Lesson 17 · Real-World Data Analytics

Python Data Analytics #17: Real Cryptocurrency Market Analysis in Python

Video seventeen of the hundred-video real-world data analytics series. Analyzing Ethereum's own real early trading history, real on-chain activity included,…

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CourseReal-World Data Analytics
Lesson17 of 26
Video30 min
FormatJupyter notebook · 30 code cells
Data1 dataset

pandas NumPy Matplotlib

What you'll learn

Crypto Data Includes More Than Price
Real Data Preview
Real Price History
Real Daily Returns
Real Annualized Volatility, Crypto vs Stocks
Real Best and Real Worst Single Days
Real Cumulative Growth
Real Maximum Drawdown

Datasets used in this lesson

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

eth_price_onchain.csv13.0 KB

📓 Full notebook

Download .ipynb

Data Analytics 100, Video 17: Real Cryptocurrency Market Analysis#

Video seventeen of the hundred-video real-world data analytics series.
Analyzing Ethereum's own real early trading history, real on-chain activity included, not just a price chart.
Let's get into it.

Part 1: Crypto Data Includes More Than Price#

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
eth = pd.read_csv('eth_price_onchain.csv', parse_dates=['Date'])
eth.shape

(158, 7)

Part 2: Real Data Preview#

eth.head(3)
eth.columns.tolist()

['Date',
 'PriceUSD',
 'CapMrktCurUSD',
 'TxCnt',
 'AdrActCnt',
 'HashRate',
 'VolumeUSD']

Part 3: Real Price History#

plt.figure(figsize=(11, 5))
plt.plot(eth['Date'], eth['PriceUSD'], color='darkslateblue')
plt.xlabel('Real Date')
plt.ylabel('Real Price (USD)')
plt.title("Real Ethereum Price, Aug 2015 to Jan 2016")
plt.tight_layout()
plt.savefig('eth_price_history.png', dpi=120)
plt.close()

Part 4: Real Daily Returns#

eth['Return'] = eth['PriceUSD'].pct_change()
eth['Return'].describe().round(4)

count    157.0000
mean       0.0047
std        0.0999
min       -0.2371
25%       -0.0450
50%       -0.0029
75%        0.0479
max        0.4634
Name: Return, dtype: float64

Part 5: Real Annualized Volatility, Crypto vs Stocks#

eth_annual_vol = eth['Return'].std() * np.sqrt(365)
round(eth_annual_vol * 100, 1)

np.float64(190.8)

Part 6: Real Best and Real Worst Single Days#

eth_best_day = eth.loc[eth['Return'].idxmax(), 'Date']
eth_best_pct = round(eth['Return'].max() * 100, 1)
eth_worst_day = eth.loc[eth['Return'].idxmin(), 'Date']
eth_worst_pct = round(eth['Return'].min() * 100, 1)
eth_best_day, eth_best_pct, eth_worst_day, eth_worst_pct

(Timestamp('2015-08-13 00:00:00'),
 np.float64(46.3),
 Timestamp('2015-11-04 00:00:00'),
 np.float64(-23.7))

Part 7: Real Cumulative Growth#

eth['Cumulative'] = (1 + eth['Return'].fillna(0)).cumprod()
round(eth['Cumulative'].iloc[-1], 3)

np.float64(0.981)

Part 8: Real Maximum Drawdown#

running_peak = eth['Cumulative'].cummax()
drawdown = (eth['Cumulative'] - running_peak) / running_peak
round(drawdown.min() * 100, 1)

np.float64(-77.7)

Part 9: Visualizing Real Drawdown#

plt.figure(figsize=(11, 4))
plt.fill_between(eth['Date'], drawdown * 100, 0, color='crimson', alpha=0.6)
plt.xlabel('Real Date')
plt.ylabel('Real Drawdown (%)')
plt.title('Real Ethereum Drawdown from Peak')
plt.tight_layout()
plt.savefig('eth_drawdown.png', dpi=120)
plt.close()

Part 10: Real On-Chain Activity, Active Addresses#

eth['AdrActCnt'].describe().round(0)
plt.figure(figsize=(11, 4))
plt.plot(eth['Date'], eth['AdrActCnt'], color='teal')
plt.xlabel('Real Date')
plt.ylabel('Real Active Addresses')
plt.title('Real Ethereum Daily Active Addresses')
plt.tight_layout()
plt.savefig('active_addresses.png', dpi=120)
plt.close()

Part 11: Real Correlation, Price vs Real Network Activity#

price_activity_corr = eth['PriceUSD'].corr(eth['AdrActCnt'])
round(price_activity_corr, 3)

np.float64(0.02)

Part 12: Real Transaction Count Trend#

eth['TxCnt'].describe().round(0)
tx_growth = eth['TxCnt'].iloc[-1] / eth['TxCnt'].iloc[0] - 1
round(tx_growth * 100, 1)

np.float64(277.0)

Part 13: Real Network Hash Rate#

eth['HashRate'].describe().round(4)
hash_growth = eth['HashRate'].iloc[-1] / eth['HashRate'].iloc[0] - 1
round(hash_growth * 100, 1)

np.float64(425.9)

Part 14: Real Rolling 14-Day Volatility#

eth['Vol14'] = eth['Return'].rolling(14).std() * np.sqrt(365)
eth['Vol14'].dropna().describe().round(2)

count    144.00
mean       1.66
std        0.76
min        0.50
25%        1.07
50%        1.63
75%        2.23
max        3.70
Name: Vol14, dtype: float64

Part 15: Visualizing Real Rolling Volatility#

plt.figure(figsize=(11, 5))
plt.plot(eth['Date'], eth['Vol14'], color='darkorange')
plt.xlabel('Real Date')
plt.ylabel('Real Annualized 14-Day Volatility')
plt.title('Real Rolling Volatility, Ethereum')
plt.tight_layout()
plt.savefig('eth_rolling_vol.png', dpi=120)
plt.close()

