def updateFpList(self):
last_day = common.get_lastN_date(self.today, 1)
last2_day = common.get_lastN_date(self.today, 2)
day_list = [self.today, last_day, last2_day]
day_list = [common.format_date(x, "%Y-%m-%d")
for x in day_list] # 关注的复牌日
results = self.mongodb.stock.FP.find({
"fp_time": {
"$in": day_list
},
"dp_ratio": {
"$exists": True
}
})
for result in results:
if result['delta_days'] < 20:
continue
if result['dp_ratio'] >= 0:
direction = "+"
else:
direction = "-"
self.fpDict[result['stcid']] = [direction, result['target_price']]
stcid_string = "_".join(self.fpDict.keys())
# 更新mongo数据库
self.mongodb.stock.ZDT_by_date.update(
{"date": self.today}, {"$set": {
"fp_list": stcid_string
}}, True, True)
def monitored_fresh(date):
tardate = common.get_lastN_date(date, 120)
freshList = set()
results = mongodb.stock.ZDT_by_date.find({
"date": {
"$gte": tardate,
"$lte": date
},
"Add_newStocks": {
"$exists": True
}
})
for result in results:
tmp = result['Add_newStocks'].keys()
if u'未公布' in tmp:
tmp.remove(u'未公布')
freshList.update(tmp)
tmp = list(freshList)
todayfresh = mongodb.stock.ZDT_by_date.find_one({'date':
date})['freshStocks']
tmp = [x for x in freshList if x not in todayfresh]
freshList = list(set(tmp))
mongodb.stock.ZDT_by_date.update(
{"date": date},
{"$set": {
"monitorFreshStocks": "_".join(freshList),
"date": date
}}, True)
return freshList
def add_rank_chg(dframe, chg_days=[5, 25], sort_v=5):
# dframe = dframe.sort('TRADE_DATE', ascending='True')
dframe = dframe.sort_values(by=['TRADE_DATE'], ascending='True')
dframe = dframe[[
'TICKER_SYMBOL', 'TRADE_DATE', 'PRE_CLOSE_PRICE', 'CLOSE_PRICE'
]]
dframe['daily_ratio'] = 100 * (
dframe['CLOSE_PRICE'] -
dframe['PRE_CLOSE_PRICE']) / dframe['PRE_CLOSE_PRICE']
dframe['daily_ratio'] = dframe['daily_ratio'].round(2)
oframe = pd.DataFrame()
max_day = max(dframe.TRADE_DATE.values)
for chg_day in chg_days:
# 求出最近N天的涨跌幅
n_day = common.get_lastN_date(max_day, chg_day)
n_day = common.format_date(n_day, "%Y-%m-%d")
tmp_frame = dframe[dframe.TRADE_DATE >= n_day]
for stockid, stockframe in tmp_frame.groupby('TICKER_SYMBOL'):
oframe.loc[stockid, '%s_ratio' % chg_day] = 100 * (
np.prod(1 + stockframe['daily_ratio'] / 100) - 1)
oframe.reset_index(inplace=True)
oframe.rename(columns={"index": "TICKER_SYMBOL"}, inplace=True)
oframe['rank'] = oframe['%s_ratio' % sort_v].rank(method='min',
ascending=False)
# oframe = oframe.sort("rank")
oframe = oframe.sort_values(by=["rank"])
return oframe
def updatefreshlist(self):
tardate = common.get_lastN_date(self.today, 120)
tardate = common.format_date(tardate, "%Y%m%d")
results = self.mongodb.stock.ZDT_by_date.find({
"date": {
"$gte": tardate,
"$lte": self.today
},
"Add_newStocks": {
"$exists": True
}
})
for result in results:
tmp = result['Add_newStocks'].keys()
if u'未公布' in tmp:
tmp.remove(u'未公布')
self.freshList.update(tmp)
tmp = list(self.freshList)
todayfresh = self.mongodb.stock.ZDT_by_date.find_one(
{'date': self.yestoday})['freshStocks']
tmp = [x for x in self.freshList if x not in todayfresh]
self.freshList = set(tmp)
self.mongodb.stock.ZDT_by_date.update({"date": self.today}, {
"$set": {
"monitorFreshStocks": "_".join(self.freshList),
"date": self.today
}
}, True)
print "update Monitored fresh stocks finished!"
def study_plot(stcid, date, plot_dir):
# print stcid,date
tmp_array = [date, None, stcid, u'买入', 100]
LearnFrom.study_plot(tmp_array, plot_dir)
nextdate = common.get_lastN_date(date, -1)
tmp_array = [nextdate, None, stcid, u'卖出', 100]
LearnFrom.study_plot(tmp_array, plot_dir)
print "finish %s" % stcid
def __init__(self):
mongoUrl="localhost"
self.period = 120
self.mongodb = pymongo.MongoClient(mongoUrl)
self.today = datetime.date.today().strftime("%Y%m%d")
self.tday = common.get_lastN_date(self.today,self.period)
self.highFrame = pd.DataFrame()
self.dateList = common.getDate(self.tday,self.today)
def __init__(self):
mongoUrl = "localhost"
self.period = 120
self.mongodb = pymongo.MongoClient(mongoUrl)
self.today = datetime.date.today().strftime("%Y%m%d")
self.tday = common.get_lastN_date(self.today, self.period)
self.highFrame = pd.DataFrame()
self.dateList = common.getDate(self.tday, self.today)
def concept_pipeline_html(self, default_period=30, csv_dir=u'D:/Money/modeResee/复盘'):
concepts = common.get_focused_concepts() # 热力图的纵轴
back_day = common.get_lastN_date(self.day, default_period)
day_list = common.getDate(back_day, self.day) # 热力图横轴
zt_concepts_group = [0] * (len(day_list)*len(concepts))
hd_concepts_group = [0] * (len(day_list)*len(concepts))
dt_concepts_group = [0] * (len(day_list)*len(concepts))
meat_concepts_group = [0] * (len(day_list)*len(concepts))
hole_concepts_group = [0] * (len(day_list)*len(concepts))
group_list = [zt_concepts_group, hd_concepts_group, dt_concepts_group, meat_concepts_group, hole_concepts_group]
character_list = ['ZT', 'HD', 'DT', 'meat', 'hole']
# character_list = ['ZT']
group_list = group_list[:len(character_list)]
for i in range(0, len(day_list)):
tarday = day_list[i]
print tarday
tarday = common.format_date(tarday, "%Y%m%d")
dframe = pd.read_csv(os.path.join(csv_dir, "%s/daydayup.csv" %tarday), encoding='gbk')
dframe['len_status'] = dframe['stock'].apply(lambda x: len(str(x)))
dframe = dframe[dframe.len_status > 0]
for j in range(0, len(concepts)):
concept = concepts[j]
# 相近的名称
if concept in common.close_concept_dict.keys():
concept_near_list = common.close_concept_dict[concept]
elif concept in common.close_concept_value_dict.keys():
real_concept = common.close_concept_value_dict[concept]
concept_near_list = common.close_concept_dict[real_concept]
else:
concept_near_list = [concept]
cframe = dframe[dframe.group.isin(concept_near_list)]
for k in range(0, len(character_list)):
tmp_frame = cframe[cframe.type == character_list[k]]
stock_name = list(set(list(np.unique(tmp_frame.name.values))))
num = len(stock_name)
stock_name = "_".join(stock_name)
position = j*len(day_list) + i
group_list[k][position] = [j, i, num, stock_name]
dframe_list = []
day_list = [common.format_date(x, "%m%d") for x in day_list]
for i in range(0, len(group_list)):
dframe_list.append(common.get_dataframe_option3(group_list[i], "_".join(concepts), "_".join(day_list), character_list[i]))
type_list = [3] * len(dframe_list)
title_list = character_list
save_day = common.format_date(self.day, "%Y%m%d")
common.get_html_curve(dframe_list, u"concepts_flow", html_types=type_list, title_list=title_list, save_dir=os.path.join(u"D:/Money/modeResee/复盘/%s"%save_day, ""))
def updatefreshlist(self):
tardate = common.get_lastN_date(self.today, 120)
tardate = common.format_date(tardate, "%Y%m%d")
results = self.mongodb.stock.ZDT_by_date.find({"date": {"$gte": tardate, "$lte": self.today},"Add_newStocks": {"$exists": True}})
for result in results:
tmp = result['Add_newStocks'].keys()
if u'未公布' in tmp:
tmp.remove(u'未公布')
self.freshList.update(tmp)
tmp = list(self.freshList)
todayfresh = self.mongodb.stock.ZDT_by_date.find_one({'date':self.yestoday})['freshStocks']
tmp = [x for x in self.freshList if x not in todayfresh]
self.freshList = set(tmp)
self.mongodb.stock.ZDT_by_date.update({"date":self.today},{"$set":{"monitorFreshStocks":"_".join(self.freshList),"date":self.today}},True)
print "update Monitored fresh stocks finished!"
