Joe li branch

Investment Portfolio Python Notebook_03_2018_blog example.ipynb

@@ -5829,9 +5829,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "streamlit_apps",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -5843,7 +5843,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,

Investment_Portfolio_App.code-workspace

@@ -0,0 +1,8 @@
+{
+	"folders": [
+		{
+			"path": "."
+		}
+    ],
+    "settings": {}
+}

Updated App.py

@@ -0,0 +1,149 @@
+# Import initial libraries
+
+import pandas as pd
+import numpy as np
+import datetime
+import matplotlib.pyplot as plt
+import yfinance as yf
+
+
+from plotly import __version__
+from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
+
+import plotly.graph_objs as go
+
+print(__version__) # requires version >= 1.9.0
+
+init_notebook_mode(connected=True)
+
+filepath = "./New Sheet.xlsx"
+print("filepath: ", filepath)
+# Load the entire Excel file
+xls = pd.ExcelFile(filepath)
+
+# Load all sheet names
+sheet_names = xls.sheet_names
+print("sheet_names: ", sheet_names)
+# Load all sheets into a dictionary of DataFrames
+dfs = {sheet: pd.read_excel(xls, sheet) for sheet in sheet_names}
+
+# Convert the 'Transaction Date' column to datetime format (only year, month, day) in the 'transactions' DataFrame
+dfs['Transactions']['Transaction Date'] = pd.to_datetime(dfs['Transactions']['Transaction Date']).dt.date
+
+# Get unique tickers from the 'Summary' sheet
+tickers = dfs['Summary']['Ticker'].unique().tolist()  # Convert numpy array to list
+
+# Fetch today's data for all tickers
+data = yf.download(tickers, period='1d')
+
+# Convert 'Adj Close' DataFrame to a Series
+adj_close_series = data['Adj Close'].squeeze()
+
+# Update the 'Price Today' column in the 'Summary' DataFrame
+dfs['Summary']['Price Today'] = dfs['Summary']['Ticker'].map(adj_close_series).round(2)
+
+# Update the 'Current Value' column in the 'Summary' DataFrame
+dfs['Summary']['Current Value'] = dfs['Summary']['Quantity'] * dfs['Summary']['Price Today'].round(3)
+
+# Update the 'Transaction Total' column in the 'Transactions' DataFrame
+dfs['Transactions']['Transaction Total'] = dfs['Transactions']['Shares'] * dfs['Transactions']['Cost Per Share']
+
+# Copy the 'Ticker' column from the 'Transactions' DataFrame to the 'Summary' DataFrame
+dfs['Summary']['Ticker'] = dfs['Transactions']['Ticker']
+
+# Calculate the total quantity for each ticker
+quantity_series = dfs['Transactions'].groupby(['Ticker', 'Type'])['Shares'].sum().unstack().fillna(0)
+quantity_series['Total'] = quantity_series['Buy'] - quantity_series['Sell']
+
+# Update the 'Quantity' column in the 'Summary' DataFrame
+dfs['Summary'] = dfs['Summary'].set_index('Ticker')
+dfs['Summary']['Quantity'] = quantity_series['Total']
+dfs['Summary'] = dfs['Summary'].reset_index()
+
+# Calculate the total realized sales for each ticker
+realized_sales_series = dfs['Transactions'][dfs['Transactions']['Type'] == 'Sell'].groupby('Ticker')['Transaction Total'].sum()
+
+# Update the 'Realized Sales' column in the 'Summary' DataFrame
+dfs['Summary'] = dfs['Summary'].set_index('Ticker')
+dfs['Summary']['Realized Sales'] = realized_sales_series
+dfs['Summary'] = dfs['Summary'].reset_index()
+
+# Sort the 'Transactions' DataFrame by date
+dfs['Transactions'] = dfs['Transactions'].sort_values('Transaction Date')
+
+cost_basis_df = pd.DataFrame(columns=['Ticker', 'Shares', 'Transaction Total'])
+
+# Initialize a Series to hold the profit for each ticker
+profit_series = pd.Series(dtype='float64')
+
+# Iterate over the sorted 'Transactions' DataFrame
