def scan_stocks():
pd_names = pd.read_csv(ConfigUtils.get_stock("STOCK_NAME"))
# 数据库获取沪、深、中小板
zxb_stocks = [s.code for s in get_mark_stocks(mark='zxb')]
hsb_stocks = [s.code for s in get_mark_stocks(mark='hsb')]
ssb_stocks = [s.code for s in get_mark_stocks(mark='ssb')]
candidate_stocks = zxb_stocks + hsb_stocks + ssb_stocks
print("total stock size:", len(candidate_stocks))
records = get_records(dt='2020-12-15', direction='limit', period='d1')
print("涨停数量:", len(records))
for index, row in tqdm(pd_names.iterrows()):
code = row['code']
name = row['code_name']
if stock_utils.is_jiucaiban(code):
continue
if code[3:] not in candidate_stocks:
# print(name, code)
continue
code_name = (code, name)
df = stock_utils.read_data(code_name)
df.reset_index(drop=True, inplace=True)
if len(df) < 60:
continue
df = df.tail(n=5)
df.reset_index(drop=True, inplace=True)
# 涨停分析
close = df.iloc[-1]['close']
chg = df.iloc[-1]['pctChg']
volume = df.iloc[-1]['volume']
volume_rate = (volume - df.iloc[-2]['volume']) / df.iloc[-2]['volume']
chgs = [c for c in df['pctChg']] # 涨幅序列
vols = [v / df.iloc[0]['volume'] for v in df['volume']] # 量比序列
feat = standardization(chgs[:-1]) + standardization(vols[:-1])
feat = standardization(chgs[:-1])
# print(feat)
tmp_count = 0
for record in records:
tmp_chg = json.loads(record.extra)['chgs'][:-1]
tmp_vol = json.loads(record.extra)['vols'][:-1]
tmp_feat = standardization(tmp_chg) + standardization(tmp_vol)
tmp_feat = standardization(tmp_chg)
dist = cosine_dist(feat, tmp_feat)
if dist > 0.9:
# print(name, record.name, chg, "sim:", dist)
tmp_count += 1
if tmp_count > 5:
print(code, name, chg)
def gen_records(day_ago=0):
records = list()
pd_names = pd.read_csv(ConfigUtils.get_stock("STOCK_NAME"))
# 数据库获取沪、深、中小板
zxb_stocks = [s.code for s in get_mark_stocks(mark='zxb')]
hsb_stocks = [s.code for s in get_mark_stocks(mark='hsb')]
ssb_stocks = [s.code for s in get_mark_stocks(mark='ssb')]
candidate_stocks = zxb_stocks + hsb_stocks + ssb_stocks
print("total stock size:", len(candidate_stocks))
for index, row in tqdm(pd_names.iterrows()):
code = row['code']
name = row['code_name']
if stock_utils.is_jiucaiban(code):
continue
if code[3:] not in candidate_stocks:
continue
code_name = (code, name)
df = stock_utils.read_data(code_name)
df = df.head(n=len(df) - day_ago) # n天前 新高/低入口
df.reset_index(drop=True, inplace=True)
if len(df) < 60:
continue
for period in [500, 120, 60, 20, 10, 5]:
df = df.tail(n=period)
df.reset_index(drop=True, inplace=True)
idxMax = df['close'].idxmax(axis=0)
idxMin = df['close'].idxmin(axis=0)
# print(idxMax, idxMin)
dateMax = df.iloc[idxMax]['date']
dateMin = df.iloc[idxMin]['date']
date = df.iloc[-1]['date']
volume = df.iloc[-1]['volume']
record_date = datetime.datetime.strptime(date, '%Y-%m-%d').date()
d_period_inc = (df.iloc[-1]['close'] -
df.iloc[0]['close']) / df.iloc[0]['close']
volume_avg = df['volume'].sum() / period
amount_avg = df['amount'].sum() / period
period_inc_avg = d_period_inc / period
extraJson = {
'volume_avg': volume_avg,
'amount_avg': amount_avg,
'inc_avg': period_inc_avg
}
flag, direction = False, None
r = Record(name, code, record_date, df.iloc[-1]['close'],
"d{}".format(period), volume, d_period_inc)
