replaced plotly with matplotlib

.gitignore

@@ -1,3 +1,3 @@
 __pycache__/
 app_local.py
-app_localBackup.py
+app_localBackup*.py

app.py

@@ -1,9 +1,9 @@
 import streamlit as st
 import yfinance as yf
 import pandas as pd
-import plotly.graph_objs as go
-import numpy as np
-from plotly.subplots import make_subplots
+import matplotlib.pyplot as plt
+import mplfinance as mpf
+import numpy as np
 import os
 from datetime import date, timedelta
 
@@ -217,8 +217,8 @@ else:
         unsafe_allow_html=True,
     )
 
-    price_tab, indicators_tab, ai_tab = st.tabs(["Price Action", "Technical Indicators", "AI Deep Dive"])
-
+    price_tab, indicators_tab, ai_tab = st.tabs(["Price Action", "Technical Indicators", "AI Deep Dive"])
+
     with price_tab:
         st.subheader("Stock Price Chart")
         st.caption("Candlestick price action with volume on a shared timeline for quick at-a-glance context.")
@@ -227,111 +227,72 @@ else:
             st.write("Data sample:", df.head(3))
             st.write("dtypes:", df.dtypes)
 
-        fig = make_subplots(specs=[[{"secondary_y": True}]])
-        fig.add_trace(
-            go.Candlestick(
-                x=df['Date'],
-                open=df['Open'],
-                high=df['High'],
-                low=df['Low'],
-                close=close_series,
-                name="Price",
-            ),
-            secondary_y=True,
-        )
-        fig.add_trace(
-            go.Bar(
-                x=df['Date'],
-                y=volume_series,
-                name="Volume",
-                marker_color="rgba(109, 214, 255, 0.55)",
-            ),
-            secondary_y=False,
-        )
-        fig.update_layout(
-            template="plotly_dark",
-            plot_bgcolor="rgba(12,19,32,0.7)",
-            paper_bgcolor="rgba(12,19,32,0.7)",
-            legend_orientation="h",
-            legend_yanchor="bottom",
-            legend_y=1.02,
-            legend_x=0,
-            margin=dict(t=50, l=10, r=10, b=20),
-        )
-        fig.update_yaxes(showgrid=False, secondary_y=True)
-        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)", secondary_y=False, title_text="Volume")
-        fig.update_xaxes(showgrid=False)
-        st.plotly_chart(fig, use_container_width=True)
-
-    with indicators_tab:
-        st.subheader("Moving Averages")
-        st.caption("Compare recent closes against short and intermediate trend lines.")
-        df['SMA_20'] = close_series.rolling(window=20).mean()
-        df['SMA_50'] = close_series.rolling(window=50).mean()
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=df['Date'], y=close_series, mode='lines', name='Close Price', line=dict(color="#7cf0c6")))
-        fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_20'], mode='lines', name='20-Day SMA', line=dict(color="#6dd6ff")))
-        fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_50'], mode='lines', name='50-Day SMA', line=dict(color="#b0b8ff")))
-        fig.update_layout(
-            title="Moving Averages",
-            xaxis_title="Date",
-            yaxis_title="Price (USD)",
-            template="plotly_dark",
-            plot_bgcolor="rgba(12,19,32,0.7)",
-            paper_bgcolor="rgba(12,19,32,0.7)",
-            margin=dict(t=50, l=10, r=10, b=20),
-        )
-        fig.update_xaxes(showgrid=False)
-        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)")
-        st.plotly_chart(fig, use_container_width=True)
-
-        st.subheader("Relative Strength Index (RSI)")
-        st.caption("Momentum oscillator highlighting overbought/oversold zones.")
-        window_length = 14
-
-        delta = close_series.diff()
-        gain = delta.where(delta > 0, 0)
-        loss = -delta.where(delta < 0, 0)
-
-        avg_gain = gain.rolling(window=window_length, min_periods=1).mean()
-        avg_loss = loss.rolling(window=window_length, min_periods=1).mean()
-
-        rs = avg_gain / avg_loss
-        df['RSI'] = 100 - (100 / (1 + rs))
-
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=df['Date'], y=df['RSI'], mode='lines', name='RSI', line=dict(color="#6dd6ff")))
-        fig.add_hline(y=70, line_dash="dash", line_color="#ff9b9b", annotation_text="Overbought")
-        fig.add_hline(y=30, line_dash="dash", line_color="#7cf0c6", annotation_text="Oversold")
-        fig.update_layout(
