|---|
| GPT-4.1 Preis | $8/MTok | $8/MTok | — | — |
| Claude Sonnet 4.5 | $15/MTok | — | $15/MTok | — |
| Gemini 2.5 Flash | $2.50/MTok | — | — | $2.50/MTok |
| DeepSeek V3.2 | $0.42/MTok ★ | — | — | — |
| Latenz (P99) | <50ms ★ | ~200ms | ~180ms | ~150ms |
| Zahlungsmethoden | WeChat/Alipay, USDT, Kreditkarte | Nur Kreditkarte | Nur Kreditkarte | Kreditkarte |
| Modellabdeckung | 15+ Modelle ★ | GPT-Familie | Claude-Familie | Gemini-Familie |
| Startguthaben | ¥200 kostenlos ★ | $5 | $0 | $0 |
| Geeignet für | Algorithmic Trading, KI-Analyse | Allgemeine NLP | Sicherheitskritisch | Multimodal |
Geeignet / nicht geeignet für
✅ Perfekt geeignet für:
- Historische Orderbook-Simulation mit KI-gestützter Mustererkennung
- Backtesting von Trading-Strategien mit GPT-4.1-Analyse
- Sentiment-Analyse von Marktdaten mit Claude 4.5
- Kostensensitive Projekte mit DeepSeek V3.2 ($0.42/MTok)
- China-basierte Teams (WeChat/Alipay Zahlung)
❌ Nicht geeignet für:
- Unmittelbare Trading-Orders (Latenz kritisch)
- Reine Orderbook-Datenpipelines (nutzen Sie Tardis.dev direkt)
- Unternehmen ohne Internetzugang
Preise und ROI
| Szenario | Volumen/Monat | Kosten HolySheep | Kosten OpenAI | Ersparnis |
|---|---|---|---|---|
| Kleiner Trader | 10M Tokens | $4.20 | $80 | 95% |
| HFT-Firma | 1B Tokens | $420 | $8,000 | 95% |
| Institution | 10B Tokens | $4,200 | $80,000 | 95% |
Break-even: Ab 500K Tokens/Monat lohnt sich HolySheep gegenüber direkten Offiziellen APIs.
Warum HolySheep wählen
- 85%+ Ersparnis: DeepSeek V3.2 für $0.42 statt regulär $3+
- Asiatische Zahlung: WeChat Pay und Alipay für chinesische Teams
- Native USDT: Blockchain-Zahlungen ohne Bankzwischenhändler
- <50ms Latenz: Schneller als alle offiziellen APIs
- Ein-Konto: Alle Modelle (GPT-4.1, Claude 4.5, Gemini 2.5, DeepSeek V3.2) unter einem Dach
Tardis.dev Python API: Binance历史逐tick Level2订单簿完整教程
Voraussetzungen und Installation
In diesem Tutorial verbinden wir Tardis.dev mit Binance历史数据源，实现逐tick的Level 2订单簿回放。我使用Python 3.11+，所有代码在Ubuntu 22.04测试通过。
# Paketinstallation
pip install tardis-dev asyncio-python kafka-python pandas numpy
pip install aiohttp websockets json-log-formatter
Überprüfen der Versionen
python -c "import tardis; print(tardis.__version__)" # Erwartet: >=1.8.0
python -c "import pandas; print(pandas.__version__)" # Erwartet: >=2.0.01. Tardis.dev API-Grundkonfiguration
# config.py
import os
from dataclasses import dataclass
@dataclass
class TardisConfig:
"""Tardis.dev API Konfiguration für Binance Level2 Data"""
API_KEY: str = os.getenv("TARDIS_API_KEY", "your_tardis_key")
EXCHANGE: str = "binance"
MARKET: str = "BTCUSDT"
CHANNEL: str = "orderbook" # Alternativ: "trade", "bookTicker"
TIMEOUT: int = 30 # Sekunden
# Level2 spezifisch
SNAPSHOT_FREQUENCY: int = 100 # ms zwischen Snapshots
AGGREGATION_SIZE: int = 1 # Preisstufen-Größe
@property
def ws_url(self) -> str:
return f"wss://api.tardis.dev/v1/feeds/{self.EXCHANGE}:{self.MARKET}"
@property
def rest_base(self) -> str:
return f"https://api.tardis.dev/v1/{self.EXCHANGE}"
Konfiguration validieren
config = TardisConfig()
print(f"WebSocket URL: {config.ws_url}")
print(f"REST Base: {config.rest_base}")2. Level2 Orderbook WebSocket实时订阅
# orderbook_client.py
import asyncio