def add_rank_chg(dframe, chg_days=[5, 25], sort_v=5):
# dframe = dframe.sort('TRADE_DATE', ascending='True')
dframe = dframe.sort_values(by=['TRADE_DATE'], ascending='True')
dframe = dframe[['TICKER_SYMBOL', 'TRADE_DATE', 'PRE_CLOSE_PRICE', 'CLOSE_PRICE']]
dframe['daily_ratio'] = 100*(dframe['CLOSE_PRICE'] - dframe['PRE_CLOSE_PRICE'])/dframe['PRE_CLOSE_PRICE']
dframe['daily_ratio'] = dframe['daily_ratio'].round(2)
oframe = pd.DataFrame()
max_day = max(dframe.TRADE_DATE.values)
for chg_day in chg_days:
# 求出最近N天的涨跌幅
n_day = common.get_lastN_date(max_day, chg_day)
n_day = common.format_date(n_day, "%Y-%m-%d")
tmp_frame = dframe[dframe.TRADE_DATE >= n_day]
for stockid, stockframe in tmp_frame.groupby('TICKER_SYMBOL'):
oframe.loc[stockid, '%s_ratio' % chg_day] = 100*(np.prod(1+stockframe['daily_ratio']/100)-1)
oframe.reset_index(inplace=True)
oframe.rename(columns={"index":"TICKER_SYMBOL"}, inplace=True)
oframe['rank'] = oframe['%s_ratio' % sort_v].rank(method='min', ascending=False)
# oframe = oframe.sort("rank")
oframe = oframe.sort_values(by=["rank"])
return oframe
def parse_content(self, content):
content = content.replace(u'var kcSTlIop=', u'').replace(
u'pages', u'"pages"').replace(u'data', u'"data"')
con_dict = eval(content)
# 当前时间点
ttime = time.localtime()
thour = ttime.tm_hour
tmin = ttime.tm_min
day_list = []
if thour >= 18: # 晚上6点以后，可以得到当天收盘价了，关注复牌时间点为[明天，今天，昨天]
for n in range(2 - self.dayRange, 2):
day_list.append(common.get_lastN_date(self.date, n))
else: # 否则，关注时间点为[今天，昨天，前天]
for n in range(1 - self.dayRange, 1):
day_list.append(common.get_lastN_date(self.date, n))
for infos in con_dict['data']:
elements = infos.split(",")
if len(elements) < 2:
continue
secid = elements[0]
secname = elements[1]
tp_time = elements[-2]
fp_time = elements[-1]
reason = elements[5]
db_num = self.mongodb.stock.FP.find({
"stcid": "%s" % secid,
"tp_time": "%s" % tp_time,
"fp_time": "%s" % fp_time,
"target_ratio": {
"$exists": True
}
}).count()
if db_num > 0:
continue
else:
self.mongodb.stock.FP.update(
{
"stcid": "%s" % secid,
"tp_time": "%s" % tp_time
}, {
"$set": {
"stcid": "%s" % secid,
"tp_time": "%s" % tp_time,
"fp_time": "%s" % fp_time,
"reason": "%s" % reason
}
}, True, True)
# 仅仅记录目标日期在关注日期内的信息
if fp_time in day_list:
# 期间大盘涨幅
if secid[:2] == '60':
dp_stockid = 'ZS000001'
else:
dp_stockid = 'ZS399006'
dp_frame = common.get_mysqlData(
[dp_stockid],
[tp_time, common.get_last_date(fp_time)])
if len(dp_frame) < 2:
continue
dp_ratio = (dp_frame.loc[1, 'CLOSE_PRICE'] -
dp_frame.loc[0, 'CLOSE_PRICE']
) / dp_frame.loc[0, 'CLOSE_PRICE']
dp_ratio = round(dp_ratio, 2)
# 停牌时价格
stock_price = common.get_mysqlData(
[secid], [tp_time]).loc[0, 'CLOSE_PRICE']
if dp_ratio > 0:
diff = 0.05
else:
diff = -0.05
# 目标涨幅
target_ratio = dp_ratio + diff
# 目标价格
target_price = round(stock_price * (1 + target_ratio), 2)
# 停牌时间
delta_days = common.get_mongoDicts(dateStart=tp_time,
dateEnd=fp_time).count()
self.mongodb.stock.FP.update(
{
"stcid": "%s" % secid,
"tp_time": "%s" % tp_time
}, {
"$set": {
"dp_ratio": dp_ratio,
"target_ratio": target_ratio,
"target_price": target_price,
'delta_days': delta_days
}
}, True, True)
def plot_stocks_html(self):
n_day_before = common.get_lastN_date(self.running_day, 80)
concept_frame_list = []
all_local_frame = common.FindConceptStocks(n_day_before,
self.running_day)
# 收集所有关注概念以及对应的股票代码
for concept in self.concepts:
concept_stocks = []
# 云财经中相关股票
ycj_stocks = common.get_ycj_stocks(concept)
# 金融界中相关股票
jrj_stocks = common.get_jrj_stocks(concept)
# 本地csv中相关股票
csv_stocks = all_local_frame.filter_by_concept(concept)
# 合并到一起
concept_stocks.extend(ycj_stocks)
concept_stocks.extend(jrj_stocks)
concept_stocks.extend(csv_stocks.ticker.values)
concept_stocks = list(set(concept_stocks))
tmp_concept_frame = pd.DataFrame({"stockid": concept_stocks})
tmp_concept_frame['concept'] = concept
concept_frame_list.append(tmp_concept_frame)
# index stockid, concept
concept_frame = pd.concat(concept_frame_list, axis=0)
daily_stock_list = list(np.unique(concept_frame.stockid.values))
daily_stock_list.extend(self.extra_stocks)
daily_stock_list = list(set(daily_stock_list))
# 拿到这些股票，指定日期的所有日线数据
daily_frame = common.get_mysqlData(
daily_stock_list, common.getDate(n_day_before, self.running_day))
# daily_frame.to_csv(u'all_daily_frame.csv', encoding='gbk')
daily_frame['rate'] = 100 * (
daily_frame['CLOSE_PRICE'] -
daily_frame['PRE_CLOSE_PRICE']) / daily_frame['PRE_CLOSE_PRICE']
daily_frame['rate'] = daily_frame['rate'].round(2)
daily_frame['lrate'] = 100 * (
daily_frame['LOWEST_PRICE'] -
daily_frame['PRE_CLOSE_PRICE']) / daily_frame['PRE_CLOSE_PRICE']
daily_frame['lrate'] = daily_frame['lrate'].round(2)
daily_frame['hrate'] = 100 * (
daily_frame['HIGHEST_PRICE'] -
daily_frame['PRE_CLOSE_PRICE']) / daily_frame['PRE_CLOSE_PRICE']
daily_frame['hrate'] = daily_frame['hrate'].round(2)
daily_frame['TRADE_DATE'] = daily_frame['TRADE_DATE'].apply(
lambda x: x.strftime("%Y-%m-%d"))
# 复权数据，以便画k线图
fq_daily_frame = common.get_mysqlData(daily_stock_list,
common.getDate(
n_day_before,
self.running_day),
db_table='vmkt_equd_adj')
fq_daily_frame['rate'] = 100 * (fq_daily_frame['CLOSE_PRICE'] -
fq_daily_frame['PRE_CLOSE_PRICE']
) / fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['rate'] = fq_daily_frame['rate'].round(2)
fq_daily_frame['lrate'] = 100 * (fq_daily_frame['LOWEST_PRICE'] -
fq_daily_frame['PRE_CLOSE_PRICE']
) / fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['lrate'] = fq_daily_frame['lrate'].round(2)
fq_daily_frame['hrate'] = 100 * (fq_daily_frame['HIGHEST_PRICE'] -
fq_daily_frame['PRE_CLOSE_PRICE']
) / fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['hrate'] = fq_daily_frame['hrate'].round(2)
fq_daily_frame['TRADE_DATE'] = fq_daily_frame['TRADE_DATE'].apply(
lambda x: x.strftime("%Y-%m-%d"))
# 筛选出符合条件的股票
# 连续3天以上涨停
# condition1_frame = filter_stock_by_zt_condition(daily_frame, condition='ZT', zt_num=3)
#{"concept":concept_frame, "concept":concept_frame2}
# concept_frame: index TICKER_SYMBOL 5_chg 25_chg N_chg rank
self.concept_rank_dict = {}
for concept in self.concepts:
print concept
