def upsert_chn_stock_name():
tushare = TuShareProData()
df = tushare.stock_basic(exchange="SSE", cols=["ts_code", "name"])
for idx, row in df.iterrows():
symbol = row["ts_code"].replace("SH", "SS") # YAHOO 上海股市的后缀是 SS
symbol_obj = FinancialInstrumentSymbol(symbol=symbol,
full_name=row['name'])
upsert_document(symbol_obj, False)
df = tushare.stock_basic(exchange="SZSE", cols=["ts_code", "name"])
for idx, row in df.iterrows():
symbol = row["ts_code"] # 深圳股市的后缀是相同的
symbol_obj = FinancialInstrumentSymbol(symbol=symbol,
full_name=row['name'])
upsert_document(symbol_obj, False)
def find_equity(symbol_or_name: str) -> Optional[FinancialInstrumentSymbol]:
""" 使用 symbol or name 在 FinancialInstrumentSymbol 中查询匹配的 symbol
前提是该 symbol 已经填入过 yahoo 的基本信息内容（前置的环境准备）
"""
ls_symbol_guess = [
f"{symbol_or_name}{suffix}" for suffix in ["", ".SS", ".SZ", ".HK"]
]
raw_query = {
"_id": {
"$in": ls_symbol_guess
},
"$or": [{
"chn_name": symbol_or_name
}, {
"eng_name": symbol_or_name
}],
"info_from_yahoo": {
"$exists": True
}
}
symbol_obj = FinancialInstrumentSymbol.objects(
(Q(symbol__in=ls_symbol_guess) | Q(chn_name=symbol_or_name)
| Q(eng_name=symbol_or_name) | Q(full_name=symbol_or_name))
& Q(info_from_yahoo__exists=True)).first()
if symbol_obj is not None:
return symbol_obj
else:
return None
def upsert_yahoo_recommend(symbol: str):
yahoo_ts = yFinanceData()
try:
df = yahoo_ts.recommendations(symbol_to_yahoo_symbol(symbol))
logger.info(f"upsert_yahoo_recommend : {symbol}-{df.shape}")
for t, row in df.iterrows():
firm = GlobalEntity(entity_id=row["Firm"])
dict_info = {
"t":
t,
"symbol":
FinancialInstrumentSymbol(symbol=symbol),
"firm":
firm,
"uid":
md5_str(f"{t.isoformat()}-{symbol}-{row['Firm']}"),
"to_grade":
None if row["To Grade"] == "" else row["To Grade"],
"from_grade":
None if row["From Grade"] == "" else row["From Grade"],
"action":
None if row["Action"] == "" else row["Action"]
}
upsert_document(firm)
upsert_document(FinancialInstrumentRecommendInYahoo(**dict_info),
False)
except Exception as ex:
logger.error(ex)
return
def kw_search(rlt_path: str,
batch_action_uuid: str,
action_uuid: str,
save_doc: bool = True) -> List[Article]:
rlt_articles: List[Article] = list()
rlt_files = glob.glob(os.path.join(rlt_path, "*_kw_search_result.csv"))
if rlt_files:
for i, f in enumerate(rlt_files):
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f,
header=0,
parse_dates=["extract_t"])
kw, df_rlt = SeekingAlphaDataProcess.proc_kw_search(
df_from_csv)
# print(df_rlt)
for idx, row in df_rlt.iterrows():
dict_row = row.to_dict()
dict_article = dict()
if not pd.isna(dict_row.get("title", np.NaN)):
dict_article["title"] = dict_row["title"]
if not pd.isna(dict_row.get("news_url", np.NaN)):
article_url = furl(dict_row["news_url"])
abs_url_str = f"{article_url.origin}{article_url.path}"
# 仅 seeking alpha 内部的链接考虑去掉参数项，其他站点的行文，不确定url参数是否也构成了 unique 的情况
if abs_url_str.find("seekingalpha") > 0:
dict_article["full_text_url"] = abs_url_str
dict_article["uuid"] = md5_str(abs_url_str)
else:
dict_article["full_text_url"] = article_url.url
dict_article["uuid"] = md5_str(article_url.url)
if dict_row.get("publish_time", pd.NaT) is not pd.NaT:
dict_article["publish_time"] = dict_row[
"publish_time"].to_pydatetime()
if not pd.isna(dict_row.get("symbols", np.NaN)):
symbols = [
x.strip()
for x in dict_row.get("symbols").split(",")
]
ls_related_symbols: List[FinancialInstrumentSymbol] = [
FinancialInstrumentSymbol(symbol=x)
for x in symbols
]
if save_doc:
map(upsert_document, ls_related_symbols)
dict_article["related_symbols"] = ls_related_symbols
dict_article["from_searching_phase"] = SearchingPhrase(
searching_phrase=kw)
dict_article["engine_site"] = "SeekingAlpha"
