def run_pipline_stg(filter_kc=True):
logging.info(
"************************ process start ***************************************"
)
pd_names = pd.read_csv(ConfigUtils.get_stock("STOCK_NAME"))
strategies = {
'海龟交易法则': turtle_trade.check_enter,
'潜伏慢阳3d': turtle_trade.continue_increase3,
'潜伏慢阳5d': turtle_trade.continue_increase5,
'潜伏慢阳7d': turtle_trade.continue_increase7,
'BigInc': turtle_trade.big_inc,
'BreakInc': turtle_trade.break_inc,
'BackInc': turtle_trade.feedback_inc,
'Demon': turtle_trade.demon_inc,
'连续含1y': turtle_trade.inc_cyin1,
'连续含2y': turtle_trade.inc_cyin2,
'放量上涨_1.5': enter.check_volume,
'大涨': enter.check_continuous_inc,
'ATR': enter.check_breakthrough,
'突破平台': breakthrough_platform.check,
'均线多头': keep_increasing.check,
'停机坪': parking_apron.check,
'回踩年线': backtrace_ma250.check,
'低ATR成长策略': low_atr.check_low_increase,
# 'pe': pe.check,
}
stg_result_dict = collections.defaultdict(list)
for index, row in pd_names.iterrows():
code = row['code']
name = row['code_name']
if filter_kc and stock_utils.is_jiucaiban(code):
continue
if "ST" in name:
continue
code_name = (code, name)
print(code_name)
data = stock_utils.read_data(code_name)
if data is None:
continue
for strategy, strategy_func in strategies.items():
r = strategy_func(code_name, data)
if r and r > 0.0:
stg_result_dict[strategy].append((code, name, r))
for strategy, results in stg_result_dict.items():
outs = sorted(results, key=lambda e: e[2], reverse=True)
stock_utils.persist(strategy, outs)
logging.info(
"************************ process end ***************************************"
)
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 filter_stock(df, code, name):
if len(df) < 60 or is_jiucaiban(code) or is_index(
code, name): # 去除一些上市不久的企业 688 300 399
return True
else:
return False
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