class StockInfoSpyder(Spyder):
def __init__(self, database_name, collection_name):
super(StockInfoSpyder, self).__init__()
self.db_obj = Database()
self.col_basic_info = self.db_obj.get_collection(database_name, collection_name)
self.database_name = database_name
self.collection_name = collection_name
def get_stock_code_info(self):
stock_info_df = ak.stock_info_a_code_name() # 获取所有A股code和name
stock_symbol_code = ak.stock_zh_a_spot().get(["symbol", "code"]) # 获取A股所有股票的symbol和code
for _id in range(stock_info_df.shape[0]):
_symbol = stock_symbol_code[stock_symbol_code.code == stock_info_df.iloc[_id].code].symbol.values
if len(_symbol) != 0:
_dict = {"symbol": _symbol[0]}
_dict.update(stock_info_df.iloc[_id].to_dict())
self.col_basic_info.insert_one(_dict)
return stock_info_df
def get_historical_news(self, start_date, end_date, freq="day"):
stock_symbol_list = self.col_basic_info.distinct("symbol")
if len(stock_symbol_list) == 0:
stock_symbol_list = self.get_stock_code_info()
if freq == "day":
if os.path.exists(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH):
with open(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH, "r") as file:
start_stock_code = file.read()
logging.info("read {} to get start code number is {} ... "
.format(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH, start_stock_code))
else:
start_stock_code = 0
for symbol in stock_symbol_list:
if int(symbol[2:]) >= int(start_stock_code):
try:
stock_zh_a_daily_hfq_df = ak.stock_zh_a_daily(symbol=symbol,
start_date=start_date,
end_date=end_date,
adjust="hfq")
stock_zh_a_daily_hfq_df.insert(0, 'date', stock_zh_a_daily_hfq_df.index.tolist())
stock_zh_a_daily_hfq_df.index = range(len(stock_zh_a_daily_hfq_df))
_col = self.db_obj.get_collection(self.database_name, symbol)
for _id in range(stock_zh_a_daily_hfq_df.shape[0]):
_col.insert_one(stock_zh_a_daily_hfq_df.iloc[_id].to_dict())
logging.info("{} finished saving ... ".format(symbol))
except Exception:
with open(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH, "w") as file:
file.write(symbol[2:])
elif freq == "week":
pass
elif freq == "month":
pass
elif freq == "5mins":
pass
elif freq == "15mins":
pass
elif freq == "30mins":
pass
elif freq == "60mins":
pass
class DeNull(object):
def __init__(self, database_name, collection_name):
self.database = Database()
self.database_name = database_name
self.collection_name = collection_name
self.delete_num = 0
def run(self):
collection = self.database.get_collection(self.database_name,
self.collection_name)
for row in self.database.get_collection(self.database_name,
self.collection_name).find():
for _key in list(row.keys()):
if _key != "RelatedStockCodes" and row[_key] == "":
collection.delete_one({'_id': row["_id"]})
self.delete_num += 1
break
logging.info(
"there are {} news contained NULL value in {} collection ... ".
format(self.delete_num, self.collection_name))
class GenStockNewsDB(object):
def __init__(self):
self.database = Database()
def get_all_news_about_specific_stock(self, database_name,
collection_name):
# 获取collection_name的key值，看是否包含RelatedStockCodes，如果没有说明，没有做将新闻中所涉及的
# 股票代码保存在新的一列
_keys_list = list(
next(
self.database.get_collection(database_name,
collection_name).find()).keys())
if "RelatedStockCodes" not in _keys_list:
tokenization = Tokenization(import_module="jieba",
user_dict="./Leorio/financedict.txt")
tokenization.update_news_database_rows(database_name,
collection_name)
# 创建stock_code为名称的collection
stock_code_list = self.database.get_data("stock",
"basic_info",
keys=["code"
])["code"].to_list()
for code in stock_code_list:
_collection = self.database.get_collection(
config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, code)
_tmp_num_stat = 0
for row in self.database.get_collection(
database_name, collection_name).find(): # 迭代器
if code in row["RelatedStockCodes"].split(" "):
_collection.insert_one({
"Date": row["Date"],
"Url": row["Url"],
"Title": row["Title"],
"Article": row["Article"],
"OriDB": database_name,
"OriCOL": collection_name
})
_tmp_num_stat += 1
logging.info(
"there are {} news mentioned {} in {} collection ... ".format(
_tmp_num_stat, code, collection_name))
class Deduplication(object):
def __init__(self, database_name, collection_name):
self.database = Database()
self.database_name = database_name
self.collection_name = collection_name
self.delete_num = 0
def run(self):
date_list = self.database.get_data(self.database_name,
self.collection_name,
keys=["Date"])["Date"].tolist()
collection = self.database.get_collection(self.database_name,
self.collection_name)
date_list.sort() # 升序
# start_date, end_date = date_list[1].split(" ")[0], date_list[-1].split(" ")[0]
start_date, end_date = min(date_list).split(" ")[0], max(
date_list).split(" ")[0]
for _date in utils.get_date_list_from_range(start_date, end_date):
# 获取特定时间对应的数据并根据URL去重
# logging.info(_date)
try:
data_df = self.database.get_data(
self.database_name,
