first commit

app.py

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+# Importing required modules
+import pandas as pd
+import numpy as np
+import numpy as np
+import plotly.express as px
+
+# To extract and parse fundamental data like beta and growth estimates from finviz website
+import requests
+from bs4 import BeautifulSoup as bs
+
+# For parsing financial statements data from financialmodelingprep api
+from urllib.request import urlopen
+import json
+
+# For Gradio App
+import gradio as gr
+
+import os
+# uncomment and set API Key in the environment variable below
+# or you can choose to set it using any other method you know
+#os.environ['FMP_API_KEY'] = "your_api_key"
+
+# read the environment variable to use in API requests later
+apiKey = os.environ['FMP_API_KEY']
+
+
+############################################################################################################
+###### GET DATA FROM FINANCIAL MODELING PREP
+############################################################################################################
+
+# Financialmodelingprep api url
+base_url = "https://financialmodelingprep.com/api/v3/"
+
+def get_jsonparsed_data(url):
+    response = urlopen(url)
+    data = response.read().decode("utf-8")
+    return json.loads(data)
+
+# get financial statements using financial modelling prep API
+def get_financial_statements(ticker):
+    # quarterly cash flow statements for calculating latest trailing twelve months (TTM) free cash flow
+    columns_drop = ['acceptedDate', 'period', 'symbol', 'reportedCurrency', 'cik', 'fillingDate', 'depreciationAndAmortization']
+    q_cash_flow_statement = pd.DataFrame(get_jsonparsed_data(base_url+'cash-flow-statement/' + ticker + '?period=quarter' + '&apikey=' + apiKey))
+    q_cash_flow_statement = q_cash_flow_statement.set_index('date').drop(columns_drop, axis=1).iloc[:4] # extract for last 4 quarters
+    latest_year = int(q_cash_flow_statement.iloc[0]['calendarYear'])
+
+    # annual cash flow statements
+    cash_flow_statement = pd.DataFrame(get_jsonparsed_data(base_url+'cash-flow-statement/' + ticker + '?apikey=' + apiKey))
+    cash_flow_statement = cash_flow_statement.set_index('date').drop(columns_drop, axis=1)
+    
+    # combine annual and latest TTM cash flow statements
+    ttm_cash_flow_statement = q_cash_flow_statement.sum() # sum up last 4 quarters to get TTM cash flow
+    cash_flow_statement = cash_flow_statement[::-1].append(ttm_cash_flow_statement.rename('TTM')).drop(['netIncome'], axis=1)
+    final_cash_flow_statement = cash_flow_statement[::-1] # reverse list to show most recent ones first
+    
+    # quarterly balance sheet statements
+    columns_drop = ['acceptedDate', 'calendarYear', 'period', 'symbol', 'reportedCurrency', 'cik', 'fillingDate']
+    q_balance_statement = pd.DataFrame(get_jsonparsed_data(base_url+'balance-sheet-statement/' + ticker + '?' + '&apikey=' + apiKey))
+    q_balance_statement = q_balance_statement.set_index('date').drop(columns_drop, axis=1)
+    q_balance_statement = q_balance_statement.apply(pd.to_numeric, errors='coerce')
+        
+    return q_cash_flow_statement, cash_flow_statement, final_cash_flow_statement, q_balance_statement, latest_year
+ 
+ 
+# check stability of cash flows
+def plot_cash_flow(ticker, cash_flow_statement):       
+    # DCF model works best only if the free cash flows are POSITIVE, STABLE and STEADILY INCREASING. 
+    # So let's plot the graph and verify if this is the case.
+    fig_cash_flow = px.bar(cash_flow_statement , y='freeCashFlow', title=ticker + ' Free Cash Flows')
+    fig_cash_flow.update_xaxes(type='category', tickangle=270, title='Date')
+    fig_cash_flow.update_yaxes(title='Free Cash Flows')
+    #fig_cash_flow.show()
+    return fig_cash_flow
+
+
+# get ttm cash flow, most recent total debt and cash & short term investment data from statements
+def get_statements_data(final_cash_flow_statement, q_balance_statement):
+    cash_flow = final_cash_flow_statement.iloc[0]['freeCashFlow'] # ttm cash flow
+    total_debt = q_balance_statement.iloc[0]['totalDebt'] 
+    cash_and_ST_investments = q_balance_statement.iloc[0]['cashAndShortTermInvestments']
+    return cash_flow, total_debt, cash_and_ST_investments
+
+
+############################################################################################################
+###### GET DATA FROM FINVIZ WEBSITE 
+############################################################################################################
+
+# Price, EPS next Y/5Y, Beta, Number of Shares Outstanding
+# Extract (using requests.get) and Parse (using Beautiful Soup) data from Finviz table in the Finviz website (see screenshot above), needed to calculate intrinsic value of stock.