concept_stock_list = list(
concept_frame[concept_frame.concept == concept].stockid.values)
# 去除掉rmv对应的股票，以及加上mst对于的股票
if concept in self.rmv_dict.keys():
concept_stock_list = [
x for x in concept_stock_list
if x not in self.rmv_dict[concept]
]
if concept in self.mst_dict.keys():
concept_stock_list.extend(self.mst_dict[concept])
# 概念对应的所有股票的日线数据
concept_daily_frame = daily_frame[daily_frame.TICKER_SYMBOL.isin(
concept_stock_list)]
# 统计出其涨幅排行
concept_rank_chg_frame = add_rank_chg(concept_daily_frame,
chg_days=[5, 25],
sort_v=5)
# 得到连续N个涨停以上的股票
condition1_frame = filter_stock_by_zt_condition(
daily_frame[daily_frame.TICKER_SYMBOL.isin(
concept_stock_list)],
condition='ZT',
zt_num=self.direct10_num[concept])
# print concept,self.direct10_num[concept]
# 得到涨幅排行前N的股票
if len(condition1_frame) < 3:
tail_n = 10 - len(condition1_frame)
else:
tail_n = 5
stock_list = list(
concept_rank_chg_frame.sort_values(
by=['rank'],
ascending=False).tail(tail_n).TICKER_SYMBOL.values)
stock_list.extend(condition1_frame.TICKER_SYMBOL.values)
# 再加上必须关注的股票
if concept in self.mst_dict.keys():
stock_list.extend(self.mst_dict[concept])
stock_list = list(set(stock_list))
# 记录到mongo中daily_interesting_concept_stocks
self.write_mongo_concept_day(stock_list, concept, self.running_day)
# 画图
k_line_frame_list = []
for stock in stock_list:
stock_frame = fq_daily_frame[
fq_daily_frame.TICKER_SYMBOL == stock][[
u'TRADE_DATE', 'OPEN_PRICE', 'CLOSE_PRICE',
'HIGHEST_PRICE', 'LOWEST_PRICE', 'TURNOVER_VOL',
'TICKER_SYMBOL', 'rate', 'lrate', 'hrate',
'SEC_SHORT_NAME'
]]
stock_frame.columns = [
'date', 'open', 'close', 'high', 'low', 'volume', 'code',
'rate', 'lrate', 'hrate', 'name'
]
stock_frame['name'] = stock_frame['name'].apply(
lambda x: x.encode('gb2312'))
# index row，date，open, close, high, low, volume, code
# stock_frame = stock_frame.sort('date', ascending='True')
stock_frame = stock_frame.sort_values(by=['date'],
ascending='True')
stock_frame.reset_index(inplace=True)
stock_frame['name'] = stock_frame['name'].apply(
lambda x: x.decode("gbk").encode("utf-8"))
del stock_frame['index']
k_line_frame_list.append(stock_frame)
# 只显示感兴趣的股票的排行和涨幅数据
concept_rank_chg_frame = concept_rank_chg_frame[
concept_rank_chg_frame.TICKER_SYMBOL.isin(stock_list)]
concept_rank_chg_frame = concept_rank_chg_frame.sort_values(
by=['rank'], ascending=True)
concept_rank_chg_frame['stock_name'] = concept_rank_chg_frame[
'TICKER_SYMBOL'].apply(lambda x: common.QueryStockMap(x)[0])
names = [
x for x in concept_rank_chg_frame.columns
if x not in ['stock_name', 'TICKER_SYMBOL']
]
valid_name = ['stock_name']
valid_name.extend(names)
concept_rank_chg_frame = concept_rank_chg_frame[valid_name]
# 构造一个OPTION4&6，左边为K线图，右边为条形图，以及下面一系列的k线图
plot_frame_list = [[k_line_frame_list[0], concept_rank_chg_frame]]
plot_frame_list.extend(k_line_frame_list[1:])
# 构造对应的stock_list，标识对应的股票代码
plot_stock_list = []
for kline_frame in k_line_frame_list:
plot_stock_list.append(kline_frame['code'].values[0])
html_types = ['4and6']
html_types.extend([4] * len(k_line_frame_list[1:]))
title_list = [['A_stock', 'accum_ratio']]
title_list.extend(['A_stocks'] * len(k_line_frame_list[1:]))
# 在加一个统计区间涨跌幅
# range_ratio = {
# concept_name:[[start1, end1],[start2, end2], [start3, end3], ...],
# concept_name2:[[start1, end1],[start2, end2], [start3, end3], ...],
# }
# 说明该概念需要画出不同时期的涨跌幅对比
if concept in self.range_ratio_dict.keys():
range_ratio_frame = pd.DataFrame()
for [sday, eday] in self.range_ratio_dict[concept]:
col_name = "%s-%s" % (common.format_date(
sday, "%Y%m%d"), common.format_date(eday, "%Y%m%d"))
for stock in stock_list:
range_ratio_frame.loc[
stock, col_name] = common.time_range_accum_ratio(
stock, sday, eday)
range_ratio_frame.reset_index(inplace=True)
range_ratio_frame.rename(columns={"index": "stock_name"},
inplace=True)
range_ratio_frame['stock_name'] = range_ratio_frame[
'stock_name'].apply(lambda x: common.QueryStockMap(x)[0])
# 添加到画图中
plot_frame_list.append(range_ratio_frame)
plot_stock_list.append(0)
html_types.append(7)
title_list.append('ACCUM_RATIO')
# 输出html
common.get_html_curve(plot_frame_list,
'%s' % (concept),
html_types=html_types,
title_list=title_list,
save_dir=os.path.join(
self.dump_dir, self.running_day),
chart_connect=1,
plot_stock_list=plot_stock_list,
generate_date=self.running_day)
logging.getLogger().info("plot html finished for %s, %s" %
(concept, self.running_day))
print "Finished for %s" % self.running_day
def get_stopDetail(stockid, dates, mysqls):
global diOut_dict, backTen_dict, tenMonitor_dict
stockid = '0' * (6 - len(stockid)) + stockid
edates = common.get_lastN_date(dates, -50)
dates = common.format_date(dates, "%Y-%m-%d")
edates = common.format_date(edates, "%Y-%m-%d")
table = "vmkt_equd"
query = 'SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_PRICE, OPEN_PRICE, HIGHEST_PRICE, LOWEST_PRICE, CLOSE_PRICE, ACT_PRE_CLOSE_PRICE ' \
'from %s where TICKER_SYMBOL = "%s" and TRADE_DATE >= "%s" and TRADE_DATE <= "%s"' % (table, stockid, dates, edates)
z = mysqls.dydb_query(query)
z['actualRatio'] = (z['CLOSE_PRICE'].astype(np.float64) /
z['PRE_CLOSE_PRICE'].astype(np.float64)) - 1
z['actualRatio'] = (100 * z['actualRatio']).round(2)
z['priceTen'] = z['PRE_CLOSE_PRICE'].astype(np.float64) * 1.1
z['priceTen'] = z['priceTen'].round(2)
z['closeStatus'] = z['CLOSE_PRICE'] - z['priceTen']
z['closeStatus'] = z['closeStatus'].apply(lambda x: True
if x >= 0 else False)
z['lowStatus'] = z['LOWEST_PRICE'] - z['priceTen']
z['lowStatus'] = z['lowStatus'].apply(lambda x: True if x >= 0 else False)
print stockid
# 记录第一次的时间点
# 如果当前还在涨停期间，跳过
if len(z[z.lowStatus != True].index.values) == 0:
return 100, "2018-01-01", "No Result", "INF", "2018-01-01", "100"
print stockid
first_low_out_index = z[z.lowStatus != True].index.values[0]
first_low_out_day = z.loc[first_low_out_index, 'TRADE_DATE']
# 当前还在回封期， 开板日设置为2018-01-01， 开板的index设置为无穷大
if len(z[z.closeStatus != True].index.values) == 0:
first_close_out_index = 10000
first_close_out_day = "2018-01-01"
else:
first_close_out_index = z[z.closeStatus != True].index.values[0]
first_close_out_day = z.loc[first_close_out_index, 'TRADE_DATE']
