写在前面
大家好，今天我将介绍一个基于Python快速构建应用程序的库 Streamlit，而为了更好的学习，我会带领大家编写一个的股票市场分析仪表盘。
Streamlit是一个用于构建数据科学和机器学习应用程序的开源Python库。它旨在使应用程序的创建过程变得简单而快速，无需繁琐的前端开发知识。使用Streamlit，您可以通过简单的Python脚本创建交互式应用程序，并实时预览结果。
印度股市在这几年里增长了很多，因为越来越多的人意识到储蓄和投资的重要性。这是一个可以帮助您在最初几天跟踪股票市场的工具。
准备工作
下载并安装python3.8+
依赖包：
yfinance
plotly
streamlit
安装方法：
pip install 包名
例如
pip install yfinance
编写代码
新建app.py文件，并输入以下内容。
from math import floor
import streamlit as st
import pandas as pd
import datetime
import yfinance as yf
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.title("Stock Price Screener and Analysis")"Stock Price Screener and Analysis")
pd.set_option('max_colwidth', 400)
df = pd.DataFrame()
st.header("S&P BSE SENSEX")
today = datetime.date.today()
sensex = yf.download("^BSESN",start=today, interval="5m")
sensex_current = floor(sensex['Close'].iloc[-1])
if datetime.datetime.today().weekday()!=5 or datetime.datetime.today().weekday()!=6 :
if datetime.datetime.now().hour > 16:
st.write(f"Closing Price: {sensex_current}")
else:
st.write(f"Current Price: {sensex_current}")
if datetime.datetime.today().weekday() == 5 or datetime.datetime.today().weekday() == 6:
sensex_current = floor(sensex['Close'].iloc[-2])
st.write(f"Closing Price: {sensex_current}")
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.03, row_heights=[
0.7, 0.3], specs=[[{"type": "candlestick"}], [{"type": "bar"}]])
fig.update_xaxes(rangeslider_visible=False)
fig.add_trace(go.Candlestick(x=sensex.index, open=sensex['Open'], high=sensex['High'],
low=sensex['Low'], close=sensex['Close'], name='market data'), row=1, col=1)
st.plotly_chart(fig, use_container_width=True)
st.header("NIFTY 50")
nifty = yf.download("^NSEI",start=today, interval="5m")
nifty_current = floor(nifty['Close'].iloc[-1])
if datetime.datetime.today().weekday()!=5 or datetime.datetime.today().weekday()!=6 :
if datetime.datetime.now().hour > 16:
st.write(f"Closing Price: {nifty_current}")
else:
st.write(f"Current Price: {nifty_current}")
if datetime.datetime.today().weekday() == 5 or datetime.datetime.today().weekday() == 6:
nifty_current = floor(sensex['Close'].iloc[-2])
st.write(f"Closing Price: {nifty_current}")
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.03, row_heights=[
0.7, 0.3], specs=[[{"type": "candlestick"}], [{"type": "bar"}]])
fig.update_xaxes(rangeslider_visible=False)
fig.add_trace(go.Candlestick(x=nifty.index, open=nifty['Open'], high=nifty['High'],
low=nifty['Low'], close=nifty['Close'], name='market data'), row=1, col=1)
st.plotly_chart(fig, use_container_width=True)
todays_stock, stocks, indicators = st.tabs(
["Stock price for Today ", "Historical Price of Stock", "Indicators"])
with todays_stock:
st.title("Today's Price of Stock")
stock = st.text_input("Enter a stock ticker symbol", "LT")
Today_stock = st.button("Get Today's Price")
today = datetime.date.today()
if Today_stock:
start_date = today
df = yf.download(f"{stock}.NS", start=start_date, interval="2m")
df['% Change'] = df['Close'].pct_change()*100
df['% Change'] = df['% Change'].round(2)
st.write(df)
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.03, row_heights=[
0.7, 0.3], specs=[[{"type": "candlestick"}], [{"type": "bar"}]])
fig.update_xaxes(rangeslider_visible=False)
fig.add_trace(go.Candlestick(x=df.index, open=df['Open'], high=df['High'],
low=df['Low'], close=df['Close'], name='market data'), row=1, col=1)
fig.add_trace(
go.Bar(x=df.index, y=df['Volume'], name='Volume'), row=2, col=1)
st.plotly_chart(fig, use_container_width=True)
with stocks:
st.title("Stocks")
stock_name = st.text_input("Enter a stock ticker symbol")
start_date = st.date_input("Start date")
goto_today = st.button("Get Price from start date till today")
end_date = st.date_input("End date")
get_stock = st.button("Get Stock Price")
today_date = datetime.date.today() + datetime.timedelta(days=1)
if end_date < start_date:
st.error("Error: End date must fall after start date.")
elif goto_today:
end_date = today_date
df = yf.download(f"{stock}.NS", start=start_date,
end=end_date, interval="15m")
df.style.set_properties(**{'text-align': 'left'})
st.write(df)
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.03, row_heights=[
0.7, 0.3], specs=[[{"type": "candlestick"}], [{"type": "bar"}]])
fig.update_xaxes(rangeslider_visible=False)
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]),
dict(pattern='hour', bounds=[16, 9])
]
)
fig.add_trace(go.Candlestick(x=df.index, open=df['Open'], high=df['High'],
low=df['Low'], close=df['Close'], name='market data'), row=1, col=1)
fig.add_trace(
go.Bar(x=df.index, y=df['Volume'], name='Volume'), row=2, col=1)
st.plotly_chart(fig, use_container_width=True)
elif get_stock:
df = yf.download(f"{stock}.NS", start=start_date,
end=end_date+datetime.timedelta(days=1), interval="15m")
df.style.set_properties(**{'text-align': 'left'})
st.write(df)
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.03, row_heights=[
0.7, 0.3], specs=[[{"type": "candlestick"}], [{"type": "bar"}]])
fig.update_xaxes(rangeslider_visible=False)
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]),
dict(pattern='hour', bounds=[16, 9])
]
)
fig.add_trace(go.Candlestick(x=df.index, open=df['Open'], high=df['High'],
low=df['Low'], close=df['Close'], name='market data'), row=1, col=1)
# bar chart
fig.add_trace(
go.Bar(x=df.index, y=df['Volume'], name='Volume'), row=2, col=1)
st.plotly_chart(fig, use_container_width=True)
运行效果
现在，在终端中只需运行以下命令启动服务。
streamlit run app.py
然后在浏览器中输入以下地址，进入应在端口端口 8501。
localhost:8501
写在最后
1. 为什么不用大A股的数据？
A股股民们很受伤了，还是多看看别人的数据。
2. Streamlit的代码我看不太懂。
这里只是一个示例，让你能够运行Streamlit，并对其有一个直观的认识。具体的编码规则还需要你去官网学习。
原文始发于微信公众号（harvey的网络日志）：使用 Python 制作股市分析仪表板
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