710 lines
28 KiB
Python
710 lines
28 KiB
Python
import asyncio
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import csv
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import json
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import logging
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import os
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import threading
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import time
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from contextlib import asynccontextmanager
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Dict, Iterable, List, Optional
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import ccxt # type: ignore
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import pandas as pd # type: ignore
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from fastapi import FastAPI, HTTPException, Query
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from fastapi.middleware.cors import CORSMiddleware
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import uvicorn
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from technical.util import resample_to_interval
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# docker compose logs --tail=200
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# docker compose down && docker compose build --no-cache && docker compose up -d
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TIMEFRAME_ORDER = ["1m", "1h", "1d", "1w"]
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ALLOWED_TIMEFRAMES = set(TIMEFRAME_ORDER)
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TIMEFRAME_TO_MS: Dict[str, int] = {
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"1m": 60_000,
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"1h": 3_600_000,
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"1d": 86_400_000,
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"1w": 604_800_000,
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}
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DERIVED_TIMEFRAME_PLAN: Dict[str, List[str]] = {
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"1m": ["2m", "3m", "4m", "5m", "10m", "15m", "20m", "25m", "30m"],
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"1h": ["2h", "3h", "4h", "6h", "8h", "12h", "16h"],
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"1d": ["2d", "3d", "4d", "5d", "6d"],
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"1w": ["2w"],
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}
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CSV_FIELDNAMES = ["timestamp", "datetime", "open", "high", "low", "close", "volume"]
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DEFAULT_LIMIT = 500
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RECENT_CANDLE_LIMIT = 10
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RECENT_FETCH_INTERVAL = 5
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PERSIST_INTERVAL = 600
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logger = logging.getLogger("data_provider")
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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)
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def to_utc_iso(timestamp_ms: int) -> str:
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dt = datetime.fromtimestamp(timestamp_ms / 1000, tz=timezone.utc)
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return dt.isoformat().replace("+00:00", "Z")
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def parse_timestamp(value: Optional[object]) -> Optional[int]:
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if value is None:
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return None
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if isinstance(value, (int, float)):
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return int(value)
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if isinstance(value, str):
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text = value.strip()
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if not text:
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return None
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if text.isdigit():
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return int(text)
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if text.endswith("Z"):
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text = text[:-1] + "+00:00"
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try:
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dt = datetime.fromisoformat(text)
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except ValueError as exc: # pragma: no cover - informative logging
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raise ValueError(f"无法解析时间字符串: {value}") from exc
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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else:
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dt = dt.astimezone(timezone.utc)
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return int(dt.timestamp() * 1000)
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raise ValueError(f"不支持的时间格式: {value}")
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def candle_to_dict(candle: Iterable[float]) -> Dict[str, float]:
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ts = int(candle[0])
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return {
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"timestamp": ts,
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"datetime": to_utc_iso(ts),
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"open": float(candle[1]),
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"high": float(candle[2]),
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"low": float(candle[3]),
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"close": float(candle[4]),
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"volume": float(candle[5]),
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}
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def timeframe_to_minutes(tf: str) -> Optional[int]:
