theme updates and chart constant

app.py

@@ -435,73 +435,70 @@ def runapp() -> None:
                 c = st.columns(9)
                 with c[4]: 
                     submitted = st.form_submit_button("Get Cookin'!")           
-
+            signal_map = {'Long': 1, 'Short':-1}  
             if submitted and principal_balance * lev > dollar_cap:
                 lev = np.floor(dollar_cap/principal_balance)
                 st.error(f"WARNING: (Starting Balance)*(Leverage) exceeds the ${dollar_cap} limit. Using maximum available leverage of {lev}")
 
-            if submitted and no_errors:
-                df = df[(df[dateheader] >= startdate) & (df[dateheader] <= enddate)]
-                signal_map = {'Long': 1, 'Short':-1}  
-            
-                
-                if len(df) == 0:
-                        st.error("There are no available trades matching your selections. Please try again!")
-                        no_errors = False
-                        
-                if no_errors:
-                    if bot_selections == "Cinnamon Toast":
-                        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
-                        df['DCA %'] = df['DCA'].map(dca_map)
-                        df['Calculated Return %'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
-                        df['DCA'] = np.floor(df['DCA'].values)
-                        
-                        df['Return Per Trade'] = np.nan
-                        df['Balance used in Trade'] = np.nan
-                        df['New Balance'] = np.nan
-
-                        g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
-                        df.loc[df['DCA']==1.0,'Return Per Trade'] = 1+lev*g['Return Per Trade'].values
-                        
-                        df['Compounded Return'] = df['Return Per Trade'].cumprod()
-                        df.loc[df['DCA']==1.0,'New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df.loc[df['DCA']==1.0,'Compounded Return']]
-                        df.loc[df['DCA']==1.0,'Balance used in Trade'] = np.concatenate([[principal_balance], df.loc[df['DCA']==1.0,'New Balance'].values[:-1]])
-                    else: 
-                        df['Calculated Return %'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
-                        df['Return Per Trade'] = np.nan
-                        g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
-                        df['Return Per Trade'] = 1+lev*g['Return Per Trade'].values
-                        
-                        df['Compounded Return'] = df['Return Per Trade'].cumprod()
-                        df['New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df['Compounded Return']]
-                        df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
-                    df['Net P/L Per Trade'] = (df['Return Per Trade']-1)*df['Balance used in Trade']
-                    df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
-                    
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
-                        cum_pl = df.loc[df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L'] + principal_balance
-                        #cum_sdp = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
-                        #cum_sdm = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
-                    else: 
-                        cum_pl = df.loc[df.dropna().index[-1],'Cumulative P/L'] + principal_balance
-                        #cum_sdp = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
-                        #cum_sdm = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
-                    #sd = 2*.00026
-                    #sd_df = get_sd_df(get_sd_df(df.copy(), sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fees, lev, dollar_cap, principal_balance)
-                    
-                    effective_return = 100*((cum_pl - principal_balance)/principal_balance)
+            df = df[(df[dateheader] >= startdate) & (df[dateheader] <= enddate)]
 