+
+# List of data we want to extract from Finviz Table
+# Price is the current stock price
+# EPS next Y is the estimated earnings growth for next year
+# EPS next 5Y is the estimated earnings growth for next 5 years (if this is not present on finviz, we will use EPS next Y instead)
+# Beta captures the volatility of the stock, used for estimating discount rate later
+# Shs Outstand is the number of shares present in the market
+metric = ['Price', 'EPS next Y', 'EPS next 5Y', 'Beta', 'Shs Outstand']
+
+def fundamental_metric(soup, metric):
+    # the table which stores the data in Finviz has html table attribute class of 'snapshot-td2'
+    return soup.find_all(text = metric)[-1].find_next(class_='snapshot-td2').text
+
+# get above metrics from finviz and store as a dict 
+def get_finviz_data(ticker):
+    try:
+        url = ("http://finviz.com/quote.ashx?t=" + ticker.lower())
+        soup = bs(requests.get(url,headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) Gecko/20100101 Firefox/20.0'}).content)
+        dict_finviz = {}        
+        for m in metric:   
+            dict_finviz[m] = fundamental_metric(soup,m)
+        for key, value in dict_finviz.items():
+            # replace percentages
+            if (value[-1]=='%'):
+                dict_finviz[key] = value[:-1]
+                dict_finviz[key] = float(dict_finviz[key])
+            # billion
+            if (value[-1]=='B'):
+                dict_finviz[key] = value[:-1]
+                dict_finviz[key] = float(dict_finviz[key])*1000000000  
+            # million
+            if (value[-1]=='M'):
+                dict_finviz[key] = value[:-1]
+                dict_finviz[key] = float(dict_finviz[key])*1000000
+            try:
+                dict_finviz[key] = float(dict_finviz[key])
+            except:
+                pass 
+    except Exception as e:
+        print (e)
+        print ('Not successful parsing ' + ticker + ' data.')        
+    return dict_finviz
+
+
+def parse_finviz_dict(finviz_dict):
+    EPS_growth_5Y = finviz_dict['EPS next 5Y']
+    # sometimes EPS next 5Y is empty and shows as a '-' string, in this case use EPS next Y
+    if isinstance(EPS_growth_5Y, str):
+        if not EPS_growth_5Y.isdigit():
+            EPS_growth_5Y = finviz_dict['EPS next Y']
+    EPS_growth_6Y_to_10Y = EPS_growth_5Y/2  # Half the previous growth rate, conservative estimate
+    #EPS_growth_11Y_to_20Y  = np.minimum(EPS_growth_6Y_to_10Y, 4)  # Slightly higher than long term inflation rate, conservative estimate
+    long_term_growth_rate = np.minimum(EPS_growth_6Y_to_10Y, 3)  # Slightly higher than long term inflation rate, conservative estimate
+    shares_outstanding = finviz_dict['Shs Outstand']
+    beta = finviz_dict['Beta']
+    current_price = finviz_dict['Price']
+    
+    return EPS_growth_5Y, EPS_growth_6Y_to_10Y, long_term_growth_rate, beta, shares_outstanding, current_price
+
+
+## Estimate Discount Rate from Beta
+def estimate_discount_rate(beta):
+    # Beta shows the volatility of the stock, 
+    # the higher the beta, we want to be more conservative by increasing the discount rate also.
