Merge pull request #2643 from freqtrade/mins

Remove min (plural) from codebase

freqtrade/data/history.py

@@ -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:

freqtrade/optimize/backtesting.py

@@ -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:

freqtrade/optimize/hyperopt.py

@@ -426,7 +426,7 @@ 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:

freqtrade/optimize/hyperopt_interface.py

@@ -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_t_scale = timeframe_min / 5
-        roi_p_scale = math.log1p(timeframe_mins) / math.log1p(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),

freqtrade/plot/plotting.py

@@ -121,7 +121,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
         )
         # Create description for sell summarizing the trade
         desc = trades.apply(lambda row: f"{round(row['profitperc'], 3)}%, {row['sell_reason']}, "
-                                        f"{row['duration']}min",
+                                        f"{row['duration']} min",
                             axis=1)
         trade_sells = go.Scatter(
             x=trades["close_time"],

tests/optimize/test_backtest_detail.py

@@ -250,7 +250,7 @@ tc15 = BTContainer(data=[
             BTrade(sell_reason=SellType.STOP_LOSS, open_tick=2, close_tick=2)]
 )
 
-# Test 16: Buy, hold for 65 mins, then forcesell using roi=-1
+# Test 16: Buy, hold for 65 min, then forcesell using roi=-1
 # Causes negative profit even though sell-reason is ROI.
 # stop-loss: 10%, ROI: 10% (should not apply), -100% after 65 minutes (limits trade duration)
 tc16 = BTContainer(data=[

tests/optimize/test_hyperopt.py

@@ -642,7 +642,7 @@ def test_generate_optimizer(mocker, default_conf) -> None:
     response_expected = {
         'loss': 1.9840569076926293,
         'results_explanation': ('     1 trades. Avg profit   2.31%. Total profit  0.00023300 BTC '
-                                '(   2.31\N{GREEK CAPITAL LETTER SIGMA}%). Avg duration 100.0 mins.'
+                                '(   2.31\N{GREEK CAPITAL LETTER SIGMA}%). Avg duration 100.0 min.'
                                 ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
         'params_details': {'buy': {'adx-enabled': False,
                                    'adx-value': 0,