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11/22/2024

Sachin Chandra

Optimizing Financial Portfolio Performance: Analyzing Trends, Risk, and Return for Better Asset Allocation

This notebook leverages advanced data analysis techniques to evaluate and optimize a financial portfolio. It offers a comprehensive approach to understanding asset price ...

Introduction:

In the fast-paced world of finance, every investor is on a quest to maximize returns while minimizing risk. The key to successful investing lies in understanding the complex interplay between asset prices, market trends, and volatility. Imagine you’re managing a diverse portfolio of stocks, bonds, and commodities, but how do you know which assets are performing the best? How can you predict the future to stay ahead of the curve?

This notebook takes you on a journey through the core principles of portfolio management, blending data science with financial expertise. We’ll dive deep into analyzing price trends, measuring risk, and uncovering the assets that drive your portfolio’s performance. Through advanced optimization techniques, we’ll show you how to fine-tune your asset allocation, balancing potential returns with calculated risk.

By the end of this notebook, you’ll not only have a clearer picture of where your portfolio stands but also the tools to make smarter, data-driven decisions for the future. Whether you’re a seasoned investor or just starting out, this guide will equip you with the insights needed to navigate the unpredictable world of finance with confidence.

1. Data Understanding

Understanding the data is the foundation of any analysis. Here, we dive into the structure of our dataset, exploring its attributes, time range, and the diversity of assets it represents. By ensuring data completeness and quality, we lay the groundwork for reliable insights.

Key questions addressed in this section:

• What is the time range of our dataset, and how many unique assets does it contain?

• Are there missing or duplicate entries that could affect the analysis?

• How balanced is the dataset in terms of representation for each asset?

Through this exploration, we establish a clean and robust dataset ready for further analysis.

1  import pandas as pd
2  import numpy as np
3  import matplotlib.pyplot as plt
4  import seaborn as sns
5  from datetime import datetime
6  pd.set_option('display.max_columns', None)
7  pd.set_option('display.max_rows', 50)
8  data = pd.read_csv(r'''/app/financial_portfolio_data.csv''')
9  print("Dataset Preview:")
10  display(data.head())
11  print("\nDataset Info:")
12  data.info()
13  print("\nSummary Statistics:")
14  display(data.describe())

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 50)
data = pd.read_csv(r'''/app/financial_portfolio_data.csv''')
print("Dataset Preview:")
display(data.head())
print("\nDataset Info:")
data.info()
print("\nSummary Statistics:")
display(data.describe())

1  data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
2  missing_values = data.isnull().sum()
3  print("Missing Values:\n", missing_values)
4  duplicate_rows = data.duplicated().sum()
5  print(f"\nNumber of Duplicate Rows: {duplicate_rows}")
6  date_range = (data['Date'].min(), data['Date'].max())
7  print(f"\nDataset Time Range: {date_range[0].strftime('%Y-%m-%d')} to {date_range[1].strftime('%Y-%m-%d')}")
8  unique_assets = data['Asset'].nunique()
9  print(f"\nNumber of Unique Assets: {unique_assets}")
10  print("Assets List:", data['Asset'].unique())
11  asset_counts = data['Asset'].value_counts()
12  print("\nData Entries per Asset:\n", asset_counts)
13  plt.figure(figsize=(10, 6))
14  sns.countplot(data=data, y='Asset', order=asset_counts.index, palette='viridis', hue='Asset', legend=False)
15  plt.title("Number of Data Points per Asset")
16  plt.xlabel("Count")
17  plt.ylabel("Asset")
18  plt.show()

data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
missing_values = data.isnull().sum()
print("Missing Values:\n", missing_values)
duplicate_rows = data.duplicated().sum()
print(f"\nNumber of Duplicate Rows: {duplicate_rows}")
date_range = (data['Date'].min(), data['Date'].max())
print(f"\nDataset Time Range: {date_range[0].strftime('%Y-%m-%d')} to {date_range[1].strftime('%Y-%m-%d')}")
unique_assets = data['Asset'].nunique()
print(f"\nNumber of Unique Assets: {unique_assets}")
print("Assets List:", data['Asset'].unique())
asset_counts = data['Asset'].value_counts()
print("\nData Entries per Asset:\n", asset_counts)
plt.figure(figsize=(10, 6))
sns.countplot(data=data, y='Asset', order=asset_counts.index, palette='viridis', hue='Asset', legend=False)
plt.title("Number of Data Points per Asset")
plt.xlabel("Count")
plt.ylabel("Asset")
plt.show()

2. Data Preprocessing

Raw financial data is often messy and inconsistent. In this section, we transform our dataset into a structured format suitable for analysis. Key steps include:

• Converting dates to a proper format for chronological analysis.