dict_article["channel_in_site"] = "Search"
dict_article["batch_action_uuid"] = batch_action_uuid
article = Article(**dict_article)
rlt_articles.append(article)
if save_doc:
upsert_document(article, True)
return rlt_articles
def upsert_yahoo_financial_instrument_info(symbol: str,
insert_only: bool = False):
if insert_only:
symbol_obj = FinancialInstrumentSymbol.objects(
symbol=symbol, info_from_yahoo__exists=True).first()
if symbol_obj is not None:
return
set_http_proxy()
import yfinance as yf
symbol_data = yf.Ticker(symbol_to_yahoo_symbol(symbol))
try:
dict_info = symbol_data.info
except Exception as ex:
logger.error(f"api exception when get {symbol} data , {ex}")
return
# remove : companyOfficers = # <class 'list'>
if "companyOfficers" in dict_info:
del dict_info["companyOfficers"]
# remove symbol , 重复数据
if "symbol" in dict_info:
del dict_info["symbol"]
# rename "52WeekChange" -> "fiftyTwoWeekChange" , python 变量首字符不允许数字
if "52WeekChange" in dict_info:
dict_info["fiftyTwoWeekChange"] = dict_info["52WeekChange"]
del dict_info["52WeekChange"]
# rename "yield" -> "yieldVal" , python 关键字
if "yield" in dict_info:
dict_info["yieldVal"] = dict_info["yield"]
del dict_info["yield"]
if "err" in dict_info:
del dict_info["err"]
dict_info["mtime"] = datetime.now()
fin_symbol = FinancialInstrumentSymbol(symbol=symbol)
fin_symbol.info_from_yahoo = SymbolDataFromYahoo(**dict_info)
upsert_document(fin_symbol)
def upsert_splits(symbol: str, start_t: datetime):
yahoo_ts = yFinanceData()
try:
df = yahoo_ts.splits(symbol_to_yahoo_symbol(symbol), start=start_t)
logger.info(f"upsert_splits : {symbol}-{df.shape}")
for t, row in df.iterrows():
dict_info = {
"t": t,
"split": row["Stock Splits"],
"symbol": FinancialInstrumentSymbol(symbol=symbol),
"uid": md5_str(f"{t.isoformat()}-{symbol}")
}
upsert_document(FinancialInstrumentDividends(**dict_info))
except:
return
def upsert_us_stock_nametable_and_chn_name():
akshare_data = akshareData()
df_us_stocks = akshare_data.get_us_stock_name()
logger.info(f"table shape:{df_us_stocks.shape}")
for idx, row in df_us_stocks.iterrows():
if idx <= 9425:
continue
symbol = row["symbol"]
chn_name = row["cname"]
eng_name = row["name"]
logger.info(f"[{idx}] {symbol} - {chn_name} - {eng_name}")
fin_data = FinancialInstrumentSymbol(symbol=symbol,
full_name=eng_name,
eng_name=eng_name,
chn_name=chn_name)
upsert_document(fin_data)
upsert_yahoo_financial_instrument_info(symbol, True)
def upsert_yahoo_symbol_holder(symbol: str):
import yfinance as yf
symbol_data = yf.Ticker(symbol_to_yahoo_symbol(symbol))
try:
df = symbol_data.institutional_holders
logger.info(f"upsert_yahoo_symbol_holder : {symbol} {df.shape}")
for idx, row in df.iterrows():
t = row["Date Reported"]
dict_info = {
"t": t,
"symbol": FinancialInstrumentSymbol(symbol=symbol),
"holder": GlobalEntity(entity_id=row["Holder"]),
"shares": row["Shares"],
"value": row["Value"],
"percentage_out": row["% Out"],
"uid": md5_str(f"{t.isoformat()}-{symbol}-{row['Holder']}")
}
upsert_document(FinancialInstrumentHolders(**dict_info), True)
except Exception as ex:
logger.error(f"api exception when get data , {ex}")
def upsert_daily_market_data(symbol: str, start_t: datetime):
yahoo_ts = yFinanceData()
df = yahoo_ts.history(symbol_to_yahoo_symbol(symbol), start=start_t)
logger.info(f"upsert_daily_market_data : {symbol}-{df.shape}")
df["fifty_two_week_high"] = df["Close"].rolling(window=244).max()
df["fifty_two_week_low"] = df["Close"].rolling(window=244).min()
for t, row in df.iterrows():
dict_info = {
"t": t,
"open": row["Open"],
"high": row["High"],
"low": row["Low"],
"close": row["Close"],
"volume": row["Volume"],
"dividends": row["Dividends"],
