NVDA options trade analysis
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Market Analysis10. February 20265 min read

NVDA $180 Call: +340% in 4 Days

How the DeepSeek anniversary and three catalysts created the perfect trade.

NVDA $180 Call
+340%
Result
+$14.300
Daniel Richter
Daniel Richter·Lead Quantitative Analyst

February 5, 2026 — exactly one year after the DeepSeek crash that wiped $589 billion from NVIDIA's market cap. The stock sat at $172, forming a pivot bottom signal. What followed was a trade that returned +340% in just 4 days.

Asset
NVDA
Option
$180 Call, Feb 21
Entry
$4.20
Exit
$18.50
+$14.300(+340%)
10 Contracts

Important Notice

Market data in this article is real. Trade scenarios are hypothetical examples for educational purposes. Options trading involves significant risk of loss.

The Catalyst

On February 5, 2026 — exactly one year after the DeepSeek crash — NVDA formed a pivot bottom signal at $172. The stock had been under pressure in prior weeks, but three catalytic events converged:

1Goldman Sachs forecast Q4 revenue of $67.3B — $2B above consensus of $65.5B.
2NVIDIA announced shipment of 40,000-80,000 H200 chips to Chinese customers (worth $1.28-$2.56B).
3The Vera Rubin platform gave the company visibility to ~$500B in Blackwell and Rubin revenue through end of 2026.

Technical Analysis

  • Pattern:Bullish engulfing pattern on 4H timeframe at $172 support. Pivot buy signal confirmed on February 5.
  • Levels:Price broke above 50 EMA at $178, then resistance at $185.40 with significant volume spike (+180% vs. 20-day average).
  • Signal:MACD crossover in positive territory confirmed momentum. RSI rose from 38 to 67 without becoming overbought.
NVDAFeb 5, 2026
4H
Support / Pivot Bottom
$172.00
50 EMA
$178.00
Resistance
$185.40
Options Entry
$4.20
Options Exit
$18.50
RSI↑ 38 → 67
MACD↑ Positive Crossover
Volume↑ +180%

Key Takeaway

Anniversaries of major market events create predictable narratives. The DeepSeek crash of January 2025 created a "recovery anniversary" effect — analysts and media drove sentiment. The smart trader positioned at the pivot bottom, not at the top.

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Frequently Asked Questions

The market data (prices, dates, events) is real and verifiable. The specific trade scenarios are hypothetical examples based on this real data to illustrate learning outcomes. Names and amounts have been modified for educational purposes.
Such returns are possible but rare and carry significant risk. Most options expire worthless. Successful traders have strict risk management rules and never risk more than 2-5% of their portfolio per trade.
The most common mistake is ignoring Implied Volatility (IV). Traders buy options when IV is already elevated, pay inflated premiums, and lose money despite correct directional calls due to IV crush.

Glossary: Key Terms

The market's expectation of future price movement priced into the option premium. High IV = expensive options.
Compares current IV to its 52-week range. IV Rank >80% means options are historically expensive.
Measures the option's price change per $1 move in the underlying. Delta 0.50 = option moves ~$0.50 per $1 stock move.
The rate of change of delta. High gamma means delta changes rapidly — critical during fast price moves.
The total number of outstanding option contracts. Rising OI signals new money and conviction — falling OI signals position closing.

Disclaimer

The information on this page is for educational purposes only and does not constitute investment advice. Options trading involves significant risks and is not suitable for all investors.

*Names and amounts modified for privacy. Examples are hypothetical. Past performance is not indicative of future results.*

Sources

Daniel Richter

Author

Daniel Richter

Lead Quantitative Analyst

AI Options Strategist

15++ YearsCFA-aligned expertiseFRM framework knowledge

Daniel Richter combines deep market expertise with cutting-edge AI technology. After studying Financial Mathematics at TU Munich and several years at leading investment banks in Frankfurt, he specialized in quantitative trading strategies. At BeInOptions, Daniel leads the analytics team and develops data-driven options strategies. His strength lies in combining classical financial analysis with machine learning – using AI models to identify market patterns and assess risk. "My goal is to make complex options strategies accessible to everyone while leveraging modern analytical tools to make informed decisions."

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