def get_and_store_stock_detail_data(stock_code,date,type=1):
"""
保存单只股票数据csv文件，自定义文件名
tye：1:股票。2：指数。
"""
fn = str(stock_code) + "_" + str(date)
if type != 1:
fn = fn.replace('.','')
pd = load_csv_detail_data(fn)
if pd is None:
set_token("8e1026d2dfd455be2e1f239e50004b35a481061e")
if type == 1:
symbols = ['SZSE.'+str(stock_code),'SHSE.'+str(stock_code)]
data = get_instrumentinfos(symbols=symbols, exchanges=None, sec_types=1, names=None, fields=None, df=True)
symbol = data[data.sec_id == str(stock_code)].symbol.values[0]
else:
symbol = stock_code
start_date = datetime.datetime.strptime(date, '%Y-%m-%d').date()
end_date = start_date + datetime.timedelta(days=1)
pd = history(symbol, "60s", start_date, end_date, fields=None, skip_suspended=True,
fill_missing=None, adjust=0, adjust_end_time='', df=True)
# pd = ts.get_tick_data(stock_code, date=date, src='tt')
if pd is None:
return None
else :
pd = cacle_column(pd,fn)
return pd
else:
pd = cacle_column(pd,fn)
return pd
def save_one_symbol_monday(self, symbol):
""" 保存一个合约tick数据(周一) """
try:
print("正在下载(周一): {0}".format(symbol))
# 数据分3段下载, 每次只能下载33000根
# 周五20:00 到 周六00:00
history_data_1 = history(symbol=symbol, frequency='tick', start_time='{0} 20:00'.format(self.trading_day_friday_str), end_time='{0} 00:00'.format(self.trading_day_saturday_str), df=True,\
fields='symbol, price, cum_volume, cum_amount, cum_position, created_at')
if not history_data_1.empty:
# 周五时间加两天到周一
history_data_1.created_at = history_data_1.created_at.map(
lambda date: date + timedelta(days=2))
# 周六00:00 到周六03:00
history_data_2 = history(symbol=symbol, frequency='tick', start_time='{0} 00:00'.format(self.trading_day_saturday_str), end_time='{0} 03:00'.format(self.trading_day_saturday_str), df=True,\
fields='symbol, price, cum_volume, cum_amount, cum_position, created_at')
if not history_data_2.empty:
# 周五时间加两天到周一
history_data_2.created_at = history_data_2.created_at.map(
lambda date: date + timedelta(days=2))
# 周一03:00 到 周一16:00
history_data_3 = history(symbol=symbol, frequency='tick', start_time='{0} 03:00'.format(self.trading_day_monday_str), end_time='{0} 16:00'.format(self.trading_day_monday_str), df=True,\
fields='symbol, price, cum_volume, cum_amount, cum_position, created_at')
# 拼接
history_data = pd.concat(
[history_data_1, history_data_2, history_data_3])
# 去重
history_data.drop_duplicates('created_at', inplace=True)
# 保存
if not history_data.empty:
# 重命名时间字段, 转化为标准名称
history_data.rename(columns={'created_at': 'strtime'},
inplace=True)
with open(os.path.join(self.save_path, symbol + '.pkl'),
'wb') as fwb:
pickle.dump(history_data, fwb)
else:
print("无数据: {0}".format(symbol))
except Exception as err:
print("\033[0;36;41m下载tick数据出错: {0}\033[0m".format(symbol))
traceback.print_exc()
print(err)
def isTrading(symbol, datetime_):
from gm.api import history
dt_ = datetime.datetime.strptime(datetime_, '%Y-%m-%d')
tempHQ = history(symbol=symbol,
frequency='1d',
start_time=dt_,
end_time=dt_,
fields=None,
df=True)
if tempHQ.empty:
return False
else:
return True
def increment_build():
count = 0
read = read_conn()
write = write_conn()
cursor = write.cursor()
infos = instrumentinfos.infos(read)
for i, info in enumerate(infos):
print('\rBuilding history_1d: %.2f%%' % (i * 100 / len(infos)), end='')
last_eob = max_eob(read, info['symbol'])
start = info[
'listed_date'] if last_eob is None else last_eob + timedelta(
days=1)
while True:
end = start + timedelta(days=1000)
bars = history(symbol=info['symbol'],
frequency='1d',
start_time=start,
end_time=end,
fields='bob,eob,open,close,high,low,amount,volume',
skip_suspended=False,
fill_missing='Last',
adjust=ADJUST_NONE)
bars.sort(key=lambda x: x['eob'])
for bar in bars:
cursor.execute(
