mirror of
https://github.com/OpenBB-finance/OpenBB.git
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Enhanced ratios (#867)
* Initial function * Added sister_data * Added ratios for other companies * Didier and Artem updates * Didier and Artem updates * Fixed lock file Co-authored-by: Colin Delahunty <colindelahunty@Colins-MacBook-Air.local> Co-authored-by: Colin Delahunty <colindelahunty@Colins-Air.indiana.edu>
This commit is contained in:
Colin Delahunty
@@ -1183,27 +1183,29 @@ Current Expiry: {self.selected_date or None}
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help="Choose from already created graphs",
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)
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# try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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if not self.ticker and not self.selected_date:
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print("Ticker and expiration required. \n")
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return
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if (ns_parser.x is None or ns_parser.y is None) and ns_parser.custom is None:
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print("Please submit an X and Y value, or select a preset.\n")
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return
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yfinance_view.plot_plot(
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self.ticker,
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self.selected_date,
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ns_parser.put,
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ns_parser.x,
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ns_parser.y,
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ns_parser.custom,
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)
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print("")
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# except Exception as e:
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# print(e, "\n")
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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if not self.ticker and not self.selected_date:
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print("Ticker and expiration required. \n")
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return
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if (
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ns_parser.x is None or ns_parser.y is None
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) and ns_parser.custom is None:
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print("Please submit an X and Y value, or select a preset.\n")
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return
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yfinance_view.plot_plot(
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self.ticker,
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self.selected_date,
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ns_parser.put,
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ns_parser.x,
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ns_parser.y,
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ns_parser.custom,
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)
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print("")
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except Exception as e:
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print(e, "\n")
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def call_stocks(self, _):
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"""Process stocks command"""
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@@ -9,6 +9,7 @@ from io import BytesIO
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from sklearn.linear_model import LinearRegression
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from openpyxl.styles import Border, Side, Font, PatternFill, Alignment
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from openpyxl import worksheet
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import FinanceDatabase as fd
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import yfinance as yf
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import pandas as pd
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@@ -29,7 +30,9 @@ def string_float(string: str) -> float:
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number : float
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Analysis of filings text
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"""
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return float(string.replace(",", ""))
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if string.strip().replace(",", "").replace("-", "") == "":
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return 0
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return float(string.strip().replace(",", "").replace("-", ""))
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def insert_row(
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@@ -200,6 +203,31 @@ def get_fama_coe(ticker: str) -> float:
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) * 12
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def others_in_sector(ticker: str, sector: str, industry: str) -> List[str]:
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"""Get other stocks in a ticker's sector
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Parameters
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----------
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ticker : str
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The ticker to be excluded
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sector : str
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The sector to pull from
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industry : str
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The industry to pull from
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Returns
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-------
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tickers : List[str]
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List of tickers in the same sector
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"""
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sister_ticks = list(
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fd.select_equities(country="United States", sector=sector, industry=industry)
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)
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if ticker in sister_ticks:
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sister_ticks.remove(ticker)
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return sister_ticks
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letters = [
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"A",
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"B",
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@@ -4,6 +4,7 @@ __docformat__ = "numpy"
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from typing import List, Union
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from datetime import datetime
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from pathlib import Path
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import random
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import os
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from openpyxl.styles.numbers import FORMAT_PERCENTAGE_00
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@@ -52,6 +53,10 @@ class CreateExcelFA:
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self.info: pd.DataFrame = yf.Ticker(ticker).info
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self.t_bill: float = get_rf()
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self.r_ff: float = dcf_model.get_fama_coe(self.ticker)
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self.sisters: List[str] = dcf_model.others_in_sector(
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self.ticker, self.info["sector"], self.info["industry"]
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)
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self.sister_data: List[List[pd.DataFrame]] = [[pd.DataFrame()]]
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def create_workbook(self):
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self.ws1.column_dimensions["A"].width = 25
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@@ -136,7 +141,13 @@ class CreateExcelFA:
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body = table.find("tbody")
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rows = body.find_all("tr")
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all_data = [[x.get_text().strip() for x in y.find_all("td")] for y in rows]
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all_data = [
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[
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x.get_text().strip() if x.get_text().strip() != "-" else "0"
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for x in y.find_all("td")
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]
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for y in rows
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]
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df = pd.DataFrame(data=all_data)
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df = df.set_index(0)
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@@ -153,36 +164,19 @@ class CreateExcelFA:
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self.ws1[f"A{row}"] = title
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self.ws1[f"A{row}"].font = dcf_model.bold_font
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# Refactor in the future
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if statement == "IS":
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if "Revenue" in df.index:
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blank_list = ["0" for x in df.loc["Revenue"].to_list()]
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else:
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raise ValueError("Dataframe does not have key information.")
