131 lines
5.2 KiB
Python
131 lines
5.2 KiB
Python
"""
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Data sources for Kronos advisor.
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Two flavours:
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- TradernetHlocSource: production, uses Tradernet API (needs PRIVATE_KEY)
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- BinancePublicSource: public, no auth, for offline tests & fallback
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Both expose the same interface:
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fetch_ohlcv(lookback: int) -> pd.DataFrame
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with columns ['open','high','low','close','volume'] and DatetimeIndex
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"""
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from __future__ import annotations
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import logging
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import time
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from typing import Optional
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import pandas as pd
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import requests
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logger = logging.getLogger(__name__)
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# Map our SYMBOL (Tradernet format) → Binance symbol
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SYMBOL_MAP_TRADERNET_TO_BINANCE = {
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"BTC-USDT.IMEX": "BTCUSDT",
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"ETH-USDT.IMEX": "ETHUSDT",
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"SOL-USDT.IMEX": "SOLUSDT",
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"TON-USDT.IMEX": "TONUSDT",
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}
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class TradernetHlocSource:
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"""
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Тянет OHLCV с Tradernet (продакшн).
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Требует приватный ключ → не используй в офлайн-тестах.
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"""
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def __init__(self, api, symbol: str, tf_min: int = 60):
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self.api = api # экземпляр TradernetAPI
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self.symbol = symbol
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self.tf_min = tf_min
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def fetch_ohlcv(self, lookback: int = 500, timeout: float = 20.0) -> pd.DataFrame:
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# Берём запас побольше — на случай пропусков
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# ВАЖНО: Tradernet по (date_from="", date_to="", count=N) возвращает
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# САМЫЕ СТАРЫЕ N свечей, а не свежие. Подставляем явный date_to=NOW.
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from datetime import datetime, timedelta
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now = datetime.now()
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# lookback свечей × tf_min минут = сколько часов назад начинать
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hours_back = max(1, int(lookback * self.tf_min / 60) + 1)
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date_from = (now - timedelta(hours=hours_back)).strftime("%d.%m.%Y %H:%M")
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date_to = now.strftime("%d.%m.%Y %H:%M")
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resp = self.api.get_hloc_sync(
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ticker=self.symbol,
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timeframe_min=self.tf_min,
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date_from=date_from,
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date_to=date_to,
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count=0, # count=0 — берём только между датами
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timeout=timeout,
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)
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if not resp or "hloc" not in resp:
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raise RuntimeError(f"Tradernet getHloc returned empty for {self.symbol}")
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hloc_map = resp["hloc"]
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x_map = resp.get("xSeries", {})
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vl_map = resp.get("vl", {})
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rows = hloc_map.get(self.symbol) or next(iter(hloc_map.values()))
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ts_list = x_map.get(self.symbol) or next(iter(x_map.values()), [])
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vols = vl_map.get(self.symbol) or next(iter(vl_map.values()), []) if vl_map else []
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if not rows or not ts_list:
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raise RuntimeError(f"Tradernet getHloc: empty series for {self.symbol}")
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df = pd.DataFrame(rows, columns=["open", "high", "low", "close"])
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df["volume"] = vols if len(vols) == len(df) else 0.0
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# xSeries — unix-секунды
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df.index = pd.to_datetime(ts_list, unit="s", utc=True).tz_convert(None)
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df = df.sort_index()
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return df.tail(lookback)
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class BinancePublicSource:
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"""
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Публичный API Binance (https://api.binance.com). Без ключей.
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Используется для offline-тестов Kronos и как fallback, если Tradernet недоступен.
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"""
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BASE_URL = "https://api.binance.com"
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def __init__(self, symbol: str, tf_min: int = 60):
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# symbol в формате Binance: BTCUSDT
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self.symbol = symbol
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self.tf_min = tf_min
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@classmethod
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def from_tradernet(cls, tradernet_symbol: str, tf_min: int = 60) -> "BinancePublicSource":
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"""Конвертирует Tradernet-символ в Binance-символ."""
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bsym = SYMBOL_MAP_TRADERNET_TO_BINANCE.get(tradernet_symbol)
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if not bsym:
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raise ValueError(f"No Binance mapping for {tradernet_symbol}")
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return cls(bsym, tf_min)
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def fetch_ohlcv(self, lookback: int = 500, timeout: float = 20.0) -> pd.DataFrame:
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# Binance: 1m/3m/5m/15m/30m/1h/2h/4h/... (не "60m", а "1h")
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interval_map = {1: "1m", 3: "3m", 5: "5m", 15: "15m", 30: "30m",
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60: "1h", 120: "2h", 240: "4h", 360: "6h",
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720: "12h", 1440: "1d"}
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interval = interval_map.get(self.tf_min, f"{self.tf_min}m")
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limit = min(1000, lookback)
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url = f"{self.BASE_URL}/api/v3/klines"
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params = {"symbol": self.symbol, "interval": interval, "limit": limit}
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r = requests.get(url, params=params, timeout=timeout)
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r.raise_for_status()
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data = r.json()
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if not data:
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raise RuntimeError(f"Binance returned empty for {self.symbol}")
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cols = ["open_time", "open", "high", "low", "close", "volume",
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"close_time", "quote_vol", "trades", "taker_buy_base", "taker_buy_quote", "_"]
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df = pd.DataFrame(data, columns=cols)
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for c in ("open", "high", "low", "close", "volume"):
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df[c] = df[c].astype(float)
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df.index = pd.to_datetime(df["open_time"], unit="ms", utc=True)
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df.index = df.index.tz_convert(None)
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df = df[["open", "high", "low", "close", "volume"]].sort_index()
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return df.tail(lookback)
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