diff --git a/backend/app/api/kline.py b/backend/app/api/kline.py
index 68f2404..333bec1 100644
--- a/backend/app/api/kline.py
+++ b/backend/app/api/kline.py
@@ -33,16 +33,38 @@ def search_instruments(
request: Request,
q: str = Query("", min_length=0, max_length=50, description="搜索关键词"),
limit: int = Query(20, ge=1, le=50),
+ asset_types: str = Query("stock", description="逗号分隔的资产类型: stock,etf"),
):
- """模糊搜索标的 (代码 / 名称)。从内存 instruments 缓存中查。"""
- repo = request.app.state.repo
- df = repo.get_instruments()
- if df.is_empty() or not q.strip():
+ """模糊搜索标的 (代码 / 名称)。从内存 instruments 缓存中查。
+
+ 默认只搜股票, 保持既有调用方行为不变; 自选等场景传 asset_types=stock,etf
+ 可一并搜出 ETF, 结果附带 asset_type 字段供前端区分。
+ """
+ if not q.strip():
return {"results": []}
- keyword = q.strip().upper()
+ repo = request.app.state.repo
import polars as pl
+ types = [t.strip() for t in asset_types.split(",") if t.strip()]
+ parts: list[pl.DataFrame] = []
+ for t in types:
+ df_t = repo.get_instruments_asset(t)
+ if df_t.is_empty() or "symbol" not in df_t.columns:
+ continue
+ # dtype 全部归一到 Utf8: 股票/ETF 两份缓存来源不同 (ETF 含 legacy 合并), 防 concat SchemaError
+ parts.append(df_t.with_columns([
+ pl.col("symbol").cast(pl.Utf8).alias("symbol"),
+ (pl.col("name").cast(pl.Utf8) if "name" in df_t.columns else pl.lit("")).alias("name"),
+ (pl.col("code").cast(pl.Utf8) if "code" in df_t.columns else pl.lit("")).alias("code"),
+ pl.lit(t).alias("asset_type"),
+ ]).select(["symbol", "name", "code", "asset_type"]))
+ if not parts:
+ return {"results": []}
+ df = pl.concat(parts, how="vertical")
+
+ keyword = q.strip().upper()
+
# code/symbol 前缀优先，再 name 包含匹配
prefix_mask = (
pl.col("code").str.starts_with(keyword)
@@ -64,23 +86,17 @@ def search_instruments(
prefix_symbols = set(prefix_hits["symbol"].to_list()) if not prefix_hits.is_empty() else set()
contain_hits = df.filter(contains_mask & ~pl.col("symbol").is_in(prefix_symbols)).head(remaining)
matched = pl.concat([prefix_hits, contain_hits]) if not prefix_hits.is_empty() else contain_hits
- rows = matched.select(["symbol", "name", "code"]).to_dicts()
+ rows = matched.select(["symbol", "name", "code", "asset_type"]).to_dicts()
return {"results": rows}
@router.post("/instruments/names")
def instruments_names(request: Request, symbols: list[str]):
- """批量查股票名称。传入 symbol 列表, 返回 {symbol: name}。"""
+ """批量查标的名称 (股票 + ETF)。传入 symbol 列表, 返回 {symbol: name}。"""
if not symbols:
return {"names": {}}
repo = request.app.state.repo
- df = repo.get_instruments()
- if df.is_empty():
- return {"names": {}}
- import polars as pl
- matched = df.filter(pl.col("symbol").is_in(symbols)).select(["symbol", "name"])
- names = {row["symbol"]: row["name"] for row in matched.iter_rows(named=True)}
- return {"names": names}
+ return {"names": repo.get_name_map(symbols)}
def _get_stock_info(repo, symbol: str) -> dict:
@@ -101,6 +117,21 @@ def _get_stock_info(repo, symbol: str) -> dict:
}
+def _get_asset_info(repo, symbol: str, asset_type: str) -> dict:
+ """非股票标的 (ETF / 指数) 的名称信息 — 从对应 instruments 缓存查, 无股本概念。"""
+ import polars as pl
+ try:
+ df = repo.get_instruments_asset(asset_type)
+ if df.is_empty() or "symbol" not in df.columns or "name" not in df.columns:
+ return {}
+ hit = df.filter(pl.col("symbol") == symbol).head(1)
+ if hit.is_empty():
+ return {}
+ return {"name": hit["name"][0]}
