2) The exact Python script (run this locally or ask me to run it)<\/strong><\/p>\n\n\n\nRequirements: Python 3.8+, yfinance, pandas, numpy, matplotlib, scipy (optional).
# btc_vs_equities_performance.py<\/p>\n\n\n\n
# Reproducible script to produce:<\/p>\n\n\n\n
# – log-scale cumulative returns chart for BTC, QQQ, SPY, XIT (10 years)<\/p>\n\n\n\n
# – CAGR table for 1\/3\/5\/10 years (exact dates)<\/p>\n\n\n\n
# – Risk-adjusted metrics: Annualized Sharpe, Annualized Volatility, Max Drawdown<\/p>\n\n\n\n
#<\/p>\n\n\n\n
# Usage: python btc_vs_equities_performance.py<\/p>\n\n\n\n
# (or run each cell in a Jupyter Notebook)<\/p>\n\n\n\n
import yfinance as yf<\/p>\n\n\n\n
import pandas as pd<\/p>\n\n\n\n
import numpy as np<\/p>\n\n\n\n
import matplotlib.pyplot as plt<\/p>\n\n\n\n
from datetime import datetime, timedelta<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# User-configurable:<\/p>\n\n\n\n
tickers = {<\/p>\n\n\n\n
“Bitcoin”: “BTC-USD”,<\/p>\n\n\n\n
“QQQ”: “QQQ”,<\/p>\n\n\n\n
“S&P500 (SPY)”: “SPY”,<\/p>\n\n\n\n
“XIT (TSX Tech)”: “XIT.TO” # change if your broker uses a different suffix<\/p>\n\n\n\n
}<\/p>\n\n\n\n
end_date = pd.to_datetime(“today”).normalize() # or set explicit e.g. pd.to_datetime(“2025-11-28”)<\/p>\n\n\n\n
start_date = end_date – pd.DateOffset(years=10) # 10 years back<\/p>\n\n\n\n
risk_free_rate_annual = 0.03 # example: 3% annual risk-free (adjust if you prefer)<\/p>\n\n\n\n
trading_days = 252<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# Fetch data<\/p>\n\n\n\n
def fetch_close_prices(tickers, start, end):<\/p>\n\n\n\n
data = yf.download(list(tickers.values()), start=start.strftime(“%Y-%m-%d”),<\/p>\n\n\n\n
end=(end + pd.Timedelta(days=1)).strftime(“%Y-%m-%d”),<\/p>\n\n\n\n
progress=False)[“Adj Close”]<\/p>\n\n\n\n
data.columns = list(tickers.keys())<\/p>\n\n\n\n
return data.dropna(how=”all”)<\/p>\n\n\n\n
prices = fetch_close_prices(tickers, start_date, end_date)<\/p>\n\n\n\n
if prices.isnull().any().any():<\/p>\n\n\n\n
prices = prices.fillna(method=’ffill’).dropna(how=’all’) # simple fill-forward<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# Compute daily returns<\/p>\n\n\n\n
returns = prices.pct_change().dropna()<\/p>\n\n\n\n
# Cumulative returns (log scale for plotting)<\/p>\n\n\n\n
cum_returns = (1 + returns).cumprod()<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# Functions for metrics<\/p>\n\n\n\n
def annualized_return_from_period(start_price, end_price, n_years):<\/p>\n\n\n\n
return (end_price \/ start_price) ** (1.0 \/ n_years) – 1.0<\/p>\n\n\n\n
def compute_cagr(series, start_date, end_date):<\/p>\n\n\n\n
# series is price series (pandas Series)<\/p>\n\n\n\n
p0 = series.loc[start_date]<\/p>\n\n\n\n
p1 = series.loc[end_date]<\/p>\n\n\n\n
days = (end_date – start_date).days<\/p>\n\n\n\n
years = days \/ 365.25<\/p>\n\n\n\n
return (p1 \/ p0) ** (1.0 \/ years) – 1.0<\/p>\n\n\n\n
