chore: 移除不再使用的 ChanMacro、system、tests。
这些目录已废弃，从仓库中清理。 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
jackyu66git
@@ -1,5 +0,0 @@
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"""Data fetchers — L0 raw data acquisition."""
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from .base import BaseFetcher
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from .ohlcv import OHLCVFetcher
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from .breadth import BreadthFetcher
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from .derivatives import DerivativesFetcher
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@@ -1,69 +0,0 @@
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"""
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fetchers/base.py — Abstract base class for all macro data fetchers.
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Provides retry logic, rate limiting, and a common interface.
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"""
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from abc import ABC, abstractmethod
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from datetime import date as Date
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from typing import Optional
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import logging
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import time
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import requests
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class BaseFetcher(ABC):
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"""Abstract base for all macro data fetchers."""
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def __init__(self, timeout: int = 30, max_retries: int = 3):
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self.timeout = timeout
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self.max_retries = max_retries
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self.logger = logging.getLogger(self.__class__.__name__)
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def _get(self, url: str, params: Optional[dict] = None,
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headers: Optional[dict] = None) -> dict:
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"""GET with retry and exponential backoff."""
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for attempt in range(self.max_retries):
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try:
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resp = requests.get(
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url, params=params, headers=headers, timeout=self.timeout
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)
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resp.raise_for_status()
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return resp.json()
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except requests.RequestException as e:
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wait = 2 ** attempt
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self.logger.warning(
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f"Request failed (attempt {attempt+1}/{self.max_retries}): {e}. "
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f"Retrying in {wait}s"
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)
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if attempt < self.max_retries - 1:
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time.sleep(wait)
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else:
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raise
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def _get_raw(self, url: str, params: Optional[dict] = None,
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headers: Optional[dict] = None) -> bytes:
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"""GET raw bytes with retry (for non-JSON endpoints)."""
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for attempt in range(self.max_retries):
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try:
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resp = requests.get(
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url, params=params, headers=headers, timeout=self.timeout
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)
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resp.raise_for_status()
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return resp.content
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except requests.RequestException as e:
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wait = 2 ** attempt
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if attempt < self.max_retries - 1:
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time.sleep(wait)
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else:
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raise
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@abstractmethod
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def fetch(self, target_date: Optional[Date] = None) -> list[dict]:
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"""Fetch raw data. Returns list of record dicts."""
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...
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@abstractmethod
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def store(self, db_path: str, records: list[dict]) -> int:
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"""Store raw records into SQLite. Returns count of new rows."""
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...
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@@ -1,189 +0,0 @@
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"""
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fetchers/breadth.py — Fetches TOP50 OHLCV and computes market breadth metrics.
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Multi-tier: Top20 / Top30 / Top50 for advance/decline, EMA20%, new highs, BTC.D.
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"""
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from datetime import date as Date, datetime
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from typing import Optional
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import logging
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import pandas as pd
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import numpy as np
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import requests
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from .base import BaseFetcher
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from config import config
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class BreadthFetcher(BaseFetcher):
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"""Fetches TOP50 coin OHLCV data and computes breadth metrics."""
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def __init__(self, provider_url: Optional[str] = None):
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super().__init__(timeout=60, max_retries=3)
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self.provider_url = provider_url or config.provider_url
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self.symbols = config.top50_symbols
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self.ema_period = config.breadth_ema_period
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self.new_high_window = config.breadth_new_high_window
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self.logger = logging.getLogger(__name__)
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def fetch(self, target_date: Optional[Date] = None) -> dict:
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"""
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Fetch daily OHLCV for all TOP50 symbols and compute breadth.
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Returns a dict suitable for storing in breadth_daily table.
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"""
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if target_date is None:
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target_date = Date.today()
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# Fetch last 60 days of daily data for each symbol to compute EMAs and new highs
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all_data = {}
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for symbol in self.symbols:
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try:
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df = self._fetch_symbol(symbol)
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if df is not None and not df.empty:
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all_data[symbol] = df
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except Exception as e:
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self.logger.debug(f"Failed to fetch {symbol}: {e}")
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if not all_data:
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self.logger.error("No symbol data fetched for breadth")
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return {}
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# Compute breadth metrics for the target date
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breadth = self._compute_breadth(all_data, target_date)
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return breadth
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def _fetch_symbol(self, symbol: str) -> Optional[pd.DataFrame]:
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"""Fetch daily OHLCV for a single symbol."""
