async def sync_bars(frame: str = None, codes: str = None):
"""立即同步行情数据
如果`frame`, `codes`没有提供，则从配置文件中读取相关信息
Args:
frame:
codes:
Returns:
"""
await _init()
if frame:
frame_type = FrameType(frame)
params = sync.read_sync_params(FrameType(frame))
if codes:
params["secs"] = list(map(lambda x: x.strip(" "),
codes.split(",")))
await sync.trigger_bars_sync(frame_type, params, force=True)
logger.info("request %s,%s send to workers.", params, codes)
else:
for frame_type in itertools.chain(tf.day_level_frames,
tf.minute_level_frames):
params = sync.read_sync_params(frame_type)
if params:
await sync.trigger_bars_sync(frame_type, params, force=True)
logger.info("request %s,%s send to workers.", params, codes)
async def sync_bars(frame: str = None, codes: str = None):
"""立即同步行情数据
如果`frame`, `codes`没有提供，则从配置文件中读取相关信息
Args:
frame:
codes:
Returns:
"""
await _init()
if frame:
frame_type = FrameType(frame)
params = syncjobs.load_sync_params(frame_type)
if codes:
params["cat"] = None
params["include"] = codes
await syncjobs.trigger_bars_sync(params, force=True)
logger.info("request %s,%s send to workers.", params, codes)
else:
for frame_type in itertools.chain(tf.day_level_frames, tf.minute_level_frames):
params = syncjobs.load_sync_params(frame_type)
if not params:
continue
if codes:
params["cat"] = None
params["include"] = codes
await syncjobs.trigger_bars_sync(params, force=True)
logger.info("request %s,%s send to workers.", params, codes)
async def copy(self,
code: str,
frame_type: Union[str, FrameType],
frame: Union[str, Frame],
ma_wins=None):
frame_type = FrameType(frame_type)
frame = arrow.get(frame, tzinfo=cfg.tz)
ma_wins = ma_wins or [5, 10, 20]
fit_win = 7
stop = frame
start = tf.shift(stop, -fit_win - max(ma_wins), frame_type)
sec = Security(code)
bars = await sec.load_bars(start, stop, frame_type)
features = []
for win in ma_wins:
ma = signal.moving_average(bars['close'], win)
if len(ma) < fit_win:
raise ValueError(
f"{sec.display_name} doesn't have enough bars for "
f"extracting features")
err, (a, b, c), (vx,
_) = signal.polyfit(ma[-fit_win:] / ma[-fit_win])
features.append((err, (a, b, c), vx))
return features
async def list_momentum_pool(day_offset: int = 1, sort_by='y'):
start = tf.day_shift(arrow.now().date(), -day_offset)
key = f"plots.momentum.pool"
recs = await cache.sys.hgetall(key)
data = []
for k, v in recs.items():
frame, code = k.split(":")
if arrow.get(frame) < arrow.get(start):
continue
sec = Security(code)
v = json.loads(v)
frame_type = FrameType(v.get("frame_type"))
fired = tf.int2time(frame) if frame_type in tf.minute_level_frames else \
tf.int2date(frame)
data.append({
"name": sec.display_name,
"code": code,