+    discount_rate = 7
+    if(beta<0.80):
+        discount_rate = 5
+    elif(beta>=0.80 and beta<1):
+        discount_rate = 6
+    elif(beta>=1 and beta<1.1):
+        discount_rate = 6.5
+    elif(beta>=1.1 and beta<1.2):
+        discount_rate = 7
+    elif(beta>=1.2 and beta<1.3):
+        discount_rate = 7.5
+    elif(beta>=1.3 and beta<1.4):
+        discount_rate = 8
+    elif(beta>=1.4 and beta<1.6):
+        discount_rate = 8.5
+    elif(beta>=1.61):
+        discount_rate = 9   
+
+    return discount_rate
+
+
+############################################################################################################
+## Calculate Intrinsic Value
+############################################################################################################
+
+# 1. First Project Cash Flows from Year 1 to Year 10 using Present (TTM) Free Cash Flow
+# 2. Discount the Cash Flows to Present Value
+# 3. Calculate the Terminal Value after Year 10 (Discounted to Present Value) Assuming the Company will Grow at a Constant Steady Rate Forever (https://corporatefinanceinstitute.com/resources/financial-modeling/dcf-terminal-value-formula/)
+# 4. Add the Cash Flows and the Terminal Value Up
+# 5. Then Account for the Cash + Short Term Investments and Subtract Total Debt
+# 6. Divide by Total Number of Shares Outstanding
+
+def calculate_intrinsic_value(latest_year, cash_flow, total_debt, cash_and_ST_investments, 
+                                  EPS_growth_5Y, EPS_growth_6Y_to_10Y, long_term_growth_rate,
+                                  shares_outstanding, discount_rate, current_price):   
+    
+    # Convert all percentages to decmials
+    EPS_growth_5Y_d = EPS_growth_5Y/100
+    EPS_growth_6Y_to_10Y_d = EPS_growth_6Y_to_10Y/100
+    long_term_growth_rate_d = long_term_growth_rate/100
+    discount_rate_d = discount_rate/100
+    # print("Discounted Cash Flows\n")
+    
+    # Lists of projected cash flows from year 1 to year 20
+    cash_flow_list = []
+    cash_flow_discounted_list = []
+    year_list = []
+        
+    # Years 1 to 5
+    for year in range(1, 6):
+        year_list.append(year + latest_year)
+        cash_flow*=(1 + EPS_growth_5Y_d)        
+        cash_flow_list.append(cash_flow)
+        cash_flow_discounted = cash_flow/((1 + discount_rate_d)**year)
+        cash_flow_discounted_list.append(cash_flow_discounted)
+        # print("Year " + str(year + latest_year) + ": $" + str(cash_flow_discounted)) ## Print out the projected discounted cash flows
+    
+    # Years 6 to 10
+    for year in range(6, 11):
+        year_list.append(year + latest_year)
+        cash_flow*=(1 + EPS_growth_6Y_to_10Y_d)
+        cash_flow_list.append(cash_flow)
+        cash_flow_discounted = cash_flow/((1 + discount_rate_d)**year)
+        cash_flow_discounted_list.append(cash_flow_discounted)
+        # print("Year " + str(year + latest_year) + ": $" + str(cash_flow_discounted)) ## Print out the projected discounted cash flows
+    
+    # Store all forecasted cash flows in dataframe
+    forecast_cash_flows_df = pd.DataFrame.from_dict({'Year': year_list, 'Cash Flow': cash_flow_list, 'Discounted Cash Flow': cash_flow_discounted_list})
+    forecast_cash_flows_df = forecast_cash_flows_df.set_index('Year')
+    
+    # Growth in Perpuity Approach    
+    cashflow_10Y = cash_flow_discounted_list[-1]
+    # Formula to Calculate: https://corporatefinanceinstitute.com/resources/financial-modeling/dcf-terminal-value-formula/
+    terminal_value = cashflow_10Y*(1+long_term_growth_rate_d)/(discount_rate_d-long_term_growth_rate_d)
+    
+    # Yay finally
+    intrinsic_value = (sum(cash_flow_discounted_list) + terminal_value - total_debt + cash_and_ST_investments)/shares_outstanding
+    margin_of_safety = (1-current_price/intrinsic_value)*100
+    
+    return forecast_cash_flows_df, terminal_value, intrinsic_value, margin_of_safety
+
+
+# Plot forecasted cash flows from years 1 to 10, as well as the discounted cash flows
+def plot_forecasted_cash_flows(ticker, forecast_cash_flows_df):
+
+    fig_cash_forecast = px.bar(forecast_cash_flows_df, barmode='group', title=ticker + ' Projected Free Cash Flows')
+    fig_cash_forecast.update_xaxes(type='category', tickangle=270)
+    fig_cash_forecast.update_xaxes(tickangle=270, title='Forecasted Year')
+    fig_cash_forecast.update_yaxes(title='Free Cash Flows')
+    # fig_cash_forecast.show()
+    
+    return fig_cash_forecast
+
+
+# chain all the steps from the functions above together
+def run_all_steps(ticker):
+    q_cash_flow_statement, cash_flow_statement, final_cash_flow_statement, q_balance_statement, latest_year = get_financial_statements(ticker)