'INSERT INTO history_1d (symbol, bob, eob, open, close, high, low, amount, volume) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s)',
(info['symbol'], bar['bob'], bar['eob'], bar['open'],
bar['close'], bar['high'], bar['low'], bar['amount'],
bar['volume']))
count += 1
if end < min(info['delisted_date'], today().date()):
start = end + timedelta(days=1)
else:
break
cursor.close()
write.close()
read.close()
print('\rBuilding history_1d: Finish')
return count
def __saveing_work(code, coll):
QA_util_log_info("##JOB03 Now Saving STOCK_MIN ==== {}".format(code),
ui_log=ui_log)
try:
for type in ["1min"]:
ref_ = coll.find({"code": str(code)[0:6], "type": type})
end_time = str(now_time())[0:19]
if ref_.count() > 0:
start_time = ref_[ref_.count() - 1]["datetime"]
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min"].index(type),
str(code)[0:6],
start_time,
end_time,
type,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(
symbol=code,
start_time=start_time,
end_time=end_time,
frequency=type,
)
__data = __transform_gm_to_qa(df, code=code[:6])
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
__data == __transform_gm_to_qa(df, code=code[:6])
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
else:
start_time = "2015-01-01 09:30:00"
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min"].index(type),
str(code)[0:6],
start_time,
end_time,
type,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(
symbol=code,
start_time=start_time,
end_time=end_time,
frequency=type,
)
__data = __transform_gm_to_qa(df, code=code[:6])
if len(__data) > 1:
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
def plot_order_jubaopen_myself(order, start=60, end=10):
"""
绘制订单分时图
:param order: AbuOrder对象序列
"""
stock_code = order.symbol
set_token("8e1026d2dfd455be2e1f239e50004b35a481061e")
data = get_instrumentinfos(symbols=None,
exchanges=None,
sec_types=1,
names=None,
fields=None,
df=True)
symbol = data[data.sec_id == stock_code].symbol.values[0]
start_date_order = datetime.datetime.strptime(
str(order.buy_time)[0:18], "%Y-%m-%d %H:%M:%S").date()
start_date = start_date_order + datetime.timedelta(days=-1)
end_date = start_date_order + datetime.timedelta(days=9)
kl_pd = history(symbol,
"60s",
start_date,
end_date,
fields=None,
skip_suspended=True,
fill_missing=None,
adjust=ADJUST_PREV,
adjust_end_time='',
df=True)
bench_kl_pd = history('SHSE.000001',
"60s",
start_date,
end_date,
fields=None,
skip_suspended=True,
fill_missing=None,
adjust=ADJUST_PREV,
adjust_end_time='',
df=True)
bench_kl_pd = bench_kl_pd[bench_kl_pd['bob'].isin(kl_pd['bob'].tolist())]
bench_kl_pd.index = np.arange(0, len(bench_kl_pd))
kl_pd['date'] = kl_pd['bob'].apply(
lambda x: ABuDateUtil.date_time_str_to_int(str(x)))
kl_pd['time'] = kl_pd['bob'].apply(
lambda x: ABuDateUtil.date_time_str_to_time_str(str(x)))
kl_pd_time = kl_pd[kl_pd.time == '093000']
kl_pd_buy_time = kl_pd[kl_pd.bob == order.buy_time]
kl_pd_sell_time = kl_pd[kl_pd.bob == order.sell_time]
kl_pd['p_change'] = (kl_pd.close - kl_pd['close'][0]) / kl_pd['close'][0]
bench_kl_pd['p_change'] = (
bench_kl_pd.close - bench_kl_pd['close'][0]) / bench_kl_pd['close'][0]
kl_pd['p_change_update'] = (kl_pd.p_change - bench_kl_pd.p_change)
window_volume = 30
window_close = 30
kl_pd['p_change_5ma'] = kl_pd.p_change.rolling(window=window_close).mean()
kl_pd['p_change_update_5ma'] = kl_pd.p_change_update.rolling(
window=window_close).mean()
bench_kl_pd['p_change_5ma'] = bench_kl_pd.p_change.rolling(
window=window_close).mean()
kl_pd['volume_ma'] = kl_pd.volume.rolling(window=window_volume).mean()
kl_pd['p_change_5ma_up_rate'] = (kl_pd.p_change_5ma -
kl_pd.p_change_5ma.shift(5))
kl_pd['p_change_update_5ma_up_rate'] = (kl_pd.p_change_update_5ma -