self.collection_name,
query={"Date": {
"$regex": _date
}})
except Exception:
continue
if data_df is None:
continue
data_df_drop_duplicate = data_df.drop_duplicates(["Url"])
for _id in list(
set(data_df["_id"]) - set(data_df_drop_duplicate["_id"])):
collection.delete_one({'_id': _id})
self.delete_num += 1
# logging.info("{} finished ... ".format(_date))
logging.info(
"DB:{} - COL:{} had {} data length originally, now has deleted {} depulications ... "
.format(self.database_name, self.collection_name,
str(len(date_list)), self.delete_num))
class Tokenization(object):
def __init__(self,
import_module="jieba",
user_dict=None,
chn_stop_words_dir=None):
#self.database = Database().conn[config.DATABASE_NAME] #.get_collection(config.COLLECTION_NAME_CNSTOCK)
self.database = Database()
self.import_module = import_module
self.user_dict = user_dict
if self.user_dict:
self.update_user_dict(self.user_dict)
if chn_stop_words_dir:
self.stop_words_list = utils.get_chn_stop_words(chn_stop_words_dir)
else:
self.stop_words_list = list()
def update_user_dict(self, old_user_dict_dir, new_user_dict_dir=None):
# 将缺失的(或新的)股票名称、金融新词等，添加进金融词典中
word_list = []
with open(old_user_dict_dir, "r", encoding="utf-8") as file:
for row in file:
word_list.append(row.split("\n")[0])
name_code_df = self.database.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
new_words_list = list(set(name_code_df["name"].tolist()))
for word in new_words_list:
if word not in word_list:
word_list.append(word)
new_user_dict_dir = old_user_dict_dir if not new_user_dict_dir else new_user_dict_dir
with open(new_user_dict_dir, "w", encoding="utf-8") as file:
for word in word_list:
file.write(word + "\n")
def cut_words(self, text):
outstr = list()
sentence_seged = None
if self.import_module == "jieba":
if self.user_dict:
jieba.load_userdict(self.user_dict)
sentence_seged = list(jieba.cut(text))
elif self.import_module == "pkuseg":
seg = pkuseg.pkuseg(user_dict=self.user_dict) # 添加自定义词典
sentence_seged = seg.cut(text) # 进行分词
if sentence_seged:
for word in sentence_seged:
if word not in self.stop_words_list and word != "\t" and word != " ":
outstr.append(word)
return outstr
else:
return False
def find_relevant_stock_codes_in_article(self, article,
stock_name_code_dict):
stock_codes_set = list()
cut_words_list = self.cut_words(article)
if cut_words_list:
for word in cut_words_list:
try:
stock_codes_set.append(stock_name_code_dict[word])
except Exception:
pass
return list(set(stock_codes_set))
def update_news_database_rows(self, database_name, collection_name):
name_code_df = self.database.get_data(
config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["name", "code"])
name_code_dict = dict(name_code_df.values)
data = self.database.get_collection(database_name,
collection_name).find()
for row in data:
# if row["Date"] > "2019-05-20 00:00:00":
related_stock_codes_list = self.find_relevant_stock_codes_in_article(
row["Article"], name_code_dict)
self.database.update_row(
database_name, collection_name, {"_id": row["_id"]},
{"RelatedStockCodes": " ".join(related_stock_codes_list)})
logging.info(
"[{} -> {} -> {}] updated RelatedStockCodes key value ... ".
format(database_name, collection_name, row["Date"]))
class StockInfoSpyder(Spyder):
def __init__(self, database_name, collection_name):
super(StockInfoSpyder, self).__init__()
self.db_obj = Database()
self.col_basic_info = self.db_obj.get_collection(
database_name, collection_name)
self.database_name = database_name
self.collection_name = collection_name
self.start_program_date = datetime.datetime.now().strftime("%Y%m%d")
self.redis_client = redis.StrictRedis(
host="localhost",
port=6379,
db=config.REDIS_CLIENT_FOR_CACHING_STOCK_INFO_DB_ID)
self.redis_client.set("today_date",
datetime.datetime.now().strftime("%Y-%m-%d"))
def get_stock_code_info(self):
# TODO:每半年需要更新一次
stock_info_df = ak.stock_info_a_code_name() # 获取所有A股code和name
stock_symbol_code = ak.stock_zh_a_spot().get(
["symbol", "code"]) # 获取A股所有股票的symbol和code
for _id in range(stock_info_df.shape[0]):
_symbol = stock_symbol_code[stock_symbol_code.code == stock_info_df
.iloc[_id].code].symbol.values
if len(_symbol) != 0:
_dict = {"symbol": _symbol[0]}
_dict.update(stock_info_df.iloc[_id].to_dict())
self.col_basic_info.insert_one(_dict)
def get_historical_news(self, start_date=None, end_date=None, freq="day"):
if end_date is None:
end_date = datetime.datetime.now().strftime("%Y%m%d")
stock_symbol_list = self.col_basic_info.distinct("symbol")
if len(stock_symbol_list) == 0:
self.get_stock_code_info()
stock_symbol_list = self.col_basic_info.distinct("symbol")
if freq == "day":
start_stock_code = 0 if self.redis_client.get(
"start_stock_code") is None else int(
self.redis_client.get("start_stock_code").decode())
for symbol in stock_symbol_list:
if int(symbol[2:]) > start_stock_code:
if start_date is None:
# 如果该symbol有历史数据，如果有则从API获取从数据库中最近的时间开始直到现在的所有价格数据
# 如果该symbol无历史数据，则从API获取从2015年1月1日开始直到现在的所有价格数据
_latest_date = self.redis_client.get(symbol)
