financial-data-analysis-with-python/app.py at main · gunal-official/financial-data-analysis-with-python · GitHub

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import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff

st.title("Financial Data Analysis - NIFTY50 Stocks")

uploaded_file = st.file_uploader("Upload your NIFTY50 closing prices CSV file", type=["csv"])

def calculate_rsi(prices, window=14):
    delta = prices.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

if uploaded_file:
    data = pd.read_csv(uploaded_file)
    st.write("Data Preview:", data.head())

    # Data preparation
    if 'HDFC.NS' in data.columns:
        data = data.drop(columns=['HDFC.NS'])
    data['Date'] = pd.to_datetime(data['Date'])
    data = data.sort_values('Date').reset_index(drop=True)

    # Descriptive statistics
    desc_stats = data.describe().T[['mean', 'std', 'min', 'max']]
    desc_stats.columns = ['Mean', 'Std Dev', 'Min', 'Max']
    st.subheader("Descriptive Statistics")
    st.dataframe(desc_stats)

    # Portfolio returns example - using 3 stocks
    weights = np.array([0.4, 0.35, 0.25])
    stocks = ['RELIANCE.NS', 'HDFCBANK.NS', 'ICICIBANK.NS']
    if all(stock in data.columns for stock in stocks):
        portfolio_data = data[stocks]
        daily_returns = portfolio_data.pct_change().dropna()
        portfolio_returns = (daily_returns * weights).sum(axis=1)
        st.subheader("Portfolio Daily Returns")
        st.line_chart(portfolio_returns)

        # Risk assessment
        volatility = daily_returns.std()
        VaR = daily_returns.quantile(0.05)
        risk_metrics = pd.DataFrame({'Volatility (Std Dev)': volatility, 'Value at Risk (VaR)': VaR})
        st.subheader("Risk Metrics")
        st.dataframe(risk_metrics)

        # Correlation heatmap
        corr_matrix = daily_returns.corr()
        fig_corr = ff.create_annotated_heatmap(
            z=corr_matrix.values,
            x=list(corr_matrix.columns),
            y=list(corr_matrix.index),
            annotation_text=corr_matrix.round(2).values,
            colorscale='RdBu',
            showscale=True
        )
        fig_corr.update_layout(title="Correlation Matrix of Stock Returns", title_x=0.5)
        st.plotly_chart(fig_corr)

        # Moving averages for RELIANCE.NS
        data['RELIANCE_5d_MA'] = data['RELIANCE.NS'].rolling(window=5).mean()
        data['RELIANCE_20d_MA'] = data['RELIANCE.NS'].rolling(window=20).mean()
        fig_ma = go.Figure()
        fig_ma.add_trace(go.Scatter(x=data['Date'], y=data['RELIANCE.NS'], mode='lines', name='RELIANCE.NS Price'))
        fig_ma.add_trace(go.Scatter(x=data['Date'], y=data['RELIANCE_5d_MA'], mode='lines', name='5-Day MA'))
        fig_ma.add_trace(go.Scatter(x=data['Date'], y=data['RELIANCE_20d_MA'], mode='lines', name='20-Day MA'))
        fig_ma.update_layout(title="Moving Averages for RELIANCE.NS", xaxis_title="Date", yaxis_title="Price")
        st.plotly_chart(fig_ma)

        # RSI for RELIANCE.NS
        data['RELIANCE_RSI'] = calculate_rsi(data['RELIANCE.NS'])
        fig_rsi = go.Figure()
        fig_rsi.add_trace(go.Scatter(x=data['Date'], y=data['RELIANCE_RSI'], mode='lines', name='RSI'))
        fig_rsi.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="Overbought (70)")
        fig_rsi.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="Oversold (30)")
        fig_rsi.update_layout(title="RSI for RELIANCE.NS", xaxis_title="Date", yaxis_title="RSI")
        st.plotly_chart(fig_rsi)

        # Sharpe Ratio
        mean_returns = daily_returns.mean()
        volatility = daily_returns.std()
        risk_free_rate = 0.04 / 252
        sharpe_ratios = (mean_returns - risk_free_rate) / volatility
        sharpe_df = pd.DataFrame({'Stock': sharpe_ratios.index, 'Sharpe Ratio': sharpe_ratios.round(2)})
        st.subheader("Sharpe Ratios")
        st.dataframe(sharpe_df)

        # Monte Carlo Simulation for RELIANCE.NS
        num_simulations = 1000
        num_days = 252
        last_price = data['RELIANCE.NS'].iloc[-1]
        volatility_mc = data['RELIANCE.NS'].pct_change().std()
        simulated_prices = np.zeros((num_simulations, num_days))
        for i in range(num_simulations):
            simulated_prices[i, 0] = last_price
            for j in range(1, num_days):
                simulated_prices[i, j] = simulated_prices[i, j-1] * np.exp(np.random.normal(0, volatility_mc))

        fig_mc = go.Figure()
        for i in range(num_simulations):
            fig_mc.add_trace(go.Scatter(x=list(range(num_days)), y=simulated_prices[i], mode='lines', line=dict(width=0.5), opacity=0.1, showlegend=False))
        fig_mc.update_layout(title="Monte Carlo Simulation for RELIANCE.NS Prices",
                             xaxis_title="Days", yaxis_title="Simulated Price")
        st.plotly_chart(fig_mc)

    else:
        st.warning("Some portfolio stocks not found in data columns.")

else:
    st.info("Please upload a CSV file to start the analysis.")