def annualized_volatility(daily_ret):<\/p>\n\n\n\n
return daily_ret.std() * np.sqrt(trading_days)<\/p>\n\n\n\n
def sharpe_ratio(daily_ret, rf_annual=0.03):<\/p>\n\n\n\n
excess_daily = daily_ret – (rf_annual \/ trading_days)<\/p>\n\n\n\n
ann_excess = excess_daily.mean() * trading_days<\/p>\n\n\n\n
ann_vol = annualized_volatility(daily_ret)<\/p>\n\n\n\n
if ann_vol == 0:<\/p>\n\n\n\n
return np.nan<\/p>\n\n\n\n
return ann_excess \/ ann_vol<\/p>\n\n\n\n
def max_drawdown(price_series):<\/p>\n\n\n\n
running_max = price_series.cummax()<\/p>\n\n\n\n
drawdown = (price_series – running_max) \/ running_max<\/p>\n\n\n\n
return drawdown.min() # negative number<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# Prepare CAGR windows (end dates inclusive)<\/p>\n\n\n\n
end = prices.index[-1]<\/p>\n\n\n\n
windows = {<\/p>\n\n\n\n
“1Y”: end – pd.DateOffset(years=1),<\/p>\n\n\n\n
“3Y”: end – pd.DateOffset(years=3),<\/p>\n\n\n\n
“5Y”: end – pd.DateOffset(years=5),<\/p>\n\n\n\n
“10Y”: end – pd.DateOffset(years=10)<\/p>\n\n\n\n
}<\/p>\n\n\n\n
# Align window start dates to nearest available index<\/p>\n\n\n\n
def align_date(series_index, dt):<\/p>\n\n\n\n
# choose the first available index on or after dt<\/p>\n\n\n\n
idx = series_index[series_index >= dt]<\/p>\n\n\n\n
if len(idx) == 0:<\/p>\n\n\n\n
return series_index[0]<\/p>\n\n\n\n
return idx[0]<\/p>\n\n\n\n
cagr_table = []<\/p>\n\n\n\n
metrics_table = []<\/p>\n\n\n\n
for name in prices.columns:<\/p>\n\n\n\n
series = prices[name]<\/p>\n\n\n\n
daily = returns[name]<\/p>\n\n\n\n
# CAGR for windows<\/p>\n\n\n\n
cagr_row = {“Instrument”: name}<\/p>\n\n\n\n
for label, dt in windows.items():<\/p>\n\n\n\n
start_aligned = align_date(prices.index, dt)<\/p>\n\n\n\n
try:<\/p>\n\n\n\n
cagr = compute_cagr(series, start_aligned, end)<\/p>\n\n\n\n
except Exception as e:<\/p>\n\n\n\n
cagr = np.nan<\/p>\n\n\n\n
cagr_row[label] = cagr<\/p>\n\n\n\n
cagr_row[f”{label}_start_date”] = start_aligned.date()<\/p>\n\n\n\n
cagr_table.append(cagr_row)<\/p>\n\n\n\n
# Risk metrics (annualized)<\/p>\n\n\n\n
ann_vol = annualized_volatility(daily)<\/p>\n\n\n\n
sr = sharpe_ratio(daily, risk_free_rate_annual)<\/p>\n\n\n\n
md = max_drawdown(series)<\/p>\n\n\n\n
metrics_table.append({<\/p>\n\n\n\n
“Instrument”: name,<\/p>\n\n\n\n
“Annualized Volatility”: ann_vol,<\/p>\n\n\n\n
“Sharpe (ann.)”: sr,<\/p>\n\n\n\n
“Max Drawdown”: md<\/p>\n\n\n\n
})<\/p>\n\n\n\n
cagr_df = pd.DataFrame(cagr_table).set_index(“Instrument”)<\/p>\n\n\n\n
metrics_df = pd.DataFrame(metrics_table).set_index(“Instrument”)<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# Plot log-scale cumulative returns (normalized to 1 at the start of the plotted period)<\/p>\n\n\n\n
plt.style.use(‘seaborn-darkgrid’)<\/p>\n\n\n\n
plt.figure(figsize=(12,6))<\/p>\n\n\n\n
# Normalize each series to 1 at the first date<\/p>\n\n\n\n
normed = prices \/ prices.iloc[0]<\/p>\n\n\n\n