if _latest_date is None:
symbol_start_date = config.STOCK_PRICE_REQUEST_DEFAULT_DATE
else:
tmp_date_dt = datetime.datetime.strptime(
_latest_date.decode(), "%Y-%m-%d").date()
offset = datetime.timedelta(days=1)
symbol_start_date = (tmp_date_dt +
offset).strftime('%Y%m%d')
if symbol_start_date < end_date:
stock_zh_a_daily_hfq_df = ak.stock_zh_a_daily(
symbol=symbol,
start_date=symbol_start_date,
end_date=end_date,
adjust="qfq")
stock_zh_a_daily_hfq_df.insert(
0, 'date', stock_zh_a_daily_hfq_df.index.tolist())
stock_zh_a_daily_hfq_df.index = range(
len(stock_zh_a_daily_hfq_df))
_col = self.db_obj.get_collection(
self.database_name, symbol)
for _id in range(stock_zh_a_daily_hfq_df.shape[0]):
_tmp_dict = stock_zh_a_daily_hfq_df.iloc[
_id].to_dict()
_tmp_dict.pop("outstanding_share")
_tmp_dict.pop("turnover")
_col.insert_one(_tmp_dict)
self.redis_client.set(
symbol,
str(_tmp_dict["date"]).split(" ")[0])
logging.info(
"{} finished saving from {} to {} ... ".format(
symbol, symbol_start_date, end_date))
self.redis_client.set("start_stock_code", int(symbol[2:]))
self.redis_client.set("start_stock_code", 0)
elif freq == "week":
pass
elif freq == "month":
pass
elif freq == "5mins":
pass
elif freq == "15mins":
pass
elif freq == "30mins":
pass
elif freq == "60mins":
pass
def get_realtime_news(self, freq="day"):
while True:
if_updated = input(
"Has the stock price dataset been updated today? (Y/N) \n")
if if_updated == "Y":
self.redis_client.set("is_today_updated", "1")
break
elif if_updated == "N":
self.redis_client.set("is_today_updated", "")
break
self.get_historical_news() # 对所有股票补充数据到最新
while True:
if freq == "day":
time_now = datetime.datetime.now().strftime(
"%Y-%m-%d %H:%M:%S")
if time_now.split(" ")[0] != self.redis_client.get(
"today_date").decode():
self.redis_client.set("today_date", time_now.split(" ")[0])
self.redis_client.set("is_today_updated",
"") # 过了凌晨，该参数设置回空值，表示今天未进行数据更新
if not bool(
self.redis_client.get("is_today_updated").decode()):
update_time = "{} {}".format(
time_now.split(" ")[0], "15:30:00")
if time_now >= update_time:
stock_zh_a_spot_df = ak.stock_zh_a_spot() # 当天的日数据行情下载
for _id, sym in enumerate(
stock_zh_a_spot_df["symbol"]):
_col = self.db_obj.get_collection(
self.database_name, sym)
_tmp_dict = {}
_tmp_dict.update({
"date":
Timestamp("{} 00:00:00".format(
time_now.split(" ")[0]))
})
_tmp_dict.update(
{"open": stock_zh_a_spot_df.iloc[_id].open})
_tmp_dict.update(
{"high": stock_zh_a_spot_df.iloc[_id].high})
_tmp_dict.update(
{"low": stock_zh_a_spot_df.iloc[_id].low})
_tmp_dict.update(
{"close": stock_zh_a_spot_df.iloc[_id].trade})
_tmp_dict.update({
"volume":
stock_zh_a_spot_df.iloc[_id].volume
})
_col.insert_one(_tmp_dict)
self.redis_client.set(sym, time_now.split(" ")[0])
logging.info(
"finished updating {} price data of {} ... ".
format(sym,
time_now.split(" ")[0]))
self.redis_client.set("is_today_updated", "1")
class GenStockNewsDB(object):
def __init__(self):
self.database = Database()
# 获取从1990-12-19至2020-12-31股票交易日数据
self.trade_date = ak.tool_trade_date_hist_sina()["trade_date"].tolist()
self.label_range = {
3: "3DaysLabel",
5: "5DaysLabel",
10: "10DaysLabel",
15: "15DaysLabel",
30: "30DaysLabel",
60: "60DaysLabel"
}
def get_all_news_about_specific_stock(self, database_name,
collection_name):
# 获取collection_name的key值，看是否包含RelatedStockCodes，如果没有说明，没有做将新闻中所涉及的
# 股票代码保存在新的一列
_keys_list = list(
next(
self.database.get_collection(database_name,
collection_name).find()).keys())
if "RelatedStockCodes" not in _keys_list:
tokenization = Tokenization(import_module="jieba",
user_dict="./Leorio/financedict.txt")
tokenization.update_news_database_rows(database_name,
collection_name)
# 创建stock_code为名称的collection
stock_symbol_list = self.database.get_data(
"stock", "basic_info", keys=["symbol"])["symbol"].to_list()
for symbol in stock_symbol_list:
_collection = self.database.get_collection(
config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, symbol)
_tmp_num_stat = 0
for row in self.database.get_collection(
database_name, collection_name).find(): # 迭代器
if symbol[2:] in row["RelatedStockCodes"].split(" "):
# 返回新闻发布后n天的标签
_tmp_dict = {}
for label_days, key_name in self.label_range.items():
_tmp_res = self._label_news(
datetime.datetime.strptime(
row["Date"].split(" ")[0], "%Y-%m-%d"), symbol,
label_days)
if _tmp_res:
_tmp_dict.update({key_name: _tmp_res})
_data = {
"Date": row["Date"],
"Url": row["Url"],
"Title": row["Title"],
"Article": row["Article"],
"OriDB": database_name,
"OriCOL": collection_name
}
_data.update(_tmp_dict)
_collection.insert_one(_data)
_tmp_num_stat += 1
logging.info(
"there are {} news mentioned {} in {} collection ... ".format(
_tmp_num_stat, symbol, collection_name))
def _label_news(self, date, symbol, n_days):
"""
:param date: 类型datetime.datetime，表示新闻发布的日期，只包括年月日，不包括具体时刻，如datetime.datetime(2015, 1, 5, 0, 0)
:param symbol: 类型str，表示股票标的，如sh600000
