zz

app.py

@@ -33,6 +33,7 @@ class Stonks:
         self.stochastic_oscillator_period = 14
         self.stochastic_oscillator_upper_band = 80
         self.stochastic_oscillator_lower_band = 20
+        self.roc_period = 9
         self.bollinger_band_period = 20
         self.on_balance_volumne_period = 20
 
@@ -385,9 +386,9 @@ class Stonks:
     def plot_stochastic_oscillator(self, data, title_txt: str):
         fig, ax = plt.subplots(figsize=(20, 10))
         data[['Strategy Returns','Market Returns']].cumsum().plot(ax=ax)
-        ax.set_title('Strategy returns versus AZN.L returns', color = 'black', fontsize = 20)
+        ax.set_title(title_txt, color = 'black', fontsize = 20)
         return fig
-
+    
     def plot_bollinger_bands(self, data, column_list, title_txt: str):
         fig, ax = plt.subplots(figsize=(20, 10))
         data[column_list].plot(ax=ax)
@@ -478,6 +479,8 @@ class Stonks:
         self.stochastic_oscillator_upper_band = st.sidebar.number_input('Stochastic Oscillator Upper Band', 50, 100, 80)
         self.stochastic_oscillator_lower_band = st.sidebar.number_input('Stochastic Oscillator Lower Band', 0, 50, 20)
         st.sidebar.markdown("""---""")
+        self.roc_period = st.sidebar.number_input('ROC Period', 9, 100, 9)
+        st.sidebar.markdown("""---""")
         self.bollinger_band_period = st.sidebar.number_input('Bollinger Band Period', 20, 100, 20)
         st.sidebar.markdown("""---""")
         self.on_balance_volumne_period = st.sidebar.number_input('On Balance Volumne Period', 20, 100, 20)
@@ -787,6 +790,22 @@ class Stonks:
         temp_df = get_stochastic_oscillator()
         st.pyplot(self.plot_stochastic_oscillator(temp_df, title_txt=f"Stochastic Oscillator for {self.selected_stock} stock"))
 
+        st.markdown("""
+            ###  Rate of Change (ROC) 
+
+            The ROC indicator is a pure momentum oscillator. The ROC calculation compares the current price with the price "n" periods ago e.g. when we compute the ROC of the daily price with a 9-day lag, we are simply looking at how much, in percentage, the price has gone up (or down) compared to 9 days ago. Like other momentum indicators, ROC has overbought and oversold zones that may be adjusted according to market conditions. 
+        """)
+        
+        def get_roc():
+            temp_df =  self.stock_df.copy()
+            temp_df['ROC'] = ((temp_df['Adj Close'] - temp_df['Adj Close'].shift(self.roc_period)) / temp_df['Adj Close'].shift(self.roc_period) -1 ) * 100
+
+            return temp_df
+        
+        temp_df = get_roc()
+        st.set_option('deprecation.showPyplotGlobalUse', False)
+        st.pyplot(mpf.plot(temp_df, type='candle',  style='yahoo', figsize=(15,8),  title=f"{self.selected_stock} Daily Price", volume=True))
+
         st.markdown("""
             ## Volatility trading strategies
 
@@ -907,6 +926,5 @@ class Stonks:
             It is almost certainly better to choose technical indicators that complement each other, not just those that move in unison and generate the same signals. The intuition here is that the more indicators you have that confirm each other, the better your chances are to profit. This can be done by combining strategies to form a system, and looking for multiple signals.                    
         """)
 
-
 stonks = Stonks(stocks_filepath="Models/stocknames.csv")
 stonks.ui_renderer()