Move trim_dataframe from history to converter

freqtrade/data/converter.py

@@ -2,10 +2,12 @@
 Functions to convert data from one format to another
 """
 import logging
+from datetime import datetime, timezone
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
+from freqtrade.configuration.timerange import TimeRange
 
 logger = logging.getLogger(__name__)
 
@@ -112,6 +114,23 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     return df
 
 
+def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    :param df: Dataframe to trim
+    :param timerange: timerange (use start and end date if available)
+    :param: df_date_col: Column in the dataframe to use as Date column
+    :return: trimmed dataframe
+    """
+    if timerange.starttype == 'date':
+        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] >= start, :]
+    if timerange.stoptype == 'date':
+        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] <= stop, :]
+    return df
+
+
 def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     """
     Gets order book list, returns dataframe with below format per suggested by creslin

freqtrade/data/datahandlers/jsondatahandler.py

@@ -7,8 +7,7 @@
 
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.data.converter import clean_ohlcv_dataframe
-from freqtrade.data.history import trim_dataframe
+from freqtrade.data.converter import clean_ohlcv_dataframe, trim_dataframe
 
 from .idatahandler import IDataHandler
 

freqtrade/data/history.py

@@ -50,23 +50,6 @@ def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
     return tickerlist[start_index:stop_index]
 
 
-def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
-    """
-    Trim dataframe based on given timerange
-    :param df: Dataframe to trim
-    :param timerange: timerange (use start and end date if available)
-    :param: df_date_col: Column in the dataframe to use as Date column
-    :return: trimmed dataframe
-    """
-    if timerange.starttype == 'date':
-        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
-        df = df.loc[df[df_date_col] >= start, :]
-    if timerange.stoptype == 'date':
-        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
-        df = df.loc[df[df_date_col] <= stop, :]
-    return df
-
-
 def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
                          timerange: Optional[TimeRange] = None) -> List[Dict]:
     """
@@ -115,15 +98,6 @@ def store_trades_file(datadir: Path, pair: str,
     misc.file_dump_json(filename, data, is_zip=is_zip)
 
 
-def _validate_pairdata(pair, pairdata, timerange: TimeRange):
-    if timerange.starttype == 'date' and pairdata[0][0] > timerange.startts * 1000:
-        logger.warning('Missing data at start for pair %s, data starts at %s',
-                       pair, arrow.get(pairdata[0][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-    if timerange.stoptype == 'date' and pairdata[-1][0] < timerange.stopts * 1000:
-        logger.warning('Missing data at end for pair %s, data ends at %s',
-                       pair, arrow.get(pairdata[-1][0] // 1000).strftime('%Y-%m-%d %H:%M:%S'))
-
-
 def load_pair_history(pair: str,
                       timeframe: str,
                       datadir: Path, *,

freqtrade/optimize/backtesting.py

@@ -16,6 +16,7 @@
 from freqtrade.configuration import (TimeRange, remove_credentials,
                                      validate_config_consistency)
 from freqtrade.data import history
+from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.misc import file_dump_json
@@ -482,7 +483,7 @@ def start(self) -> None:
 
             # Trim startup period from analyzed dataframe
             for pair, df in preprocessed.items():
-                preprocessed[pair] = history.trim_dataframe(df, timerange)
+                preprocessed[pair] = trim_dataframe(df, timerange)
             min_date, max_date = history.get_timerange(preprocessed)
 
             logger.info(

freqtrade/optimize/hyperopt.py

@@ -23,7 +23,8 @@
 from pandas import DataFrame
 
 from freqtrade import OperationalException
-from freqtrade.data.history import get_timerange, trim_dataframe
+from freqtrade.data.history import get_timerange
+from freqtrade.data.converter import trim_dataframe
 from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules

freqtrade/plot/plotting.py

@@ -5,6 +5,7 @@
 import pandas as pd
 from freqtrade.configuration import TimeRange
 from freqtrade.data import history
+from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.btanalysis import (combine_tickers_with_mean,
                                        create_cum_profit,
                                        extract_trades_of_period, load_trades)
@@ -47,7 +48,7 @@ def init_plotscript(config):
                          db_url=config.get('db_url'),
                          exportfilename=config.get('exportfilename'),
                          )
-    trades = history.trim_dataframe(trades, timerange, 'open_time')
+    trades = trim_dataframe(trades, timerange, 'open_time')
     return {"tickers": tickers,
             "trades": trades,
             "pairs": pairs,

tests/data/test_converter.py

@@ -1,8 +1,11 @@
 # pragma pylint: disable=missing-docstring, C0103
 import logging
 
