Update app.py

app.py

@@ -7,6 +7,7 @@ from sklearn.cluster import AgglomerativeClustering
 import streamlit as st
 import requests
 from streamlit_lottie import st_lottie
+import datetime
 
 st.set_page_config(page_title = "Support and resistance levels",
                    page_icon = ':📈:',
@@ -14,6 +15,11 @@ st.set_page_config(page_title = "Support and resistance levels",
 
 st.title('📈 Technical analysis 📉')
 st.header('Find support and resistance levels for :blue[price action] analysis!')
+st.markdown('''<span style="font-size:18px; font-weight:500;">
+This demo includes an implemented <em>Agglomerative Clustering</em>
+algorithm that can assist you in automatically detecting 
+potential support and resistance levels in financial markets.
+</span>''', unsafe_allow_html = True)
 st.markdown('##')
 
 def load_lottieurl(url: str):
@@ -27,33 +33,42 @@ lottie_money = load_lottieurl(lottie_url__money)
 
 st.sidebar.header('Please choose parameters: ')
 
-ticker = st.text_input('Select stock to analyse: (Make sure the ticker you search for is supported by _Yahoo! Finance_).', 'BNB-USD')
+ticker = st.text_input('''Select stock to analyse: 
+(Make sure the ticker you search for is supported 
+by _Yahoo! Finance_).''', 'BNB-USD')
 
-interval = '1h'
+interval = st.sidebar.selectbox(
+    'Select the time interval',
+    ('1d', '5d', '1wk', '1mo', '3mo'))
 
-num_clusters = st.sidebar.select_slider(
-    'Select a number of clusters',
-    options=[i for i in range(1,8)])
+timedelta = {'1d': 1, '5d': 5, '1wk' : 7, '1mo' : 30, '3mo' : 90}
 
-rolling_wave_length = st.sidebar.select_slider(
-    '''Select a length of rolling wave 
-    (how much data to cluster at one time)''',
-    options=[i for i in range(5, 21)])
+start = st.sidebar.date_input(
+    "Select the beginning date",
+    datetime.date(2022, 1, 1))
 
-period_num = st.sidebar.select_slider(
-    'Select number of days to display on chart',
-    options=[i for i in range(1, 31)])
+end = st.sidebar.date_input(
+    "Select the ending date",
+    datetime.date(2023, 1, 1), min_value = start + datetime.timedelta(timedelta[interval]))
 
-period = '{}d'.format(period_num)
-df = yf.download(ticker, period = period, interval = interval)
-df.index = pd.to_datetime(df.index).strftime("%d-%m-%Y %H:%M")
+df = yf.download(ticker, start = start, end = end, interval = interval)
+df.index = pd.to_datetime(df.index).strftime("%d-%m-%Y")
 df = df.drop(columns = ["Adj Close"])
 
+num_clusters = st.sidebar.slider(
+    'Select the number of clusters (affects number of levels you will get)',
+    1, 7, 3)
+
+rolling_wave_length = st.sidebar.slider(
+    '''Select the length of rolling wave 
+    (select more the more long-term biased you are)''',
+    1, len(df)//5, 1)
+
 left_column, right_column = st.columns(2)
 
 left_column.markdown('<span style="font-size:20px; font-weight:600; letter-spacing:2px;">Preview data:</span>',
             unsafe_allow_html = True)
-left_column.dataframe(df, height = 400)
+left_column.dataframe(df, height = 400, use_container_width=True)
 
 with right_column:
     st_lottie(lottie_money, key="money")
@@ -98,20 +113,6 @@ fig.add_trace(go.Candlestick(x=df.index,
                 low=df['Low'],
                 close=df['Close'], name = "Market data"), row = 1, col = 1)
 
-fig.update_xaxes(
-    rangeslider_visible = False,
-    rangeselector=dict(
-        buttons=list([
-            dict(count=1, label="1d", 
-                step="day", stepmode="backward"),
-            dict(count=3, label="3d", 
-                step="day", stepmode="backward"),
-            dict(count=7, label="7d",
-                 step="day", stepmode="backward"),
-            dict(count=30, label="30d",
-                 step="day", stepmode="backward"),
-            dict(step="all")])))
-
 i = 0
 for level in support_resistance_levels.to_list():
     fig.add_hline(y=level, line_width=1, 
@@ -119,6 +120,9 @@ for level in support_resistance_levels.to_list():
                   line_color="snow")
     i += 1
 
+fig.update_xaxes(
+    rangeslider_visible = False)
+
 colors = []
 
 for i in range(len(df.Close)):
@@ -144,4 +148,37 @@ fig.update_layout(
         x=0))
 
 #show chart
-st.plotly_chart(fig, use_container_width=True)
+st.plotly_chart(fig, use_container_width=True)
+
+st.markdown("""<span style="font-size:13px; font-weight:400;">
+Disclaimer: It's important to note that while this demonstration provides a useful approach to 
+identifying support and resistance levels in financial markets, 
+it is not intended to be taken as financial advice. 
+Trading decisions should be made based on careful analysis of multiple factors, 
+including market conditions, 
+risk tolerance, 
+and individual financial goals.
+</span>""", unsafe_allow_html=True)
+
+hide_streamlit_style = """
+            <style>
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+st.markdown('''
+            <div style="position: relative; bottom: -180px; width: 100%;">
+                <span class="e1_33">
+                    <p style="text-align:center">
+                        Designed with ❤️ by 
+                        <a href="https://www.linkedin.com/in/amelia-doli%C5%84ska-55613a270/">
+                        <em>
+                        Amelia Dolińska
+                        </em>
+                        </a> 
+                    </p>
+                </span>
+            </div>
+            ''',
+            unsafe_allow_html=True)