:param n_days: 类型int，表示根据多少天后的价格设定标签，如新闻发布后n_days天，如果收盘价格上涨，则认为该则新闻是利好消息
"""
# 计算新闻发布当天经过n_days天后的具体年月日
this_date_data = self.database.get_data(config.STOCK_DATABASE_NAME,
symbol,
query={"date": date})
# 考虑情况：新闻发布日期是非交易日，因此该日期没有价格数据，则往前寻找，比如新闻发布日期是2020-12-12是星期六，
# 则考虑2020-12-11日的收盘价作为该新闻发布时的数据
tmp_date = date
if this_date_data is None:
i = 1
while this_date_data is None and i <= 10:
tmp_date -= datetime.timedelta(days=i)
# 判断日期是否是交易日，如果是再去查询数据库；如果this_date_data还是NULL值，则说明数据库没有该交易日数据
if tmp_date.strftime("%Y-%m-%d") in self.trade_date:
this_date_data = self.database.get_data(
config.STOCK_DATABASE_NAME,
symbol,
query={"date": tmp_date})
i += 1
try:
close_price_this_date = this_date_data["close"][0]
except Exception:
close_price_this_date = None
# 考虑情况：新闻发布后n_days天是非交易日，或者没有采集到数据，因此向后寻找，如新闻发布日期是2020-12-08，5天
# 后的日期是2020-12-13是周日，因此将2020-12-14日周一的收盘价作为n_days后的数据
new_date = date + datetime.timedelta(days=n_days)
n_days_later_data = self.database.get_data(config.STOCK_DATABASE_NAME,
symbol,
query={"date": new_date})
if n_days_later_data is None:
i = 1
while n_days_later_data is None and i <= 10:
new_date = date + datetime.timedelta(days=n_days + i)
if new_date.strftime("%Y-%m-%d") in self.trade_date:
n_days_later_data = self.database.get_data(
config.STOCK_DATABASE_NAME,
symbol,
query={"date": new_date})
i += 1
try:
close_price_n_days_later = n_days_later_data["close"][0]
except Exception:
close_price_n_days_later = None
# 判断条件：
# （1）如果n_days个交易日后且n_days<=10天，则价格上涨(下跌)超过3%，则认为该新闻是利好(利空)消息；如果价格在3%的范围内，则为中性消息
# （2）如果n_days个交易日后且10<n_days<=15天，则价格上涨(下跌)超过5%，则认为该新闻是利好(利空)消息；如果价格在5%的范围内，则为中性消息
# （3）如果n_days个交易日后且15<n_days<=30天，则价格上涨(下跌)超过10%，则认为该新闻是利好(利空)消息；如果价格在10%的范围内，则为中性消息
# （4）如果n_days个交易日后且30<n_days<=60天，则价格上涨(下跌)超过15%，则认为该新闻是利好(利空)消息；如果价格在15%的范围内，则为中性消息
# Note：中性消息定义为，该消息迅速被市场消化，并没有持续性影响
param = 0.01
if n_days <= 10:
param = 0.03
elif 10 < n_days <= 15:
param = 0.05
elif 15 < n_days <= 30:
param = 0.10
elif 30 < n_days <= 60:
param = 0.15
if close_price_this_date is not None and close_price_n_days_later is not None:
if (close_price_n_days_later -
close_price_this_date) / close_price_this_date > param:
return "利好"
elif (close_price_n_days_later -
close_price_this_date) / close_price_this_date < -param:
return "利空"
else:
return "中性"
else:
return False
class TopicModelling(object):
def __init__(self):
self.tokenization = Tokenization(
import_module="jieba",
user_dict=config.USER_DEFINED_DICT_PATH,
chn_stop_words_dir=config.CHN_STOP_WORDS_PATH)
self.database = Database()
self.classifier = Classifier()
def create_dictionary(self,
raw_documents_list,
save_path=None,
is_saved=False):
"""
将文中每个词汇关联唯一的ID，因此需要定义词汇表
:param: raw_documents_list, 原始语料列表，每个元素即文本，如["洗尽铅华...", "风雨赶路人...", ...]
:param: savepath, corpora.Dictionary对象保存路径
"""
documents_token_list = []
for doc in raw_documents_list:
documents_token_list.append(self.tokenization.cut_words(doc))
_dict = corpora.Dictionary(documents_token_list)
# 找到只出现一次的token
once_items = [
_dict[tokenid] for tokenid, docfreq in _dict.dfs.items()
if docfreq == 1
]
# 在documents_token_list的每一条语料中，删除只出现一次的token
for _id, token_list in enumerate(documents_token_list):
documents_token_list[_id] = list(
filter(lambda token: token not in once_items, token_list))
# 极端情况，某一篇语料所有token只出现一次，这样该篇新闻语料的token列表就变为空，因此删除掉
documents_token_list = [
token_list for token_list in documents_token_list
if (len(token_list) != 0)
]
# 找到只出现一次的token对应的id
once_ids = [
tokenid for tokenid, docfreq in _dict.dfs.items() if docfreq == 1
]
# 删除仅出现一次的词
_dict.filter_tokens(once_ids)
# 消除id序列在删除词后产生的不连续的缺口
_dict.compactify()
if is_saved and save_path:
_dict.save(save_path)
logging.info(
"new generated dictionary saved in path -> {} ...".format(
save_path))
return _dict, documents_token_list
def renew_dictionary(self,
old_dict_path,
new_raw_documents_list,
new_dict_path=None,
is_saved=False):
documents_token_list = []
for doc in new_raw_documents_list:
documents_token_list.append(self.tokenization.cut_words(doc))
_dict = corpora.Dictionary.load(old_dict_path)
_dict.add_documents(documents_token_list)
if new_dict_path:
old_dict_path = new_dict_path
if is_saved:
_dict.save(old_dict_path)
logging.info(
"updated dictionary by another raw documents serialized in {} ... "
.format(old_dict_path))
return _dict, documents_token_list
def create_bag_of_word_representation(self,
raw_documents_list,
old_dict_path=None,
new_dict_path=None,
bow_vector_save_path=None,
is_saved_dict=False):
if old_dict_path:
# 如果存在旧的语料词典，就在原先词典的基础上更新，增加未见过的词
corpora_dictionary, documents_token_list = self.renew_dictionary(
old_dict_path, raw_documents_list, new_dict_path=new_dict_path)
else:
# 否则重新创建词典
start_time = time.time()
corpora_dictionary, documents_token_list = self.create_dictionary(
raw_documents_list,
save_path=new_dict_path,
is_saved=is_saved_dict)
end_time = time.time()
logging.info(
"there are {} mins spent to create a new dictionary ... ".