if first_low_out_index == first_close_out_index: # 开板没有回封
diout_ratio = z.loc[first_close_out_index, 'actualRatio']
diout_ratio = round(diout_ratio, 2)
if first_low_out_day not in diOut_dict.keys():
diOut_dict[first_low_out_day] = {
"stock": [stockid],
"ratio": [diout_ratio]
}
else:
diOut_dict[first_low_out_day]['stock'].append(stockid)
diOut_dict[first_low_out_day]['ratio'].append(diout_ratio)
else: # 开板后回封
diback_ratio = z.loc[first_low_out_index, 'actualRatio']
diback_ratio = round(diback_ratio, 2)
# 记录每天的回封个数
if first_low_out_day not in backTen_dict.keys():
backTen_dict[first_low_out_day] = {
"stock": [stockid],
"ratio": [diback_ratio]
}
else:
backTen_dict[first_low_out_day]['stock'].append(stockid)
backTen_dict[first_low_out_day]['ratio'].append(diback_ratio)
# 记录开板
if first_close_out_index != 10000:
diout_ratio = z.loc[first_close_out_index, 'actualRatio']
diout_ratio = round(diout_ratio, 2)
if first_close_out_day not in diOut_dict.keys():
diOut_dict[first_close_out_day] = {
"stock": [stockid],
"ratio": [diout_ratio]
}
else:
diOut_dict[first_close_out_day]['stock'].append(stockid)
diOut_dict[first_close_out_day]['ratio'].append(diout_ratio)
# 记录每天回封后的涨幅（即：A日回封，B日不再涨停，记录A+1日到B日至今的涨幅）
for idx in range(first_low_out_index + 1, first_close_out_index + 1):
if idx > max(z.index.values):
break
# idx = max(z.index.values)
day = z.loc[idx, 'TRADE_DATE']
# today = datetime.datetime.now()
# print day, today
# if day == datetime.date(2016, 11, 7) and stockid == "300548":
# print 'abc'
# if day > datetime.date(today.year, today.month, today.day):
# print 'yes'
# break
# 这里记录的是收盘涨幅，开板再回封的情况暂时不考虑
ratio = z.loc[idx, 'actualRatio']
if day not in tenMonitor_dict.keys():
tenMonitor_dict[day] = {"stock": [stockid], "ratio": [ratio]}
else:
tenMonitor_dict[day]['stock'].append(stockid)
tenMonitor_dict[day]['ratio'].append(ratio)
## 此处不全，diOut不仅仅是first_low_out_index， 还包括回封后开板的
# return
# 返回涨停数，开板日，开板状态（是否回封）， 非涨停日，非涨停日涨幅
if first_close_out_index == 10000:
actualRatio = 100
else:
actualRatio = z.loc[first_close_out_index, 'actualRatio']
return first_close_out_index, first_low_out_day, first_close_out_index > first_low_out_index, first_close_out_index - first_low_out_index, first_close_out_day, actualRatio
def parse_content(self, content):
content = content.replace(u'var kcSTlIop=', u'').replace(u'pages', u'"pages"').replace(u'data', u'"data"')
con_dict = eval(content)
# 当前时间点
ttime = time.localtime()
thour = ttime.tm_hour
tmin = ttime.tm_min
day_list = []
if thour >= 18: # 晚上6点以后，可以得到当天收盘价了，关注复牌时间点为[明天，今天，昨天]
for n in range(2-self.dayRange, 2):
day_list.append(common.get_lastN_date(self.date, n))
else: # 否则，关注时间点为[今天，昨天，前天]
for n in range(1-self.dayRange, 1):
day_list.append(common.get_lastN_date(self.date, n))
for infos in con_dict['data']:
elements = infos.split(",")
if len(elements) < 2:
continue
secid = elements[0]
secname = elements[1]
tp_time = elements[-2]
fp_time = elements[-1]
reason = elements[5]
db_num = self.mongodb.stock.FP.find({"stcid":"%s" % secid, "tp_time":"%s" % tp_time, "fp_time":"%s" % fp_time, "target_ratio":{"$exists":True}}).count()
if db_num > 0:
continue
else:
self.mongodb.stock.FP.update({"stcid":"%s" % secid, "tp_time":"%s" % tp_time},
{"$set":{"stcid":"%s" % secid, "tp_time":"%s" % tp_time, "fp_time":"%s" % fp_time, "reason":"%s" %reason}},
True, True)
# 仅仅记录目标日期在关注日期内的信息
if fp_time in day_list:
# 期间大盘涨幅
if secid[:2] == '60':
dp_stockid = 'ZS000001'
else:
dp_stockid = 'ZS399006'
dp_frame = common.get_mysqlData([dp_stockid], [tp_time, common.get_last_date(fp_time)])
if len(dp_frame) < 2:
continue
dp_ratio = (dp_frame.loc[1, 'CLOSE_PRICE'] - dp_frame.loc[0, 'CLOSE_PRICE']) / dp_frame.loc[0, 'CLOSE_PRICE']
dp_ratio = round(dp_ratio, 2)
# 停牌时价格
stock_price = common.get_mysqlData([secid], [tp_time]).loc[0, 'CLOSE_PRICE']
if dp_ratio > 0:
diff = 0.05
else:
diff = -0.05
# 目标涨幅
target_ratio = dp_ratio + diff
# 目标价格
target_price = round(stock_price*(1+target_ratio), 2)
# 停牌时间
delta_days = common.get_mongoDicts(dateStart=tp_time,dateEnd=fp_time).count()
self.mongodb.stock.FP.update({"stcid":"%s" % secid, "tp_time":"%s" % tp_time},
{"$set":{"dp_ratio":dp_ratio, "target_ratio":target_ratio, "target_price": target_price, 'delta_days':delta_days}},
True, True)
def get_stopDetail(stockid, dates, mysqls):
global diOut_dict, backTen_dict, tenMonitor_dict
stockid = '0'*(6-len(stockid)) + stockid
edates = common.get_lastN_date(dates, -50)
dates = common.format_date(dates,"%Y-%m-%d")
edates = common.format_date(edates,"%Y-%m-%d")
table="vmkt_equd"
query = 'SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_PRICE, OPEN_PRICE, HIGHEST_PRICE, LOWEST_PRICE, CLOSE_PRICE, ACT_PRE_CLOSE_PRICE ' \
'from %s where TICKER_SYMBOL = "%s" and TRADE_DATE >= "%s" and TRADE_DATE <= "%s"' % (table, stockid, dates, edates)
z = mysqls.dydb_query(query)
z['actualRatio'] = (z['CLOSE_PRICE'].astype(np.float64) /z['PRE_CLOSE_PRICE'].astype(np.float64)) - 1
z['actualRatio'] = (100*z['actualRatio']).round(2)
z['priceTen'] = z['PRE_CLOSE_PRICE'].astype(np.float64) * 1.1
z['priceTen'] = z['priceTen'].round(2)
z['closeStatus'] = z['CLOSE_PRICE'] - z['priceTen']
z['closeStatus'] = z['closeStatus'].apply(lambda x: True if x >= 0 else False)
z['lowStatus'] = z['LOWEST_PRICE'] - z['priceTen']
z['lowStatus'] = z['lowStatus'].apply(lambda x: True if x >= 0 else False)
print stockid
# 记录第一次的时间点
# 如果当前还在涨停期间，跳过
if len(z[z.lowStatus != True].index.values) == 0:
return 100, "2018-01-01", "No Result", "INF", "2018-01-01", "100"
print stockid
first_low_out_index = z[z.lowStatus != True].index.values[0]
first_low_out_day = z.loc[first_low_out_index, 'TRADE_DATE']
# 当前还在回封期， 开板日设置为2018-01-01， 开板的index设置为无穷大
if len(z[z.closeStatus != True].index.values) == 0:
first_close_out_index = 10000
first_close_out_day = "2018-01-01"
else:
first_close_out_index = z[z.closeStatus != True].index.values[0]
first_close_out_day = z.loc[first_close_out_index, 'TRADE_DATE']
if first_low_out_index == first_close_out_index: # 开板没有回封
diout_ratio = z.loc[first_close_out_index, 'actualRatio']
diout_ratio = round(diout_ratio,2)