+ except Exception:
+ return {}
+
+
@router.get("/daily")
def get_daily(
request: Request,
@@ -126,11 +157,12 @@ def get_daily(
else:
start = end - timedelta(days=days)
- stock_info = _get_stock_info(repo, symbol)
+ asset_type = repo.resolve_asset_type(symbol)
+ stock_info = _get_stock_info(repo, symbol) if asset_type == "stock" else _get_asset_info(repo, symbol, asset_type)
stock_name = stock_info.get("name")
- # 从 enriched 表读取 (已含前复权 OHLCV + 技术指标 + 信号)
- df = repo.get_daily(symbol, start, end)
+ # 从 enriched 表读取 (已含前复权 OHLCV + 技术指标 + 信号); ETF/指数走独立存储
+ df = repo.get_daily_asset(asset_type, symbol, start, end)
if df.is_empty():
try:
@@ -151,14 +183,14 @@ def get_daily(
enriched = compute_enriched(raw, factors=factors)
rows = enriched.tail(days).to_dicts()
# 即使 live 模式也尝试追加实时蜡烛
- rows = _maybe_inject_live_candle(request, symbol, rows)
+ rows = _maybe_inject_live_candle(request, symbol, rows, asset_type)
resp = {"symbol": symbol, "name": stock_name, "stock_info": stock_info, "rows": rows, "source": "live"}
return _attach_ext(resp, repo, symbol, ext_columns)
rows = df.to_dicts()
# 追加/覆盖今日实时蜡烛
- rows = _maybe_inject_live_candle(request, symbol, rows)
+ rows = _maybe_inject_live_candle(request, symbol, rows, asset_type)
resp = {"symbol": symbol, "name": stock_name, "stock_info": stock_info, "rows": rows, "source": "enriched"}
return _attach_ext(resp, repo, symbol, ext_columns)
@@ -229,13 +261,21 @@ def _attach_ext(resp: dict, repo, symbol: str, ext_columns: Optional[str]) -> di
return resp
-def _maybe_inject_live_candle(request: Request, symbol: str, rows: list[dict]) -> list[dict]:
- """如果 QuoteService 有实时 enriched 数据, 用实时数据生成今日蜡烛并追加/覆盖。"""
- qs = getattr(request.app.state, "quote_service", None)
- if not qs:
- return rows
+def _maybe_inject_live_candle(request: Request, symbol: str, rows: list[dict], asset_type: str = "stock") -> list[dict]:
+ """如果有当日实时 enriched 数据, 用实时数据生成今日蜡烛并追加/覆盖。
- df_today, enriched_date = qs.get_enriched_today()
+ stock 走 QuoteService 的股票实时缓存; etf 走 ETF enriched 缓存 (开启实时 ETF
+ 拉取时为盘中数据, 否则为磁盘最新日, 由下方"非今日不注入"守卫自然跳过)。
+ """
+ if asset_type == "stock":
+ qs = getattr(request.app.state, "quote_service", None)
+ if not qs:
+ return rows
+ df_today, enriched_date = qs.get_enriched_today()
+ elif asset_type == "etf":
+ df_today, enriched_date = request.app.state.repo.get_enriched_latest_asset("etf")
+ else:
+ return rows
if df_today.is_empty():
return rows
@@ -377,8 +417,17 @@ def get_minute_batch(request: Request, body: dict):
if recent_date is not None:
trade_date = recent_date
- # Step 1: 本地优先 — 一次 scan 读全部 symbol 当日分钟K
- df_local = repo.get_minute_batch(symbols, trade_date)
+ # Step 1: 本地优先 — 一次 scan 读全部 symbol 当日分钟K (股票 / ETF 分钟数据分开存储)
+ etf_set = repo.get_etf_symbol_set()
+ stock_syms = [s for s in symbols if s not in etf_set]
+ etf_syms = [s for s in symbols if s in etf_set]
+ df_local = repo.get_minute_batch(stock_syms, trade_date)
+ if etf_syms:
+ df_etf = repo.get_minute_batch(etf_syms, trade_date, asset_type="etf")
+ if df_local.is_empty():
+ df_local = df_etf
+ elif not df_etf.is_empty():
+ df_local = pl.concat([df_local, df_etf], how="diagonal_relaxed")
# 期望条数 (盘中按当前时刻估算, 盘后 240)
now = datetime.now()
@@ -442,11 +491,12 @@ def get_minute(
- 本地无数据或不完整 → 从 TickFlow 实时拉取返回（不写入）
"""
repo = request.app.state.repo
