def get(self):
ts_code = self.get_argument('ts_code')
freq = self.get_argument('freq')
trade_date = self.get_argument('trade_date')
if trade_date == 'null':
trade_date = datetime.now().date()
else:
trade_date = datetime.strptime(trade_date, "%Y%m%d")
kline = get_kline(symbol=ts_code,
end_date=trade_date,
freq=freq,
count=5000)
kline.loc[:, "dt"] = pd.to_datetime(kline.dt)
# kline.loc[:, "is_end"] = True
if czsc.__version__ < "0.5":
ka = KlineAnalyze(kline, bi_mode="new", xd_mode='strict')
kline = pd.DataFrame(ka.kline_new)
else:
ka = KlineAnalyze(kline, bi_mode="new", verbose=False)
kline = ka.to_df(ma_params=(5, 20), use_macd=True, max_count=5000)
kline = kline.fillna("")
kline.loc[:, "dt"] = kline.dt.apply(str)
columns = [
"dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi',
'xd'
]
self.finish({'kdata': kline[columns].values.tolist()})
def get(self):
ts_code = self.get_argument('ts_code')
freq = self.get_argument('freq')
asset = self.get_argument('asset', "E")
trade_date = self.get_argument('trade_date')
if trade_date == 'null':
trade_date = datetime.now().date().__str__().replace("-", "")
kline = get_kline(ts_code=ts_code,
end_date=trade_date,
freq=freq,
asset=asset)
kline.loc[:, "dt"] = pd.to_datetime(kline.dt)
if czsc.__version__ < "0.5":
ka = KlineAnalyze(kline, bi_mode="new")
kline = pd.DataFrame(ka.kline_new)
else:
ka = KlineAnalyze(kline, verbose=False)
kline = ka.to_df(ma_params=(5, 20), use_macd=True, max_count=5000)
kline = kline.fillna("")
kline.loc[:, "dt"] = kline.dt.apply(str)
columns = [
"dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi',
'xd'
]
self.finish({'kdata': kline[columns].values.tolist()})
def get(self):
ts_code = self.get_argument('ts_code')
freq = self.get_argument('freq')
trade_date = self.get_argument('trade_date')
if trade_date == 'null':
trade_date = datetime.now().date().__str__().replace("-", "")
kline = get_gm_kline(symbol=ts_code,
end_date=trade_date,
freq=freq,
k_count=3000)
ka = KlineAnalyze(kline,
bi_mode="new",
verbose=False,
use_xd=True,
max_count=5000)
kline = ka.to_df(ma_params=(5, 20),
use_macd=True,
max_count=5000,
mode='new')
kline = kline.fillna("")
kline.loc[:, "dt"] = kline.dt.apply(str)
columns = [
"dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi',
'xd'
]
self.finish({'kdata': kline[columns].values.tolist()})
def use_kline_analyze():
kline = get_kline(symbol="000001.XSHG",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="D",
count=5000)
ka = KlineAnalyze(kline, name="日线", max_raw_len=5000, verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
kline = get_kline(symbol="300803.XSHE",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="30min",
count=5000)
ka = KlineAnalyze(kline, name="30分钟", max_raw_len=5000, verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
kline = get_kline(symbol="300803.XSHE",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="5min",
count=5000)
ka = KlineAnalyze(kline, name="5分钟", max_raw_len=5000, verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
def analysis(symbol=None):
end_date = '2021-04-19'
start_date = '2020-05-20'
kline_1min = get_local_kline(symbol=[symbol], end=end_date, freq='1min', start=start_date)
kline_5min = get_local_kline(symbol=[symbol], end=end_date, freq='5min', start=start_date)
kline_30min = get_local_kline(symbol=[symbol], end=end_date, freq='30min', start=start_date)
kline_day = get_local_day_kline(symbol, end_date, start_date)
if kline_5min is None or kline_1min is None or kline_30min is None or kline_day is None:
return
ka_1m = KlineAnalyze(kline_1min, name="1min", verbose=False)
tig_5m = TechIndicatorGenerator(kline_5min, name="5min", verbose=False)
tig_30m = TechIndicatorGenerator(kline_30min, name="30min", verbose=False)
tig_day = TechIndicatorGenerator(kline_day, name='day', verbose=False)