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for i, value in enumerate(dcf_model.gaap_is[1:]):
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df = dcf_model.insert_row(
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dcf_model.gaap_is[i + 1], dcf_model.gaap_is[i], df, blank_list
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)
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vals = ["Revenue", dcf_model.gaap_is]
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elif statement == "BS":
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vals = ["Cash & Equivalents", dcf_model.gaap_bs]
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elif statement == "CF":
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vals = ["Net Income", dcf_model.gaap_cf]
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if statement == "BS":
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if "Cash & Equivalents" in df.index:
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blank_list = ["0" for x in df.loc["Cash & Equivalents"].to_list()]
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else:
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raise ValueError("Dataframe does not have key information.")
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for i, value in enumerate(dcf_model.gaap_bs[1:]):
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df = dcf_model.insert_row(
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dcf_model.gaap_bs[i + 1], dcf_model.gaap_bs[i], df, blank_list
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)
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if statement == "CF":
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if "Net Income" in df.index:
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blank_list = ["0" for x in df.loc["Net Income"].to_list()]
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else:
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raise ValueError("Dataframe does not have key information.")
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for i, value in enumerate(dcf_model.gaap_cf[1:]):
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df = dcf_model.insert_row(
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dcf_model.gaap_cf[i + 1], dcf_model.gaap_cf[i], df, blank_list
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)
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if vals[0] in df.index:
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blank_list = ["0" for _ in df.loc[vals[0]].to_list()]
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else:
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raise ValueError("Dataframe does not have key information.")
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for i, _ in enumerate(vals[1][1:]):
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df = dcf_model.insert_row(vals[1][i + 1], vals[1][i], df, blank_list)
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rowI = row + 1
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names = df.index.values.tolist()
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@@ -618,195 +612,265 @@ class CreateExcelFA:
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self.ws4.column_dimensions["A"].width = 27
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dcf_model.set_cell(self.ws4, "B4", "Sector:")
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dcf_model.set_cell(self.ws4, "C4", self.info["sector"])
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dcf_model.set_cell(
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self.ws4,
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"A6",
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"Liquidity Ratios",
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border=dcf_model.thin_border,
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font=dcf_model.bold_font,
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)
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dcf_model.set_cell(self.ws4, "A7", "Current Ratio")
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dcf_model.set_cell(self.ws4, "A8", "Quick Ratio")
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dcf_model.set_cell(self.ws4, "A10", "Activity Ratios", font=dcf_model.bold_font)
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dcf_model.set_cell(self.ws4, "A11", "AR Turnover")
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dcf_model.set_cell(self.ws4, "A12", "Days Sales in AR")
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dcf_model.set_cell(self.ws4, "A13", "Inventory Turnover")
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dcf_model.set_cell(self.ws4, "A14", "Days in Inventory")
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dcf_model.set_cell(self.ws4, "A15", "Average Payable Turnover")
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dcf_model.set_cell(self.ws4, "A16", "Days of Payables Outstanding")
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dcf_model.set_cell(self.ws4, "A17", "Cash Conversion Cycle")
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dcf_model.set_cell(self.ws4, "A18", "Asset Turnover")
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dcf_model.set_cell(
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self.ws4,
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"A20",
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"Profitability Ratios",
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border=dcf_model.thin_border,
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font=dcf_model.bold_font,
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)
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dcf_model.set_cell(self.ws4, "A21", "Profit Margin")
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dcf_model.set_cell(self.ws4, "A22", "Return on Assets")
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dcf_model.set_cell(self.ws4, "A23", "Return on Equity")
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dcf_model.set_cell(self.ws4, "A24", "Return on Sales")
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dcf_model.set_cell(self.ws4, "A25", "Gross Margin")
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dcf_model.set_cell(self.ws4, "A26", "Operating Cash Flow Ratio")
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dcf_model.set_cell(
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self.ws4,
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"A28",
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"Coverage Ratios",
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border=dcf_model.thin_border,
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font=dcf_model.bold_font,
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)
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dcf_model.set_cell(self.ws4, "A29", "Debt-to-Equity")
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dcf_model.set_cell(self.ws4, "A30", "Total Debt Ratio")
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dcf_model.set_cell(self.ws4, "A31", "Equity Multiplier")