r.set_extra(json.dumps(extraJson))
if df.iloc[-1]['date'] == dateMax:
flag, direction = True, 'up'
if df.iloc[-1]['date'] == dateMin and 'ST' not in name:
flag, direction = True, 'down'
if flag:
r.set_direction(direction)
records.append(r)
# 涨停分析
close = df.iloc[-1]['close']
chg = df.iloc[-1]['pctChg']
volume = df.iloc[-1]['volume']
volume_rate = (volume - df.iloc[-2]['volume']) / df.iloc[-2]['volume']
if float(chg) >= 9:
chgs = [c for c in df['pctChg']] # 涨幅序列
vols = [v / df.iloc[0]['volume'] for v in df['volume']] # 量比序列
extraJson = {
'volume_rate': volume_rate,
'chgs': chgs,
'vols': vols
}
r = Record(name, code, record_date, close, "d1", volume, chg)
r.set_direction(direction='limit')
r.set_extra(json.dumps(extraJson))
records.append(r)
return records
def period_records():
outResult = collections.defaultdict(dict)
extraResult = collections.defaultdict(list)
hsb = 1696
ssb = 484
zxb = 961
HSB_NUM = 10
SSB_NUM = 5
ZXB_NUM = 5
# 数据库获取沪、深、中小板
zxb_stocks = [s.code for s in get_mark_stocks(mark='zxb')]
hsb_stocks = [s.code for s in get_mark_stocks(mark='hsb')]
ssb_stocks = [s.code for s in get_mark_stocks(mark='ssb')]
candidate_stocks = zxb_stocks + hsb_stocks + ssb_stocks
pool = {}
bestStocks = []
dt = get_recently_trade_date()
print("recently date:", dt)
# dt = '2020-12-22'
new_highs = [
r for r in get_records(dt=dt, direction='up', period='d500')
if r.code[3:] in candidate_stocks
]
new_lows = [
r for r in get_records(dt=dt, direction='down', period='d500')
if r.code[3:] in candidate_stocks
]
pool['d60'] = get_records(dt=dt, direction='up', period='d60')
pool['d20'] = get_records(dt=dt, direction='up', period='d20')
pool['d10'] = get_records(dt=dt, direction='up', period='d10')
pool['d5'] = get_records(dt=dt, direction='up', period='d5')
print("d5:", len(pool['d5']), "d10:", len(pool['d10']), "d20:",
len(pool['d20']), "d60:", len(pool['d60']))
for k, records in pool.items():
hsb = [r for r in records if r.code[3:] in hsb_stocks][:HSB_NUM]
ssb = [r for r in records if r.code[3:] in ssb_stocks][:SSB_NUM]
zxb = [r for r in records if r.code[3:] in zxb_stocks][:ZXB_NUM]
for r in hsb + ssb + zxb:
bestStocks.append((r.code, r.name))
outResult[k]['hsb'] = hsb
outResult[k]['ssb'] = ssb
outResult[k]['zxb'] = zxb
# 近一周的新高股票
day_week_ago = datetime.datetime.now() + datetime.timedelta(days=-7)
target_date = day_week_ago.strftime('%Y-%m-%d')
records = get_period_records(start_date=target_date)
week_bests = []
for r in records:
if r.code[3:] not in candidate_stocks:
continue
week_bests.append((r.code, r.name))
# print(collections.Counter(bestStocks))
# best = sorted(collections.Counter(bestStocks).items(), key=lambda x: x[1], reverse=True)
# print(best) # [(('sh.600121', '郑州煤电'), 4), (('sh.601015', '陕西黑猫'), 4), (('sh.600418', '江淮汽车'), 4),...
extraResult['best'] = sorted(collections.Counter(bestStocks).items(),
key=lambda x: x[1],
reverse=True)
extraResult['week_best'] = sorted(collections.Counter(week_bests).items(),
key=lambda x: x[1],
reverse=True)
extraResult['high'] = new_highs
extraResult['low'] = new_lows
# # 月线趋势选股
# # 结构 [(('sh.600196', '复星医药'), 44517688035.19, 2269476415.0), (('sh.600887', '伊利股份'), 29118278005.35, 1554822026.0), ..]
