0.3.10 优化KlineAnalyze性能

test/profile_df.py

 from cobra.data.kline import get_kline
+from czsc import KlineAnalyze
+import pandas as pd
+from czsc.analyze import KlineAnalyze
+from datetime import datetime, timedelta
+from contextlib import closing
+from tqsdk import TqApi
+from tqsdk.tools import DataDownloader
+
+
+def use_large_df():
+    symbol = "KQ.m@CZCE.MA"
+    freq = '5min'
+
+    file_csv = f"{symbol}_kline_{freq}.csv"
+    start_dt = datetime(2017, 1, 1, 6, 0, 0)
+    end_dt = datetime(2020, 5, 1, 6, 0, 0)
+    freq_dur_sec = {"1min": 60, '5min': 300, '30min': 1800, 'D': 3600*24}
+    freq_delta = {"1min": timedelta(days=20), '5min': timedelta(days=100),
+                  '30min': timedelta(days=300), 'D': timedelta(days=3000)}
+
+    api = TqApi()
+    k = DataDownloader(api, symbol_list=symbol, dur_sec=freq_dur_sec[freq],
+                       start_dt=start_dt-freq_delta[freq],
+                       end_dt=end_dt, csv_file_name=file_csv)
+
+    with closing(api):
+        while not k.is_finished():
+            api.wait_update()
+            print("download progress: %.2f%%" % k.get_progress())
+
+    kline = pd.read_csv(file_csv)
+    kline.columns = [x.replace(symbol + ".", "") for x in kline.columns]
+    kline.rename({"volume": "vol"}, axis=1, inplace=True)
+    kline.loc[:, "symbol"] = symbol
+    kline.loc[:, "dt"] = kline['datetime'].apply(lambda x: x.split(".")[0])
+    kline = kline[['symbol', 'dt', 'open', 'close', 'high', 'low', 'vol']]
+    print(kline.shape)
+    ka = KlineAnalyze(kline)
+    return ka
 
 
 def convert_to_list_v1(df):
@@ -18,8 +57,10 @@ def convert_to_list_v3(df):
 
 
 if __name__ == '__main__':
-    df = get_kline(ts_code="000001.SH", end_dt="2020-04-28 15:00:00", freq='D', asset='I')
-    # convert_to_list_v1(df)
+    df = get_kline(ts_code="000001.SH", end_dt="2020-04-28 15:00:00", freq='1min', asset='I')
+    print(df.shape)
+    convert_to_list_v1(df)
     convert_to_list_v2(df)
+    ka = use_large_df()