• Sorting the data by asset and time to maintain consistency.

• Engineering features such as rolling averages, volatility, and returns to uncover hidden trends.

By addressing outliers and crafting new features, we unlock the full potential of the data, enabling deeper exploration and meaningful insights.

1  data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
2  data.sort_values(by=['Asset', 'Date'], inplace=True)
3  missing_values = data.isnull().sum()
4  print("Missing Values After Sorting:\n", missing_values)
5  data['Price'] = data['Price'].ffill()  
6  plt.figure(figsize=(10, 6))
7  sns.boxplot(x='Asset', y='Price', data=data)
8  plt.title("Price Distribution by Asset")
9  plt.show()
10  data['Return'] = data.groupby('Asset')['Price'].pct_change()
11  data['7-Day MA'] = data.groupby('Asset')['Price'].rolling(window=7).mean().reset_index(0, drop=True)
12  data['30-Day MA'] = data.groupby('Asset')['Price'].rolling(window=30).mean().reset_index(0, drop=True)
13  data['7-Day Volatility'] = data.groupby('Asset')['Price'].rolling(window=7).std().reset_index(0, drop=True)
14  print("\nData after Preprocessing:")
15  display(data.head())
16  print("\nSummary Statistics After Preprocessing:")
17  display(data[['Price', 'Return', '7-Day MA', '30-Day MA', '7-Day Volatility']].describe())

data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
data.sort_values(by=['Asset', 'Date'], inplace=True)
missing_values = data.isnull().sum()
print("Missing Values After Sorting:\n", missing_values)
data['Price'] = data['Price'].ffill()  
plt.figure(figsize=(10, 6))
sns.boxplot(x='Asset', y='Price', data=data)
plt.title("Price Distribution by Asset")
plt.show()
data['Return'] = data.groupby('Asset')['Price'].pct_change()
data['7-Day MA'] = data.groupby('Asset')['Price'].rolling(window=7).mean().reset_index(0, drop=True)
data['30-Day MA'] = data.groupby('Asset')['Price'].rolling(window=30).mean().reset_index(0, drop=True)
data['7-Day Volatility'] = data.groupby('Asset')['Price'].rolling(window=7).std().reset_index(0, drop=True)
print("\nData after Preprocessing:")
display(data.head())
print("\nSummary Statistics After Preprocessing:")
display(data[['Price', 'Return', '7-Day MA', '30-Day MA', '7-Day Volatility']].describe())

3. Exploratory Data Analysis (EDA)

With a clean dataset, we now embark on a journey to uncover patterns and relationships within the data. This section focuses on answering critical questions about portfolio performance:

1. Price Trends: Which assets have shown consistent growth, and which have fluctuated significantly?

2. Volatility Analysis: How does the risk of each asset compare, and which assets are the most stable or erratic?

3. Correlation Analysis: Are there relationships between asset movements that can guide diversification?

4. Risk-Return Tradeoff: What is the balance between potential rewards and risks across our portfolio?

Through vivid visualizations, we bring the numbers to life, providing an intuitive understanding of the portfolio’s dynamics.

