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finance
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In order to achieve the expression like "Power($close, Rank($close, 14))" which is fairly common in fomulla alpha.
I guess we could use following code:
class Power(NpPairOperator): def __init__(self, feature_left, feature_right): super(PairPower, self).__init__(feature_left, feature_right, "power")
Finally, we could add a rpow magic method to Expression class
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Expected Behavior
The description of the EMA indicator mentions that the only way to warm up an EMA is
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I want to know do agents learn? Any tips are appreciated
Reading currencies, alphavantage returns a greeting note ("welcome") and this note raises an error in alphavantage.py line 363.
elif "Note" in json_response and self.treat_info_as_error:
raise ValueError(json_response["Note"])
For this reason, alphavantage does not work in home assistant.
Heston model has accurate density approximations for European option prices, which are of interest.
The module implementing this method should live under tf_quant_finance/volatility/heston_approximation.py. It should support both European option puts and calls approximations. Tests should be in heston_approximation_test.py in the same folder.
Can we display an asset name in https://pyportfolioopt.readthedocs.io/en/latest/_images/ef_plot.png ?
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When I ran Center of Gravity: cg over 3 months of Bitcoin prices ("20200801" to "20201101"), I got
| Close | cg | |
|---|---|---|
| count | 132481.000000 | 132472.000000 |
| mean | 11378.306788 | -5.499988 |
| std | 844.355621 | 0.001991 |
| min | 9881.820000 | -5.616297 |
| 25% | 10710.500000 | -5.500833 |
| 50% | 11368.680000 | -5.499987 |
| 75% | 11742.540000 | -5.499146 |
| 1 |
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