SpyGlass Pharma Inc

SpyGlass Pharma Inc (SGP) Price Prediction & Forecast

$24.000

-0.698 (-2.91%)At close

Price Predictions

1-Day Price Prediction
-0.30%
1-Week Prediction
+3.02%
1-Month Prediction
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2026 Prediction
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2030 Prediction
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Open in app to unlock longer-horizon forecasts.

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SGP Prediction & Forecast by Similar Chart Analysis

Alphio's SGP stock price prediction model matches the current SpyGlass Pharma Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $23.93 (-0.30%). The 1-week path is $24.72 (+3.02%). Longer windows use the same pattern set plus seasonality; the 1-month and multi-year forecasts sit behind unlock because they include precise min / avg / max bands.

Should I Buy SpyGlass Pharma Inc Stock?

SpyGlass Pharma, Inc. is not a good buy right now due to its significant net losses and poor technical indicators. The stock is currently priced at $24.00, with a 5-day change of -3.69% and a 20-day change of -17.47%. The RSI is at 39.059, indicating that the stock is nearing oversold territory, but the overall trend remains negative. The company reported a net loss of $19.9 million for Q2 2026, which is a substantial increase from previous losses, highlighting ongoing financial struggles. The main risk is the continued increase in expenses, particularly R&D and administrative costs, which could further impact profitability.

SGP stock price ended at $24.00 on Friday, after losing -2.91%

SpyGlass Pharma Inc (SGP) last closed at $24, losing -2.91%. These facts feed the 1-day and 1-week SGP price prediction above.

Forecast Drivers

SpyGlass Pharma Inc (SGP) forecast drivers combine similar chart patterns, seasonality, and moving averages.

Similar patterns

Positive

Bullish

Seasonality

Positive

Bullish

SMA 20

Negative

Bearish

SMA 200

Negative

Bearish

Similar Chart Patterns

Stocks whose recent price path most closely matches SGP, ranked by similarity.

Seasonality analysis for SGP

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This page is for research only and is not investment advice. Models can be wrong. Past performance does not guarantee future results.

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