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MIRMT.code-workspace

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+{
+	"folders": [
+		{
+			"uri": "vscode-remote://codespaces+cuddly-space-doodle-rxrj99x7p6vf5v5w/workspaces/FinRisk"
+		}
+	],
+	"remoteAuthority": "codespaces+cuddly-space-doodle-rxrj99x7p6vf5v5w",
+	"settings": {}
+}

MindInventory-Financial-Risk-Analysis/LICENSE

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+MIT License
+
+Copyright (c) 2024 Samar Patel
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+![cover_photo](./readmefile/Cover.png)
+
+# MindInventory - Portfolio & Risk Management System
+*FinRisk allows you to build a custom portfolio and assess its risk within 
+few clicks!*
+
+*Portfolio Risk management is like wearing a helmet while riding a bike—it 
+shields your money during investments. For average investors, it's hard to 
+understand the risk of their portfolio, FinRisk is here to help.*
+
+## Core Features
+### Real Time Market Condition Previewer
+Quick Glimpse of the Financial Frenzy! The market preview includes current
+market status of Dow Jones, S&P 500, Nasdaq, and Russell 2000 index. Hold onto 
+your seat as we zoom into the exciting world of the top 8 tech and meme stocks! 
+🚀🎢 Get the scoop on current prices, price changes, and the percentage shake-ups.
+![market_preview](./readmefile/market_preview.png)
+### Portfolio Builder
+Dream big and curate a portfolio that reflects your financial aspirations. You 
+have the power to choose up to 10 stocks!For each stock in your portfolio, some 
+fundamental information required: stock tickers, the specific quantity of shares, 
+and the record of purchase dates.
+![portfolio_builder](./readmefile/portfolio_builder.png)
+
+### Portfolio Risk Simulator
+The portfolio risk simulator is a powerful tool that allows you to assess your portfolio's risk using
+Monte Carlo Simulation. The simulation unravels crucial metrics: Value at Risk (VaR) and Conditional Value 
+at Risk (CVaR). VaR adn CVaR are two of the most popular risk metrics used by financial institutions to 
+measure the risk of their portfolios. Simulation can be tailored to your needs by adjusting the number 
+of simulations, historical data of your selected stocks, the confidence levels (alpha). The simulation 
+will also generate a line chart for your portfolio's daily returns. All data can be downloaded as a CSV file for
+further analysis.
+![portfolio_risk_simulator](./readmefile/risk_model.png)
+## How to Use
+👉 click [MRMST](https://mrmst.streamlit.app/) to launch the app!

assets/Collector.py

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+import yfinance
+import pandas as pd
+
+
+class InfoCollector:
+
+    @staticmethod
+    def get_ticker(stock_name: str) -> yfinance.Ticker:
+        return yfinance.Ticker(stock_name)
+
+    @staticmethod
+    def get_history(ticker: yfinance.Ticker,
+                    period="1mo", interval="1d",
+                    start=None, end=None):
+        """
+         period : str
+            Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
+            Either Use period parameter or use start and end
+        interval : str
+            Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
+            Intraday data cannot extend last 60 days
+        start: str
+            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
+            Default is 99 years ago
+            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
+        end: str
+            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
+            Default is now
+            E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
+        """
+        return ticker.history(period=period, interval=interval,
+                              start=start, end=end)
+
+    @staticmethod
+    def get_demo_daily_history(interval: str):
+        return InfoCollector.get_history(
+            ticker=yfinance.Ticker("AAPL"),
+            period="1d",
+            interval=interval,
+            start="2023-11-15",
+            end="2023-11-16")
+
+    @staticmethod
+    def get_prev_date(stock_info: pd.DataFrame):
+        return stock_info.index[0]
+
+    @staticmethod
+    def get_daily_info(stock_info: pd.DataFrame, key_info: str):
+        return stock_info.loc[stock_info.index[0], key_info]
+
+    @staticmethod
+    def download_batch_history(stocks: list, start_time, end_time):
+        return yfinance.download(stocks, start=start_time, end=end_time)