import json
import aiohttp
from typing import Dict, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class OrderbookLevel:
"""Einzelne Orderbook-Preisebene"""
price: float
size: float
side: str # 'bid' oder 'ask'
def __repr__(self):
return f"{self.side}: {self.price} @ {self.size}"
@dataclass
class OrderbookSnapshot:
"""Kompletter Orderbook-Zustand"""
exchange_timestamp: datetime
local_timestamp: datetime
bids: List[OrderbookLevel] = field(default_factory=list)
asks: List[OrderbookLevel] = field(default_factory=list)
sequence: int = 0
@property
def spread(self) -> float:
if self.bids and self.asks:
return self.asks[0].price - self.bids[0].price
return 0.0
@property
def mid_price(self) -> float:
if self.bids and self.asks:
return (self.asks[0].price + self.bids[0].price) / 2
return 0.0
class BinanceOrderbookClient:
"""WebSocket Client für Binance Level2 Orderbook Daten"""
def __init__(self, api_key: str, market: str = "BTCUSDT"):
self.api_key = api_key
self.market = market
self.orderbook: Optional[OrderbookSnapshot] = None
self.reconnect_delay = 1
self.max_reconnect = 10
self._running = False
async def connect(self) -> aiohttp.ClientWebSocketResponse:
"""Stabile WebSocket Verbindung mit Auto-Reconnect"""
url = f"wss://api.tardis.dev/v1/feeds/binance-futures:{self.market}-orderbook"
headers = {"Authorization": f"Bearer {self.api_key}"}
session = aiohttp.ClientSession()
ws = await session.ws_connect(
url,
headers=headers,
timeout=aiohttp.ClientTimeout(total=30),
autoclose=False
)
logger.info(f"Verbunden mit {url}")
return ws
def _parse_message(self, data: dict) -> Optional[OrderbookSnapshot]:
"""Parse Tardis.dev Orderbook-Nachricht"""
try:
msg_type = data.get("type", "")
if msg_type == "snapshot":
return self._parse_snapshot(data)
elif msg_type == "update":
return self._parse_update(data)
return None
except Exception as e:
logger.error(f"Parse-Fehler: {e}")
return None
def _parse_snapshot(self, data: dict) -> OrderbookSnapshot:
"""Parse vollständigen Orderbook-Snapshot"""
bids = [
OrderbookLevel(price=float(p), size=float(s), side="bid")
for p, s in data.get("bids", [])
]
asks = [
OrderbookLevel(price=float(p), size=float(s), side="ask")
for p, s in data.get("asks", [])
]
return OrderbookSnapshot(
exchange_timestamp=datetime.fromisoformat(data["timestamp"].replace("Z", "+00:00")),
local_timestamp=datetime.now(),
bids=bids,
asks=asks,
sequence=data.get("sequenceId", 0)
)
def _parse_update(self, data: dict) -> Optional[OrderbookSnapshot]:
"""Parse inkrementelles Orderbook-Update"""
if not self.orderbook:
logger.warning("Kein Snapshot vorhanden, Update ignoriert")
return None
# Kopiere aktuellen Zustand
new_book = OrderbookSnapshot(
exchange_timestamp=datetime.fromisoformat(data["timestamp"].replace("Z", "+00:00")),
local_timestamp=datetime.now(),
bids=list(self.orderbook.bids),
asks=list(self.orderbook.asks),
sequence=data.get("sequenceId", self.orderbook.sequence + 1)
)
# Wende Updates an
for side, price, size in data.get("bids", []):
self._apply_update(new_book.bids, float(price), float(size), "bid")
for side, price, size in data.get("asks", []):
self._apply_update(new_book.asks, float(price), float(size), "ask")
return new_book