"fired": fired,
"frame": frame_type.value,
"y": round(v.get("y"), 2),
"vx": round(v.get("vx"), 1),
"a": round(v.get("a"), 4),
"b": round(v.get("b"), 4),
"err": round(v.get("err"), 4)
})
if len(data) == 0:
print("no data")
else:
df = DataFrame(data)
df.set_index('fired', inplace=True)
display(df.sort_values(sort_by))
async def evaluate(self,
code: str,
frame_type: Union[str, FrameType] = '30m',
win: int = 5,
flag: str = 'both',
slip: float = 0.015):
"""
测试当前股价是否达到均线(frame_type和win指定）附近
Args:
code:
slip:
params: frame_type, win, flag
Returns:
"""
frame_type = FrameType(frame_type)
bars = await self.get_bars(code, win, frame_type)
ma = signal.moving_average(bars['close'], win)
c0 = bars[-1]['close']
if abs(c0 / ma[-1] - 1) <= slip:
await self.fire_trade_signal(flag,
code,
bars[-1]['frame'],
frame_type,
slip=slip,
win=win)
async def _test_sync_bars(self):
# fixme: recover this test later
config_items = [
[
{
"frame": "1d",
"start": "2020-01-01",
"delay": 3,
"type": [],
"include": "000001.XSHE,000004.XSHE",
"exclude": "000001.XSHG",
}
]
]
sync_request = []
async def on_sync_bars(params: dict):
sync_request.append(params)
emit.register(Events.OMEGA_DO_SYNC, on_sync_bars)
for config in config_items:
cfg.omega.sync.bars = config
for frame_config in config:
frame_type = FrameType(frame_config.get("frame"))
sync_params = frame_config
await sync.trigger_bars_sync(frame_type, sync_params, force=True)
await asyncio.sleep(0.2)
self.assertDictEqual(
{"start": "2020-01-01", "stop": None, "frame_type": FrameType.DAY},
sync_request[0],
)
def __init__(self, frame_type: Union[str, FrameType], jitter: str = None):
"""构造函数
jitter的格式用正则式表达为`r"([-]?)(\\d+)([mshd])"`，其中第一组为符号，'-'表示提前；
第二组为数字，第三组为单位，可以为`m`(分钟), `s`(秒), `h`（小时）,`d`(天)。
下面的示例构造了一个只在交易日，每30分钟触发一次，每次提前15秒触的trigger。即它的触发时
间是每个交易日的09:29:45, 09:59:45, ...
Examples:
>>> FrameTrigger(FrameType.MIN30, '-15s')
... # doctest: +ELLIPSIS
<triggers.FrameTrigger object at 0x...>
Args:
frame_type:
jitter: 单位秒。其中offset必须在一个FrameType的长度以内
"""
self.frame_type = FrameType(frame_type)
if jitter is None:
_jitter = 0
else:
matched = re.match(r"([-]?)(\d+)([mshd])", jitter)
if matched is None: # pragma: no cover
raise ValueError(
"malformed. jitter should be [-](number)(unit), "
"for example, -30m, or 30s")
sign, num, unit = matched.groups()
num = int(num)
if unit.lower() == "m":
_jitter = 60 * num
elif unit.lower() == "s":
_jitter = num
elif unit.lower() == "h":
_jitter = 3600 * num
elif unit.lower() == "d":
_jitter = 3600 * 24 * num
else: # pragma: no cover
raise ValueError("bad time unit. only s,h,m,d is acceptable")
if sign == "-":
_jitter = -_jitter
self.jitter = datetime.timedelta(seconds=_jitter)
