def scrawl_single_tick(i, path, ex, tdates):
the_dir1 = os.path.join(path, ex.upper(), str(i[2].year))
if not os.path.exists(the_dir1):
os.makedirs(the_dir1)
the_dir = os.path.join(path, ex.upper(), str(i[2].year), i[0] + ".csv.gz")
the_dir2 = os.path.join(path, ex.upper(), str(i[2].year), i[0] + ".csv")
if not os.path.exists(the_dir):
print(the_dir)
print(i)
print(tdates[i[2]])
print(i[2])
api = TqApi(account=TqSim())
# api = TqApi(account=TqSim(),url="ws://192.168.56.1:7777")
td = DataDownloader(api,
symbol_list=[ex.upper() + "." + i[1]],
dur_sec=0,
start_dt=tdates[i[2]] + timedelta(hours=17),
end_dt=i[2] + timedelta(hours=16),
csv_file_name=the_dir2)
while not td.is_finished():
api.wait_update()
# print("progress: tick:%.2f%%" % td.get_progress())
print("done:" + the_dir)
api.close()
with open(the_dir2, 'rb') as f:
with gzip.GzipFile(filename=the_dir2 + ".gz",
mode='w',
compresslevel=9) as gf:
content = f.read()
gf.write(content)
os.remove(the_dir2)
del td
del api
gc.collect()
def scrawl_day_tick(date, ex):
agg = agg_future_dayk()
logging.info("start filter existed symbols")
path = TICK_PATH
logging.info("start getting tick data")
api = TqApi(account=TqSim())
logging.info(ex + ": start getting tick")
currentYearData = agg.getCurrentYearData(ex)
currentYearData = currentYearData[currentYearData['date'] == date]
pathpair = list(
map(
lambda x: (x[1].strftime('%Y%m%d') + "-" + x[0], x[0],
datetime.utcfromtimestamp(x[1].timestamp())),
currentYearData[['symbol', 'date']].values))
trading_dates = get_trading_calendar(security_type="future",
exchange="shfe")
tdates = {}
for i in range(len(trading_dates)):
if i > 0:
tdates[datetime.strptime(trading_dates[i],
'%Y%m%d')] = datetime.strptime(
trading_dates[i - 1], '%Y%m%d')
for i in pathpair:
if i[1].startswith("sc"):
continue
the_dir1 = os.path.join(path, ex.upper(), str(i[2].year))
if not os.path.exists(the_dir1):
os.makedirs(the_dir1)
the_dir = os.path.join(path, ex.upper(), str(i[2].year),
i[0] + ".csv.gz")
the_dir2 = os.path.join(path, ex.upper(), str(i[2].year),
i[0] + ".csv")
# print(the_dir)
if not os.path.exists(the_dir):
td = DataDownloader(api,
symbol_list=[ex.upper() + "." + i[1]],
dur_sec=0,
start_dt=tdates[i[2]] + timedelta(hours=17),
end_dt=i[2] + timedelta(hours=15),
csv_file_name=the_dir2)
while not td.is_finished():
api.wait_update()
# print("progress: tick:%.2f%%" % td.get_progress())
print("done:" + the_dir)
logging.info(ex + ": complete getting tick")
from tqsdk.lib import TargetPosTask
'''
连续3根阴线就做空，连续3根阳线就做多，否则空仓
'''
api = TqApi("SIM")
# 设定连续多少根阳线/阴线
length = 3
# 获得 rb1901 10秒K线的引用, 长度为 length+1
klines = api.get_kline_serial("SHFE.rb1901", 10, data_length = length + 1)
# 创建 rb1901 的目标持仓 task，该 task 负责调整 rb1901 的仓位到指定的目标仓位, offset_priority的用法详见文档
target_pos = TargetPosTask(api, "SHFE.rb1901", offset_priority="今昨开")
while True:
api.wait_update()
# 只有在新创建出K线时才判断开平仓条件
if api.is_changing(klines[-1], "datetime"):
# 将K线转为pandas.DataFrame, 跳过最后一根刚生成的K线
df = klines.to_dataframe()[:-1]
# 比较收盘价和开盘价，判断是阳线还是阴线
# df["close"] 为收盘价序列, df["open"] 为开盘价序列, ">"(pandas.Series.gt) 返回收盘价是否大于开盘价的一个新序列
up = df["close"] > df["open"]
down = df["close"] < df["open"]
if all(up):
print("连续阳线: 目标持仓 多头1手")
# 设置目标持仓为正数表示多头，负数表示空头，0表示空仓
target_pos.set_target_volume(1)
elif all(down):
print("连续阴线: 目标持仓 空头1手")
target_pos.set_target_volume(-1)