def test_kline_analyze():
df = get_kline(ts_code="300008.SZ", end_dt="2020-03-23 15:00:00", freq='30min', asset='E')
ka = KlineAnalyze(df)
# 测试识别结果
assert ka.bi[-1]['fx_mark'] == 'g'
assert ka.xd[-1]['fx_mark'] == 'g'
# 测试背驰识别
assert not ka.bi_bei_chi()
assert not ka.xd_bei_chi()
def make_one_day(ts_code, trade_date, asset="E"):
if "-" in trade_date:
end_date = datetime.strptime(trade_date, '%Y-%m-%d')
else:
end_date = datetime.strptime(trade_date, '%Y-%m-%d')
start_date = end_date - timedelta(days=1)
end_dt = end_date + timedelta(days=30)
start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")
if not is_trade_day(start_date):
return
print(f"start trade simulator on {start_date}")
trade_simulator(ts_code=ts_code,
start_date=start_date,
end_date=end_date,
asset=asset,
watch_interval=1)
for freq in ['1min', '5min', '30min']:
file_signals = os.path.join(
data_path, f"{ts_code}_{start_date}_{end_date}_signals.txt")
signals = [
eval(x) for x in open(file_signals, encoding='utf-8').readlines()
]
df = pd.DataFrame(signals)
kline = get_kline(ts_code,
end_dt=end_dt.strftime("%Y-%m-%d %H:%M:%S"),
freq=freq,
asset=asset)
ka = KlineAnalyze(kline)
print(kline.head(), "\n\n")
xd = deepcopy(ka.xd)
xd = sorted(xd, key=lambda row: row['dt'], reverse=False)
print(xd, "\n\n")
def ___xd_status(dt):
for x in xd:
if x['dt'] >= dt:
if x['fx_mark'] == 'd':
s = "向下段"
elif x['fx_mark'] == 'g':
s = "向上段"
else:
raise ValueError
return s
return "o"
col = f'{freq}线段状态'
df[col] = df['交易时间'].apply(___xd_status)
file_excel = "./data/%s_%s_%s_%s.xlsx" % (ts_code, start_date,
end_date, freq)
df.to_excel(file_excel, index=False)
def test_bei_chi():
df = get_kline(ts_code="000001.SH",
end_dt="2020-04-28 15:00:00",
freq='D',
asset='I')
ka = KlineAnalyze(df, name="日线")
plot_ka(ka, file_image="test.jpg")
# 线段背驰
zs1 = {
"start_dt": '2018-07-26 15:00:00',
"end_dt": '2018-10-19 15:00:00',
"direction": "down"
}
zs2 = {
"start_dt": '2018-01-29 15:00:00',
"end_dt": '2018-07-06 15:00:00',
"direction": "down"
}
assert is_bei_chi(ka, zs1, zs2, mode='xd', adjust=0.9)
zs1 = {
"start_dt": '2013-12-10 15:00:00',
"end_dt": '2014-01-20 15:00:00',
"direction": "down"
}
zs2 = {
"start_dt": '2013-09-12 15:00:00',
"end_dt": '2013-11-14 15:00:00',
"direction": "down"
}
assert not is_bei_chi(ka, zs1, zs2, mode='xd', adjust=0.9)
# 笔背驰
zs1 = {"start_dt": '2019-05-17 15:00:00', "end_dt": '2019-06-10 15:00:00'}
zs2 = {"start_dt": '2019-04-08 15:00:00', "end_dt": '2019-05-10 15:00:00'}
assert is_bei_chi(ka, zs1, zs2, mode='bi', adjust=0.9)
zs1 = {"start_dt": '2018-09-28 15:00:00', "end_dt": '2018-10-19 15:00:00'}
zs2 = {"start_dt": '2018-08-28 15:00:00', "end_dt": '2018-09-12 15:00:00'}
assert not is_bei_chi(ka, zs1, zs2, mode='bi', adjust=0.9)
def test_bei_chi():
df = get_kline(ts_code="000001.SH", end_dt="2020-04-28 15:00:00", freq='D', asset='I')
ka = KlineAnalyze(df)
# 线段背驰
zs1 = ['2018-07-26 15:00:00', '2018-10-19 15:00:00']
zs2 = ['2018-01-29 15:00:00', '2018-07-06 15:00:00']
assert is_bei_chi(ka, zs1, zs2, direction='down', mode='xd', adjust=0.9)
zs1 = ['2013-12-10 15:00:00', '2014-01-20 15:00:00']
zs2 = ['2013-09-12 15:00:00', '2013-11-14 15:00:00']
assert not is_bei_chi(ka, zs1, zs2, direction='down', mode='xd', adjust=0.9)
# 笔背驰
zs1 = ['2019-05-17 15:00:00', '2019-06-10 15:00:00']
zs2 = ['2019-04-08 15:00:00', '2019-05-10 15:00:00']
assert is_bei_chi(ka, zs1, zs2, mode='bi', adjust=0.9)
zs1 = ['2018-09-28 15:00:00', '2018-10-19 15:00:00']
zs2 = ['2018-08-28 15:00:00', '2018-09-12 15:00:00']
assert not is_bei_chi(ka, zs1, zs2, mode='bi', adjust=0.9)
# coding: utf-8
import sys
import warnings
from cobra.data.kline import get_kline
sys.path.insert(0, '.')
sys.path.insert(0, '..')
import czsc
from czsc import KlineAnalyze
from czsc.analyze import is_bei_chi, find_zs
warnings.warn(f"czsc version is {czsc.__version__}")
df = get_kline(ts_code="000001.SH",
end_dt="2020-04-28 15:00:00",
freq='D',
asset='I')
ka = KlineAnalyze(df, name="日线", bi_mode='old')
print(ka)
def test_kline_analyze():
assert ka.bi[-1]['fx_mark'] == 'g'
assert ka.xd[-1]['fx_mark'] == 'd'
# 测试背驰识别
assert ka.bi_bei_chi()
assert ka.xd_bei_chi()
print(ka.zs[-2])
ka.to_html("kline.html")
ka.to_image("kline.png")