if (frame_type == FrameType.MIN1 and abs(_jitter) >= 60
or frame_type == FrameType.MIN5 and abs(_jitter) >= 300
or frame_type == FrameType.MIN15 and abs(_jitter) >= 900
or frame_type == FrameType.MIN30 and abs(_jitter) >= 1800
or frame_type == FrameType.MIN60 and abs(_jitter) >= 3600
or frame_type == FrameType.DAY and abs(_jitter) >= 24 * 3600
# it's still not allowed if offset > week, month, etc. Would anybody
# really specify an offset longer than that?
):
raise ValueError("offset must be less than frame length")
async def evaluate(self,
code: str,
frame_type: Union[str, FrameType],
flag: str,
win: int,
price: float,
slip: float = 0.015):
frame_type = FrameType(frame_type)
bars = await self.get_bars(code, 1, frame_type)
if abs(bars[-1]['close'] / price - 1) <= slip:
await self.fire_trade_signal(flag, code, bars[-1]['frame'],
frame_type)
async def evaluate(self, code: str, frame_type: Union[FrameType, str], flag: str,
win: int,
c1: float, d1: Frame,
c2: float, d2: Frame,
slip: float = 0.015):
frame_type = FrameType(frame_type)
n1 = tf.count_frames(d1, d2, frame_type)
slp = (c2 - c1) / n1
n2 = tf.count_frames(d2, tf.floor(arrow.now(), frame_type),
frame_type)
c_ = c2 + slp * n2
bars = await self.get_bars(code, 1, frame_type)
if abs(c_ / bars[-1]['close'] - 1) <= slip:
await self.fire_trade_signal(flag, code, bars[-1]['frame'], frame_type)
async def get_bars_handler(self, request):
try:
sec = request.json.get("sec")
frame_type = FrameType(request.json.get("frame_type"))
end = arrow.get(request.json.get("end"), tzinfo=cfg.tz)
end = end.date() if frame_type in tf.day_level_frames else end.datetime
n_bars = request.json.get("n_bars")
include_unclosed = request.json.get("include_unclosed", False)
bars = await aq.get_bars(sec, end, n_bars, frame_type, include_unclosed)
body = pickle.dumps(bars, protocol=cfg.pickle.ver)
return response.raw(body)
except Exception as e:
logger.exception(e)
return response.raw(pickle.dumps(None, protocol=cfg.pickle.ver))
async def build_train_data(self, save_to: str,
frame_type: str = '1d',
n=10) -> List:
await self.init()
frame_type = FrameType(frame_type)
data = await self._build_train_data(frame_type, n)
date = tf.date2int(arrow.now().date())
path = os.path.abspath(save_to)
path = os.path.join(path, f"momemtum.{frame_type.value}.tsv")
with open(path, "w") as f:
cols = "code,date,a5,b5,err5,a10,b10,err10,a20,b20,err20,y".split(",")
f.writelines("\t".join(cols))
f.writelines("\n")
for item in data:
f.writelines("\t".join(map(lambda x: str(x), item)))
f.writelines("\n")
return data
async def get_stock_pool(request):
args = request.args
frames = int(args.get('frames')[0])
frame_types = args.getlist("frame_types")
if frame_types:
frame_types = [FrameType(frame_type) for frame_type in frame_types]
else:
frame_types = tf.day_level_frames
frame_types.extend(tf.minute_level_frames)
plots = args.getlist('plots') or ['momentum']
results = []
for plot_name in plots:
plot = create_plot(plot_name)
results.append(await plot.list_stock_pool(frames, frame_types))
return response.json(body=results)
def test_comparison(self):
day = FrameType("1d")
week = FrameType("1w")
month = FrameType("1M")
quater = FrameType("1Q")
year = FrameType("1Y")
min_1 = FrameType("1m")
min_5 = FrameType("5m")
self.assertTrue(week > day)
self.assertTrue(day < week)
self.assertTrue(week < month)
self.assertTrue(month < quater)
self.assertTrue(quater < year)
self.assertTrue(min_1, min_5)
self.assertTrue(day >= day)
self.assertTrue(day <= day)
def __init__(self, frame_type: Union[str, FrameType], jitter: str = None):
"""
Args:
frame_type:
jitter: in seconds unit, offset must within one frame
"""
self.frame_type = FrameType(frame_type)
if jitter is None:
_jitter = 0
else:
matched = re.match(r"([-]?)(\d+)([mshd])", jitter)
if matched is None:
raise ValueError(
"malformed. jitter should be [-](number)(unit), "
"for example, -30m, or 30s")
sign, num, unit = matched.groups()
num = int(num)
if unit.lower() == "m":
_jitter = 60 * num
elif unit.lower() == "s":
_jitter = num
elif unit.lower() == "h":
_jitter = 3600 * num
elif unit.lower() == "d":
_jitter = 3600 * 24 * num
else:
raise ValueError("bad time unit. only s,h,m,d is acceptable")
if sign == "-":
_jitter = -_jitter
self.jitter = datetime.timedelta(seconds=_jitter)