A. Descriptive Statistics for Key Features

1  print("\nDescriptive Statistics for Key Features:")
2  display(data[['Price', 'Return', '7-Day MA', '30-Day MA', '7-Day Volatility']].describe())
3  plt.figure(figsize=(12, 8))
4  for asset in data['Asset'].unique():
5      asset_data = data[data['Asset'] == asset]
6      plt.plot(asset_data['Date'], asset_data['Price'], label=asset)
7  plt.title("Price Trend Over Time for Each Asset")
8  plt.xlabel("Date")
9  plt.ylabel("Price")
10  plt.legend()
11  plt.xticks(rotation=45)
12  plt.show()

print("\nDescriptive Statistics for Key Features:")
display(data[['Price', 'Return', '7-Day MA', '30-Day MA', '7-Day Volatility']].describe())
plt.figure(figsize=(12, 8))
for asset in data['Asset'].unique():
    asset_data = data[data['Asset'] == asset]
    plt.plot(asset_data['Date'], asset_data['Price'], label=asset)
plt.title("Price Trend Over Time for Each Asset")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
plt.xticks(rotation=45)
plt.show()

B. Volatility Over Time

1  plt.figure(figsize=(12, 8))
2  for asset in data['Asset'].unique():
3      asset_data = data[data['Asset'] == asset]
4      plt.plot(asset_data['Date'], asset_data['7-Day Volatility'], label=asset)
5  plt.title("7-Day Volatility Over Time for Each Asset")
6  plt.xlabel("Date")
7  plt.ylabel("7-Day Volatility")
8  plt.legend()
9  plt.xticks(rotation=45)
10  plt.show()

plt.figure(figsize=(12, 8))
for asset in data['Asset'].unique():
    asset_data = data[data['Asset'] == asset]
    plt.plot(asset_data['Date'], asset_data['7-Day Volatility'], label=asset)
plt.title("7-Day Volatility Over Time for Each Asset")
plt.xlabel("Date")
plt.ylabel("7-Day Volatility")
plt.legend()
plt.xticks(rotation=45)
plt.show()

C. Correlation Matrix of Asset Returns

1  returns_data = data.groupby(['Asset', 'Date'])['Return'].mean().reset_index()
2  returns_data = returns_data.pivot(index='Date', columns='Asset', values='Return')
3  correlation_matrix = returns_data.corr()
4  plt.figure(figsize=(10, 6))
5  sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
6  plt.title("Correlation Matrix of Asset Returns")
7  plt.show()

returns_data = data.groupby(['Asset', 'Date'])['Return'].mean().reset_index()
returns_data = returns_data.pivot(index='Date', columns='Asset', values='Return')
correlation_matrix = returns_data.corr()
plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title("Correlation Matrix of Asset Returns")
plt.show()

D. Risk Return Tradeoff: Volatility Vs. Returns

1  plt.figure(figsize=(10, 6))
2  for asset in data['Asset'].unique():
3      asset_data = data[data['Asset'] == asset]
4      plt.scatter(asset_data['7-Day Volatility'], asset_data['Return'], label=asset)
5  plt.title("Risk-Return Tradeoff: Volatility vs. Returns")
6  plt.xlabel("7-Day Volatility")
7  plt.ylabel("Return")
8  plt.legend()
9  plt.show()

plt.figure(figsize=(10, 6))
for asset in data['Asset'].unique():
    asset_data = data[data['Asset'] == asset]
    plt.scatter(asset_data['7-Day Volatility'], asset_data['Return'], label=asset)
plt.title("Risk-Return Tradeoff: Volatility vs. Returns")
plt.xlabel("7-Day Volatility")
plt.ylabel("Return")
plt.legend()
plt.show()

4. Modeling

Financial success isn’t just about understanding the past—it’s about predicting the future and optimizing decisions. In this section, we leverage advanced models to:

1. Forecast Asset Prices: Using time-series models, we project future price trends to guide investment decisions. The accuracy of our predictions, measured by metrics like Mean Absolute Error (MAE), provides a glimpse into what lies ahead.

2. Optimize Portfolio Allocation: By simulating different combinations of assets, we identify the optimal mix that maximizes returns while minimizing risk. Techniques like the Efficient Frontier and Monte Carlo simulations ensure a data-driven approach to asset allocation.

This section bridges the gap between analysis and actionable strategy, equipping investors with the tools to make informed decisions.

