The provided Python code creates an interactive web application using Streamlit to analyze and visualize the seasonality performance of various financial assets. Here's a detailed breakdown of the code:

1. Imports and Initial Setup

import streamlit as st
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import numpy as np
import datetime

• streamlit: For creating the web app.
• yfinance: For downloading financial data.
• pandas: For data manipulation.
• plotly.graph_objects & plotly.express: For creating interactive visualizations.
• numpy: For numerical operations.
• datetime: For date handling.

2. Streamlit Page Configuration

st.set_page_config(page_title="Seasonality", layout="wide", initial_sidebar_state="expanded", page_icon="📈")
st.header("Seasonality Performance", divider="gray")
st.sidebar.header("Seasonality")

• st.set_page_config: Configures the page with a title, layout, and icon.
• st.header: Sets the main header of the app.
• st.sidebar.header: Sets the sidebar header.

3. User Inputs

ticker = st.sidebar.selectbox('Ticker:', ('^GSPC', 'ETH-USD', 'BTC-USD', "^IXIC"))
max_value = 2035
year = st.slider("Start Year", min_value=1960, max_value=max_value, value=2000, step=1)

• st.sidebar.selectbox: Dropdown for selecting a financial asset.
• st.slider: Slider for selecting the start year for analysis.

4. Data Downloading

@st.cache_data
def download_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date)
    return data

data = download_data(ticker, datetime.date(year, 1, 1), datetime.date(max_value, 1, 1))

• @st.cache_data: Caches the data download function to improve performance.
• download_data: Function to download data from Yahoo Finance.
• data: Downloads data based on the selected ticker and date range.

5. Adjusting Ticker Names

if ticker == "^GSPC":
    ticker = "S&P500"
if ticker == "^IXIC":
    ticker = "NASDAQ"

• Adjusts the ticker names for display purposes.

6. Calculating Returns

log_returns = data["Adj Close"].pct_change()
monthly_returns = log_returns.resample('M').apply(lambda x: (1 + x).prod() - 1)
monthly_returns = monthly_returns.dropna()
y = (1 + log_returns).cumprod()

• log_returns: Calculates daily log returns.
• monthly_returns: Resamples daily returns to monthly and calculates monthly returns.
• y: Computes cumulative returns over time.

7. Cumulative Returns Chart

ret = go.Figure()
ret.add_trace(go.Scatter(x=data.index, y=y, mode='lines', name='Cumulative Returns', line=dict(color='gray', width=2)))
ret.add_trace(go.Scatter(x=data.index, y=y, mode='lines', fill='tozeroy', fillcolor='rgba(220, 220, 220, 0.5)', line=dict(color='rgba(255, 255, 255, 0)')))
ret.update_layout(title=ticker + " Cumulative Returns Chart")

• go.Figure: Creates a new Plotly figure.
• go.Scatter: Adds line traces for cumulative returns.
• ret.update_layout: Updates the layout of the chart with a title.

8. Monthly Returns Heatmap

monthly_returns_df = pd.DataFrame({'Date': monthly_returns.index, 'Monthly_Return': monthly_returns.values})
monthly_returns_df['Year'] = monthly_returns_df['Date'].dt.year
monthly_returns_df['Month'] = monthly_returns_df['Date'].dt.month
monthly_percentage_changes = monthly_returns_df.groupby('Month')['Monthly_Return'].mean() * 100
heatmap_data = monthly_returns_df.pivot_table(index='Year', columns='Month', values='Monthly_Return')
heatmap_data.columns = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
heatmap_data.index.name = "Year"
heatmap_fig = px.imshow(np.round(heatmap_data * 100, 2), labels=dict(x="Month", y="Year", color="Monthly Return"), title=f"Heatmap of Monthly Returns for {ticker}", color_continuous_scale=["red", "white", "green"], text_auto=True, height=1000)
heatmap_fig.update_xaxes(tickvals=list(range(12)), ticktext=["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"])
heatmap_fig.update_yaxes(title_text="Year")

• monthly_returns_df: Creates a DataFrame for monthly returns.
• monthly_percentage_changes: Calculates average monthly returns.
• heatmap_data: Pivots the DataFrame to create heatmap data.
• heatmap_fig: Creates the heatmap using Plotly Express with custom labels and color scales.

9. Average Monthly Returns Histogram

percentage_changes_fig = go.Figure()
percentage_changes_fig.add_trace(go.Bar(x=heatmap_data.columns, y=np.round(monthly_percentage_changes * 100, 2), orientation='v', marker=go.bar.Marker(color=monthly_percentage_changes, colorscale="Greens", colorbar=dict(title="value"), line=dict(color="rgb(0, 0, 0)", width=1))))
percentage_changes_fig.update_layout(title_text='Average monthly performance', height=500)
percentage_changes_fig.update_xaxes(title_text="Avg monthly returns")
percentage_changes_fig.add_hline(y=0)
percentage_changes_fig.add_annotation(text=("@VanHelsing"), showarrow=False, x=1, y=0.1, xref='paper', yref='paper', xanchor='right', yanchor='bottom', font=dict(size=12, color="grey"), align="left")

• percentage_changes_fig: Creates a new Plotly figure for the histogram.
• go.Bar: Adds bar traces for average monthly returns.
• percentage_changes_fig.update_layout: Updates the layout with a title and height.
• percentage_changes_fig.update_xaxes: Updates x-axis labels.
• percentage_changes_fig.add_hline: Adds a horizontal line at y=0.
• percentage_changes_fig.add_annotation: Adds an annotation for credit.

10. Streamlit Integration

st.plotly_chart(ret, use_container_width=True)
st.plotly_chart(heatmap_fig, use_container_width=True)
st.plotly_chart(percentage_changes_fig, use_container_width=False)

• st.plotly_chart: Renders the Plotly charts in the Streamlit app.

11. Footer Information

st.write(
    "About\n",
    "\nSeasonality Performance is not a financial advisor\n",
    "\nCopyright 2023 Snowflake Inc. All rights reserved.\n"
)

• Adds footer information with a disclaimer and copyright notice.

link to the app: https://finance-seasonality.streamlit.app/