move exchange_helpers to data module

freqtrade/exchange/__init__.py

@@ -14,7 +14,7 @@ import ccxt.async_support as ccxt_async
 from pandas import DataFrame
 
 from freqtrade import constants, OperationalException, DependencyException, TemporaryError
-from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+from freqtrade.data.convert import parse_ticker_dataframe
 
 logger = logging.getLogger(__name__)
 

freqtrade/exchange/exchange_helpers.py

@@ -1,59 +0,0 @@
-"""
-Functions to analyze ticker data with indicators and produce buy and sell signals
-"""
-import logging
-import pandas as pd
-from pandas import DataFrame, to_datetime
-
-logger = logging.getLogger(__name__)
-
-
-def parse_ticker_dataframe(ticker: list) -> DataFrame:
-    """
-    Analyses the trend for the given ticker history
-    :param ticker: ticker list, as returned by exchange.async_get_candle_history
-    :return: DataFrame
-    """
-    logger.debug("Parsing tickerlist to dataframe")
-    cols = ['date', 'open', 'high', 'low', 'close', 'volume']
-    frame = DataFrame(ticker, columns=cols)
-
-    frame['date'] = to_datetime(frame['date'],
-                                unit='ms',
-                                utc=True,
-                                infer_datetime_format=True)
-
-    # group by index and aggregate results to eliminate duplicate ticks
-    frame = frame.groupby(by='date', as_index=False, sort=True).agg({
-        'open': 'first',
-        'high': 'max',
-        'low': 'min',
-        'close': 'last',
-        'volume': 'max',
-    })
-    frame.drop(frame.tail(1).index, inplace=True)     # eliminate partial candle
-    return frame
-
-
-def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
-    """
-    Gets order book list, returns dataframe with below format per suggested by creslin
-    -------------------------------------------------------------------
-     b_sum       b_size       bids       asks       a_size       a_sum
-    -------------------------------------------------------------------
-    """
-    cols = ['bids', 'b_size']
-
-    bids_frame = DataFrame(bids, columns=cols)
-    # add cumulative sum column
-    bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
-    cols2 = ['asks', 'a_size']
-    asks_frame = DataFrame(asks, columns=cols2)
-    # add cumulative sum column
-    asks_frame['a_sum'] = asks_frame['a_size'].cumsum()
-
-    frame = pd.concat([bids_frame['b_sum'], bids_frame['b_size'], bids_frame['bids'],
-                       asks_frame['asks'], asks_frame['a_size'], asks_frame['a_sum']], axis=1,
-                      keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
-    # logger.info('order book %s', frame )
-    return frame