python、talib选股：自动工具介绍以及倒锤头形态搜索并可视化显示

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_cdl2cdf_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("-")continuesum_total = 0.0for j in range(day_count):sum_total += float(data["values"][i - j][1])result.append(abs(float("%.3f" % (sum_total / day_count))))return resultdef 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 Lineoverlap_kline_line = kline.overlap(line)# Grid Overlap + Bargrid_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：

谁想用用试试程序就在评论区留下邮箱吧，我直接发你邮箱。

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。