1)Calculate 1m quote from tick

2)Add quote stats and ts api
3)Looser constraints on tag

api-tests/trading/get_quote_stats.http

@@ -0,0 +1,2 @@
+GET http://127.0.0.1:8090/api/trading/get_quote_stats
+accept: application/json

api-tests/trading/query_kdata.http

@@ -0,0 +1,13 @@
+POST http://127.0.0.1:8090/api/trading/query_kdata
+accept: application/json
+Content-Type: application/json
+
+
+{
+  "data_provider": "em",
+  "entity_ids": [
+    "stock_sz_002085",
+    "stock_sz_300133"
+  ],
+  "adjust_type": "hfq"
+}

api-tests/factor/query_kdata.http → api-tests/trading/query_ts.http

@@ -1,4 +1,4 @@
-POST http://127.0.0.1:8090/api/factor/query_kdata
+POST http://127.0.0.1:8090/api/trading/query_ts
 accept: application/json
 Content-Type: application/json
 

src/zvt/api/kdata.py

@@ -9,10 +9,17 @@
 from zvt.contract.api import decode_entity_id, get_schema_by_name
 from zvt.domain import Index1dKdata
 from zvt.utils.pd_utils import pd_is_not_null
-from zvt.utils.time_utils import to_time_str, TIME_FORMAT_DAY, TIME_FORMAT_ISO8601, to_pd_timestamp
+from zvt.utils.time_utils import (
+    to_time_str,
+    TIME_FORMAT_DAY,
+    TIME_FORMAT_ISO8601,
+    to_pd_timestamp,
+    date_time_by_interval,
+    current_date,
+)
 
 
-def get_trade_dates(start, end):
+def get_trade_dates(start, end=None):
     df = Index1dKdata.query_data(
         entity_id="index_sh_000001",
         provider="em",
@@ -25,6 +32,12 @@ def get_trade_dates(start, end):
     return df["timestamp"].tolist()
 
 
+def get_recent_trade_dates(days_count=5):
+    max_start = date_time_by_interval(current_date(), -days_count - 15)
+    dates = get_trade_dates(start=max_start)
+    return dates[-days_count:]
+
+
 def get_latest_kdata_date(
     entity_type: str,
     provider: str = None,
@@ -185,9 +198,14 @@ def to_sum(s):
     return df
 
 
+if __name__ == "__main__":
+    print(get_recent_trade_dates())
+
+
 # the __all__ is generated
 __all__ = [
     "get_trade_dates",
+    "get_recent_trade_dates",
     "get_latest_kdata_date",
     "get_kdata_schema",
     "get_kdata",

src/zvt/broker/qmt/qmt_quote.py

@@ -9,9 +9,16 @@
 from zvt.contract import IntervalLevel, AdjustType
 from zvt.contract.api import decode_entity_id, df_to_db, get_db_session
 from zvt.domain import StockQuote, Stock
-from zvt.domain.quotes.stock.stock_quote import Stock1mQuote
+from zvt.domain.quotes.stock.stock_quote import Stock1mQuote, StockQuoteLog
 from zvt.utils.pd_utils import pd_is_not_null
-from zvt.utils.time_utils import to_time_str, current_date, to_pd_timestamp, now_pd_timestamp, TIME_FORMAT_MINUTE
+from zvt.utils.time_utils import (
+    to_time_str,
+    current_date,
+    to_pd_timestamp,
+    now_pd_timestamp,
+    TIME_FORMAT_MINUTE,
+    date_time_by_interval,
+)
 
 # https://dict.thinktrader.net/nativeApi/start_now.html?id=e2M5nZ
 
@@ -137,7 +144,7 @@ def get_kdata(
     code = _to_qmt_code(entity_id=entity_id)
     period = level.value
     # 保证qmt先下载数据到本地
-    xtdata.download_history_data(stock_code=code, period=period, start_time="", end_time="")
+    xtdata.download_history_data(stock_code=code, period=period)
     records = xtdata.get_market_data(
         stock_list=[code],
         period=period,
@@ -223,14 +230,22 @@ def on_data(datas, stock_df=entity_df):
         df["float_cap"] = df["float_volume"] * df["price"]
         df["total_cap"] = df["total_volume"] * df["price"]
 
