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| import streamlit as st | |
| from streamlit_option_menu import option_menu | |
| import pandas as pd | |
| import yfinance as yf | |
| import plotly.express as px | |
| import datetime | |
| from datetime import date | |
| import numpy as np | |
| from dataclasses import dataclass | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| np.set_printoptions(suppress=True) | |
| # Define the commodities and their Yahoo Finance tickers | |
| commodities = { | |
| 'Gold': 'GC=F', | |
| 'Silver': 'SI=F', | |
| 'Crude Oil': 'CL=F', | |
| 'Natural Gas': 'NG=F', | |
| 'Copper': 'HG=F', | |
| 'Platinum': 'PL=F', | |
| 'Paladium': 'PA=F', | |
| 'RBOB Gasoline': 'RB=F', | |
| 'Brent Crude Oil': 'BZ=F', | |
| 'Corn Futures': 'ZC=F', | |
| 'Oat Futures': 'ZO=F', | |
| 'KC HRW Wheat Futures': 'KE=F', | |
| 'Soybean Oil Futures': 'ZL=F', | |
| 'Soybean Futures': 'ZS=F', | |
| 'Live Cattle Futures': 'LE=F', | |
| 'Cocoa': 'CC=F', | |
| 'Coffee': 'KC=F', | |
| 'Cotton': 'CT=F', | |
| 'Sugar': 'SB=F' | |
| } | |
| # Define the Class Structure | |
| class Data: | |
| collectedData: pd.DataFrame | |
| commTickers: dict | |
| years: int | |
| upperBoundLimit: float | |
| import plotly.express as px | |
| import pandas as pd | |
| import streamlit as st | |
| def plot_optimal_weights(weights: pd.DataFrame, title: str = "Weights Allocation Bar Chart"): | |
| """ | |
| Plot optimal weights using Plotly and display it on Streamlit. | |
| Parameters: | |
| - weights: pd.DataFrame containing asset names and their corresponding weights. | |
| - title: Title of the plot. | |
| """ | |
| # Ensure the DataFrame has the correct format | |
| if weights.shape[1] != 2: | |
| raise ValueError("DataFrame should have two columns: 'Tickers' and 'Weights'.") | |
| # Fill NaN weights with 0 | |
| weights['Weights'] = weights['Weights'].fillna(0) | |
| fig = px.bar(weights, | |
| x='Tickers', | |
| y='Weights', | |
| title=title, | |
| labels={"Tickers": "Assets", "Weights": "Weights"}, | |
| template='plotly_dark') | |
| fig.update_layout( | |
| width=1000, | |
| height=475, | |
| xaxis_title="Assets", | |
| yaxis_title="Weights", | |
| title_font=dict(size=24, color='white'), | |
| legend=dict( | |
| title_font=dict(size=36, color='white'), | |
| font=dict(size=28, color='white') | |
| ), | |
| plot_bgcolor='black', | |
| paper_bgcolor='black' | |
| ) | |
| fig.update_traces(marker=dict(color='blue', line=dict(color='white', width=1.5))) | |
| return fig | |
| # Function to retrieve ticker data or Stock Prices from Yahoo Finance | |
| def retrieve_tickers(): | |
| global dt | |
| if len(st.session_state.dtVariable.commTickers) <= 0: | |
| return | |
| startDate = date.today() - datetime.timedelta(days=st.session_state.dtVariable.years * 365) | |
| endDate = date.today() | |
| for com, tick in st.session_state.dtVariable.commTickers.items(): | |
| # st.write("Hello",st.session_state.dtVariable.collectedData.columns) | |
| if com not in st.session_state.dtVariable.collectedData.columns: | |
| st.session_state.dtVariable.collectedData[com] = yf.download(tickers=tick, start=startDate, end=endDate)['Adj Close'] | |
| st.session_state.dtVariable.collectedData.dropna(inplace=True) | |
| # Function to Visualize Ticker Data | |
| def visualize(data: pd.DataFrame, | |
| title: str, hoverLabel: dict, | |
| xTitle: str, | |
| yTitle: str, | |
| legendTitle: str, | |
| width: int = 1000 # Default width set to 1000, you can adjust as needed | |
| ): | |
| # Create interactive plots | |
| fig = px.line(data, | |
| title=title, | |
| labels=hoverLabel) | |
| fig.update_layout( | |
| width=width, # Set the width of the plot | |
| xaxis_title=xTitle, | |
| yaxis_title=yTitle, | |
| legend_title_text=legendTitle, | |
| template='plotly_dark', | |
| hovermode='x unified', | |
| plot_bgcolor='black', # Set plot background color to black | |
| paper_bgcolor='black', # Set paper background color to black | |
| title_font=dict(color='white'), | |