if first_low_out_day not in diOut_dict.keys():
diOut_dict[first_low_out_day] = {"stock":[stockid], "ratio":[diout_ratio]}
else:
diOut_dict[first_low_out_day]['stock'].append(stockid)
diOut_dict[first_low_out_day]['ratio'].append(diout_ratio)
else: # 开板后回封
diback_ratio = z.loc[first_low_out_index, 'actualRatio']
diback_ratio = round(diback_ratio,2)
# 记录每天的回封个数
if first_low_out_day not in backTen_dict.keys():
backTen_dict[first_low_out_day] = {"stock":[stockid], "ratio":[diback_ratio]}
else:
backTen_dict[first_low_out_day]['stock'].append(stockid)
backTen_dict[first_low_out_day]['ratio'].append(diback_ratio)
# 记录开板
if first_close_out_index != 10000:
diout_ratio = z.loc[first_close_out_index, 'actualRatio']
diout_ratio = round(diout_ratio,2)
if first_close_out_day not in diOut_dict.keys():
diOut_dict[first_close_out_day] = {"stock":[stockid], "ratio":[diout_ratio]}
else:
diOut_dict[first_close_out_day]['stock'].append(stockid)
diOut_dict[first_close_out_day]['ratio'].append(diout_ratio)
# 记录每天回封后的涨幅（即：A日回封，B日不再涨停，记录A+1日到B日至今的涨幅）
for idx in range(first_low_out_index+1, first_close_out_index+1):
if idx > max(z.index.values):
break
# idx = max(z.index.values)
day = z.loc[idx, 'TRADE_DATE']
# today = datetime.datetime.now()
# print day, today
# if day == datetime.date(2016, 11, 7) and stockid == "300548":
# print 'abc'
# if day > datetime.date(today.year, today.month, today.day):
# print 'yes'
# break
# 这里记录的是收盘涨幅，开板再回封的情况暂时不考虑
ratio = z.loc[idx, 'actualRatio']
if day not in tenMonitor_dict.keys():
tenMonitor_dict[day] = {"stock":[stockid], "ratio":[ratio]}
else:
tenMonitor_dict[day]['stock'].append(stockid)
tenMonitor_dict[day]['ratio'].append(ratio)
## 此处不全，diOut不仅仅是first_low_out_index， 还包括回封后开板的
# return
# 返回涨停数，开板日，开板状态（是否回封）， 非涨停日，非涨停日涨幅
if first_close_out_index == 10000:
actualRatio = 100
else:
actualRatio = z.loc[first_close_out_index, 'actualRatio']
return first_close_out_index, first_low_out_day, first_close_out_index > first_low_out_index, first_close_out_index - first_low_out_index,first_close_out_day,actualRatio
# coding:utf-8
import pandas as pd
import numpy as np
import hot_district
import common
import dragon_catcher
x = dragon_catcher.DRAGON_POOL()
start_date = "20170908"
while 1:
if start_date >= "20170918":
break
next_date = common.get_lastN_date(start_date, -1)
next_date = common.format_date(next_date, "%Y%m%d")
x.copy_pool_dragon(start_date, next_date)
print "copy %s to %s" %(start_date, next_date)
start_date = next_date
# daily_frame = pd.read_csv(u'all_daily_frame.csv', encoding='gbk',dtype={"TICKER_SYMBOL":np.str}, index_col=0)
# daily_frame.head()
#
#
# tpath = u'D:/Money/modeResee/彼战'
# concept_frame = pd.read_excel(os.path.join(tpath, u'战区.xlsx'), encoding='gbk', sheetname=u'战区位置')
# concept_frame = concept_frame[concept_frame[u'开战状态'] == 1]
# concepts = concept_frame[u'战区'].values
#
def concept_pipeline_html(self,
default_period=30,
csv_dir=u'D:/Money/modeResee/复盘'):
concepts = common.get_focused_concepts() # 热力图的纵轴
back_day = common.get_lastN_date(self.day, default_period)
day_list = common.getDate(back_day, self.day) # 热力图横轴
zt_concepts_group = [0] * (len(day_list) * len(concepts))
hd_concepts_group = [0] * (len(day_list) * len(concepts))
dt_concepts_group = [0] * (len(day_list) * len(concepts))
meat_concepts_group = [0] * (len(day_list) * len(concepts))
hole_concepts_group = [0] * (len(day_list) * len(concepts))
group_list = [
zt_concepts_group, hd_concepts_group, dt_concepts_group,
meat_concepts_group, hole_concepts_group
]
character_list = ['ZT', 'HD', 'DT', 'meat', 'hole']
# character_list = ['ZT']
group_list = group_list[:len(character_list)]
for i in range(0, len(day_list)):
tarday = day_list[i]
print tarday
tarday = common.format_date(tarday, "%Y%m%d")
dframe = pd.read_csv(os.path.join(csv_dir,
"%s/daydayup.csv" % tarday),
encoding='gbk')
dframe['len_status'] = dframe['stock'].apply(lambda x: len(str(x)))
dframe = dframe[dframe.len_status > 0]
for j in range(0, len(concepts)):
concept = concepts[j]
# 相近的名称
if concept in common.close_concept_dict.keys():
concept_near_list = common.close_concept_dict[concept]
elif concept in common.close_concept_value_dict.keys():
real_concept = common.close_concept_value_dict[concept]
concept_near_list = common.close_concept_dict[real_concept]
else:
concept_near_list = [concept]
cframe = dframe[dframe.group.isin(concept_near_list)]
for k in range(0, len(character_list)):
tmp_frame = cframe[cframe.type == character_list[k]]
stock_name = list(
set(list(np.unique(tmp_frame.name.values))))
num = len(stock_name)
stock_name = "_".join(stock_name)
position = j * len(day_list) + i
group_list[k][position] = [j, i, num, stock_name]
dframe_list = []
day_list = [common.format_date(x, "%m%d") for x in day_list]
for i in range(0, len(group_list)):
dframe_list.append(
common.get_dataframe_option3(group_list[i], "_".join(concepts),
"_".join(day_list),
character_list[i]))
type_list = [3] * len(dframe_list)
title_list = character_list
save_day = common.format_date(self.day, "%Y%m%d")
common.get_html_curve(dframe_list,
u"concepts_flow",
html_types=type_list,
title_list=title_list,
save_dir=os.path.join(
u"D:/Money/modeResee/复盘/%s" % save_day, ""))
results = self.mongodb.concepts.dragon_pool.find({"date":tdate})
if results.count() > 0:
save_dict = results[0]
save_dict[concept_name] = {}
if len(dragon_list) > 0:
save_dict[concept_name]['dragon'] = dragon_list
if len(wind_list) > 0:
save_dict[concept_name]['wind'] = wind_list
self.mongodb.concepts.dragon_pool.update({"date":tdate}, save_dict, upsert=True)
# 将up, down, flat存放到mongo中的pattern, trend表中
for code_str in catch_dict.keys():
tmp_list = catch_dict[code_str]
for ticker in tmp_list:
self.mongodb.pattern.trend.update({"date":tdate, "ticker":ticker},
{"date":tdate, "ticker":ticker, "trend": code_str},
upsert=True)
# 将昨天以及之前记录的龙头、排除等信息更新到存储中，今天生成新的文件就会包含昨天的笔记
if __name__ == '__main__':
x = DRAGON_POOL()
# 将上一日的copy到今日
today = datetime.datetime.today().strftime("%Y%m%d")
lastday = common.get_lastN_date(today, 1)
x.copy_pool_dragon(lastday, today)
x.fresh_dir()
print "dragon pool update finished!"