+
+    fig_cash_flow = plot_cash_flow(ticker, cash_flow_statement)
+
+    cash_flow, total_debt, cash_and_ST_investments = get_statements_data(final_cash_flow_statement, q_balance_statement)
+
+    finviz_dict = get_finviz_data(ticker)
+
+    EPS_growth_5Y, EPS_growth_6Y_to_10Y, long_term_growth_rate, beta, shares_outstanding, current_price = parse_finviz_dict(finviz_dict)
+
+    discount_rate = estimate_discount_rate(beta)
+
+    forecast_cash_flows_df, terminal_value, intrinsic_value, margin_of_safety = calculate_intrinsic_value(latest_year, cash_flow, total_debt, cash_and_ST_investments, 
+                                      EPS_growth_5Y, EPS_growth_6Y_to_10Y, long_term_growth_rate,
+                                      shares_outstanding, discount_rate, current_price)
+
+    fig_cash_forecast = plot_forecasted_cash_flows(ticker, forecast_cash_flows_df)
+    
+    return q_cash_flow_statement.reset_index(), final_cash_flow_statement.reset_index(), q_balance_statement.reset_index(), fig_cash_flow, \
+        str(EPS_growth_5Y) + '%' , str(EPS_growth_6Y_to_10Y) + '%', str(long_term_growth_rate) + '%', \
+        beta, shares_outstanding, current_price, \
+        discount_rate, forecast_cash_flows_df.reset_index(), terminal_value, intrinsic_value, fig_cash_forecast, margin_of_safety
+      
+
+# Gradio App and UI
+with gr.Blocks() as app:
+    with gr.Row():
+        gr.HTML("<h1>Bohmian's Stock Intrinsic Value Calculator</h1>")
+
+    with gr.Row():        
+        ticker = gr.Textbox("AAPL", label='Enter stock ticker to calculate its intrinsic value e.g. "AAPL"')
+        btn = gr.Button("Calculate Intrinsic Value")
+        
+    # Show intrinsic value calculation results
+    with gr.Row():
+        gr.HTML("<h2>Calculated Intrinsic Value</h2>")
+        
+    with gr.Row():
+        intrinsic_value = gr.Text(label="Intrinsic Value")
+        current_price = gr.Text(label="Actual Stock Price")
+        margin_of_safety = gr.Text(label="Margin of Safety")
+    
+    # Show metrics obtained and estimated from FinViz website that were essential for calculations
+    with gr.Row():
+        gr.HTML("<h2>Metrics Obtained (and Estimated) from FinViz Website</h2>")
+    with gr.Row():
+        gr.HTML("<h3>https://finviz.com/</h3>")
+        
+    with gr.Row():
+            EPS_growth_5Y = gr.Text(label="EPS Next 5Y (Estimated EPS growth for next 5 years)")
+            EPS_growth_6Y_to_10Y = gr.Text(label="EPS growth for 6th to 10th year (estimated as half of above)")
+            long_term_growth_rate = gr.Text(label="Long Term Growth Rate (estimated as half of above or 3%, whichever is lower)")  
+    
+    with gr.Row():
+        beta = gr.Text(label="Beta (Measures volatility of stock)")
+        discount_rate = gr.Text(label="Discount Rate (estimated from beta)") 
+        shares_outstanding = gr.Text(label="Shares Outstanding")             
+
+       
+    # Show detailed actual historical financial statements
+    with gr.Row():
+        gr.HTML("<h2>Actual Historical Financial Statements Data</h2>")
+    with gr.Row():
+        gr.HTML("<h3>IMPORTANT NOTE: DCF model works best only if the free cash flows are POSITIVE, STABLE and STEADILY INCREASING. Check if this is the case.</h3>")
+    
+    with gr.Row():        
+        fig_cash_flow = gr.Plot(label="Historical Cash Flows")
+        
+    with gr.Row():   
+        q_cash_flow_statement = gr.DataFrame(label="Last 4 Quarterly Cash Flow Statements")
+        
+    with gr.Row():   
+        final_cash_flow_statement = gr.DataFrame(label="TTM + Annual Cash Flow Statements")
+    
+    with gr.Row():   
+        q_balance_statement = gr.DataFrame(label="Quarterly Balance Statements")
+    
+    
+    # Show forecasted cash flows and terminal value
+    with gr.Row():
+        gr.HTML("<h2>Forecasted Cash Flows for Next 10 Years</h2>")
+        
+    with gr.Row():
+        fig_cash_forecast = gr.Plot(label="Forecasted Cash Flows")
+        forecast_cash_flows_df = gr.DataFrame(label="Forecasted Cash Flows")
+
+    with gr.Row():
+        terminal_value = gr.Text(label="Terminal Value (after 10th year)")
+        
+
+    btn.click(fn=run_all_steps, inputs=[ticker], 
+              outputs=[q_cash_flow_statement, final_cash_flow_statement, q_balance_statement, fig_cash_flow, \
+        EPS_growth_5Y, EPS_growth_6Y_to_10Y, long_term_growth_rate, beta, shares_outstanding, current_price, \
+        discount_rate, forecast_cash_flows_df, terminal_value, intrinsic_value, fig_cash_forecast, margin_of_safety])
+
+app.launch()

requirements.txt

@@ -0,0 +1,3 @@
+pandas
+plotly
+bs4