AMD Breakout Pattern: Success Rate and Average Return
AMD Breakouts: Frequent and Moderately Reliable
AMD has printed roughly 150 20-day breakouts across 2016-2026 — about once every 17 trading days, a frequency similar to NVDA and Tesla. This reflects AMD's volatility profile and the fact that the stock has spent long stretches in uptrends during the AI cycle.
The base rates are solid: roughly 60% of AMD 20-day breakouts have produced a positive 10-day forward return, with an average move near +2.6%. That's better than Tesla's 56% but slightly worse than NVDA's 62%. AMD sits in the middle of the semiconductor-momentum pack.
Base Rates by Timeframe
The breakout edge grows with holding period. AMD's 5-day forward return after a 20-day breakout has averaged roughly +1.4% with a 58% win rate. The 10-day return has averaged around +2.6% with a 60% win rate. The 20-day return has averaged roughly +4.3% with a 62% win rate.
This increasing-with-horizon pattern is classic momentum. Winning breakouts compound their gains; losing breakouts tend to fail early and get cut from the sample. Over a 20-day window, the winners dominate the average.
- 5-day post-breakout: ~58% win rate, ~+1.4% average return
- 10-day post-breakout: ~60% win rate, ~+2.6% average return
- 20-day post-breakout: ~62% win rate, ~+4.3% average return
- Unconditional 20-day average (any day): ~+2.0%
Volume Filter Effect
Adding a volume filter (requiring breakout-day volume at least 50% above the 20-day average) improves the 10-day win rate to roughly 68% with average returns near +4.1%. This is meaningful alpha versus the unfiltered baseline — and versus most rule-based systems traders apply to AMD.
Low-volume breakouts on AMD perform poorly: 10-day win rate around 52%, average return near 0.5%. As with other semis, the volume filter is essentially screening for institutional participation. Breakouts without that participation are usually drift-through-resistance events with no real demand behind them.
AMD Breakouts in Macro Context
AMD's breakout performance depends heavily on the broader semiconductor cycle. During periods when the SOXX ETF was in an uptrend (SOXX above its 50-day moving average), AMD's breakouts have had a 10-day win rate near 68% with average returns of roughly +3.4%. During periods when SOXX was below its 50-day, the win rate dropped to roughly 50% with average returns near 0%.
This is one of the cleanest examples of macro conditioning improving a momentum signal. Buying AMD breakouts only when semis are in an uptrend has historically eliminated most of the losing trades without sacrificing much upside.
Note:Chart Library's sector rotation endpoint gives you the current state of semiconductor strength in one call. Combining it with the AMD pattern search provides a cleaner signal than either alone.
Using the Data
The most effective workflow: check SOXX trend first (via the sector rotation endpoint), then run AMD pattern search. If semis are strong and AMD's pattern matches historical breakouts with positive forward returns, the setup is statistically supported.
from chartlibrary import ChartLibrary cl = ChartLibrary(api_key="cl_...") sectors = cl.sector_rotation() amd = cl.intelligence("AMD") print(f"Semis rank: {sectors['XLK']['rank']}") print(f"AMD 10d avg: {amd.forward_returns['10d']['mean']:.1%}")
Related reading: our posts on NVDA breakout success rate and stock breakouts 2025 data offer comparable data on adjacent tickers.
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