| legend=dict( | |
| title_font=dict(color='white'), # Set legend title color to white | |
| font=dict(color='white') # Set legend items color to white | |
| ) # Set title color to white | |
| ) | |
| return fig | |
| # Function to Calculate Key metrics | |
| class KeyMetrics: | |
| def __init__(self, data, logNormal=False): | |
| self.data = data | |
| self.initialWeights = np.array([1/len(self.data.columns) for _ in range(len(self.data.columns))]) | |
| self.logNormal = logNormal | |
| self.dailyReturns = None | |
| self.covarianceMatrix = None | |
| self.variance = None | |
| self.standardDeviation = None | |
| self.expectedReturns = None | |
| self.sharpeRatio = None | |
| self.negSharpeRatio = None | |
| def calculate_daily_returns(self): | |
| self.dailyReturns = np.log(self.data / self.data.shift(1)).dropna() if self.logNormal else self.data.pct_change().dropna() | |
| def calculate_covariance(self): | |
| if self.dailyReturns is None: | |
| return "Please Calculate Daily Returns First" | |
| self.covarianceMatrix = self.dailyReturns.cov() * 252 | |
| def calculate_standard_deviation(self): | |
| if self.covarianceMatrix is None: | |
| return "Please Calculate Covariance Matrix First" | |
| self.variance = self.initialWeights.T @ self.covarianceMatrix @ self.initialWeights | |
| self.standardDeviation = np.sqrt(self.variance) | |
| def calculate_expected_return(self): | |
| if self.standardDeviation is None: | |
| return "Please Calculate Standard Deviation First" | |
| self.expectedReturns = np.sum(self.dailyReturns.mean() * self.initialWeights) * 252 | |
| def calculate_sharpe_ratio(self, riskFreeRate): | |
| if self.expectedReturns is None: | |
| return "Please Calculate Expected Returns First" | |
| self.sharpeRatio = (self.expectedReturns - riskFreeRate) / self.standardDeviation | |
| def negative_sharpe_ratio(self): | |
| if self.sharpeRatio is None: | |
| return "Please Calculate Sharpe Ratio First" | |
| self.negSharpeRatio = - self.sharpeRatio | |
| # Navigation Bar | |
| def navigationBar(): | |
| # Use the following link to get whichever icon you'd like: | |
| # https://getbootstrap.com/ | |
| options = [ | |
| {"label": "Sharpe Ratio Technique", "icon": "bezier"} | |
| ] | |
| selected = option_menu( | |
| menu_title= None, #"Ask Me Anything", # Menu title | |
| options=[option["label"] for option in options], | |
| icons=[option["icon"] for option in options], | |
| menu_icon="lightbulb-fill", | |
| default_index=0, | |
| orientation="horizontal", | |
| styles={ | |
| "container": { | |
| "display": "flex", | |
| "flex-direction": "column", | |
| "justify-content": "center", | |
| "padding": "20px 40px 20px 40px", # Increased top and bottom padding | |
| "background-color": "#00E0FF", # Dark background color | |
| "border-radius": "20px", | |
| "width":"100%", | |
| "box-shadow": "0px 2px 10px rgba(0, 0, 0, 0.2)", # Shadow effect | |
| "margin": "auto", # Center align the navigation bar | |
| "overflow-x": "auto", # Allow horizontal scrolling for small screens | |
| }, | |
| "menu-title": { | |
| "font-size": "36px", | |
| "font-weight": "bold", | |
| "background-color": "#292", | |
| "color": "#FFFFFF", # White text color | |
| "margin-bottom": "20px", # Spacing below the menu title | |
| }, | |
| "menu-icon": { | |
| "color": "#FFD700", # Golden yellow icon color | |
| "font-size": "36px", | |
| "margin-right": "10px", | |
| }, | |
| "icon": { | |
| "color": "#FFD700", # Golden yellow icon color | |
| "font-size": "36px", | |
| "margin-right": "10px", | |
| }, | |
| "nav-link": { | |
| "font-size": "20px", | |
| "text-align": "center", | |
| "color": "#FFFFFF", # White text color | |
| "padding": "10px 20px", | |
| "border-radius": "15px", | |
| "transition": "background-color 0.3s ease", | |
| }, | |
| "nav-link-selected": { | |
| "background-color": "#07A2F4", # Tomato red when selected | |
| "color": "#FFFFFF", # White text color when selected | |
| } | |
| }, | |
| ) | |
| return selected |