if (frame_type == FrameType.MIN1 and abs(_jitter) >= 60
or frame_type == FrameType.MIN5 and abs(_jitter) >= 300
or frame_type == FrameType.MIN15 and abs(_jitter) >= 900
or frame_type == FrameType.MIN30 and abs(_jitter) >= 1800
or frame_type == FrameType.MIN60 and abs(_jitter) >= 3600
or frame_type == FrameType.DAY and abs(_jitter) >= 24 * 3600
# it's still not allowed if offset > week, month, etc. Would anybody
# really specify an offset longer than that?
):
raise ValueError("offset must be less than frame length")
async def visualize(self, code: Union[str, List[str]], frame: Union[str,
Frame],
frame_type: Union[str, FrameType]):
"""
将code列表中的股票的动量特征图象化
Args:
code:
frame:
frame_type:
Returns:
"""
import matplotlib.pyplot as plt
if isinstance(code, str):
code = [code]
col = 4
row = len(code) // col + 1
plt.figure(figsize=(5 * row * col, 7))
plt.subplots_adjust(wspace=0.2, hspace=0.2)
fit_win = 7
colors = {"5": '#808080', "10": '#00cc80', "20": '#00ccff'}
frame = arrow.get(frame)
frame_type = FrameType(frame_type)
for i, code in enumerate(code):
_code = code.split(".")[0]
start = tf.shift(frame, -25, frame_type)
bars = await Security(code).load_bars(start, frame, frame_type)
plt.subplot(len(code) // col + 1, col, i + 1)
y_lim = 0
text = ""
for win in [5, 10, 20]:
ma = signal.moving_average(bars['close'], win)
_ma = ma[-fit_win:]
plt.plot(_ma, color=colors[f"{win}"])
err, (a, b, c), (vx, _) = signal.polyfit(_ma / _ma[0])
p = np.poly1d((a * _ma[0], b * _ma[0], c * _ma[0]))
y = p(fit_win + 2) / p(fit_win - 1) - 1
y_lim = max(y_lim, np.max(_ma))
if win == 5:
text = f"{_code} a:{a:.4f} b:{b:.4f} vx:{vx:.1f} y:{y:.2f}"
if err < self.baseline(f"ma{win}:{frame_type.value}:err"):
# 如果拟合在误差范围内，则画出拟合线
plt.plot([p(i) for i in range(len(_ma))],
"--",
color=colors[f"{win}"])
plt.plot([p(i) for i in range(len(_ma))],
"o",
color=colors[f"{win}"])
if 0 < vx < fit_win:
plt.plot([vx], p(vx), 'x')
plt.plot(0, y_lim * 1.035)
plt.text(0.1, y_lim * 1.02, text, color='r')
async def quick_scan():
# fixme
secs = Securities()
report = logging.getLogger("quickscan")
counters = {}
for sync_config in cfg.omega.sync.bars:
frame = sync_config.get("frame")
start = sync_config.get("start")
if frame is None or start is None:
logger.warning("skipped %s: required fields are [frame, start]",
sync_config)
continue
frame_type = FrameType(frame)
start = arrow.get(start).date()
start = tf.floor(start, FrameType.DAY)
stop = sync_config.get("stop") or arrow.now().date()
if frame_type in tf.minute_level_frames:
minutes = tf.ticks[frame_type][0]
h, m = minutes // 60, minutes % 60
start = datetime.datetime(start.year,
start.month,
start.day,
h,
m,
tzinfo=tz.gettz(cfg.tz))
stop = datetime.datetime(stop.year,
stop.month,
stop.day,
15,
tzinfo=tz.gettz(cfg.tz))
counters[frame] = [0, 0]
codes = secs.choose(sync_config.get("type"))
include = filter(lambda x: x,
sync_config.get("include", "").split(","))
include = map(lambda x: x.strip(" "), include)
