目录
1 前言
2 工具介绍
1.1 界面
3 测试搜索倒锤头形态
1 前言
本来想研究金融，可是看到代码就烦，难道还要特意去学习python编程？那样岂不浪费好多发cai的时间？估计很多股友跟我的经历很相似。想从网上找个好的python工具，但是在网上找来找去都没找到特别中意的，全都是一堆代码，没法直接拿来主义。没办法还是边学习编程边炒gu养家吧。
2 工具介绍
这个工具的特点是，一是不用安装，直接运行；二是后台集成了python，功能强大；三是扩展性强，后面需要什么功能模块直接安装就行；四是不用敲代码，一行代码都不用敲，点几下鼠标就出结果了；五是后面会不断扩充功能，因为我要用它炒gu挣钱养家糊口，功能不强大不行；六是增加了功能我会马上发布新程序来。股友们拿来主义随便用；七是。。。。。。
1.1 界面
刚开始界面有点简陋啊，将就吧。
3 测试搜索倒锤头形态
选中一个已经导出的代码，然后点击“搜：倒锤头”，几秒钟后浏览器显示结果。下面的滑块可以左右平移、放大缩小。
记录一下实际使用的python代码：
from typing import List, Union
import talib
from pyecharts import options as opts
from pyecharts.charts import Kline, Line, Bar, Grid
import os
import pandas as pd
'''
def net_split_data(data):
category_data = []
values = []
volumes = []
for i, tick in enumerate(data):
category_data.append(tick[0])
values.append(tick)
volumes.append([i, tick[4], 1 if tick[1] > tick[2] else -1])
return {"categoryData": category_data, "values": values, "volumes": volumes}
def net_get_data():
response = requests.get(
url="https://echarts.apache.org/examples/data/asset/data/stock-DJI.json"
)
json_response = response.json()
# 解析数据
return net_split_data(data=json_response)
'''
def split_data(data):
category_data = []
values = []
volumes = []
# flags = []
for i, tick in enumerate(data.values.tolist()):
category_data.append(tick[0])
values.append(tick)
volumes.append([i, tick[5], 1 if tick[1] > tick[2] else -1])
# flags.append([i, 0])
open_p = pd.DataFrame(values)[1]
close_p = pd.DataFrame(values)[2]
low_p = pd.DataFrame(values)[3]
high_p = pd.DataFrame(values)[4]
array_cdl2c = talib.CDLINVERTEDHAMMER(open_p, high_p, low_p, close_p) # 倒锤头
# l_array_cdl2c = array_cdl2c.values.tolist()
# 由于不知道如何在k线图中叠加标记，使用这种变通方法，即替换成交量图中
# 的成交量为乌鸦标记
# 即：用 array_cdl2c 的值替换 df_volumes 中的成交量
# 列表转化为DataFrame方便列操作
df_volumes = pd.DataFrame(volumes)
df_volumes[1] = array_cdl2c
df_volumes[2] = 1 # 2只乌鸦标志颜色统一设置为绿色
volumes = df_volumes.values.tolist()
return {"categoryData": category_data, "values": values, "volumes": volumes}
def get_data(code):
# df_tdx = pd.read_feather(r'./dataout/tdx/'+code+r'.day.feather')
# df_tdx.index=pd.to_datetime(df_tdx.Date, format = '%Y%m%d')
# df_tdx_b=df_tdx.truncate(before=start_date, after = end_date)
# df_tdx_b['Openinterest']=0
# df_tdx_b.rename(columns={'vol':'volume'}, inplace = True)
# df_tdx_b=df_tdx_b[['Open','High','Low','Close','Volume','Openinterest']]
# return split_data(data=df_tdx_b)
df_tdx = pd.read_feather(r'./data/tdx/'+code+r'.day.feather')
df_tdx.drop('Amout', axis=1, inplace=True)
df_tdx.Date=pd.to_datetime(df_tdx.Date, format = '%Y%m%d')
df_tdx.Date=df_tdx.Date.map(lambda x:x.strftime('%Y-%m-%d'))
# df_tdx.index=pd.to_datetime(df_tdx.Date, format = '%Y%m%d')
# 调整列顺序
df_tdx = df_tdx.loc[:,['Date', 'Open', 'Close', 'Low', 'High', 'Volume']]
# df_tdx_b=df_tdx.truncate(before=start, after = end)
# df_tdx_b['Openinterest']=0
# df_tdx.rename(columns={'vol':'Volume'}, inplace = True)
# df_tdx_b=df_tdx_b[['Open','High','Low','Close','Volume','Openinterest']]
return split_data(data=df_tdx)
def calculate_ma(day_count: int, data):
result: List[Union[float, str]] = []
for i in range(len(data["values"])):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(data["values"][i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
def draw_charts():
kline_data = [data[1:-1] for data in chart_data["values"]]
kline = (
Kline()
