Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,59 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import numpy as np
+import plotly.graph_objects as go
+
+# Title
+st.title("Financial Modeling App")
+
+# Tab setup
+tabs = st.tabs(["Time Value of Money", "Consistent Cash Flow Investment", "Monte Carlo Simulation"])
+
+# --- Time Value of Money ---
+with tabs[0]:
+    st.header("Time Value of Money")
+    a = st.number_input("Base Amount (a)", min_value=0.0, value=1000.0)
+    r = st.number_input("Annual Return Rate (r)", min_value=0.0, value=0.05)
+    T = st.number_input("Number of Years (T)", min_value=0, value=10)
+
+    future_value = a * ((1 + r) ** T)
+    st.write(f"Future Value = {a} * (1 + {r})^{T} = **{future_value:,.2f}**")
+
+# --- Consistent Cash Flow Investment ---
+with tabs[1]:
+    st.header("Consistent Cash Flow Investment")
+    a_cf = st.number_input("Annual Cash Flow (a)", min_value=0.0, value=1000.0)
+    r_cf = st.number_input("Annual Return Rate (r)", min_value=0.0, value=0.05)
+    T_cf = st.number_input("Number of Years (T)", min_value=0, value=10)
+
+    if r_cf > 0:
+        fv_cf = a_cf * (((1 + r_cf) ** T_cf - 1) / r_cf)
+    else:
+        fv_cf = a_cf * T_cf
+
+    st.write(f"Future Value of Cash Flows = **{fv_cf:,.2f}**")
+
+# --- Monte Carlo Simulation ---
+with tabs[2]:
+    st.header("Monte Carlo Simulation")
+    mean_return = st.number_input("Mean Annual Return", value=0.07)
+    std_dev = st.number_input("Standard Deviation", value=0.15)
+    years = st.slider("Number of Years", 1, 100, 30)
+    simulations = 50
+
+    # Simulate paths
+    np.random.seed(42)
+    results = np.zeros((simulations, years))
+    for i in range(simulations):
+        returns = np.random.normal(loc=mean_return, scale=std_dev, size=years)
+        results[i] = np.cumprod(1 + returns)
+
+    # Plot
+    fig = go.Figure()
+    for i in range(simulations):
+        fig.add_trace(go.Scatter(y=results[i], mode='lines', name=f'Sim {i+1}', line=dict(width=1)))
+    fig.update_layout(title="Monte Carlo Simulations of Portfolio Growth",
+                      xaxis_title="Years",
+                      yaxis_title="Portfolio Value (normalized)",
+                      showlegend=False)
+
+    st.plotly_chart(fig)