+for idx, row in dfs['Transactions'].iterrows():
+    if row['Type'] == 'Buy':
+        # Add the shares to the cost basis DataFrame
+        cost_basis_df.loc[len(cost_basis_df)] = row[['Ticker', 'Shares', 'Transaction Total']]
+    elif row['Type'] == 'Sell':
+        # Subtract the shares from the cost basis DataFrame in the order they were added (FIFO)
+        sell_shares = row['Shares']
+        sell_ticker = row['Ticker']
+        sell_price = row['Transaction Total'] / sell_shares
+        profit = 0
+        for i, cost_row in cost_basis_df[cost_basis_df['Ticker'] == sell_ticker].iterrows():
+            if sell_shares <= 0:
+                break
+            shares_to_sell = min(sell_shares, cost_row['Shares'])
+            profit += shares_to_sell * (sell_price - (cost_row['Transaction Total'] / cost_row['Shares']))
+            sell_shares -= shares_to_sell
+            cost_basis_df.loc[i, 'Shares'] -= shares_to_sell
+        cost_basis_df = cost_basis_df[cost_basis_df['Shares'] > 0]
+        profit_series[sell_ticker] = profit_series.get(sell_ticker, 0) + profit
+
+# Initialize 'Cost Basis' column in the 'Summary' DataFrame
+dfs['Summary']['Cost Basis'] = 0
+
+# Convert 'Cost Basis', 'Realized Profit' and 'Unrealized Profit' to float
+for column in ['Cost Basis', 'Realized Profit', 'Unrealized Profit']:
+    dfs['Summary'][column] = dfs['Summary'][column].astype(float)
+
+# Create a dictionary to hold the cost basis and shares of each ticker
+cost_basis_dict = {ticker: [] for ticker in dfs['Summary']['Ticker'].unique()}
+
+# Iterate over the 'Transactions' DataFrame
+for idx, row in dfs['Transactions'].iterrows():
+    if row['Type'] == 'Buy':
+        # Add the transaction total and shares to the cost basis dictionary for the corresponding ticker
+        cost_basis_dict[row['Ticker']].append((row['Transaction Total'], row['Shares']))
+    elif row['Type'] == 'Sell':
+        # Subtract the cost basis of the sold shares from the cost basis dictionary for the corresponding ticker
+        sell_shares = row['Shares']
+        while sell_shares > 0 and cost_basis_dict[row['Ticker']]:
+            buy_total, buy_shares = cost_basis_dict[row['Ticker']][0]
+            if buy_shares > sell_shares:
+                cost_basis_dict[row['Ticker']][0] = (buy_total * (buy_shares - sell_shares) / buy_shares, buy_shares - sell_shares)
+                sell_shares = 0
+            else:
+                sell_shares -= buy_shares
+                cost_basis_dict[row['Ticker']].pop(0)
+
+# Update the 'Cost Basis' column in the 'Summary' DataFrame
+for ticker in dfs['Summary']['Ticker'].unique():
+    dfs['Summary'].loc[dfs['Summary']['Ticker'] == ticker, 'Cost Basis'] = sum(total for total, shares in cost_basis_dict[ticker])
+
+
+# Update the 'Realized Profit' column in the 'Summary' DataFrame
+dfs['Summary'] = dfs['Summary'].set_index('Ticker')
+dfs['Summary']['Realized Profit'] = profit_series
+dfs['Summary'] = dfs['Summary'].reset_index()
+
+# Calculate 'Unrealized Profit' for each ticker
+dfs['Summary']['Unrealized Profit'] = dfs['Summary']['Current Value'] - dfs['Summary']['Cost Basis']
+
+# Round 'Cost Basis', 'Realized Profit' and 'Unrealized Profit' to 2 decimal places
+for column in ['Cost Basis', 'Realized Profit', 'Unrealized Profit']:
+    dfs['Summary'][column] = dfs['Summary'][column].round(2)
+
+# Write all DataFrames back to the Excel file
+with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+    for sheet, df in dfs.items():
+        df.to_excel(writer, sheet_name=sheet, index=False)
+
+

