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import pandas as pd
import numpy as np
import yfinance as yf
import streamlit as st
import plotly.graph_objects as go
list_df = pd.read_csv("Data/Company List.csv")
company_name = list_df["Name"].to_list()
company_symbol = (list_df["Ticker"] + '.NS').to_list()
company_dict = dict()
for CSymbol, CName in zip(company_symbol, company_name):
company_dict[CName] = CSymbol
com_sel_name = st.multiselect('Select Multiple Companies', company_name, default = None)
com_sel = [company_dict[i] for i in com_sel_name]
num_tick = len(com_sel)
if num_tick > 1:
com_data = yf.download(com_sel, start="2019-03-01", end="2024-03-01")['Adj Close']
com_data.dropna(inplace = True)
st.dataframe(com_data, use_container_width=True)
## Log-Return of Company Dataset
log_return = np.log(1 + com_data.pct_change())
## Generate Random Weights
rand_weig = np.array(np.random.random(num_tick))
## Rebalancing Random Weights
rebal_weig = rand_weig / np.sum(rand_weig)
## Calculate the Expected Returns, Annualize it by * 247.0
exp_ret = np.sum((log_return.mean() * rebal_weig) * 247)
## Calculate the Expected Volatility, Annualize it by * 247.0
exp_vol = np.sqrt(
np.dot(
rebal_weig.T,
np.dot(
log_return.cov() * 247,
rebal_weig
)
)
)
## Calculate the Sharpe Ratio.
sharpe_ratio = exp_ret / exp_vol
# Put the weights into a data frame to see them better.
weights_df = pd.DataFrame(data={
'company_name': com_sel_name,
'random_weights': rand_weig,
'rebalance_weights': rebal_weig
})
st.write('PORTFOLIO WEIGHTS:')
st.dataframe(weights_df, use_container_width=True)
# Do the same with the other metrics.
metrics_df = pd.DataFrame(data={
'Expected Portfolio Returns': exp_ret,
'Expected Portfolio Volatility': exp_vol,
'Portfolio Sharpe Ratio': sharpe_ratio
}, index=[0])
st.write('PORTFOLIO METRICS:')
st.dataframe(metrics_df, use_container_width=True)
## Let's get started with Monte Carlo Simulations
## How many times should we run Monte Carlo
num_of_port = 5000
## Create an Array to store the weights as they are generated
all_weights = np.zeros((num_of_port, num_tick))
## Create an Array to store the returns as they are generated
ret_arr = np.zeros(num_of_port)
## Create an Array to store the volatilities as they are generated
vol_arr = np.zeros(num_of_port)
## Create an Array to store the Sharpe Ratios as they are generated
sharpe_arr = np.zeros(num_of_port)
## Let's start the Monte Carlo Simulation
for ind in range(num_of_port):
## Let's first Calculate the Weights
weig = np.array(np.random.random(num_tick))
weig = weig / np.sum(weig)
## Append the Weights to Weigths array
all_weights[ind, :] = weig
## Calculate and Append the Expected Log Returns to Returns Array
ret_arr[ind] = np.sum((log_return.mean() * weig) * 247)
## Calculate and Append the Volatility to the Volatitlity Array
vol_arr[ind] = np.sqrt(
np.dot(weig.T, np.dot(log_return.cov() * 247, weig))
)
## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array
sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]
## Let's create a Data Frame with Weights, Returns, Volatitlity, and the Sharpe Ratio
sim_data = [ret_arr, vol_arr, sharpe_arr, all_weights]
## Create a Data Frame using above, then Transpose it
sim_df = pd.DataFrame(data=sim_data).T
## Give the columns in Simulation Data Proper Names
sim_df.columns = [
'Returns',
'Volatility',
'Sharpe Ratio',
'Portfolio Weights'
]
## Make sure the Data Types are correct in the Data Frame
sim_df = sim_df.infer_objects()
# Print out the results.
st.write('SIMULATIONS RESULT:')
st.dataframe(sim_df.head(), use_container_width=True)
# Return the Max Sharpe Ratio from the run.
max_sharpe_ratio = sim_df.loc[sim_df['Sharpe Ratio'].idxmax()]
# Return the Min Volatility from the run.
min_volatility = sim_df.loc[sim_df['Volatility'].idxmin()]
max_sharpe_weights_df = pd.DataFrame(data={
'company_name': com_sel_name,
'random_weights': max_sharpe_ratio["Portfolio Weights"],
})
st.write('MAX SHARPE RATIO:')
st.dataframe(max_sharpe_ratio, use_container_width=True)
st.dataframe(max_sharpe_weights_df, use_container_width=True)
min_volatility_weights_df = pd.DataFrame(data={
'company_name': com_sel_name,
'random_weights': min_volatility["Portfolio Weights"],
})
st.write('MIN VOLATILITY:')
st.dataframe(min_volatility, use_container_width=True)
st.dataframe(min_volatility_weights_df, use_container_width=True)
fig = go.Figure(data=go.Scatter(
x=sim_df['Volatility'],
y=sim_df['Returns'],
mode='markers',
marker=dict(
color=sim_df['Sharpe Ratio'],
colorscale='RdYlBu',
size=10
)
))
# Add color bar
fig.update_layout(
coloraxis_colorbar=dict(
title='Sharpe Ratio'
)
)
# Add title and axis labels
fig.update_layout(
title='Portfolio Returns Vs. Risk',
xaxis=dict(title='Standard Deviation'),
yaxis=dict(title='Returns')
)
# Plot the Max Sharpe Ratio, using a `Red Star`.
fig.add_trace(go.Scatter(
x=[max_sharpe_ratio[1]],
y=[max_sharpe_ratio[0]],
mode='markers',
marker=dict(
color='red',
symbol='star',
size=20
),
name='Max Sharpe Ratio'
))
# Plot the Min Volatility, using a `Blue Star`.
fig.add_trace(go.Scatter(
x=[min_volatility[1]],
y=[min_volatility[0]],
mode='markers',
marker=dict(
color='blue',
symbol='star',
size=20
),
name='Min Volatility'
))
st.plotly_chart(fig, use_container_width=True)
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