+        # 实时行情统计，只保留最新
         df_to_db(df, data_schema=StockQuote, provider="qmt", force_update=True, drop_duplicates=False)
-        cost_time = time.time() - start_time
-        logger.info(f"Quotes cost_time:{cost_time} for {len(datas.keys())} stocks")
 
+        # 1分钟分时
         df["id"] = df[["entity_id", "timestamp"]].apply(
             lambda se: "{}_{}".format(se["entity_id"], to_time_str(se["timestamp"], TIME_FORMAT_MINUTE)), axis=1
         )
         df_to_db(df, data_schema=Stock1mQuote, provider="qmt", force_update=True, drop_duplicates=False)
+        # 历史记录
+        # df["id"] = df[["entity_id", "timestamp"]].apply(
+        #     lambda se: "{}_{}".format(se["entity_id"], to_time_str(se["timestamp"], TIME_FORMAT_MINUTE2)), axis=1
+        # )
+        # df_to_db(df, data_schema=StockQuoteLog, provider="qmt", force_update=True, drop_duplicates=False)
+
+        cost_time = time.time() - start_time
+        logger.info(f"Quotes cost_time:{cost_time} for {len(datas.keys())} stocks")
 
     return on_data
 
@@ -245,6 +260,9 @@ def download_capital_data():
 def clear_history_quote():
     session = get_db_session("qmt", data_schema=StockQuote)
     session.query(StockQuote).filter(StockQuote.timestamp < current_date()).delete()
+    start_date = date_time_by_interval(current_date(), -20)
+    session.query(Stock1mQuote).filter(Stock1mQuote.timestamp < start_date).delete()
+    session.query(StockQuoteLog).filter(StockQuoteLog.timestamp < start_date).delete()
     session.commit()
 
 
@@ -253,7 +271,7 @@ def record_tick():
     Stock.record_data(provider="em")
     stocks = get_qmt_stocks()
     print(stocks)
-    xtdata.subscribe_whole_quote(stocks, callback=tick_to_quote())
+    sid = xtdata.subscribe_whole_quote(stocks, callback=tick_to_quote())
 
     """阻塞线程接收行情回调"""
     import time
@@ -267,6 +285,7 @@ def record_tick():
         if current_timestamp.hour >= 15 and current_timestamp.minute >= 10:
             logger.info(f"record tick finished at: {current_timestamp}")
             break
+    xtdata.unsubscribe_quote(sid)
 
 
 if __name__ == "__main__":
@@ -278,7 +297,6 @@ def record_tick():
     sched.start()
     sched._thread.join()
 
-
 # the __all__ is generated
 __all__ = [
     "get_qmt_stocks",

src/zvt/contract/recorder.py

@@ -13,14 +13,13 @@
 from zvt.contract.api import get_entities, get_data
 from zvt.contract.base_service import OneStateService
 from zvt.contract.schema import Mixin, TradableEntity
+from zvt.contract.utils import is_in_same_interval, evaluate_size_from_timestamp
 from zvt.contract.zvt_info import RecorderState
 from zvt.utils.pd_utils import pd_is_not_null
 from zvt.utils.time_utils import (
     to_pd_timestamp,
     TIME_FORMAT_DAY,
     to_time_str,
-    evaluate_size_from_timestamp,
-    is_in_same_interval,
     now_pd_timestamp,
     now_time_str,
 )

