BATT | GMET | BATT / GMET | |
Gain YTD | 22.045 | 24.948 | 88% |
Net Assets | 65.9M | 21M | 314% |
Total Expense Ratio | 0.59 | 0.61 | 97% |
Turnover | 69.00 | 20.00 | 345% |
Yield | 2.82 | 1.63 | 173% |
Fund Existence | 7 years | 4 years | - |
BATT | GMET | |
---|---|---|
RSI ODDS (%) | 3 days ago87% | 3 days ago90% |
Stochastic ODDS (%) | 3 days ago86% | 3 days ago90% |
Momentum ODDS (%) | 3 days ago88% | 3 days ago84% |
MACD ODDS (%) | 3 days ago85% | 3 days ago90% |
TrendWeek ODDS (%) | 3 days ago88% | 3 days ago86% |
TrendMonth ODDS (%) | 3 days ago85% | 3 days ago81% |
Advances ODDS (%) | 4 days ago89% | 9 days ago84% |
Declines ODDS (%) | 16 days ago90% | 17 days ago90% |
BollingerBands ODDS (%) | 3 days ago90% | 3 days ago88% |
Aroon ODDS (%) | 3 days ago86% | 3 days ago86% |
A.I.dvisor indicates that over the last year, BATT has been closely correlated with ALB. These tickers have moved in lockstep 71% of the time. This A.I.-generated data suggests there is a high statistical probability that if BATT jumps, then ALB could also see price increases.
Ticker / NAME | Correlation To BATT | 1D Price Change % | ||
---|---|---|---|---|
BATT | 100% | -0.65% | ||
ALB - BATT | 71% Closely correlated | -0.29% | ||
SQM - BATT | 69% Closely correlated | -1.96% | ||
BHP - BATT | 64% Loosely correlated | -1.66% | ||
SGML - BATT | 62% Loosely correlated | -3.58% | ||
LAC - BATT | 61% Loosely correlated | -3.05% | ||
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A.I.dvisor tells us that GMET and SQM have been poorly correlated (+12% of the time) for the last year. This A.I.-generated data suggests there is low statistical probability that GMET and SQM's prices will move in lockstep.
Ticker / NAME | Correlation To GMET | 1D Price Change % | ||
---|---|---|---|---|
GMET | 100% | -0.89% | ||
SQM - GMET | 12% Poorly correlated | -1.96% | ||
LAR - GMET | 11% Poorly correlated | -2.60% | ||
AAL - GMET | 9% Poorly correlated | -2.21% | ||
ALB - GMET | 8% Poorly correlated | -0.29% | ||
AMS - GMET | 6% Poorly correlated | +0.19% | ||
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