# breakstocks, highstocks = get_m_candidates()
# extraResult['m_break'] = breakstocks
# extraResult['m_high'] = highstocks
send_hot_share_mail(outResult, extraResult)
def get_m_candidates():
pd_names = pd.read_csv(ConfigUtils.get_stock("STOCK_NAME"))
# 数据库获取沪、深、中小板
zxb_stocks = [s.code for s in get_mark_stocks(mark='zxb')]
hsb_stocks = [s.code for s in get_mark_stocks(mark='hsb')]
ssb_stocks = [s.code for s in get_mark_stocks(mark='ssb')]
candidate_stocks = zxb_stocks + hsb_stocks + ssb_stocks
print("total stock size:", len(candidate_stocks))
t_date = datetime.date.today().strftime('%Y-%m')
break_stocks = list()
high_stocks = list()
for index, row in tqdm(pd_names.iterrows()):
code = row['code']
name = row['code_name']
if stock_utils.is_jiucaiban(code):
continue
if code[3:] not in candidate_stocks:
continue
code_name = (code, name)
df = stock_utils.read_data(code_name,
root=ConfigUtils.get_stock("DATA_M_DIR"))
if df is None or len(df) < 2:
continue
df = df.tail(n=12) # n天前 新高/低入口
df.reset_index(drop=True, inplace=True)
close_df = df.sort_values(by='close', ascending=False)
volume_df = df.sort_values(by='volume', ascending=False)
amount_df = df.sort_values(by='amount', ascending=False)
pctChg_df = df.sort_values(by='pctChg', ascending=False)
idxCloseMax0, idxCloseMax1 = close_df.index[0], close_df.index[1]
idxVolMax0, idxVolMax1 = volume_df.index[0], volume_df.index[1]
idxAmountMax0, idxAmountMax1 = amount_df.index[0], amount_df.index[1]
idxChgMax0, idxChgMax1 = pctChg_df.index[0], pctChg_df.index[1]
volMax0, volMax1 = df.iloc[idxVolMax0]['volume'], df.iloc[idxVolMax1][
'volume']
pctMax0, pctMax1 = df.iloc[idxChgMax0]['pctChg'], df.iloc[idxChgMax1][
'pctChg']
closeMax0, closeMax1 = df.iloc[idxCloseMax0]['close'], df.iloc[
idxCloseMax1]['close']
date0, date1 = df.iloc[idxCloseMax0]['date'], df.iloc[idxCloseMax1][
'date']
pch0, pch1 = df.iloc[idxCloseMax0]['pctChg'], df.iloc[idxCloseMax1][
'pctChg']
amount0, amount1 = df.iloc[idxCloseMax0]['amount'], df.iloc[
idxCloseMax1]['amount']
# 经过3个月洗盘, 最近股价将赶超新高
if t_date == date1 and (idxCloseMax1 - idxCloseMax0) > 2:
# print(code_name, volMax1, date0, date1, idxVolMax0, idxChgMax0, idxCloseMax0, closeMax0)
r = Record(name, code, t_date, closeMax1, 'm', amount1, pch1)
break_stocks.append(r)
# 持续新高
if t_date == date0:
# print(code_name, dateMax0, dateMax1, idxVolMax0, idxChgMax0, idxCloseMax0, closeMax0)
r = Record(name, code, t_date, closeMax0, 'm', amount0, pch0)
high_stocks.append(r)
break_stocks = sorted(break_stocks, key=lambda x: x.volume, reverse=True)
high_stocks = sorted(high_stocks, key=lambda x: x.volume, reverse=True)
# print(break_stocks[:30])
# print(high_stocks[:30])
break_stocks = [r for r in break_stocks if r.volume < 5e11]
high_stocks = [r for r in high_stocks if r.volume < 90081936793]
return break_stocks, high_stocks
# @Author : zhangzhen12
# @Site :
# @File : monitor.py
# @Software: PyCharm
from data_modules.sina_reptile import get_real_price
from stock_analytic_modules.utils.helper import get_before_day
from common.sql_utils import get_records, get_mark_stocks
from common.stock_utils import get_recently_trade_date
import datetime
import threading
from time import ctime, sleep
dt = get_before_day(ago=1)
# print(dt)
# 数据库获取沪、深、中小板
zxb_stocks = [s.code for s in get_mark_stocks(mark='zxb')]
hsb_stocks = [s.code for s in get_mark_stocks(mark='hsb')]
ssb_stocks = [s.code for s in get_mark_stocks(mark='ssb')]
candidate_stocks = zxb_stocks + hsb_stocks + ssb_stocks
def monitor_low():
new_lows = [
r for r in get_records(dt=dt, direction='down', period='d500')
if r.code[3:] in candidate_stocks
]
print("low:", len(new_lows))
while True:
sleep(10)
for r in new_lows:
code = str(r.code).replace('.', '')