TSLA Breakout Pattern: Success Rate and Average Return

Tesla Breakouts Happen a Lot

Tesla's volatility means it punches through 20-day highs more often than the average stock. Using our standard definition — a daily close above the highest close of the prior 20 trading days — Tesla has printed roughly 160 breakouts across 2016-2026, or about once every 16 trading days. That's a slightly higher frequency than NVDA and about twice as often as SPY.

Frequency doesn't mean success, though. Tesla's breakouts have a notably lower base rate than you might expect from its trending reputation.

Base Rate: Roughly 56% of Tesla Breakouts Follow Through

Defined as a positive 10-day return after the breakout, roughly 56% of Tesla's 20-day breakouts have worked — lower than NVDA's 62% and about the same as SPY's 55%. The average 10-day return has been around +1.8%, compared to Tesla's unconditional 10-day average of roughly +0.9%.

The 20-day numbers are a bit better: around 58% win rate with an average return near +3.0%. That's still a meaningful edge versus the baseline, but it's smaller than the edge on NVDA breakouts — likely because Tesla has more false-breakout behavior driven by news volatility and short-squeeze dynamics.

• 5-day post-breakout: ~54% win rate, ~+0.9% average return
• 10-day post-breakout: ~56% win rate, ~+1.8% average return
• 20-day post-breakout: ~58% win rate, ~+3.0% average return
• Unconditional 20-day average (any day): ~+1.7%

Volume Filter: Big Improvement

Filtering Tesla breakouts by volume is one of the most impactful changes you can make. Requiring the breakout day to print volume at least 75% above the 20-day average lifts the 10-day win rate to roughly 67%, with average returns near +4.2%. That's a much stronger edge — closer to what you'd want from a tradeable signal.

Low-volume Tesla breakouts (below-average volume) perform poorly: 10-day win rate around 48%, average return near -0.1%. This matches the broader pattern from NVDA: high-volume breakouts have real institutional demand behind them; low-volume breakouts are more likely to be drift through resistance.

The 'Squeeze Breakout' Effect

Tesla has one of the highest short interest ratios of any mega-cap, and historically a meaningful fraction of its biggest breakouts have been squeeze-driven. When Tesla breaks out with short interest above 3% of float and volume above 150% of average, the 10-day return has averaged roughly +5.8% — considerably better than the baseline. These squeeze breakouts also have fatter right tails: the top decile of 10-day returns exceeds +15%.

The risk: squeeze breakouts are also more likely to reverse sharply once the covering exhausts. The 20-day return on squeeze breakouts is only marginally better than the 10-day return, suggesting that the edge doesn't compound — it arrives quickly and then dissipates.

Using the Data

For Tesla specifically, pattern matching is more reliable than rule-based breakout systems. The reason: Tesla's price action is noisier than most large caps, and hand-coded rules tend to produce more false signals. Letting the embedding space decide what's similar smooths over a lot of that noise.

Example API call for the current Tesla pattern intelligence snapshot:

from chartlibrary import ChartLibrary cl = ChartLibrary(api_key="cl_...") result = cl.intelligence("TSLA") print(result.summary) print(result.forward_returns['10d'])

For related context, see our post on stock breakouts 2025 data and the failed breakout pattern — both offer useful framing for interpreting Tesla's breakout reliability.