-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathMetricCalcFMP.py
213 lines (159 loc) · 9 KB
/
MetricCalcFMP.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
import numpy as np
import pandas as pd
import csv
from io import StringIO
import json
import csv
import requests
def get_jsonparsed_data(url):
response = requests.get(url)
return response.json()
def getObj(url):
url = get_jsonparsed_data(url)
string = json.dumps(url)
obj = json.loads(string)
return obj
metricNames = ['P/E', 'EV/EBITDA', 'P/B', 'P/CF', 'P/S', 'ROE', 'ROA', "ROD",'ROI',"Revenue", 'Profit', "Equity", "Assets", "D/A", "Ebitda Growth", "CF Growth", "Equity Multiplier"]
# ROE: [(TTM Net Income/ TTM Shareholder Equity) - (15yrPast Net Income/ 15yrPast Shareholder Equity)] / (15yrPast Net Income/ 15yrPast Shareholder Equity)
# OCF Per Share: TTM-OCF/Shares-Outstanding
# P/CF: Share-Price (30 day avg) / OCF Per Share **If Share Price not available, another formula: https://www.investopedia.com/terms/p/price-to-cash-flowratio.asp
# ROA: [(TTM Net Income/TTM Total Assets) - (15yrPast Net Income/15yrPast Total Assets)] / (15yrPast Net Income/15yrPast Total Assets)
consumDiscStockList = ["AEO", "AMZN", "ANF", "APTV", "AZO", "BABA", "BBW", "BBY", "BNED", "BURL", "BWA", "CAAS", "CAKE", "CMG", "CZR", "EDU", "ETSY", "FL", "F", "GM", "GRMN", "GRPN", "HAS", "HD", "HMC", "HOG", "HRB", "H", "KMX", "LCII", "LE", "LOW", "LULU", "LVS", "MAR", "MAT", "MCD", "MOV", "M", "NCLH", "NKE", "ORLY", "PLNT", "PTON", "PZZA", "SBUX", "SFIX", "SONY", "SWBI", "TAL", "TCS", "TJX", "TM", "TSLA", "TXRH", "UA", "ULTA", "URBN", "VFC", "VRA", "WEN", "WH", "WSM", "YUM"]
healthStockList = ["ABBV", "ABT", "ACAD", "AMGN", "AMN", "BAX", "BMY", "CI", "CNC", "CPRX", "CVS", "DOC", "GILD", "GMAB", "GMED", "GSK", "HBIO", "ILMN", "IQV", "JNJ", "LQDA", "MCK", "MRK", "MYGN", "NVO", "NVS", "PBH", "PFE", "REGN", "TEVA", "UNH", "VEEV"]
infoTechStockList = ["AAPL", "ACN", "ADBE", "ADI", "ADP", "AEHR", "AMAT", "AMD", "ANET", "ASGN", "ASML", "AVGO", "BILL", "CRM", "CRWD", "CSCO", "CTSH", "DBX", "DELL", "DJCO", "DXC", "FICO", "FTNT", "IBM", "INTC", "INTU", "MANH", "MSFT", "MSI", "NCTY", "NOW", "NVDA", "NXPI", "ORCL", "PANW", "PLTR", "QCOM", "SAP", "SEDG", "SNOW", "TSM", "TXN", "UTSI", "XRX"]
financialsStockList = ["AFL", "ALL", "APAM", "AXP", "BAC", "BAM", "BCS", "BEN", "BLK", "BX", "C", "DB", "DFS", "GBCI", "GEG", "GS", "ICE", "JPM", "KEY", "KKR", "L", "LAZ", "MA", "MCO", "MET", "MS", "MTB", "ONB", "OPY", "OZK", "PNC", "PRU", "PYPL", "RF", "SCHW", "SEIC", "TROW", "UBS", "V", "WFC"]
stockList = ["AAPL"]
stock15yrMetrics = []
keyMetrics = getObj("https://financialmodelingprep.com/api/v3/key-metrics/AAPL?period=annual&apikey=V6J4FVBPQvPyJAWmcPry9kb8pVlTgibE")
for m in range(len(stockList)):
