Tesla Stock Performance 2010-2025: A Data-Driven Overview

The Dataset That Tells a Story

Tesla’s stock history from 2010 to 2025 is one of the most dramatic runs in modern market history. The Kaggle dataset covering this period isn’t just a collection of OHLC (Open, High, Low, Close) values—it’s a record of a company that went from near-bankruptcy to the most valuable automaker on the planet, then back to earth, then up again.

I pulled the dataset expecting clean, boring numbers. What I got was a masterclass in volatility.

The data structure is straightforward: daily records with Date, Open, High, Low, Close, Adj Close, and Volume. Standard stuff for stock price analysis in Python, though this dataset predates some of the more convenient APIs. The interesting part isn’t the schema—it’s what happens when you plot it.

Early Years: The Quiet Period (2010-2013)

Tesla went public on June 29, 2010, at $17 per share (split-adjusted to around $1.13 after the 5-for-1 split in 2020). The first three years were remarkably stable by Tesla standards. The stock traded in a range, occasionally dipping below the IPO price, occasionally spiking on delivery news.

This is the period most analysts ignore because nothing “exciting” happened. But it’s actually the most informative baseline. The daily volatility $sigma$ during 2010-2012 averaged around 3-4%, calculated as:

$$sigma_{text{daily}} = sqrt{frac{1}{N-1} sum_{i=1}^{N} (r_i – bar{r})^2}$$

where $r_i = frac{P_{i} – P_{i-1}}{P_{i-1}}$ is the daily return. That’s high for a typical stock, but calm for what was coming.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load the dataset (assuming CSV with Date, Open, High, Low, Close, Volume)
df = pd.read_csv('tesla_2010_2025.csv', parse_dates=['Date'])
df = df.sort_values('Date').reset_index(drop=True)

# Calculate daily returns
df['Return'] = df['Close'].pct_change()

# Early period volatility (2010-2013)
early_period = df[(df['Date'] >= '2010-06-29') & (df['Date'] < '2014-01-01')]
early_vol = early_period['Return'].std() * np.sqrt(252)  # Annualized
print(f"Early period annualized volatility: {early_vol:.2%}")
# Output: Early period annualized volatility: 52.34%

That 52% annualized volatility is already higher than most tech stocks, but it’s nothing compared to what came later.

The First Breakout: Model S and Profitability (2013-2014)

In 2013, Tesla did something unexpected: it reported a profit. Not for the full year, but for Q1 2013. The stock jumped from around $2.50 (split-adjusted) in January 2013 to over $12 by September. That’s a 380% run in eight months.

This is where the dataset starts showing extreme daily moves. On May 9, 2013, the stock jumped 24% in a single day after the profitability announcement. The distribution of daily returns during this period stopped looking Gaussian and started showing fat tails—a classic sign of regime change.

The cumulative return from IPO to the 2013 peak was roughly 580%. For context, the S&P 500 returned about 60% over the same period. But here’s the thing: if you bought at the 2013 peak, you’d have been underwater for months. The stock pulled back to $8 by February 2014 before resuming the climb.

# Plot cumulative returns vs S&P 500 (assuming we have SPY data)
df['Cumulative_Return'] = (1 + df['Return']).cumprod()

plt.figure(figsize=(12, 6))
plt.plot(df['Date'], df['Cumulative_Return'], label='Tesla', linewidth=1.5)
plt.axhline(y=1, color='gray', linestyle='--', alpha=0.5)
plt.xlabel('Date')
plt.ylabel('Cumulative Return (Normalized to 1.0)')
plt.title('Tesla Cumulative Returns 2010-2025')
plt.legend()
plt.grid(alpha=0.3)
plt.yscale('log')  # Log scale because this gets ridiculous
plt.tight_layout()
plt.show()

The log scale is necessary because the returns span multiple orders of magnitude. Linear scale makes the early years invisible.

The 2017-2019 Rollercoaster: Production Hell

Model 3 was supposed to save Tesla. Instead, it nearly killed the company. The stock peaked at around $23 (split-adjusted) in June 2017, then spent the next two years in what Elon Musk called “production hell.”

The dataset during this period is a mess of contradictory signals. Record deliveries would send the stock up 10%, then a cash flow warning would erase it the next week. The average daily trading volume during 2018 was 2.5x higher than 2016—everyone was trying to trade the volatility, and most were getting chopped up.

I’m not entirely sure why the stock bottomed at $10.50 in June 2019 (down 54% from the 2017 peak) when the fundamentals were arguably improving. My best guess is that the market was pricing in bankruptcy risk that never materialized. The bond market certainly thought Tesla was toast—yields on their debt hit distressed levels.

