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Industrial Logistics Properties Trust Stock Price Chart

  • Based on the share price being below its 5, 20 & 50 day exponential moving averages, the current trend is considered strongly bearish and ILPT is experiencing buying pressure, which is a positive indicator for future bullish movement.

Industrial Logistics Properties Trust Price Chart Indicators

Moving Averages Level Buy or Sell
8-day SMA: 3.25 Sell
20-day SMA: 3.41 Sell
50-day SMA: 3.71 Sell
200-day SMA: 4.06 Sell
8-day EMA: 3.17 Sell
20-day EMA: 3.39 Sell
50-day EMA: 3.58 Sell
200-day EMA: 3.87 Sell

Industrial Logistics Properties Trust Technical Analysis Indicators

Chart Indicators Level Buy or Sell
MACD (12, 26): -0.18 Sell
Relative Strength Index (14 RSI): 25.33 Sell
Chaikin Money Flow: -441006 -
Bollinger Bands Level Buy or Sell
Bollinger Bands (25): ( - ) Buy
Bollinger Bands (100): ( - ) Buy

Industrial Logistics Properties Trust Technical Analysis

Technical Analysis: Buy or Sell?
8-day SMA:
20-day SMA:
50-day SMA:
200-day SMA:
8-day EMA:
20-day EMA:
50-day EMA:
200-day EMA:
MACD (12, 26):
Relative Strength Index (14 RSI):
Bollinger Bands (25):
Bollinger Bands (100):

Technical Analysis for Industrial Logistics Properties Trust Stock

Is Industrial Logistics Properties Trust Stock a Buy?

ILPT Technical Analysis vs Fundamental Analysis

Sell
1
Industrial Logistics Properties Trust (ILPT) is a Sell

Is Industrial Logistics Properties Trust a Buy or a Sell?

Industrial Logistics Properties Trust Stock Info

Market Cap:
185.2M
Price in USD:
2.80
Share Volume:
525K

Industrial Logistics Properties Trust 52-Week Range

52-Week High:
5.45
52-Week Low:
2.78
Sell
1
Industrial Logistics Properties Trust (ILPT) is a Sell

Industrial Logistics Properties Trust Share Price Forecast

Is Industrial Logistics Properties Trust Stock a Buy?

Technical Analysis of Industrial Logistics Properties Trust

Should I short Industrial Logistics Properties Trust stock?

* Industrial Logistics Properties Trust stock forecasts short-term for next days and weeks may differ from long term prediction for next month and year based on timeline differences.