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update docs #685
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update docs #685
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4594d93
added test to confirm hat shorting more, when already short works.
brettelliot 21e39ee
Remove assumption about the columns in csv data.
brettelliot 91a95a6
numpy fix and deploy
grzesir 7be4373
Merge pull request #677 from Lumiwealth/be-drift-rebalancer-shorting-…
grzesir a16216d
improved yahoo downloads for large amounts of assets
grzesir 1337664
Merge branch 'dev' of https://github.com/Lumiwealth/lumibot into dev
grzesir b6651d0
small change to trigger github tests
grzesir 2a9e73e
added tests for polygon
grzesir 865c21e
Merge pull request #683 from Lumiwealth/add-polygon-tests
grzesir 7350fec
added LUMIWEALTH_API_KEY to docs
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Original file line number | Diff line number | Diff line change |
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@@ -5,7 +5,7 @@ | |
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setuptools.setup( | ||
name="lumibot", | ||
version="3.8.24", | ||
version="3.8.26", | ||
author="Robert Grzesik", | ||
author_email="[email protected]", | ||
description="Backtesting and Trading Library, Made by Lumiwealth", | ||
|
@@ -21,7 +21,8 @@ | |
"yfinance>=0.2.46", | ||
"matplotlib>=3.3.3", | ||
"quandl", | ||
"numpy>=1.20.0", | ||
# Numpy greater than 1.20.0 and less than 2 because v2 has compatibility issues with a few libraries | ||
"numpy>=1.20.0, <2", | ||
"pandas>=2.2.0", | ||
"pandas_market_calendars>=4.3.1", | ||
"plotly>=5.18.0", | ||
|
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Optimize DataFrame column assignments by avoiding .loc.![category Performance](https://camo.githubusercontent.com/c214095a9a15cdec09fcba5f2705c7a01bded9162b954ab44b2a0f892734daaf/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f506572666f726d616e63652d346634366535)
Tell me more
In the init method of the Bars class, you are using .loc to assign values to the 'price_change', 'dividend_yield', and 'return' columns. While .loc is useful for label-based indexing, it has some performance overhead compared to directly assigning values to the DataFrame.
To optimize performance, consider changing the assignments to:
This way, you avoid the additional checks and computations performed by .loc, resulting in faster execution, especially when working with large datasets.
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