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flake8 cleanup

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werkkrew 2021-03-13 19:10:34 -05:00
parent 6ac220982c
commit a3d6b472a7
5 changed files with 29 additions and 21 deletions
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13
docs/hyperopt.md
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@ -643,14 +643,23 @@ In order to use these best dynamic ROI parameters found by Hyperopt in backtesti
If you are optimizing dynamic ROI values, Freqtrade creates the 'dynamic-roi' optimization hyperspace for you. By default, the `enabled` parameter will try both True and False values. The value the `type` vary between `linear`, `exponential`, and `connect`.
Other values have default ranges of:
| Param | Range |
|------------|-------------|
| decay-time | 180..1440 |
| decay-rate | 0.001..0.03 |
| start | 0.05..0.25 |
| end | 0..0.005 |
Override the `dynamic_roi_space()` method and define the desired range in it if you want values of the dynamic ROI parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
Just as the standard ROI table, the time decay value (in minutes) has a range which varies based on your candle size, for example:
| Candle | Range |
|----------|---------------|
| 1m | 30..144 |
| 5m | 150..720 |
| 1h | 1800..8640 |
| 1d | 3456..207360 |
It is **highly** recommended to override the `dynamic_roi_space()` method and define the desired ranges in it if you want values of the dynamic ROI parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
## Show details of Hyperopt results

4
freqtrade/optimize/hyperopt.py
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@ -500,7 +500,7 @@ class Hyperopt:
if space == 'roi' or (space is None and self.has_space('roi')):
logger.debug("Hyperopt has 'roi' space")
spaces += self.custom_hyperopt.roi_space()
if space == 'dynamic-roi' or (space is None and self.has_space('dynamic-roi')):
logger.debug("Hyperopt has 'dynamic-roi' space")
spaces += self.custom_hyperopt.dynamic_roi_space()
@ -526,7 +526,7 @@ class Hyperopt:
if self.has_space('roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
if self.has_space('dynamic-roi'):
self.backtesting.strategy.dynamic_roi = ( # type: ignore
self.custom_hyperopt.generate_dynamic_roi_table(params_dict))

5
freqtrade/optimize/hyperopt_interface.py
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@ -238,14 +238,14 @@ class IHyperOpt(ABC):
Create a dynamic ROI space.
You may override it in your custom Hyperopt class.
"""
"""
# Time and percentage scaling are the same as in roi_space()
# Refer to the notes there for additional details
roi_t_alpha = 1.0
timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
roi_t_scale = timeframe_min / 5
# scaled range in minutes
# scaled range in minutes
# candle min max
# 1m 30 144
# 5m 150 720
@ -263,7 +263,6 @@ class IHyperOpt(ABC):
Real(0, 0.005, name='dynamic_roi_end')
]
# This is needed for proper unpickling the class attribute ticker_interval
# which is set to the actual value by the resolver.
# Why do I still need such shamanic mantras in modern python?

20
freqtrade/strategy/interface.py
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@ -637,21 +637,21 @@ class IStrategy(ABC):
"""
dynamic_roi = self.dynamic_roi
minimal_roi = self.minimal_roi
if not dynamic_roi:
return None, None
if 'dynamic_roi_type' in dynamic_roi and dynamic_roi['dynamic_roi_type'] \
in ['linear', 'exponential', 'connect']:
in ['linear', 'exponential', 'connect']:
roi_type = dynamic_roi['dynamic_roi_type']
# linear decay: f(t) = start - (rate * t)
if roi_type == 'linear':
if 'dynamic_roi_start' in dynamic_roi and 'dynamic_roi_end' in dynamic_roi and \
'dynamic_roi_time' in dynamic_roi:
start = dynamic_roi['dynamic_roi_start']
end = dynamic_roi['dynamic_roi_end']
time = dynamic_roi['dynamic_roi_time']
rate = (start - end) / time
end = dynamic_roi['dynamic_roi_end']
time = dynamic_roi['dynamic_roi_time']
rate = (start - end) / time
min_roi = max(end, start - (rate * trade_dur))
return trade_dur, min_roi
else:
@ -661,8 +661,8 @@ class IStrategy(ABC):
if 'dynamic_roi_start' in dynamic_roi and 'dynamic_roi_end' in dynamic_roi and \
'dynamic_roi_rate' in dynamic_roi:
start = dynamic_roi['dynamic_roi_start']
end = dynamic_roi['dynamic_roi_end']
rate = dynamic_roi['dynamic_roi_rate']
end = dynamic_roi['dynamic_roi_end']
rate = dynamic_roi['dynamic_roi_rate']
min_roi = max(end, start * np.exp(-rate*trade_dur))
return trade_dur, min_roi
else:
@ -673,7 +673,7 @@ class IStrategy(ABC):
return None, None
# figure out where we are in the defined roi table
past_roi = list(filter(lambda x: x <= trade_dur, minimal_roi.keys()))
next_roi = list(filter(lambda x: x > trade_dur, minimal_roi.keys()))
next_roi = list(filter(lambda x: x > trade_dur, minimal_roi.keys()))
# if we are past the final point in the table, use that key/vaule pair
if not past_roi:
return None, None
@ -714,9 +714,9 @@ class IStrategy(ABC):
"""
# Check if time matches and current rate is above threshold
trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
if self.dynamic_roi and 'dynamic_roi_enabled' in self.dynamic_roi \
and self.dynamic_roi['dynamic_roi_enabled']:
and self.dynamic_roi['dynamic_roi_enabled']:
_, roi = self.min_roi_reached_dynamic(trade_dur)
else:
_, roi = self.min_roi_reached_entry(trade_dur)

8
freqtrade/templates/sample_hyperopt_advanced.py
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@ -275,10 +275,10 @@ class AdvancedSampleHyperOpt(IHyperOpt):
You may override it in your custom Hyperopt class.
If you are reducing the types, you may also remove the parameters that will not be used on the
reduced scope. For example, if you reduce the types to only 'connect' you do not need to specify
the ranges for decay-rate, decay-time, start, or end.
"""
If you are reducing the types, you may also remove the parameters that will not be used on
the reduced scope. For example, if you reduce the types to only 'connect' you do not
need to specify the ranges for decay-rate, decay-time, start, or end.
"""
return [
Categorical([True, False], name='dynamic_roi_enabled'),
Categorical(['linear', 'exponential', 'connect'], name='dynamic_roi_type'),