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dcf_model.set_cell(self.ws4, "A32", "Times Interest Earned")
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dcf_model.set_cell(
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self.ws4,
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"A34",
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"Investor Ratios",
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border=dcf_model.thin_border,
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font=dcf_model.bold_font,
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)
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dcf_model.set_cell(self.ws4, "A35", "Earnings Per Share")
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dcf_model.set_cell(self.ws4, "A36", "Price Earnings Ratio")
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for i in range(len(self.df_bs.columns) - 1):
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lt = dcf_model.letters[i + 1]
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cace1 = float(
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self.df_bs.at[
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"Cash & Cash Equivalents", self.df_bs.columns[i + 1]
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].replace(",", "")
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)
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ar0 = float(
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self.df_bs.at["Receivables", self.df_bs.columns[i]].replace(",", "")
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)
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ar1 = float(
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self.df_bs.at["Receivables", self.df_bs.columns[i + 1]].replace(",", "")
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)
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inv0 = float(
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self.df_bs.at["Inventory", self.df_bs.columns[i]].replace(",", "")
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)
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inv1 = float(
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self.df_bs.at["Inventory", self.df_bs.columns[i + 1]].replace(",", "")
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)
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ca1 = float(
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self.df_bs.at[
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"Total Current Assets", self.df_bs.columns[i + 1]
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].replace(",", "")
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)
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ta0 = float(
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self.df_bs.at["Total Assets", self.df_bs.columns[i]].replace(",", "")
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)
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ta1 = float(
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self.df_bs.at["Total Assets", self.df_bs.columns[i + 1]].replace(
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",", ""
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)
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)
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ap0 = float(
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self.df_bs.at["Accounts Payable", self.df_bs.columns[i]].replace(
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",", ""
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)
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)
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ap1 = float(
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self.df_bs.at["Accounts Payable", self.df_bs.columns[i + 1]].replace(
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",", ""
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)
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)
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cl1 = float(
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self.df_bs.at[
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"Total Current Liabilities", self.df_bs.columns[i + 1]
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].replace(",", "")
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)
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tl1 = float(
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self.df_bs.at["Total Liabilities", self.df_bs.columns[i + 1]].replace(
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",", ""
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)
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)
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te0 = float(
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self.df_bs.at["Shareholders' Equity", self.df_bs.columns[i]].replace(
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",", ""
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)
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)
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te1 = float(
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self.df_bs.at[
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"Shareholders' Equity", self.df_bs.columns[i + 1]
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].replace(",", "")
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)
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sls1 = float(
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self.df_is.at["Revenue", self.df_bs.columns[i + 1]].replace(",", "")
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)
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cogs1 = float(
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self.df_is.at["Cost of Revenue", self.df_bs.columns[i + 1]].replace(
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",", ""
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)
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)
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inte1 = float(
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self.df_is.at[
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"Interest Expense / Income", self.df_bs.columns[i + 1]
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].replace(",", "")
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)
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tax1 = float(
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self.df_is.at["Income Tax", self.df_bs.columns[i + 1]].replace(",", "")
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)
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ni1 = float(
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self.df_is.at["Net Income", self.df_bs.columns[i + 1]].replace(",", "")
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)
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pdiv1 = float(
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self.df_is.at["Preferred Dividends", self.df_bs.columns[i + 1]].replace(
