# Path: fooltrader/api/technical.py
# def get_security_list(security_type='stock', exchanges=None, start_code=None, end_code=None,
#                       mode='simple', start_list_date=None, codes=None):
#     """
#     get security list.
# 
#     Parameters
#     ----------
#     security_type : str
#         {‘stock’, 'future'},default: stock
#     exchanges : str or list
#         ['sh', 'sz','nasdaq','nyse','amex','shfe','dce','zce'],default: ['sh','sz']
#     start_code : str
#         the start code,work with end,default:None
#         if using codes,it would be ignored
#     end_code : str
#         the end code,works with start,default:None
#         if using codes,it would be ignored
#     mode : str
#         whether parse more security info,{'simple','es'},default:'simple'
#     start_list_date : Timestamp str or Timestamp
#         the filter for start list date,default:None
#     codes : list
#         the exact codes to query,default:None
# 
#     Returns
#     -------
#     DataFrame
#         the security list
# 
#     """
#     df = pd.DataFrame()
#     if type(exchanges) == str:
#         exchanges = [exchanges]
# 
#     if not exchanges:
#         exchanges = SECURITY_TYPE_MAP_EXCHANGES[security_type]
# 
#     if security_type == 'index':
#         df = df.append(pd.DataFrame(CHINA_STOCK_SH_INDEX), ignore_index=True)
#         df = df.append(pd.DataFrame(CHINA_STOCK_SZ_INDEX), ignore_index=True)
#         df = df.append(pd.DataFrame(USA_STOCK_NASDAQ_INDEX), ignore_index=True)
#     else:
#         for exchange in exchanges:
#             the_path = get_security_list_path(security_type, exchange)
#             if os.path.exists(the_path):
#                 converters = None
# 
#                 if mode == 'es' and security_type == 'stock':
#                     converters = {'code': str,
#                                   'sinaIndustry': convert_to_list_if_need,
#                                   'sinaConcept': convert_to_list_if_need,
#                                   'sinaArea': convert_to_list_if_need}
#                 if converters:
#                     df = df.append(pd.read_csv(the_path, converters=converters), ignore_index=True)
#                 else:
#                     df = df.append(pd.read_csv(the_path, dtype=str), ignore_index=True)
# 
#     if not df.empty > 0:
#         df = df_for_date_range(df, start_date=start_list_date)
# 
#         df = df.set_index(df['code'], drop=False)
#         df = df.sort_index()
# 
#         if codes:
#             df = df[df["code"].isin(codes)]
#         elif start_code and end_code:
#             df = df[(df["code"] >= start_code) & (df["code"] <= end_code)]
# 
#         if security_type != 'cryptocurrency':
#             df = df.drop_duplicates(subset='code', keep='last')
# 
#     return df
# 
# Path: fooltrader/contract/files_contract.py
# def get_finance_path(item):
#     return os.path.join(get_finance_dir(item), "finance.csv")
# 
# Path: fooltrader/utils/utils.py
# def index_df_with_time(df, index='timestamp'):
#     df = df.set_index(df[index], drop=False)
#     df.index = pd.to_datetime(df.index)
#     df = df.sort_index()
#     return df


import io
import pandas as pd
import scrapy
from scrapy import Request
from scrapy import signals
from fooltrader.api.technical import get_security_list
from fooltrader.contract.files_contract import get_finance_path
from fooltrader.utils.utils import index_df_with_time
                "BVPS",
                "PB",
                "PE",
                "cumulativeDividendsPerShare",
                "dividendPayoutRatio",
                "longTermDebtToEquityRatio",
                "equityToAssetsRatio",
                "netMargin",
                "assetTurnover",
                "freeCashFlowPerShare",
                "currentRatio"]

            df['code'] = security_item['code']
            df['securityId'] = security_item['id']
            df['id'] = df[['securityId', 'reportDate']].apply(lambda x: '_'.join(x.astype(str)), axis=1)

            df = index_df_with_time(df, index='reportDate')

            df.fillna(0, inplace=True)

            df.to_csv(path, index=False)
        else:
            self.logger.exception(
                "get finance csv error:url={} content type={} body={}".format(response.url, content_type_header,
                                                                              response.body))

    @classmethod
    def from_crawler(cls, crawler, *args, **kwargs):
        spider = super(AmericaStockFinanceSpider, cls).from_crawler(crawler, *args, **kwargs)
        crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed)