.add_xaxis(xaxis_data=chart_data["categoryData"])
.add_yaxis(
series_name="stock index",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
.set_global_opts(
legend_opts=opts.LegendOpts(
is_show=False, pos_bottom=10, pos_left="center"
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="85%",
range_start=98,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{"value": 1, "color": "#00da3c"},
{"value": -1, "color": "#ec0000"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
line = (
Line()
.add_xaxis(xaxis_data=chart_data["categoryData"])
.add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="MA10",
y_axis=calculate_ma(day_count=10, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="MA20",
y_axis=calculate_ma(day_count=20, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="MA30",
y_axis=calculate_ma(day_count=30, data=chart_data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
bar = (
Bar()
.add_xaxis(xaxis_data=chart_data["categoryData"])
.add_yaxis(
series_name="Volume",
y_axis=chart_data["volumes"],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid(
init_opts=opts.InitOpts(
width="1400px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
)
)
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="8%", pos_top="63%", height="16%"
),
)
grid_chart.render("render.html")
# 打开网页
os.system("render.html")
if __name__ == "__main__":
'''
df_tdx = pd.read_feather(r'./dataout/tdx/bj871396.day.feather')
df_tdx.drop('Amout', axis=1, inplace=True)
df_tdx.Date=pd.to_datetime(df_tdx.Date, format = '%Y%m%d')
df_tdx.Date=df_tdx.Date.map(lambda x:x.strftime('%Y-%m-%d'))
# df_tdx.index=pd.to_datetime(df_tdx.Date, format = '%Y%m%d')
# df_tdx.Date = df_tdx.astype({'Date':'str'})
# df_tdx.Date = df_tdx.Date.map(lamda x:)
# df_tdx.rename(columns={'vol':'Volume'}, inplace = True)
# df_tdx_b=df_tdx_b[['Open','High','Low','Close','Volume','Openinterest']]
# print(df_tdx.dtypes)
# print(list(df_tdx))
df_tdx = df_tdx.loc[:,['Date', 'Open', 'Close', 'Low', 'High', 'Volume']]
# print(list(df_tdx))
d_category_data = []
d_values = []
d_volumes = []
# d_flags = []
for i, tick in enumerate(df_tdx.values.tolist()):
d_category_data.append(tick[0])
d_values.append(tick)
d_volumes.append([i, tick[5], 1 if tick[1] > tick[2] else -1])
# d_flags.append([i, 0])
open_p = pd.DataFrame(d_values)[1]
close_p = pd.DataFrame(d_values)[2]
low_p = pd.DataFrame(d_values)[3]
high_p = pd.DataFrame(d_values)[4]
array_cdl2c = talib.CDLINVERTEDHAMMER(open_p, high_p, low_p, close_p)
# array_cdl2c 与 d_volumes合并，
# 然后用 array_cdl2c 的之替换 df_volumes 中的成交量
# 列表转化为DataFrame方便列操作
df_volumes = pd.DataFrame(d_volumes)
df_volumes[1] = array_cdl2c
# l_array_cdl2c = array_cdl2c.values.tolist()
'''
'''
response = requests.get(
url="https://echarts.apache.org/examples/data/asset/data/stock-DJI.json"
)
json_response = response.json()
# 解析数据
category_data = []
values = []
volumes = []
for i, tick in enumerate(json_response):
category_data.append(tick[0])
values.append(tick)
volumes.append([i, tick[4], 1 if tick[1] > tick[2] else -1])
# return {"categoryData": category_data, "values": values, "volumes": volumes}
'''
# net_chart_data = net_get_data()
chart_data = get_data('bj430198')
# chart_data = net_get_data()
draw_charts()程序有点大，近90M：
有什么建议请在评论区留言，不接受其他交流方式，有合适的建议我就加到程序里。
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