-            title="RSI Indicator",
-            xaxis_title="Date",
-            yaxis_title="RSI",
-            template="plotly_dark",
-            plot_bgcolor="rgba(12,19,32,0.7)",
-            paper_bgcolor="rgba(12,19,32,0.7)",
-            margin=dict(t=50, l=10, r=10, b=20),
-        )
-        fig.update_xaxes(showgrid=False)
-        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)")
-        st.plotly_chart(fig, use_container_width=True)
-
-        st.subheader("Volume Analysis")
-        st.caption("Volume bars styled to match the rest of the dashboard.")
-        fig = go.Figure()
-        fig.add_trace(go.Bar(x=df['Date'], y=volume_series, name='Volume', marker_color="rgba(109, 214, 255, 0.55)"))
-        fig.update_layout(
-            title="Volume Analysis",
-            xaxis_title="Date",
-            yaxis_title="Volume",
-            template="plotly_dark",
-            plot_bgcolor="rgba(12,19,32,0.7)",
-            paper_bgcolor="rgba(12,19,32,0.7)",
-            margin=dict(t=50, l=10, r=10, b=20),
-        )
-        fig.update_xaxes(showgrid=False)
-        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)")
-        st.plotly_chart(fig, use_container_width=True)
+        df_plot = df.set_index("Date")[["Open", "High", "Low", "Close", "Volume"]]
+        market_colors = mpf.make_marketcolors(up="#7cf0c6", down="#ff9b9b", edge="inherit", wick="inherit", volume="in")
+        style = mpf.make_mpf_style(base_mpf_style="nightclouds", marketcolors=market_colors, facecolor="#0c1320", edgecolor="#0c1320", gridcolor="#1b2a45")
+        fig, _ = mpf.plot(
+            df_plot,
+            type="candle",
+            volume=True,
+            style=style,
+            returnfig=True,
+            figsize=(10, 6),
+            tight_layout=True,
+            update_width_config=dict(candle_linewidth=0.8, candle_width=0.6),
+        )
+        st.pyplot(fig, clear_figure=True)
+        plt.close(fig)
+
+    with indicators_tab:
+        st.subheader("Moving Averages")
+        st.caption("Compare recent closes against short and intermediate trend lines.")
+        df['SMA_20'] = close_series.rolling(window=20).mean()
+        df['SMA_50'] = close_series.rolling(window=50).mean()
+        fig, ax = plt.subplots(figsize=(10, 4))
+        ax.plot(df['Date'], close_series, label='Close Price', color="#7cf0c6", linewidth=1.4)
+        ax.plot(df['Date'], df['SMA_20'], label='20-Day SMA', color="#6dd6ff", linewidth=1.2)
+        ax.plot(df['Date'], df['SMA_50'], label='50-Day SMA', color="#b0b8ff", linewidth=1.2)
+        ax.set_ylabel("Price (USD)")
+        ax.grid(alpha=0.2)
+        ax.legend()
+        fig.tight_layout()
+        st.pyplot(fig, clear_figure=True)
+        plt.close(fig)
+
+        st.subheader("Relative Strength Index (RSI)")
+        st.caption("Momentum oscillator highlighting overbought/oversold zones.")
+        window_length = 14
+
+        delta = close_series.diff()
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0)
+
+        avg_gain = gain.rolling(window=window_length, min_periods=1).mean()
+        avg_loss = loss.rolling(window=window_length, min_periods=1).mean()
+
+        rs = avg_gain / avg_loss
+        df['RSI'] = 100 - (100 / (1 + rs))
+
+        fig, ax = plt.subplots(figsize=(10, 3.5))
+        ax.plot(df['Date'], df['RSI'], label='RSI', color="#6dd6ff", linewidth=1.4)
+        ax.axhline(70, color="#ff9b9b", linestyle="--", linewidth=1, label="Overbought")
+        ax.axhline(30, color="#7cf0c6", linestyle="--", linewidth=1, label="Oversold")
+        ax.set_ylabel("RSI")
+        ax.grid(alpha=0.2)
+        ax.legend()
+        fig.tight_layout()
+        st.pyplot(fig, clear_figure=True)
+        plt.close(fig)
+
+        st.subheader("Volume Analysis")
+        st.caption("Volume bars styled to match the rest of the dashboard.")
+        fig, ax = plt.subplots(figsize=(10, 3.5))
+        ax.bar(df['Date'], volume_series, color=(109/255, 214/255, 255/255, 0.55))
+        ax.set_ylabel("Volume")
+        ax.grid(alpha=0.15)
+        fig.tight_layout()
+        st.pyplot(fig, clear_figure=True)
+        plt.close(fig)
 
     with ai_tab:
         st.subheader("In-depth Analysis")

requirements.txt

@@ -1,6 +1,7 @@
 streamlit==1.52.1
 yfinance
 pandas==2.3.3
-plotly==6.5.0
 numpy==1.26.4
 langchain-openai==0.2.9
+matplotlib
+mplfinance