Shulou(Shulou.com)06/02 Report--

In this issue, the editor will bring you about the permission setting in Python Flask. The article is rich in content and analyzed and described from a professional point of view. I hope you can get something after reading this article.

We have set up the registration and login functions of the system, which has basically met the needs of a small Web application. If we want to make a little money from this site, we need to provide more advanced functions, of course, these advanced features are not free, design a permission system to control the use of advanced applications.

Write advanced features

The so-called advanced features are features that users are willing to spend money to buy, and people like me who like wool only use basic features.

The advanced function of my design here is to enrich the K chart and add the moving average and trading volume on the basis of our original K chart.

Moving average

Moving average is a very common index in technical analysis. "average" refers to the average closing price per unit cycle, while "moving" refers to excluding the earliest closing price while bringing the new trading day closing price into the calculation cycle.

Let's first take a look at the data obtained through tushare.

As you can see, the Ma5, Ma10 and Ma20 values in the data can be used to make moving averages and can be displayed in the form of a line chart.

Import pyecharts.options as optsfrom pyecharts.charts import Linedef moving_average ()-> Line: C = (Line () .add _ xaxis (df.index.tolist () .add _ yaxis ("Ma5", df ['ma5'] .values.tolist (), is_smooth=True) .add _ yaxis ("Ma10", df [' ma10'] .values.tolist (), is_smooth=True) .add _ yaxis ("Ma20", df ['ma20'] .values.tolist () Is_smooth=True) .set _ global_opts (title_opts=opts.TitleOpts (title= "moving average")) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False),) return cmoving_average () .render_notebook ()

Trading volume

For the volume, it can be shown by the bar chart, the height of the bar chart is the size of the volume. Show the rising trading volume in red and the falling trading volume in green.

Import pyecharts.options as optsfrom pyecharts.charts import Line, Barvolume_rise= [x] if df.close [x] > df.open [x] else "0" for x in range (0, len (df.index))] volume_drop= [df.volum [x] if df.close [x] Bar: C = (Bar () .add _ xaxis (df.index.tolist ()) .add _ yaxis ("volume_rise", volume_rise, stack=True) Color= ["# ec0000"]) .add _ yaxis ("volume_drop", volume_drop, stack=True, color= ["# 00da3c"]) .set _ global_opts (title_opts=opts.TitleOpts (title= "volume"), datazoom_opts= [opts.DataZoomOpts ()],) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False),) return cvolume () .render_notebook ()

Integrate three charts

Let's combine the three icons, the K chart, the moving average chart and the volume chart.

First, stack the K chart and the moving average layer together.

Def kline_base ()-> Kline: kline = (Kline () .add _ xaxis (df.index.tolist () .add _ yaxis ("daily K chart", df ['open',' close', 'low',' high']] .values.tolist (), markpoint_opts=opts.MarkLineOpts (data= [opts.MarkLineItem (type_= "max", value_dim= "close")), markline_opts=opts.MarkLineOpts (data= [opts.MarkLineItem (type_= "max")) Value_dim= "close")], itemstyle_opts=opts.ItemStyleOpts (color= "# ec0000", color0= "# 00da3c", border_color= "# 8A0000", border_color0= "# 008F28",),) .set _ global_opts (yaxis_opts=opts.AxisOpts (is_scale=True, splitarea_opts=opts.SplitAreaOpts (is_show=True, areastyle_opts=opts.AreaStyleOpts (opacity=1)), xaxis_opts=opts.AxisOpts (is_scale=True, axislabel_opts=opts.LabelOpts (rotate=-30), title_opts=opts.TitleOpts (title= "Stock movements")) Datazoom_opts= [opts.DataZoomOpts ()], toolbox_opts=opts.ToolboxOpts (is_show=True)) line = (Line () .add _ xaxis (df.index.tolist ()) .add _ yaxis ("Ma5", df ['ma5'] .values.tolist (), is_smooth=True) .add _ yaxis ("Ma10", df [' ma10'] .values.tolist (), is_smooth=True) .add _ yaxis ("Ma20", df ['ma20'] .values.tolist ()) Is_smooth=True) .set _ global_opts (title_opts=opts.TitleOpts (title= "moving average")) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False),) kline.overlap (line) return kline

Next, add the volume chart to the main chart through grid.