- stock_info = _get_stock_info(repo, symbol)
+ asset_type = repo.resolve_asset_type(symbol)
+ stock_info = _get_stock_info(repo, symbol) if asset_type == "stock" else _get_asset_info(repo, symbol, asset_type)
stock_name = stock_info.get("name")
if trade_date is None:
- trade_date = repo.latest_minute_date(symbol)
+ trade_date = repo.latest_minute_date(symbol, asset_type=asset_type)
if trade_date is None:
# 本地无任何分钟K，尝试从 TickFlow 拉取当天
trade_date = date.today()
@@ -456,7 +506,7 @@ def get_minute(
"date": str(trade_date), "rows": df.to_dicts(), "source": "live",
}
- df = repo.get_minute(symbol, trade_date)
+ df = repo.get_minute(symbol, trade_date, asset_type=asset_type)
# 完整交易日应有 240 条分钟K；如果是今天(盘中)，期望条数按已交易分钟估算
expected = 240
diff --git a/backend/app/api/stock_analysis.py b/backend/app/api/stock_analysis.py
index 8926fc1..0eb4b58 100644
--- a/backend/app/api/stock_analysis.py
+++ b/backend/app/api/stock_analysis.py
@@ -121,7 +121,8 @@ def get_levels(
repo = request.app.state.repo
end = date.today()
start = end - timedelta(days=days * 2)
- df = repo.get_daily(symbol, start, end)
+ # 按资产类型分流: ETF/指数走独立 enriched 存储, 股票保持原路径
+ df = repo.get_daily_asset(repo.resolve_asset_type(symbol), symbol, start, end)
if df.is_empty():
return {"levels": {"sr": [], "pivot": [], "extreme": [],
"boll": [], "keltner_s": [], "keltner_m": [], "keltner_l": [],
diff --git a/backend/app/api/watchlist.py b/backend/app/api/watchlist.py
index 81fc34d..213a530 100644
--- a/backend/app/api/watchlist.py
+++ b/backend/app/api/watchlist.py
@@ -31,10 +31,10 @@ def _with_names(rows: list[dict], request: Request) -> list[dict]:
if not rows:
return rows
try:
- df_i = request.app.state.repo.get_instruments()
- if df_i.is_empty() or "symbol" not in df_i.columns or "name" not in df_i.columns:
+ # 股票 + ETF 名称统一由 repo.get_name_map 解析, 自选列表可混合持有
+ name_by_symbol = request.app.state.repo.get_name_map([r.get("symbol") for r in rows])
+ if not name_by_symbol:
return rows
- name_by_symbol = dict(df_i.select(["symbol", "name"]).iter_rows())
return [{**row, "name": name_by_symbol.get(row.get("symbol"))} for row in rows]
except Exception as e: # noqa: BLE001
logger.debug("attach watchlist names failed: %s", e)
@@ -119,20 +119,42 @@ def watchlist_enriched(
if not symbols:
return {"rows": [], "as_of": None, "elapsed_ms": 0}
+ # 按资产拆分自选 symbol; ETF enriched 是独立缓存, 仅自选真的含 ETF 才去加载
+ # (避免无 ETF 用户在缓存冷启动时触发 ETF 全量懒加载)
+ etf_set = repo.get_etf_symbol_set()
+ stock_symbols = [s for s in symbols if s not in etf_set]
+ etf_symbols = [s for s in symbols if s in etf_set]
+
df_e, cache_date = repo.get_enriched_latest()
- if df_e.is_empty():
+ # 保持原契约: 自选含股票但股票 enriched 未就绪 (预热中) → 返回"未就绪"而非部分结果
+ if stock_symbols and df_e.is_empty():
return {"rows": [], "as_of": None, "elapsed_ms": 0}
- # 按 symbol 过滤
- df = df_e.filter(pl.col("symbol").is_in(symbols))
- if df.is_empty():
- return {"rows": [], "as_of": str(cache_date) if cache_date else None, "elapsed_ms": 0}
+ df = df_e.filter(pl.col("symbol").is_in(stock_symbols)) if stock_symbols else pl.DataFrame()
- # JOIN instruments 取 name + float_shares
+ # ETF 行合并; 缺失列 (换手率/涨跌停信号等) 为 null
+ etf_date = None
+ if etf_symbols:
+ df_etf_all, etf_date = repo.get_enriched_latest_asset("etf")
+ if not df_etf_all.is_empty():
+ df_etf = df_etf_all.filter(pl.col("symbol").is_in(etf_symbols))
+ if not df_etf.is_empty():
