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.streamlit/config.toml

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+[theme]
+base="dark"
+primaryColor="#32d18b"

app.py

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+import pandas as pd
+import yfinance as yf
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from sklearn.cluster import AgglomerativeClustering
+import streamlit as st
+import requests
+from streamlit_lottie import st_lottie
+
+st.set_page_config(page_title = "Support and resistance levels",
+                   page_icon = ':📈:',
+                   layout = 'wide')
+
+st.title('📈 Technical analysis 📉')
+st.header('Find support and resistance levels for :blue[price action] analysis!')
+st.divider()
+
+def load_lottieurl(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+lottie_url__money = "https://assets1.lottiefiles.com/packages/lf20_06a6pf9i.json"
+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')
+
+interval = '1h'
+
+num_clusters = st.sidebar.select_slider(
+    'Select a number of clusters',
+    options=[i for i in range(1,8)])
+
+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)])
+
+period_num = st.sidebar.select_slider(
+    'Select number of days to display on chart',
+    options=[i for i in range(1, 31)])
+
+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 = df.drop(columns = ["Adj Close"])
+
+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)
+
+with right_column:
+    st_lottie(lottie_money, key="money")
+
+#creating function
+def calculate_support_resistance(df, rolling_wave_length, num_clusters):
+    date = df.index
+    df.reset_index(inplace=True)
+    
+    max_waves_temp = df.High.rolling(rolling_wave_length).max().rename('waves')
+    min_waves_temp = df.Low.rolling(rolling_wave_length).min().rename('waves')
+   
+    max_waves = pd.concat([max_waves_temp, pd.Series(np.zeros(len(max_waves_temp)) + 1)], axis=1)
+    min_waves = pd.concat([min_waves_temp, pd.Series(np.zeros(len(min_waves_temp)) + -1)], axis=1)
+    max_waves.drop_duplicates('waves', inplace=True)
+    min_waves.drop_duplicates('waves', inplace=True)
+    
+    waves = pd.concat([max_waves, min_waves]).sort_index()
+    waves = waves[waves[0] != waves[0].shift()].dropna()
+    
+    x = np.concatenate((waves.waves.values.reshape(-1, 1),
+                        (np.zeros(len(waves)) + 1).reshape(-1, 1)), axis=1)
+    
+    cluster = AgglomerativeClustering(n_clusters=num_clusters, linkage='ward')
+    cluster.fit_predict(x)
+    waves['clusters'] = cluster.labels_
+    waves2 = waves.loc[waves.groupby('clusters')['waves'].idxmax()]
+    df.index = date
+    waves2.waves.drop_duplicates(keep='first', inplace=True)
+    
+    return waves2.reset_index().waves
+support_resistance_levels = calculate_support_resistance(df, rolling_wave_length, num_clusters)
+
+#creating a plot
+fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
+               vertical_spacing=0.06, subplot_titles=('OHLC', 'Volume'), 
+               row_width=[0.3, 0.7])
+
+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.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, 
+                  line_dash="dash", row=1, col=1,
+                  line_color="snow")
+    i += 1
+
+colors = []
+
+for i in range(len(df.Close)):
+    if i != 0:
+        if df.Close[i] > df.Close[i-1]:
+            colors.append('lightgreen')
+        else:
+            colors.append('lightcoral')
+    else:
+        colors.append('lightcoral')
+
+fig.add_trace(go.Bar(x=df.index, y=df['Volume'], showlegend=False, 
+                     marker=dict(color=colors)), row=2, col=1)
+
+fig.update_traces(name= 'Volume', selector=dict(type='bar'))
+
+text = f'{ticker} Chart'
+
+fig.update_layout(
+    title=go.layout.Title(
+        text=text,
+        xref="paper",
+        x=0))
+
+#show chart
+st.plotly_chart(fig, use_container_width=True)