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url = f"{self.provider_url}/api/candles"
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params = {
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"symbol": symbol,
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"tf": "1d",
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"limit": 100,
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}
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try:
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resp = requests.get(url, params=params, timeout=15)
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resp.raise_for_status()
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data = resp.json()
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if not data:
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return None
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df = pd.DataFrame(data)
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df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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df["date"] = df["timestamp"].dt.date
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df = df.drop_duplicates(subset="date").sort_values("date").reset_index(drop=True)
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df["close"] = df["close"].astype(float)
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df["ema20"] = df["close"].ewm(span=self.ema_period, adjust=False).mean()
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return df
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except Exception:
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return None
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def _compute_breadth(self, all_data: dict, target_date: Date) -> dict:
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"""Compute breadth metrics for a specific date across all symbols."""
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total = len(all_data)
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advances_50 = declines_50 = 0
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above_ema20_50 = 0
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new_highs_50 = 0
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advances_30 = declines_30 = 0
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above_ema20_30 = 0
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new_highs_30 = 0
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advances_20 = declines_20 = 0
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above_ema20_20 = 0
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new_highs_20 = 0
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for i, (symbol, df) in enumerate(all_data.items()):
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# Get data for target date
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df["date_str"] = df["date"].astype(str)
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target_str = str(target_date)
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idx = df[df["date_str"] == target_str].index
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if len(idx) == 0:
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continue
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row_idx = idx[0]
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if row_idx < 1:
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continue
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current_close = df.loc[row_idx, "close"]
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prev_close = df.loc[row_idx - 1, "close"]
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# Advance/Decline
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if current_close > prev_close:
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if i < 50: advances_50 += 1
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if i < 30: advances_30 += 1
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if i < 20: advances_20 += 1
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elif current_close < prev_close:
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if i < 50: declines_50 += 1
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if i < 30: declines_30 += 1
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if i < 20: declines_20 += 1
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# Above EMA20
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ema20_val = df.loc[row_idx, "ema20"]
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if not pd.isna(ema20_val) and current_close > ema20_val:
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if i < 50: above_ema20_50 += 1
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if i < 30: above_ema20_30 += 1
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if i < 20: above_ema20_20 += 1
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# New 20-day highs
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lookback_start = max(0, row_idx - self.new_high_window)
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recent_highs = df.loc[lookback_start:row_idx - 1, "high"].astype(float)
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current_high = df.loc[row_idx, "high"]
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if len(recent_highs) > 0 and float(current_high) > recent_highs.max():
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if i < 50: new_highs_50 += 1
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if i < 30: new_highs_30 += 1
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if i < 20: new_highs_20 += 1
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return {
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"date": str(target_date),
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"total_tracked": total,
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"advance_top50": advances_50,
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"decline_top50": declines_50,
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"above_ema20_top50": above_ema20_50,
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"new_highs_20d_top50": new_highs_50,
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"advance_top30": advances_30,
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"advance_top20": advances_20,
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"above_ema20_top30": above_ema20_30,
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"above_ema20_top20": above_ema20_20,
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"new_highs_20d_top30": new_highs_30,
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"new_highs_20d_top20": new_highs_20,
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"btc_dominance": None, # Reserved for Coinglass API integration
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}
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def store(self, db_path: Optional[str] = None, record: Optional[dict] = None) -> int:
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"""Store a breadth record into SQLite. Returns 1 if inserted/updated."""
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import sqlite3
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db_path = db_path or config.db_path
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conn = sqlite3.connect(db_path)
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if record is None:
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conn.close()
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return 0
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try:
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conn.execute("""
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INSERT OR REPLACE INTO breadth_daily
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(date, total_tracked,
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advance_top50, decline_top50, above_ema20_top50, new_highs_20d_top50,
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advance_top30, advance_top20,
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above_ema20_top30, above_ema20_top20,
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new_highs_20d_top30, new_highs_20d_top20,
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btc_dominance)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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record["date"], record.get("total_tracked", 50),
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record.get("advance_top50", 0), record.get("decline_top50", 0),
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record.get("above_ema20_top50", 0), record.get("new_highs_20d_top50", 0),
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record.get("advance_top30", 0), record.get("advance_top20", 0),
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record.get("above_ema20_top30", 0), record.get("above_ema20_top20", 0),
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record.get("new_highs_20d_top30", 0), record.get("new_highs_20d_top20", 0),
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record.get("btc_dominance"),
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))
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conn.commit()
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return 1
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except Exception as e:
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self.logger.error(f"Failed to store breadth: {e}")
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return 0
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finally:
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conn.close()
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@@ -1,66 +0,0 @@
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"""
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fetchers/derivatives.py — Fetches derivatives data from data_provider API.
