{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DFC foracst\n",
"### Author: Karozis Stelios\n",
"### Date: 12/4/2021\n",
"### Source: https://codingandfun.com/discounted-cash-flow-with-python-valuing-a-company/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Libs import"
]
},
{
"cell_type": "code",
"execution_count": 743,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pandas_datareader.data as web\n",
"import datetime\n",
"import plotly.graph_objects as go\n",
"pd.options.display.float_format = '{:,.2f}'.format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## User inputs"
]
},
{
"cell_type": "code",
"execution_count": 684,
"metadata": {},
"outputs": [],
"source": [
"y_ahead=5\n",
"date_start = [2019,3,1]\n",
"timeframe=0 #analysis for current year: 0, analysis for previous year: 1 etc\n",
"company = 'GOOG' #'VWS.DE' #VESTAS\n",
"demo = '34c9e7a6a6fcfb7b1d531f1267eac987'"
]
},
{
"cell_type": "code",
"execution_count": 652,
"metadata": {},
"outputs": [],
"source": [
"IS = requests.get('https://financialmodelingprep.com/api/v3/income-statement/'+company+'?apikey='+demo).json()"
]
},
{
"cell_type": "code",
"execution_count": 653,
"metadata": {},
"outputs": [],
"source": [
"BS = requests.get('https://financialmodelingprep.com/api/v3/balance-sheet-statement/'+company+'?apikey='+demo).json()"
]
},
{
"cell_type": "code",
"execution_count": 654,
"metadata": {},
"outputs": [],
"source": [
"FR = requests.get('https://financialmodelingprep.com/api/v3/ratios/'+company+'?apikey='+demo).json()"
]
},
{
"cell_type": "code",
"execution_count": 655,
"metadata": {},
"outputs": [],
"source": [
"profile = requests.get('https://financialmodelingprep.com/api/v3/company/profile/'+company+'?apikey='+demo).json()"
]
},
{
"cell_type": "code",
"execution_count": 729,
"metadata": {},
"outputs": [],
"source": [
"historical_prices = requests.get('https://financialmodelingprep.com/api/v3/historical-price-full/'+company+'?apikey='+demo).json()"
]
},
{
"cell_type": "code",
"execution_count": 741,
"metadata": {},
"outputs": [],
"source": [
"history = requests.get('https://financialmodelingprep.com/api/v3/historical-price-full/'+company+'?apikey='+demo).json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FCF forecast"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Functions"
]
},
{
"cell_type": "code",
"execution_count": 594,
"metadata": {},
"outputs": [],
"source": [
"def income_perc_years_ahead(IS,timeframe, y_ahead, revenue_g):\n",
" #Get net income\n",
" #net_income = dataframe[0]['netIncome']\n",
" #get income statement as % of revenue for future predictions and forecast 5 next IS years\n",
" income_statement = pd.DataFrame.from_dict(IS[timeframe],orient='index')\n",
"\n",
"\n",
" #income_statement = income_statement[6:27]\n",
" income_statement.columns = ['current_year']\n",
" income_statement['as_ratio_of_revenue'] = np.nan\n",
" for i in income_statement.index:\n",
" try:\n",
" #print(type(income_statement['current_year'][i]), income_statement['current_year'][i], type(net_income),net_income)\n",
" income_statement['as_ratio_of_revenue'][i] = float(income_statement['current_year'][i]) / float(income_statement['current_year']['revenue'])\n",
