refactor(rotation): 移除数据缓存 + 修复空值和pct_change警告
- 移除CSV本地缓存（cache_dir、_cache_path、_premium_cache_path、_save_premium_cache） - 每次运行直接从API获取数据，简化DataCache类 - 修复_get_etf_prices中open/close为None时的空值处理（中证指数API不提供OHLC） - 修复pct_change的FutureWarning（显式传fill_method=None） - 更新trade_cost注释
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@@ -151,7 +151,7 @@ rotation:
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rebalance:
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min_hold_days: 1
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score_threshold: 0.0
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trade_cost: 0.001 # 0.1% 交易成本
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trade_cost: 0.001 # 万1 交易成本（场内ETF万0.5免5）
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# ============================================================
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# 溢价控制配置
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@@ -81,53 +81,18 @@ def is_crash(prices: np.ndarray) -> bool:
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# ============================================================
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class DataCache:
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"""CSV-cached data fetching for index (raw) and ETF (hfq)"""
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"""Data fetching (no local cache, always from API)"""
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def __init__(self, base_url: str, cache_dir: str = None, timeout: int = 120):
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def __init__(self, base_url: str, timeout: int = 120):
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self.base_url = base_url.rstrip('/')
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self.api_path = '/api/v1/ohlcv'
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self.timeout = timeout
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if cache_dir is None:
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cache_dir = PROJECT_ROOT / 'data' / 'simple_rotation_cache'
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self.cache_dir = Path(cache_dir)
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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# premium data cache: {trade_code: {date_str: premium_ratio}}
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# premium data in memory: {trade_code: {date_str: premium_ratio}}
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self.premium_data: Dict[str, Dict[str, float]] = {}
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def _cache_path(self, code: str, adj: str) -> Path:
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prefix = 'index' if adj == 'raw' else 'etf'
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safe_code = code.replace('=', '_').replace('^', '_')
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return self.cache_dir / f"{prefix}_{safe_code}.csv"
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def _premium_cache_path(self, code: str) -> Path:
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safe_code = code.replace('=', '_').replace('^', '_')
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return self.cache_dir / f"premium_{safe_code}.csv"
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def preload(self, code: str, start_date: str, end_date: str, adj: str = 'raw') -> Optional[pd.DataFrame]:
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"""Preload full history and cache to CSV"""
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cache_path = self._cache_path(code, adj)
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if cache_path.exists():
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try:
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df = pd.read_csv(cache_path, index_col='date', parse_dates=True)
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if len(df) > 0:
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cs = df.index.min().strftime('%Y-%m-%d')
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ce = df.index.max().strftime('%Y-%m-%d')
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if cs <= start_date and ce >= end_date:
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return df
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new_start = (df.index.max() + timedelta(days=1)).strftime('%Y-%m-%d')
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if new_start <= end_date:
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new_df = self._fetch_api(code, new_start, end_date, adj)
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if new_df is not None and len(new_df) > 0:
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df = pd.concat([df, new_df])
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df = df[~df.index.duplicated(keep='last')]
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df.to_csv(cache_path)
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return df
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except Exception:
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pass
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df = self._fetch_api(code, start_date, end_date, adj)
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if df is not None and len(df) > 0:
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df.to_csv(cache_path)
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return df
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"""Fetch data from API"""
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return self._fetch_api(code, start_date, end_date, adj)
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def _fetch_api(self, code: str, start_date: str, end_date: str, adj: str) -> Optional[pd.DataFrame]:
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"""Fetch from Flask API, also extracts premium_series for ETFs"""
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@@ -155,11 +120,13 @@ class DataCache:
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df = df.set_index('date').sort_index()
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keep = [c for c in ['open', 'high', 'low', 'close', 'volume'] if c in df.columns]
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df = df[keep]
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# Extract and cache premium_series (ETF only)
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# Extract premium_series (ETF only) and store in memory
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premium_series = data.get('premium_series', [])
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if premium_series:
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df.attrs['premium_series'] = {item['date']: item['premium'] for item in premium_series}
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self._save_premium_cache(code, df.attrs['premium_series'])
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if code not in self.premium_data:
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self.premium_data[code] = {}
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self.premium_data[code].update(df.attrs['premium_series'])
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print(f" + {code}: {len(df)} rows ({adj})")
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return df
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except (requests.exceptions.Timeout, requests.exceptions.SSLError, requests.exceptions.ConnectionError) as e:
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@@ -174,50 +141,20 @@ class DataCache:
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return None
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return None
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def _save_premium_cache(self, code: str, premium_dict: Dict[str, float]):
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"""Save premium data to CSV cache"""
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try:
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cache_path = self._premium_cache_path(code)
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pd.DataFrame(
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[{'date': k, 'premium': v} for k, v in premium_dict.items()]
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).to_csv(cache_path, index=False)
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except Exception:
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pass
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def preload_premium(self, code: str, end_date: str = None) -> Optional[Dict[str, float]]:
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"""Load premium data for an ETF code from cache, with incremental update.