kl_pd.p_change_update_5ma.shift(5))
bench_kl_pd['p_change_5ma_up_rate'] = (bench_kl_pd.p_change_5ma -
bench_kl_pd.p_change_5ma.shift(5))
kl_pd['zero_line'] = 0
kl_pd['volume_ma_up_rate'] = (kl_pd.volume_ma - kl_pd.volume_ma.shift(5))
kl_pd[kl_pd['p_change_5ma_up_rate'] > 0.01] = 0.01
kl_pd[kl_pd['p_change_5ma_up_rate'] < -0.01] = -0.01
max_p_change = kl_pd['p_change_5ma_up_rate'].max()
min_p_change = kl_pd['p_change_5ma_up_rate'].min()
max_volume = kl_pd['volume_ma_up_rate'].max()
min_volume = kl_pd['volume_ma_up_rate'].min()
vs_rate1 = max_p_change / max_volume
vs_rate2 = min_p_change / min_volume
vs_rate = vs_rate1 if vs_rate1 >= vs_rate2 else vs_rate2
kl_pd['volume_ma_up_rate'] = (kl_pd.volume_ma -
kl_pd.volume_ma.shift(5)) * vs_rate
# kl_pd[kl_pd['volume_ma_up_rate'] > 0.0025] = 0.0025
# kl_pd[kl_pd['volume_ma_up_rate'] < -0.0025] = -0.0025
# kl_pd['volume_ma_up_rate'] = kl_pd['volume_ma_up_rate'] * 4
# max_volume = kl_pd['volume_ma_up_rate'].max()
# min_volume = kl_pd['volume_ma_up_rate'].min()
#
# vs_rate1 = max_p_change / max_volume
# vs_rate2 = min_p_change / min_volume
# vs_rate = vs_rate1 if vs_rate1 >= vs_rate2 else vs_rate2
# kl_pd['volume_ma_up_rate'] = (kl_pd.volume_ma - kl_pd.volume_ma.shift(5)) * vs_rate
title = str(stock_code) + '_' + str(order.buy_time)[0:10]
# plt.plot(kl_pd.index, kl_pd['p_change'], label='p_change', color='blue') #基础p_change
# plt.plot(kl_pd.index, bench_kl_pd['p_change'], label='bench_p_change', color='green') #大盘p_change
# plt.plot(kl_pd.index, kl_pd['p_change_5ma'], label='close60', color='red') #基础p_change均线
# plt.plot(kl_pd.index, bench_kl_pd['p_change_5ma'], label='close60', color='red') #基础大盘p_change均线
# plt.plot(kl_pd.index, kl_pd['p_change_update'],'--', label='p_change_update', color='red') #修正后涨跌幅
plt.plot(kl_pd.index, kl_pd['p_change'], label='p_change',
color='blue') #基础p_change
plt.plot(bench_kl_pd.index,
bench_kl_pd['p_change'],
label='bench_p_change',
color='green') #大盘p_change
# plt.plot(kl_pd.index, kl_pd['p_change_5ma'], label='close60', color='red') #基础p_change均线
# plt.plot(kl_pd.index, bench_kl_pd['p_change_5ma'], label='close60', color='red') #基础大盘p_change均线
# plt.plot(kl_pd.index, kl_pd['p_change_update'],'--', label='p_change_update', color='red') #修正后涨跌幅
plt.plot(kl_pd.index, kl_pd['zero_line'], label='0_line',
color='black') # 0线
plt.vlines(kl_pd_time.index, -0.005, 0.005, color="black") #日期分割线
plt.vlines(kl_pd_buy_time.index, -0.01, 0.01, color="red") #买入时间线
plt.vlines(kl_pd_sell_time.index, -0.02, 0.02, color="blue") #卖出时间线
plt.title(title)
plt.legend(loc='upper left')
# plt.show()
png_name = generatePngName(stock_code)
plt.savefig(png_name)
plt.close()
# 获得日分时数据。
kl_pd = get_and_store_stock_detail_data(stock_code, str(start_date_order))
kl_pd['zero_line'] = 0
# plt.plot(kl_pd.index, kl_pd['volume_30ma_up_rate'], label='volume_30ma_up_rate', color='blue') #基础p_change
plt.plot(kl_pd.index,
kl_pd['volume_30ma'],
label='volume_30ma',
color='blue') #基础p_change
plt.plot(kl_pd.index,
kl_pd['volume_5ma'],
label='volume_5ma',
color='green') #基础p_change
plt.plot(kl_pd.index, kl_pd['zero_line'], label='0_line',
color='black') # 0线
plt.title(title)
plt.legend(loc='upper left')
# plt.show()
png_name = generatePngName(stock_code)
plt.savefig(png_name)
plt.close()
bench_kl_pd = get_and_store_SHSE000001_detail_data(str(start_date_order))
plt.plot(kl_pd.index,
kl_pd['p_change_30ma_up_rate'],
label='p_change_30ma_up_rate',
color='red') # 基础均线增长斜率
# plt.plot(kl_pd.index, kl_pd['p_change_update_5ma_up_rate'], '--', label='close60', color='blue') # 修正均线增长斜率
plt.plot(bench_kl_pd.index,
bench_kl_pd['p_change_30ma_up_rate'],
label='bench_p_change_30ma_up_rate',
color='green') # 大盘增长斜率
plt.plot(kl_pd.index, kl_pd['zero_line'], label='0_line',
