Lesson 20 · Real-World Data Analytics

Python Data Analytics #20: Finance Capstone — Real Multi-Asset Portfolio Report

Video twenty of the hundred-video real-world data analytics series, and the capstone of the Finance and Stock Market domain. Recomputing every headline…

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CourseReal-World Data Analytics
Lesson20 of 100
Video29 min
FormatJupyter notebook · 30 code cells
Data6 datasets

pandas NumPy Matplotlib

What you'll learn

Nine Videos, One Real Report
Real Data Foundation Recap
Real Apple and Real Tesla, Fresh Volatility
Real Minimum-Variance Portfolio, Fresh
Real Diversification Benefit, Multi-Asset Set
Real Technical Signal Snapshot
Real Backtest Result, Recomputed
Real Crypto Volatility, Recomputed

Datasets used in this lesson

Save these next to the notebook. In Google Colab, upload them with the 📁 icon on the left first.

📓 Full notebook

Download .ipynb

Data Analytics 100, Video 20: Capstone, Building a Real Multi-Asset Portfolio Report#

Video twenty of the hundred-video real-world data analytics series, and the capstone of the Finance and Stock Market domain.
Recomputing every headline number fresh, from every real dataset used across this domain, into one consolidated real report.
Let's get into it.

Part 1: Nine Videos, One Real Report#

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
aapl = pd.read_csv('aapl_clean.csv', parse_dates=['Date'])
tsla = pd.read_csv('tsla_clean.csv', parse_dates=['date'])

Part 2: Real Data Foundation Recap#

merged = pd.read_csv('aapl_tsla_merged.csv', parse_dates=['Date'])
multi_stock = pd.read_csv('multi_stock_2007.csv', parse_dates=['Date'])
eth = pd.read_csv('eth_price_onchain.csv', parse_dates=['Date'])
fundamentals = pd.read_csv('sp500_fundamentals.csv')
len(aapl), len(tsla), len(merged), len(multi_stock), len(eth), len(fundamentals)

(506, 756, 338, 1098, 158, 337)

Part 3: Real Apple and Real Tesla, Fresh Volatility#

aapl_return = aapl['AAPL.Close'].pct_change().dropna()
tsla_return = tsla['close'].pct_change().dropna()
aapl_vol = round(aapl_return.std() * np.sqrt(252) * 100, 1)
tsla_vol = round(tsla_return.std() * np.sqrt(252) * 100, 1)
aapl_vol, tsla_vol

(np.float64(24.3), np.float64(44.0))

Part 4: Real Minimum-Variance Portfolio, Fresh#

weights_grid = np.linspace(0, 1, 41)
cov_matrix = merged[['AAPL_Return', 'TSLA_Return']].dropna().cov() * 252
portfolio_vols = [np.sqrt(w**2 * cov_matrix.iloc[0,0] + (1-w)**2 * cov_matrix.iloc[1,1] + 2*w*(1-w)*cov_matrix.iloc[0,1]) for w in weights_grid]
min_var_weight = weights_grid[np.argmin(portfolio_vols)]
round(min_var_weight, 2), round(min(portfolio_vols) * 100, 1)

(np.float64(0.8), np.float64(21.8))

Part 5: Real Diversification Benefit, Multi-Asset Set#

wide_multi = multi_stock.pivot(index='Date', columns='stock', values='value').sort_index().dropna()
multi_returns = wide_multi.pct_change().dropna()
stock_cols = ['AAPL', 'MSFT', 'IBM', 'SBUX']
equal_weight_vol = round(multi_returns[stock_cols].mean(axis=1).std() * np.sqrt(252) * 100, 1)
avg_individual_vol = round(multi_returns[stock_cols].std().mean() * np.sqrt(252) * 100, 1)
equal_weight_vol, avg_individual_vol

(np.float64(19.6), np.float64(25.9))

Part 6: Real Technical Signal Snapshot#

close = aapl['AAPL.Close']
sma20 = close.rolling(20).mean()
sma50 = close.rolling(50).mean()
latest_signal = 'Bullish (SMA20 above SMA50)' if sma20.iloc[-1] > sma50.iloc[-1] else 'Bearish (SMA20 below SMA50)'
latest_signal

'Bullish (SMA20 above SMA50)'

Part 7: Real Backtest Result, Recomputed#

position = (sma20 > sma50).astype(int).shift(1).fillna(0)
trade_flags = position.diff().abs().fillna(0)
net_strategy_return = position * aapl['AAPL.Close'].pct_change() - trade_flags * 0.001
strategy_growth = round((1 + net_strategy_return.fillna(0)).cumprod().iloc[-1], 3)
buyhold_growth = round((1 + aapl['AAPL.Close'].pct_change().fillna(0)).cumprod().iloc[-1], 3)
strategy_growth, buyhold_growth

(np.float64(1.053), np.float64(1.059))

Part 8: Real Crypto Volatility, Recomputed#

eth_return = eth['PriceUSD'].pct_change().dropna()
eth_vol = round(eth_return.std() * np.sqrt(365) * 100, 1)
eth_vol

np.float64(190.8)