# Path: fooltrader/api/technical.py
# def get_security_list(security_type='stock', exchanges=None, start_code=None, end_code=None,
#                       mode='simple', start_list_date=None, codes=None):
#     """
#     get security list.
# 
#     Parameters
#     ----------
#     security_type : str
#         {‘stock’, 'future'},default: stock
#     exchanges : str or list
#         ['sh', 'sz','nasdaq','nyse','amex','shfe','dce','zce'],default: ['sh','sz']
#     start_code : str
#         the start code,work with end,default:None
#         if using codes,it would be ignored
#     end_code : str
#         the end code,works with start,default:None
#         if using codes,it would be ignored
#     mode : str
#         whether parse more security info,{'simple','es'},default:'simple'
#     start_list_date : Timestamp str or Timestamp
#         the filter for start list date,default:None
#     codes : list
#         the exact codes to query,default:None
# 
#     Returns
#     -------
#     DataFrame
#         the security list
# 
#     """
#     df = pd.DataFrame()
#     if type(exchanges) == str:
#         exchanges = [exchanges]
# 
#     if not exchanges:
#         exchanges = SECURITY_TYPE_MAP_EXCHANGES[security_type]
# 
#     if security_type == 'index':
#         df = df.append(pd.DataFrame(CHINA_STOCK_SH_INDEX), ignore_index=True)
#         df = df.append(pd.DataFrame(CHINA_STOCK_SZ_INDEX), ignore_index=True)
#         df = df.append(pd.DataFrame(USA_STOCK_NASDAQ_INDEX), ignore_index=True)
#     else:
#         for exchange in exchanges:
#             the_path = get_security_list_path(security_type, exchange)
#             if os.path.exists(the_path):
#                 converters = None
# 
#                 if mode == 'es' and security_type == 'stock':
#                     converters = {'code': str,
#                                   'sinaIndustry': convert_to_list_if_need,
#                                   'sinaConcept': convert_to_list_if_need,
#                                   'sinaArea': convert_to_list_if_need}
#                 if converters:
#                     df = df.append(pd.read_csv(the_path, converters=converters), ignore_index=True)
#                 else:
#                     df = df.append(pd.read_csv(the_path, dtype=str), ignore_index=True)
# 
#     if not df.empty > 0:
#         df = df_for_date_range(df, start_date=start_list_date)
# 
#         df = df.set_index(df['code'], drop=False)
#         df = df.sort_index()
# 
#         if codes:
#             df = df[df["code"].isin(codes)]
#         elif start_code and end_code:
#             df = df[(df["code"] >= start_code) & (df["code"] <= end_code)]
# 
#         if security_type != 'cryptocurrency':
#             df = df.drop_duplicates(subset='code', keep='last')
# 
#     return df
# 
# Path: fooltrader/contract/files_contract.py
# def get_finance_path(item):
#     return os.path.join(get_finance_dir(item), "finance.csv")
# 
# Path: fooltrader/utils/utils.py
# def index_df_with_time(df, index='timestamp'):
#     df = df.set_index(df[index], drop=False)
#     df.index = pd.to_datetime(df.index)
#     df = df.sort_index()
#     return df


# Path: fooltrader/spiders/america/america_stock_finance_spider.py
import io
import pandas as pd
import scrapy
from scrapy import Request
from scrapy import signals
from fooltrader.api.technical import get_security_list
from fooltrader.contract.files_contract import get_finance_path
from fooltrader.utils.utils import index_df_with_time

                "BVPS",
                "PB",
                "PE",
                "cumulativeDividendsPerShare",
                "dividendPayoutRatio",
                "longTermDebtToEquityRatio",
                "equityToAssetsRatio",
                "netMargin",
                "assetTurnover",
                "freeCashFlowPerShare",
                "currentRatio"]

            df['code'] = security_item['code']
            df['securityId'] = security_item['id']
            df['id'] = df[['securityId', 'reportDate']].apply(lambda x: '_'.join(x.astype(str)), axis=1)

            df = index_df_with_time(df, index='reportDate')

            df.fillna(0, inplace=True)

            df.to_csv(path, index=False)
        else:
            self.logger.exception(
                "get finance csv error:url={} content type={} body={}".format(response.url, content_type_header,
                                                                              response.body))

    @classmethod
    def from_crawler(cls, crawler, *args, **kwargs):
        spider = super(AmericaStockFinanceSpider, cls).from_crawler(crawler, *args, **kwargs)
        crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed)

        return spider