color='black') # 0线
# plt.plot(kl_pd.index, kl_pd['volume_ma'], label='volume_ma', color='blue') #量均值
# plt.plot(kl_pd.index, kl_pd['volume_30ma_up_rate'], '--', label='volume_30ma_up_rate', color='blue') # 量增长斜率
plt.title(title)
plt.legend(loc='upper left')
# plt.show()
png_name = generatePngName(stock_code)
plt.savefig(png_name)
plt.close()
plt.plot(kl_pd.index,
kl_pd['p_change_30ma'],
label='p_change_30ma',
color='red') # 基础均线增长斜率
plt.plot(bench_kl_pd.index,
bench_kl_pd['p_change_30ma'],
label='bench_p_change_30ma',
color='green') # 大盘增长斜率
plt.plot(kl_pd.index, kl_pd['zero_line'], label='0_line',
color='black') # 0线
plt.title(title)
plt.legend(loc='upper left')
png_name = generatePngName(stock_code)
plt.savefig(png_name)
plt.close()
pass
def __saving_work(code, coll):
QA_util_log_info("##JOB03 Now Saving STOCK_MIN ==== {}".format(code),
ui_log=ui_log)
try:
for type_ in ["1min", "5min", "15min", "30min", "60min"]:
col_filter = {"code": str(code)[5:], "type": type_}
ref_ = coll.find(col_filter)
end_time = str(now_time())[0:19]
if coll.count_documents(col_filter) > 0:
start_time = ref_[coll.count_documents(col_filter) -
1]["datetime"]
print(start_time)
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min", "5min", "15min", "30min",
"60min"].index(type_),
str(code)[5:],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(symbol=code,
start_time=start_time,
end_time=end_time,
frequency=MIN_SEC[type_],
df=True)
__data = __transform_gm_to_qa(df, type_)
if len(__data) > 1:
# print(QA_util_to_json_from_pandas(__data)[1::])
# print(__data)
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
else:
start_time = "2015-01-01 09:30:00"
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min", "5min", "15min", "30min",
"60min"].index(type_),
str(code)[5:],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(symbol=code,
start_time=start_time,
end_time=end_time,
frequency=MIN_SEC[type_],
df=True)
__data = __transform_gm_to_qa(df, type_)
if len(__data) > 1:
# print(__data)
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
# print(QA_util_to_json_from_pandas(__data)[1::])
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)
def __saving_work(code, coll):
QA_util_log_info(
"##JOB03 Now Saving STOCK_MIN ==== {}".format(code), ui_log=ui_log)
try:
for type_ in ["1min", "5min", "15min", "30min", "60min"]:
col_filter = {"code": str(code)[5:], "type": type_}
ref_ = coll.find(col_filter)
end_time = str(now_time())[0:19]
if coll.count_documents(col_filter) > 0:
start_time = ref_[coll.count_documents(
col_filter) - 1]["datetime"]
print(start_time)
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min",
"5min",
"15min",
"30min",
"60min"
].index(type_),
str(code)[5:],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(
symbol=code,
start_time=start_time,
end_time=end_time,
frequency=MIN_SEC[type_],
df=True
)
__data = __transform_gm_to_qa(df, type_)
if len(__data) > 1:
# print(QA_util_to_json_from_pandas(__data)[1::])
# print(__data)
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
else:
start_time = "2015-01-01 09:30:00"
QA_util_log_info(
"##JOB03.{} Now Saving {} from {} to {} == {}".format(
["1min",
"5min",
"15min",
"30min",
"60min"
].index(type_),
str(code)[5:],
start_time,
end_time,
type_,
),
ui_log=ui_log,
)
if start_time != end_time:
df = history(
symbol=code,
start_time=start_time,
end_time=end_time,
frequency=MIN_SEC[type_],
df=True
)
__data = __transform_gm_to_qa(df, type_)
if len(__data) > 1:
# print(__data)
coll.insert_many(
QA_util_to_json_from_pandas(__data)[1::])
# print(QA_util_to_json_from_pandas(__data)[1::])
except Exception as e:
QA_util_log_info(e, ui_log=ui_log)
err.append(code)
QA_util_log_info(err, ui_log=ui_log)