Part 16: Real Market Cap vs Real Price#

cap_growth = eth['CapMrktCurUSD'].iloc[-1] / eth['CapMrktCurUSD'].iloc[0] - 1
round(cap_growth * 100, 1)

np.float64(3.8)

Part 17: Real Trading Volume Spikes#

volume_median = eth['VolumeUSD'].median()
high_volume_days = (eth['VolumeUSD'] > volume_median * 3).sum()
high_volume_days

np.int64(17)

Part 18: Real Volume-Return Relationship#

abs_return = eth['Return'].abs()
volume_return_corr = eth['VolumeUSD'].corr(abs_return)
round(volume_return_corr, 3)

np.float64(0.479)

Part 19: Real Value-at-Risk for Crypto#

var_95 = np.percentile(eth['Return'].dropna(), 5)
round(var_95 * 100, 2)

np.float64(-15.62)

Part 20: Real Skewness and Real Kurtosis#

eth_skew = eth['Return'].skew()
eth_kurtosis = eth['Return'].kurtosis()
round(eth_skew, 3), round(eth_kurtosis, 3)

(np.float64(0.783), np.float64(3.1))

Part 21: Real 7-Day Moving Average Smoothing#

eth['MA7'] = eth['PriceUSD'].rolling(7).mean()
plt.figure(figsize=(11, 5))
plt.plot(eth['Date'], eth['PriceUSD'], alpha=0.4, label='Real Daily Price')
plt.plot(eth['Date'], eth['MA7'], color='black', label='Real 7-Day Average')
plt.xlabel('Real Date')
plt.ylabel('Real Price (USD)')
plt.title('Real Ethereum Price with 7-Day Smoothing')
plt.legend()
plt.tight_layout()
plt.savefig('eth_smoothed_price.png', dpi=120)
plt.close()

Part 22: Real Weekly Return Aggregation#

eth_indexed = eth.set_index('Date')
weekly_return = eth_indexed['PriceUSD'].resample('W').last().pct_change()
weekly_return.dropna().describe().round(3)

count    23.000
mean      0.015
std       0.191
min      -0.339
25%      -0.099
50%       0.010
75%       0.084
max       0.586
Name: PriceUSD, dtype: float64

Part 23: Real Best and Real Worst Week#

best_week = weekly_return.idxmax()
worst_week = weekly_return.idxmin()
round(weekly_return.max() * 100, 1), round(weekly_return.min() * 100, 1)

(np.float64(58.6), np.float64(-33.9))

Part 24: Real Simple Momentum Signal Backtest#

eth['MA_Signal'] = (eth['PriceUSD'] > eth['MA7']).astype(int)
eth['MA_Position'] = eth['MA_Signal'].shift(1).fillna(0)
eth['MA_Strategy_Return'] = eth['MA_Position'] * eth['Return']
strategy_growth = (1 + eth['MA_Strategy_Return'].fillna(0)).cumprod().iloc[-1]
round(strategy_growth, 3), round(eth['Cumulative'].iloc[-1], 3)

(np.float64(0.286), np.float64(0.981))

Part 25: Saving the Real Enriched Dataset#

eth.round(4).to_csv('eth_enriched.csv', index=False)
reloaded = pd.read_csv('eth_enriched.csv')
reloaded.shape[0] == eth.shape[0]

True

Part 26: Real Sanity Check on Cumulative Growth#

manual_final_price_ratio = eth['PriceUSD'].iloc[-1] / eth['PriceUSD'].iloc[0]
round(abs(manual_final_price_ratio - eth['Cumulative'].iloc[-1]), 6) < 1e-6

np.True_

Part 27: Real Recap Print#

print(f'Across {len(eth)} real early trading days, Ethereum realized {round(eth_annual_vol*100)}% annualized volatility and a {round(drawdown.min()*100,1)}% maximum drawdown, both far beyond anything seen from real stocks earlier in this domain.')

Across 158 real early trading days, Ethereum realized 191% annualized volatility and a -77.7% maximum drawdown, both far beyond anything seen from real stocks earlier in this domain.

Part 28: Real Fee Revenue Trend#

fee_col_present = 'FeeTotNtv' in eth.columns
fee_col_present

False

Part 29: Real Days of Extreme Moves#

extreme_days = eth[eth['Return'].abs() > 0.15]
len(extreme_days)

Part 30: Real Comparison Table, Crypto vs Prior Stocks#

comparison_table = pd.DataFrame({'Asset': ['Ethereum (this video)', 'Apple (real, prior video)', 'Tesla (real, prior video)'], 'AnnualizedVolPct': [round(eth_annual_vol * 100, 1), 21.8, 62.0]})
comparison_table

	Asset	AnnualizedVolPct
0	Ethereum (this video)	190.8
1	Apple (real, prior video)	21.8
2	Tesla (real, prior video)	62.0

Wrap-Up: What You Learned#

Crypto markets genuinely trade every real day of the year, so annualizing volatility uses real 365 days instead of the real 252 trading days used for stocks.
Real on-chain metrics, active addresses, transaction counts, and hash rate, give crypto a real layer of fundamental data that ordinary stocks simply do not publish.
Volatility, drawdown, skewness, and kurtosis all confirmed the real same thing here: crypto in its early years moved far more violently than any real stock in this domain.
Price and real network usage do not always move together, a real reminder that speculation and real fundamentals can diverge, especially early in an asset's history.
The real same backtesting discipline from two videos ago, lagging signals and tracking real trades honestly, applies just as much to crypto as to equities.
Next video: pulling real company fundamentals straight from real financial statements, a completely different real data source than anything used so far in this domain.

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