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if not tf:
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return None
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unit = tf[-1]
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try:
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value = int(tf[:-1])
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except ValueError:
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return None
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multiplier = {
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"m": 1,
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"h": 60,
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"d": 1_440,
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"w": 10_080,
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}.get(unit)
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if multiplier is None:
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return None
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return value * multiplier
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class DataProvider:
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def __init__(self, config_path: Path) -> None:
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self.config_path = config_path
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self.config = self._load_config()
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self.exchange_name: str = self.config["exchange"]
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self.symbols: List[str] = self._load_symbols(self.config)
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self.timeframes: List[str] = self._validate_timeframes(self.config.get("timeframes"))
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self.data_dir = Path(self.config.get("data_dir", "./data")).expanduser()
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start = parse_timestamp(self.config.get("start_time"))
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if start is None:
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raise ValueError("配置文件必须包含 start_time 字段")
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self.start_time_ms: int = start
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self.exchange = self._init_exchange()
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self.data: Dict[str, Dict[str, List[Dict[str, float]]]] = {
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symbol: {tf: [] for tf in self.timeframes} for symbol in self.symbols
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}
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self.derived_map: Dict[str, str] = {}
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for base_tf in self.timeframes:
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for derived_tf in DERIVED_TIMEFRAME_PLAN.get(base_tf, []):
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self.derived_map.setdefault(derived_tf, base_tf)
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derived_order: List[str] = []
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for base_tf in TIMEFRAME_ORDER:
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if base_tf not in self.timeframes:
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continue
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for derived_tf in DERIVED_TIMEFRAME_PLAN.get(base_tf, []):
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if derived_tf in self.derived_map and derived_tf not in derived_order:
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derived_order.append(derived_tf)
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self.available_timeframes: List[str] = list(self.timeframes) + derived_order
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self._lock = threading.RLock()
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self._ready = threading.Event()
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self._stop_event = threading.Event()
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self._fetch_thread: Optional[threading.Thread] = None
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self._persist_thread: Optional[threading.Thread] = None
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# 记录断线后需要从哪个 since 重新拉取（symbol -> timeframe -> since_ms）
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self._resume_since: Dict[str, Dict[str, int]] = {}
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# 恢复点持久化文件
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self._resume_file: Path = self.data_dir / "resume_since.json"
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# 尝试加载历史恢复点
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self._load_resume_since()
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def _load_config(self) -> Dict[str, object]:
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if not self.config_path.exists():
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raise FileNotFoundError(f"未找到配置文件: {self.config_path}")
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with self.config_path.open("r", encoding="utf-8") as fp:
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return json.load(fp)
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def _load_symbols(self, config: Dict[str, object]) -> List[str]:
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raw_symbols: List[str] = []
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symbols_value = config.get("symbols")
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if isinstance(symbols_value, list):
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raw_symbols = [str(item).strip() for item in symbols_value if isinstance(item, str) and item.strip()]
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elif isinstance(symbols_value, str) and symbols_value.strip():
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raw_symbols = [item.strip() for item in symbols_value.split(",") if item.strip()]
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symbol_single = config.get("symbol")
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if not raw_symbols and isinstance(symbol_single, str) and symbol_single.strip():