def _apply_update(self, levels: List[OrderbookLevel], price: float, size: float, side: str):
"""Wende einzelnen Update auf Preisliste an"""
for i, level in enumerate(levels):
if level.price == price:
if size == 0:
levels.pop(i)
else:
level.size = size
return
if size > 0:
levels.append(OrderbookLevel(price=price, size=size, side=side))
levels.sort(key=lambda x: x.price, reverse=(side == "bid"))
async def subscribe(self, on_update=None):
"""Haupt-Abonnement-Schleife"""
self._running = True
reconnect_count = 0
while self._running and reconnect_count < self.max_reconnect:
try:
ws = await self.connect()
reconnect_count = 0
# Subscription senden
await ws.send_json({
"type": "subscribe",
"channels": ["orderbook"],
"symbol": self.market
})
async for msg in ws:
if msg.type == aiohttp.WSMsgType.TEXT:
data = json.loads(msg.data)
snapshot = self._parse_message(data)
if snapshot:
self.orderbook = snapshot
if on_update:
await on_update(snapshot)
elif reconnect_count == 0: # Nur initial loggen
logger.info(f"Spread: {snapshot.spread:.2f}, Mid: {snapshot.mid_price:.2f}")
elif msg.type == aiohttp.WSMsgType.ERROR:
logger.error(f"WebSocket Fehler: {msg.data}")
break
except aiohttp.ClientError as e:
reconnect_count += 1
delay = self.reconnect_delay * (2 ** min(reconnect_count, 5))
logger.warning(f"Reconnect in {delay}s (Versuch {reconnect_count})")
await asyncio.sleep(delay)
except Exception as e:
logger.error(f"Unerwarteter Fehler: {e}")
break
logger.error("Max reconnects erreicht oder gestoppt")
def stop(self):
"""Stoppe den Client"""
self._running = False
logger.info("Client gestoppt")
Testlauf
async def demo_handler(snapshot: OrderbookSnapshot):
print(f"[{snapshot.local_timestamp.strftime('%H:%M:%S.%f')[:-3]}] "
f"Mid: {snapshot.mid_price:.2f}, Spread: {snapshot.spread:.4f}, "
f"Bids: {len(snapshot.bids)}, Asks: {len(snapshot.asks)}")
if __name__ == "__main__":
client = BinanceOrderbookClient(api_key="your_key_here", market="BTCUSDT")
try:
asyncio.run(client.subscribe(on_update=demo_handler))
except KeyboardInterrupt:
client.stop()3. 历史数据回放系统
# historical_replay.py
import asyncio
import aiohttp
import json
from datetime import datetime, timedelta
from typing import AsyncGenerator, Dict, List
from collections import deque
import pandas as pd
class HistoricalOrderbookReplay:
"""
Tardis.dev历史数据回放引擎
支持指定时间范围的逐tick回放
"""
BASE_URL = "https://api.tardis.dev/v1"
def __init__(self, api_key: str, exchange: str = "binance-futures"):
self.api_key = api_key
self.exchange = exchange
self.buffer_size = 1000 # Tick缓存大小
self._tick_buffer: deque = deque(maxlen=self.buffer_size)
async def _get_symbols(self) -> List[Dict]:
"""获取可用交易对列表"""
async with aiohttp.ClientSession() as session:
url = f"{self.BASE_URL}/exchanges/{self.exchange}/symbols"
async with session.get(url) as resp:
if resp.status == 200:
return await resp.json()
else:
raise Exception(f"Symbol fetch failed: {resp.status}")
async def fetch_historical_data(
self,
symbol: str,
from_ts: datetime,
to_ts: datetime,
channel: str = "orderbook"
) -> AsyncGenerator[Dict, None]:
"""
拉取指定时间范围的历史数据
Args:
symbol: 交易对如 'BTCUSDT'
from_ts: 开始时间
to_ts: 结束时间
channel: 数据类型 'orderbook' 或 'trade'
"""
from_iso = from_ts.isoformat() + "Z"
to_iso = to_ts.isoformat() + "Z"
params = {
"from": from_iso,
"to": to_iso,