1  from statsmodels.tsa.arima.model import ARIMA
2  from sklearn.metrics import mean_absolute_error
3  asset = 'Stock A'
4  asset_data = data[data['Asset'] == asset].set_index('Date').sort_index()
5  train_data = asset_data[:-30]
6  test_data = asset_data[-30:]
7  model = ARIMA(train_data['Price'], order=(5, 1, 0)) 
8  model_fit = model.fit()
9  forecast = model_fit.forecast(steps=30)
10  forecast_index = test_data.index
11  forecast_df = pd.DataFrame({'Actual': test_data['Price'], 'Forecast': forecast}, index=forecast_index)
12  plt.figure(figsize=(10, 6))
13  plt.plot(train_data.index, train_data['Price'], label='Training Data')
14  plt.plot(test_data.index, test_data['Price'], label='Actual Prices', color='red')
15  plt.plot(forecast_df.index, forecast_df['Forecast'], label='Forecasted Prices', color='green')
16  plt.title(f"ARIMA Forecast for {asset}")
17  plt.xlabel("Date")
18  plt.ylabel("Price")
19  plt.legend()
20  plt.xticks(rotation=45)
21  plt.show()
22  mae = mean_absolute_error(test_data['Price'], forecast)
23  print(f"Mean Absolute Error for {asset}: {mae}")

from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_absolute_error
asset = 'Stock A'
asset_data = data[data['Asset'] == asset].set_index('Date').sort_index()
train_data = asset_data[:-30]
test_data = asset_data[-30:]
model = ARIMA(train_data['Price'], order=(5, 1, 0)) 
model_fit = model.fit()
forecast = model_fit.forecast(steps=30)
forecast_index = test_data.index
forecast_df = pd.DataFrame({'Actual': test_data['Price'], 'Forecast': forecast}, index=forecast_index)
plt.figure(figsize=(10, 6))
plt.plot(train_data.index, train_data['Price'], label='Training Data')
plt.plot(test_data.index, test_data['Price'], label='Actual Prices', color='red')
plt.plot(forecast_df.index, forecast_df['Forecast'], label='Forecasted Prices', color='green')
plt.title(f"ARIMA Forecast for {asset}")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
plt.xticks(rotation=45)
plt.show()
mae = mean_absolute_error(test_data['Price'], forecast)
print(f"Mean Absolute Error for {asset}: {mae}")

1  # Prepare returns data for portfolio optimization
2  returns_data = data.pivot_table(index='Date', columns='Asset', values='Return', aggfunc='mean')
3  # Calculate annualized returns and covariance matrix
4  annual_returns = returns_data.mean() * 252  # Annualize returns
5  cov_matrix = returns_data.cov() * 252  # Annualized covariance
6  # Monte Carlo Simulation for Portfolio Optimization
7  np.random.seed(42)
8  num_portfolios = 10000
9  results = np.zeros((3, num_portfolios))
10  weights_record = []
11  for i in range(num_portfolios):
12      weights = np.random.random(len(annual_returns))
13      weights /= np.sum(weights)
14      weights_record.append(weights)
15      portfolio_return = np.dot(weights, annual_returns)
16      portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
17      sharpe_ratio = portfolio_return / portfolio_volatility
18      results[0, i] = portfolio_return
19      results[1, i] = portfolio_volatility
20      results[2, i] = sharpe_ratio
21  # Convert results into a DataFrame
22  portfolio_df = pd.DataFrame(results.T, columns=['Return', 'Volatility', 'Sharpe Ratio'])
23  weights_df = pd.DataFrame(weights_record, columns=returns_data.columns)
24  print("done")

# Prepare returns data for portfolio optimization
returns_data = data.pivot_table(index='Date', columns='Asset', values='Return', aggfunc='mean')
# Calculate annualized returns and covariance matrix
annual_returns = returns_data.mean() * 252  # Annualize returns
cov_matrix = returns_data.cov() * 252  # Annualized covariance
# Monte Carlo Simulation for Portfolio Optimization
np.random.seed(42)
num_portfolios = 10000
results = np.zeros((3, num_portfolios))
weights_record = []
for i in range(num_portfolios):
    weights = np.random.random(len(annual_returns))
    weights /= np.sum(weights)
    weights_record.append(weights)
    portfolio_return = np.dot(weights, annual_returns)
    portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
    sharpe_ratio = portfolio_return / portfolio_volatility
    results[0, i] = portfolio_return
    results[1, i] = portfolio_volatility
    results[2, i] = sharpe_ratio
# Convert results into a DataFrame
portfolio_df = pd.DataFrame(results.T, columns=['Return', 'Volatility', 'Sharpe Ratio'])
weights_df = pd.DataFrame(weights_record, columns=returns_data.columns)
print("done")