sharePrice1yravg = 0
sharePrice15yravg = 0
def extract15yrData(obj, metricName):
metricVal = 0
numIterated = 0
for i in range(len(obj)):
numIterated += 1
metricVal += obj[i][metricName]
return metricVal
# --------------- Financials CSV ---------------
dfFinancials = getObj("https://financialmodelingprep.com/api/v3/income-statement/"+stockList[m]+"?period=annual&apikey=V6J4FVBPQvPyJAWmcPry9kb8pVlTgibE")
netIncmAnnual = dfFinancials[0]["netIncome"]
revenueAnnual = dfFinancials[0]["revenue"]
profitAnnual = dfFinancials[0]["grossProfit"]
netIncm15yr = extract15yrData(dfFinancials, "netIncome")
revenue15yr = extract15yrData(dfFinancials, "revenue")
profit15yr = extract15yrData(dfFinancials, "grossProfit")
print(netIncmAnnual)
print(revenueAnnual)
print(profitAnnual)
print(netIncm15yr)
print(revenue15yr)
print(profit15yr)
# --------------- Balance Sheet CSV ---------------
dfBalance = getObj("https://financialmodelingprep.com/api/v3/balance-sheet-statement-as-reported/"+stockList[m]+"?period=annual&apikey=V6J4FVBPQvPyJAWmcPry9kb8pVlTgibE")
totAssetsAnnual = dfBalance[0]["assets"]
shEqAnnual = dfBalance[0]["stockholdersequity"]
shOutstAnnual = dfBalance[0]["commonstocksharesoutstanding"]
totAssets15yr = extract15yrData(dfBalance, "assets")
shEq15yr = extract15yrData(dfBalance, "stockholdersequity")
shOutst15yr = extract15yrData(dfBalance, "commonstocksharesoutstanding")
longDebt15yr = extract15yrData(dfBalance, "longtermdebtnoncurrent")
# --------------- Cash Flow CSV ---------------
with open('./Information-Technology/' + stockList[m] + '_annual_cash-flow.csv', 'r') as file:
csv_content = file.read()
# Replace non-breaking spaces with regular spaces
csv_content = csv_content.replace('\xA0', ' ')
dfCashFlow = pd.read_csv(StringIO(csv_content))
for i in range(dfCashFlow["name"].size):
if (dfCashFlow.loc[i]["name"] == "OperatingCashFlow"):
try:
ocfTTM = int(dfCashFlow.loc[i][1].replace(',',""))
except AttributeError:
ocfTTM += dfCashFlow.loc[i][1]
ocf15yr = extract15yrData(i, dfCashFlow, 0)
if (dfCashFlow.loc[i]["name"] == "CapitalExpenditure"):
capex15yr = extract15yrData(i, dfCashFlow, 0)
# --------------- Share Price CSV ---------------
dfSharePrice = pd.read_csv('./Information-Technology/' + stockList[m] +".csv")
len = dfSharePrice.shape[0]
for i in range(len):
if (i < 252):
# print(dfSharePrice.loc[dfSharePrice.shape[0]-1-i][4])
sharePrice1yravg += dfSharePrice.loc[dfSharePrice.shape[0]-1-i][4]
# print(sharePrice1yravg)
if (i == 251):
sharePrice1yravg = sharePrice1yravg/252
if (i < 3780):
sharePrice15yravg += dfSharePrice.loc[dfSharePrice.shape[0]-1-i][4]
if (i == 3779):
sharePrice15yravg = sharePrice15yravg/3780
pe15yr = 0
evEbtida15yr = 0
pb15yr = 0
pcf15yr = 0
ps15yr = 0
roe15yr = 0
roa15yr = 0
rod15yr = 0
roi15yr = 0