"splits": row["Stock Splits"],
"fifty_two_week_low": row["fifty_two_week_low"],
"fifty_two_week_high": row["fifty_two_week_high"],
"symbol": FinancialInstrumentSymbol(symbol=symbol),
"uid": md5_str(f"{t.isoformat()}-{symbol}")
}
upsert_document(FinancialInstrumentDailyMarketData(**dict_info))
def upsert_yahoo_earning_analysis(symbol: str):
"""see https://finance.yahoo.com/quote/MSFT/analysis?p=MSFT"""
import yfinance as yf
symbol_data = yf.Ticker(symbol_to_yahoo_symbol(symbol))
try:
df = symbol_data.calendar.T
logger.info(f"upsert_yahoo_earning_analysis : {symbol} {df.shape}")
for idx, row in df.iterrows():
t = row["Earnings Date"]
dict_info = {
"t": t,
"symbol": FinancialInstrumentSymbol(symbol=symbol),
"earnings_average": _convert_non_t_v(row["Earnings Average"]),
"earnings_low": _convert_non_t_v(row["Earnings Low"]),
"earnings_high": _convert_non_t_v(row["Earnings High"]),
"revenue_average": _convert_non_t_v(row["Revenue Average"]),
"revenue_low": _convert_non_t_v(row["Revenue Low"]),
"revenue_high": _convert_non_t_v(row["Revenue High"]),
"uid": md5_str(f"{t.isoformat()}-{symbol}")
}
upsert_document(FinancialInstrumentCalendarFromYahoo(**dict_info),
True)
except Exception as ex:
logger.error(f"api exception when get data , {ex}")
def symbol_analysis_report(rlt_path: str,
batch_action_uuid: str,
action_uuid: str,
save_doc: bool = True) -> List[Article]:
rlt_articles: List[Article] = list()
rlt_files = glob.glob(os.path.join(rlt_path, "*_symbol_analysis.csv"))
if rlt_files:
for i, f in enumerate(rlt_files):
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f, header=0)
df_rlt = EastMoneyDataProcess.proc_stock_analysis(df_from_csv)
if df_rlt is not None:
for idx, row in df_rlt.iterrows():
dict_row = row.to_dict()
dict_article = dict()
if not pd.isna(dict_row.get("article", np.NaN)):
dict_article["title"] = dict_row["article"]
if not pd.isna(dict_row.get("article_url", np.NaN)):
dict_article["full_text_url"] = dict_row[
"article_url"]
dict_article["uuid"] = md5_str(
dict_row["article_url"])
if not pd.isna(dict_row.get("org_url", np.NaN)):
# 暂时先把机构名直接存在 seeking alpha 的 author 数据中，这样画图方便一些
author_url: str = dict_row["org_url"]
author_id = dict_row["org"]
# NO author_name extracted!!
# author_name = None
# if not pd.isna(dict_row.get("author", np.NaN)):
# author_name = dict_row["author"]
author = AuthorInSeekingAlpha(author_id=author_id,
url=author_url)
if not pd.isna(
dict_row.get("reports_within_one_month",
np.NaN)):
author.articles = dict_row[
"reports_within_one_month"]
dict_article["seeking_alpha_author"] = author
if save_doc:
upsert_document(author, True)
if not pd.isna(dict_row.get("rating", np.NaN)):
dict_article["rating"] = dict_row["rating"]
if not pd.isna(dict_row.get("rating_chg", np.NaN)):
dict_article["rating_change"] = dict_row[
"rating_chg"]
if not pd.isna(dict_row.get("pred_2020_ret", np.NaN)):
dict_article["pred_ret_this_yr"] = dict_row[
"pred_2020_ret"]
if not pd.isna(dict_row.get("pred_2020_pe", np.NaN)):
dict_article["pred_pe_this_yr"] = dict_row[
"pred_2020_pe"]
if not pd.isna(dict_row.get("pred_2021_pe", np.NaN)):
dict_article["pred_pe_next_yr"] = dict_row[
"pred_2021_pe"]
if not pd.isna(dict_row.get("pred_2021_ret", np.NaN)):
dict_article["pred_ret_next_yr"] = dict_row[
"pred_2021_ret"]
if dict_row.get("report_date", pd.NaT) is not pd.NaT:
dict_article["publish_time"] = dict_row[
"report_date"].to_pydatetime()
symbol = dict_row["symbol"]
ls_related_symbols: List[FinancialInstrumentSymbol] = [
FinancialInstrumentSymbol(symbol=symbol)
]
if save_doc:
ls_related_symbols[0].full_name = dict_row[
"symbol_name"]
upsert_document(ls_related_symbols[0])
dict_article["related_symbols"] = ls_related_symbols