1  portfolio_weights = {'Bond A': 0.215, 'Bond B': 0.319, 'Commodity A': 0.315, 'Stock A': 0.021, 'Stock B': 0.131}
2  labels = portfolio_weights.keys()
3  sizes = portfolio_weights.values()
4  plt.figure(figsize=(8, 8))
5  plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('Blues', len(labels)))
6  plt.title('Optimal Portfolio Allocation', fontsize=16)
7  plt.show()

portfolio_weights = {'Bond A': 0.215, 'Bond B': 0.319, 'Commodity A': 0.315, 'Stock A': 0.021, 'Stock B': 0.131}
labels = portfolio_weights.keys()
sizes = portfolio_weights.values()
plt.figure(figsize=(8, 8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('Blues', len(labels)))
plt.title('Optimal Portfolio Allocation', fontsize=16)
plt.show()

1  # Identify the optimal portfolio
2  optimal_idx = portfolio_df['Sharpe Ratio'].idxmax()
3  optimal_portfolio = portfolio_df.iloc[optimal_idx]
4  optimal_weights = weights_df.iloc[optimal_idx]
5  print(f"\nOptimal Portfolio Allocation (Highest Sharpe Ratio):")
6  print(f"Expected Return: {optimal_portfolio['Return']*100:.2f}%")
7  print(f"Volatility: {optimal_portfolio['Volatility']*100:.2f}%")
8  print(f"Sharpe Ratio: {optimal_portfolio['Sharpe Ratio']:.4f}")
9  print("\nOptimal Weights:")
10  print(optimal_weights)
11  # Visualize the Efficient Frontier
12  plt.figure(figsize=(10, 6))
13  plt.scatter(portfolio_df['Volatility'], portfolio_df['Return'], c=portfolio_df['Sharpe Ratio'], cmap='viridis', marker='o')
14  plt.colorbar(label='Sharpe Ratio')
15  plt.scatter(optimal_portfolio['Volatility'], optimal_portfolio['Return'], c='red', marker='X', s=200, label='Optimal Portfolio')
16  plt.title('Efficient Frontier')
17  plt.xlabel('Volatility (Risk)')
18  plt.ylabel('Return')
19  plt.legend()
20  plt.show()

# Identify the optimal portfolio
optimal_idx = portfolio_df['Sharpe Ratio'].idxmax()
optimal_portfolio = portfolio_df.iloc[optimal_idx]
optimal_weights = weights_df.iloc[optimal_idx]
print(f"\nOptimal Portfolio Allocation (Highest Sharpe Ratio):")
print(f"Expected Return: {optimal_portfolio['Return']*100:.2f}%")
print(f"Volatility: {optimal_portfolio['Volatility']*100:.2f}%")
print(f"Sharpe Ratio: {optimal_portfolio['Sharpe Ratio']:.4f}")
print("\nOptimal Weights:")
print(optimal_weights)
# Visualize the Efficient Frontier
plt.figure(figsize=(10, 6))
plt.scatter(portfolio_df['Volatility'], portfolio_df['Return'], c=portfolio_df['Sharpe Ratio'], cmap='viridis', marker='o')
plt.colorbar(label='Sharpe Ratio')
plt.scatter(optimal_portfolio['Volatility'], optimal_portfolio['Return'], c='red', marker='X', s=200, label='Optimal Portfolio')
plt.title('Efficient Frontier')
plt.xlabel('Volatility (Risk)')
plt.ylabel('Return')
plt.legend()
plt.show()

1  labels = optimal_weights.index  # Asset names
2  sizes = optimal_weights.values  # Corresponding weights
3  plt.figure(figsize=(8, 8))
4  plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('Blues', len(labels)))
5  plt.title('Optimal Portfolio Allocation', fontsize=16)
6  plt.show()

labels = optimal_weights.index  # Asset names
sizes = optimal_weights.values  # Corresponding weights
plt.figure(figsize=(8, 8))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('Blues', len(labels)))
plt.title('Optimal Portfolio Allocation', fontsize=16)
plt.show()

5. Results and Insights

The analysis culminates in actionable insights that inform portfolio strategy:

• Price Forecasts: Highlighting expected trends for key assets, helping to anticipate market movements.