But here’s where the dataset gets interesting for statistical analysis. The autocorrelation of daily returns during 2018-2019 was near zero, meaning yesterday’s move told you nothing about today’s. But the autocorrelation of absolute returns (volatility clustering) was significant:

$$rho_{|r|}(k) = frac{text{Cov}(|r_t|, |r_{t-k}|)}{sigma_{|r|}^2}$$

High volatility days tended to follow high volatility days, even if the direction was random. This is a hallmark of markets driven by news flow rather than fundamental revaluation.

The Pandemic Surge: 2020-2021

Then COVID hit, and the dataset stops making sense by any traditional valuation model. The stock went from $30 in March 2020 to $240 (split-adjusted) by January 2021. That’s a 700% gain in ten months, during a global pandemic that shut down car factories.

The mechanics were straightforward: S&P 500 inclusion in December 2020 forced index funds to buy $80 billion worth of shares, retail traders piled in via Robinhood, and the options market went parabolic. The gamma squeeze dynamics meant dealers had to buy stock to hedge call options, which pushed the price higher, which triggered more call buying.

The daily volume during the S&P inclusion week averaged 200 million shares—roughly 10% of the float turning over every single day. The dataset shows multiple days where the intraday range (High – Low) was over 15%. You don’t see that in mature large-cap stocks.

# Calculate intraday range as percentage of open
df['Intraday_Range_Pct'] = (df['High'] - df['Low']) / df['Open'] * 100

# Find the wildest days
wild_days = df.nlargest(20, 'Intraday_Range_Pct')[['Date', 'Open', 'Close', 'Intraday_Range_Pct', 'Volume']]
print(wild_days)
# Most of these will be from 2020-2021 or the 2022 crash

The wildest day I found was December 21, 2020, with a 17.3% intraday range. The stock opened at $1.130 hit $1.131 dropped to $1.132 and closed at $1.133. If you were trading intraday without stop-losses, this would have destroyed you.

The 2022 Crash: Reality Check

Peak Tesla was $1.134 on November 4, 2021. By January 2023, it had fallen to $1.135—an 86% drawdown. The Nasdaq fell 33% over the same period, so this wasn’t just “tech stocks down.” This was a full repricing.

The dataset during the crash shows a different pattern than 2018-2019. The decline was more systematic, fewer violent bounces, higher correlation with the overall market. The beta to the Nasdaq spiked to 2.5+, meaning Tesla was moving 2.5x as much as the index on average.

What changed? My best guess is that the shareholder base shifted from “believers” to “tourists.” The 2020 buyers were there for the ride; the 2022 sellers were institutions rebalancing. When the Fed started hiking rates, high-duration growth stocks got crushed, and Tesla had the highest implied duration of any mega-cap.

The 2023-2025 Recovery (Sort Of)

From the January 2023 low, the stock rallied back to around $1.136-$1.137 by mid-2025 (as of this dataset). That’s a 200%+ gain from the bottom, but still 55% below the all-time high.

The distribution of returns since 2023 looks more “normal” than any prior period. The fat tails have shrunk, the volatility has dropped to 40-50% annualized (still high, but not insane), and the stock now trades more like a traditional automaker with a tech premium.

Volume has also normalized. The 2020-2021 frenzy averaged 120M shares/day; 2024 averaged 80M. Retail participation (measured by odd-lot volume) has dropped from 25% to 15%. The tourists left.

# Calculate rolling 30-day volatility over the full period
df['Rolling_Vol_30d'] = df['Return'].rolling(30).std() * np.sqrt(252)

plt.figure(figsize=(12, 6))
plt.plot(df['Date'], df['Rolling_Vol_30d'], linewidth=1)
plt.xlabel('Date')
plt.ylabel('Annualized Volatility (30-day rolling)')
plt.title('Tesla 30-Day Rolling Volatility 2010-2025')
plt.axhline(y=0.50, color='red', linestyle='--', alpha=0.5, label='50% vol threshold')
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.show()

The volatility chart looks like a cardiogram for someone having a heart attack in 2020-2021, then slowly recovering.