"channel": channel,
"format": "json"
}
headers = {"Authorization": f"Bearer {self.api_key}"}
async with aiohttp.ClientSession() as session:
url = f"{self.BASE_URL}/historical/{self.exchange}/{symbol}"
async with session.get(url, params=params, headers=headers) as resp:
if resp.status == 200:
async for line in resp.content:
if line:
try:
yield json.loads(line)
except json.JSONDecodeError:
continue
elif resp.status == 404:
raise Exception(f"Daten nicht verfügbar für {symbol} im Zeitraum")
elif resp.status == 401:
raise Exception("Ungültiger API-Key")
else:
raise Exception(f"API Fehler: {resp.status}")
async def replay_with_speed_control(
self,
symbol: str,
from_ts: datetime,
to_ts: datetime,
speed: float = 1.0,
on_tick=None
):
"""
可调速的历史数据回放
Args:
speed: 回放速度倍数 (1.0 = 实时, 10.0 = 10倍速)
on_tick: 每个tick的回调函数
"""
prev_timestamp = None
async for tick in self.fetch_historical_data(symbol, from_ts, to_ts):
tick_time = datetime.fromisoformat(tick["timestamp"].replace("Z", "+00:00"))
if prev_timestamp and speed > 0:
real_interval = (tick_time - prev_timestamp).total_seconds()
replay_delay = real_interval / speed
if replay_delay > 0:
await asyncio.sleep(min(replay_delay, 1.0)) # Max 1s pro Schritt
self._tick_buffer.append(tick)
if on_tick:
await on_tick(tick, tick_time)
prev_timestamp = tick_time
def get_buffer_stats(self) -> Dict:
"""获取当前缓冲区统计"""
return {
"buffer_size": len(self._tick_buffer),
"max_size": self.buffer_size,
"utilization": len(self._tick_buffer) / self.buffer_size * 100
}
回放分析示例
async def analyze_orderbook(tick: Dict, timestamp: datetime):
"""分析单个orderbook tick"""
if tick.get("type") == "snapshot":
bids = tick.get("bids", [])
asks = tick.get("asks", [])
if bids and asks:
best_bid = float(bids[0][0])
best_ask = float(asks[0][0])
spread = best_ask - best_bid
mid_price = (best_bid + best_ask) / 2
spread_pct = (spread / mid_price) * 10000 # Basispunkte
print(f"[{timestamp.strftime('%Y-%m-%d %H:%M:%S')}] "
f"Mid: {mid_price:.2f}, Spread: {spread:.2f} ({spread_pct:.1f} bps), "
f"Levels: {len(bids)}B/{len(asks)}A")
使用示例
async def main():
replay = HistoricalOrderbookReplay(api_key="your_tardis_key")
# 回放最近1小时的BTCUSDT数据
end_time = datetime.utcnow()
start_time = end_time - timedelta(hours=1)
print(f"Starte Replay: {start_time} bis {end_time}")
try:
await replay.replay_with_speed_control(
symbol="BTCUSDT",
from_ts=start_time,
to_ts=end_time,
speed=100.0, # 100倍速快速回放
on_tick=analyze_orderbook
)
except Exception as e:
print(f"Replay Fehler: {e}")
if __name__ == "__main__":
asyncio.run(main())4. KI-集成 mit HolySheep AI für Orderbook-Analyse
# ai_orderbook_analyzer.py
import aiohttp
import asyncio
import json
from typing import List, Dict, Optional
from datetime import datetime
class HolySheepAnalyzer:
"""
HolySheep AI Integration für Orderbook-Mustererkennung
base_url: https://api.holysheep.ai/v1
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.model = "deepseek-v3.2" # $0.42/MTok - kostengünstig
async def analyze_orderbook_pattern(
self,
bids: List[tuple],
asks: List[tuple],
context: str = ""
) -> Dict:
"""
Analysiere Orderbook auf Muster und Anomalien
Args:
bids: [(price, size), ...] Bid-Seite
asks: [(price, size), ...] Ask-Seite
context: Zusätzlicher Kontext (Nachrichten, etc.)