for col in normed.columns:<\/p>\n\n\n\n
plt.plot(normed.index, np.log(normed[col]), label=col) # log scale by plotting log of normed<\/p>\n\n\n\n
plt.legend()<\/p>\n\n\n\n
plt.title(“Log Cumulative Returns (normalized) \u2014 BTC vs QQQ vs SPY vs XIT”)<\/p>\n\n\n\n
plt.xlabel(“Date”)<\/p>\n\n\n\n
plt.ylabel(“Log of Normalized Price (log scale equivalent)”)<\/p>\n\n\n\n
plt.tight_layout()<\/p>\n\n\n\n
plt.show()<\/p>\n\n\n\n
# ———————–<\/p>\n\n\n\n
# Present table outputs<\/p>\n\n\n\n
pd.set_option(‘display.float_format’, ‘{:.2%}’.format)<\/p>\n\n\n\n
print(“\\nCAGR Table (exact windows):”)<\/p>\n\n\n\n
display_cagr = cagr_df.copy()<\/p>\n\n\n\n
display_cagr = display_cagr[[c for c in [“1Y”, “3Y”, “5Y”, “10Y”] if c in display_cagr.columns]]<\/p>\n\n\n\n
print(display_cagr)<\/p>\n\n\n\n
print(“\\nRisk Metrics (annualized):”)<\/p>\n\n\n\n
print(metrics_df)<\/p>\n\n\n\n
# Save results to CSV for convenience<\/p>\n\n\n\n
cagr_df.to_csv(“cagr_table.csv”)<\/p>\n\n\n\n
metrics_df.to_csv(“risk_metrics.csv”)<\/p>\n\n\n\n
normed.to_csv(“normalized_prices.csv”)<\/p>\n\n\n\n
print(“\\nSaved cagr_table.csv, risk_metrics.csv, normalized_prices.csv”)<\/p>\n\n\n\n
3) How the code works & exact dates<\/strong><\/p>\n\n\n\n\nThe script grabs adjusted close<\/strong> prices using yfinance for the tickers:\n\nBitcoin: BTC-USD<\/li>\n\n\n\n QQQ: QQQ<\/li>\n\n\n\n S&P 500 proxy: SPY<\/li>\n\n\n\n XIT (Canadian tech): XIT.TO (update suffix if your broker uses another form)<\/li>\n<\/ul>\n<\/li>\n\n\n\n End date is today by default (you can set end_date manually \u2014 e.g., 2025-11-28 to match a specific snapshot).<\/li>\n\n\n\n The CAGR windows are 1Y \/ 3Y \/ 5Y \/ 10Y<\/strong> computed precisely using the aligned start date (first trading day on or after the exact date n years ago).<\/li>\n\n\n\nThe Sharpe ratio uses an annual risk-free rate you set (default 3%). It annualizes returns and volatilities.<\/li>\n\n\n\n Max drawdown is computed on the price series (largest peak-to-trough drop).<\/li>\n<\/ul>\n\n\n\n4) Interpreting the results (what to expect when you run it)<\/strong><\/p>\n\n\n\nWhen you run the script you will get:<\/p>\n\n\n\n
\nA log-scale cumulative chart<\/strong> that shows how $1 invested in each asset grew (log axis makes compound growth lines look like slopes, so higher slope = higher CAGR).<\/li>\n\n\n\nA CAGR table<\/strong> with exact start dates for each window \u2014 this tells you annualized growth for each period.<\/li>\n\n\n\nA Risk table<\/strong> showing:\n\nAnnualized volatility<\/strong> (higher = more price movement)<\/li>\n\n\n\nSharpe<\/strong> (higher = more return per unit of risk; if Sharpe is negative or near zero, reward is poor relative to risk)<\/li>\n\n\n\nMax drawdown<\/strong> (a negative number, e.g. \u22120.65 = \u221265% worst peak-to-trough)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\nTypical historical patterns you\u2019re likely to see (once you run it):<\/p>\n\n\n\n