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",", ""
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)
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)
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opcf1 = float(
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self.df_cf.at["Operating Cash Flow", self.df_bs.columns[i + 1]].replace(
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",", ""
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)
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)
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self.get_sister_dfs()
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self.sister_data.insert(0, [self.ticker, [self.df_bs, self.df_is, self.df_cf]])
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row = 6
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for i in self.sister_data:
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self.ws4.merge_cells(f"A{row}:J{row}")
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dcf_model.set_cell(self.ws4, f"A{row}", i[0], alignment=dcf_model.center)
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dcf_model.set_cell(
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self.ws4,
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f"{lt}6",
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int(self.df_bs.columns[i + 1]),
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f"A{row+1}",
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"Liquidity Ratios",
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border=dcf_model.thin_border,
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font=dcf_model.bold_font,
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)
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dcf_model.set_cell(self.ws4, f"{lt}7", ca1 / cl1)
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dcf_model.set_cell(self.ws4, f"{lt}8", (cace1 + ar1) / cl1)
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dcf_model.set_cell(self.ws4, f"{lt}11", sls1 / ((ar0 + ar1) / 2))
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dcf_model.set_cell(self.ws4, f"{lt}12", ar1 / (sls1 / 365))
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dcf_model.set_cell(self.ws4, f"{lt}13", cogs1 / ((inv0 + inv1) / 2))
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dcf_model.set_cell(self.ws4, f"{lt}14", inv1 / (cogs1 / 365))
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dcf_model.set_cell(self.ws4, f"{lt}15", cogs1 / ((ap0 + ap1) / 2))
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dcf_model.set_cell(self.ws4, f"{lt}16", ap1 / (cogs1 / 365))
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dcf_model.set_cell(self.ws4, f"A{row+2}", "Current Ratio")
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dcf_model.set_cell(self.ws4, f"A{row+3}", "Quick Ratio")
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dcf_model.set_cell(
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self.ws4,
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f"{lt}17",
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(ar1 / (sls1 / 365)) + (inv1 / (cogs1 / 365)) - (ap1 / (cogs1 / 365)),
|
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f"A{row+5}",
|
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"Activity Ratios",
|
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border=dcf_model.thin_border,
|
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font=dcf_model.bold_font,
|
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)
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dcf_model.set_cell(self.ws4, f"{lt}18", sls1 / ((ta0 + ta1) / 2))
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dcf_model.set_cell(self.ws4, f"{lt}21", ni1 / sls1)
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dcf_model.set_cell(self.ws4, f"{lt}22", ni1 / ((ar0 + ar1) / 2))
|
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dcf_model.set_cell(self.ws4, f"{lt}23", ni1 / ((te0 + te1) / 2))
|
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dcf_model.set_cell(self.ws4, f"{lt}24", (ni1 + inte1 + tax1) / sls1)
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dcf_model.set_cell(self.ws4, f"{lt}25", (sls1 - cogs1) / sls1)
|
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dcf_model.set_cell(self.ws4, f"{lt}26", opcf1 / cl1)
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dcf_model.set_cell(self.ws4, f"{lt}29", tl1 / te1)
|
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dcf_model.set_cell(self.ws4, f"{lt}30", tl1 / ta1)
|
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dcf_model.set_cell(self.ws4, f"{lt}31", ta1 / te1)
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if inte1 == 0:
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dcf_model.set_cell(self.ws4, f"{lt}32", "N/A")
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else:
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dcf_model.set_cell(self.ws4, f"{lt}32", (ni1 + inte1 + tax1) / inte1)
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dcf_model.set_cell(self.ws4, f"A{row+6}", "AR Turnover")
|
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dcf_model.set_cell(self.ws4, f"A{row+7}", "Days Sales in AR")
|
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dcf_model.set_cell(self.ws4, f"A{row+8}", "Inventory Turnover")
|
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dcf_model.set_cell(self.ws4, f"A{row+9}", "Days in Inventory")
|
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dcf_model.set_cell(self.ws4, f"A{row+10}", "Average Payable Turnover")
|
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dcf_model.set_cell(self.ws4, f"A{row+11}", "Days of Payables Outstanding")
|
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dcf_model.set_cell(self.ws4, f"A{row+12}", "Cash Conversion Cycle")
|
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dcf_model.set_cell(self.ws4, f"A{row+13}", "Asset Turnover")
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dcf_model.set_cell(
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self.ws4,
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f"{lt}35",
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(ni1 - pdiv1) / float(self.info["sharesOutstanding"]),
|
||||
f"A{row+15}",
|
||||
"Profitability Ratios",
|
||||
border=dcf_model.thin_border,
|
||||
font=dcf_model.bold_font,
|
||||
)
|
||||
dcf_model.set_cell(self.ws4, f"A{row+16}", "Profit Margin")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+17}", "Return on Assets")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+18}", "Return on Equity")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+19}", "Return on Sales")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+20}", "Gross Margin")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+21}", "Operating Cash Flow Ratio")
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}36",
|
||||
float(self.info["previousClose"])
|
||||
/ ((ni1 - pdiv1) / float(self.info["sharesOutstanding"])),
|
||||
f"A{row+23}",
|
||||
"Coverage Ratios",
|
||||
border=dcf_model.thin_border,
|
||||
font=dcf_model.bold_font,
|
||||
)
|
||||
