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app.py
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import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
# Import your core models from core/ folder
from core.stochastic import GBMModel, HestonModel, VarianceGammaModel
from core.fixed_income import CIRModel
from core.pricing import BlackScholesPricing, MonteCarloPricing
from core.risk import VaRCalculator
from core.fixed_income import VasicekModel, CIRModel, BondPricing, YieldCurve
from utils.visualization import generate_volatility_surface, plot_volatility_surface
# Set up the main page
st.set_page_config(page_title="Finance Dashboard", layout="wide")
# Sidebar for navigation
st.sidebar.title("Navigation")
page = st.sidebar.radio("Go to", ["Home", "Stochastic Models", "Option Pricing", "Risk Analytics", "Fixed Income"])
# Sidebar for global parameters
st.sidebar.title("Global Parameters")
S0 = st.sidebar.number_input("Initial Stock Price (S0)", min_value=1, value=100)
T = st.sidebar.number_input("Time to Maturity (T)", min_value=0.1, value=1.0)
paths = st.sidebar.number_input("Number of Paths", min_value=10, value=1000)
steps = st.sidebar.number_input("Number of Steps", min_value=10, value=252)
run_button = st.sidebar.button("Run Simulation")
# Home Page (Dashboard)
if page == "Home":
st.title("📊 Financial Analytics Dashboard")
st.write("Welcome to the Financial Analytics Dashboard. Use the navigation on the left to explore different models, option pricing, and risk analytics.")
st.markdown("### Key Metrics")
st.write("Track important metrics like VaR, bond prices, and option prices here once they are computed from other sections.")
# Stochastic Models Page
elif page == "Stochastic Models":
st.title("📈 Stochastic Models")
model_type = st.selectbox("Select Model", ["GBM", "Heston", "CIR", "Variance Gamma"])
if run_button:
if model_type == "GBM":
gbm = GBMModel(S0, mu=0.05, sigma=0.2, T=T, steps=steps, paths=paths)
paths = gbm.generate_path()
elif model_type == "Heston":
heston = HestonModel(S0, T, steps, paths, v0=0.04, kappa=2.0, theta=0.04, rho=-0.7, mu=0.05,sigma=0.3)
paths, _ = heston.generate_path()
elif model_type == "CIR":
cir = CIRModel(r0=0.03, a=0.1, b=0.04, sigma=0.01, T=T, steps=steps, paths=paths)
paths = cir.simulate()
elif model_type == "Variance Gamma":
vg = VarianceGammaModel(S0, T, steps, paths, mu=0.05, sigma=0.2, kappa=0.1)
paths = vg.generate_path()
st.line_chart(paths.T)
# **NEW** Plot the histogram of final prices
st.subheader(f"Histogram of Final Prices for {model_type} Model")
# Extract final prices from the paths
final_prices = paths[:, -1]
# **Plotly Interactive Histogram**
fig = px.histogram(
final_prices,
nbins=50,
title=f"Histogram of Final Prices for {model_type} Model",
labels={'value': 'Final Price', 'count': 'Frequency'}
)
fig.update_layout(bargap=0.1)
st.plotly_chart(fig, use_container_width=True)
# Option Pricing Page
elif page == "Option Pricing":
st.title("💲 Option Pricing")
option_type = st.selectbox("Option Type", ["Call", "Put"])
pricing_method = st.selectbox("Pricing Method", ["Black-Scholes", "Monte Carlo"])
K = st.number_input("Strike Price (K)", min_value=1, value=100)
if run_button:
if pricing_method == "Black-Scholes":
bs = BlackScholesPricing(S0, K, r=0.05, T=T, sigma=0.2, option_type=option_type.lower())
price = bs.price()
elif pricing_method == "Monte Carlo":
mc = MonteCarloPricing(S0, K, r=0.05, T=T, paths=paths, steps=steps, option_type=option_type.lower())
# Reuse previously simulated paths from Stochastic Models
price = mc.price(paths)
st.write(f"Option Price: {price:.2f}")
# === NEW SECTION FOR VOLATILITY SURFACE ===
st.write("---")
st.title("📈 Volatility Surface Visualization")
# Volatility Surface Parameters (Sliders in the Sidebar)
st.sidebar.title("Volatility Surface Controls")
strike_min = st.sidebar.number_input("Minimum Strike Price", min_value=0, max_value=200, value=50)
strike_max = st.sidebar.number_input("Maximum Strike Price", min_value=0, max_value=200, value=150)
strike_step = st.sidebar.slider("Strike Step", min_value=1, max_value=10, value=5)
maturity_min = st.sidebar.slider("Minimum Maturity (Years)", min_value=0.01, max_value=2.0, value=0.1, step=0.01)
maturity_max = st.sidebar.slider("Maximum Maturity (Years)", min_value=0.1, max_value=5.0, value=2.0, step=0.1)
maturity_step = st.sidebar.slider("Maturity Step", min_value=0.01, max_value=0.5, value=0.1)
base_vol = st.sidebar.slider("Base Volatility (ATM)", min_value=0.01, max_value=1.0, value=0.2, step=0.01)
smile_factor = st.sidebar.slider("Smile Factor", min_value=0.01, max_value=0.5, value=0.1, step=0.01)
time_decay_factor = st.sidebar.slider("Time Decay Factor", min_value=0.01, max_value=0.1, value=0.02, step=0.01)
if st.button('Show Volatility Surface'):
st.write("### Interactive Volatility Surface")
# Call the function to generate the surface
strikes, maturities, vol_surface = generate_volatility_surface(
strike_range=(strike_min, strike_max),
maturity_range=(maturity_min, maturity_max),
strike_step=strike_step,
maturity_step=maturity_step,
base_vol=base_vol,
smile_factor=smile_factor,
time_decay_factor=time_decay_factor
)
# Call the function to plot the surface **inside Streamlit**
# Store the returned figure in a variable
fig = plot_volatility_surface(strikes, maturities, vol_surface)
# Pass the figure to Streamlit
st.plotly_chart(fig, use_container_width=True)
# Risk Analytics Page
elif page == "Risk Analytics":
st.title("📉 Risk Analytics")
VaR_level = st.slider("Select VaR Confidence Level", min_value=0.01, max_value=0.10, step=0.01, value=0.05)
if run_button:
# Simulate returns
returns = np.random.normal(0, 1, 1000)
var_calculator = VaRCalculator(returns, alpha=VaR_level)
var_value = var_calculator.calculate_var()
st.write(f"Value-at-Risk (VaR) at {VaR_level*100}% level: {var_value:.2f}")
# Fixed Income Page
elif page == "Fixed Income":
st.title("💸 Fixed Income Analytics")
model_type = st.selectbox("Select Interest Rate Model", ["Vasicek", "CIR"])
if run_button:
if model_type == "Vasicek":
vasicek = VasicekModel(r0=0.03, a=0.1, b=0.04, sigma=0.01, T=T, steps=steps, paths=paths)
rates = vasicek.simulate()
elif model_type == "CIR":
cir = CIRModel(r0=0.03, a=0.1, b=0.04, sigma=0.01, T=T, steps=steps, paths=paths)
rates = cir.simulate()
st.line_chart(rates.T)