-                    st.header(f"{bot_selections} Results")
-                    with st.container(): 
-                        
-                        if len(bot_selections) > 1:
-                            col1, col2 = st.columns(2)
-                            with col1: 
-                                st.metric(
-                                    "Total Account Balance",
-                                    f"${cum_pl:.2f}",
-                                    f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
-                                )
+            if submitted and len(df) == 0:
+                    st.error("There are no available trades matching your selections. Please try again!")
+                    no_errors = False
+
+            if no_errors:
+                if bot_selections == "Cinnamon Toast":
+                    dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
+                    df['DCA %'] = df['DCA'].map(dca_map)
+                    df['Calculated Return %'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
+                    df['DCA'] = np.floor(df['DCA'].values)
+
+                    df['Return Per Trade'] = np.nan
+                    df['Balance used in Trade'] = np.nan
+                    df['New Balance'] = np.nan
+
+                    g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
+                    df.loc[df['DCA']==1.0,'Return Per Trade'] = 1+lev*g['Return Per Trade'].values
+
+                    df['Compounded Return'] = df['Return Per Trade'].cumprod()
+                    df.loc[df['DCA']==1.0,'New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df.loc[df['DCA']==1.0,'Compounded Return']]
+                    df.loc[df['DCA']==1.0,'Balance used in Trade'] = np.concatenate([[principal_balance], df.loc[df['DCA']==1.0,'New Balance'].values[:-1]])
+                else: 
+                    df['Calculated Return %'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
+                    df['Return Per Trade'] = np.nan
+                    g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
+                    df['Return Per Trade'] = 1+lev*g['Return Per Trade'].values
+
+                    df['Compounded Return'] = df['Return Per Trade'].cumprod()
+                    df['New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df['Compounded Return']]
+                    df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
+                df['Net P/L Per Trade'] = (df['Return Per Trade']-1)*df['Balance used in Trade']
+                df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
+
+                if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
+                    cum_pl = df.loc[df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L'] + principal_balance
+                    #cum_sdp = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
+                    #cum_sdm = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
+                else: 
+                    cum_pl = df.loc[df.dropna().index[-1],'Cumulative P/L'] + principal_balance
+                    #cum_sdp = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
+                    #cum_sdm = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
+                #sd = 2*.00026
+                #sd_df = get_sd_df(get_sd_df(df.copy(), sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fees, lev, dollar_cap, principal_balance)
+
+                effective_return = 100*((cum_pl - principal_balance)/principal_balance)
+
+                st.header(f"{bot_selections} Results")
+                with st.container(): 
+
+                    if len(bot_selections) > 1:
+                        col1, col2 = st.columns(2)
+                        with col1: 
+                            st.metric(
+                                "Total Account Balance",
+                                f"${cum_pl:.2f}",
+                                f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
+                            )
 
 #                             with col2:
 #                                 st.write("95% of trades should fall within this 2 std. dev. range.")
@@ -515,67 +512,70 @@ def runapp() -> None:
 #                                     f"" ,#${cum_sdm:.2f}"
 #                                     f"{100*(cum_sdm-principal_balance)/(principal_balance):.2f} %",
 #                                 )
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
-                        #st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
-                        dfdata = df.drop('Drawdown %', axis=1).dropna()
-                        #sd_df = sd_df.drop('Drawdown %', axis=1).dropna()
-                    else: 
-                        #st.line_chart(data=df.dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
-                        dfdata = df.dropna()
-                        #sd_df = sd_df.dropna()
-                        
-                    # Create figure
-                    fig = go.Figure()
-
-                    pyLogo = Image.open("logo.png")
-                    
+                if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
+                    #st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
+                    dfdata = df.drop('Drawdown %', axis=1).dropna()
+                    #sd_df = sd_df.drop('Drawdown %', axis=1).dropna()
+                else: 
+                    #st.line_chart(data=df.dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
+                    dfdata = df.dropna()
+                    #sd_df = sd_df.dropna()
+
+                # Create figure
+                fig = go.Figure()
+
+                pyLogo = Image.open("logo.png")
+
 #                     fig.add_traces(go.Scatter(x=sd_df['Exit Date'], y = sd_df['Cumulative P/L (+)'],line_shape='spline',
 #                              line = dict(smoothing = 1.3, color='rgba(31, 119, 200,0)'),  showlegend = False)
 #                                   )
-                    
+
 #                     fig.add_traces(go.Scatter(x=sd_df['Exit Date'], y = sd_df['Cumulative P/L (-)'],
 #                              line = dict(smoothing = 1.3, color='rgba(31, 119, 200,0)'), line_shape='spline',
 #                              fill='tonexty', 
 #                              fillcolor = 'rgba(31, 119, 200,.2)', name = '+/- Standard Deviation')
 #                                  )
 
