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 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():
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, freq, file_bs, file_png="bs.png"):
"""在图上画出买卖点"""
bs = pd.read_excel(file_bs)
bs['f'] = bs['操作提示'].apply(lambda x: 1 if freq_map[freq] in x and "同级别" not in x else 0)
bs = bs[bs.f == 1]
df = get_kline(ts_code, freq=freq, end_date="20200314", asset='E')
ka = KlineAnalyze(df)
df = pd.DataFrame(ka.kline)
df = macd(df)
df = df.merge(bs[['操作提示', '出现时间', '基准价格']], left_on='dt', right_on='出现时间', how='left')
draw(df, file_png)
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 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 = ka.chan_result
kline = kline.fillna("")
columns = ["dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi', 'xd']
self.finish({'kdata': kline[columns].values.tolist()})
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['f'] = bs['操作提示'].apply(lambda x: 1
if freq_map[freq] in x and "线" not in x else 0)
bs = bs[bs.f == 1]
bs['操作提示'] = bs['操作提示'].apply(lambda x: x.replace(freq_map[freq], ""))
df = get_kline(ts_code, freq=freq, end_date=end_date, asset=asset)
ka = KlineAnalyze(df)
plot_kline(ka, bs, file_html)
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 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)
ka = KlineAnalyze(kline)
kline = pd.DataFrame(ka.kline)
kline = kline.fillna("")
columns = [
"dt", "open", "close", "low", "high", "vol", 'fx_mark', 'fx', 'bi',
'xd'
]
self.finish({'kdata': kline[columns].values.tolist()})
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)
def test_kline_analyze():
df = get_kline(ts_code="300803.SZ", freq='1min', end_date="20200216")
ka = KlineAnalyze(df)