assets/Portfolio.py

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+from assets.Stock import Stock
+
+
+class Portfolio:
+
+    def __init__(self) -> None:
+        self.stocks = {}
+        self.book_amount = 0
+        self.market_value = 0
+
+    def add_stock(self, stock: Stock) -> None:
+        if stock.stock_name in self.stocks.keys():
+            raise Exception("Stock included in portfolio. Please remove stock to add again")
+
+        self.stocks[stock.stock_name] = stock
+        self.book_amount += stock.get_book_cost()
+
+    def remove_stock(self, stock_name: str) -> None:
+        if stock_name not in self.stocks.keys():
+            raise Exception("Stock not in portfolio")
+        self.book_amount -= self.stocks[stock_name].get_book_cost()
+        self.stocks.pop(stock_name)
+
+    def update_market_value(self) -> None:
+        for stock in self.stocks.values():
+            stock_market_value = stock.get_market_value()
+            self.market_value += stock_market_value

assets/Stock.py

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+import datetime
+from assets.Collector import InfoCollector
+
+
+class Stock:
+
+    def __init__(self, stock_name: str):
+        self.stock_name = stock_name
+        self.ticker = InfoCollector.get_ticker(stock_name)
+        self.owned_quantity = 0
+        self.average_price = 0
+        self.previous_close = None
+        self.previous_open = None
+        self.previous_volume = None
+        self.previous_date = None
+
+        self._update_stock()
+
+    def __eq__(self, other):
+        if self.stock_name == other.stock_name:
+            return True
+        return False
+
+    def _update_stock(self) -> None:
+        """
+        Updates the stock information, used as a check function to check if
+        stock exist
+        """
+        stock_info = InfoCollector.get_history(self.ticker, period="1d")
+        if len(stock_info) == 0:
+            raise Exception("Invalid stock, enter a valid stock")
+        else:
+            self.previous_date = InfoCollector.get_prev_date(stock_info)
+            self.previous_open = InfoCollector.get_daily_info(stock_info, "Open")
+            self.previous_close = InfoCollector.get_daily_info(stock_info, "Close")
+            self.previous_volume = InfoCollector.get_daily_info(stock_info, "Volume")
+
+    def _get_purchase_price(self, purchase_date: datetime.datetime) -> float:
+        """
+        Gets the purchase price (assumed be closed price) of the stock based
+        on given date if price at given date not found, track back for 5 days,
+        thought: smart implementation might be required for caching
+        """
+        time_delta = datetime.timedelta(days=1)
+        start_date = purchase_date
+        end_date = purchase_date + time_delta
+
+        for _ in range(5):
+            info = InfoCollector.get_history(self.ticker,
+                                             start=start_date,
+                                             end=end_date)
+            if len(info) > 0:
+                purchased_price = InfoCollector.get_daily_info(info, "Close")
+                return purchased_price
+            start_date = start_date - time_delta
+            end_date = end_date - time_delta
+
+        raise Exception("Purchase price not found, please check the date or stock sticker")
+
+    def add_buy_action(self, quantity: int,
+                       purchase_date: datetime.datetime) -> None:
+        """
+        Add a purchase to the stock. Currently, do not support to add another
+        purchase to the stock
+        """
+
+        # update own quantity
+
+        self.owned_quantity += quantity
+
+        if self.average_price == 0:
+
+            self.average_price = self._get_purchase_price(purchase_date=purchase_date)
+        else:
+            cur_purchase_price = self._get_purchase_price(purchase_date=purchase_date)
+            purchase_cost = quantity * cur_purchase_price
+
+            # update average price and owned_quantity
+            total_cost = purchase_cost + cur_purchase_price
+            self.average_price = total_cost / self.owned_quantity
+
+    def get_book_cost(self) -> float:
+        if self.owned_quantity == 0:
+            raise Exception("Stock not owned, please purchase first")
+
+        if self.average_price is None:
+            raise Exception("Purchase price not found, please check the date or stock sticker")
+
+        return self.average_price * self.owned_quantity
+
+    def get_market_value(self) -> float:
+        self._update_stock()
+        if self.owned_quantity == 0:
+            raise Exception("Stock not owned, please purchase first")
+
+        if self.previous_close is None:
+            raise Exception("Stock price not found, please check the date or stock sticker")
+
+        return self.previous_close * self.owned_quantity
+
+    def get_gain_loss(self) -> float:
+        return self.get_market_value() - self.get_book_cost()
+
+    def get_pct_change(self) -> float:
+        return (self.get_gain_loss() / self.get_book_cost()) * 100