codes.extend(include)
exclude = sync_config.get("exclude", "")
exclude = map(lambda x: x.strip(" "), exclude)
codes = set(codes) - set(exclude)
counters[frame][1] = len(codes)
for code in codes:
head, tail = await cache.get_bars_range(code, frame_type)
if head is None or tail is None:
report.info("ENOSYNC,%s,%s", code, frame)
counters[frame][0] = counters[frame][0] + 1
continue
expected = tf.count_frames(head, tail, frame_type)
# 'head', 'tail' should be excluded
actual = (await
cache.security.hlen(f"{code}:{frame_type.value}")) - 2
if actual != expected:
report.info("ELEN,%s,%s,%s,%s,%s,%s", code, frame, expected,
actual, head, tail)
counters[frame][0] = counters[frame][0] + 1
continue
sec = Security(code)
if start != head:
if (type(start) == datetime.date and start > sec.ipo_date
or (type(start) == datetime.datetime
and start.date() > sec.ipo_date)):
report.info("ESTART,%s,%s,%s,%s,%s", code, frame, start,
head, sec.ipo_date)
counters[frame][0] = counters[frame][0] + 1
continue
if tail != stop:
report.info("EEND,%s,%s,%s,%s", code, frame, stop, tail)
counters[frame][0] = counters[frame][0] + 1
return counters
async def evaluate(self,
code: str,
frame_type: str = '30m',
dt: str = None,
win=5,
flag='long'):
"""
如果股价从高点下来，或者从低点上来，则发出信号。高点和低点的确定，由于曲线拟合的原因，
可能产生上一周期未发出信号，这一周期发出信号，但高点或者低点已在几个周期之前。这里的
策略是，在新的趋势未形成之前，只报一次
Args:
code:
frame_type: frame_type
dt:
win:
flag:
Returns:
"""
stop = arrow.get(dt, tzinfo=cfg.tz) if dt else arrow.now(tz=cfg.tz)
frame_type = FrameType(frame_type)
ft = frame_type.value
bars = await self.get_bars(code, win + self.fit_win, frame_type, stop)
ma = signal.moving_average(bars['close'], win)
_ma = ma[-self.fit_win:]
err, (a, b, c), (vx, _) = signal.polyfit(_ma / _ma[0])
logger.debug("%s, %s, %s, %s, %s", code, err, a, b, vx)
if err > self.baseline(f"ma{win}:{ft}:err"):
self.remember(code, frame_type, "trend", "dunno")
return
p = np.poly1d((a, b, c))
y = p(self.fit_win + 2) / p(self.fit_win - 1) - 1
previous_status = self.recall(code, frame_type, "trend")
# 如果b > 10 * a * x，则走势主要由b决定。这里x即fit_win序列，我们向后看3周期
if abs(b) > 10 * (self.fit_win + 3) * abs(a):
if b > 0 and previous_status != "long" and flag in [
'both', "long"
]:
await self.fire_trade_signal('long',
code,
stop,
frame_type,
a=a,
b=b,
err=err,
vx=vx,
y=y)
if b < 0 and previous_status != "short" and flag in [
'both', "short"
]:
await self.fire_trade_signal('short',
code,
stop,
frame_type,
err=err,
a=a,
b=b,
y=y)
return
t1 = int(vx) < self.fit_win - 1
# 判断是否为看多信号
t2 = a > self.baseline(f"ma{win}:{ft}:a")
if t1 and t2 and previous_status != "long" and flag in [
"long", "both"
]:
await self.fire_trade_signal('long',
code,
stop,
frame_type,
err=err,
a=a,
b=b,
y=y)
# 判断是否为看空信号
t2 = a < -self.baseline(f"ma{win}:{ft}:a")
if t1 and t2 and previous_status != "short" and flag in [
"short", "both"
]:
await self.fire_trade_signal('short',
code,
stop,
frame_type,
err=err,
a=a,
b=b,
y=y)
async def list_stock_pool(self,
frames: int,
