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Application update
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‎frontend/app.py‎

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# Import required packages
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import streamlit as st
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import snowflake.connector
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import pandas as pd
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# Snowflake connection function
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@st.cache_resource
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def create_snowflake_connection():
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return snowflake.connector.connect(
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user=st.secrets["snowflake"]["user"],
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password=st.secrets["snowflake"]["password"],
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account=st.secrets["snowflake"]["account"],
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warehouse=st.secrets["snowflake"]["warehouse"],
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database=st.secrets["snowflake"]["database"],
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schema=st.secrets["snowflake"]["schema"]
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)
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# Function to load data from Snowflake
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# @st.cache_data()
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def load_data(conn):
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daily_query = "SELECT * FROM FRED_DB.PROD_ANALYTICS.FRED_COMBINED_DAILY"
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weekly_query = "SELECT * FROM FRED_DB.PROD_ANALYTICS.FRED_COMBINED_WEEKLY"
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monthly_query = "SELECT * FROM FRED_DB.PROD_ANALYTICS.FRED_COMBINED_MONTHLY"
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daily_agg = pd.read_sql(daily_query, conn)
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weekly_agg = pd.read_sql(weekly_query, conn)
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monthly_agg = pd.read_sql(monthly_query, conn)
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return daily_agg, weekly_agg, monthly_agg
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def daily_data_analytics(daily_agg):
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st.subheader('Daily Yield Analytics')
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# Ensure OBS_DATE is in datetime format
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daily_agg['OBS_DATE'] = pd.to_datetime(daily_agg['OBS_DATE'])
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# Get min and max date
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min_date = daily_agg['OBS_DATE'].min()
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max_date = daily_agg['OBS_DATE'].max()
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# Date range input
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date_range = st.date_input(
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"Date range:",
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[min_date.date(), max_date.date()], # Convert min/max to .date()
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min_value=min_date.date(),
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max_value=max_date.date(),
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key='date_range'
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)
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# Ensure date_range returns a tuple of two dates
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if isinstance(date_range, tuple) and len(date_range) == 2:
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start_date, end_date = date_range
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start_date = pd.to_datetime(start_date)
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end_date = pd.to_datetime(end_date)
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# Apply filtering
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df_filter = daily_agg[(daily_agg['OBS_DATE'] >= start_date) & (daily_agg['OBS_DATE'] <= end_date)]
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# Remove rows where 10Y_YIELD or 2Y_YIELD is 0
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df_filter = df_filter[(df_filter['10Y_YIELD'] > 0) & (df_filter['2Y_YIELD'] > 0)]
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st.line_chart(df_filter, x = 'OBS_DATE', y = ['10Y_YIELD', '2Y_YIELD'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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data = df_filter[['OBS_DATE', 'SPREAD']]
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data['Positive Change'] = data['SPREAD'].apply(lambda x: x if x >= 0 else None)
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data['Negative Change'] = data['SPREAD'].apply(lambda x: x if x < 0 else None)
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st.line_chart(
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data.set_index('OBS_DATE')[['Positive Change', 'Negative Change']],
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use_container_width=True
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)
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else:
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st.warning("Please select a valid date range.")
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def weekly_data_analytics(weekly_agg):
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st.subheader('Weekly Yield Spread Analytics')
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# Ensure WEEK_START is in datetime format
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weekly_agg['WEEK_START'] = pd.to_datetime(weekly_agg['WEEK_START'])
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# Get min and max date
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min_date = weekly_agg['WEEK_START'].min()
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max_date = weekly_agg['WEEK_START'].max()
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# Date range selection
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date_range = st.date_input(
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"Select Date Range:",
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[min_date.date(), max_date.date()],
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min_value=min_date.date(),
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max_value=max_date.date(),
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key='weekly_date_range'
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)
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if isinstance(date_range, tuple) and len(date_range) == 2:
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start_date, end_date = date_range
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start_date = pd.to_datetime(start_date)
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end_date = pd.to_datetime(end_date)
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# Filter data based on selected date range
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df_filter = weekly_agg[(weekly_agg['WEEK_START'] >= start_date) & (weekly_agg['WEEK_START'] <= end_date)]
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st.line_chart(df_filter, x = 'WEEK_START', y = ['AVG_10Y', 'AVG_2Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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st.line_chart(df_filter, x = 'WEEK_START', y = ['AVG_10Y', 'MIN_10Y', 'MAX_10Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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st.line_chart(df_filter, x = 'WEEK_START', y = ['AVG_2Y', 'MIN_2Y', 'MAX_2Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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st.line_chart(df_filter, x = 'WEEK_START', y = ['AVG_10Y', 'AVG_2Y',
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'MIN_10Y', 'MAX_10Y',
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'MIN_2Y', 'MAX_2Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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data = df_filter[['WEEK_START', 'AVG_SPREAD']]
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data['Positive Change'] = data['AVG_SPREAD'].apply(lambda x: x if x >= 0 else None)
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data['Negative Change'] = data['AVG_SPREAD'].apply(lambda x: x if x < 0 else None)
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st.line_chart(
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data.set_index('WEEK_START')[['Positive Change', 'Negative Change']],
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use_container_width=True
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)
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else:
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st.warning("Please select a valid date range.")