-from freqtrade.data.converter import parse_ticker_dataframe, ohlcv_fill_up_missing_data
-from freqtrade.data.history import load_pair_history, validate_backtest_data, get_timerange
+from freqtrade.configuration.timerange import TimeRange
+from freqtrade.data.converter import (ohlcv_fill_up_missing_data,
+                                      parse_ticker_dataframe, trim_dataframe)
+from freqtrade.data.history import (get_timerange, load_data,
+                                    load_pair_history, validate_backtest_data)
 from tests.conftest import log_has
 
 
@@ -145,3 +148,43 @@ def test_ohlcv_drop_incomplete(caplog):
     assert len(data) == 3
 
     assert log_has("Dropping last candle", caplog)
+
+
+def test_trim_dataframe(testdatadir) -> None:
+    data = load_data(
+        datadir=testdatadir,
+        timeframe='1m',
+        pairs=['UNITTEST/BTC']
+    )['UNITTEST/BTC']
+    min_date = int(data.iloc[0]['date'].timestamp())
+    max_date = int(data.iloc[-1]['date'].timestamp())
+    data_modify = data.copy()
+
+    # Remove first 30 minutes (1800 s)
+    tr = TimeRange('date', None, min_date + 1800, 0)
+    data_modify = trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 30
+    assert all(data_modify.iloc[-1] == data.iloc[-1])
+    assert all(data_modify.iloc[0] == data.iloc[30])
+
+    data_modify = data.copy()
+    # Remove last 30 minutes (1800 s)
+    tr = TimeRange(None, 'date', 0, max_date - 1800)
+    data_modify = trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 30
+    assert all(data_modify.iloc[0] == data.iloc[0])
+    assert all(data_modify.iloc[-1] == data.iloc[-31])
+
+    data_modify = data.copy()
+    # Remove first 25 and last 30 minutes (1800 s)
+    tr = TimeRange('date', 'date', min_date + 1500, max_date - 1800)
+    data_modify = trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 55
+    # first row matches 25th original row
+    assert all(data_modify.iloc[0] == data.iloc[25])

tests/data/test_history.py

@@ -18,10 +18,8 @@
                                     load_tickerdata_file, pair_data_filename,
                                     pair_trades_filename,
                                     refresh_backtest_ohlcv_data,
-                                    refresh_backtest_trades_data,
-                                    refresh_data,
-                                    trim_dataframe, trim_tickerlist,
-                                    validate_backtest_data)
+                                    refresh_backtest_trades_data, refresh_data,
+                                    trim_tickerlist, validate_backtest_data)
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import file_dump_json
 from freqtrade.strategy.default_strategy import DefaultStrategy
@@ -444,46 +442,6 @@ def test_trim_tickerlist(testdatadir) -> None:
     assert not ticker
 
 
-def test_trim_dataframe(testdatadir) -> None:
-    data = load_data(
-        datadir=testdatadir,
-        timeframe='1m',
-        pairs=['UNITTEST/BTC']
-    )['UNITTEST/BTC']
-    min_date = int(data.iloc[0]['date'].timestamp())
-    max_date = int(data.iloc[-1]['date'].timestamp())
-    data_modify = data.copy()
-
-    # Remove first 30 minutes (1800 s)
-    tr = TimeRange('date', None, min_date + 1800, 0)
-    data_modify = trim_dataframe(data_modify, tr)
-    assert not data_modify.equals(data)
-    assert len(data_modify) < len(data)
-    assert len(data_modify) == len(data) - 30
-    assert all(data_modify.iloc[-1] == data.iloc[-1])
-    assert all(data_modify.iloc[0] == data.iloc[30])
-
-    data_modify = data.copy()
-    # Remove last 30 minutes (1800 s)
-    tr = TimeRange(None, 'date', 0, max_date - 1800)
-    data_modify = trim_dataframe(data_modify, tr)
-    assert not data_modify.equals(data)
-    assert len(data_modify) < len(data)
-    assert len(data_modify) == len(data) - 30
-    assert all(data_modify.iloc[0] == data.iloc[0])
-    assert all(data_modify.iloc[-1] == data.iloc[-31])
-
-    data_modify = data.copy()
-    # Remove first 25 and last 30 minutes (1800 s)
-    tr = TimeRange('date', 'date', min_date + 1500, max_date - 1800)
-    data_modify = trim_dataframe(data_modify, tr)
-    assert not data_modify.equals(data)
-    assert len(data_modify) < len(data)
-    assert len(data_modify) == len(data) - 55
-    # first row matches 25th original row
-    assert all(data_modify.iloc[0] == data.iloc[25])
-
-
 def test_file_dump_json_tofile(testdatadir) -> None:
     file = testdatadir / 'test_{id}.json'.format(id=str(uuid.uuid4()))
     data = {'bar': 'foo'}