frame_types: List[FrameType] = None):
key = "plots.momentum.pool"
recs = await cache.sys.hgetall(key)
items = []
now = arrow.now()
for k, v in recs.items():
frame, code = k.split(":")
sec = Security(code)
v = json.loads(v)
frame_type = FrameType(v.get("frame_type"))
if frame_type not in frame_types:
continue
latest_frame = tf.floor(now, frame_type)
start = tf.shift(latest_frame, -frames, frame_type)
fired = tf.int2time(frame) if frame_type in tf.minute_level_frames else \
tf.int2date(frame)
if fired < start:
continue
items.append({
"name": sec.display_name,
"code": code,
"fired": str(fired),
"frame": frame_type.value,
"y": round(v.get("y"), 2),
"vx": round(v.get("vx"), 1),
"a": round(v.get("a"), 4),
"b": round(v.get("b"), 4),
"err": round(v.get("err"), 4)
})
return {
"name":
self.display_name,
"plot":
self.name,
"items":
items,
"headers": [{
"text": '名称',
"value": 'name'
}, {
"text": '代码',
"value": 'code'
}, {
"text": '信号时间',
"value": 'fired'
}, {
"text": '预测涨幅',
"value": 'y'
}, {
"text": '动能',
"value": 'a'
}, {
"text": '势能',
"value": 'b'
}, {
"text": '周期',
"value": 'frame'
}, {
"text": '底部距离(周期)',
"value": 'vx'
}, {
"text": '拟合误差',
"value": 'err'
}]
}
async def scan(self,
frame_type: Union[str, FrameType] = FrameType.DAY,
end: Frame = None,
codes: List[str] = None):
logger.info("running momentum scan at %s level", frame_type)
if end is None:
end = arrow.now(cfg.tz).datetime
assert type(end) in (datetime.date, datetime.datetime)
frame_type = FrameType(frame_type)
ft = frame_type.value
codes = codes or Securities().choose(['stock'])
day_bars = {}
async for code, bars in Security.load_bars_batch(
codes, end, 2, FrameType.DAY):
day_bars[code] = bars
if len(day_bars) == 0:
return
async for code, bars in Security.load_bars_batch(
codes, end, 11, frame_type):
if len(bars) < 11:
continue
fired = bars[-1]['frame']
day_bar = day_bars.get(code)
if day_bar is None:
continue
c1, c0 = day_bars.get(code)[-2:]['close']
cmin = min(bars['close'])
# 还处在下跌状态、或者涨太多
if c0 == cmin or (c0 / c1 - 1) > self.baseline(f"up_limit"):
continue
ma5 = signal.moving_average(bars['close'], 5)
err, (a, b, c), (vx, _) = signal.polyfit(ma5[-7:] / ma5[-7])
# 无法拟合，或者动能不足
if err > self.baseline(f"ma5:{ft}:err") or a < self.baseline(
f"ma5:{ft}:a"):
continue
# 时间周期上应该是信号刚出现，还在窗口期内
vx_range = self.baseline(f"ma5:{ft}:vx")
if not vx_range[0] < vx < vx_range[1]:
continue
p = np.poly1d((a, b, c))
y = p(9) / p(6) - 1
# 如果预测未来三周期ma5上涨幅度不够
if y < self.baseline(f"ma5:{ft}:y"):
continue
sec = Security(code)
if frame_type == FrameType.DAY:
start = tf.shift(tf.floor(end, frame_type), -249, frame_type)
bars250 = await sec.load_bars(start, end, frame_type)
ma60 = signal.moving_average(bars250['close'], 60)
ma120 = signal.moving_average(bars250['close'], 120)
ma250 = signal.moving_average(bars250['close'], 250)
# 上方无均线压制
if (c0 > ma60[-1]) and (c0 > ma120[-1]) and (c0 > ma250[-1]):
logger.info("%s, %s, %s, %s, %s, %s", sec, round(a, 4),
round(b, 4), round(vx, 1),