dict_article["engine_site"] = "EastMoney"
dict_article["channel_in_site"] = "StockAnalysis"
dict_article["batch_action_uuid"] = batch_action_uuid
article = Article(**dict_article)
rlt_articles.append(article)
if save_doc:
upsert_document(article, True)
return rlt_articles
def add_equity_sentiment_actions(equity_symbol: FinancialInstrumentSymbol,
batch_action_uuid: str) -> List[str]:
assert equity_symbol is not None
# NOTE: equity_symbol 的数据内容可能并不全，这里调用 reload 以便于能够加载全部数据
equity_symbol.reload()
ls_action_uuid_rlt: List[str] = list()
# 以 company name 搜 google news
kw = google_kws_or([
corp_name_remove_stop_words(equity_symbol.chn_name),
corp_name_remove_stop_words(equity_symbol.eng_name),
equity_symbol.symbol
])
act_uuid = add_one_equity_search_action(
act_uuid=generate_uuid(),
batch_action_uuid=batch_action_uuid,
generate_func=add_equity_sentiment_actions,
finished_triggered_func=gs_nlp_process,
equity_symbol=equity_symbol,
webapp_cfg=AllGeneralWebApp.GOOGLE_NEWS_TAB.value,
kw=kw,
additional_kw="",
category="CorpNews",
sub_category="GoogleNews",
action_description=f"直接搜索公司相关新闻，避免财经站点的新闻采编出现遗漏的情况,kw={kw}")
ls_action_uuid_rlt.append(act_uuid)
# 有关评级的搜索
ls_recommend = FinancialInstrumentRecommendInYahoo.objects(
symbol=equity_symbol.symbol).order_by("-t")[:10]
set_recommend_corp_name: Set[str] = set()
for recommend in ls_recommend:
firm = recommend.firm.fetch()
recommend_corp_name = firm.name
if not recommend_corp_name:
recommend_corp_name = firm.entity_id
if recommend_corp_name in set_recommend_corp_name:
continue
set_recommend_corp_name.add(recommend_corp_name)
kw = google_kws_and([
equity_symbol.symbol,
corp_name_remove_stop_words(recommend_corp_name)
])
description = f"'{equity_symbol.symbol}' achieve grade '{recommend.to_grade}'(pre '{recommend.from_grade}') by '{recommend_corp_name}' at {recommend.t}"
act_uuid = add_one_equity_search_action(
act_uuid=generate_uuid(),
batch_action_uuid=batch_action_uuid,
generate_func=add_equity_sentiment_actions,
finished_triggered_func=gs_nlp_process,
equity_symbol=equity_symbol,
webapp_cfg=AllGeneralWebApp.GOOGLE_NEWS_TAB.value,
kw=kw,
additional_kw="",
category="CorpNews",
sub_category="CorpRecommendation",
action_description=description)
ls_action_uuid_rlt.append(act_uuid)
# print(f"create google news query : {recommend.firm} + {equity_symbol.symbol}")
# 有关机构持有人的搜索
ls_holders = FinancialInstrumentHolders.objects(
symbol=equity_symbol.symbol).order_by("-t")[:10]
set_holder_corp_name: Set[str] = set()
for holder in ls_holders:
holder_corp_name = holder.holder.pk
if holder_corp_name in set_holder_corp_name:
continue
kw = google_kws_and([
equity_symbol.symbol,
corp_name_remove_stop_words(holder_corp_name)
])
description = f"{holder_corp_name} holds '{equity_symbol.symbol}' {holder.shares} shares , market_value={holder.value} , at {holder.t}"
act_uuid = add_one_equity_search_action(
act_uuid=generate_uuid(),
batch_action_uuid=batch_action_uuid,
generate_func=add_equity_sentiment_actions,
finished_triggered_func=gs_nlp_process,
equity_symbol=equity_symbol,
webapp_cfg=AllGeneralWebApp.GOOGLE_NEWS_TAB.value,
kw=kw,
additional_kw="",
category="CorpNews",
sub_category="InstitutionHolderOpinion",
action_description=description)
ls_action_uuid_rlt.append(act_uuid)
# 在雪球中用不同的关键词搜索
# for additional_kw in [""] + CHN_KW_EQUITY_RELATED: # 需叠加一个空白关键词，以便于拿到所有的新闻内容
# cfg_obj = AllGeneralWebApp.XUEQIU_NEWS.value
# act_uuid = add_one_equity_search_action(act_uuid=generate_uuid(), batch_action_uuid=batch_action_uuid,
# generate_func=add_equity_sentiment_actions,
# finished_triggered_func=gs_nlp_process,
# equity_symbol=equity_symbol,
# webapp_cfg=cfg_obj,
# kw=cfg_obj.symbol_func(equity_symbol.symbol),
# additional_kw=additional_kw,
# category="CorpNews", sub_category="ChnXueqiu",
# action_description=f"Search {equity_symbol.symbol} news with keyword '{additional_kw}' in xueqiu"
# )
# ls_action_uuid_rlt.append(act_uuid)
return ls_action_uuid_rlt
