update docs #685
update docs #685
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…tests Drift rebalancer shorting tests
small change to trigger github tests
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Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Fix Detected |
|---|---|---|
 |
Optimize DataFrame column assignments by avoiding .loc. ▹ view |
Files scanned
Due to the exceptionally large size of this PR, I've limited my review to the following files:
| File Path | Reviewed |
|---|---|
| setup.py | ✅ |
| lumibot/entities/bars.py | ✅ |
| lumibot/data_sources/data_source.py | ✅ |
| lumibot/tools/yahoo_helper.py | ✅ |
| docs/searchindex.js | ✅ |
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| df.loc[:, "price_change"] = df["close"].pct_change() | ||
| df.loc[:, "dividend_yield"] = df["dividend"] / df["close"] | ||
| df.loc[:, "return"] = df["dividend_yield"] + df["price_change"] |
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Optimize DataFrame column assignments by avoiding .loc. 
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:
df["price_change"] = df["close"].pct_change()
df["dividend_yield"] = df["dividend"] / df["close"]
df["return"] = df["dividend_yield"] + df["price_change"]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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Description by Korbit AI
What change is being made?
Add the
LUMIWEALTH_API_KEYto the deployment documentation, enhance data retrieval error handling indata_source.py, implement retry logic for data download inyahoo_helper.py, update Numpy dependency constraints insetup.py, and append new tests to validate recent code modifications.Why are these changes being made?
The
LUMIWEALTH_API_KEYdocumentation update ensures users can track bot performance via BotSpot.trade. Enhanced error handling and retry logic improve data reliability by managing exceptions and transient rate limit errors effectively. Adjusting the Numpy version constraints prevents compatibility issues with newer versions. Lastly, the new tests confirm that changes do not unintentionally alter data retrieval functionality, maintaining code integrity.