src/zvt/contract/utils.py

@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+import math
+
+import pandas as pd
+
+from zvt.contract import IntervalLevel
+from zvt.utils.time_utils import to_pd_timestamp
+
+
+def is_in_same_interval(t1: pd.Timestamp, t2: pd.Timestamp, level: IntervalLevel):
+    t1 = to_pd_timestamp(t1)
+    t2 = to_pd_timestamp(t2)
+    if level == IntervalLevel.LEVEL_1WEEK:
+        return t1.week == t2.week
+    if level == IntervalLevel.LEVEL_1MON:
+        return t1.month == t2.month
+
+    return level.floor_timestamp(t1) == level.floor_timestamp(t2)
+
+
+def evaluate_size_from_timestamp(
+    start_timestamp, level: IntervalLevel, one_day_trading_minutes, end_timestamp: pd.Timestamp = None
+):
+    """
+    given from timestamp,level,one_day_trading_minutes,this func evaluate size of kdata to current.
+    it maybe a little bigger than the real size for fetching all the kdata.
+
+    :param start_timestamp:
+    :type start_timestamp: pd.Timestamp
+    :param level:
+    :type level: IntervalLevel
+    :param one_day_trading_minutes:
+    :type one_day_trading_minutes: int
+    """
+    if not end_timestamp:
+        end_timestamp = pd.Timestamp.now()
+    else:
+        end_timestamp = to_pd_timestamp(end_timestamp)
+
+    time_delta = end_timestamp - to_pd_timestamp(start_timestamp)
+
+    one_day_trading_seconds = one_day_trading_minutes * 60
+
+    if level == IntervalLevel.LEVEL_1DAY:
+        return time_delta.days + 1
+
+    if level == IntervalLevel.LEVEL_1WEEK:
+        return int(math.ceil(time_delta.days / 7)) + 1
+
+    if level == IntervalLevel.LEVEL_1MON:
+        return int(math.ceil(time_delta.days / 30)) + 1
+
+    if time_delta.days > 0:
+        seconds = (time_delta.days + 1) * one_day_trading_seconds
+        return int(math.ceil(seconds / level.to_second())) + 1
+    else:
+        seconds = time_delta.total_seconds()
+        return min(int(math.ceil(seconds / level.to_second())) + 1, one_day_trading_seconds / level.to_second() + 1)
+
+
+def next_timestamp_on_level(current_timestamp: pd.Timestamp, level: IntervalLevel) -> pd.Timestamp:
+    current_timestamp = to_pd_timestamp(current_timestamp)
+    return current_timestamp + pd.Timedelta(seconds=level.to_second())
+
+
+def is_finished_kdata_timestamp(timestamp, level: IntervalLevel):
+    timestamp = to_pd_timestamp(timestamp)
+    if level.floor_timestamp(timestamp) == timestamp:
+        return True
+    return False

src/zvt/domain/quotes/stock/stock_quote.py

@@ -41,6 +41,39 @@ class StockQuote(StockQuoteBase, Mixin):
     total_cap = Column(Float)
 
 
+class StockQuoteLog(StockQuoteBase, Mixin):
+    __tablename__ = "stock_quote_log"
+    code = Column(String(length=32))
+    name = Column(String(length=32))
+
+    #: UNIX时间戳
+    time = Column(Integer)
+    #: 最新价
+    price = Column(Float)
+    # 涨跌幅
+    change_pct = Column(Float)
+    # 成交金额
+    turnover = Column(Float)
+    # 换手率
+    turnover_rate = Column(Float)
+    #: 是否涨停
+    is_limit_up = Column(Boolean)
+    #: 封涨停金额
+    limit_up_amount = Column(Float)
+    #: 是否跌停
+    is_limit_down = Column(Boolean)
+    #: 封跌停金额
+    limit_down_amount = Column(Float)
+    #: 5挡卖单金额
+    ask_amount = Column(Float)
+    #: 5挡买单金额
+    bid_amount = Column(Float)
+    #: 流通市值
+    float_cap = Column(Float)
+    #: 总市值
+    total_cap = Column(Float)
+
+
 class Stock1mQuote(StockQuoteBase, Mixin):
     __tablename__ = "stock_1m_quote"
     code = Column(String(length=32))
@@ -60,10 +93,14 @@ class Stock1mQuote(StockQuoteBase, Mixin):
     turnover = Column(Float)
     # 换手率
     turnover_rate = Column(Float)
+    #: 是否涨停
+    is_limit_up = Column(Boolean)
+    #: 是否跌停
+    is_limit_down = Column(Boolean)
 
 
 register_schema(providers=["qmt"], db_name="stock_quote", schema_base=StockQuoteBase, entity_type="stock")
 