fds = get_fd_from_points(ka_1m.bi_list, ka_1m.macd, symbol=ka_1m.symbol)
ka_1m = TrendAnalyser(fds, ka_1m.macd)
ta_5m = TrendAnalyser(ka_1m.get_zoushi_list(), tig_5m.macd)
ta_30m = TrendAnalyser(ta_5m.get_zoushi_list(), tig_30m.macd)
ta_day = TrendAnalyser(ta_30m.get_zoushi_list(), tig_day.macd)
print("result is:")
print(ta_30m.getAnalysisResult())
def kline():
if request.method == "POST":
data = json.loads(request.get_data(as_text=True))
elif request.method == "GET":
data = request.args
else:
raise ValueError
ts_code = data.get('ts_code')
freq = data.get('freq')
k = get_kline(symbol=ts_code, freq=freq, k_count=5000)
if czsc.__version__ < "0.5":
ka = KlineAnalyze(k, bi_mode="new", xd_mode='strict')
k = pd.DataFrame(ka.kline_new)
else:
ka = KlineAnalyze(k, min_bi_k=5, verbose=False)
k = ka.to_df(ma_params=(5, 20), use_macd=True, max_count=5000)
k = k.fillna("")
kline.loc[:, "dt"] = kline.dt.apply(str)
columns = ["dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi', 'xd']
res = make_response(jsonify({'kdata': k[columns].values.tolist()}))
res.headers['Access-Control-Allow-Origin'] = '*'
res.headers['Access-Control-Allow-Method'] = '*'
res.headers['Access-Control-Allow-Headers'] = '*'
return res
def __init__(self, klines):
self.klines = klines
self.ka_1min = KlineAnalyze(self.klines['1分钟'], name='1分钟')
self.ka_5min = KlineAnalyze(self.klines['5分钟'], name="5分钟")
self.ka_D = KlineAnalyze(self.klines['日线'], name='日线')
self.symbol = self.ka_1min.symbol
self.end_dt = self.ka_1min.end_dt
self.latest_price = self.ka_1min.latest_price
self.s = self.signals()
self.desc = self.__doc__
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()
print(ka.zs[-2])
def use_kline_analyze():
# kline = get_kline(symbol="000001.XSHG", end_date=datetime.strptime("20200830", "%Y%m%d"), freq="D", count=5000)
# ka = KlineAnalyze(kline, name="日线", verbose=False)
# print("分型识别结果：", ka.fx_list[-3:])
# print("笔识别结果：", ka.bi_list[-3:])
# print("线段识别结果：", ka.xd_list[-3:])
#
# kline = get_kline(symbol="300803.XSHE", end_date=datetime.strptime("20200830", "%Y%m%d"), freq="30min", count=5000)
# ka = KlineAnalyze(kline, name="30分钟", verbose=False)
# print("分型识别结果：", ka.fx_list[-3:])
# print("笔识别结果：", ka.bi_list[-3:])
# print("线段识别结果：", ka.xd_list[-3:])
kline = get_kline(symbol="000001.XSHE",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="5min",
count=5000)
ka = KlineAnalyze(kline, name="5分钟", verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
zx = find_zs(ka)
print(jsonpath.jsonpath(zx, '$..third_buy'))
def use_kline_analyze():
print('=' * 100, '\n')
print("KlineAnalyze 的使用方法：\n")
kline = get_kline(symbol='SHSE.000300', end_date="2020-02-02")
ka = KlineAnalyze(kline)
print("线段：", ka.xd, "\n")
print("中枢：", ka.zs, "\n")
def use_kline_analyze():
kline = get_kline(symbol="000001.XSHG",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="D",
count=5000)
ka = KlineAnalyze(kline,
name="日线",
bi_mode="new",
use_xd=True,
max_count=2000,
ma_params=(5, 34, 120),
verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
kline = get_kline(symbol="300803.XSHE",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="30min",
count=5000)
ka = KlineAnalyze(kline,
name="30分钟",
bi_mode="new",
use_xd=True,
max_count=2000,
ma_params=(5, 34, 120),
verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
kline = get_kline(symbol="300803.XSHE",
end_date=datetime.strptime("20200830", "%Y%m%d"),
freq="5min",
count=5000)
ka = KlineAnalyze(kline,
name="5分钟",
bi_mode="new",
use_xd=True,
max_count=2000,
ma_params=(5, 34, 120),
verbose=False)
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
def use_kline_analyze():
print('=' * 100, '\n')
print("KlineAnalyze 的使用方法：\n")
kline = get_kline(ts_code="000009.SZ",
end_date="20200210",
freq='30min',
asset="I")
ka = KlineAnalyze(kline)
print("线段：", ka.xd, "\n")
print("中枢：", ka.zs, "\n")