format((end_time - start_time) / 60))
# 根据新词典对文档(或语料)生成对应的词袋向量
start_time = time.time()
bow_vector = [
corpora_dictionary.doc2bow(doc_token)
for doc_token in documents_token_list
]
end_time = time.time()
logging.info(
"there are {} mins spent to calculate bow-vector ... ".format(
(end_time - start_time) / 60))
if bow_vector_save_path:
corpora.MmCorpus.serialize(bow_vector_save_path, bow_vector)
return documents_token_list, corpora_dictionary, bow_vector
@staticmethod
def transform_vectorized_corpus(corpora_dictionary,
bow_vector,
model_type="lda",
model_save_path=None):
# 如何没有保存任何模型，重新训练的情况下，可以选择该函数
model_vector = None
if model_type == "lsi":
# LSI(Latent Semantic Indexing)模型，将文本从词袋向量或者词频向量(更好)，转为一个低维度的latent空间
# 对于现实语料，目标维度在200-500被认为是"黄金标准"
model_tfidf = models.TfidfModel(bow_vector)
# model_tfidf.save("model_tfidf.tfidf")
tfidf_vector = model_tfidf[bow_vector]
model = models.LsiModel(tfidf_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[tfidf_vector]
if model_save_path:
model.save(model_save_path)
elif model_type == "lda":
model = models.LdaModel(bow_vector,
id2word=corpora_dictionary,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[bow_vector]
if model_save_path:
model.save(model_save_path)
elif model_type == "tfidf":
model = models.TfidfModel(bow_vector) # 初始化
# model = models.TfidfModel.load("model_tfidf.tfidf")
model_vector = model[bow_vector] # 将整个语料进行转换
if model_save_path:
model.save(model_save_path)
return model_vector
def classify_stock_news(self,
unseen_raw_document,
database_name,
collection_name,
label_name="60DaysLabel",
topic_model_type="lda",
classifier_model="svm",
ori_dict_path=None,
bowvec_save_path=None,
is_saved_bow_vector=False):
historical_raw_documents_list = []
Y = []
for row in self.database.get_collection(database_name,
collection_name).find():
if label_name in row.keys():
if row[label_name] != "":
historical_raw_documents_list.append(row["Article"])
Y.append(row[label_name])
logging.info(
"fetch symbol '{}' historical news with label '{}' from [DB:'{}' - COL:'{}'] ... "
.format(collection_name, label_name, database_name,
collection_name))
le = preprocessing.LabelEncoder()
Y = le.fit_transform(Y)
logging.info(
"encode historical label list by sklearn preprocessing for training ... "
)
label_name_list = le.classes_ # ['中性' '利好' '利空'] -> [0, 1, 2]
# 根据历史新闻数据库创建词典，以及计算每个历史新闻的词袋向量；如果历史数据库创建的字典存在，则加载进内存
# 用未见过的新闻tokens去更新该词典
if not os.path.exists(ori_dict_path):
if not os.path.exists(bowvec_save_path):
_, _, historical_bow_vec = self.create_bag_of_word_representation(
historical_raw_documents_list,
new_dict_path=ori_dict_path,
bow_vector_save_path=bowvec_save_path,
is_saved_dict=True)
logging.info(
"create dictionary of historical news, and serialized in path -> {} ... "
.format(ori_dict_path))
logging.info(
"create bow-vector of historical news, and serialized in path -> {} ... "
.format(bowvec_save_path))
else:
_, _, _ = self.create_bag_of_word_representation(
historical_raw_documents_list,
new_dict_path=ori_dict_path,
is_saved_dict=True)
logging.info(
"create dictionary of historical news, and serialized in path -> {} ... "
.format(ori_dict_path))
else:
if not os.path.exists(bowvec_save_path):
_, _, historical_bow_vec = self.create_bag_of_word_representation(
historical_raw_documents_list,
new_dict_path=ori_dict_path,
bow_vector_save_path=bowvec_save_path,
is_saved_dict=True)
logging.info(
"historical news dictionary existed, which saved in path -> {}, but not the historical bow-vector"
" ... ".format(ori_dict_path))
else:
historical_bow_vec_mmcorpus = corpora.MmCorpus(
bowvec_save_path
) # type -> <gensim.corpora.mmcorpus.MmCorpus>
historical_bow_vec = []
for _bow in historical_bow_vec_mmcorpus:
historical_bow_vec.append(_bow)
logging.info(
"both historical news dictionary and bow-vector existed, load historical bow-vector to memory ... "
)
start_time = time.time()
updated_dictionary_with_old_and_unseen_news, unssen_documents_token_list = self.renew_dictionary(
ori_dict_path, [unseen_raw_document], is_saved=True)
end_time = time.time()
logging.info(
"renew dictionary with unseen news tokens, and serialized in path -> {}, "
"which took {} mins ... ".format(ori_dict_path,
(end_time - start_time) / 60))
unseen_bow_vector = [
updated_dictionary_with_old_and_unseen_news.doc2bow(doc_token)
for doc_token in unssen_documents_token_list
]
updated_bow_vector_with_old_and_unseen_news = []
updated_bow_vector_with_old_and_unseen_news.extend(historical_bow_vec)
updated_bow_vector_with_old_and_unseen_news.extend(unseen_bow_vector)
# 原先updated_bow_vector_with_old_and_unseen_news是list类型，
# 但是经过下面序列化后重新加载进来的类型是gensim.corpora.mmcorpus.MmCorpus
if is_saved_bow_vector and bowvec_save_path:
corpora.MmCorpus.serialize(