def symbol_summary(
rlt_path: str,
batch_action_uuid: str,
action_uuid: str,
save_doc: bool = True
) -> Tuple[List[FinancialInstrumentSymbol], List[Article]]:
rlt_articles: List[Article] = list()
rlt_symbols: List[FinancialInstrumentSymbol] = list()
# region symbol analysis
rlt_files = glob.glob(os.path.join(rlt_path, "*_symbol_analysis.csv"))
if rlt_files:
for i, f in enumerate(rlt_files):
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f,
header=0,
parse_dates=["extract_t"])
symbol, df_rlt = SeekingAlphaDataProcess.proc_symbol_analysis(
df_from_csv)
if df_rlt is not None:
for idx, row in df_rlt.iterrows():
dict_row = row.to_dict()
dict_article = dict()
if not pd.isna(dict_row.get("title", np.NaN)):
dict_article["title"] = dict_row["title"]
if not pd.isna(dict_row.get("article_url", np.NaN)):
article_url = furl(dict_row["article_url"])
abs_url_str = f"{article_url.origin}{article_url.path}"
dict_article["full_text_url"] = abs_url_str
dict_article["uuid"] = md5_str(abs_url_str)
if not pd.isna(dict_row.get("author_url", np.NaN)):
author_url: str = dict_row["author_url"]
author_id = dict_row["author_id"]
# NO author_name extracted!!
# author_name = None
# if not pd.isna(dict_row.get("author", np.NaN)):
# author_name = dict_row["author"]
author = AuthorInSeekingAlpha(author_id=author_id,
url=author_url)
dict_article["seeking_alpha_author"] = author
if save_doc:
upsert_document(author, True)
if not pd.isna(dict_row.get("rating", np.NaN)):
dict_article["rating"] = dict_row["rating"]
if dict_row.get("publish_time", pd.NaT) is not pd.NaT:
dict_article["publish_time"] = dict_row[
"publish_time"].to_pydatetime()
if not pd.isna(dict_row.get("comments", np.NaN)):
dict_article[
"seeking_alpha_extra"] = SeekingAlphaArticleExtra(
comments=dict_row["comments"])
ls_related_symbols: List[FinancialInstrumentSymbol] = [
FinancialInstrumentSymbol(symbol=symbol)
]
if save_doc:
upsert_document(ls_related_symbols[0])
dict_article["related_symbols"] = ls_related_symbols
dict_article["engine_site"] = "SeekingAlpha"
dict_article["channel_in_site"] = "analysis"
dict_article["batch_action_uuid"] = batch_action_uuid
article = Article(**dict_article)
rlt_articles.append(article)
if save_doc:
upsert_document(article, True)
# endregion
# region symbol news
rlt_files = glob.glob(os.path.join(rlt_path, "*_symbol_news.csv"))
for i, f in enumerate(rlt_files):
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f, header=0, parse_dates=["extract_t"])
symbol, df_rlt = SeekingAlphaDataProcess.proc_symbol_news(
df_from_csv)
for idx, row in df_rlt.iterrows():
dict_row = row.to_dict()
dict_article = dict()
if not pd.isna(dict_row.get("title", np.NaN)):
dict_article["title"] = dict_row["title"]
if not pd.isna(dict_row.get("news_url", np.NaN)):
article_url = furl(dict_row["news_url"])
abs_url_str = f"{article_url.origin}{article_url.path}"
# 仅 seeking alpha 内部的链接考虑去掉参数项，其他站点的行文，不确定url参数是否也构成了 unique 的情况
if abs_url_str.find("seekingalpha") > 0:
dict_article["full_text_url"] = abs_url_str
dict_article["uuid"] = md5_str(abs_url_str)
else:
dict_article["full_text_url"] = article_url.url
dict_article["uuid"] = md5_str(article_url.url)
if dict_row.get("publish_time", pd.NaT) is not pd.NaT:
dict_article["publish_time"] = dict_row[
"publish_time"].to_pydatetime()
if not pd.isna(dict_row.get("comments", np.NaN)):
dict_article[
"seeking_alpha_extra"] = SeekingAlphaArticleExtra(
comments=dict_row["comments"])