" except:\n",
" pass\n",
" \n",
" \n",
" for y in range(1,y_ahead+1):\n",
" if y==1:\n",
" cl_nm='next'+str(y)+'year'\n",
" prev='current_year'\n",
" else:\n",
" cl_nm='next'+str(y)+'year'\n",
" prev='next'+str(y-1)+'year'\n",
" #print(cl_nm)\n",
" income_statement[cl_nm] = np.nan\n",
" #for i in income_statement.index:\n",
" income_statement[cl_nm] = (income_statement[prev]['revenue'] * (1+revenue_g)) \\\n",
" * income_statement['as_ratio_of_revenue'] \n",
" \n",
" return income_statement"
]
},
{
"cell_type": "code",
"execution_count": 568,
"metadata": {},
"outputs": [],
"source": [
"def balance_perc_years_ahead(BS, timeframe, income_statement, y_ahead):\n",
" #Get Balance sheet as a percentage of revenue\n",
" balance_sheet = pd.DataFrame.from_dict(BS[timeframe],orient='index')\n",
" balance_sheet.columns = ['current_year']\n",
" balance_sheet['as_ratio_of_revenue'] = np.nan\n",
" for i in balance_sheet.index:\n",
" try:\n",
" balance_sheet['as_ratio_of_revenue'][i] = float(balance_sheet['current_year'][i]) \\\n",
" / float(income_statement['current_year']['revenue'])\n",
" except:\n",
" pass\n",
" \n",
" for y in range(1,y_ahead+1):\n",
" cl_nm='next'+str(y)+'year'\n",
" #print(cl_nm)\n",
" balance_sheet[cl_nm] = income_statement[cl_nm]['revenue'] \\\n",
" * balance_sheet['as_ratio_of_revenue'] \n",
" \n",
" return balance_sheet"
]
},
{
"cell_type": "code",
"execution_count": 602,
"metadata": {},
"outputs": [],
"source": [
"def CF_forecast_perc_years_ahead(income_statement, balance_sheet, y_ahead):\n",
" CF_forecast = {}\n",
" \n",
" for y in range(1,y_ahead+1):\n",
" if y==1:\n",
" cl_nm='next'+str(y)+'year'\n",
" prev='current_year'\n",
" else:\n",
" cl_nm='next'+str(y)+'year'\n",
" prev='next'+str(y-1)+'year'\n",
" #print(cl_nm)\n",
" CF_forecast[cl_nm] = {}\n",
" CF_forecast[cl_nm]['netIncome'] = income_statement[cl_nm]['netIncome']\n",
" CF_forecast[cl_nm]['inc_depreciation'] = income_statement[cl_nm]['depreciationAndAmortization'] - income_statement[prev]['depreciationAndAmortization']\n",
" CF_forecast[cl_nm]['inc_receivables'] = balance_sheet[cl_nm]['netReceivables'] - balance_sheet[prev]['netReceivables']\n",
" CF_forecast[cl_nm]['inc_inventory'] = balance_sheet[cl_nm]['inventory'] - balance_sheet[prev]['inventory']\n",
" CF_forecast[cl_nm]['inc_payables'] = balance_sheet[cl_nm]['accountPayables'] - balance_sheet[prev]['accountPayables']\n",
" CF_forecast[cl_nm]['CF_operations'] = CF_forecast[cl_nm]['netIncome'] + CF_forecast[cl_nm]['inc_depreciation'] + (CF_forecast[cl_nm]['inc_receivables'] * -1) + (CF_forecast[cl_nm]['inc_inventory'] *-1) + CF_forecast[cl_nm]['inc_payables']\n",
" CF_forecast[cl_nm]['CAPEX'] = balance_sheet[cl_nm]['propertyPlantEquipmentNet'] - balance_sheet[prev]['propertyPlantEquipmentNet'] + income_statement[cl_nm]['depreciationAndAmortization']\n",
"\n",
" CF_forecast[cl_nm]['FCF'] = CF_forecast[cl_nm]['CAPEX'] + CF_forecast[cl_nm]['CF_operations']\n",
" \n",
" #add the forecasted cash flows into a Pandas\n",
" CF_forec = pd.DataFrame.from_dict(CF_forecast,orient='columns')\n",
"\n",
" #add below option to format the dataframe with thousand separators\n",
" #pd.options.display.float_format = '{:,.2f}'.format\n",