+ df = df_etf if df.is_empty() else pl.concat([df, df_etf], how="diagonal_relaxed")
+
+ # as_of 取两类缓存中较旧者, 避免把旧的 ETF 行标成股票缓存日期
+ dates = [d for d in (cache_date if stock_symbols else None, etf_date) if d is not None]
+ as_of = min(dates) if dates else None
+ if df.is_empty():
+ return {"rows": [], "as_of": str(as_of) if as_of else None, "elapsed_ms": 0}
+
+ # JOIN float_shares (仅股票有) + 名称 (股票/ETF 统一走 get_name_map)
df_i = repo.get_instruments()
- if not df_i.is_empty() and "name" in df_i.columns:
- inst_cols = [c for c in ["symbol", "name", "float_shares"] if c in df_i.columns]
- df = df.join(df_i.select(inst_cols), on="symbol", how="left")
+ if not df_i.is_empty() and "float_shares" in df_i.columns:
+ df = df.join(df_i.select(["symbol", "float_shares"]), on="symbol", how="left")
+ name_map = repo.get_name_map(df["symbol"].to_list())
+ df = df.with_columns(
+ pl.col("symbol").replace_strict(name_map, default=None, return_dtype=pl.Utf8).alias("name")
+ )
# 选择内置需要的列
keep = [c for c in _WATCHLIST_COLS + ["name", "float_shares"] if c in df.columns]
@@ -204,7 +226,7 @@ def watchlist_enriched(
rows = df.to_dicts()
elapsed = (time.perf_counter() - t0) * 1000
- return {"rows": rows, "as_of": str(cache_date) if cache_date else None, "elapsed_ms": elapsed}
+ return {"rows": rows, "as_of": str(as_of) if as_of else None, "elapsed_ms": elapsed}
def _parse_ext_columns(ext_columns: str) -> list[tuple[str, str]]:
diff --git a/backend/app/services/stock_analyzer.py b/backend/app/services/stock_analyzer.py
index c312459..db45c78 100644
--- a/backend/app/services/stock_analyzer.py
+++ b/backend/app/services/stock_analyzer.py
@@ -45,7 +45,8 @@ def _load_kline(repo, symbol: str) -> pl.DataFrame:
end = date.today()
start = end - timedelta(days=_KLINE_WINDOW * 2) # 多取一些保证交易日够
- df = repo.get_daily(symbol, start, end)
+ # 按资产类型分流: ETF/指数走独立 enriched 存储 (无财务数据, 提示词已有兜底)
+ df = repo.get_daily_asset(repo.resolve_asset_type(symbol), symbol, start, end)
if df.is_empty():
return df
return df.tail(_KLINE_WINDOW)
diff --git a/backend/app/tickflow/repository.py b/backend/app/tickflow/repository.py
index 90efae3..857fdf7 100644
--- a/backend/app/tickflow/repository.py
+++ b/backend/app/tickflow/repository.py
@@ -301,6 +301,9 @@ class KlineRepository:
self._etf_live_agg_cache: pl.DataFrame | None = None
self._etf_live_agg_cache_date: date | None = None
self._etf_instruments_cache: pl.DataFrame | None = None
+ # symbol 集合 memo (随对应 instruments 缓存失效): 供每请求资产分流用
+ self._index_symbol_set_cache: set[str] | None = None
+ self._etf_symbol_set_cache: set[str] | None = None
# ---- enriched 后台预热 ----
# 启动时 compute_indicators (107万行, 低配机 50s+) 移出 lifespan 关键路径,
@@ -362,6 +365,11 @@ class KlineRepository:
self._refresh_etf_instruments()
logger.info("cache refresh step done: ETF instruments (%.2fs)", time.perf_counter() - step)
+ # ETF enriched 只失效不重建: 下次访问时按新数据懒加载,
+ # 避免自选无 ETF 的用户在管道后白付全量重算成本
+ self._etf_enriched_cache = None
+ self._etf_enriched_cache_date = None
+
if background:
logger.info("cache refresh: enriched 推后台线程预热")
self._start_enriched_warmup()
@@ -438,6 +446,8 @@ class KlineRepository:
self._etf_live_agg_cache = None
self._etf_live_agg_cache_date = None
self._etf_instruments_cache = None
+ self._index_symbol_set_cache = None
+ self._etf_symbol_set_cache = None