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raw_symbols = [symbol_single.strip()]
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if not raw_symbols:
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raise ValueError("配置文件必须提供 symbols（列表或逗号分隔字符串）或 symbol 字段")
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unique: List[str] = []
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for item in raw_symbols:
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if item not in unique:
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unique.append(item)
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return unique
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def _validate_timeframes(self, configured: Optional[Iterable[str]]) -> List[str]:
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if not configured:
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return list(TIMEFRAME_ORDER)
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invalid = [tf for tf in configured if tf not in ALLOWED_TIMEFRAMES]
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if invalid:
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raise ValueError(f"不支持的时间周期: {invalid}. 允许值: {sorted(ALLOWED_TIMEFRAMES)}")
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unique = []
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seen = set()
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for tf in TIMEFRAME_ORDER:
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if tf in configured and tf not in seen:
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unique.append(tf)
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seen.add(tf)
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for tf in configured:
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if tf not in seen:
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unique.append(tf)
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seen.add(tf)
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return unique
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def _init_exchange(self):
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if not hasattr(ccxt, self.exchange_name):
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raise ValueError(f"不支持的交易所: {self.exchange_name}")
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exchange_class = getattr(ccxt, self.exchange_name)
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exchange = exchange_class({"enableRateLimit": True})
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if exchange.id == "binance":
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exchange.options.setdefault("defaultType", "future")
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exchange.load_markets()
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logger.info("已初始化交易所 %s", exchange.id)
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return exchange
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def _data_file_path(self, symbol: str, timeframe: str) -> Path:
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symbol_safe = symbol.replace("/", "_").replace(":", "_")
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return self.data_dir / timeframe / f"{self.exchange.id}_{symbol_safe}_{timeframe}.csv"
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def _load_local(self, symbol: str, timeframe: str) -> List[Dict[str, float]]:
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path = self._data_file_path(symbol, timeframe)
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if not path.exists():
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return []
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loaded: List[Dict[str, float]] = []
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with path.open("r", encoding="utf-8", newline="") as fp:
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reader = csv.DictReader(fp)
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for row in reader:
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try:
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loaded.append(
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{
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"timestamp": int(row["timestamp"]),
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"datetime": row.get("datetime") or to_utc_iso(int(row["timestamp"])),
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"open": float(row["open"]),
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"high": float(row["high"]),
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"low": float(row["low"]),
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"close": float(row["close"]),
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"volume": float(row["volume"]),
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}
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)
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except (KeyError, ValueError):
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logger.warning("忽略损坏的行: %s", row)
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loaded.sort(key=lambda item: item["timestamp"])
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logger.info("交易对 %s 时间周期 %s 加载本地K线数量: %s", symbol, timeframe, len(loaded))
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return loaded
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def _merge_candles(
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self,
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timeframe: str,
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base: List[Dict[str, float]],
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new_candles: Iterable[Iterable[float]],
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) -> List[Dict[str, float]]:
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merged = {entry["timestamp"]: entry for entry in base}
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for candle in new_candles:
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entry = candle_to_dict(candle)