• Optimal Portfolio Allocation: A detailed breakdown of the best asset mix, including a pie chart for visualization and the Efficient Frontier to balance risk and reward.

• Risk-Return Recommendations: Suggestions for balancing high-return, high-risk assets with more stable investments.

These insights are not just numbers—they’re the roadmap for building a resilient and profitable portfolio.

1  # ARIMA Results
2  print("\nARIMA Forecast Results:")
3  print(f"Mean Absolute Error (MAE) for the forecasted asset ('{asset}'): {mae:.4f}")
4  # Optimal Portfolio Insights
5  print("\nOptimal Portfolio Insights:")
6  print("1. The optimal portfolio maximizes the Sharpe Ratio, indicating the best risk-adjusted return.")
7  print("2. Based on historical data, these weights provide an ideal balance between risk and return.")
8  print("3. Investors should consider diversifying across these weights for a stable investment strategy.")
9  # Actionable Insights
10  print("\nActionable Insights:")
11  print("1. Forecasted price trends can guide short-term investment decisions.")
12  print("2. Diversification across assets reduces portfolio volatility while maintaining reasonable returns.")
13  print("3. Continuous monitoring and rebalancing of weights are essential for long-term success.")

# ARIMA Results
print("\nARIMA Forecast Results:")
print(f"Mean Absolute Error (MAE) for the forecasted asset ('{asset}'): {mae:.4f}")
# Optimal Portfolio Insights
print("\nOptimal Portfolio Insights:")
print("1. The optimal portfolio maximizes the Sharpe Ratio, indicating the best risk-adjusted return.")
print("2. Based on historical data, these weights provide an ideal balance between risk and return.")
print("3. Investors should consider diversifying across these weights for a stable investment strategy.")
# Actionable Insights
print("\nActionable Insights:")
print("1. Forecasted price trends can guide short-term investment decisions.")
print("2. Diversification across assets reduces portfolio volatility while maintaining reasonable returns.")
print("3. Continuous monitoring and rebalancing of weights are essential for long-term success.")

1  # Final Remarks
2  print("\nConclusion:")
3  print("1. The analysis provided a comprehensive understanding of portfolio dynamics through trend analysis, forecasting, and optimization.")
4  print("2. The ARIMA model, while effective for short-term forecasting, should be complemented with external factors for improved accuracy.")
5  print("3. The portfolio optimization identified the optimal allocation strategy, maximizing returns for a given level of risk.")
6  print("4. Future work should focus on integrating macroeconomic data, real-time monitoring, and advanced models like LSTMs for enhanced predictions.")

# Final Remarks
print("\nConclusion:")
print("1. The analysis provided a comprehensive understanding of portfolio dynamics through trend analysis, forecasting, and optimization.")
print("2. The ARIMA model, while effective for short-term forecasting, should be complemented with external factors for improved accuracy.")
print("3. The portfolio optimization identified the optimal allocation strategy, maximizing returns for a given level of risk.")
print("4. Future work should focus on integrating macroeconomic data, real-time monitoring, and advanced models like LSTMs for enhanced predictions.")

7. Conclusion

The journey through this notebook highlights the power of data-driven portfolio management. We began by cleaning and understanding the data, progressed through exploratory analysis and advanced modeling, and concluded with actionable insights. Key takeaways include:

• The importance of balancing risk and return through optimal asset allocation.

• The value of forecasting future trends for proactive investment strategies.

• The benefits of visualizing complex relationships for better comprehension.

This notebook serves as a guide for navigating the complexities of financial markets. Whether you’re managing your own portfolio or advising others, the methods and insights here provide a strong foundation for success in the ever-evolving world of finance.

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