ls_related_symbols: List[FinancialInstrumentSymbol] = [
FinancialInstrumentSymbol(symbol=symbol)
]
if save_doc:
upsert_document(ls_related_symbols[0])
dict_article["related_symbols"] = ls_related_symbols
dict_article["engine_site"] = "SeekingAlpha"
dict_article["batch_action_uuid"] = batch_action_uuid
if not pd.isna(dict_row.get("orig_source", np.NaN)):
dict_article["channel_in_site"] = dict_row["orig_source"]
article = Article(**dict_article)
rlt_articles.append(article)
if save_doc:
upsert_document(article, True)
# endregion
# region symbol info
rlt_files = glob.glob(os.path.join(rlt_path,
"*_symbol_indicators.csv"))
if rlt_files:
for f in rlt_files:
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f, header=0)
dict_symbol_info = SeekingAlphaDataProcess.proc_symbol_indicator(
df_from_csv)
if not dict_symbol_info:
continue
if "symbol" not in dict_symbol_info:
continue
symbol = FinancialInstrumentSymbol(
symbol=dict_symbol_info.get("symbol"),
info_from_seeking_alpha=SymbolInfoBySeekingAlpha(
followers=dict_symbol_info.get("followers", None),
high_52wk=dict_symbol_info.get("52wk high", None),
low_52wk=dict_symbol_info.get("52wk low", None),
eps_fwd=dict_symbol_info.get("EPS (FWD)", None),
pe_fwd=dict_symbol_info.get("PE (FWD)", None),
yield_fwd=dict_symbol_info.get("Yield (FWD)", None),
div_rate_fwd=dict_symbol_info.get(
"Div Rate (FWD)", None),
mkt_cap=dict_symbol_info.get("Market Cap", None),
volume=dict_symbol_info.get("Volume", None),
mtime=datetime.now()))
if save_doc:
upsert_document(symbol, True)
rlt_symbols.append(symbol)
# endregion
return rlt_symbols, rlt_articles
def all_chn_symbols() -> List[str]:
symbols = FinancialInstrumentSymbol.objects(
info_from_yahoo__market="cn_market").order_by("symbol").only("symbol")
return [x.symbol for x in symbols]
def author_detail(
rlt_path: str,
batch_action_uuid: str,
action_uuid: str,
save_doc: bool = True
) -> Tuple[List[AuthorInSeekingAlpha], List[Article]]:
rlt_articles: List[Article] = list()
rlt_authors: List[AuthorInSeekingAlpha] = list()
# region author articles
rlt_files = glob.glob(os.path.join(rlt_path, "*_author_articles.csv"))
if rlt_files:
for i, f in enumerate(rlt_files):
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f,
header=0,
parse_dates=["extract_t"])
author_id, df_rlt = SeekingAlphaDataProcess.proc_author_articles(
df_from_csv)
if df_rlt is not None:
for idx, row in df_rlt.iterrows():
dict_row = row.to_dict()
dict_article = dict()
if not pd.isna(dict_row.get("title", np.NaN)):
dict_article["title"] = dict_row["title"]
if not pd.isna(dict_row.get("news_url", np.NaN)):
article_url = furl(dict_row["news_url"])
abs_url_str = f"{article_url.origin}{article_url.path}"
dict_article["full_text_url"] = abs_url_str
dict_article["uuid"] = md5_str(abs_url_str)
if dict_row.get("publish_time", pd.NaT) is not pd.NaT:
dict_article["publish_time"] = dict_row[
"publish_time"].to_pydatetime()
if not pd.isna(dict_row.get("comments", np.NaN)):
dict_article[
"seeking_alpha_extra"] = SeekingAlphaArticleExtra(
comments=dict_row["comments"])
if not pd.isna(dict_row.get("symbols", np.NaN)):
ls_related_symbols: List[
FinancialInstrumentSymbol] = [
FinancialInstrumentSymbol(symbol=s.strip())
for s in dict_row["symbols"].split(",")
]
if ls_related_symbols:
for symbol in ls_related_symbols:
if save_doc:
upsert_document(symbol, True)
dict_article[
"related_symbols"] = ls_related_symbols
dict_article["engine_site"] = "SeekingAlpha"
dict_article["batch_action_uuid"] = batch_action_uuid
article = Article(**dict_article)
rlt_articles.append(article)
if save_doc:
upsert_document(article, True)
# endregion
# region author info
rlt_files = glob.glob(os.path.join(rlt_path, "*_author_info.csv"))