def check_trade(ts_code, file_bs, freq, end_date="20200314", asset="E", file_html="bs.html"):
"""在图上画出买卖点"""
bs = pd.read_excel(file_bs)
bs.loc[:, 'f'] = bs.apply(lambda x: 1 if x['交易级别'] == freq_map[freq] else 0, axis=1)
bs = bs[bs.f == 1]
bs.loc[:, '操作提示'] = bs['操作提示'].apply(lambda x: x.replace(freq_map[freq], ""))
bs = bs[["操作提示", "交易时间", "交易价格"]]
print(bs)
df = get_kline(ts_code, freq=freq, end_date=end_date, asset=asset)
ka = KlineAnalyze(df)
plot_kline(ka, bs, file_html, width="1400px", height="680px")
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 level_1_analysis(symbol=None):
end_date = '2021-02-05'
start_date = '2020-10-11'
kline_day = get_local_day_kline(symbol, end_date, start_date)
ka_day = KlineAnalyze(kline_day, name="30min", verbose=False)
tig_day = TechIndicatorGenerator(kline_day, name="day", verbose=False)
fds = get_fd_from_points(ka_day.bi_list, ka_day.macd, symbol=ka_day.symbol)
ta_day = TrendAnalyser(fds, tig_day.macd)
print(ta_day.getAnalysisResult())
def use_kline_analyze():
print('=' * 100, '\n')
print("KlineAnalyze 的使用方法：\n")
kline = get_kline(ts_code="000009.SZ",
end_date="20200210",
freq='30min',
asset="I")
ka = KlineAnalyze(kline,
name="本级别",
min_bi_k=5,
max_raw_len=10000,
verbose=False)
print("分型：", ka.fx_list, "\n")
print("线段：", ka.xd_list, "\n")
def get(self):
ts_code = self.get_argument('ts_code')
freq = self.get_argument('freq')
trade_date = self.get_argument('trade_date')
if trade_date == 'null':
trade_date = datetime.now().date().__str__().replace("-", "")
kline = get_gm_kline(symbol=ts_code, end_date=trade_date, freq=freq, k_count=3000)
ka = KlineAnalyze(kline)
kline = pd.DataFrame(ka.kline)
kline = kline.fillna("")
kline.loc[:, "dt"] = kline.dt.apply(str)
columns = ["dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi', 'xd']
self.finish({'kdata': kline[columns].values.tolist()})
def use_kline_analyze():
print('=' * 100, '\n')
print("KlineAnalyze 的使用方法：\n")
kline = get_kline(ts_code="000009.SZ",
end_date="20200210",
freq='30min',
asset="I")
ka = KlineAnalyze(kline,
name="本级别",
bi_mode="new",
max_xd_len=20,
ma_params=(5, 34, 120),
verbose=False)
print("分型：", ka.fx_list, "\n")
print("线段：", ka.xd_list, "\n")
def use_kline_analyze():
print('=' * 100, '\n')
print("KlineAnalyze 的使用方法：\n")
ts_code = "300033.SZ"
asset = "E"
kline = get_kline("{}-{}".format(ts_code, asset),
end_date="20201212",
freq='30min',
count=2000)
ka = KlineAnalyze(kline,
name="本级别",
bi_mode="new",
max_count=2000,
use_ta=False,
use_xd=True)
print("分型：", ka.fx_list, "\n")
print("线段：", ka.xd_list, "\n")
def use_kline_analyze():
kline = get_kline(symbol="000001.XSHG", end_date="20200616", freq="D", count=5000)
# 输入K线即完成分析
ka = KlineAnalyze(kline, name="日线", min_bi_k=5, max_raw_len=10000, verbose=False)
# 查看结果
print("分型识别结果：", ka.fx_list[-3:])
print("笔识别结果：", ka.bi_list[-3:])
print("线段识别结果：", ka.xd_list[-3:])
# 用图片或者HTML可视化
ka.to_image("test.png")
ka.to_html("test.html")
def kline():
if request.method == "POST":
data = json.loads(request.get_data(as_text=True))
elif request.method == "GET":
data = request.args
else:
raise ValueError
print(data)
ts_code = data.get('ts_code')
freq = data.get('freq')
k = get_kline(symbol=ts_code, freq=freq, k_count=5000)
ka = KlineAnalyze(k, bi_mode="new", xd_mode='strict')
k = pd.DataFrame(ka.kline)
k = k.fillna("")
columns = ["dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi', 'xd']
res = make_response(jsonify({'kdata': k[columns].values.tolist()}))
res.headers['Access-Control-Allow-Origin'] = '*'
res.headers['Access-Control-Allow-Method'] = '*'
res.headers['Access-Control-Allow-Headers'] = '*'
return res
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 enhanced_analysis(symbol=None, start_date='2020-05-20', end_date='2021-02-22'):
kline_1min = get_local_kline(symbol=[symbol], end=end_date, freq='1min', start=start_date)
