def test():
pl = publish.Publish()
base_boll_up = get_boll_up_base()
base_boll_lower = get_boll_lower_base()
def get_data():
code = jx.HCGD
df = tdx.getFive(code)
return df
key = myredis.gen_keyname(__file__, Recognize_boll.test)
df = myredis.createRedisVal(key, get_data).get()
df = stock.TDX_BOLL_df(df)
for i in range(60, len(df)):
df_cur = df[:i]
c = Recognize_boll(base_boll_up, df_cur)
c2 = Recognize_boll(base_boll_lower, df_cur)
b = c._calc_beta_up()
b2 = c._calc_beta_lower()
#if c2.is_matched():
#print(b2)
if abs(c.sign()) > 0 or abs(c2.sign()) > 0:
df_cur = df_cur[['c', 'boll_up', 'boll_mid', 'boll_lower']]
df_cur.index = range(len(df_cur))
df_cur.plot()
pl.show()
def watch_four():
"""观察当前数据源的four情况"""
from autoxd.pinyin import stock_pinyin3 as jx
code = jx.ZXJT
df = stock.getFiveHisdatDf(code, method='tdx')
#print(df)
four = stock.FOUR(df['c'].values)
print(four)
from autoxd import ui
from autoxd.pypublish import publish
pl = publish.Publish()
ui.DrawTs(pl, four)
def _test_recog_boll(self):
from pypublish import publish
pl = publish.Publish()
from policy_report import df_to_html_table
report = []
for i in range(200):
try:
recog_boll(pl, report)
except:
continue
df = pd.DataFrame(report)
df = df[df[df.columns[0]] > 0.7]
pl.reset(df_to_html_table(df))
pl.publish()
def df_to_img_martix(df, n):
"""生成df到目录img
return: np.ndarray shape(n,n)"""
pl = publish.Publish(explicit=True, is_clear_path=True)
df.plot(legend=False)
pl.axis('off')
pl.show()
fname = pl.get_CurImgFname()
pl.close()
#print(fname)
img = cv2.imread(fname, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (n,n))
return img
def show_first():
fname = 'center_indexs_mid.csv'
datas = pearson_clust.load_data()
df = pd.read_csv(fname)
df = df['0']
indexs = df.get_values()
print(len(indexs))
#print(len(indexs))
#print(len(datas))
pl = publish.Publish()
for index in indexs:
pl.figure
pearson_clust.draw(datas[index])
pl.show()
pl.close()
def test():
width = 300
height = 300
a = np.random.rand(30,4)*100
print(a)
df = pd.DataFrame(a)
pl = publish.Publish()
df.plot()
pl.show()
pl.close()
v = convert(width, height, df)
#filter(lambda x:pl.scatter(x[0],x[1]), v )
for x in v:
pl.scatter(x[:,0], x[:,1])
pl.show()
pl.close()
print("")
def test_recog_history_boll():
from pypublish import publish
pl = publish.Publish(explicit=True)
code = jx.ZCKJ.b
def get_local_codes():
data_path = 'cnn_boll/datasources/'
return np.array([str(f).split('.')[0] for f in os.listdir(data_path)])
from sklearn.utils import shuffle
key = myredis.gen_keyname(__file__, test_recog_history_boll)
codes = myredis.createRedisVal(key, get_local_codes).get()
code = shuffle(codes)[0]
#ui.DrawTs(pl, x)
#x = get_boll_up_base()
x = get_boll_lower_base()
p = 0.75
#report = ['init', pl.get_CurImgFname()]
report = recog_history_boll(pl, x, p, code)
pl.RePublish(report)
输出符合技术指标组合的结果
"""
from autoxd import stock, ui, myredis, agl, sign_observation
from autoxd.cnn_boll.judge_boll_sign import getBolls
from autoxd.pinyin import stock_pinyin3 as jx
from autoxd.myenum import MYCOLS_NAME as colname
from autoxd.hard_recog import kurtosis
from autoxd.cnn_boll.judge_boll_sign import g_scope_len
import pylab as pl
import pandas as pd
import random
import numpy as np
from collections.abc import Iterator, Iterable
from autoxd.pypublish import publish
pl = publish.Publish(is_clear_path=True)
#需要处理的字段
#col_names = [""]
#cols = ["close_zz_0", "close_zz_1", "boll_low_zz_0", "boll_low_zz_1", "boll_w"]
class calc_property:
close_zz_0 = np.nan
close_zz_1 = np.nan
boll_low_zz_0 = np.nan
boll_low_zz_1 = np.nan
boll_w = np.nan
adx = 0
boll_x = 0 # x, 时间周期
boll_y = 0.1 # (mid-v)/(mid-low)
def setParams(s):
if 0: s = Strategy_Boll
s.setParams(pl=publish.Publish(), )
def setParams(s):
if 0: s = Strategy_Boll
s.setParams(
trade_num=300,
pl=publish.Publish(explicit=True),
)
def setParams(s):
s.setParams(
pl=publish.Publish(),
)