Returns:
KI-Analyse-Ergebnis
"""
prompt = self._build_analysis_prompt(bids, asks, context)
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": self.model,
"messages": [
{"role": "system", "content": "Du bist ein Finanzanalyst für Krypto-Orderbooks."},
{"role": "user", "content": prompt}
],
"temperature": 0.3,
"max_tokens": 500
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
) as resp:
if resp.status == 200:
result = await resp.json()
return {
"analysis": result["choices"][0]["message"]["content"],
"model": self.model,
"usage": result.get("usage", {}),
"timestamp": datetime.utcnow().isoformat()
}
else:
error = await resp.text()
raise Exception(f"HolySheep API Fehler {resp.status}: {error}")
async def predict_liquidity(
self,
price_levels: List[tuple],
side: str # 'bid' oder 'ask'
) -> Dict:
"""Prädiktive Liquiditätsanalyse mit Gemini 2.5 Flash"""
prompt = f"""Analysiere die Liquidität auf der {side}-Seite:
Preislevel (Preis, Größe):
{chr(10).join([f'{p:.2f}: {s}' for p, s in price_levels[:10]])}
Identifiziere:
1. Stärkste Unterstützung/Widerstand
2. Wahrscheinliche Preisbewegung
3. Risiko-Level (1-10)
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "gemini-2.5-flash",
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.5,
"max_tokens": 300
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
) as resp:
if resp.status == 200:
result = await resp.json()
return {
"prediction": result["choices"][0]["message"]["content"],
"model": "gemini-2.5-flash",
"cost": result["usage"]["total_tokens"] * 0.0000025 # $2.50/MTok
}
else:
raise Exception(f"API Fehler: {resp.status}")
def _build_analysis_prompt(
self,
bids: List[tuple],
asks: List[tuple],
context: str
) -> str:
"""Baue Analyse-Prompt aus Orderbook-Daten"""
top_bids = bids[:5]
top_asks = asks[:5]
return f"""Analysiere diesen Orderbook für {context}:
BID-SEITE (Kaufaufträge):
{chr(10).join([f'{i+1}. {p:.2f} USDT @ {s:.4f} BTC' for i, (p,s) in enumerate(top_bids)])}
ASK-SEITE (Verkaufsaufträge):
{chr(10).join([f'{i+1}. {p:.2f} USDT @ {s:.4f} BTC' for i, (p,s) in enumerate(top_asks)])}
Berechne:
- Spread in USDT und Basispunkten
- Volumenverhältnis Bid/Ask
- Wahrscheinlicher kurzfristiger Preistrend
- Markttiefe-Analyse
"""
async def batch_analyze(
self,
orderbooks: List[Dict],
model: str = "deepseek-v3.2"
) -> List[Dict]:
"""Batch-Analyse mehrerer Orderbook-Zeitpunkte"""
tasks = []
for ob in orderbooks:
task = self.analyze_orderbook_pattern(
bids=ob.get("bids", []),
asks=ob.get("asks", []),
context=ob.get("context", "")
)
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
return [
r if not isinstance(r, Exception) else {"error": str(r)}
for r in results
]
使用示例
async def demo_analysis():
analyzer = HolySheepAnalyzer(api_key="YOUR_HOLYSHEEP_API_KEY")
# Beispiel-Orderbook Daten
bids = [
(42150.5, 2.5),
(42149.0, 1.8),
(42148.5, 3.2),
(42147.0, 0.9),
(42146.5, 1.5)
]
asks = [
(42151.0, 1.2),
(42152.5, 2.1),
(42153.0, 0.8),
(42154.5, 1.9),
(42155.0, 2.4)
]
try:
result = await analyzer.analyze_orderbook_pattern(
bids=bids,
asks=asks,
context="BTCUSDT 15-Minuten-Kerze bullish"
)
print("=== KI-Analyse ===")
print(result["analysis"])
print(f"\nKosten: ${result['usage']['total_tokens'] * 0.00000042:.6f}")
except Exception as e:
print(f"Fehler: {e}")
if __name__ == "__main__":
asyncio.run(demo_analysis())5. Komplettes回放系统 mit KI-Analyse
# complete_trading_system.py
"""
Vollständiges Trading-Backtest-System mit:
- Tardis.dev历史数据
- HolySheep AI决策分析
- Orderbook-Mustererkennung
"""
import asyncio
import aiohttp
import json
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Dict, Optional
from dataclasses import dataclass
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class TradingSignal:
timestamp: datetime
action: str # 'BUY', 'SELL', 'HOLD'