dcf_model.set_cell(self.ws4, f"A{row+24}", "Debt-to-Equity")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+25}", "Total Debt Ratio")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+26}", "Equity Multiplier")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+27}", "Times Interest Earned")
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"A{row+29}",
|
||||
"Investor Ratios",
|
||||
border=dcf_model.thin_border,
|
||||
font=dcf_model.bold_font,
|
||||
)
|
||||
dcf_model.set_cell(self.ws4, f"A{row+30}", "Earnings Per Share")
|
||||
dcf_model.set_cell(self.ws4, f"A{row+31}", "Price Earnings Ratio")
|
||||
for j in range(len(self.df_bs.columns) - 1):
|
||||
lt = dcf_model.letters[j + 1]
|
||||
|
||||
cace1 = float(
|
||||
i[1][0]
|
||||
.at["Cash & Cash Equivalents", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
ar0 = float(
|
||||
i[1][0].at["Receivables", i[1][0].columns[j]].replace(",", "")
|
||||
)
|
||||
ar1 = float(
|
||||
i[1][0].at["Receivables", i[1][0].columns[j + 1]].replace(",", "")
|
||||
)
|
||||
inv0 = float(
|
||||
i[1][0].at["Inventory", i[1][0].columns[j]].replace(",", "")
|
||||
)
|
||||
inv1 = float(
|
||||
i[1][0].at["Inventory", i[1][0].columns[j + 1]].replace(",", "")
|
||||
)
|
||||
ca1 = float(
|
||||
i[1][0]
|
||||
.at["Total Current Assets", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
ta0 = float(
|
||||
i[1][0].at["Total Assets", i[1][0].columns[j]].replace(",", "")
|
||||
)
|
||||
ta1 = float(
|
||||
i[1][0].at["Total Assets", i[1][0].columns[j + 1]].replace(",", "")
|
||||
)
|
||||
ap0 = float(
|
||||
i[1][0].at["Accounts Payable", i[1][0].columns[j]].replace(",", "")
|
||||
)
|
||||
ap1 = float(
|
||||
i[1][0]
|
||||
.at["Accounts Payable", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
cl1 = float(
|
||||
i[1][0]
|
||||
.at["Total Current Liabilities", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
tl1 = float(
|
||||
i[1][0]
|
||||
.at["Total Liabilities", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
te0 = float(
|
||||
i[1][0]
|
||||
.at["Shareholders' Equity", i[1][0].columns[j]]
|
||||
.replace(",", "")
|
||||
)
|
||||
te1 = float(
|
||||
i[1][0]
|
||||
.at["Shareholders' Equity", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
sls1 = float(
|
||||
i[1][1].at["Revenue", i[1][1].columns[j + 1]].replace(",", "")
|
||||
)
|
||||
cogs1 = float(
|
||||
i[1][1]
|
||||
.at["Cost of Revenue", i[1][1].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
inte1 = float(
|
||||
i[1][1]
|
||||
.at["Interest Expense / Income", i[1][1].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
tax1 = float(
|
||||
i[1][1].at["Income Tax", i[1][1].columns[j + 1]].replace(",", "")
|
||||
)
|
||||
ni1 = float(
|
||||
i[1][1].at["Net Income", i[1][1].columns[j + 1]].replace(",", "")
|
||||
)
|
||||
pdiv1 = float(
|
||||
i[1][1]
|
||||
.at["Preferred Dividends", i[1][0].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
opcf1 = float(
|
||||
i[1][2]
|
||||
.at["Operating Cash Flow", i[1][2].columns[j + 1]]
|
||||
.replace(",", "")
|
||||
)
|
||||
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+1}",
|
||||
int(i[1][0].columns[j + 1]),
|
||||
font=dcf_model.bold_font,
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+2}", "N/A" if cl1 == 0 else ca1 / cl1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+3}", "N/A" if cl1 == 0 else (cace1 + ar1) / cl1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+6}",
|
||||
"N/A" if ar0 + ar1 == 0 else sls1 / ((ar0 + ar1) / 2),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+7}", "N/A" if sls1 == 0 else ar1 / (sls1 / 365)
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+8}",
|
||||
"N/A" if inv0 + inv1 == 0 else cogs1 / ((inv0 + inv1) / 2),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+9}",
|
||||
"N/A" if cogs1 == 0 else inv1 / (cogs1 / 365),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+10}",
|
||||
"N/A" if ap0 + ap1 == 0 else cogs1 / ((ap0 + ap1) / 2),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+11}",
|
||||
"N/A" if cogs1 == 0 else ap1 / (cogs1 / 365),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+12}",
|
||||
"N/A"
|
||||
if cogs1 == 0
|
||||
else (ar1 / (sls1 / 365))
|
||||
+ (inv1 / (cogs1 / 365))
|
||||
- (ap1 / (cogs1 / 365)),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+13}",
|
||||
"N/A" if ta0 + ta1 == 0 else sls1 / ((ta0 + ta1) / 2),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+16}", "N/A" if sls1 == 0 else ni1 / sls1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+17}",
|
||||
"N/A" if ar0 + ar1 == 0 else ni1 / ((ar0 + ar1) / 2),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+18}",
|
||||
"N/A" if te0 + te1 == 0 else ni1 / ((te0 + te1) / 2),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+19}",
|
||||
"N/A" if sls1 == 0 else (ni1 + inte1 + tax1) / sls1,
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+20}",
|
||||
"N/A" if sls1 == 0 else (sls1 - cogs1) / sls1,
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+21}", "N/A" if cl1 == 0 else opcf1 / cl1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+24}", "N/A" if te1 == 0 else tl1 / te1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+25}", "N/A" if ta1 == 0 else tl1 / ta1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4, f"{lt}{row+26}", "N/A" if te1 == 0 else ta1 / te1
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+27}",
|
||||
"N/A" if inte1 == 0 else (ni1 + inte1 + tax1) / inte1,
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+30}",
|
||||
(ni1 - pdiv1) / float(self.info["sharesOutstanding"]),
|
||||
)
|
||||
dcf_model.set_cell(
|
||||
self.ws4,
|
||||
f"{lt}{row+31}",
|
||||
float(self.info["previousClose"])
|
||||
/ ((ni1 - pdiv1) / float(self.info["sharesOutstanding"])),
|
||||
)
|
||||
row += 35
|
||||
|
||||
def create_header(self, ws: Workbook):
|
||||
for i in range(10):
|
||||
@@ -1138,3 +1202,100 @@ class CreateExcelFA:
|
||||
)
|
||||
dcf_model.set_cell(self.ws3, f"B{self.letter+4}", text)
|
||||
self.letter += 1
|
||||
|
||||
def get_sister_dfs(self):
|
||||
# Once mcap is added to this, we can add as an additional filters for more comparative results
|
||||
sisters = self.sisters
|
||||
random.shuffle(sisters)
|
||||
i = 0
|
||||
new_list = []
|
||||
while i < 3 and sisters:
|
||||
try:
|
||||
vals = [
|
||||
sisters[0],
|
||||
[self.get_sister_data(x, sisters[0]) for x in ["BS", "IS", "CF"]],
|
||||
]
|
||||
new_list.append(vals)
|
||||
i += 1
|
||||
sisters.pop(0)
|
||||
except ValueError:
|
||||
sisters.pop(0)
|
||||
self.sister_data = new_list
|
||||
|
||||
def get_sister_data(self, statement: str, ticker: str) -> pd.DataFrame:
|
||||
URL = f"https://stockanalysis.com/stocks/{ticker}/financials/"
|
||||
if statement == "BS":
|
||||
URL += "balance-sheet/"
|
||||
ignores = dcf_model.non_gaap_bs
|
||||
if statement == "CF":
|
||||
URL += "cash-flow-statement/"
|
||||
ignores = dcf_model.non_gaap_cf
|
||||
if statement == "IS":
|
||||
ignores = dcf_model.non_gaap_is
|
||||
|
||||
r = requests.get(URL, headers=dcf_model.headers)
|
||||
|
||||
if "404 - Page Not Found" in r.text:
|
||||
raise ValueError("The ticker given is not in the stock analysis website.")