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merged[entry["timestamp"]] = entry
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ordered = list(sorted(merged.values(), key=lambda item: item["timestamp"]))
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logger.debug("时间周期 %s 合并后K线数量: %s", timeframe, len(ordered))
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return ordered
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def _write_to_disk(self, symbol: str, timeframe: str, data: List[Dict[str, float]]) -> None:
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path = self._data_file_path(symbol, timeframe)
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path.parent.mkdir(parents=True, exist_ok=True)
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tmp_path = path.with_suffix(path.suffix + ".tmp")
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try:
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with tmp_path.open("w", encoding="utf-8", newline="") as fp:
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writer = csv.DictWriter(fp, fieldnames=CSV_FIELDNAMES)
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writer.writeheader()
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writer.writerows(data)
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os.replace(tmp_path, path)
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finally:
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if tmp_path.exists():
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try:
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tmp_path.unlink()
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except OSError:
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pass
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logger.info("交易对 %s 时间周期 %s 已写入磁盘 (%s 根K线)", symbol, timeframe, len(data))
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def _fetch_history(self, symbol: str, timeframe: str, since_ms: int) -> List[List[float]]:
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results: List[List[float]] = []
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limit = 1500
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now_ms = self.exchange.milliseconds()
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tf_ms = TIMEFRAME_TO_MS[timeframe]
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fetch_since = since_ms
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max_rounds = 5000
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rounds = 0
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while fetch_since < now_ms and rounds < max_rounds:
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rounds += 1
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try:
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candles = self.exchange.fetch_ohlcv(
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symbol,
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timeframe=timeframe,
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since=fetch_since,
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limit=limit,
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)
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except ccxt.RateLimitExceeded as exc:
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logger.warning("触发频率限制，等待: %s", exc)
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time.sleep(self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 1)
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continue
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except ccxt.BaseError as exc:
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logger.error("拉取历史K线失败 (%s, %s): %s", timeframe, fetch_since, exc)
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time.sleep(5)
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continue
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if not candles:
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break
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results.extend(candles)
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last_ts = candles[-1][0]
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fetch_since = last_ts + tf_ms
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if last_ts >= now_ms - tf_ms:
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break
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time.sleep(self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 0.2)
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logger.info("交易对 %s 时间周期 %s 拉取历史K线数量: %s", symbol, timeframe, len(results))
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return results
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def initialize(self) -> None:
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logger.info("开始初始化数据提供商")
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for symbol in self.symbols:
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for timeframe in self.timeframes:
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existing = self._load_local(symbol, timeframe)
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tf_ms = TIMEFRAME_TO_MS[timeframe]
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last_ts = existing[-1]["timestamp"] if existing else None
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if last_ts is not None:
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if len(existing) >= 2:
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fetch_since = existing[-2]["timestamp"]
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else:
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fetch_since = max(0, last_ts - tf_ms)
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else:
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fetch_since = self.start_time_ms
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logger.debug(
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"初始化拉取参数",
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extra={
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"symbol": symbol,
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"timeframe": timeframe,