kline_5min = get_local_kline(symbol=[symbol], end=end_date, freq='5min', start=start_date)
kline_30min = get_local_kline(symbol=[symbol], end=end_date, freq='30min', start=start_date)
kline_day = get_local_day_kline(symbol, end_date, start_date)
if kline_5min is None or kline_1min is None or kline_30min is None or kline_day is None:
return
ka_1m = KlineAnalyze(kline_1min, bi_mode='old', name="1min", verbose=False)
tig_5m = TechIndicatorGenerator(kline_5min, name="5min", verbose=False)
tig_30m = TechIndicatorGenerator(kline_30min, name="30min", verbose=False)
tig_day = TechIndicatorGenerator(kline_day, name='day', verbose=False)
fds = get_fd_from_points(ka_1m.bi_list, ka_1m.macd, symbol=ka_1m.symbol)
ta_1m = EnhancedTrendAnalyser(fds, ka_1m.macd)
ta_5m = EnhancedTrendAnalyser(ta_1m.get_zoushi_list(), tig_5m.macd)
ta_30m = EnhancedTrendAnalyser(ta_5m.get_zoushi_list(), tig_30m.macd)
ta_day = EnhancedTrendAnalyser(ta_30m.get_zoushi_list(), tig_day.macd)
judge_No2_buy_point(ta_day, ta_30m, end=end_date)
def use_kline_analyze():
kline = get_kline(symbol="000001.XSHG",
end_date="20200616",
freq="D",
count=5000)
# 输入K线即完成分析
ka = KlineAnalyze(kline, name="日线")
# 查看结果
print("分型识别结果：", ka.fx[-3:])
print("笔识别结果：", ka.bi[-3:])
print("线段识别结果：", ka.xd[-3:])
print("中枢识别结果：", ka.zs[-1])
# 用图片或者HTML可视化
ka.to_image("test.png")
ka.to_html("test.html")
def test_use_local_data():
kline_1min = get_local_kline(symbol=['300494'], end='2020-12-31', freq='1min', start='2020-01-01')
kline_5min = get_local_kline(symbol=['300494'], end='2020-12-31', freq='5min', start='2020-01-01')
# kline = get_local_day_kline(symbol=['000001'], end='2019-04-31', start='2018-01-01')
# kline = QA_fetch_stock_week('000002', end='2019-12-13', start='2019-03-29')
ka_1min = KlineAnalyze(kline_1min, name="1min", verbose=False)
ka_5min = KlineAnalyze(kline_5min, name="5min", verbose=False)
print("分型识别结果：", ka_1min.fx_list[-3:])
print("笔识别结果：", ka_1min.bi_list[-3:])
print("线段识别结果：", ka_1min.xd_list[-3:])
fds = get_fd_from_points(ka_1min.bi_list, ka_1min.macd, symbol=ka_1min.symbol)
# zoushi = construct_zoushi(fds, ka.macd)
ta_1min = TrendAnalyser(fds, ka_1min.macd)
ta_5min = TrendAnalyser(ta_1min.get_zoushi_list(), ka_5min.macd)
result = ta_5min.getAnalysisResult()
bc = common_check_beichi(ka_1min.xd_list[-5:])
zx = find_zs_enhanced_v1(ka_1min.bi_list, ka_1min.macd)
print("中枢识别结果：", zx)
bei_chi = jsonpath.jsonpath(zx, '$..bei_chi')
if isinstance(bei_chi, bool):
bei_chi = pd.DataFrame()
else:
bei_chi = json_normalize(bei_chi)
bei_chi.set_index('dt', inplace=True)
third_buy = jsonpath.jsonpath(zx, '$..third_buy')
if isinstance(third_buy, bool):
third_buy = pd.DataFrame()
else:
third_buy = json_normalize(third_buy)
third_buy['type'] = "3"
second_buy = jsonpath.jsonpath(zx, '$..second_buy')
if isinstance(second_buy, bool):
second_buy = pd.DataFrame()
else:
second_buy = json_normalize(second_buy)
second_buy['type'] = "2"
first_buy = jsonpath.jsonpath(zx, '$..first_buy')
if isinstance(first_buy, bool):
first_buy = pd.DataFrame()
else:
first_buy = json_normalize(first_buy)
first_buy['type'] = 1
# third_bs = jsonpath.jsonpath(zx, '$..third_bs_section')
# third_bs = json_normalize(third_bs)
# third_bs.set_index('dt', inplace=True)
dfs = [first_buy, second_buy, third_buy]
points = reduce(lambda left, right: pd.concat([left, right], axis=0, join='outer'), dfs)
if len(points) == 0:
pass
else:
points = pd.DataFrame(points).set_index('dt').sort_index()
points = pd.concat([points, bei_chi], axis=1, join='outer')
# points = pd.concat([points, third_bs], axis=1, join='outer')
kline_1min = pd.DataFrame(kline_1min).set_index(['dt'])
result = pd.concat([kline_1min, points], axis=1, join='outer').reset_index()
result = result.fillna(0).set_index(['symbol', 'dt'])
print(result)
# 用图片或者HTML可视化
ka_1min.to_image("test.png")
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")