 
 # the __all__ is generated
-__all__ = ["StockQuote", "Stock1mQuote"]
+__all__ = ["StockQuote", "StockQuoteLog", "Stock1mQuote"]

src/zvt/factors/factor_models.py

@@ -4,7 +4,7 @@
 
 from pydantic import BaseModel, Field
 
-from zvt.contract import IntervalLevel, AdjustType
+from zvt.contract import IntervalLevel
 from zvt.trader import TradingSignalType
 from zvt.utils.time_utils import date_time_by_interval, current_date
 
@@ -17,23 +17,6 @@ class FactorRequestModel(BaseModel):
     level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)
 
 
-class KdataRequestModel(BaseModel):
-    entity_ids: List[str]
-    data_provider: str = Field(default="em")
-    start_timestamp: datetime = Field(default=date_time_by_interval(current_date(), -365))
-    end_timestamp: Optional[datetime] = Field(default=None)
-    level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)
-    adjust_type: AdjustType = Field(default=AdjustType.hfq)
-
-
-class KdataModel(BaseModel):
-    entity_id: str
-    code: str
-    name: str
-    level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY)
-    datas: List
-
-
 class TradingSignalModel(BaseModel):
     entity_id: str
     happen_timestamp: datetime
@@ -51,4 +34,4 @@ class FactorResultModel(BaseModel):
 
 
 # the __all__ is generated
-__all__ = ["FactorRequestModel", "KdataRequestModel", "KdataModel", "TradingSignalModel", "FactorResultModel"]
+__all__ = ["FactorRequestModel", "TradingSignalModel", "FactorResultModel"]

src/zvt/factors/factor_service.py

@@ -1,40 +1,11 @@
 # -*- coding: utf-8 -*-
 import pandas as pd
 
-from zvt.api import kdata as kdata_api
 from zvt.contract import zvt_context
 from zvt.domain import Stock
-from zvt.factors.factor_models import FactorRequestModel, KdataRequestModel
+from zvt.factors.factor_models import FactorRequestModel
 from zvt.factors.technical_factor import TechnicalFactor
 from zvt.trader import TradingSignalType
-from zvt.utils.pd_utils import pd_is_not_null
-
-
-def query_kdata(kdata_request_model: KdataRequestModel):
-
-    kdata_df = kdata_api.get_kdata(
-        entity_ids=kdata_request_model.entity_ids,
-        provider=kdata_request_model.data_provider,
-        start_timestamp=kdata_request_model.start_timestamp,
-        end_timestamp=kdata_request_model.end_timestamp,
-        adjust_type=kdata_request_model.adjust_type,
-    )
-    if pd_is_not_null(kdata_df):
-        kdata_df["timestamp"] = kdata_df["timestamp"].apply(lambda x: int(x.timestamp()))
-        kdata_df["data"] = kdata_df.apply(
-            lambda x: x[
-                ["timestamp", "open", "high", "low", "close", "volume", "turnover", "change_pct", "turnover_rate"]
-            ].values.tolist(),
-            axis=1,
-        )
-        df = kdata_df.groupby("entity_id").agg(
-            code=("code", "first"),
-            name=("name", "first"),
-            level=("level", "first"),
-            datas=("data", lambda data: list(data)),
-        )
-        df = df.reset_index(drop=False)
-        return df.to_dict(orient="records")
 
 
 def query_factor_result(factor_request_model: FactorRequestModel):
@@ -70,4 +41,4 @@ def to_trading_signal(order_type):
 
 
 # the __all__ is generated
-__all__ = ["query_kdata", "query_factor_result"]
+__all__ = ["query_factor_result"]