import pytse_client as tse
tickers = tse.download(symbols="all", write_to_csv=True)
tickers["ولملت"] # history

            date     open     high  ...     volume  count    close
0     2009-02-18   1050.0   1050.0  ...  330851245    800   1050.0
1     2009-02-21   1051.0   1076.0  ...  335334212   6457   1057.0
2     2009-02-22   1065.0   1074.0  ...    8435464    603   1055.0
3     2009-02-23   1066.0   1067.0  ...    8570222    937   1060.0
4     2009-02-25   1061.0   1064.0  ...    7434309    616   1060.0
...          ...      ...      ...  ...        ...    ...      ...
2323  2020-04-14   9322.0   9551.0  ...  105551315  13536   9400.0
2324  2020-04-15   9410.0   9815.0  ...  201457026  11322   9815.0
2325  2020-04-18  10283.0  10283.0  ...  142377245   8929  10283.0
2326  2020-04-19  10797.0  10797.0  ...  292985635  22208  10380.0
2327  2020-04-20  10600.0  11268.0  ...  295590437  16313  11268.0

import pytse_client as tse
tse.download(symbols="وبملت", write_to_csv=True)
tse.download(symbols="وبملت", write_to_csv=True, include_jdate=True)
tse.download(symbols=["وبملت", "ولملت"], write_to_csv=True)

from pytse_client import download_client_types_records  
  
if __name__ == '__main__':  

  records_dict = download_client_types_records("all")
  print(records_dict["فولاد"])
  #Output
date         individual_buy_count  ... individual_ownership_change
                            
2020-09-01                36298  ...                   -691857.0
2020-08-31                58185  ...                  83789408.0
2020-08-26                  461  ...                  21647730.0
2020-08-25                 1248  ...                  14716846.0
2020-08-24                38291  ...                -238454702.0
...                         ...  ...                         ...
2008-12-02                    7  ...                    -10000.0
2008-12-01                    8  ...                         0.0
2008-11-30                   10  ...                    -12781.0
2008-11-29                  116  ...                   4596856.0
2008-11-26                   14  ...                    -20000.0

[2518 rows x 17 columns]

from pytse_client import download_client_types_records
if __name__ == '__main__':  
   
  #Records are saved as a .csv file with the same name of ticer's 
  records = download_client_types_records("فولاد", write_to_csv=True)

import pytse_client as tse

tse.download(symbols="وبملت", write_to_csv=True)  # optional
ticker = tse.Ticker("وبملت")
print(ticker.history)  # سابقه قیمت سهم
print(ticker.client_types)  # حقیقی حقوقی
print(ticker.title)  # نام شرکت
بانك ملت (وبملت)
print(ticker.url)  # آدرس صفحه سهم
http://tsetmc.com/Loader.aspx?ParTree=151311&i=778253364357513
print(ticker.group_name)  # نام گروه
بانكها و موسسات اعتباري
print(ticker.eps)  # eps
2725.0
print(ticker.p_e_ratio)  # P/E
6.1478899082568805
print(ticker.group_p_e_ratio)  # group P/E
18.0
print(ticker.base_volume)  # حجم مبنا
7322431.0
print(ticker.last_price)  # آخرین معامله
17316
print(ticker.adj_close)  # قیمت پایانی
16753
print(ticker.best_supply_price)  # قیمت بهترین تقاضا
26700
print(ticker.best_supply_vol)  # حجم بهترین تقاضا
357062
print(ticker.best_demand_price)  # قیمت بهترین عرضه
26700
print(ticker.best_demand_vol)  # حجم بهترین عرضه
576608