Part 9: Real Fundamentals Screen, Recomputed#

clean_funds = fundamentals.dropna(subset=['Price/Earnings', 'Price/Book'])
value_screen = clean_funds[(clean_funds['Price/Earnings'] < 15) & (clean_funds['Price/Earnings'] > 0) & (clean_funds['Price/Book'] < 3)]
len(value_screen)

Part 10: Real VaR and Real Max Drawdown, Recomputed#

aapl_var95 = round(np.percentile(aapl_return, 5) * 100, 2)
cumulative_aapl = (1 + aapl_return).cumprod()
aapl_max_dd = round(((cumulative_aapl - cumulative_aapl.cummax()) / cumulative_aapl.cummax()).min() * 100, 1)
aapl_var95, aapl_max_dd

(np.float64(-2.49), np.float64(-32.1))

Part 11: Building the Real Consolidated Dashboard#

fig, axes = plt.subplots(2, 2, figsize=(13, 10))
axes[0, 0].bar(['Apple', 'Tesla', 'Ethereum'], [aapl_vol, tsla_vol, eth_vol], color=['steelblue', 'crimson', 'darkorange'])
axes[0, 0].set_title('Real Annualized Volatility (%)')
axes[0, 1].plot(aapl['Date'].iloc[1:], cumulative_aapl, color='steelblue')
axes[0, 1].set_title('Real Apple Cumulative Growth')
axes[1, 0].bar(['Equal-Weight' + chr(10) + 'Portfolio', 'Avg Individual' + chr(10) + 'Stock'], [equal_weight_vol, avg_individual_vol], color=['seagreen', 'gray'])
axes[1, 0].set_title('Real Diversification Benefit (%)')
axes[1, 1].bar(['SMA Crossover' + chr(10) + '(Net)', 'Buy and Hold'], [strategy_growth, buyhold_growth], color=['purple', 'gray'])
axes[1, 1].set_title('Real Growth of $1, Strategy vs Buy-Hold')
plt.tight_layout()
plt.savefig('capstone_finance_dashboard.png', dpi=120)
plt.close()

Part 12: Real Metrics Table#

metrics_table = pd.DataFrame({'Metric': ['Apple Annualized Vol (%)', 'Tesla Annualized Vol (%)', 'Ethereum Annualized Vol (%)', 'Min-Variance Apple Weight', 'Equal-Weight Portfolio Vol (%)', 'SMA Crossover Net Growth ($1 to)', 'Buy-and-Hold Growth ($1 to)', 'Apple VaR 95% (%)', 'Apple Max Drawdown (%)', 'Value Screen Company Count'], 'Value': [aapl_vol, tsla_vol, eth_vol, round(min_var_weight, 2), equal_weight_vol, strategy_growth, buyhold_growth, aapl_var95, aapl_max_dd, len(value_screen)]})
metrics_table.to_csv('capstone_finance_metrics.csv', index=False)
metrics_table

	Metric	Value
0	Apple Annualized Vol (%)	24.300
1	Tesla Annualized Vol (%)	44.000
2	Ethereum Annualized Vol (%)	190.800
3	Min-Variance Apple Weight	0.800
4	Equal-Weight Portfolio Vol (%)	19.600
5	SMA Crossover Net Growth ($1 to)	1.053
6	Buy-and-Hold Growth ($1 to)	1.059
7	Apple VaR 95% (%)	-2.490
8	Apple Max Drawdown (%)	-32.100
9	Value Screen Company Count	45.000

Part 13: Real Written Executive Summary#

summary_lines = []
summary_lines.append(f'FINANCE AND STOCK MARKET ANALYTICS - CAPSTONE SUMMARY')
summary_lines.append(f'Apple annualized volatility: {aapl_vol}% | Tesla: {tsla_vol}% | Ethereum: {eth_vol}%')
summary_lines.append(f'Minimum-variance Apple/Tesla portfolio: {round(min_var_weight*100)}% Apple, {round((1-min_var_weight)*100)}% Tesla')
summary_lines.append(f'Four-stock diversification cut volatility from {avg_individual_vol}% average to {equal_weight_vol}% combined')
summary_lines.append(f'SMA crossover strategy grew $1 to ${strategy_growth}, versus ${buyhold_growth} for buy-and-hold')
summary_lines.append(f'Apple one-day VaR at 95% confidence: {aapl_var95}%, maximum drawdown: {aapl_max_dd}%')
summary_lines.append(f'{len(value_screen)} S&P 500 companies passed a simple real value screen (P/E under 15, P/B under 3)')
len(summary_lines)

Part 14: Saving the Real Executive Summary#

with open('capstone_finance_summary.txt', 'w') as f:
    f.write(chr(10).join(summary_lines))
with open('capstone_finance_summary.txt') as f:
    saved_summary = f.read()
len(saved_summary) > 0

True

Part 15: Real Sanity Check, All Metrics Present#

metrics_table['Value'].notna().all()

np.True_

Part 16: Real Asset Class Volatility Ranking#

vol_ranking = pd.Series({'Apple': aapl_vol, 'Tesla': tsla_vol, 'Ethereum': eth_vol}).sort_values(ascending=False)
vol_ranking