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def monthly_data_analytics(monthly_agg):
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st.subheader('Monthly Yield Spread Analytics')
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# Ensure MONTH_START is in datetime format
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monthly_agg['MONTH_START'] = pd.to_datetime(monthly_agg['MONTH_START'])
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# Get min and max date
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min_date = monthly_agg['MONTH_START'].min()
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max_date = monthly_agg['MONTH_START'].max()
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# Date range selection
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date_range = st.date_input(
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"Select Date Range:",
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[min_date.date(), max_date.date()],
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min_value=min_date.date(),
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max_value=max_date.date(),
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key='monthly_date_range'
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)
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if isinstance(date_range, tuple) and len(date_range) == 2:
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start_date, end_date = date_range
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start_date = pd.to_datetime(start_date)
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end_date = pd.to_datetime(end_date)
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# Filter data based on selected date range
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df_filter = monthly_agg[(monthly_agg['MONTH_START'] >= start_date) & (monthly_agg['MONTH_START'] <= end_date)]
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st.line_chart(df_filter, x = 'MONTH_START', y = ['AVG_10Y', 'AVG_2Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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st.line_chart(df_filter, x = 'MONTH_START', y = ['AVG_10Y', 'MIN_10Y', 'MAX_10Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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st.line_chart(df_filter, x = 'MONTH_START', y = ['AVG_2Y', 'MIN_2Y', 'MAX_2Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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st.line_chart(df_filter, x = 'MONTH_START', y = ['AVG_10Y', 'AVG_2Y',
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'MIN_10Y', 'MAX_10Y',
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'MIN_2Y', 'MAX_2Y'],
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height = 500,
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x_label= "Financial Period",
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y_label= "Yield")
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data = df_filter[['MONTH_START', 'AVG_SPREAD']]
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data['Positive Change'] = data['AVG_SPREAD'].apply(lambda x: x if x >= 0 else None)
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data['Negative Change'] = data['AVG_SPREAD'].apply(lambda x: x if x < 0 else None)
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st.line_chart(
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data.set_index('MONTH_START')[['Positive Change', 'Negative Change']],
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use_container_width=True
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)
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else:
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st.warning("Please select a valid date range.")
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def main():
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# Set the title of the app
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st.title("FRED Yield Curve Analysis")
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st.header("Analysis of the Yield Curve Spread between 10Y and 2Y Treasury Bonds")
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# Establish Snowflake session
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conn = create_snowflake_connection()
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# Load data
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daily_agg, weekly_agg, monthly_agg = load_data(conn)
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# Select period
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period = st.selectbox("Select Period", ["DAILY", "WEEKLY", "MONTHLY"])
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if period == "DAILY":
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daily_data_analytics(daily_agg)
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elif period == "WEEKLY":
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weekly_data_analytics(weekly_agg)
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elif period == "MONTHLY":
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monthly_data_analytics(monthly_agg)
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if __name__ == "__main__":
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# Set page configuration
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st.set_page_config(
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page_title="FRED - Snowflake Data Pipeline", # Name of the app
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layout="wide", # Layout: "centered" or "wide"
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initial_sidebar_state="expanded" # Sidebar: "expanded" or "collapsed"
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)
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main()
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