Master

app.py

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+import pandas as pd
+import streamlit as st
+from dotenv import load_dotenv
+from alpha_vantage.timeseries import TimeSeries
+import matplotlib.pyplot as plt
+import altair as alt
+import plotly.express as px
+import requests
+import os
+
+st.title('TDI Milestone Project')
+st.header('Select Plot Parameters')
+st.text('An interactive chart of stock closing prices using Streamlit and Plot.ly.')
+
+Ticker = st.selectbox('Ticker', options =
+                  ['IBM',
+                   'TSLA',
+                   'AAPL'])
+Year = st.selectbox('Year', options =
+                  [2010,
+                   2011,
+                   2012,
+                   2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021])
+Month = st.selectbox('Month:', options =
+                  ['January',
+                   'February',
+                   'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])
+@st.cache
+def load_data(Tic, YEAR, MONTH):
+    load_dotenv()
+    api_key = '7BVCOMX48DYGXDOY'
+    ts = TimeSeries(key=api_key, output_format='pandas')
+    data, meta_dats = ts.get_daily(Tic, outputsize = 'full')
+    month_dict = {}
+    keys = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]
+    values = list(range(1, 13))
+    i = 0
+    for key in keys:
+        month_dict[key] = values[i]
+        i = i+1
+    m_int = month_dict.get(MONTH)
+    data['date'] = data.index.time
+    s = (data['date'].index.year == YEAR) * (data['date'].index.month == m_int)
+    g = data[s]['4. close']
+    return g
+
+#### Chart ###
+fig = px.line(load_data(Ticker, Year, Month),
+                y='4. close',
+                )
+fig.update_layout(
+    xaxis_title="Timestamp",
+    yaxis_title="Closing Price",
+)
+st.plotly_chart(fig)
+
+
+
+
+
+