def plot_stocks_html(self):
n_day_before = common.get_lastN_date(self.running_day, 80)
concept_frame_list = []
all_local_frame = common.FindConceptStocks(n_day_before, self.running_day)
# 收集所有关注概念以及对应的股票代码
for concept in self.concepts:
concept_stocks = []
# 云财经中相关股票
ycj_stocks = common.get_ycj_stocks(concept)
# 金融界中相关股票
jrj_stocks = common.get_jrj_stocks(concept)
# 本地csv中相关股票
csv_stocks = all_local_frame.filter_by_concept(concept)
# 合并到一起
concept_stocks.extend(ycj_stocks)
concept_stocks.extend(jrj_stocks)
concept_stocks.extend(csv_stocks.ticker.values)
concept_stocks = list(set(concept_stocks))
tmp_concept_frame = pd.DataFrame({"stockid":concept_stocks})
tmp_concept_frame['concept'] = concept
concept_frame_list.append(tmp_concept_frame)
# index stockid, concept
concept_frame = pd.concat(concept_frame_list, axis=0)
daily_stock_list = list(np.unique(concept_frame.stockid.values))
daily_stock_list.extend(self.extra_stocks)
daily_stock_list = list(set(daily_stock_list))
# 拿到这些股票，指定日期的所有日线数据
daily_frame = common.get_mysqlData(daily_stock_list, common.getDate(n_day_before, self.running_day))
# daily_frame.to_csv(u'all_daily_frame.csv', encoding='gbk')
daily_frame['rate'] = 100*(daily_frame['CLOSE_PRICE'] - daily_frame['PRE_CLOSE_PRICE'])/daily_frame['PRE_CLOSE_PRICE']
daily_frame['rate'] = daily_frame['rate'].round(2)
daily_frame['lrate'] = 100*(daily_frame['LOWEST_PRICE'] - daily_frame['PRE_CLOSE_PRICE'])/daily_frame['PRE_CLOSE_PRICE']
daily_frame['lrate'] = daily_frame['lrate'].round(2)
daily_frame['hrate'] = 100*(daily_frame['HIGHEST_PRICE'] - daily_frame['PRE_CLOSE_PRICE'])/daily_frame['PRE_CLOSE_PRICE']
daily_frame['hrate'] = daily_frame['hrate'].round(2)
daily_frame['TRADE_DATE'] = daily_frame['TRADE_DATE'].apply(lambda x: x.strftime("%Y-%m-%d"))
# 复权数据，以便画k线图
fq_daily_frame = common.get_mysqlData(daily_stock_list, common.getDate(n_day_before, self.running_day), db_table='vmkt_equd_adj')
fq_daily_frame['rate'] = 100*(fq_daily_frame['CLOSE_PRICE'] - fq_daily_frame['PRE_CLOSE_PRICE'])/fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['rate'] = fq_daily_frame['rate'].round(2)
fq_daily_frame['lrate'] = 100*(fq_daily_frame['LOWEST_PRICE'] - fq_daily_frame['PRE_CLOSE_PRICE'])/fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['lrate'] = fq_daily_frame['lrate'].round(2)
fq_daily_frame['hrate'] = 100*(fq_daily_frame['HIGHEST_PRICE'] - fq_daily_frame['PRE_CLOSE_PRICE'])/fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['hrate'] = fq_daily_frame['hrate'].round(2)
fq_daily_frame['TRADE_DATE'] = fq_daily_frame['TRADE_DATE'].apply(lambda x: x.strftime("%Y-%m-%d"))
# 筛选出符合条件的股票
# 连续3天以上涨停
# condition1_frame = filter_stock_by_zt_condition(daily_frame, condition='ZT', zt_num=3)
#{"concept":concept_frame, "concept":concept_frame2}
# concept_frame: index TICKER_SYMBOL 5_chg 25_chg N_chg rank
self.concept_rank_dict = {}
for concept in self.concepts:
print concept
concept_stock_list = list(concept_frame[concept_frame.concept==concept].stockid.values)
# 去除掉rmv对应的股票，以及加上mst对于的股票
if concept in self.rmv_dict.keys():
concept_stock_list = [x for x in concept_stock_list if x not in self.rmv_dict[concept]]
if concept in self.mst_dict.keys():
concept_stock_list.extend(self.mst_dict[concept])
# 概念对应的所有股票的日线数据
concept_daily_frame = daily_frame[daily_frame.TICKER_SYMBOL.isin(concept_stock_list)]
# 统计出其涨幅排行
concept_rank_chg_frame = add_rank_chg(concept_daily_frame, chg_days=[5, 25], sort_v=5)
# 得到连续N个涨停以上的股票
condition1_frame = filter_stock_by_zt_condition(daily_frame[daily_frame.TICKER_SYMBOL.isin(concept_stock_list)],
condition='ZT', zt_num=self.direct10_num[concept])
# print concept,self.direct10_num[concept]
# 得到涨幅排行前N的股票
if len(condition1_frame) < 3:
tail_n = 10 - len(condition1_frame)
else:
tail_n = 5
stock_list = list(concept_rank_chg_frame.sort_values(by=['rank'], ascending=False).tail(tail_n).TICKER_SYMBOL.values)
stock_list.extend(condition1_frame.TICKER_SYMBOL.values)
# 再加上必须关注的股票
if concept in self.mst_dict.keys():
stock_list.extend(self.mst_dict[concept])
stock_list = list(set(stock_list))
# 记录到mongo中daily_interesting_concept_stocks
self.write_mongo_concept_day(stock_list, concept, self.running_day)
# 画图
k_line_frame_list = []
for stock in stock_list:
stock_frame = fq_daily_frame[fq_daily_frame.TICKER_SYMBOL == stock][[u'TRADE_DATE', 'OPEN_PRICE', 'CLOSE_PRICE', 'HIGHEST_PRICE', 'LOWEST_PRICE',
'TURNOVER_VOL', 'TICKER_SYMBOL', 'rate', 'lrate', 'hrate', 'SEC_SHORT_NAME']]
stock_frame.columns=['date', 'open', 'close', 'high', 'low', 'volume', 'code', 'rate', 'lrate', 'hrate', 'name']
stock_frame['name'] = stock_frame['name'].apply(lambda x: x.encode('gb2312'))
# index row，date，open, close, high, low, volume, code
# stock_frame = stock_frame.sort('date', ascending='True')
stock_frame = stock_frame.sort_values(by=['date'], ascending='True')
stock_frame.reset_index(inplace=True)
stock_frame['name'] = stock_frame['name'].apply(lambda x: x.decode("gbk").encode("utf-8") )
del stock_frame['index']
k_line_frame_list.append(stock_frame)
# 只显示感兴趣的股票的排行和涨幅数据
concept_rank_chg_frame = concept_rank_chg_frame[concept_rank_chg_frame.TICKER_SYMBOL.isin(stock_list)]
concept_rank_chg_frame = concept_rank_chg_frame.sort_values(by=['rank'], ascending=True)
concept_rank_chg_frame['stock_name'] = concept_rank_chg_frame['TICKER_SYMBOL'].apply(lambda x: common.QueryStockMap(x)[0])
names = [x for x in concept_rank_chg_frame.columns if x not in ['stock_name', 'TICKER_SYMBOL']]
valid_name = ['stock_name']
valid_name.extend(names)
concept_rank_chg_frame = concept_rank_chg_frame[valid_name]
# 构造一个OPTION4&6，左边为K线图，右边为条形图，以及下面一系列的k线图
plot_frame_list = [[k_line_frame_list[0], concept_rank_chg_frame]]
