Replaces public_trades_to_dataframe with trades_list_to_df

freqtrade/data/converter/__init__.py

@@ -2,7 +2,6 @@ from freqtrade.data.converter.converter import (clean_ohlcv_dataframe, convert_o
                                                 ohlcv_fill_up_missing_data, ohlcv_to_dataframe,
                                                 order_book_to_dataframe,
                                                 populate_dataframe_with_trades,
-                                                public_trades_to_dataframe,
                                                 reduce_dataframe_footprint, trim_dataframe,
                                                 trim_dataframes)
 from freqtrade.data.converter.trade_converter import (convert_trades_format,
@@ -24,7 +23,6 @@ __all__ = [
     'convert_trades_format',
     'convert_trades_to_ohlcv',
     'populate_dataframe_with_trades',
-    'public_trades_to_dataframe',
     'trades_convert_types',
     'trades_df_remove_duplicates',
     'trades_dict_to_list',

freqtrade/data/converter/converter.py

@@ -195,30 +195,6 @@ def populate_dataframe_with_trades(config: Config,
     return dataframe
 
 
-def public_trades_to_dataframe(trades: List, pair: str) -> DataFrame:
-    """
-    Converts a list with candle (TRADES) data (in format returned by ccxt.fetch_trades)
-    to a Dataframe
-    :param trades: list with candle (TRADES) data, as returned by exchange.async_get_candle_history
-    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
-    :param pair: Pair this data is for (used to warn if fillup was necessary)
-    :param fill_missing: fill up missing candles with 0 candles
-                         (see trades_fill_up_missing_data for details)
-    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
-    :return: DataFrame
-    """
-    logger.debug(f"Converting candle (TRADES) data to dataframe for pair {pair}.")
-    cols = DEFAULT_TRADES_COLUMNS
-    df = DataFrame(trades, columns=cols)
-    df['date'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
-
-    # Some exchanges return int values for Volume and even for OHLC.
-    # Convert them since TA-LIB indicators used in the strategy assume floats
-    # and fail with exception...
-    df = df.astype(dtype={'amount': 'float', 'cost': 'float', 'price': 'float'})
-    return df
-
-
 def trades_to_volumeprofile_with_total_delta_bid_ask(trades: DataFrame, scale: float):
     """
     :param trades: dataframe

freqtrade/exchange/exchange.py

@@ -25,9 +25,8 @@ from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, DEFAULT_TRADES_
                                  ExchangeConfig, ListPairsWithTimeframes, MakerTaker, OBLiteral,
                                  PairWithTimeframe)
 from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
-from freqtrade.data.converter.converter import (_calculate_ohlcv_candle_start_and_end,
+from freqtrade.data.converter.converter import _calculate_ohlcv_candle_start_and_end
-                                                public_trades_to_dataframe)
+from freqtrade.data.converter.trade_converter import trades_df_remove_duplicates, trades_list_to_df
-from freqtrade.data.converter.trade_converter import trades_df_remove_duplicates
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, PriceType, RunMode, TradingMode
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, PricingError,
@@ -2106,7 +2105,7 @@ class Exchange:
                            cache: bool,
                            first_required_candle_date: Optional[int]) -> DataFrame:
         # keeping parsed dataframe in cache
-        trades_df = public_trades_to_dataframe(ticks, pair=pair)
+        trades_df = trades_list_to_df(ticks, True)
         # keeping last candle time as last refreshed time of the pair
         if ticks and cache:
             idx = -1

tests/data/test_converter_public_trades.py

@@ -3,8 +3,10 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from freqtrade.data.converter import populate_dataframe_with_trades, public_trades_to_dataframe
+from freqtrade.constants import DEFAULT_TRADES_COLUMNS
-from freqtrade.data.converter.converter import trades_to_volumeprofile_with_total_delta_bid_ask
+from freqtrade.data.converter import populate_dataframe_with_trades
+from freqtrade.data.converter.orderflow import trades_to_volumeprofile_with_total_delta_bid_ask
+from freqtrade.data.converter.trade_converter import trades_list_to_df
 
 
 BIN_SIZE_SCALE = 0.5
@@ -74,7 +76,7 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
                   'imbalance_volume': 0,
                   'imbalance_ratio': 300,
                   'stacked_imbalance_range': 3
-                  }}
+              }}
     df = populate_dataframe_with_trades(config,
                                         dataframe, trades, pair='unitttest')
     results = df.iloc[0]
@@ -138,8 +140,8 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
             'imbalance_volume': 0,
             'imbalance_ratio': 300,
             'stacked_imbalance_range': 3
-            }
         }
+    }
     df = populate_dataframe_with_trades(config,
                                         dataframe, trades, pair='unitttest')
     row = df.iloc[0]
@@ -164,7 +166,8 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
 
 
 def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple, candles):
-    df = public_trades_to_dataframe(public_trades_list_simple,  'doesntmatter')
+    df = trades_list_to_df(
+        public_trades_list_simple[DEFAULT_TRADES_COLUMNS].values.tolist())
     df = trades_to_volumeprofile_with_total_delta_bid_ask(
         df, scale=BIN_SIZE_SCALE)
     candles['vp'] = np.nan
@@ -176,7 +179,8 @@ def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_
 
 def test_public_trades_binned_big_sample_list(public_trades_list):
     BIN_SIZE_SCALE = 0.05
-    trades = public_trades_to_dataframe(public_trades_list,  'doesntmatter')
+    trades = trades_list_to_df(
+        public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist())
     df = trades_to_volumeprofile_with_total_delta_bid_ask(
         trades, scale=BIN_SIZE_SCALE)
     assert df.columns.tolist() == ['bid', 'ask', 'delta',
@@ -203,7 +207,7 @@ def test_public_trades_binned_big_sample_list(public_trades_list):
     assert 57.551 == df['delta'].iat[-1]  # delta
 
     BIN_SIZE_SCALE = 1
-    trades = public_trades_to_dataframe(public_trades_list, 'doesntmatter')
+    trades = trades_list_to_df(public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist())
     df = trades_to_volumeprofile_with_total_delta_bid_ask(
         trades, scale=BIN_SIZE_SCALE)
     assert 2 == len(df)