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"existing_last": last_ts,
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"fetch_since": fetch_since,
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"tf_ms": tf_ms,
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},
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)
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history = self._fetch_history(symbol, timeframe, fetch_since)
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merged = self._merge_candles(timeframe, existing, history)
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with self._lock:
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self.data.setdefault(symbol, {})[timeframe] = merged
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self._write_to_disk(symbol, timeframe, merged)
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self._ready.set()
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logger.info("数据初始化完成")
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def resample_df(self, df: pd.DataFrame, interval: int) -> pd.DataFrame:
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return resample_to_interval(df, interval)
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def _save_resume_since(self) -> None:
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path = self._resume_file
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path.parent.mkdir(parents=True, exist_ok=True)
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tmp_path = path.with_suffix(path.suffix + ".tmp")
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with self._lock:
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snapshot = {
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symbol: {tf: int(since) for tf, since in tf_map.items()}
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for symbol, tf_map in self._resume_since.items()
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}
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try:
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with tmp_path.open("w", encoding="utf-8") as fp:
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json.dump(snapshot, fp, ensure_ascii=False, separators=(",", ":"))
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os.replace(tmp_path, path)
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finally:
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if tmp_path.exists():
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try:
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tmp_path.unlink()
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except OSError:
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pass
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logger.debug("恢复点已保存到磁盘: %s", path)
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def _load_resume_since(self) -> None:
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path = self._resume_file
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if not path.exists():
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return
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try:
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with path.open("r", encoding="utf-8") as fp:
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raw = json.load(fp)
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except Exception as exc:
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logger.warning("恢复点文件读取失败，忽略: %s (%s)", path, exc)
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return
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if not isinstance(raw, dict):
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logger.warning("恢复点文件格式错误，忽略: %s", path)
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return
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loaded: Dict[str, Dict[str, int]] = {}
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for symbol, tf_map in raw.items():
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if not isinstance(tf_map, dict):
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continue
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per_symbol: Dict[str, int] = {}
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for timeframe, since in tf_map.items():
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try:
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per_symbol[str(timeframe)] = int(since)
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except Exception:
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continue
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if per_symbol:
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loaded[str(symbol)] = per_symbol
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if not loaded:
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return
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with self._lock:
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# 合并为更早的 since，避免遗漏
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for symbol, tf_map in loaded.items():
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cur = self._resume_since.setdefault(symbol, {})
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for timeframe, since in tf_map.items():
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prev = cur.get(timeframe)
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if prev is None or since < prev:
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cur[timeframe] = since
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logger.info("已加载恢复点: %s", path)
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def _get_resume_since(self, symbol: str, timeframe: str) -> Optional[int]:
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with self._lock:
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return self._resume_since.get(symbol, {}).get(timeframe)
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def _set_resume_since(self, symbol: str, timeframe: str, since_ms: int) -> None:
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with self._lock:
