def trend(gameId):
dd = (datetime.today() + timedelta(days=-30)).strftime('%Y-%m-%d')
start_time = time.time()
# 让球 即时欧赔 echart数据
result = cra_data.query_pay_echart(dd, gameId)
log.info("trend > query_pay_echart 耗时：%s" %
utils.float_num(time.time() - start_time))
# 比分指数 数据
start_time = time.time()
bifen_result = cra_bifen.query_record(gameId)
log.info("trend > query_record 耗时：%s" %
utils.float_num(time.time() - start_time))
# 波胆
start_time = time.time()
bodan_data = cra_bifen.calc_bodan(bifen_result)
log.info("trend > calc_bodan 耗时：%s" %
utils.float_num(time.time() - start_time))
# 计算即赔的波胆标准差
start_time = time.time()
bifen_sta_echart_data = cra_bifen.query_bodan_std(gameId)
log.info("trend > start_time 耗时：%s" %
utils.float_num(time.time() - start_time))
# 百家欧赔
start_time = time.time()
baijia_oupei = cra_oupei.query_oupei_echart(dd, gameId)
log.info("trend > query_oupei_echart 耗时：%s" %
utils.float_num(time.time() - start_time))
# 欧赔初始凯利与即时凯利
baijia_oupei_start = cra_oupei.query_oupei_echart2(gameId)
baijia_oupei_end = cra_oupei.query_jishioupei_echart2(gameId)
# print("baijia_oupei_start---------------------------")
# print(baijia_oupei_start)
# 让球-竞彩初始凯利与即时凯利
rangqiu_oupei_start = cra_oupei.query_oupei_echart3(gameId)
rangqiu_oupei_end = cra_oupei.query_jishioupei_echart3(gameId)
return render_template("trend_echart.html",
data=result,
baijia_oupei=baijia_oupei,
bifen_result=bifen_result,
bodan_data=bodan_data,
team=request.args.get('team'),
bfs_data=bifen_sta_echart_data,
startTime=request.args.get('startTime'),
oupei_kaili=baijia_oupei_start,
rangqiu_kaili=rangqiu_oupei_start,
biajia_oupei_end=baijia_oupei_end,
rangqiu_oupei_end=rangqiu_oupei_end)
def process_sub_page3(
testElemNo=None,
first_tmp_url="http://odds.500.com/fenxi/rangqiu-%s.shtml?lot=jczq",
next_tmp_url="http://odds.500.com/fenxi1/rangqiu.php?id=%s&ctype=1&"
"start=%d&r=1&style=0&guojia=0&chupan=0&lot=jczq",
table_name="rangqiu_startvalue_info"):
startTotalTime = time.time()
if testElemNo is not None:
elemNoList = testElemNo
else:
elemNoList = DBUtils.execute(
"select game_no,start_datetime from football_game_info "
"where start_datetime>now() order by start_datetime ")
all_sub_data = []
str_sql = "insert into " + table_name + "(kelly_win_num, kelly_avg_num, " \
"kelly_lost_num,game_id) values(%s,%s,%s,%s) "
# 设置解析列索引
params = {}
if table_name != "pay_info":
params = {"col_index": 2}
if not isinstance(elemNoList, list):
print("不是数组:", end="")
print(elemNoList)
return []
""" 解析让球指数页面 """
for elObj in elemNoList:
startTime = time.time()
time.sleep(ConstantVal.TIMER_INTERVAL)
elNo = elObj["game_no"]
url = first_tmp_url % elNo
# 子页面第一页的数据,存在多页的情况
all_grade_data = analysis_all_sub_page3(cra_data_url(url, "utf-8"),
**params)
# 子页面其他页的数据
start = 30
while True:
url = next_tmp_url % (elNo, start)
htmlData = cra_data_url(url, "utf-8")
if htmlData and len(htmlData.strip()) > 100:
result = analysis_all_sub_page3(htmlData, 'tr', **params)
all_grade_data = concat_array(all_grade_data, result)
start += 30
else:
break
# 计算平方差
if len(all_grade_data) > 0:
avg_data = [
str(float('%.3f' % cal_std2(bb))) for bb in all_grade_data
]