" \n",
" return CF_forec"
]
},
{
"cell_type": "code",
"execution_count": 740,
"metadata": {},
"outputs": [],
"source": [
"def historical_prices(history):\n",
"\n",
" historical_prices = history['historical']\n",
" price_data = {}\n",
"\n",
" for item in historical_prices:\n",
" price_data[item['date']] = {}\n",
" price_data[item['date']]['date'] = item['date']\n",
" price_data[item['date']]['open']= item['open']\n",
" price_data[item['date']]['high'] = item['high']\n",
" price_data[item['date']]['low'] = item['low']\n",
" price_data[item['date']]['adjClose'] = item['adjClose']\n",
"\n",
" price_DF = pd.DataFrame.from_dict(price_data)\n",
" price_DF = price_DF.T\t\n",
" fig = go.Figure(data=[go.Candlestick(x=price_DF['date'],\n",
" open=price_DF['open'],\n",
" high=price_DF['high'],\n",
" low=price_DF['low'],\n",
" close=price_DF['adjClose'])])\n",
"\n",
" fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculations"
]
},
{
"cell_type": "code",
"execution_count": 744,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"output_type": "display_data"
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"2021-03-05",
"2021-03-08",
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"2021-03-10",
"2021-03-11",
"2021-03-12",
"2021-03-15",
"2021-03-16",
"2021-03-17",
"2021-03-18",
"2021-03-19",
"2021-03-22",
"2021-03-23",
"2021-03-24",
"2021-03-25",
"2021-03-26",
"2021-03-29",
"2021-03-30",
"2021-03-31",
"2021-04-01",
"2021-04-05",
"2021-04-06",
"2021-04-07",
"2021-04-08",
"2021-04-09",
"2021-04-12",
"2021-04-13",
"2021-04-14",
"2021-04-15",
"2021-04-16",
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"text/html": [
"\n",
" \n",
" \n",
"
\n",
" \n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" next1year | \n",
" next2year | \n",
" next3year | \n",
" next4year | \n",
" next5year | \n",
"
\n",
" \n",
" \n",
" \n",
" | CAPEX | \n",
" 27,828,487,609.43 | \n",
" 31,382,333,528.28 | \n",
" 35,390,024,477.88 | \n",
" 39,909,518,883.18 | \n",
" 45,006,176,768.32 | \n",
"
\n",
" \n",
" | CF_operations | \n",
" 43,773,617,100.28 | \n",
" 49,363,740,885.24 | \n",
" 55,667,753,217.72 | \n",
" 62,776,821,463.21 | \n",
" 70,793,755,544.81 | \n",
"
\n",
" \n",
" | FCF | \n",
" 71,602,104,709.71 | \n",
" 80,746,074,413.52 | \n",
" 91,057,777,695.60 | \n",
" 102,686,340,346.39 | \n",
" 115,799,932,313.12 | \n",
"
\n",
" \n",
" | inc_depreciation | \n",
" 1,749,179,769.80 | \n",
" 1,972,559,332.26 | \n",
" 2,224,465,653.26 | \n",
" 2,508,541,751.62 | \n",
" 2,828,895,879.07 | \n",
"
\n",
" \n",
" | inc_inventory | \n",
" 92,969,473.05 | \n",
" 104,842,169.37 | \n",
" 118,231,072.17 | \n",
" 133,329,809.10 | \n",
" 150,356,735.05 | \n",
"
\n",
" \n",
" | inc_payables | \n",
" 713,745,034.20 | \n",
" 804,894,072.28 | \n",
" 907,683,327.45 | \n",
" 1,023,599,317.36 | \n",
" 1,154,318,395.86 | \n",
"
\n",
" \n",
" | inc_receivables | \n",
" 4,007,903,766.91 | \n",
" 4,519,734,400.50 | \n",
" 5,096,928,529.01 | \n",
" 5,747,833,418.48 | \n",
" 6,481,862,325.23 | \n",
"
\n",
" \n",
" | netIncome | \n",
" 45,411,565,536.24 | \n",
" 51,210,864,050.58 | \n",
" 57,750,763,838.20 | \n",
" 65,125,843,621.81 | \n",
" 73,442,760,330.16 | \n",
"
\n",
" \n",
"
\n",
"