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per_symbol = self._resume_since.setdefault(symbol, {})
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prev = per_symbol.get(timeframe)
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# 取更早的 since，避免跳过数据
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if prev is None or since_ms < prev:
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per_symbol[timeframe] = since_ms
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logger.warning(
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"记录断线恢复点: %s %s since=%s (%s)",
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symbol,
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timeframe,
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since_ms,
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to_utc_iso(since_ms),
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)
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# 同步写盘
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self._save_resume_since()
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def _clear_resume_since(self, symbol: str, timeframe: str) -> None:
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with self._lock:
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if symbol in self._resume_since and timeframe in self._resume_since[symbol]:
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del self._resume_since[symbol][timeframe]
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if not self._resume_since[symbol]:
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del self._resume_since[symbol]
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logger.info("清除断线恢复点: %s %s", symbol, timeframe)
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# 同步写盘
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self._save_resume_since()
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def start_background_workers(self) -> None:
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if self._fetch_thread and self._fetch_thread.is_alive():
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return
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self._stop_event.clear()
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self._fetch_thread = threading.Thread(target=self._refresh_loop, name="refresh-loop", daemon=True)
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self._persist_thread = threading.Thread(target=self._persist_loop, name="persist-loop", daemon=True)
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self._fetch_thread.start()
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self._persist_thread.start()
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logger.info("后台线程已启动")
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def stop(self) -> None:
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self._stop_event.set()
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if self._fetch_thread:
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self._fetch_thread.join(timeout=5)
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if self._persist_thread:
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self._persist_thread.join(timeout=5)
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logger.info("数据提供商已停止")
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def _refresh_loop(self) -> None:
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while not self._stop_event.is_set():
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for symbol in self.symbols:
|
||
for timeframe in self.timeframes:
|
||
try:
|
||
# 若存在断线恢复点，则优先从该 since 补齐历史数据
|
||
resume_since = self._get_resume_since(symbol, timeframe)
|
||
if resume_since is not None:
|
||
logger.info(
|
||
"开始断线后补数: %s %s since=%s (%s)",
|
||
symbol,
|
||
timeframe,
|
||
resume_since,
|
||
to_utc_iso(resume_since),
|
||
)
|
||
history = self._fetch_history(symbol, timeframe, resume_since)
|
||
with self._lock:
|
||
current = self.data.setdefault(symbol, {}).get(timeframe, [])
|
||
merged = self._merge_candles(timeframe, current, history)
|
||
self.data[symbol][timeframe] = merged
|
||
self._clear_resume_since(symbol, timeframe)
|
||
else:
|
||
# 正常增量获取最近若干根K线
|
||
candles = self.exchange.fetch_ohlcv(
|
||
symbol,
|
||
timeframe=timeframe,
|
||
limit=RECENT_CANDLE_LIMIT,
|
||
)
|
||
if not candles:
|
||
continue
|
||
with self._lock:
|
||
current = self.data.setdefault(symbol, {}).get(timeframe, [])
|
||
merged = self._merge_candles(timeframe, current, candles)
|
||
self.data[symbol][timeframe] = merged
|
||
except ccxt.BaseError as exc:
|
||
logger.error("更新最新K线失败 (%s %s): %s", symbol, timeframe, exc)
|
||
# 记录应当从何时恢复拉取，避免重连后从当前时间开始导致丢K
|
||
with self._lock:
|
||
current = self.data.get(symbol, {}).get(timeframe, [])
|
||
if current:
|
||
last_ts = int(current[-1]["timestamp"])
|
||
else:
|
||
last_ts = self.start_time_ms
|
||
tf_ms = TIMEFRAME_TO_MS[timeframe]
|
||
# 回退一个周期，确保包含可能未完全收盘的K线，去重由 _merge_candles 处理
|
||
since_ms = max(self.start_time_ms, last_ts - tf_ms)
|
||
self._set_resume_since(symbol, timeframe, since_ms)
|
||
time.sleep(2)
|
||
continue
|
||
if self._stop_event.wait(RECENT_FETCH_INTERVAL):
|
||
break
|
||
|
||
def _persist_loop(self) -> None:
|
||
while not self._stop_event.wait(PERSIST_INTERVAL):
|
||
self._persist_all()
|
||
|
||
def _persist_all(self) -> None:
|
||
if not self._ready.is_set():
|
||
return
|
||
with self._lock:
|
||
snapshot = {
|
||
symbol: {tf: list(data) for tf, data in tf_map.items()}
|
||
for symbol, tf_map in self.data.items()
|
||
}
|
||
for symbol, tf_map in snapshot.items():
|
||
for timeframe, data in tf_map.items():
|
||
self._write_to_disk(symbol, timeframe, data)
|
||
# 周期性也保存一次恢复点，保证一致性
|
||
self._save_resume_since()
|
||
|
||
def is_ready(self) -> bool:
|
||
return self._ready.is_set()
|
||
|
||
def wait_ready(self, timeout: Optional[float] = None) -> bool:
|
||
return self._ready.wait(timeout)
|
||
|
||
def get_available_timeframes(self) -> List[str]:
|
||
return list(self.available_timeframes)
|
||
|
||
def get_derived_timeframes(self) -> List[str]:
|
||
return list(self.derived_map.keys())
|
||
|
||
def _get_base_klines(
|
||
self,
|
||
symbol: str,
|
||
timeframe: str,
|
||