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If cache exists but doesn't cover end_date, fetches the gap."""
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"""Load premium data for an ETF code, fetch from API if not in memory."""
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if code in self.premium_data:
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# Already in memory - check if up-to-date
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if end_date:
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dates = sorted(self.premium_data[code].keys())
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if dates and dates[-1] >= end_date:
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return self.premium_data[code]
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else:
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return self.premium_data[code]
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cache_path = self._premium_cache_path(code)
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if cache_path.exists():
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try:
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df = pd.read_csv(cache_path)
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if len(df) > 0 and 'date' in df.columns and 'premium' in df.columns:
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self.premium_data[code] = dict(zip(df['date'].astype(str), df['premium']))
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# Check if cache covers end_date
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if end_date:
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latest_cached = max(self.premium_data[code].keys())
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if latest_cached >= end_date:
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return self.premium_data[code]
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# Cache is stale - fetch gap from latest_cached+1 to end_date
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fetch_start = (pd.Timestamp(latest_cached) + timedelta(days=1)).strftime('%Y-%m-%d')
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self._fetch_premium_api(code, fetch_start, end_date)
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return self.premium_data[code]
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except Exception:
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pass
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# No cache: fetch full history from API
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self._fetch_premium_api(code, '2000-01-01', end_date or datetime.now().strftime('%Y-%m-%d'))
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return self.premium_data.get(code)
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def _fetch_premium_api(self, code: str, start_date: str, end_date: str):
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"""Fetch premium_series from API and merge into cache"""
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"""Fetch premium_series from API and store in memory"""
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url = f"{self.base_url}{self.api_path}"
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params = {'code': code, 'start': start_date, 'end': end_date, 'adj': 'raw'}
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for attempt in range(3):
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@@ -237,7 +174,6 @@ class DataCache:
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if code not in self.premium_data:
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self.premium_data[code] = {}
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self.premium_data[code].update(new_data)
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self._save_premium_cache(code, self.premium_data[code])
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print(f" + premium {code}: +{len(new_data)} days (total {len(self.premium_data[code])})")
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return
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except (requests.exceptions.Timeout, requests.exceptions.SSLError, requests.exceptions.ConnectionError):
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@@ -474,14 +410,17 @@ class SimpleRotationStrategy:
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today_date = recent.index[-1]
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if (date - today_date).days > 5:
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return None
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close = float(today_row['close'])
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prev_close = float(prev_row['close'])
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# Handle missing open (common for index data like 931862.CSI)
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open_price = float(today_row.get('open', close)) if 'open' in today_row.index else close
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close_raw = today_row.get('close')
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prev_close_raw = prev_row.get('close')
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if close_raw is None or pd.isna(close_raw) or prev_close_raw is None or pd.isna(prev_close_raw):
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return None
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close = float(close_raw)
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prev_close = float(prev_close_raw)
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# Handle missing/invalid open (common for index data like 931862.CSI)
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raw_open = today_row.get('open')
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open_price = float(raw_open) if raw_open is not None and not pd.isna(raw_open) else close
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if pd.isna(open_price) or open_price == 0:
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open_price = close
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if pd.isna(close) or pd.isna(prev_close):
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return None
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return {
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'open': open_price,
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'close': close,
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@@ -995,7 +934,7 @@ class SimpleRotationStrategy:
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if self.benchmark_data is not None:
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bm_close = self.benchmark_data['close'].reindex(df.index, method='ffill')
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if bm_close is not None and not bm_close.isna().all():
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benchmark_nav = (1 + bm_close.pct_change()).cumprod()
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benchmark_nav = (1 + bm_close.pct_change(fill_method=None)).cumprod()
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first_valid = benchmark_nav.dropna().iloc[0] if len(benchmark_nav.dropna()) > 0 else 1
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benchmark_nav = benchmark_nav / first_valid
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@@ -1005,7 +944,7 @@ class SimpleRotationStrategy:
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if code in self.index_data:
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price = self.index_data[code]['close'].reindex(df.index, method='ffill')
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if price is not None and not price.isna().all():
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nav_s = (1 + price.pct_change()).cumprod()
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nav_s = (1 + price.pct_change(fill_method=None)).cumprod()
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fv = nav_s.dropna().iloc[0] if len(nav_s.dropna()) > 0 else 1
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asset_navs[code] = nav_s / fv
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@@ -1023,7 +962,7 @@ class SimpleRotationStrategy:
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# Benchmark metrics
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b_total_return = b_annual = b_sharpe = b_dd = 0
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if benchmark_nav is not None:
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bm_ret = benchmark_nav.pct_change()
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bm_ret = benchmark_nav.pct_change(fill_method=None)
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b_total_return = benchmark_nav.iloc[-1] - 1
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b_annual = (1 + b_total_return) ** (252 / n_days) - 1 if n_days > 0 else 0
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b_sharpe = bm_ret.mean() / bm_ret.std() * np.sqrt(252) if bm_ret.std() > 0 else 0
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