app.py

import numpy as np
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from plotly.subplots import make_subplots
from itertools import product
import warnings
from datetime import datetime

warnings.filterwarnings("ignore")

st.set_page_config(page_title="Expected Stock Price Movement Using Volatility Multipliers", layout="wide")
st.title('Expected Stock Price Movement Using Volatility Multipliers')

# Sidebar section with instructions
st.sidebar.title('Input Parameters')

with st.sidebar.expander("How to use:", expanded=False):
    st.markdown("""
    1. **Input Parameters**: Enter the stock ticker or cryptocurrency pair, date range, time horizon, standard deviation multipliers, and rolling window period.
    2. **Run the Analysis**: Click the "Run" button to perform the analyses and visualize the results.
    """)

# Wrapping ticker and date settings in an expander
with st.sidebar.expander("Ticker and Date Settings", expanded=True):
    ticker = st.text_input('Enter Stock Ticker or Crypto Pair', 'ADS.DE', help="Enter the ticker symbol of the stock or cryptocurrency pair you want to analyze (e.g., ADS.DE for Adidas, BTC-USD for Bitcoin).")
    start_date = st.date_input('Start Date', pd.to_datetime('2020-01-01'), help="Select the start date for fetching historical stock data.")
    end_date = st.date_input('End Date', pd.to_datetime('today') + pd.DateOffset(1), help="Select the end date for fetching historical stock data.")

# Wrapping parameter settings in an expander
with st.sidebar.expander("Parameter Settings", expanded=True):
    time_horizon = st.slider('Time Horizon (Days)', min_value=1, max_value=60, value=30, help="Set the number of days into the future for which you want to estimate asset prices.")
    std_multipliers = st.multiselect('Select Std Multipliers', [1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3], default=[1, 1.25, 1.5, 1.75], help="Choose the standard deviation multipliers to calculate future price ranges.")
    rolling_window = st.slider('Rolling Window (Days)', min_value=10, max_value=90, value=30, step=5, help="Set the number of days to use for calculating rolling volatility.")

st.write("""
This tool estimates the potential price movement of a selected stock or cryptocurrency pair over a specified time horizon. 
The predictions are based on historical volatility, calculated from the asset's daily returns. 
You can adjust the time horizon and standard deviation multipliers to see how the expected price range changes.
""")

with st.expander("Click here to read more about the methodology", expanded=False):
    st.latex(r'''
    P_t = P_0 \times e^{\sigma \times \sqrt{t} \times z}
    ''')
    st.markdown("""
    **Formula for price movement estimation explained:**
    - **Pt**: Estimated price at time (t)
    - **P0**: Current price
    - **σ (sigma)**: Standard deviation of the stock's returns, representing volatility
    - **t**: Time horizon in days
    - **z**: Multiplier corresponding to the desired confidence level, which adjusts for standard deviation
    To read more about the methodologies, visit [this link](https://entreprenerdly.com/expected-stock-price-movement-with-volatility-multipliers/).
    """)

if st.sidebar.button('Run Analysis'):
    # Download data; use "Close" and squeeze to ensure a 1D Series.
    stock_data = yf.download(ticker, start=start_date, end=end_date)
    if not stock_data.empty:
        stock_data = stock_data['Close'].squeeze()
        # Compute returns separately (do not add to stock_data to avoid modifying the index)
        returns = stock_data.pct_change()
        current_price = stock_data.iloc[-1]

        # Method 1: Volatility over dynamic periods
        fig1 = go.Figure()
        plot_data = stock_data[-rolling_window:]
        # Ensure the last index is a Timestamp (avoid concatenation error)
        last_date = pd.to_datetime(plot_data.index[-1])
        date_range = pd.date_range(last_date + pd.DateOffset(1), periods=time_horizon, freq='D')
        
        st.markdown("""
        ### Method 1: Dynamic Volatility
        This method assesses stock price movement by calculating volatility over dynamically changing periods based on a rolling window.
        """)

        # Plot historical prices once
        fig1.add_trace(go.Scatter(x=plot_data.index, y=plot_data, mode='lines', name='Historical Prices'))

        for std_multiplier in std_multipliers:
            expected_upper_bounds = pd.Series(index=date_range)
            expected_lower_bounds = pd.Series(index=date_range)

            for i in range(time_horizon):
                if i == 0:
                    volatility = stock_data.iloc[-rolling_window:].pct_change().std()
                else:
                    volatility = stock_data.iloc[-(rolling_window + i):-i].pct_change().std()
                expected_price_movement = current_price * volatility * np.sqrt(i + 1) * std_multiplier
                expected_upper_bounds.iloc[i] = current_price + expected_price_movement
                expected_lower_bounds.iloc[i] = current_price - expected_price_movement

            fig1.add_trace(go.Scatter(x=date_range, y=expected_upper_bounds, mode='lines', line=dict(dash='dash', color='green'), name=f'Upper Bound ({std_multiplier}x std)'))
            fig1.add_trace(go.Scatter(x=date_range, y=expected_lower_bounds, mode='lines', line=dict(dash='dash', color='red'), name=f'Lower Bound ({std_multiplier}x std)'))
            fig1.add_trace(go.Scatter(x=date_range, y=expected_upper_bounds, fill='tonexty', fillcolor='rgba(128, 128, 128, 0.3)', mode='none', showlegend=False))

        fig1.update_layout(title=f'{ticker} - Dynamic Volatility Expected Price Movement',
                           xaxis_title='Date',
                           yaxis_title='Price',
                           legend_title='Legend',
                           width=1600,
                           height=800)

        # Method 2: Single volatility measure over the period
        fig2 = go.Figure()
        
        # Plot historical prices once
        fig2.add_trace(go.Scatter(x=plot_data.index, y=plot_data, mode='lines', name='Historical Prices'))
        
        for std_multiplier in std_multipliers:
            expected_upper_bounds = pd.Series(index=date_range)
            expected_lower_bounds = pd.Series(index=date_range)

            for i in range(time_horizon):
                volatility = stock_data.pct_change().std() * std_multiplier
                expected_price_movement = current_price * volatility * np.sqrt(i + 1)
                expected_upper_bounds.iloc[i] = current_price + expected_price_movement
                expected_lower_bounds.iloc[i] = current_price - expected_price_movement

            fig2.add_trace(go.Scatter(x=date_range, y=expected_upper_bounds, mode='lines', line=dict(dash='dash', color='green'), name=f'Upper Bound ({std_multiplier}x std)'))
            fig2.add_trace(go.Scatter(x=date_range, y=expected_lower_bounds, mode='lines', line=dict(dash='dash', color='red'), name=f'Lower Bound ({std_multiplier}x std)'))
            fig2.add_trace(go.Scatter(x=date_range, y=expected_upper_bounds, fill='tonexty', fillcolor='rgba(128, 128, 128, 0.3)', mode='none', showlegend=False))

        fig2.update_layout(title=f'{ticker} - Single Volatility Measure Expected Price Movement',
                           xaxis_title='Date',
                           yaxis_title='Price',
                           legend_title='Legend',
                           width=1600,
                           height=800)

        st.plotly_chart(fig1)
        
        st.markdown("""
        ### Method 2: Single Volatility Measure
        This method calculates stock price movement based on a single, constant measure of volatility derived from the entire historical data set available.
        """)
        st.plotly_chart(fig2)
    else:
        st.write("No data found for the given ticker and date range.")

# Hide Streamlit's menu and footer
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)