if rlt_files:
for f in rlt_files:
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f, header=0)
dict_author_info = SeekingAlphaDataProcess.proc_author_info(
df_from_csv)
if not dict_author_info:
continue
if "author" not in dict_author_info:
continue
author = AuthorInSeekingAlpha(
author_id=dict_author_info.get("author"),
intro=dict_author_info.get("author_intro", ""),
articles=dict_author_info.get("articles", None),
picks=dict_author_info.get("authors_picks", None),
blog_posts=dict_author_info.get("instablogs", None),
comments=dict_author_info.get("comments", None),
stock_talks=dict_author_info.get("stocktalks", None),
likes=dict_author_info.get("likes", None),
followers=dict_author_info.get("followers", None),
following=dict_author_info.get("following", None),
mtime=datetime.now(),
batch_action_uuid=batch_action_uuid)
if save_doc:
upsert_document(author, True)
rlt_authors.append(author)
# endregion
return rlt_authors, rlt_articles
def set_hk_stock_chn_name():
for symbol, chn_name in ALL_HK_SYMBOLS_WITH_NAME:
fin_data = FinancialInstrumentSymbol(symbol=symbol, full_name=chn_name)
upsert_document(fin_data)
def set_symbol_important_flag(symbol: str):
symbol_doc = FinancialInstrumentSymbol(symbol=symbol, important=True)
upsert_document(symbol_doc)
def create_equity_workflow(req: WorkflowRequest):
assert req.workflow_name in GSPredefinedWorkflow._value2member_map_
equity_entity = find_equity(req.entity_str)
if equity_entity is None:
wf_batch_uuid = md5_str(
f"{req.request_from_account}-{req.ctime.isoformat()}-{req.entity_str}"
)
doc_wf = TriggeredWebPagesCrawlWorkflow(
uuid=wf_batch_uuid,
workflow=PredefinedWorkflow(workflow_name=req.workflow_name),
para_begin=req.para_begin,
para_end=req.para_begin,
submit_account=req.request_from_account,
submit_type=WorkflowSubmitType.HotKey.value,
submit_time=req.ctime,
finish_or_error_flag=WorkflowStatusFlag.WithError.value,
error_msg=f"Can't find equity symbol or name by '{req.entity_str}'"
)
upsert_document(doc_wf, False)
return
# 找到了 entity， 生成 workflow 的内容
wf_batch_uuid = md5_str(
f"{equity_entity.symbol}-{req.workflow_name}-{req.para_begin}-{req.para_end}-{req.request_from_account}-{req.ctime.isoformat()}"
)
# 查询 workflow 预设的更新频率
# wf_freq = "D"
wf_freq = "1s"
workflow_def = PredefinedWorkflow.objects(
workflow_name=req.workflow_name).first()
if workflow_def is not None:
wf_freq = workflow_def.refresh_freq
# 找一下该 symbol 的 workflow 最近一次的执行时间(假定 Per Symbol + Per Account)
latest_workflow_inst = TriggeredWebPagesCrawlWorkflow.objects(
fin_instrument=equity_entity.symbol,
workflow=req.workflow_name,
submit_account=req.request_from_account,
finish_or_error_flag__in=[
WorkflowStatusFlag.WaitToRun.value,
WorkflowStatusFlag.SuccessFinished.value
]).order_by("-submit_time").first()
# 如果在同一个周期的，直接记录一条错误的记录内容
if latest_workflow_inst is not None and is_same_period(
latest_workflow_inst.submit_time, req.ctime, wf_freq):
logger.error(
f"Workflow(uuid={latest_workflow_inst.uuid},ctime='{latest_workflow_inst.submit_time}') in the same period is existed."
)
doc_wf = TriggeredWebPagesCrawlWorkflow(
uuid=wf_batch_uuid,
main_entity_type=EntityType.Equity.value,
fin_instrument=FinancialInstrumentSymbol(
symbol=equity_entity.symbol),
workflow=PredefinedWorkflow(workflow_name=req.workflow_name),
para_begin=req.para_begin,
para_end=req.para_begin,
submit_account=req.request_from_account,
submit_type=WorkflowSubmitType.HotKey.value,
submit_time=req.ctime,
finish_or_error_flag=WorkflowStatusFlag.WithError.value,
error_msg=
f"workflow '{req.workflow_name}'({equity_entity.symbol}) is executed at {latest_workflow_inst.submit_time} . No need to rerun now."
