Qtum Price Prediction for July 2025
Qtum is projected to start July 2025 at $2.508** and close the month at **$1.899.
- Maximum forecasted price: $2.285
- Minimum forecasted price: $1.554
- Average price: $1.828 (-10.84% change)
Qtum Price Prediction for August 2025
Qtum is expected to begin August 2025 at $1.937** and conclude at **$1.717.
- Maximum forecasted price: $2.265
- Minimum forecasted price: $1.540
- Average price: $1.812 (-11.62% change)
Qtum Price Prediction for September 2025
Qtum is anticipated to open September 2025 at $1.734** and end the month at **$1.610.
- Maximum forecasted price: $2.242
- Minimum forecasted price: $1.525
- Average price: $1.794 (-12.50% change)
Weekly Qtum Price Forecast (04.07.2025 – 10.07.2025)
| Day | Price Range | Predicted Close |
|---|---|---|
| Today | $1.745 – $2.566 | $2.053 |
| Tomorrow | $1.725 – $2.537 | $2.090 |
| Saturday | Data pending | Data pending |
| Sunday | Data pending | Data pending |
Qtum Price Prediction for 2025
| Month | Minimum Price | Maximum Price | Average Price | Change |
|---|---|---|---|---|
| July 2025 | $1.554 | $2.285 | $1.828 | -10.84% |
| August 2025 | $1.540 | $2.265 | $1.812 | -11.62% |
| December 2025 | $2.105 | $3.095 | $2.476 | +20.79% |
👉 Explore Qtum's long-term growth potential
Qtum Price Prediction for 2026–2029
Key Projections:
- 2026: Average price peaks at $2.453 (January).
- 2027: Volatility expected, with lows of $1.476 (March).
- 2028: Gradual recovery, averaging $2.591 by December.
- 2029: Potential highs of $3.673 (December).
Qtum Price Forecast FAQ
1. What is Qtum’s predicted price for 2025?
Qtum is forecasted to reach $2.476** by December 2025, with a yearly average of **$1.906.
2. What factors influence Qtum’s price?
Market sentiment, adoption rates, and broader crypto trends are key drivers.
3. Is Qtum a good long-term investment?
While past performance doesn’t guarantee future results, Qtum’s hybrid blockchain technology positions it uniquely.
👉 Dive deeper into Qtum’s market analysis
Disclaimer
Predictions are based on historical data and algorithmic models. Never invest more than you can afford to lose.
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