|
||||
soup = BeautifulSoup(r.content, "html.parser")
|
||||
|
||||
table = soup.find(
|
||||
"table", attrs={"class": "FinancialTable_table_financial__1RhYq"}
|
||||
)
|
||||
head = table.find("thead")
|
||||
if head is None:
|
||||
raise ValueError("Incorrect website format")
|
||||
columns = head.find_all("th")
|
||||
|
||||
if self.years == []:
|
||||
self.years = [x.get_text().strip() for x in columns]
|
||||
self.len_data = len(self.years) - 1
|
||||
|
||||
if self.rounding == 0:
|
||||
phrase = soup.find(
|
||||
"div", attrs={"class": "text-sm pb-1 text-gray-600"}
|
||||
).get_text()
|
||||
if "thousand" in phrase:
|
||||
self.rounding = 1_000
|
||||
elif "millions" in phrase:
|
||||
self.rounding = 1_000_000
|
||||
elif "billions" in phrase:
|
||||
self.rounding = 1_000_000_000
|
||||
else:
|
||||
raise ValueError(
|
||||
"Stock Analysis did not specify a proper rounding amount"
|
||||
)
|
||||
|
||||
body = table.find("tbody")
|
||||
rows = body.find_all("tr")
|
||||
|
||||
all_data = [[x.get_text().strip() for x in y.find_all("td")] for y in rows]
|
||||
|
||||
df = pd.DataFrame(data=all_data)
|
||||
df = df.set_index(0)
|
||||
n = df.shape[1] - self.len_data
|
||||
if n > 0:
|
||||
df = df.iloc[:, :-n]
|
||||
|
||||
df.columns = self.years[1:]
|
||||
|
||||
for ignore in ignores:
|
||||
if ignore in df.index:
|
||||
df = df.drop([ignore])
|
||||
df = df[df.columns[::-1]]
|
||||
|
||||
if statement == "IS":
|
||||
vals = ["Revenue", dcf_model.gaap_is]
|
||||
elif statement == "BS":
|
||||
vals = ["Cash & Equivalents", dcf_model.gaap_bs]
|
||||
elif statement == "CF":
|
||||
vals = ["Net Income", dcf_model.gaap_cf]
|
||||
|
||||
if vals[0] in df.index:
|
||||
blank_list = ["0" for _ in df.loc[vals[0]].to_list()]
|
||||
else:
|
||||
raise ValueError("Dataframe does not have key information.")
|
||||
for i, _ in enumerate(vals[1][1:]):
|
||||
df = dcf_model.insert_row(vals[1][i + 1], vals[1][i], df, blank_list)
|
||||
|
||||
return df
|
||||
|
||||
@@ -744,7 +744,7 @@ Other Sources:
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="dcf",
|
||||
description="""
|
||||
Generates a completed discounted cash flow statement. The statement uses machine
|
||||
Generates a discounted cash flow statement. The statement uses machine
|
||||
learning to predict the future financial statement, and then predicts the future
|
||||
value of the stock based on the predicted financials.""",
|
||||
)
|
||||
|
||||
11463
poetry.lock
generated
vendored
11463
poetry.lock
generated
vendored
File diff suppressed because it is too large
Load Diff
224
pyproject.toml
vendored
224
pyproject.toml
vendored
@@ -1,112 +1,112 @@
|
||||
# https://python-poetry.org/docs/dependency-specification/
|
||||
[tool.poetry]
|
||||
name = "GamestonkTerminal"
|
||||
version = "0.1.0"
|
||||
description = ""
|
||||
authors = ["Didier Rodrigues Lopes"]
|
||||
packages = [
|
||||
{ include = "gamestonk_terminal" },
|
||||
]
|
||||
|
||||
[tool.poetry.scripts]
|
||||
gst = 'terminal:terminal'
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.7"
|
||||
pandas = "^1.1.5,<1.2"
|
||||
iso8601 = "^0.1.14"
|
||||
FundamentalAnalysis = "^0.2.6"
|
||||
requests = "^2.25.1"
|
||||
alpha-vantage = "^2.3.1"
|
||||
finviz = "^1.3.4"
|
||||
bs4 = "^0.0.1"
|
||||
rapidfuzz = "^1.1.1"
|
||||
yfinance = "^0.1.63"
|
||||