plot_frame_list.extend(k_line_frame_list[1:])
# 构造对应的stock_list，标识对应的股票代码
plot_stock_list = []
for kline_frame in k_line_frame_list:
plot_stock_list.append(kline_frame['code'].values[0])
html_types=['4and6']
html_types.extend([4]*len(k_line_frame_list[1:]))
title_list = [['A_stock', 'accum_ratio']]
title_list.extend(['A_stocks']*len(k_line_frame_list[1:]))
# 在加一个统计区间涨跌幅
# range_ratio = {
# concept_name:[[start1, end1],[start2, end2], [start3, end3], ...],
# concept_name2:[[start1, end1],[start2, end2], [start3, end3], ...],
# }
# 说明该概念需要画出不同时期的涨跌幅对比
if concept in self.range_ratio_dict.keys():
range_ratio_frame = pd.DataFrame()
for [sday, eday] in self.range_ratio_dict[concept]:
col_name = "%s-%s"%(common.format_date(sday, "%Y%m%d"), common.format_date(eday, "%Y%m%d"))
for stock in stock_list:
range_ratio_frame.loc[stock, col_name] = common.time_range_accum_ratio(stock, sday, eday)
range_ratio_frame.reset_index(inplace=True)
range_ratio_frame.rename(columns={"index":"stock_name"}, inplace=True)
range_ratio_frame['stock_name'] = range_ratio_frame['stock_name'].apply(lambda x: common.QueryStockMap(x)[0])
# 添加到画图中
plot_frame_list.append(range_ratio_frame)
plot_stock_list.append(0)
html_types.append(7)
title_list.append('ACCUM_RATIO')
# 输出html
common.get_html_curve(plot_frame_list, '%s' %(concept), html_types=html_types,
title_list = title_list, save_dir=os.path.join(self.dump_dir, self.running_day),
chart_connect=1, plot_stock_list=plot_stock_list, generate_date=self.running_day)
logging.getLogger().info("plot html finished for %s, %s" %(concept, self.running_day))
print "Finished for %s" %self.running_day
def study_plot(tmp_array,dirs, name_tail = u'', end_date_interval = 0, start_date_interval = 0):
## 日线dir
daydir = dirs
minsdir = "%s/mins"%dirs
bothdir = "%s/both"%dirs
if os.path.exists(minsdir):
pass
else:
os.mkdir(minsdir)
if os.path.exists(bothdir):
pass
else:
os.mkdir(bothdir)
[ddate,dtime,code,direction,price]=tmp_array
end_date = common.format_date(ddate,"%Y-%m-%d")
start_date = common.get_lastN_date(end_date, start_date_interval)
day_end_date = common.get_lastN_date(end_date, end_date_interval)
code = "0"*(6-len(str(int(code))))+str(int(code))
# 日线数据
sql = "SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_PRICE, OPEN_PRICE, HIGHEST_PRICE, LOWEST_PRICE, CLOSE_PRICE, \
DEAL_AMOUNT from vmkt_equd where TRADE_DATE >= '%s' and TRADE_DATE <='%s' and TICKER_SYMBOL = '%s'"%(start_date,day_end_date,code)
sub = common.get_mysqlData_sqlquery(sql)
sub = regulate_data(sub)
idxcode = "000001"
idxsql = "SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_INDEX, OPEN_INDEX, HIGHEST_INDEX, LOWEST_INDEX, CLOSE_INDEX, \
TURNOVER_VOL from vmkt_idxd where TRADE_DATE >= '%s' and TRADE_DATE <='%s' and TICKER_SYMBOL = '%s'"%(start_date,day_end_date,idxcode)
idx = common.get_mysqlData_sqlquery(idxsql)
idx.columns=sub.columns
add_mean(sub)
add_mean(idx)
tableTime = common.format_date(ddate,"%Y%m")
endDate = common.format_date(ddate,"%Y%m%d")
endDate = int(endDate)
stockid = int(code)
# table = "equity_pricefenbi%s"%tableTime
table = "MarketDataTDB.equity_pricemin%s"%tableTime
dtsql = "SELECT * from %s where ticker = %s and datadate = %s"%(table,stockid,endDate)
dtv = common.get_mydb_sqlquery(dtsql)
if len(dtv) == 0:
table = "MarketDataL1.equity_pricemin%s"%tableTime
dtsql = "SELECT * from %s where ticker = %s and datadate = %s"%(table,stockid,endDate)
dtv = common.get_mydb_sqlquery(dtsql)
table = "MarketDataTDB.equity_pricemin%s"%tableTime
zssql = 'SELECT * from %s where datadate = %s and ticker = 1 and shortnm = "上证指数"'%(table,endDate)
sh = common.get_mydb_sqlquery(zssql)
if len(sh) == 0:
table = "MarketDataL1.equity_pricemin%s"%tableTime
zssql = 'SELECT * from %s where datadate = %s and ticker = 1 and shortnm = "上证指数"'%(table,endDate)
sh = common.get_mydb_sqlquery(zssql)
# ##临时增加，需要保存数据
# tmp_dir = "D:\Money\lilton_code\Market_Mode\study_fresh_fail_Module\data"
# dtv.to_csv(os.path.join(tmp_dir,"%s_%s.csv"%(ddate,code)),encoding='utf8')
[name,tmp_id]=common.QueryStockMap(id=code)
##日线，daydir
fig1 = plt.figure(figsize=[12,8])
##日线图
point = 10
# plt.title("%s"%code)
plt.title(u"%s_%s_%s_d.png"%(ddate, code, name_tail))
ax1 = fig1.add_subplot(211)
plot_candlestick(sub, ax1, point=10, direction=direction, mount_flag=1, mark_date=end_date)
ax2 = fig1.add_subplot(212)
plot_candlestick(idx, ax2, point=10, mount_flag=1, mark_date=end_date)
# plt.savefig(os.path.join(daydir,"%s_%s_%s.pdf"%(ddate,direction,code)),dpi=300)
plt.savefig(os.path.join(daydir, "%s_%s_%s_%s_m.png"%(ddate, code, name, name_tail)),dpi=300)
plt.close()
##分时图,分钟线，在dir/mins
fig2 = plt.figure(figsize=(12,8))
ax3 = fig2.add_subplot(211)
plt.title("%s"%code)
plot_dealDetail(dtv,ax3,time=dtime,direction=direction,mount_flag=1)
ax4 = fig2.add_subplot(212)
plot_dealDetail(sh,ax4,time=dtime,direction=direction,mount_flag=1)
# plt.savefig(os.path.join(minsdir,"%s_%s_%s_%s.pdf"%(ddate,direction,code,name)),dpi=300)
plt.savefig(os.path.join(minsdir,"%s_%s_%s_%s.png"%(ddate, code, name, name_tail)),dpi=300)
plt.close()
self.mongodb.concepts.dragon_pool.update({"date": tdate},
save_dict,
upsert=True)
# 将up, down, flat存放到mongo中的pattern, trend表中
for code_str in catch_dict.keys():
tmp_list = catch_dict[code_str]
for ticker in tmp_list:
self.mongodb.pattern.trend.update(
{
"date": tdate,
"ticker": ticker
}, {
"date": tdate,
"ticker": ticker,
"trend": code_str
},
upsert=True)
# 将昨天以及之前记录的龙头、排除等信息更新到存储中，今天生成新的文件就会包含昨天的笔记
if __name__ == '__main__':
x = DRAGON_POOL()
# 将上一日的copy到今日
today = datetime.datetime.today().strftime("%Y%m%d")
lastday = common.get_lastN_date(today, 1)
x.copy_pool_dragon(lastday, today)
x.fresh_dir()
print "dragon pool update finished!"