investment_portfoli_v2.py

@@ -0,0 +1,134 @@
+"""
+Based on the Streamlit Handover Delays Dashboard
+
+Created on Tue Apr 16 2024
+@author: JL
+
+"""
+import streamlit as st
+import time
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import subprocess
+# import yfinance as yf
+
+st.set_page_config(page_title='Investment Portfolio App',  layout='wide', page_icon=':ambulance:')
+
+#this is the header
+
+
+# t1, t2 = st.columns((0.07,1)) 
+t1, t2 = st.columns((0.08,1)) 
+
+t1.image('images/NTAI.png', width = 120)
+t2.title("Networking Technology Academy Institute:")
+t2.title("\n Personal Investment Portfolio Report")
+t2.markdown(" **tel:** 617-123-4567 **| website:** https://www.networktechnologyacademy.org **| email:** xyz@ntai.com")
+
+dataFile = "New Sheet.xlsx"
+
+## Data
+
+with st.spinner('Updating Report...'):
+
+    #Metrics setting and rendering
+    subprocess.call(["python", "Investment_Portfolio_App.py"])
+    invstr_df = pd.read_excel(dataFile,sheet_name = 'Investors')
+    invstr = st.selectbox('Choose Investor', invstr_df, help = 'Filter report to show only one investor', index = 0)
+    subprocess.call(["python", "Investment_Portfolio_App.py"])
+    # Current Investment Table
+    cw1, cw2 = st.columns((2.7, 2.5))
+    # cwdf = pd.read_excel(dataFile,sheet_name = 'All')    
+    if invstr == 'All':
+        cwdf = pd.read_excel(dataFile,sheet_name = 'Summary')
+    elif invstr != 'All':
+        print("inverstor selected is: ", invstr)         
+        cwdf = pd.read_excel(dataFile,sheet_name = invstr)
+
+
+    fig = go.Figure(
+            data = [go.Table (columnorder = [0,1,2,3,4,5,6,7], columnwidth = [10,20,20,20,20,20,20,20],
+                header = dict(
+                 values = list(cwdf.columns),
+                 font=dict(size=12, color = 'white'),
+                 fill_color = '#264653',
+                 align = 'center',
+                 height=20
+                 )
+              , cells = dict(
+                  values = [cwdf[K].tolist() for K in cwdf.columns], 
+                  font=dict(size=12),
+                  align = 'center',
+                  fill_color='#F0F2F6',
+                  height=20))]) 
+
+    fig.update_layout(title_text="Summary",title_font_color = '#264653',title_x=0,margin= dict(l=0,r=10,b=10,t=30), height=480)
+
+    cw1.plotly_chart(fig, use_container_width=True)
+
+    cwdf = pd.read_excel(dataFile,sheet_name = 'Transactions')    
+    if invstr == 'All':
+        cwdf = cwdf
+    elif invstr != 'All':
+        cwdf = cwdf[cwdf['Investor']==invstr]   
+
+
+    fig = go.Figure(
+            data = [go.Table (columnorder = [0,1,2,3,4,5,6,7], columnwidth = [25,20,20,20,20,20,20,20],
+                header = dict(
+                 values = list(cwdf.columns),
+                 font=dict(size=12, color = 'white'),
+                 fill_color = '#264653',
+                 align = 'center',
+                 height=20
+                 )
+              , cells = dict(
+                  values = [cwdf[K].tolist() for K in cwdf.columns], 
+                  font=dict(size=12),
+                  align = 'left',
+                  fill_color='#F0F2F6',
+                  height=20))]) 
+
+    fig.update_layout(title_text="Transactions",title_font_color = '#264653',title_x=0,margin= dict(l=0,r=10,b=10,t=30), height=480)
+
+    cw2.plotly_chart(fig, use_container_width=True)
+
+
+with st.spinner('Report updated!'):
+    time.sleep(1)     
+
+# Assume we have a DataFrame
+# df = pd.DataFrame({
+#     'A': [1, 2, 3, 4, 5],
+#     'B': [10, 20, 30, 40, 50]
+# })
+
+# row_number = st.number_input('Input row number', min_value=0, max_value=len(df)-1, format="%i")
+# column_name = st.selectbox('Select column', df.columns)
+# new_value = st.number_input('Input new value', value=df.loc[row_number, column_name])
+
+# if st.button('Update Data'):
+#     df.loc[row_number, column_name] = new_value
+#     st.write(df)
+
+
+# Contact Form
+
+with st.expander("Contact us"):
+    with st.form(key='contact', clear_on_submit=True):
+
+        email = st.text_input('Contact Email')
+        st.text_area("Query","Please fill in all the information or we may not be able to process your request")  
+
+        submit_button = st.form_submit_button(label='Send Information')
+
+
+
+
+
+
+
+
+
+