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Building an Advanced Portfolio Analysis and Market Intelligence Tool with OpenBB

In this tutorial, we dive deep into the advanced capabilities of OpenBB to perform comprehensive portfolio analysis and market intelligence. We start by constructing a tech-focused portfolio, fetching historical market data, and computing key performance metrics. We then explore advanced technical indicators, sector-level performance, market sentiment, and correlation-based risk analysis. Along the way, we integrate visualizations and insights to make the analysis more intuitive and actionable, ensuring that we cover both the quantitative and qualitative aspects of investment decision-making.

Target Audience Analysis

The target audience for this tutorial includes finance professionals, data analysts, and investment managers who are interested in leveraging AI tools for portfolio management. Their pain points include:

• Difficulty in accessing and analyzing large datasets.
• Need for advanced analytical tools to make informed investment decisions.
• Desire for intuitive visualizations to communicate insights effectively.

Their goals are to:

• Enhance portfolio performance through data-driven strategies.
• Utilize advanced analytics to identify market trends and risks.
• Improve decision-making processes with actionable insights.

Interests include financial modeling, risk assessment, and market analysis. They prefer clear, concise communication with a focus on practical applications and technical specifications.

1. Building and Analyzing a Tech Portfolio

We begin by installing and importing OpenBB along with essential Python libraries for data analysis and visualization. We configure our environment to suppress warnings, set display options for pandas, and get ready to perform advanced financial analysis.

!pip install openbb[all] --quiet

We define our tech stocks and their initial weights:

tech_stocks = ['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'NVDA']

initial_weights = [0.25, 0.20, 0.25, 0.15, 0.15]

We fetch historical data for the past year and compute daily returns:

for i, symbol in enumerate(tech_stocks):
    data = obb.equity.price.historical(symbol=symbol, start_date=start_date, end_date=end_date)
    df = data.to_df()
    returns = df['close'].pct_change().dropna()
    portfolio_returns[symbol] = returns

2. Portfolio Performance Analysis

We analyze the portfolio’s performance by calculating annual return, volatility, Sharpe ratio, and maximum drawdown:

annual_return = weighted_returns.mean() * 252
annual_volatility = weighted_returns.std() * np.sqrt(252)
sharpe_ratio = annual_return / annual_volatility

3. Advanced Technical Analysis

We perform advanced technical analysis on NVDA, calculating SMAs, EMAs, MACD, RSI, and Bollinger Bands:

df['SMA_20'] = df['close'].rolling(window=20).mean()

4. Sector Analysis & Stock Screening

We analyze sector performance by fetching data for multiple stocks within defined sectors:

sectors = {
    'Technology': ['AAPL', 'GOOGL', 'MSFT'],
    'Electric Vehicles': ['TSLA', 'RIVN', 'LCID'],
    'Semiconductors': ['NVDA', 'AMD', 'INTC']
}

5. Market Sentiment Analysis

We incorporate market sentiment by fetching recent news headlines for selected stocks:

news = obb.news.company(symbol=symbol, limit=3)

6. Risk Analysis

We quantify portfolio risk via correlations and annualized volatility:

correlation_matrix = portfolio_returns.corr()

7. Creating Performance Visualizations

We visualize performance with cumulative returns, rolling volatility, and a correlation heatmap:

fig, axes = plt.subplots(2, 2, figsize=(15, 10))

8. Investment Summary & Recommendations

We conclude with key insights and next steps for further analysis:

• Diversification across tech sectors reduces portfolio risk.
• Technical indicators help identify entry/exit points.
• Regular rebalancing maintains target allocations.

Next steps include backtesting different allocation strategies and exploring ESG and factor-based screening.

We have successfully leveraged OpenBB to build, analyze, and visualize a diversified portfolio while extracting sector insights, technical signals, and risk metrics. This approach allows us to continuously monitor and refine our strategies, ensuring that we remain agile in changing market conditions and confident in the data-driven choices we make.

For more features and documentation, visit OpenBB.

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