-                    # Add trace
-                    fig.add_trace(
-                        go.Scatter(x=dfdata['Exit Date'], y=np.round(dfdata['Cumulative P/L'].values,2), line_shape='spline', 
-                                   line = {'smoothing': 1.0, 'color' : 'rgba(31, 119, 200,.8)'},
-                                   name='Cumulative P/L')
-                    )
-                    buyhold  = (principal_balance/dfdata['Buy Price'][dfdata.index[0]])*(dfdata['Buy Price']-dfdata['Buy Price'][dfdata.index[0]])
-                    fig.add_trace(go.Scatter(x=dfdata['Exit Date'], y=np.round(buyhold.values,2), line_shape='spline', 
-                                             line = {'smoothing': 1.0, 'color' :'red'}, name = 'Buy & Hold Return')
-                    )
-
-                    fig.add_layout_image(
-                            dict(
-                                source=pyLogo,
-                                xref="paper",
-                                yref="paper",
-                                x = 0.05, #dfdata['Exit Date'].astype('int64').min() // 10**9, 
-                                y = .85, #dfdata['Cumulative P/L'].max(),
-                                sizex= .9, #(dfdata['Exit Date'].astype('int64').max() - dfdata['Exit Date'].astype('int64').min()) // 10**9,
-                                sizey= .9, #(dfdata['Cumulative P/L'].max() - dfdata['Cumulative P/L'].min()),
-                                sizing="contain",
-                                opacity=0.2, 
-                            layer = "below")
-                    )
-                    
-                    #style layout 
-                    fig.update_layout(
-                        height = 600,
-                        xaxis=dict(
-                            title="Exit Date", 
-                            tickmode='array',
-                        ),
-                        yaxis=dict(
-                            title="Cumulative P/L"
-                        ) ) 
-                    
-                    st.plotly_chart(fig, theme=None, use_container_width=True,height=600)
+                # Add trace
+                fig.add_trace(
+                    go.Scatter(x=dfdata['Exit Date'], y=np.round(dfdata['Cumulative P/L'].values,2), line_shape='spline', 
+                               line = {'smoothing': 1.0, 'color' : 'rgba(90, 223, 137, 1)'},
+                               name='Cumulative P/L')
+                )
+                buyhold  = (principal_balance/dfdata['Buy Price'][dfdata.index[0]])*(dfdata['Buy Price']-dfdata['Buy Price'][dfdata.index[0]])
+                fig.add_trace(go.Scatter(x=dfdata['Exit Date'], y=np.round(buyhold.values,2), line_shape='spline', 
+                                         line = {'smoothing': 1.0, 'color' :'rgba(33, 212, 225, 1)'}, name = 'Buy & Hold Return')
+                )
+
+                fig.add_layout_image(
+                        dict(
+                            source=pyLogo,
+                            xref="paper",
+                            yref="paper",
+                            x = 0.05, #dfdata['Exit Date'].astype('int64').min() // 10**9, 
+                            y = .85, #dfdata['Cumulative P/L'].max(),
+                            sizex= .9, #(dfdata['Exit Date'].astype('int64').max() - dfdata['Exit Date'].astype('int64').min()) // 10**9,
+                            sizey= .9, #(dfdata['Cumulative P/L'].max() - dfdata['Cumulative P/L'].min()),
+                            sizing="contain",
+                            opacity=0.2, 
+                        layer = "below")
+                )
+
+                #style layout 
+                fig.update_layout(
+                    height = 600,
+                    xaxis=dict(
+                        title="Exit Date", 
+                        tickmode='array',
+                    ),
+                    yaxis=dict(
+                        title="Cumulative P/L"
+                    ), 
+                    plot_bgcolor = 'rgba(10, 10, 10, 1)'
+                ) 
+
+                st.plotly_chart(fig, theme=None, use_container_width=True,height=600)
+                if submitted:
                     st.write()
                     df['Per Trade Return Rate'] = df['Return Per Trade']-1
 
@@ -588,7 +588,7 @@ def runapp() -> None:
 
                     totals['Cum. P/L'] = cum_pl-principal_balance
                     totals['Cum. P/L (%)'] = 100*(cum_pl-principal_balance)/principal_balance
-                    
+
                     if df.empty:
                         st.error("Oops! None of the data provided matches your selection(s). Please try again.")
                     else:
@@ -650,7 +650,7 @@ def runapp() -> None:
                                         "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
                                         f"{get_rolling_stats(df,lev, otimeheader, 180):.2f}%",
                                     )
-            
+
             if bot_selections == "Cinnamon Toast":
                 if submitted:                     
                     grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
@@ -663,10 +663,7 @@ def runapp() -> None:
                                                'Net P/L Per Trade':'Net P/L', 
                                                'Calculated Return %':'P/L %'}, inplace=True)
                 else:
-                    dca_map = {1: 25/100, 2: 25/100, 3: 25/100, 4: 25/100, 1.1: 50/100, 2.1: 50/100}
-                    df['DCA %'] = df['DCA'].map(dca_map)
-                    df['Calculated Return %'] = (df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade
-                        
+
                     grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
                                              'Sell Price' : 'max',
                                              'P/L per token': 'mean',