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 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")