\nBitcoin<\/strong> usually shows the highest CAGRs<\/strong> over 5\u201310 years, but also the highest vol & largest max drawdowns<\/strong> (e.g., many samples show drawdowns of 60\u201380% in bad cycles).<\/li>\n\n\n\nQQQ<\/strong> tends to outperform SPY over 5\u201310 year periods when tech is strong (QQQ is concentrated in tech + growth names).<\/li>\n\n\n\nSPY<\/strong> (S&P 500) typically shows a solid medium CAGR with lower volatility than BTC and QQQ.<\/li>\n\n\n\nXIT.TO<\/strong> (TSX tech) may show varied performance \u2014 it can underperform U.S. tech over the last 10 years due to smaller tech ecosystem in Canada; check the live chart to see exact behaviour.<\/li>\n<\/ul>\n\n\n\n5) Example of how to read the final table<\/strong><\/p>\n\n\n\nAfter running, you might see something like (illustrative only \u2014 run script for real numbers):<\/p>\n\n\n\nInstrument<\/strong><\/td>1Y CAGR<\/strong><\/td>3Y CAGR<\/strong><\/td>5Y CAGR<\/strong><\/td>10Y CAGR<\/strong><\/td>Ann Vol<\/strong><\/td>Sharpe<\/strong><\/td>Max Drawdown<\/strong><\/td><\/tr><\/thead>Bitcoin<\/td> 35%<\/td> 75%<\/td> 80%<\/td> 120%<\/td> 90%<\/td> 0.6<\/td> -0.80<\/td><\/tr> QQQ<\/td> 20%<\/td> 30%<\/td> 25%<\/td> 18%<\/td> 25%<\/td> 0.9<\/td> -0.50<\/td><\/tr> SPY<\/td> 12%<\/td> 18%<\/td> 16%<\/td> 12%<\/td> 18%<\/td> 0.8<\/td> -0.34<\/td><\/tr> XIT<\/td> 8%<\/td> 12%<\/td> 10%<\/td> 7%<\/td> 22%<\/td> 0.45<\/td> -0.60<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nThese numbers are for illustration only \u2014 run the script to get exact, up-to-date numbers and a nice chart.<\/p>\n\n\n\n
6) Plain English takeaways (what the results mean)<\/strong><\/p>\n\n\n\n\nIf growth is your sole criterion<\/strong>: Bitcoin historically often shows the largest compound growth numbers (highest CAGRs). That\u2019s why it\u2019s attractive to growth-seeking investors.<\/li>\n\n\n\nIf you care about risk-adjusted returns<\/strong>: compare Sharpe<\/strong> and max drawdown<\/strong> as well as CAGR. Bitcoin\u2019s huge returns often come with very large drawdowns and higher volatility \u2014 many investors prefer a lower CAGR that comes with far lower drawdowns (easier to hold through downturns).<\/li>\n\n\n\nQQQ vs SPY<\/strong>: QQQ tends to outperform SPY in tech-led bull markets (i.e., higher CAGR) but also can be more volatile. If you believe in tech\u2019s continued outperformance, QQQ historically rewarded that view.<\/li>\n\n\n\nXIT<\/strong>: Canadian tech tends to lag the U.S. tech giants on scale and innovation; don\u2019t be surprised if XIT underperforms QQQ over long horizons \u2014 check the exact numbers after running the script.<\/li>\n<\/ul>\n\n\n\n7) Next steps I can do for you (pick one)<\/strong><\/p>\n\n\n\n