pandas-ta = "^0.2.23b0"
|
||||
prettytable = "^2.0.0"
|
||||
psaw = "^0.0.12"
|
||||
praw = "^7.1.4"
|
||||
Quandl = "^3.6.0"
|
||||
pytrends = "^4.7.3"
|
||||
timeseries-cv = "0.1.4"
|
||||
matplotlib = "^3.3.3"
|
||||
plotly = "^4.14.3"
|
||||
colorama = "^0.4.4"
|
||||
papermill = "^2.3.2"
|
||||
pmdarima = "1.8.3"
|
||||
holidays = "^0.10"
|
||||
prompt-toolkit = "^3.0.16"
|
||||
jupyter = "^1.0.0"
|
||||
jupyterlab = "^3.1.10"
|
||||
mplfinance = "^0.12.7-alpha.7"
|
||||
seaborn = "^0.11.0"
|
||||
scipy = "~1.5.4"
|
||||
fredapi = "^0.4.3"
|
||||
screeninfo = "^0.6.7"
|
||||
numpy = "~1.19.5,<1.20"
|
||||
pyrsistent = "~0.14.11"
|
||||
regex = "~2020.11.13"
|
||||
robin-stocks = "^2.0.3"
|
||||
termcolor = "^1.1.0"
|
||||
alpaca-trade-api = "^1.0.1"
|
||||
pyally = "^1.1.2"
|
||||
pycoingecko = "^1.4.0"
|
||||
detecta = "^0.0.5"
|
||||
tradingview-ta = "^3.2.3"
|
||||
finvizfinance = "^0.9.3"
|
||||
Pillow = "^8.2.0"
|
||||
statsmodels = "^0.12.2"
|
||||
tabulate = "^0.8.9"
|
||||
pyportfolioopt = "^1.4.1"
|
||||
selenium = "^3.141.0"
|
||||
python-coinmarketcap = "^0.2"
|
||||
oandapyV20 = "^0.6.3"
|
||||
valinvest = "^0.0.2"
|
||||
bt = "^0.2.9"
|
||||
python-binance = "^1.0.1"
|
||||
pyupgrade = "2.18.2"
|
||||
cvxpy = "1.1.12"
|
||||
jupyter-client = "6.1.12"
|
||||
vaderSentiment = "3.3.2"
|
||||
degiro-connector = "1.0.0"
|
||||
mypy = "^0.901"
|
||||
GitPython = "^3.1.17"
|
||||
fear-greed-index = "^0.1.3"
|
||||
pyEX = "^0.5.0"
|
||||
charset-normalizer = "2.0.1"
|
||||
sentiment-investor = "^2.0.0"
|
||||
fred = "^3.1"
|
||||
openpyxl = "3.0.7"
|
||||
vcrpy = "^4.1.1"
|
||||
coverage = "^5.5"
|
||||
tensorflow = {version = "2.6.0", optional = true}
|
||||
jsonschema = "3.2.0"
|
||||
thepassiveinvestor = "1.0.9"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest = "^6.2.2"
|
||||
pylint = "^2.7.2"
|
||||
flake8 = "^3.9.0"
|
||||
mypy = "^0.901"
|
||||
mock = "^4.0.3"
|
||||
codespell = "^2.0.0"
|
||||
pyupgrade = "^2.11.0"
|
||||
Sphinx = "4.1.1"
|
||||
black = "21.7b0"
|
||||
|
||||
[tool.poetry.extras]
|
||||
prediction = ["tensorflow"]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
# Notes for developers:
|
||||
# 1. numpy >= 1.20.0 sometimes has issues with tensorflow=2.4.1
|
||||
# 2. The following packages dropped support for python<3.7:
|
||||
# pandas >= 1.2.0
|
||||
# numpy >= 1.20.0
|
||||
# scipy >= 1.6.0
|
||||
# 3. The following often cause poetry to misbehave:
|
||||
# pyrsistent >= 0.17
|
||||
# regex >= 2021
|
||||
# https://python-poetry.org/docs/dependency-specification/
|
||||
[tool.poetry]
|
||||
name = "GamestonkTerminal"
|
||||
version = "0.1.0"
|
||||
description = ""
|
||||
authors = ["Didier Rodrigues Lopes"]
|
||||
packages = [
|
||||
{ include = "gamestonk_terminal" },
|
||||
]
|
||||
|
||||
[tool.poetry.scripts]
|
||||
gst = 'terminal:terminal'
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.7"
|
||||
pandas = "^1.1.5,<1.2"
|
||||
iso8601 = "^0.1.14"
|
||||
FundamentalAnalysis = "^0.2.6"
|
||||
requests = "^2.25.1"
|
||||
alpha-vantage = "^2.3.1"
|
||||
finviz = "^1.3.4"
|
||||
bs4 = "^0.0.1"
|
||||
rapidfuzz = "^1.1.1"
|
||||
yfinance = "^0.1.63"
|
||||
pandas-ta = "^0.2.23b0"
|
||||
prettytable = "^2.0.0"
|
||||
psaw = "^0.0.12"
|
||||
praw = "^7.1.4"
|
||||
Quandl = "^3.6.0"
|
||||
pytrends = "^4.7.3"
|
||||