bowvec_save_path, updated_bow_vector_with_old_and_unseen_news
) # 保存更新后的bow向量，即包括新旧新闻的bow向量集
logging.info(
"combined bow vector(type -> 'list') generated by historical news with unseen bow "
"vector to create a new one ... ")
if topic_model_type == "lsi":
start_time = time.time()
updated_tfidf_model_vector = self.transform_vectorized_corpus(
updated_dictionary_with_old_and_unseen_news,
updated_bow_vector_with_old_and_unseen_news,
model_type="tfidf"
) # type -> <gensim.interfaces.TransformedCorpus object>
end_time = time.time()
logging.info(
"regenerated TF-IDF model vector by updated dictionary and updated bow-vector, "
"which took {} mins ... ".format((end_time - start_time) / 60))
start_time = time.time()
model = models.LsiModel(
updated_tfidf_model_vector,
id2word=updated_dictionary_with_old_and_unseen_news,
num_topics=config.TOPIC_NUMBER) # 初始化模型
model_vector = model[
updated_tfidf_model_vector] # type -> <gensim.interfaces.TransformedCorpus object>
end_time = time.time()
logging.info(
"regenerated LSI model vector space by updated TF-IDF model vector space, "
"which took {} mins ... ".format((end_time - start_time) / 60))
elif topic_model_type == "lda":
start_time = time.time()
model_vector = self.transform_vectorized_corpus(
updated_dictionary_with_old_and_unseen_news,
updated_bow_vector_with_old_and_unseen_news,
model_type="lda")
end_time = time.time()
logging.info(
"regenerated LDA model vector space by updated dictionary and bow-vector, "
"which took {} mins ... ".format((end_time - start_time) / 60))
# 将gensim.interfaces.TransformedCorpus类型的lsi模型向量转为numpy矩阵
start_time = time.time()
latest_matrix = corpus2dense(model_vector,
num_terms=model_vector.obj.num_terms).T
end_time = time.time()
logging.info(
"transform {} model vector space to numpy.adarray, "
"which took {} mins ... ".format(topic_model_type.upper(),
(end_time - start_time) / 60))
# 利用历史数据的话题模型向量(或特征)，进一步训练新闻分类器
start_time = time.time()
train_x, train_y, test_x, test_y = utils.generate_training_set(
latest_matrix[:-1, :], Y)
clf = self.classifier.train(train_x,
train_y,
test_x,
test_y,
model_type=classifier_model)
end_time = time.time()
logging.info(
"finished training by sklearn {} using latest {} model vector space, which took {} mins ... "
.format(classifier_model.upper(), topic_model_type.upper(),
(end_time - start_time) / 60))
label_id = clf.predict(latest_matrix[-1, :].reshape(1, -1))[0]
return label_name_list[label_id]
elif ".lsi" in model_path:
return models.LsiModel.load(model_path)
elif ".lda" in model_path:
return models.LdaModel.load(model_path)
if __name__ == "__main__":
from Hisoka.classifier import Classifier
from Kite.database import Database
from sklearn import preprocessing
database = Database()
topicmodelling = TopicModelling()
raw_documents_list = []
Y = []
for row in database.get_collection("stocknews", "sz000001").find():
if "30DaysLabel" in row.keys():
raw_documents_list.append(row["Article"])
Y.append(row["30DaysLabel"])
le = preprocessing.LabelEncoder()
Y = le.fit_transform(Y)
_, corpora_dictionary, corpus = topicmodelling.create_bag_of_word_representation(
raw_documents_list)
model_vector = topicmodelling.transform_vectorized_corpus(
corpora_dictionary, corpus, model_type="lsi")
csr_matrix = utils.convert_to_csr_matrix(model_vector)
train_x, train_y, test_x, test_y = utils.generate_training_set(
csr_matrix, Y)
classifier = Classifier()
classifier.svm(train_x, train_y, test_x, test_y)
class GenStockNewsDB(object):
def __init__(self):
self.database = Database()
# 获取从1990-12-19至2020-12-31股票交易日数据
self.trade_date = ak.tool_trade_date_hist_sina()["trade_date"].tolist()
self.label_range = {3: "3DaysLabel",
5: "5DaysLabel",
10: "10DaysLabel",
15: "15DaysLabel",
30: "30DaysLabel",
60: "60DaysLabel"}
self.redis_client = redis.StrictRedis(host=config.REDIS_IP,
port=config.REDIS_PORT,
db=config.CACHE_NEWS_REDIS_DB_ID)
self.redis_client.set("today_date", datetime.datetime.now().strftime("%Y-%m-%d"))
self.redis_client.delete("stock_news_num_over_{}".format(config.MINIMUM_STOCK_NEWS_NUM_FOR_ML))
self._stock_news_nums_stat()
def get_all_news_about_specific_stock(self, database_name, collection_name):
# 获取collection_name的key值，看是否包含RelatedStockCodes，如果没有说明，没有做将新闻中所涉及的
# 股票代码保存在新的一列
_keys_list = list(next(self.database.get_collection(database_name, collection_name).find()).keys())
if "RelatedStockCodes" not in _keys_list:
tokenization = Tokenization(import_module="jieba", user_dict="./Leorio/financedict.txt")
tokenization.update_news_database_rows(database_name, collection_name)
# 创建stock_code为名称的collection
stock_symbol_list = self.database.get_data(config.STOCK_DATABASE_NAME,
config.COLLECTION_NAME_STOCK_BASIC_INFO,
keys=["symbol"])["symbol"].to_list()