def _refresh_enriched(self) -> None:
"""从 parquet 加载 enriched 最新日到内存 + 构建聚合表。
@@ -854,6 +864,7 @@ class KlineRepository:
df = pl.scan_parquet(self._index_inst_glob).collect()
if not df.is_empty():
self._index_instruments_cache = df
+ self._index_symbol_set_cache = None
logger.info("index instruments 缓存已加载: %d 只", len(df))
except Exception as e: # noqa: BLE001
logger.debug("index instruments 缓存刷新跳过: %s", e)
@@ -878,6 +889,7 @@ class KlineRepository:
if parts:
df_all = pl.concat(parts, how="diagonal_relaxed").unique(subset=["symbol"], keep="last").sort("symbol")
self._etf_instruments_cache = df_all
+ self._etf_symbol_set_cache = None
logger.info("ETF instruments 缓存已加载: %d 只", len(df_all))
def get_enriched_latest(self) -> tuple[pl.DataFrame, date | None]:
@@ -1035,11 +1047,50 @@ class KlineRepository:
return pl.DataFrame()
def get_index_symbol_set(self) -> set[str]:
- """返回已缓存指数 symbol 集合。"""
- df = self.get_index_instruments()
- if df.is_empty() or "symbol" not in df.columns:
- return set()
- return set(df["symbol"].cast(pl.Utf8).to_list())
+ """返回已缓存指数 symbol 集合 (memo, 随 instruments 缓存失效)。"""
+ if self._index_symbol_set_cache is None:
+ df = self.get_index_instruments()
+ if df.is_empty() or "symbol" not in df.columns:
+ return set()
+ self._index_symbol_set_cache = set(df["symbol"].cast(pl.Utf8).to_list())
+ return self._index_symbol_set_cache
+
+ def get_etf_symbol_set(self) -> set[str]:
+ """返回已缓存 ETF symbol 集合 (memo, 随 instruments 缓存失效)。"""
+ if self._etf_symbol_set_cache is None:
+ df = self.get_etf_instruments()
+ if df.is_empty() or "symbol" not in df.columns:
+ return set()
+ self._etf_symbol_set_cache = set(df["symbol"].cast(pl.Utf8).to_list())
+ return self._etf_symbol_set_cache
+
+ def resolve_asset_type(self, symbol: str) -> str:
+ """按 symbol 判定资产类型: etf / index / stock(默认)。
+
+ 供 API 层对单标的查询做资产分流 (get_daily_asset 等)。
+ ETF/指数集合为 memo, 每请求查询成本可忽略。
+ """
+ if symbol in self.get_etf_symbol_set():
+ return "etf"
+ if symbol in self.get_index_symbol_set():
+ return "index"
+ return "stock"
+
+ def get_name_map(self, symbols: list[str] | None = None) -> dict[str, str]:
+ """返回 {symbol: name} 映射, 合并股票 + ETF instruments (股票优先去重)。
+
+ 自选列表/名称批查等场景的统一名称解析入口, 避免各调用方自行合并两份缓存。
+ symbols 非 None 时只返回命中的条目。
+ """
+ name_map: dict[str, str] = {}
+ for df in (self.get_instruments(), self.get_etf_instruments()):
+ if df.is_empty() or "symbol" not in df.columns or "name" not in df.columns:
+ continue
+ if symbols is not None:
+ df = df.filter(pl.col("symbol").is_in(symbols))
+ for symbol, name in df.select(["symbol", "name"]).iter_rows():
+ name_map.setdefault(symbol, name)
+ return name_map
def enriched_latest_date(self) -> date | None:
"""返回缓存中的 enriched 最新日期。"""
@@ -1162,14 +1213,19 @@ class KlineRepository:
return self.get_etf_daily(symbol, start, end, columns)
return pl.DataFrame()
+ def _minute_glob_for(self, asset_type: str) -> str:
+ """按资产类型选择分钟K parquet glob。ETF 分钟数据独立存储于 kline_etf_minute。"""
+ return self._etf_minute_glob if asset_type == "etf" else self._minute_glob
+
def get_minute(
self,
symbol: str,
trade_date: date,
+ asset_type: str = "stock",
) -> pl.DataFrame:
"""分钟K查询 — Polars scan_parquet + predicate pushdown。"""
try:
- return pl.scan_parquet(self._minute_glob).filter(
+ return pl.scan_parquet(self._minute_glob_for(asset_type)).filter(