start_ms: Optional[int],
|
||
end_ms: Optional[int],
|
||
limit: Optional[int],
|
||
) -> List[Dict[str, float]]:
|
||
with self._lock:
|
||
candles = list(self.data.get(symbol, {}).get(timeframe, []))
|
||
if start_ms is not None:
|
||
candles = [row for row in candles if row["timestamp"] >= start_ms]
|
||
if end_ms is not None:
|
||
candles = [row for row in candles if row["timestamp"] <= end_ms]
|
||
if limit:
|
||
candles = candles[-limit:]
|
||
return candles
|
||
|
||
def get_klines(
|
||
self,
|
||
symbol: str,
|
||
timeframe: str,
|
||
start_time: Optional[object] = None,
|
||
end_time: Optional[object] = None,
|
||
limit: Optional[int] = None,
|
||
) -> List[Dict[str, float]]:
|
||
if symbol not in self.symbols:
|
||
raise HTTPException(status_code=404, detail=f"symbol {symbol} 不可用")
|
||
self.wait_ready()
|
||
start_ms = parse_timestamp(start_time)
|
||
end_ms = parse_timestamp(end_time)
|
||
if timeframe in self.timeframes:
|
||
return self._get_base_klines(symbol, timeframe, start_ms, end_ms, limit)
|
||
base_tf = self.derived_map.get(timeframe)
|
||
if not base_tf:
|
||
raise HTTPException(status_code=404, detail=f"{symbol} 时间周期 {timeframe} 不可用")
|
||
target_minutes = timeframe_to_minutes(timeframe)
|
||
if target_minutes is None:
|
||
raise HTTPException(status_code=400, detail=f"不支持的时间周期: {timeframe}")
|
||
target_ms = target_minutes * 60_000
|
||
adjusted_start = None if start_ms is None else max(0, start_ms - target_ms)
|
||
base_candles = self._get_base_klines(symbol, base_tf, adjusted_start, end_ms, None)
|
||
if not base_candles:
|
||
return []
|
||
df = pd.DataFrame(base_candles)
|
||
if df.empty:
|
||
return []
|
||
df = df.drop_duplicates(subset=["timestamp"], keep="last").sort_values("timestamp")
|
||
df["date"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
||
resampled = self.resample_df(df, target_minutes)
|
||
if resampled is None or resampled.empty:
|
||
return []
|
||
if "timestamp" in resampled.columns:
|
||
resampled_df = resampled.copy()
|
||
else:
|
||
resampled_df = resampled.copy()
|
||
if "date" in resampled_df.columns:
|
||
dates = pd.to_datetime(resampled_df["date"], utc=True, errors="coerce")
|
||
resampled_df["timestamp"] = (dates.view("int64") // 1_000_000).astype("int64")
|
||
elif isinstance(resampled_df.index, pd.DatetimeIndex):
|
||
idx = resampled_df.index
|
||
if idx.tz is None:
|
||
idx = idx.tz_localize("UTC")
|
||
else:
|
||
idx = idx.tz_convert("UTC")
|
||
resampled_df["timestamp"] = (idx.view("int64") // 1_000_000).astype("int64")
|
||
else:
|
||
raise HTTPException(status_code=500, detail=f"聚合结果缺少 timestamp 列 ({timeframe})")
|
||
resampled_df = resampled_df.dropna(subset=["timestamp"]).sort_values("timestamp")
|
||
if start_ms is not None:
|
||
resampled_df = resampled_df[resampled_df["timestamp"] >= start_ms]
|
||
if end_ms is not None:
|
||
resampled_df = resampled_df[resampled_df["timestamp"] <= end_ms]
|
||
if resampled_df.empty:
|
||
return []
|
||
resampled_df["datetime"] = resampled_df["timestamp"].apply(to_utc_iso)
|
||
for column in ["open", "high", "low", "close", "volume"]:
|
||
if column not in resampled_df.columns:
|
||
resampled_df[column] = 0.0
|
||
resampled_df = resampled_df[["timestamp", "datetime", "open", "high", "low", "close", "volume"]]
|
||
result = resampled_df.to_dict("records")
|
||
if limit:
|
||
result = result[-limit:]
|
||
logger.debug(
|
||
"衍生周期返回",
|
||
extra={
|
||
"symbol": symbol,
|
||
"timeframe": timeframe,
|
||
"base_timeframe": base_tf,
|
||
"count": len(result),
|
||
},
|
||
)
|
||
return result
|
||
|
||
|
||
def create_app(provider: DataProvider) -> FastAPI:
|
||
@asynccontextmanager
|
||
async def lifespan(app: FastAPI):
|
||
loop = asyncio.get_running_loop()
|
||
await loop.run_in_executor(None, provider.initialize)
|
||
provider.start_background_workers()
|
||
try:
|
||
yield
|
||
finally:
|
||
provider.stop()
|
||
|
||
app = FastAPI(title="Chan 数据提供商", version="1.0.0", lifespan=lifespan)
|
||
|
||
app.add_middleware(
|
||
CORSMiddleware,
|
||
allow_origins=["*"],
|
||
allow_credentials=True,
|
||
allow_methods=["*"],
|
||
allow_headers=["*"],
|
||
)
|
||
|
||
@app.get("/health")
|
||
async def health() -> Dict[str, object]:
|
||
return {
|
||
"status": "ok",
|
||
"exchange": provider.exchange_name,
|
||
"symbols": provider.symbols,
|
||
"base_timeframes": provider.timeframes,
|
||
"derived_timeframes": provider.get_derived_timeframes(),
|
||
"timeframes": provider.get_available_timeframes(),
|
||
"ready": provider.is_ready(),
|
||
}
|
||
|
||
@app.get("/timeframes")
|
||
async def list_timeframes() -> Dict[str, List[str]]:
|
||
provider.wait_ready()
|
||
return {
|
||
"base_timeframes": provider.timeframes,
|
||
"derived_timeframes": provider.get_derived_timeframes(),
|
||
"timeframes": provider.get_available_timeframes(),
|
||
}
|
||
|
||
@app.get("/api/candles")
|
||
async def api_candles(
|
||
symbol: str = Query(..., description="如 BTC/USDT"),
|
||
tf: str = Query("1m", description="时间周期"),
|
||
start: Optional[int] = Query(None, description="开始时间戳(ms)"),
|
||
end: Optional[int] = Query(None, description="结束时间戳(ms)"),
|
||
limit: Optional[int] = Query(None, description="可选，限制返回数量"),
|
||
):
|
||
data = provider.get_klines(symbol=symbol, timeframe=tf, start_time=start, end_time=end, limit=limit)
|
||
return data
|
||
|
||
@app.get("/")
|
||
async def root() -> Dict[str, object]:
|
||
return {
|
||
"service": "Data Provider",
|
||
"exchange": provider.exchange_name,
|
||
"symbols": provider.symbols,
|
||
"base_timeframes": provider.timeframes,
|
||
"derived_timeframes": provider.get_derived_timeframes(),
|
||
"timeframes": provider.get_available_timeframes(),
|
||
"ready": provider.is_ready(),
|
||
}
|
||
|
||
return app
|
||
|
||
|
||
def build_app() -> FastAPI:
|
||
config_path = Path(os.getenv("CONFIG_PATH", "config.json"))
|
||
provider = DataProvider(config_path)
|
||
return create_app(provider)
|
||
|
||
|
||
app = build_app()
|
||
|
||
|
||
def main() -> None:
|
||
host = os.getenv("UVICORN_HOST", "0.0.0.0")
|
||
port = int(os.getenv("UVICORN_PORT", "9009"))
|
||
|
||
uvicorn.run(app, host=host, port=port, log_level=os.getenv("UVICORN_LOG_LEVEL", "info"))
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
|