col_names = self.database.connect_database(config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE).list_collection_names(session=None)
for symbol in stock_symbol_list:
if symbol not in col_names:
# if int(symbol[2:]) > 837:
_collection = self.database.get_collection(config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, symbol)
_tmp_num_stat = 0
for row in self.database.get_collection(database_name, collection_name).find(): # 迭代器
if symbol[2:] in row["RelatedStockCodes"].split(" "):
# 返回新闻发布后n天的标签
_tmp_dict = {}
for label_days, key_name in self.label_range.items():
_tmp_res = self._label_news(
datetime.datetime.strptime(row["Date"].split(" ")[0], "%Y-%m-%d"), symbol, label_days)
_tmp_dict.update({key_name: _tmp_res})
_data = {"Date": row["Date"],
"Url": row["Url"],
"Title": row["Title"],
"Article": row["Article"],
"OriDB": database_name,
"OriCOL": collection_name}
_data.update(_tmp_dict)
_collection.insert_one(_data)
_tmp_num_stat += 1
logging.info("there are {} news mentioned {} in {} collection need to be fetched ... "
.format(_tmp_num_stat, symbol, collection_name))
# else:
# logging.info("{} has fetched all related news from {}...".format(symbol, collection_name))
def listen_redis_queue(self):
# 监听redis消息队列，当新的实时数据过来时，根据"RelatedStockCodes"字段，将新闻分别保存到对应的股票数据库
# e.g.:缓存新的一条数据中，"RelatedStockCodes"字段数据为"603386 603003 600111 603568"，则将该条新闻分别
# 都存进这四支股票对应的数据库中
crawled_url_today = set()
while True:
date_now = datetime.datetime.now().strftime("%Y-%m-%d")
if date_now != self.redis_client.get("today_date").decode():
crawled_url_today = set()
self.redis_client.set("today_date", date_now)
if self.redis_client.llen(config.CACHE_NEWS_LIST_NAME) != 0:
data = json.loads(self.redis_client.lindex(config.CACHE_NEWS_LIST_NAME, -1))
if data["Url"] not in crawled_url_today: # 排除重复插入冗余文本
crawled_url_today.update({data["Url"]})
if data["RelatedStockCodes"] != "":
for stock_code in data["RelatedStockCodes"].split(" "):
# 将新闻分别送进相关股票数据库
symbol = "sh{}".format(stock_code) if stock_code[0] == "6" else "sz{}".format(stock_code)
_collection = self.database.get_collection(config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, symbol)
_tmp_dict = {}
for label_days, key_name in self.label_range.items():
_tmp_res = self._label_news(
datetime.datetime.strptime(data["Date"].split(" ")[0], "%Y-%m-%d"), symbol, label_days)
_tmp_dict.update({key_name: _tmp_res})
_data = {"Date": data["Date"],
"Url": data["Url"],
"Title": data["Title"],
"Article": data["Article"],
"OriDB": data["OriDB"],
"OriCOL": data["OriCOL"]}
_data.update(_tmp_dict)
_collection.insert_one(_data)
logging.info("the real-time fetched news {}, which was saved in [DB:{} - COL:{}] ...".format(data["Title"],
config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE,
symbol))
#
# if symbol.encode() in self.redis_client.lrange("stock_news_num_over_{}".format(config.MINIMUM_STOCK_NEWS_NUM_FOR_ML), 0, -1):
# label_name = "3DaysLabel"
# # classifier_save_path = "{}_classifier.pkl".format(symbol)
# ori_dict_path = "{}_docs_dict.dict".format(symbol)
# bowvec_save_path = "{}_bowvec.mm".format(symbol)
#
# topicmodelling = TopicModelling()
# chn_label = topicmodelling.classify_stock_news(data["Article"],
# config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE,
# symbol,
# label_name=label_name,
# topic_model_type="lsi",
# classifier_model="rdforest", # rdforest / svm
# ori_dict_path=ori_dict_path,
# bowvec_save_path=bowvec_save_path)
# logging.info(
# "document '{}...' was classified with label '{}' for symbol {} ... ".format(
# data["Article"][:20], chn_label, symbol))
self.redis_client.rpop(config.CACHE_NEWS_LIST_NAME)
logging.info("now pop {} from redis queue of [DB:{} - KEY:{}] ... ".format(data["Title"],
config.CACHE_NEWS_REDIS_DB_ID,
config.CACHE_NEWS_LIST_NAME))
def _label_news(self, date, symbol, n_days):
"""
:param date: 类型datetime.datetime，表示新闻发布的日期，只包括年月日，不包括具体时刻，如datetime.datetime(2015, 1, 5, 0, 0)
:param symbol: 类型str，表示股票标的，如sh600000
:param n_days: 类型int，表示根据多少天后的价格设定标签，如新闻发布后n_days天，如果收盘价格上涨，则认为该则新闻是利好消息
"""
# 计算新闻发布当天经过n_days天后的具体年月日
this_date_data = self.database.get_data(config.STOCK_DATABASE_NAME,
symbol,
query={"date": date})
# 考虑情况：新闻发布日期是非交易日，因此该日期没有价格数据，则往前寻找，比如新闻发布日期是2020-12-12是星期六，
# 则考虑2020-12-11日的收盘价作为该新闻发布时的数据
tmp_date = date
if this_date_data is None:
i = 1
while this_date_data is None and i <= 10:
tmp_date -= datetime.timedelta(days=i)
# 判断日期是否是交易日，如果是再去查询数据库；如果this_date_data还是NULL值，则说明数据库没有该交易日数据
if tmp_date.strftime("%Y-%m-%d") in self.trade_date:
this_date_data = self.database.get_data(config.STOCK_DATABASE_NAME,
symbol,
query={"date": tmp_date})
i += 1
try:
close_price_this_date = this_date_data["close"][0]
except Exception:
close_price_this_date = None