def reControlCx(start_day = dt.datetime.today().strftime("%Y%m%d"), end_day = dt.datetime.today().strftime("%Y%m%d"), force = 0):
start_day = common.get_lastN_date(start_day, 10)
start_day = common.format_date(start_day, "%Y%m%d")
print "Will run reControlCx, start_day = %s, end_day = %s"%(start_day, end_day)
try:
mongodb = pymongo.MongoClient("localhost")
# 统计新股的开始日期 start_day
# 统计结束日期 end_day
results = mongodb.stock.ZDT_by_date.find({"date":{"$gte":start_day, "$lte":end_day}})
# 强势从0开始
if force == 1:
previous_back_list = [] # 记录的昨日持续涨停的、回封新股， 不包括今天的回封, 每天开盘前，该字段的值就确定了
previous_direct_ten_list = [] # 记录的昨日直接涨停的新股，未曾开板过， 每天开盘前，该字段的值就确定了
flag = 0
directTen = {} # 当天，各个一字新股的累计涨停个数，收盘之后才能确定
backTen_acc = {} # 当天，各个回封股票的累计涨幅，收盘之后才能确定
dummyDirectTen = {}
# dummyDirectTen = {"000839":15,} 考虑回封在内的连板涨停数，用不同颜色标注
# directTen = {"000839":14,} 不考虑回封，直接一字涨停的连板数
# backTen_acc = {"000839": 21.4%, }
# directOpen = {"601989":3.45, }
# backOpen = {"601989":3.45, }
# highDirectOpen = {"601989":3.45}
# highBackOpen = {"601989":3.45}
# return directTen, dummyDirectTen, backTen_acc, directOpen, highDirectOpen, backOpen, highBackOpen
last_zt_stocks = "" #昨日的涨停股票
last_new_stocks = "" # 昨日新上市的股票
else:
print start_day
last_start = common.get_lastN_date(start_day, 1)
last_start = common.format_date(last_start, "%Y%m%d")
last_mongo = mongodb.stock.ZDT_by_date.find({"date": last_start})
day = last_start
directTen = {} # 当天，各个一字新股的累计涨停个数，收盘之后才能确定
backTen_acc = {} # 当天，各个回封股票的累计涨幅，收盘之后才能确定
dummyDirectTen = {}
flag = 1
if last_mongo.count() == 1:
mongo_record = last_mongo[0]
if 'previous_direct_ten_list' in mongo_record.keys():
previous_direct_ten_list = mongo_record['previous_direct_ten_list']
if 'directTen' in mongo_record.keys():
directTen = mongo_record['directTen']
if 'dummyDirectTen' in mongo_record.keys():
dummyDirectTen = mongo_record['dummyDirectTen']
if 'backTen_acc' in mongo_record.keys():
backTen_acc = mongo_record['backTen_acc']
if "freshStocks" in mongo_record.keys():
last_zt_stocks = mongo_record['freshStocks']
else:
last_zt_stocks = ""
if "Add_newStocks" in mongo_record.keys():
last_new_stocks = "_".join(mongo_record['Add_newStocks'].keys())
else:
last_new_stocks = ""
# 更新每天的昨日回封股票list、昨日一字涨停股票list、今日的一字涨停股票和涨停数（多了数目，也多了今天新加的股票）、
# 今日的回封股票及涨停数（多了数值，以及今天回封的股票）
for result in results:
if flag == 0: # 说明是初始化的第一天
if "freshStocks" in result:
previous_direct_ten_list = result['freshStocks'].split("_") # 赋值为当天的一字涨停新股（包括了当天的新上市股，也包括了回封的新股，所有的都是当天涨停的）
flag = 1
else:
# 今日的“昨日直接涨停新股” = 昨日的“昨日直接涨停新股”中涨停的 + 昨日新增的股票， 还应该减去中间有开板过的
previous_direct_ten_list = [x for x in previous_direct_ten_list if x in last_zt_stocks.split("_")]
previous_direct_ten_list, new_back_ten_list = group_ten_list(previous_direct_ten_list, day)
previous_direct_ten_list.extend(last_new_stocks.split("_"))
previous_direct_ten_list = list(set(previous_direct_ten_list))
# 昨日涨停的新股 = 前日一字新股中昨日直接涨停的 + 昨日新上市的 + 回封的
previous_back_list = [x for x in last_zt_stocks.split("_") if x not in previous_direct_ten_list]
if "freshStocks" in result.keys():
last_zt_stocks = result['freshStocks']
else:
last_zt_stocks = ""
if "Add_newStocks" in result:
last_new_stocks = "_".join(result['Add_newStocks'].keys())
else:
last_new_stocks = ""
day = result['date']
# 更新今日收盘后，一字涨停的股票涨停数
if day == '20170426':
print 'debug'
pass
directTen, dummyDirectTen, backTen_acc, directOpen, highDirectOpen, backOpen, highBackOpen = continues_count(day, directTen, dummyDirectTen, backTen_acc, last_zt_stocks, last_new_stocks)
print day
print "will update"
print "directTen:", directTen
mongodb.stock.ZDT_by_date.update({"date":day},
{"$set":
{"previous_direct_ten_list":previous_direct_ten_list,
"previous_back_list":previous_back_list,
"directTen":directTen,
"dummyDirectTen":dummyDirectTen,
"backTen_acc":backTen_acc,
"directOpen":directOpen,
"highDirectOpen":highDirectOpen,
"backOpen":backOpen,
"highBackOpen":highBackOpen}},
True)
return True
except:
return False
def study_plot(tmp_array,dirs):
## 日线dir
daydir = dirs
minsdir = "%s/mins"%dirs
bothdir = "%s/both"%dirs
if os.path.exists(minsdir):
pass
else:
os.mkdir(minsdir)
if os.path.exists(bothdir):
pass
else:
os.mkdir(bothdir)
[ddate,dtime,code,direction,price]=tmp_array
end_date = common.format_date(ddate,"%Y-%m-%d")
start_date = common.get_lastN_date(end_date,120)
code = "0"*(6-len(str(int(code))))+str(int(code))
sql = "SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_PRICE, OPEN_PRICE, HIGHEST_PRICE, LOWEST_PRICE, CLOSE_PRICE, \
DEAL_AMOUNT from vmkt_equd where TRADE_DATE >= '%s' and TRADE_DATE <='%s' and TICKER_SYMBOL = '%s'"%(start_date,end_date,code)
sub = common.get_mysqlData_sqlquery(sql)
idxcode = "000001"
idxsql = "SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_INDEX, OPEN_INDEX, HIGHEST_INDEX, LOWEST_INDEX, CLOSE_INDEX, \
TURNOVER_VOL from vmkt_idxd where TRADE_DATE >= '%s' and TRADE_DATE <='%s' and TICKER_SYMBOL = '%s'"%(start_date,end_date,idxcode)
idx = common.get_mysqlData_sqlquery(idxsql)
idx.columns=sub.columns
add_mean(sub)
add_mean(idx)
tableTime = common.format_date(ddate,"%Y%m")
endDate = common.format_date(ddate,"%Y%m%d")
endDate = int(endDate)
stockid = int(code)
# table = "equity_pricefenbi%s"%tableTime
table = "MarketDataTDB.equity_pricemin%s"%tableTime
dtsql = "SELECT * from %s where ticker = %s and datadate = %s"%(table,stockid,endDate)
dtv = common.get_mydb_sqlquery(dtsql)
if len(dtv) == 0:
table = "MarketDataL1.equity_pricemin%s"%tableTime
dtsql = "SELECT * from %s where ticker = %s and datadate = %s"%(table,stockid,endDate)
dtv = common.get_mydb_sqlquery(dtsql)
table = "MarketDataTDB.equity_pricemin%s"%tableTime
zssql = 'SELECT * from %s where datadate = %s and ticker = 1 and shortnm = "上证指数"'%(table,endDate)
sh = common.get_mydb_sqlquery(zssql)
if len(sh) == 0:
table = "MarketDataL1.equity_pricemin%s"%tableTime
zssql = 'SELECT * from %s where datadate = %s and ticker = 1 and shortnm = "上证指数"'%(table,endDate)
sh = common.get_mydb_sqlquery(zssql)
##临时增加，需要保存数据
tmp_dir = "D:\Money\lilton_code\Market_Mode\study_fresh_fail_Module\data"
dtv.to_csv(os.path.join(tmp_dir,"%s_%s.csv"%(ddate,code)),encoding='utf8')
##日线，daydir
fig1 = plt.figure(figsize=[12,8])
##日线图
point = 10
# plt.title("%s"%code)
ax1 = fig1.add_subplot(211)
plot_candlestick(sub,ax1,point =10,direction = direction,mount_flag = 1)
ax2 = fig1.add_subplot(212)
plot_candlestick(idx,ax2,point =10,mount_flag = 1)
# plt.savefig(os.path.join(daydir,"%s_%s_%s.pdf"%(ddate,direction,code)),dpi=300)
plt.savefig(os.path.join(daydir,"%s_%s_%s.png"%(ddate,direction,code)),dpi=300)
plt.close()
##分时图,分钟线，在dir/mins
fig2 = plt.figure(figsize=(12,8))
ax3 = fig2.add_subplot(211)
[name,tmp_id]=common.QueryStockMap(id=code)
plt.title("%s"%code)
plot_dealDetail(dtv,ax3,time=dtime,direction=direction,mount_flag=1)
ax4 = fig2.add_subplot(212)
plot_dealDetail(sh,ax4,time=dtime,direction=direction,mount_flag=1)
# plt.savefig(os.path.join(minsdir,"%s_%s_%s_%s.pdf"%(ddate,direction,code,name)),dpi=300)
plt.savefig(os.path.join(minsdir,"%s_%s_%s_%s.png"%(ddate,direction,code,name)),dpi=300)
plt.close()