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Clean consumer: no direct ccxt dependency. Just HTTP GET /api/derivatives.
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"""
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from datetime import date as Date
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from typing import Optional
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import requests
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from .base import BaseFetcher
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from config import config
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class DerivativesFetcher(BaseFetcher):
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"""Fetches derivatives snapshot from data_provider /api/derivatives."""
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def __init__(self, provider_url: Optional[str] = None):
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super().__init__(timeout=15, max_retries=3)
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self.provider_url = provider_url or config.provider_url
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def fetch(self, target_date: Optional[Date] = None) -> list[dict]:
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"""Fetch derivatives data. Returns list with one record dict."""
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url = f"{self.provider_url}/api/derivatives"
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params = {"symbol": config.btc_symbol}
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try:
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data = self._get(url, params=params)
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record = {
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"date": str(target_date or Date.today()),
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"symbol": config.btc_symbol,
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"funding_rate": data.get("funding_rate"),
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"open_interest": data.get("open_interest"),
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"oi_24h_change_pct": data.get("oi_change_pct"),
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"basis_annualised_pct": data.get("basis"),
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"source": "data_provider",
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}
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return [record]
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except Exception:
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return []
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def store(self, db_path: Optional[str] = None, records: Optional[list[dict]] = None) -> int:
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"""Store derivatives records into SQLite."""
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import sqlite3
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db_path = db_path or config.db_path
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records = records or []
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conn = sqlite3.connect(db_path)
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count = 0
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for r in records:
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try:
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conn.execute("""
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INSERT OR REPLACE INTO derivatives
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(date, symbol, funding_rate, open_interest, oi_24h_change_pct,
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long_liquidations, short_liquidations, basis_annualised_pct)
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VALUES (?, ?, ?, ?, ?, NULL, NULL, ?)
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""", (
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r["date"], r.get("symbol", config.btc_symbol),
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r.get("funding_rate"), r.get("open_interest"),
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r.get("oi_24h_change_pct"), r.get("basis_annualised_pct"),
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))
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count += 1
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except Exception:
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continue
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conn.commit()
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conn.close()
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return count
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@@ -1,157 +0,0 @@
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"""
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fetchers/ohlcv.py — Fetches BTC daily OHLCV from the existing data_provider service.
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Also pre-computes EMA20/60/120, ATR(14), BB width, ADX(14).
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"""
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from datetime import date as Date, datetime, timedelta
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from typing import Optional
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import logging
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import pandas as pd
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import numpy as np
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import requests
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from .base import BaseFetcher
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from config import config
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class OHLCVFetcher(BaseFetcher):
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"""Fetches BTC daily K-line data from data_provider API."""
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def __init__(self, provider_url: Optional[str] = None):
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super().__init__(timeout=30, max_retries=3)
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self.provider_url = provider_url or config.provider_url
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self.symbol = config.btc_symbol
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self.logger = logging.getLogger(__name__)
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def fetch(self, target_date: Optional[Date] = None) -> pd.DataFrame:
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"""
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Fetch daily OHLCV for BTC. Returns DataFrame with computed indicators.
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Fetches enough history (200 bars) to compute EMAs/ATR/BB/ADX accurately.
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"""
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url = f"{self.provider_url}/api/candles"
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params = {
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"symbol": self.symbol,
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"tf": "1d",
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"limit": 200,
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}
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resp = requests.get(url, params=params, timeout=self.timeout)
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resp.raise_for_status()
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data = resp.json()
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if not data:
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self.logger.warning("OHLCV API returned empty data")
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return pd.DataFrame()
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df = pd.DataFrame(data)
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df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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df["date"] = df["timestamp"].dt.date
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df = df.drop_duplicates(subset="date").sort_values("date").reset_index(drop=True)
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# Rename columns to match expected format
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df = df.rename(columns={
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"open": "open", "high": "high", "low": "low", "close": "close",
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"volume": "volume",
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})
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# Compute indicators
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df = self._add_indicators(df)
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return df
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def _add_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Add EMA, ATR, BB, ADX indicators."""
