Add validate_backtest_data function

freqtrade/optimize/__init__.py

@@ -10,6 +10,7 @@ except ImportError:
     _UJSON = False
 import logging
 import os
+from datetime import datetime
 from typing import Optional, List, Dict, Tuple, Any
 import operator
 
@@ -76,6 +77,24 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
         max(timeframe, key=operator.itemgetter(1))[1]
 
 
+def validate_backtest_data(data: Dict[str, DataFrame], min_date: datetime,
+                           max_date: datetime, ticker_interval_mins: int) -> None:
+    """
+    Validates preprocessed backtesting data for missing values and shows warnings about it that.
+
+    :param data: dictionary with preprocessed backtesting data
+    :param min_date: start-date of the data
+    :param max_date: end-date of the data
+    :param ticker_interval_mins: ticker interval in minutes
+    """
+    # total difference in minutes / interval-minutes
+    expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
+    for pair, df in data.items():
+        if len(df) < expected_frames:
+            logger.warning('%s has missing frames: expected %s, got %s',
+                           pair, expected_frames, len(df))
+
+
 def load_tickerdata_file(
         datadir: str, pair: str,
         ticker_interval: str,

freqtrade/optimize/backtesting.py

@@ -356,8 +356,10 @@ class Backtesting(object):
             # need to reprocess data every time to populate signals
             preprocessed = self.strategy.tickerdata_to_dataframe(data)
 
-            # Print timeframe
             min_date, max_date = optimize.get_timeframe(preprocessed)
+            # Validate dataframe for missing values
+            optimize.validate_backtest_data(preprocessed, min_date, max_date,
+                                            constants.TICKER_INTERVAL_MINUTES[self.ticker_interval])
             logger.info(
                 'Measuring data from %s up to %s (%s days)..',
                 min_date.isoformat(),

freqtrade/tests/optimize/test_optimize.py

@@ -7,7 +7,7 @@ from shutil import copyfile
 
 import arrow
 
-from freqtrade import optimize
+from freqtrade import optimize, constants
 from freqtrade.arguments import TimeRange
 from freqtrade.misc import file_dump_json
 from freqtrade.optimize.__init__ import (download_backtesting_testdata,
@@ -450,3 +450,45 @@ def test_get_timeframe(default_conf, mocker) -> None:
     min_date, max_date = optimize.get_timeframe(data)
     assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
     assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
+
+
+def test_validate_backtest_data_warn(default_conf, mocker, caplog) -> None:
+    patch_exchange(mocker)
+    strategy = DefaultStrategy(default_conf)
+
+    data = strategy.tickerdata_to_dataframe(
+        optimize.load_data(
+            None,
+            ticker_interval='1m',
+            pairs=['UNITTEST/BTC']
+        )
+    )
+    min_date, max_date = optimize.get_timeframe(data)
+    caplog.clear()
+    optimize.validate_backtest_data(data, min_date, max_date,
+                                    constants.TICKER_INTERVAL_MINUTES["1m"])
+    assert len(caplog.record_tuples) == 1
+    assert log_has('UNITTEST/BTC has missing frames: expected 14396, got 13680',
+                   caplog.record_tuples)
+
+
+def test_validate_backtest_data(default_conf, mocker, caplog) -> None:
+    patch_exchange(mocker)
+    strategy = DefaultStrategy(default_conf)
+
+    timerange = TimeRange('index', 'index', 200, 250)
+    data = strategy.tickerdata_to_dataframe(
+        optimize.load_data(
+            None,
+            ticker_interval='5m',
+            pairs=['UNITTEST/BTC'],
+            timerange=timerange
+        )
+    )
+
+    min_date, max_date = optimize.get_timeframe(data)
+    caplog.clear()
+    optimize.validate_backtest_data(data, min_date, max_date,
+                                    constants.TICKER_INTERVAL_MINUTES["5m"])
+    assert len(caplog.record_tuples) == 0
+