k
145 lines
5.3 KiB
Python
145 lines
5.3 KiB
Python
"""
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历史帖子价格回溯
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对 DB 中没有价格数据的帖子，从 Binance 拉历史 K 线计算涨跌幅
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"""
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import asyncio
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import logging
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from datetime import datetime, timezone, timedelta
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from typing import Optional
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import httpx
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.config import settings
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from app.models import Post
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logger = logging.getLogger(__name__)
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# 每次从 Binance 拉多少分钟的 1m K 线（覆盖 1h 涨跌幅计算需要至少 60 根）
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FETCH_WINDOW_MINUTES = 90
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async def _fetch_klines(symbol: str, start_ms: int, limit: int = 90) -> list:
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url = (
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f"{settings.binance_rest_url}/api/v3/klines"
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f"?symbol={symbol}&interval=1m&startTime={start_ms}&limit={limit}"
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)
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async with httpx.AsyncClient(timeout=15) as client:
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resp = await client.get(url)
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resp.raise_for_status()
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return resp.json()
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def _price_at(klines: list, target_ms: int) -> Optional[float]:
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"""找最接近 target_ms 的收盘价"""
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best = None
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best_diff = float("inf")
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for row in klines:
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diff = abs(row[0] - target_ms)
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if diff < best_diff:
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best_diff = diff
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best = float(row[4]) # close
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return best
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def _pct_change(klines: list, from_ms: int, delta_minutes: int) -> Optional[float]:
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from_price = _price_at(klines, from_ms)
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to_price = _price_at(klines, from_ms + delta_minutes * 60 * 1000)
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if from_price and to_price and from_price != 0:
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return round((to_price - from_price) / from_price * 100, 4)
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return None
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async def backfill_price_impact(db_session_factory, asset: str = "BTC") -> None:
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"""
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对 DB 中 relevant=True 但没有价格数据的帖子补充价格回溯。
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对 relevant=False 的帖子，做简单判断（标题含 crypto/bitcoin/btc 关键词则标记为相关）。
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"""
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symbol = "BTCUSDT" if asset == "BTC" else "ETHUSDT"
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async with db_session_factory() as db:
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# 拿所有没有价格数据的帖子
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result = await db.execute(
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select(Post)
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.where(Post.price_at_post == None)
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.order_by(Post.published_at.asc())
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)
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posts = result.scalars().all()
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logger.info("找到 %d 条帖子需要价格回溯 (asset=%s)", len(posts), asset)
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if not posts:
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return
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# 关键词判断是否与加密相关（快速，不用 Claude API）
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crypto_keywords = [
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"bitcoin", "btc", "crypto", "cryptocurrency", "blockchain",
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"ethereum", "eth", "digital currency", "defi", "coinbase",
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"sec", "regulation", "tariff", "dollar", "inflation", "fed",
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"economy", "market", "trade", "sanctions", "iran", "china",
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]
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def is_relevant(text: str) -> bool:
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t = text.lower()
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return any(kw in t for kw in crypto_keywords)
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saved = 0
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errors = 0
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for i, post in enumerate(posts):
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try:
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published_at = post.published_at
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if published_at.tzinfo is None:
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published_at = published_at.replace(tzinfo=timezone.utc)
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start_ms = int(published_at.timestamp() * 1000)
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# 判断相关性（用关键词，不消耗 Claude API）
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relevant = is_relevant(post.text)
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sentiment = "neutral"
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if relevant:
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t = post.text.lower()
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bullish_kw = ["great", "win", "winning", "strong", "best", "love", "beautiful", "tremendous", "amazing", "pro-crypto", "bitcoin reserve"]
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bearish_kw = ["bad", "terrible", "war", "crisis", "sanction", "ban", "regulate", "crack", "fraud", "scam"]
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if any(k in t for k in bullish_kw):
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sentiment = "bullish"
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elif any(k in t for k in bearish_kw):
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sentiment = "bearish"
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# 拉 Binance 历史价格
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klines = await _fetch_klines(symbol, start_ms, limit=FETCH_WINDOW_MINUTES)
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price_at_post = _price_at(klines, start_ms)
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m5 = _pct_change(klines, start_ms, 5)
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m15 = _pct_change(klines, start_ms, 15)
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m1h = _pct_change(klines, start_ms, 60)
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# 更新帖子
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async with db_session_factory() as db:
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result = await db.execute(select(Post).where(Post.id == post.id))
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p = result.scalar_one_or_none()
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if p:
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p.relevant = relevant
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p.sentiment = sentiment
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p.price_impact_asset = asset if relevant else None
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p.price_at_post = price_at_post
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p.price_impact_m5 = m5 if relevant else None
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p.price_impact_m15 = m15 if relevant else None
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p.price_impact_m1h = m1h if relevant else None
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await db.commit()
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saved += 1
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if (i + 1) % 20 == 0:
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logger.info("进度: %d/%d 已处理，已保存 %d 条", i + 1, len(posts), saved)
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# 避免触发 Binance 限速（1200 requests/min）
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await asyncio.sleep(0.1)
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except Exception as exc:
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errors += 1
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logger.error("帖子 id=%d 回溯失败: %s", post.id, exc)
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await asyncio.sleep(1)
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logger.info("✅ 价格回溯完成: 共处理 %d 条，成功 %d 条，失败 %d 条", len(posts), saved, errors)
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