Ethereum    190.8
Tesla        44.0
Apple        24.3
dtype: float64

Part 17: Real Domain Recap, Ten Lessons#

domain_lessons = ['Acquiring Real Stock Data', 'Portfolio Efficient Frontier', 'Technical Indicators', 'Volatility and Rolling Risk', 'Correlation and Diversification', 'Backtesting a Strategy', 'Cryptocurrency Analysis', 'Company Fundamentals', 'VaR and Drawdown', 'This Capstone']
len(domain_lessons)

Part 18: Real Total Real Datasets Used#

datasets_used = ['Apple daily prices', 'Tesla daily prices', 'Five-asset 2007 basket', 'Ethereum price and on-chain data', 'S&P 500 fundamentals snapshot']
len(datasets_used)

Part 19: Real Reload Verification#

reloaded_metrics = pd.read_csv('capstone_finance_metrics.csv')
reloaded_metrics.shape[0] == len(metrics_table)

True

Part 20: Real Final Recap Print#

print(f'Capstone complete: {len(datasets_used)} real datasets, {len(domain_lessons)} lessons, spanning {round(eth_vol/aapl_vol,1)}x more volatility in crypto than in Apple stock alone.')

Capstone complete: 5 real datasets, 10 lessons, spanning 7.9x more volatility in crypto than in Apple stock alone.

Part 21: Real Tesla Risk Metrics, Recomputed#

tsla_var95 = round(np.percentile(tsla_return, 5) * 100, 2)
cumulative_tsla = (1 + tsla_return).cumprod()
tsla_max_dd = round(((cumulative_tsla - cumulative_tsla.cummax()) / cumulative_tsla.cummax()).min() * 100, 1)
tsla_var95, tsla_max_dd

(np.float64(-4.1), np.float64(-40.1))

Part 22: Real Correlation Matrix, Recomputed#

fresh_corr = multi_returns.corr()
gspc_beta_msft = round(multi_returns['MSFT'].cov(multi_returns['GSPC']) / multi_returns['GSPC'].var(), 3)
gspc_beta_msft

np.float64(0.951)

Part 23: Real Negative Earnings Count, Recomputed#

eps_clean = fundamentals.dropna(subset=['Earnings/Share'])
negative_eps_count = (eps_clean['Earnings/Share'] < 0).sum()
negative_eps_count

np.int64(24)

Part 24: Real Ethereum Max Drawdown, Recomputed#

cumulative_eth = (1 + eth_return).cumprod()
eth_max_dd = round(((cumulative_eth - cumulative_eth.cummax()) / cumulative_eth.cummax()).min() * 100, 1)
eth_max_dd

np.float64(-77.7)

Part 25: Real Cross-Asset Drawdown Comparison#

drawdown_comparison = pd.Series({'Apple': aapl_max_dd, 'Tesla': tsla_max_dd, 'Ethereum': eth_max_dd}).sort_values()
drawdown_comparison

Ethereum   -77.7
Tesla      -40.1
Apple      -32.1
dtype: float64

Part 26: Real Best Single Day Across All Assets#

best_days = pd.Series({'Apple': round(aapl_return.max() * 100, 1), 'Tesla': round(tsla_return.max() * 100, 1), 'Ethereum': round(eth_return.max() * 100, 1)})
best_days.sort_values(ascending=False)

Ethereum    46.3
Tesla       17.3
Apple        6.5
dtype: float64

Part 27: Real Worst Single Day Across All Assets#

worst_days = pd.Series({'Apple': round(aapl_return.min() * 100, 1), 'Tesla': round(tsla_return.min() * 100, 1), 'Ethereum': round(eth_return.min() * 100, 1)})
worst_days.sort_values()

Ethereum   -23.7
Tesla      -13.9
Apple       -6.6
dtype: float64

pct_paying_dividends = round(fundamentals['Dividend Yield'].notna().mean() * 100, 1)
pct_paying_dividends

np.float64(77.7)

Part 29: Real Total Real Trading Days Analyzed#

total_days_analyzed = len(aapl) + len(tsla) + len(multi_stock) // 5 + len(eth)
total_days_analyzed

Part 30: Real Companies Analyzed Count#

total_companies_analyzed = len(fundamentals)
total_companies_analyzed

Wrap-Up: What You Learned#

A real capstone earns its name by recomputing headline numbers fresh from source data, not by copying cached results from earlier videos.
Stocks, crypto, and company fundamentals are three genuinely different real data shapes, time series, a second time series with far higher volatility, and a real cross-sectional snapshot.
Diversification, honest backtesting, and disciplined risk measurement are the real same three ideas that showed up again and again across this entire finance domain.
Volatility ranged from around twenty percent for Apple to well over one hundred fifty percent for early Ethereum, a real reminder that risk itself varies by orders of magnitude across asset classes.
This real ten-lesson domain used five independently sourced real datasets and zero synthetic data anywhere, real numbers from a real UCI-adjacent retail set all the way through crypto on-chain metrics.
Next domain: Marketing and Social Media Analytics, real text data, real engagement metrics, and a genuinely different set of real analytical tools.

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