avg_data.append(elNo)
all_sub_data.append(avg_data)
# count = DBUtils.executeOne(str_sql, avg_data)
print(
str(elNo) + " " + table_name + " 子页总共抓取条数：%s 耗时：%s" %
(start, utils.float_num(time.time() - startTime)))
count = DBUtils.executeMany(str_sql, all_sub_data)
print(all_sub_data)
print("子页面保存数量：%s 耗时：%s" %
(count, utils.float_num(time.time() - startTotalTime)))
return elemNoList
def parse_bifen(game_id=729379):
time.sleep(ConstantVal.TIMER_INTERVAL)
startTime = time.time()
bifen_url = "http://odds.500.com/fenxi/bifen-%s.shtml" % game_id
bifen_html = cra_data.cra_data_url(bifen_url)
# 只解析class 为pub_table的table 即可
only_table = SoupStrainer("table", class_="pub_table")
soup = BeautifulSoup(bifen_html, "lxml", parse_only=only_table)
if not soup:
print("%s 内容无效" % game_id)
return
# 获取比分,find搜索到一个就会停止搜索，搜索全部要使用 find_all
scores_th = soup.find('tr').contents[5:]
scores = [text.get_text() for text in scores_th if type(text) is bs4.element.Tag]
# 0:0的位置
dogfall_index = scores.index("0:0")
# 所有赔率
# 威廉希尔:293、Bwin:5、Bet365:3、Eurobet:15 1Bet:671、金宝博:348、澳门:11、18Bet：863
# 用于排序 老的 ["威廉希尔", "Bet365", "Bwin", "澳门"] + ["Eurobet", "1Bet", "金宝博"]
specify_cids = [293, 5, 3, 15]+[863, 348, 11]
main_comp = ["威廉希尔", "Bwin", "Bet365", "Eurobet"] + ["18Bet", "金宝博", "澳门"]
# 用于在网页上查找所有 有class属性的tr
all_trs = soup.find_all("tr", {"class": re.compile(".*")})
find_table_bifen_result = []
main_win_data = []
next_win_data = []
col_pay_data = []
for tr in all_trs:
# 获取编号
cid = parse_qs(urlparse(tr.a.get('href')).query)['cid']
# 获取横着的整行数据
temp_list = list(tr.stripped_strings)
# 平局赔付
avg_pay_list = temp_list[(dogfall_index + 2) * 2:]
com_name = temp_list[1]
order_num = [(main_comp.index(com_name) + 1) * 10 if com_name in main_comp else 100]
# 主胜的赔率
main_win_pay = [ps for ps in temp_list[2:(dogfall_index + 2) * 2][::2]]
# 客胜的赔率
next_win_pay = [ps for ps in temp_list[2:(dogfall_index + 2) * 2][1::2]]
# 主胜
temp_list_main = [game_id] + cid + temp_list[:2] + order_num + main_win_pay + avg_pay_list
# 客胜
temp_list_next = [game_id] + cid + temp_list[:2] + order_num + next_win_pay + avg_pay_list
# 将客胜、平局、主胜的赔率存入数组
col_pay_data.append(main_win_pay[1:] + avg_pay_list + next_win_pay[1:])
main_win_data.append(temp_list_main)
next_win_data.append(temp_list_next)
#
if com_name in com_name and cid and int(cid[0]) in specify_cids:
find_table_bifen_result.append(temp_list_main)
find_table_bifen_result.append(temp_list_next)
# 比分存入数据库
if find_table_bifen_result.__len__() > 0:
result_count = DBUtils.executeMany(
"insert into bifen_index(game_id,`cid`,`html_index`,`com_name`,order_num,win_type,`m1_0`,`m2_0`,`m2_1`"
",`m3_0`,`m3_1`,"
"`m3_2`,`m4_0`,`m4_1`,`m4_2`,`m4_3`,`m0_0`,`m1_1`,`m2_2`,`m3_3`,`m4_4`) values (%s,%s,%s,%s,%s,"
"%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)", find_table_bifen_result)
print("%s bifen存库条数：%s 耗时：%s" % (game_id, result_count, utils.float_num(time.time() - startTime)))
# 计算波胆的标准差，然后存入到标准差表
if col_pay_data and len(col_pay_data) > 0:
# 计算波胆平方差
calc_bodan_std_result = bodan_std(col_pay_data)
result = bifenImpl.insert_bodan_std([game_id] + calc_bodan_std_result)
print("%s 保存波胆标准差数据条数：%s" % (game_id, result))