What the Numbers Actually Tell Us

If you bought at IPO and held through 2025, you’re up roughly 6000% (accounting for splits). The compound annual growth rate (CAGR) is:

$$text{CAGR} = left( frac{P_{text{final}}}{P_{text{initial}}} right)^{frac{1}{n}} – 1$$

Assuming $P_{text{final}} approx DOLLAR_AMOUNT_18$ and $P_{text{initial}} approx DOLLAR_AMOUNT_19$ (split-adjusted) over 15 years:

$$text{CAGR} = left( frac{100}{1.13} right)^{frac{1}{15}} – 1 approx 0.337 = 33.7$$

That’s phenomenal. But the max drawdown was 86%, and there were multiple 50%+ crashes along the way. The Sharpe ratio—risk-adjusted return—is decent but not spectacular once you account for the volatility.

And here’s the uncomfortable truth: timing mattered more than conviction. If you bought in late 2019 and sold in late 2021, you 8x’d your money in two years. If you bought in late 2021 and held through 2022, you lost 80%. “Just hold forever” works in hindsight, but the psychological toll of watching your account drop 86% is not something most people can handle.

Distribution Characteristics and Tail Risk

The daily return distribution for Tesla over the full 2010-2025 period is far from normal. The kurtosis (fourth moment) is around 12, compared to 3 for a Gaussian. This means extreme moves happen way more often than a normal distribution would predict.

from scipy import stats

# Calculate distribution stats
returns = df['Return'].dropna()
mean_return = returns.mean()
std_return = returns.std()
skewness = stats.skew(returns)
kurtosis = stats.kurtosis(returns, fisher=True)  # Excess kurtosis

print(f"Mean daily return: {mean_return:.4f}")
print(f"Std dev: {std_return:.4f}")
print(f"Skewness: {skewness:.2f}")
print(f"Excess kurtosis: {kurtosis:.2f}")
# Output:
# Mean daily return: 0.0022
# Std dev: 0.0387
# Skewness: 0.34
# Excess kurtosis: 12.18

Positive skewness (0.34) means the right tail is fatter than the left—big up days are more extreme than big down days, though both happen frequently. This is consistent with short-squeeze and gamma-squeeze dynamics.

If you were building a risk management model for a portfolio holding Tesla, using historical volatility alone would underestimate tail risk. Value-at-Risk (VaR) at the 99th percentile based on a normal distribution would miss the true downside by a wide margin.

Volume Patterns and Liquidity

One underappreciated aspect of this dataset: volume exploded over time, but not uniformly. The average daily dollar volume (price × shares) in 2010 was around $2.500M. By 2021, it was $2.501B+. That’s a 200x increase.

But liquidity isn’t just about volume—it’s about depth and resilience. The bid-ask spread (not in this dataset, but observable in tick data) during the 2020-2021 mania was often 20-30 cents on a $2.502 stock. That’s 0.1-0.15%, which is fine for retail but painful for large institutional orders. I’ve heard from traders that moving $2.503M+ in Tesla required multi-day VWAP strategies, even at peak liquidity.

Post-2022, spreads tightened and depth improved. The market microstructure normalized. This matters if you’re backtesting trading strategies—assuming infinite liquidity at the close price will give you wildly optimistic results for 2020-2021.

What This Dataset Doesn’t Tell You

Historical price data is just one dimension. It doesn’t capture:

• Sentiment shifts: The transition from “Tesla is a scam” (2018) to “Tesla is the future” (2020) to “Tesla is overvalued” (2022) happened in news flow and social media, not in OHLC bars.
• Options market dynamics: The gamma squeeze in 2020 and the put wall in 2022 were driven by derivatives, which aren’t in this dataset.
• Macroeconomic regime changes: Fed policy, interest rates, and commodity prices all moved during this period and affected Tesla disproportionately.
• Company fundamentals: Revenue, deliveries, margins, and cash flow are in earnings reports, not stock prices. The dataset shows reactions to fundamentals, not the fundamentals themselves.

If you’re serious about understanding Tesla’s stock, you need to merge this price data with fundamental data, options flow, and macro indicators. The price alone is just the outcome.

Why This Matters for Part 2

The patterns I’ve outlined here—regime changes, volatility clustering, fat tails, liquidity shifts—aren’t just historical trivia. They’re the foundation for understanding why the stock moved the way it did.

In the next part, I’ll dig into specific events: the S&P inclusion mechanics, the 2022 Twitter acquisition fallout, the China demand shocks, and how each of these translated into price action. The dataset has the footprints; now we’ll reconstruct the crime scene.

For now, the takeaway is simple: Tesla’s stock is not a random walk, and it’s not a well-behaved log-normal process. It’s a series of discrete regimes, each with different volatility, correlation, and tail characteristics. If your time series forecasting model assumes stationarity, it’s going to fail spectacularly.

Use this data to build intuition, not to extrapolate. The past 15 years were unprecedented. The next 15 won’t be the same.