... Bar = (Bar () .add _ xaxis (df.index.tolist ()) .add _ yaxis ("volume_rise", volume_rise, stack=True, color= ["# ec0000"],) .add _ yaxis ("volume_drop", volume_drop, stack=True, color= ["# 00da3c"],) .set _ global_opts (title_opts=opts.TitleOpts (), legend_opts=opts.LegendOpts (pos_right= "20%")) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False)) ) overlap_kline_line = kline.overlap (line) grid = Grid () grid.add (overlap_kline_line, grid_opts=opts.GridOpts (pos_left= "10", pos_right= "8", height= "50"),) grid.add (bar, grid_opts=opts.GridOpts (pos_left= "10", pos_right= "8", pos_top= "70", height= "16%"),).

At this point, our so-called "advanced" chart is complete, and let's start to combine Flask and embed the chart we produce.

Write each chart page

First of all, let's embed two newly generated charts into the Web application, each of which is a separate page.

Background function

Let's first create a function that generates moving averages and trading volume charts.

# moving average def moving_average_chart (mydate, data_5, data_10, data_20, name)-> Line: moving_average = (Line () .add _ xaxis (mydate) .add _ yaxis ("ma5", data_5, is_smooth=True) .add _ yaxis ("ma10", data_10, is_smooth=True) .add _ yaxis ("ma20", data_20) Is_smooth=True) .set _ global_opts (title_opts=opts.TitleOpts (title= "% s-moving average"% name), datazoom_opts= [opts.DataZoomOpts ()],) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False),) return moving_average# turnover def volume_chart (mydate, volume_rise, volume_drop, name)-> Bar: bar = (Bar () .add _ xaxis (mydate) .add _ yaxis ("volume_rise", volume_rise) Stack=True, color= ["# ec0000"]) .add _ yaxis ("volume_drop", volume_drop, stack=True, color= ["# 00da3c"]) .set _ global_opts (title_opts=opts.TitleOpts (title= "% s-volume"% name), datazoom_opts= [opts.DataZoomOpts ()],) .set _ series_opts (label_opts=opts.LabelOpts (is_show=False),) return bar

Then modify the get_stock_data function to return the data we need

Def get_stock_data (code, ctime): df = ts.get_hist_data (code) df_time = df [: ctime] mydate = df_time.index.tolist () kdata = df_time [['open',' close', 'low'' 'high'] .values.tolist () madata_5 = df_time [' ma5'] .values.tolist () madata_10 = df_time ['ma10'] .values.tolist () madata_20 = df_time [' ma20'] .values.tolist () volume_rise = [df_ time.volume [x] if df_time.close [x] > df_ time.openx] else "0" for x in range (0 Len (df_time.index)] volume_drop = [df_ time.volume [x] if df_ time.close [x] 30: if current_user.is_authenticated: pass else: abort (403) status, stock_code = check_stock (stock_name) if status = = 0: return 'error stock code or name' mydate, kdata, madata_5, madata_10, madata_20, volume_rise, volume_drop = get_stock_data (stock_code [0], int (query_time)) c = moving_average_chart (mydate) Madata_5, madata_10, madata_20, stock_code [1] return c.dump_options () @ app.route ("/ Bar", methods= ['GET'] ) def get_volume (): stock_name = request.form.get ('stockName') query_time = request.form.get (' queryTime') if not stock_name: stock_name = 'Ping an Bank' if not query_time: query_time = 30 if int (query_time) > 30: if current_user.is_authenticated: pass else: abort (403) status, stock_code = check_stock (stock_name) if status = = 0: return 'error stock code or name' mydate, kdata, madata_5 Madata_10, madata_20, volume_rise, volume_drop = get_stock_data (stock_code [0], int (query_time)) c = volume_chart (mydate, volume_rise, volume_drop, stock_code [1]) return c.dump_options ()

Then add the corresponding front-end page.

@ app.route ("/ mavg", methods= ['GET',' POST']) def moving_average (): return render_template ("mavg.html") @ app.route ("/ volume", methods= ['GET',' POST']) def volume (): return render_template ("volume.html")

Finally, create the above two html files and modify the

{% extends "base.html"%} {% block title%} my stock chart {% endblock%} {% block page_content%} {% for message in get_flashed_messages ()%} × {{message}} {% endfor%}

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