If it all worked perfectly (in an ideal world), you could in theory leave the system to run and “print money” hands-off. However, fully automated systems—especially ones reliant on social media data—typically still require ongoing effort to remain profitable and avoid pitfalls. Here’s why:

1. Market & Model Drift
   • Markets evolve constantly. A model that’s profitable now may degrade if investor sentiment, global economics, or regulatory landscapes shift.
   • You’ll need to retrain or fine-tune your NLP/LLM components and trading logic periodically to keep up with new slang, emerging platforms, or changing correlations.
2. Data & Source Shifts
   • Social media APIs, rate limits, and Terms of Service can change unexpectedly (Twitter/X is a classic example).
   • Some data sources may become paywalled, or certain influencer accounts might lose relevance. You’ll have to adapt your pipeline accordingly.
3. False Positives & Noise
   • Automated systems risk acting on bot-driven hype, fake news, or manipulated trends. You’ll want to keep refining your bot detection or source credibility metrics to minimize losses from misinformation.
4. Monitoring & Maintenance
   • Even the best algorithmic trading systems need to be monitored for software glitches, API breaks, or outlier events (e.g., “flash crashes,” black swan market conditions).
   • Without regular oversight, a system might make irrational trades if it encounters unexpected data (e.g., an incorrectly parsed message causing the AI to misinterpret sentiment).
5. Risk Management
   • Automated stop-losses, position sizing, and hedging help, but unexpected spikes in volatility or liquidity issues can still cause significant drawdowns if you’re not actively watching your portfolio and adjusting parameters.
6. Competitive Pressure
   • You aren’t the only one automating trades. Other sophisticated players—hedge funds, high-frequency trading firms—also have advanced tools. Markets can become more efficient over time, forcing continuous improvement.
7. Scaling & Cost
   • If your system expands (more data, more sources, more trades), cloud or GPU costs can skyrocket. You’ll need to optimize your architecture to keep it profitable.

Bottom Line

While automation can drastically reduce manual effort and potentially generate passive returns, zero human involvement is risky. You’ll likely want to:

• Regularly monitor key metrics (win rate, drawdowns, average trade duration, etc.).
• Review model performance and logs to spot anomalies or changes in behavior.
• Schedule maintenance (like retraining or updating rules) to ensure the system stays aligned with current market and linguistic realities.

So yes, you’ll do far less day-to-day grunt work, but maintaining a profitable, fully automated trading system still requires ongoing supervision and iteration.