$28.990
0.000 (0.00%)At close
Price Predictions
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ALOT Prediction & Forecast by Similar Chart Analysis
Alphio's ALOT stock price prediction model matches the current AstroNova Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $28.85 (-0.50%). The 1-week path is $29.62 (+2.17%). 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 AstroNova Inc Stock?
AstroNova Inc is a good buy right now due to its acquisition offer of $29 per share, which is 0.28% above the current price of $28.92. Additionally, the stock has shown a remarkable year-to-date change of +225.13%, indicating strong market performance. The main risk is its negative earnings trend, with a forward P/E of 13.97, suggesting that future earnings may not meet investor expectations.
ALOT stock price ended at $28.99 on Wednesday, after rising 0.00%
AstroNova Inc (ALOT) last closed at $28.99, gaining +0.00%. These facts feed the 1-day and 1-week ALOT price prediction above.
Analyst Consensus
AstroNova Inc (ALOT): 1 buy, 0 hold, 0 sell. Outlook: Strong Buy.
Sell / Buy
Technical signals summary based on buy and sell indicators. The gauge shows the overall market view.
Forecast Drivers
AstroNova Inc (ALOT) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bullish
Similar Chart Patterns
Stocks whose recent price path most closely matches ALOT, ranked by similarity.
Seasonality analysis for ALOT
Historically the probability of a positive August return for ALOT is 32.79%. January offers the highest probability of a positive month at 86.89%, while October is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.