default_page.py

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+import streamlit as st
+import stTools as tools
+
+
+def load_page():
+    st.markdown(
+        """
+            Welcome to :green[MRMST]! Explore this app to assess and simulate 
+            your investment portfolio's risk effortlessly! :green[Risk management] is like 
+            :blue[wearing a helmet while riding a bike]—it shields your money during investments. 
+            It's a strategy set to understand uncertainties in stocks or bonds.
+
+            Imagine your investment journey as a game; knowing rules and setbacks gives you a competitive edge. 
+            :green[Value at Risk (VaR)] and :green[Conditional Value at Risk (CVaR)] aid in smart risk navigation, 
+            keeping your game plan robust. Don't worry, I'll explain these concepts in a bit.
+            
+            Build your :green[portfolio] on the sidebar; guidance is provided! Contact me at 
+            [LinkedIn](https://www.linkedin.com/in/samarpatel/)
+            """
+    )
+
+    st.subheader(f"Market Preview")
+
+    col_stock1, col_stock_2, col_stock_3, col_stock_4 = st.columns(4)
+
+    with col_stock1:
+        tools.create_candle_stick_plot(stock_ticker_name="^DJI",
+                                       stock_name="Dow Jones Industrial")
+
+    with col_stock_2:
+        tools.create_candle_stick_plot(stock_ticker_name="^IXIC",
+                                       stock_name="Nasdaq Composite")
+
+    with col_stock_3:
+        tools.create_candle_stick_plot(stock_ticker_name="^GSPC",
+                                       stock_name="S&P 500")
+
+    with col_stock_4:
+        tools.create_candle_stick_plot(stock_ticker_name="^RUT",
+                                       stock_name="Russell 2000")
+
+    # make 2 columns for sectors
+    col_sector1, col_sector2 = st.columns(2)
+
+    with col_sector1:
+        st.subheader(f"Tech Stocks")
+        stock_list = ["AAPL", "MSFT", "AMZN", "GOOG", "META", "TSLA", "NVDA", "AVGO"]
+        stock_name = ["Apple", "Microsoft", "Amazon", "Google", "Meta", "Tesla", "Nvidia", "Broadcom"]
+
+        df_stocks = tools.create_stocks_dataframe(stock_list, stock_name)
+        tools.create_dateframe_view(df_stocks)
+
+    with col_sector2:
+        st.subheader(f"Meme Stocks")
+        # give me a list of 8 meme stocks
+        stock_list = ["GME", "AMC", "BB", "NOK", "RIVN", "SPCE", "F", "T"]
+        stock_name = ["GameStop", "AMC Entertainment", "BlackBerry", "Nokia", "Rivian",
+                      "Virgin Galactic", "Ford", "AT&T"]
+
+        df_stocks = tools.create_stocks_dataframe(stock_list, stock_name)
+        tools.create_dateframe_view(df_stocks)

main_page.py

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+import streamlit as st
+import side_bar as comp
+import stTools as tools
+import default_page
+import portfolio_page
+import model_page
+
+st.set_page_config(
+    page_title="MindInventory Financial Risk Tool",
+    page_icon="🦈",
+    layout="wide"
+)
+
+tools.remove_white_space()
+
+st.title("MindInventory Risk Management Simulation Tool")
+
+comp.load_sidebar()
+
+if "load_portfolio_check" not in st.session_state:
+    st.session_state["load_portfolio_check"] = False
+
+if "run_simulation_check" not in st.session_state:
+    st.session_state["run_simulation_check"] = False
+
+if not st.session_state.load_portfolio_check:
+    default_page.load_page()
+
+elif not st.session_state.run_simulation_check and st.session_state.load_portfolio_check:
+    portfolio_page.load_page()
+
+elif st.session_state.run_simulation_check:
+    model_page.load_page()