timeseries-cv = "0.1.4"
|
||||
matplotlib = "^3.3.3"
|
||||
plotly = "^4.14.3"
|
||||
colorama = "^0.4.4"
|
||||
papermill = "^2.3.2"
|
||||
pmdarima = "1.8.3"
|
||||
holidays = "^0.10"
|
||||
prompt-toolkit = "^3.0.16"
|
||||
jupyter = "^1.0.0"
|
||||
jupyterlab = "^3.1.10"
|
||||
mplfinance = "^0.12.7-alpha.7"
|
||||
seaborn = "^0.11.0"
|
||||
scipy = "~1.5.4"
|
||||
fredapi = "^0.4.3"
|
||||
screeninfo = "^0.6.7"
|
||||
numpy = "~1.19.5,<1.20"
|
||||
pyrsistent = "~0.14.11"
|
||||
regex = "~2020.11.13"
|
||||
robin-stocks = "^2.0.3"
|
||||
termcolor = "^1.1.0"
|
||||
alpaca-trade-api = "^1.0.1"
|
||||
pyally = "^1.1.2"
|
||||
pycoingecko = "^1.4.0"
|
||||
detecta = "^0.0.5"
|
||||
tradingview-ta = "^3.2.3"
|
||||
finvizfinance = "^0.9.3"
|
||||
Pillow = "^8.2.0"
|
||||
statsmodels = "^0.12.2"
|
||||
tabulate = "^0.8.9"
|
||||
pyportfolioopt = "^1.4.1"
|
||||
selenium = "^3.141.0"
|
||||
python-coinmarketcap = "^0.2"
|
||||
oandapyV20 = "^0.6.3"
|
||||
valinvest = "^0.0.2"
|
||||
bt = "^0.2.9"
|
||||
python-binance = "^1.0.1"
|
||||
pyupgrade = "2.18.2"
|
||||
cvxpy = "1.1.12"
|
||||
jupyter-client = "6.1.12"
|
||||
vaderSentiment = "3.3.2"
|
||||
degiro-connector = "1.0.0"
|
||||
mypy = "^0.901"
|
||||
GitPython = "^3.1.17"
|
||||
fear-greed-index = "^0.1.3"
|
||||
pyEX = "^0.5.0"
|
||||
charset-normalizer = "2.0.1"
|
||||
sentiment-investor = "^2.0.0"
|
||||
fred = "^3.1"
|
||||
openpyxl = "3.0.7"
|
||||
vcrpy = "^4.1.1"
|
||||
coverage = "^5.5"
|
||||
tensorflow = {version = "2.6.0", optional = true}
|
||||
jsonschema = "3.2.0"
|
||||
FinanceDatabase = "^0.1.11"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest = "^6.2.2"
|
||||
pylint = "^2.7.2"
|
||||
flake8 = "^3.9.0"
|
||||
mypy = "^0.901"
|
||||
mock = "^4.0.3"
|
||||
codespell = "^2.0.0"
|
||||
pyupgrade = "^2.11.0"
|
||||
Sphinx = "4.1.1"
|
||||
black = "21.7b0"
|
||||
|
||||
[tool.poetry.extras]
|
||||
prediction = ["tensorflow"]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
# Notes for developers:
|
||||
# 1. numpy >= 1.20.0 sometimes has issues with tensorflow=2.4.1
|
||||
# 2. The following packages dropped support for python<3.7:
|
||||
# pandas >= 1.2.0
|
||||
# numpy >= 1.20.0
|
||||
# scipy >= 1.6.0
|
||||
# 3. The following often cause poetry to misbehave:
|
||||
# pyrsistent >= 0.17
|
||||
# regex >= 2021
|
||||
|
||||
1
requirements-full.txt
vendored
1
requirements-full.txt
vendored
@@ -50,6 +50,7 @@ entrypoints==0.3; python_version >= "3.7"
|
||||
et-xmlfile==1.1.0; python_version >= "3.6"
|
||||
fear-greed-index==0.1.3; python_full_version >= "3.6.8" and python_full_version < "4.0.0"
|
||||
ffn==0.3.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
|
||||
financedatabase==0.1.11
|
||||
finviz==1.4.2
|
||||
finvizfinance==0.9.10; python_version >= "3.5"
|
||||
flatbuffers==1.12
|
||||
|
||||
1
requirements.txt
vendored
1
requirements.txt
vendored
@@ -45,6 +45,7 @@ entrypoints==0.3; python_version >= "3.7"
|
||||
et-xmlfile==1.1.0; python_version >= "3.6"
|
||||
fear-greed-index==0.1.3; python_full_version >= "3.6.8" and python_full_version < "4.0.0"
|
||||
ffn==0.3.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
|
||||
financedatabase==0.1.11
|
||||
finviz==1.4.2
|
||||
finvizfinance==0.9.10; python_version >= "3.5"
|
||||
fred==3.1
|
||||
|
||||
Reference in New Issue
Block a user