round(c0 / c1 - 1, 3), round(y, 3))
await self.enter_stock_pool(code,
fired,
frame_type,
a=a,
b=b,
err=err,
y=y,
vx=self.fit_win - vx)
elif frame_type == FrameType.WEEK:
await self.enter_stock_pool(code,
fired,
frame_type,
a=a,
b=b,
err=err,
y=y,
vx=self.fit_win - vx)
elif frame_type == FrameType.MIN30:
await self.fire_trade_signal('long',
code,
fired,
frame_type,
a=a,
b=b,
err=err,
y=y,
vx=self.fit_win - vx)
def parse_sync_params(
frame: Union[str, Frame],
cat: List[str] = None,
start: Union[str, datetime.date] = None,
stop: Union[str, Frame] = None,
delay: int = 0,
include: str = "",
exclude: str = "",
) -> Tuple:
"""按照[使用手册](usage.md#22-如何同步K线数据)中的规则，解析和补全同步参数。
如果`frame_type`为分钟级，则当`start`指定为`date`类型时，自动更正为对应交易日的起始帧；
当`stop`为`date`类型时，自动更正为对应交易日的最后一帧。
Args:
frame (Union[str, Frame]): frame type to be sync. The word ``frame`` is used
here for easy understand by end user. It actually implies "FrameType".
cat (List[str]): which catetories is about to be synced. Should be one of
['stock', 'index']. Defaults to None.
start (Union[str, datetime.date], optional): [description]. Defaults to None.
stop (Union[str, Frame], optional): [description]. Defaults to None.
delay (int, optional): [description]. Defaults to 5.
include (str, optional): which securities should be included, seperated by
space, for example, "000001.XSHE 000004.XSHE". Defaults to empty string.
exclude (str, optional): which securities should be excluded, seperated by
a space. Defaults to empty string.
Returns:
- codes (List[str]): 待同步证券列表
- frame_type (FrameType):
- start (Frame):
- stop (Frame):
- delay (int):
"""
frame_type = FrameType(frame)
if frame_type in tf.minute_level_frames:
if stop:
stop = arrow.get(stop, tzinfo=cfg.tz)
if stop.hour == 0: # 未指定有效的时间帧，使用当日结束帧
stop = tf.last_min_frame(tf.day_shift(stop.date(), 0),
frame_type)
else:
stop = tf.floor(stop, frame_type)
else:
stop = tf.floor(arrow.now(tz=cfg.tz).datetime, frame_type)
if stop > arrow.now(tz=cfg.tz):
raise ValueError(f"请勿将同步截止时间设置在未来: {stop}")
if start:
start = arrow.get(start, tzinfo=cfg.tz)
if start.hour == 0: # 未指定有效的交易帧，使用当日的起始帧
start = tf.first_min_frame(tf.day_shift(start.date(), 0),
frame_type)
else:
start = tf.floor(start, frame_type)
else:
start = tf.shift(stop, -999, frame_type)
else:
stop = (stop and arrow.get(stop).date()) or arrow.now().date()
if stop == arrow.now().date():
stop = arrow.now(tz=cfg.tz)
stop = tf.floor(stop, frame_type)
start = tf.floor(
(start and arrow.get(start).date()), frame_type) or tf.shift(
stop, -1000, frame_type)
secs = Securities()
codes = secs.choose(cat or [])
exclude = map(lambda x: x, exclude.split(" "))
codes = list(set(codes) - set(exclude))
include = list(filter(lambda x: x, include.split(" ")))
codes.extend(include)
return codes, frame_type, start, stop, int(delay)
def test_frame_type_convert(self):
for frame_type in FrameType:
self.assertEqual(frame_type.to_int(),
FrameType.from_int(frame_type.to_int()).to_int())