(pl.col("symbol") == symbol)
& (pl.col("datetime").dt.date() == trade_date)
).sort("datetime").collect()
@@ -1181,6 +1237,7 @@ class KlineRepository:
self,
symbols: list[str],
trade_date: date,
+ asset_type: str = "stock",
) -> pl.DataFrame:
"""批量分钟K查询 — 多 symbol 一次 scan_parquet。
@@ -1190,7 +1247,7 @@ class KlineRepository:
if not symbols:
return pl.DataFrame()
try:
- return pl.scan_parquet(self._minute_glob).filter(
+ return pl.scan_parquet(self._minute_glob_for(asset_type)).filter(
pl.col("symbol").is_in(symbols)
& (pl.col("datetime").dt.date() == trade_date)
).sort(["symbol", "datetime"]).collect()
@@ -1353,11 +1410,12 @@ class KlineRepository:
# DuckDB 查询 (冷路径: 统计/元数据/自定义SQL)
# ================================================================
- def latest_minute_date(self, symbol: str) -> date | None:
+ def latest_minute_date(self, symbol: str, asset_type: str = "stock") -> date | None:
+ table = "kline_etf_minute" if asset_type == "etf" else "kline_minute"
try:
with self._lock:
row = self.db.execute(
- "SELECT max(CAST(datetime AS DATE)) FROM kline_minute WHERE symbol = ?",
+ f"SELECT max(CAST(datetime AS DATE)) FROM {table} WHERE symbol = ?",
[symbol],
).fetchone()
if row and row[0]:
diff --git a/frontend/src/lib/api.ts b/frontend/src/lib/api.ts
index b6bceee..00a2565 100644
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -1097,9 +1097,9 @@ export const api = {
method: 'POST',
body: JSON.stringify({ symbols, date }),
}),
- instrumentSearch: (q: string, limit = 20) =>
- request<{ results: { symbol: string; name: string; code: string }[] }>(
- `/api/kline/instruments/search?q=${encodeURIComponent(q)}&limit=${limit}`,
+ instrumentSearch: (q: string, limit = 20, assetTypes?: string) =>
+ request<{ results: { symbol: string; name: string; code: string; asset_type?: string }[] }>(
+ `/api/kline/instruments/search?q=${encodeURIComponent(q)}&limit=${limit}${assetTypes ? `&asset_types=${encodeURIComponent(assetTypes)}` : ''}`,
),
/** 批量查股票名称 (传入 symbol 列表, 返回 {symbol: name}) */
diff --git a/frontend/src/lib/queryKeys.ts b/frontend/src/lib/queryKeys.ts
index 6766ef6..ab7788c 100644
--- a/frontend/src/lib/queryKeys.ts
+++ b/frontend/src/lib/queryKeys.ts
@@ -29,7 +29,7 @@ export const QK = {
// 不用 watchlist- 前缀: 避免被 SSE quotes_updated 高频失效(expert 1s/pro 2s)
// 导致每次都拉 TickFlow 触限流。分时图用固定 refetchInterval 刷新即可。
minuteBatch: (symbols: string) => ['minute-batch', symbols] as const,
- instrumentSearch: (q: string) => ['instrument-search', q] as const,
+ instrumentSearch: (q: string, assetTypes?: string) => ['instrument-search', q, assetTypes ?? 'stock'] as const,
// Screener
screener: ['screener'] as const,
diff --git a/frontend/src/pages/Watchlist.tsx b/frontend/src/pages/Watchlist.tsx
index 52c3f02..b432601 100644
--- a/frontend/src/pages/Watchlist.tsx
+++ b/frontend/src/pages/Watchlist.tsx
@@ -191,8 +191,8 @@ function StockSearchBox({
const [activeIdx, setActiveIdx] = useState(-1)
const search = useQuery({
- queryKey: QK.instrumentSearch(query),
- queryFn: () => api.instrumentSearch(query),
+ queryKey: QK.instrumentSearch(query, 'stock,etf'),
+ queryFn: () => api.instrumentSearch(query, 20, 'stock,etf'),
enabled: query.trim().length > 0,
staleTime: 30_000,
})
@@ -273,6 +273,9 @@ function StockSearchBox({
>
{r.symbol}
{r.name}
+ {r.asset_type === 'etf' && (
+ ETF
+ )}