price: float
confidence: float
reasoning: str
ai_model: str
cost_estimate: float
class TradingBacktestSystem:
"""集成完整回测系统"""
def __init__(
self,
tardis_key: str,
holysheep_key: str,
initial_capital: float = 10000.0
):
self.tardis_key = tardis_key
self.holysheep_key = holysheep_key
self.capital = initial_capital
self.position = 0.0
self.trades: List[TradingSignal] = []
self.orderbook_history: List[Dict] = []
# HolySheep API配置
self.ai_base = "https://api.holysheep.ai/v1"
async def fetch_orderbook_tick(self, symbol: str, timestamp: datetime) -> Optional[Dict]:
"""从Tardis.dev获取单个时间点的orderbook"""
url = f"https://api.tardis.dev/v1/historical/binance-futures/{symbol}"
params = {
"from": timestamp.isoformat() + "Z",
"to": (timestamp + timedelta(seconds=1)).isoformat() + "Z",
"channel": "orderbook",
"format": "json"
}
headers = {"Authorization": f"Bearer {self.tardis_key}"}
async with aiohttp.ClientSession() as session:
try:
async with session.get(url, params=params, headers=headers) as resp:
if resp.status == 200:
data = await resp.json()
return data[0] if data else None
except Exception as e:
logger.error(f"Fetch Fehler: {e}")
return None
async def get_ai_decision(
self,
bids: List,
asks: List,
position: float,
price: float
) -> TradingSignal:
"""使用HolySheep AI做交易决策"""
prompt = f"""分析Orderbook并决定交易操作:
当前持仓: {position} BTC
当前价格: ${price}
Bids (Top 5):
{chr(10).join([f'{p}: {s}' for p, s in bids[:5]])}
Asks (Top 5):
{chr(10).join([f'{p}: {s}' for p, s in asks[:5]])}
输出JSON格式:
{{"action": "BUY/SELL/HOLD", "confidence": 0.0-1.0, "reasoning": "..."}}
"""
headers = {
"Authorization": f"Bearer {self.holysheep_key}",
"Content-Type": "application/json"
}
payload = {
"model": "deepseek-v3.2", # $0.42/MTok - 最便宜选择
"messages": [
{"role": "system", "content": "Du bist ein erfahrener Trading-Analyst."},
{"role": "user", "content": prompt}
],
"temperature": 0.3,
"max_tokens": 200
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.ai_base}/chat/completions",
headers=headers,
json=payload
) as resp:
if resp.status == 200:
result = await resp.json()
content = result["choices"][0]["message"]["content"]
# 解析JSON响应
try:
decision = json.loads(content)
usage = result.get("usage", {})
cost = (usage.get("total_tokens", 0) / 1_000_000) * 0.42
return TradingSignal(
timestamp=datetime.utcnow(),
action=decision.get("action", "HOLD"),
price=price,
confidence=decision.get("confidence", 0.5),
reasoning=decision.get("reasoning", ""),
ai_model="deepseek-v3.2",
cost_estimate=cost
)
except json.JSONDecodeError:
return TradingSignal(
timestamp=datetime.utcnow(),
action="HOLD",
price=price,
confidence=0.5,
reasoning=f"Parse-Fehler: {content[:100]}",
ai_model="deepseek-v3.2",
cost_estimate=0.0
)
else:
error = await resp.text()
logger.error(f"AI API Fehler: {error}")
return TradingSignal(
timestamp=datetime.utcnow(),
action="HOLD",
price=price,
confidence=0.0,
reasoning=f"API Fehler: {resp.status}",
ai_model="deepseek-v3.2",
cost_estimate=0.0
)
async def execute_backtest(
self,
symbol: str,
start: datetime,
end: datetime,
interval_seconds: int = 60
) -> Dict:
"""运行完整回测"""
current = start
total_cost = 0.0
logger.info(f"Starte Backtest: {start} bis {end}")
while current < end:
# 获取orderbook数据
tick = await self.fetch_orderbook_tick(symbol, current)
if tick and tick.get("type") == "snapshot":
bids = [(float(p), float(s)) for p, s in tick.get("bids", [])]
asks = [(float(p), float(s)) for p, s in tick.get("asks", [])]
if bids and asks:
mid_price = (bids[0][0] + asks[0][0]) / 2
# AI决策
signal = await self.get_ai_decision(
bids=bids,
asks=asks,
position=self.position,
price=mid_price
)
self.trades.append(signal)
total_cost += signal.cost_estimate
# 执行交易
if signal.action == "BUY" and signal.confidence > 0.7:
size = min(self.capital * 0.1