rev15yr = 0
prf15yr = 0
eqi15yr = 0
ast15yr = 0
# --------------- Valuation CSV ---------------
dfValuation = pd.read_csv('./Information-Technology/' + stockList[m] +"_annual_valuation_measures.csv")
for i in range(dfValuation["name"].size):
if (dfValuation.loc[i]["name"] == "PeRatio"):
pe15yr = extract15yrData(i, dfValuation, 0)
if (dfValuation.loc[i]["name"] == "PsRatio"):
ps15yr = extract15yrData(i, dfValuation, 0)
if (dfValuation.loc[i]["name"] == "PbRatio"):
pb15yr = extract15yrData(i, dfValuation, 0)
if (dfValuation.loc[i]["name"] == "EnterprisesValueEBITDARatio"):
evEbtida15yr = extract15yrData(i, dfValuation, 0)
if pd.isnull(dfValuation.loc[i][1]):
print('nothing will be done')
evEbitdaTTM = 0
else:
evEbitdaTTM = float(dfValuation.loc[i][1].replace(',',""))
# ROE: [(TTM Net Income/ TTM Shareholder Equity) - (15yrPast Net Income/ 15yrPast Shareholder Equity)] / (15yrPast Net Income/ 15yrPast Shareholder Equity)
# OCF Per Share: TTM-OCF/Shares-Outstanding
# P/CF: Share-Price (30 day avg) / OCF Per Share **If Share Price not available, another formula: https://www.investopedia.com/terms/p/price-to-cash-flowratio.asp
# ROA: [(TTM Net Income/TTM Total Assets) - (15yrPast Net Income/15yrPast Total Assets)] / (15yrPast Net Income/15yrPast Total Assets)
roe15yr = netIncm15yr / shEq15yr
roa15yr = netIncm15yr / totAssets15yr
print(stockList[m])
try:
rod15yr = netIncm15yr / longDebt15yr
except ZeroDivisionError:
rod15yr = netIncm15yr / 1
roi15yr = netIncm15yr / capex15yr
# ocfPerShareTTM = ocfTTM/shOutstTTM
ocfPerShare15yr = ocf15yr/shOutst15yr
# pcfTTM = sharePrice1yravg / ocfPerShareTTM
pcf15yr = sharePrice15yravg / ocfPerShare15yr
revenueGrowth15yr = (revenueTTM - revenue15yr) / revenue15yr
equityGrowth15yr = (shEqTTM - shEq15yr) / shEq15yr
profitGrowth15yr = (profitTTM - profit15yr) / profit15yr
assetsGrowth15yr = (totAssetsTTM - totAssets15yr) / totAssets15yr
debtToAssets = (longDebt15yr/totAssets15yr)
try:
ebitdaGrowth15yr = (evEbitdaTTM - evEbtida15yr) / evEbtida15yr
except ZeroDivisionError:
ebitdaGrowth15yr = 0
except NameError:
ebitdaGrowth15yr = 0
cfGrowth15yr = (ocfTTM - ocf15yr) / ocf15yr
equityMult = totAssets15yr / shEq15yr
metrics = [pe15yr, evEbtida15yr, pb15yr, pcf15yr, ps15yr, roe15yr, roa15yr, rod15yr, roi15yr, revenueGrowth15yr, profitGrowth15yr, equityGrowth15yr, assetsGrowth15yr, debtToAssets, ebitdaGrowth15yr, cfGrowth15yr, equityMult ]
stock15yrMetrics.append(metrics)
fileName = "all_metrics.csv"
with open(fileName, 'w', newline="") as f:
csvWriter = csv.writer(f)
header_row = ["Stock"] + metricNames
csvWriter.writerow(header_row)
for i, metrics in enumerate(stock15yrMetrics):
# Write the stock name and the corresponding metrics
row = [stockList[i]] + metrics
csvWriter.writerow(row)
print("FINISHED")