# 考虑情况：新闻发布后n_days天是非交易日，或者没有采集到数据，因此向后寻找，如新闻发布日期是2020-12-08，5天
# 后的日期是2020-12-13是周日，因此将2020-12-14日周一的收盘价作为n_days后的数据
new_date = date + datetime.timedelta(days=n_days)
n_days_later_data = self.database.get_data(config.STOCK_DATABASE_NAME,
symbol,
query={"date": new_date})
if n_days_later_data is None:
i = 1
while n_days_later_data is None and i <= 10:
new_date = date + datetime.timedelta(days=n_days+i)
if new_date.strftime("%Y-%m-%d") in self.trade_date:
n_days_later_data = self.database.get_data(config.STOCK_DATABASE_NAME,
symbol,
query={"date": new_date})
i += 1
try:
close_price_n_days_later = n_days_later_data["close"][0]
except Exception:
close_price_n_days_later = None
# 判断条件：
# （1）如果n_days个交易日后且n_days<=10天，则价格上涨(下跌)超过3%，则认为该新闻是利好(利空)消息；如果价格在3%的范围内，则为中性消息
# （2）如果n_days个交易日后且10<n_days<=15天，则价格上涨(下跌)超过5%，则认为该新闻是利好(利空)消息；如果价格在5%的范围内，则为中性消息
# （3）如果n_days个交易日后且15<n_days<=30天，则价格上涨(下跌)超过10%，则认为该新闻是利好(利空)消息；如果价格在10%的范围内，则为中性消息
# （4）如果n_days个交易日后且30<n_days<=60天，则价格上涨(下跌)超过15%，则认为该新闻是利好(利空)消息；如果价格在15%的范围内，则为中性消息
# Note：中性消息定义为，该消息迅速被市场消化，并没有持续性影响
param = 0.01
if n_days <= 10:
param = 0.03
elif 10 < n_days <= 15:
param = 0.05
elif 15 < n_days <= 30:
param = 0.10
elif 30 < n_days <= 60:
param = 0.15
if close_price_this_date is not None and close_price_n_days_later is not None:
if (close_price_n_days_later - close_price_this_date) / close_price_this_date > param:
return "利好"
elif (close_price_n_days_later - close_price_this_date) / close_price_this_date < -param:
return "利空"
else:
return "中性"
else:
return ""
def _stock_news_nums_stat(self):
cols_list = self.database.connect_database(config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE).list_collection_names(session=None)
for sym in cols_list:
if self.database.get_collection(config.ALL_NEWS_OF_SPECIFIC_STOCK_DATABASE, sym).estimated_document_count() > config.MINIMUM_STOCK_NEWS_NUM_FOR_ML:
self.redis_client.lpush("stock_news_num_over_{}".format(config.MINIMUM_STOCK_NEWS_NUM_FOR_ML), sym)
class StockInfoSpyder(Spyder):
def __init__(self, database_name, collection_name):
super(StockInfoSpyder, self).__init__()
self.db_obj = Database()
self.col_basic_info = self.db_obj.get_collection(
database_name, collection_name)
self.database_name = database_name
self.collection_name = collection_name
def get_stock_code_info(self):
stock_info_df = ak.stock_info_a_code_name() # 获取所有A股code和name
stock_symbol_code = ak.stock_zh_a_spot().get(
["symbol", "code"]) # 获取A股所有股票的symbol和code
for _id in range(stock_info_df.shape[0]):
_symbol = stock_symbol_code[stock_symbol_code.code == stock_info_df
.iloc[_id].code].symbol.values
if len(_symbol) != 0:
_dict = {"symbol": _symbol[0]}
_dict.update(stock_info_df.iloc[_id].to_dict())
self.col_basic_info.insert_one(_dict)
def get_historical_news(self, start_date=None, end_date=None, freq="day"):
if end_date is None:
end_date = datetime.datetime.now().strftime("%Y%m%d")
stock_symbol_list = self.col_basic_info.distinct("symbol")
if len(stock_symbol_list) == 0:
self.get_stock_code_info()
stock_symbol_list = self.col_basic_info.distinct("symbol")
if freq == "day":
if os.path.exists(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH):
with open(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH,
"r") as file:
start_stock_code = file.read()
logging.info(
"read {} to get start code number is {} ... ".format(
config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH,
start_stock_code))
else:
start_stock_code = 0
for symbol in stock_symbol_list:
if int(symbol[2:]) >= int(start_stock_code):
try:
if start_date is None:
# 如果该symbol有历史数据，如果有则从API获取从数据库中最近的时间开始直到现在的所有价格数据
# 如果该symbol无历史数据，则从API获取从2015年1月1日开始直到现在的所有价格数据
symbol_date_list = self.db_obj.get_data(
self.database_name, symbol,
keys=["date"])["date"].to_list()
if len(symbol_date_list) == 0:
start_date = config.STOCK_PRICE_REQUEST_DEFAULT_DATE
else:
tmp_date = str(
max(symbol_date_list)).split(" ")[0]
tmp_date_dt = datetime.datetime.strptime(
tmp_date, "%Y-%m-%d").date()
offset = datetime.timedelta(days=1)
start_date = (tmp_date_dt +
offset).strftime('%Y%m%d')
stock_zh_a_daily_hfq_df = ak.stock_zh_a_daily(
symbol=symbol,
start_date=start_date,
end_date=end_date,
adjust="hfq")
stock_zh_a_daily_hfq_df.insert(
0, 'date', stock_zh_a_daily_hfq_df.index.tolist())
stock_zh_a_daily_hfq_df.index = range(
len(stock_zh_a_daily_hfq_df))
_col = self.db_obj.get_collection(
self.database_name, symbol)
for _id in range(stock_zh_a_daily_hfq_df.shape[0]):
_col.insert_one(
stock_zh_a_daily_hfq_df.iloc[_id].to_dict())
logging.info("{} finished saving ... ".format(symbol))
except Exception:
with open(config.STOCK_DAILY_EXCEPTION_TXT_FILE_PATH,
"w") as file:
file.write(symbol[2:])
elif freq == "week":
pass
elif freq == "month":
pass
elif freq == "5mins":
pass
elif freq == "15mins":
pass
elif freq == "30mins":
pass
elif freq == "60mins":
pass