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close = df["close"].astype(float)
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high = df["high"].astype(float)
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low = df["low"].astype(float)
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# EMAs
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df["ema20"] = close.ewm(span=20, adjust=False).mean()
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df["ema60"] = close.ewm(span=60, adjust=False).mean()
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df["ema120"] = close.ewm(span=120, adjust=False).mean()
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# ATR(14)
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tr1 = high - low
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tr2 = (high - close.shift(1)).abs()
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tr3 = (low - close.shift(1)).abs()
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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df["atr_14"] = tr.rolling(14).mean()
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# Bollinger Bands width
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sma20 = close.rolling(20).mean()
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std20 = close.rolling(20).std()
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df["bb_width"] = (2 * std20) / sma20 * 100 # as percentage
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# ADX(14)
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df["adx_14"] = self._compute_adx(df, period=14)
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return df
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@staticmethod
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def _compute_adx(df: pd.DataFrame, period: int = 14) -> pd.Series:
|
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"""Compute ADX from OHLC data."""
|
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high = df["high"].astype(float)
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low = df["low"].astype(float)
|
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close = df["close"].astype(float)
|
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|
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plus_dm = high.diff()
|
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minus_dm = low.diff().abs() * -1
|
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plus_dm = plus_dm.where(plus_dm > 0, 0)
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minus_dm = minus_dm.where(minus_dm < 0, 0).abs()
|
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tr1 = high - low
|
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tr2 = (high - close.shift(1)).abs()
|
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tr3 = (low - close.shift(1)).abs()
|
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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atr = tr.rolling(period).mean()
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plus_di = 100 * (plus_dm.rolling(period).mean() / atr)
|
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minus_di = 100 * (minus_dm.rolling(period).mean() / atr)
|
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dx = (abs(plus_di - minus_di) / (plus_di + minus_di)) * 100
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adx = dx.rolling(period).mean()
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return adx
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def store(self, db_path: str, records: list[dict]) -> int:
|
||||
"""Store OHLCV records into SQLite. Not used directly — see store_df."""
|
||||
return 0
|
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|
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def store_df(self, df: pd.DataFrame, db_path: Optional[str] = None) -> int:
|
||||
"""Store the DataFrame into the ohlcv_daily table."""
|
||||
import sqlite3
|
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db_path = db_path or config.db_path
|
||||
conn = sqlite3.connect(db_path)
|
||||
|
||||
count = 0
|
||||
for _, row in df.iterrows():
|
||||
if pd.isna(row.get("date")):
|
||||
continue
|
||||
date_str = str(row["date"])
|
||||
try:
|
||||
conn.execute("""
|
||||
INSERT OR REPLACE INTO ohlcv_daily
|
||||
(date, symbol, open, high, low, close, volume,
|
||||
ema20, ema60, ema120, atr_14, bb_width, adx_14)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
date_str, self.symbol,
|
||||
float(row["open"]), float(row["high"]),
|
||||
float(row["low"]), float(row["close"]),
|
||||
float(row.get("volume", 0)),
|
||||
float(row["ema20"]) if not pd.isna(row.get("ema20")) else None,
|
||||
float(row["ema60"]) if not pd.isna(row.get("ema60")) else None,
|
||||
float(row["ema120"]) if not pd.isna(row.get("ema120")) else None,
|
||||
float(row["atr_14"]) if not pd.isna(row.get("atr_14")) else None,
|
||||
float(row["bb_width"]) if not pd.isna(row.get("bb_width")) else None,
|
||||
float(row["adx_14"]) if not pd.isna(row.get("adx_14")) else None,
|
||||
))
|
||||
count += 1
|
||||
except Exception as e:
|
||||
self.logger.debug(f"Skip row {date_str}: {e}")
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
self.logger.info(f"Stored {count} OHLCV rows")
|
||||
return count
|
||||
Reference in New Issue
Block a user