)
upsert_document(doc_wf, False)
return
# 创建一个workflow
doc_wf = TriggeredWebPagesCrawlWorkflow(
uuid=wf_batch_uuid,
main_entity_type=EntityType.Equity.value,
fin_instrument=FinancialInstrumentSymbol(symbol=equity_entity.symbol),
workflow=PredefinedWorkflow(workflow_name=req.workflow_name),
para_begin=req.para_begin,
para_end=req.para_begin,
submit_account=req.request_from_account,
submit_type=WorkflowSubmitType.HotKey.value,
submit_time=req.ctime,
finish_or_error_flag=WorkflowStatusFlag.WaitToRun.value)
upsert_document(doc_wf, False)
# 创建 batch action
doc_batch_action = RPABatchAction(
batch_id=wf_batch_uuid,
is_dynamic_batch=True,
from_function=cls_to_str(create_equity_workflow),
ctime=req.ctime,
status=ActionStatusFlag.WaitingForRun.value)
upsert_document(doc_batch_action, False)
# 依次调用 action generator 函数
# NOTE ： 这里是直接访问 diction ， 以后改为调用函数，就可以支持 register 的功能
for func in WORKFLOW_NAME_TO_ACTION_GENERATORS.get(req.workflow_name, []):
func(equity_entity, wf_batch_uuid)
logger.info(f"Batch action '{wf_batch_uuid}' is created.")
def column_articles(rlt_path: str,
batch_action_uuid: str,
action_uuid: str,
save_doc: bool = True) -> List[Article]:
rlt_articles = list()
# region articles
rlt_files = glob.glob(os.path.join(rlt_path, "*_articles.csv"))
if rlt_files:
for f in rlt_files:
logger.info(f"proecess file : {f} ")
df_from_csv = pd.read_csv(f,
header=0,
parse_dates=["extract_t"])
kw, df_rlt = SeekingAlphaDataProcess.proc_article_data(
df_from_csv)
for idx, row in df_rlt.iterrows():
dict_row = row.to_dict()
dict_article = dict() # for Article
if not pd.isna(dict_row.get("title", np.NaN)):
dict_article["title"] = dict_row["title"]
if not pd.isna(dict_row.get("author_url", np.NaN)):
author_url: str = dict_row["author_url"]
author_id = author_url.split("/")[-1]
dict_article[
"seeking_alpha_author"] = AuthorInSeekingAlpha(
author_id=author_id, url=author_url)
if not pd.isna(dict_row.get("article_url", np.NaN)):
article_url = furl(dict_row["article_url"])
abs_url_str = f"{article_url.origin}{article_url.path}"
dict_article["full_text_url"] = abs_url_str
dict_article["uuid"] = md5_str(abs_url_str)
if dict_row.get("publish_time", pd.NaT) is not pd.NaT:
dict_article["publish_time"] = dict_row[
"publish_time"].to_pydatetime()
dict_article["engine_site"] = "SeekingAlpha"
ls_related_symbols: List[FinancialInstrumentSymbol] = list(
)
for symbol_key_pair in [
("related_symbol1", "related_symbol1_fullname"),
("related_symbol2", "related_symbol2_fullname"),
("related_symbol3", "related_symbol3_fullname")
]:
if not pd.isna(dict_row.get(
symbol_key_pair[0], np.NaN)) and not pd.isna(
dict_row.get(symbol_key_pair[1], np.NaN)):
fin_instrument_symbol = FinancialInstrumentSymbol(
symbol=dict_row[symbol_key_pair[0]],
full_name=dict_row[symbol_key_pair[1]],
batch_action_uuid=batch_action_uuid)
ls_related_symbols.append(fin_instrument_symbol)
# ListField(ReferenceField(FinancialInstrumentSymbol)) 似乎不会级联保存，这里创建的时候同时保存
if save_doc:
upsert_document(fin_instrument_symbol, True)
if ls_related_symbols:
dict_article["related_symbols"] = ls_related_symbols
if not pd.isna(dict_row.get("comments", np.NaN)):
dict_article[
"seeking_alpha_extra"] = SeekingAlphaArticleExtra(
comments=dict_row["comments"])
dict_article["channel_in_site"] = kw
dict_article["batch_action_uuid"] = batch_action_uuid
article = Article(**dict_article)
rlt_articles.append(article)
if save_doc:
upsert_document(article, True)
# endregion
return rlt_articles