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Remove min (plural) from codebase

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Matthias
2019-12-11 07:12:37 +01:00
parent b2a9b87be3
commit 7c7ca1cb90
7 changed files with 138 additions and 13 deletions
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6
freqtrade/data/history.py
View File

@@ -463,7 +463,7 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
max_date: datetime, timeframe_mins: int) -> bool:
max_date: datetime, timeframe_min: int) -> bool:
"""
Validates preprocessed backtesting data for missing values and shows warnings about it that.
@@ -471,10 +471,10 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param timeframe_mins: ticker Timeframe in minutes
:param timeframe_min: ticker Timeframe in minutes
"""
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_mins)
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)
found_missing = False
dflen = len(data)
if dflen < expected_frames:

6
freqtrade/optimize/backtesting.py
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@@ -87,7 +87,7 @@ class Backtesting:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_mins = timeframe_to_minutes(self.timeframe)
self.timeframe_min = timeframe_to_minutes(self.timeframe)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
@@ -378,7 +378,7 @@ class Backtesting:
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.timeframe_mins)
tmp = start_date + timedelta(minutes=self.timeframe_min)
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
@@ -430,7 +430,7 @@ class Backtesting:
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_mins)
tmp += timedelta(minutes=self.timeframe_min)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:

2
freqtrade/optimize/hyperopt.py
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@@ -427,7 +427,7 @@ class Hyperopt:
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['duration']:5.1f} mins."
f"Avg duration {results_metrics['duration']:5.1f} min."
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:

6
freqtrade/optimize/hyperopt_interface.py
View File

@@ -106,7 +106,7 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
# We define here limits for the ROI space parameters automagically adapted to the
# timeframe used by the bot:
@@ -117,8 +117,8 @@ class IHyperOpt(ABC):
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
roi_t_scale = timeframe_mins / 5
roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
roi_t_scale = timeframe_min / 5
roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
roi_limits = {
'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),