Update app.py

Added support + resistance analyzer

app.py

@@ -30,6 +30,21 @@ sector_etf_mapping = {
     'Communication Services': 'XLC'
 }
 
+
+# Create two functions to calculate if a level is SUPPORT or a RESISTANCE level through fractal identification
+def is_Suppport_Level(df, i):
+  support = df['Low'][i] < df['Low'][i - 1] and df['Low'][i] < df['Low'][i + 1] and df['Low'][i + 1] < df['Low'][i + 2] and df['Low'][i - 1] < df['Low'][i - 2]
+  return support
+
+def is_Resistance_Level(df, i):
+  resistance = df['High'][i] > df['High'][i - 1] and df['High'][i] > df['High'][i + 1] and df['High'][i + 1] > df['High'][i + 2] and df['High'][i - 1] > df['High'][i - 2]
+  return resistance
+
+# This function, given a price value, returns True or False depending on if it is too near to some previously discovered key level.
+def distance_from_mean(level, levels, mean):
+  return np.sum([abs(level - y) < mean for y in levels]) == 0
+
+
 # Function to get the trend of a given stock or ETF
 def get_trend(ticker, period='1mo'):
     stock = yf.Ticker(ticker)
@@ -212,6 +227,54 @@ if uploaded_file is not None:
             fig.update_layout(title='Stock and Sector Trend over the past 30 days', xaxis_title='Date', yaxis_title='Normalized Close Price', autosize=False, width=2000, height=1200)
             st.plotly_chart(fig)
 
+
+            ticker2 = yf.Ticker(ticker)
+            df = ticker2.history(period="3mo", actions=False)
+
+            # Creating a list and feeding it the identified support and resistance levels via the Support and Resistance functions
+            levels = []
+            level_types = []
+            mean = np.mean(df['High'] - df['Low'])
+
+            for i in range(2, df.shape[0] - 2):
+
+                if is_Suppport_Level(df, i):
+                    level = df['Low'][i].round(2)
+
+                    if distance_from_mean(level, levels, mean):
+                        levels.append(level)
+                        level_types.append('Support')
+
+                elif is_Resistance_Level(df, i):
+                    level = df['High'][i].round(2)
+
+                    if distance_from_mean(level, levels, mean):
+                        levels.append(level)
+                        level_types.append('Resistance')
+
+            # Plotting the data
+            fig2 = go.Figure(data=[go.Candlestick(x=df.index,
+                                                open=df['Open'],
+                                                high=df['High'],
+                                                low=df['Low'],
+                                                close=df['Close'])])
+
+            for level, level_type in zip(levels, level_types):
+                fig2.add_trace(go.Scatter(x=df.index, y=[level]*len(df.index), mode='lines', name=level_type))
+
+            fig2.update_layout(title='Support and Resistance levels for ' + ticker,
+                    xaxis_title='Date',
+                    yaxis_title='Price',
+                    autosize=False,
+                    width=2000,
+                    height=1200)
+
+
+            st.plotly_chart(fig2)
+
+
+
+
             st.subheader("Hourly and Daily Sentiment of {} Stock".format(ticker))
             news_table = get_news(ticker)
             parsed_news_df = parse_news(news_table)