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chore(docs/strategy-customization): clean up left over trailing whitespaces

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Joe Schr 2021-03-03 15:03:44 +01:00
parent d5993db064
commit 5cf3194fab
2 changed files with 4 additions and 4 deletions
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2
docs/strategy-advanced.md
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@ -142,7 +142,7 @@ class AwesomeStrategy(IStrategy):
return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss
# After reaching the desired offset, allow the stoploss to trail by half the profit # After reaching the desired offset, allow the stoploss to trail by half the profit
desired_stoploss = current_profit / 2 desired_stoploss = current_profit / 2
# Use a minimum of 2.5% and a maximum of 5% # Use a minimum of 2.5% and a maximum of 5%
return max(min(desired_stoploss, 0.05), 0.025) return max(min(desired_stoploss, 0.05), 0.025)

6
docs/strategy-customization.md
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@ -399,7 +399,7 @@ if self.dp:
### *current_whitelist()* ### *current_whitelist()*
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume. Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
The strategy might look something like this: The strategy might look something like this:
@ -418,7 +418,7 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
pairs = self.dp.current_whitelist() pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy. # Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs] informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs return informative_pairs
``` ```
### *get_pair_dataframe(pair, timeframe)* ### *get_pair_dataframe(pair, timeframe)*
@ -583,7 +583,7 @@ All columns of the informative dataframe will be available on the returning data
``` python ``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe 'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe 'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
``` ```
??? Example "Custom implementation" ??? Example "Custom implementation"