stable/freqtrade/data/converter.py

"""
Functions to convert data from one format to another
"""
import itertools
import logging
from operator import itemgetter
from typing import Dict, List

import numpy as np
import pandas as pd
from pandas import DataFrame, to_datetime

from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, Config, TradeList
from freqtrade.enums import CandleType


logger = logging.getLogger(__name__)


def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
                       fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
    """
    Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
    to a Dataframe
    :param ohlcv: list with candle (OHLCV) 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 ohlcv_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 (OHLCV) data to dataframe for pair {pair}.")
    cols = DEFAULT_DATAFRAME_COLUMNS
    df = DataFrame(ohlcv, columns=cols)

    df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=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={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
                          'volume': 'float'})
    return clean_ohlcv_dataframe(df, timeframe, pair,
                                 fill_missing=fill_missing,
                                 drop_incomplete=drop_incomplete)


def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
                          fill_missing: bool, drop_incomplete: bool) -> DataFrame:
    """
    Cleanse a OHLCV dataframe by
      * Grouping it by date (removes duplicate tics)
      * dropping last candles if requested
      * Filling up missing data (if requested)
    :param data: DataFrame containing candle (OHLCV) data.
    :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 ohlcv_fill_up_missing_data for details)
    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
    :return: DataFrame
    """
    # group by index and aggregate results to eliminate duplicate ticks
    data = data.groupby(by='date', as_index=False, sort=True).agg({
        'open': 'first',
        'high': 'max',
        'low': 'min',
        'close': 'last',
        'volume': 'max',
    })
    # eliminate partial candle
    if drop_incomplete:
        data.drop(data.tail(1).index, inplace=True)
        logger.debug('Dropping last candle')

    if fill_missing:
        return ohlcv_fill_up_missing_data(data, timeframe, pair)
    else:
        return data


def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
    """
    Fills up missing data with 0 volume rows,
    using the previous close as price for "open", "high" "low" and "close", volume is set to 0

    """
    from freqtrade.exchange import timeframe_to_minutes

    ohlcv_dict = {
        'open': 'first',
        'high': 'max',
        'low': 'min',
        'close': 'last',
        'volume': 'sum'
    }
    timeframe_minutes = timeframe_to_minutes(timeframe)
    # Resample to create "NAN" values
    df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)

    # Forwardfill close for missing columns
    df['close'] = df['close'].fillna(method='ffill')
    # Use close for "open, high, low"
    df.loc[:, ['open', 'high', 'low']] = df[['open', 'high', 'low']].fillna(
        value={'open': df['close'],
               'high': df['close'],
               'low': df['close'],
               })
    df.reset_index(inplace=True)
    len_before = len(dataframe)
    len_after = len(df)
    pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
    if len_before != len_after:
        message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
                   f" - {pct_missing:.2%}")
        if pct_missing > 0.01:
            logger.info(message)
        else:
            # Don't be verbose if only a small amount is missing
            logger.debug(message)
    return df

def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
    """ iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.        
    """
    # start_mem = df.memory_usage().sum() / 1024**2
    # logger.info(f"Memory usage of dataframe for {pair} is {start_mem:.2f} MB")
   
    for col in df.columns[1:]:
        col_type = df[col].dtype
        
        if col_type != object:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == "int":
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                    df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)  
            elif str(col_type)[:5] == "float":
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] = df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)
            # else:
            #     logger.info(f"Column not optimized because the type is {str(col_type)}")
        # else:
            # df[col] = df[col].astype('category')

    # end_mem = df.memory_usage().sum() / 1024**2
    # logger.info("Memory usage after optimization is: {:.2f} MB".format(end_mem))
    # logger.info("Decreased by {:.1f}%".format(100 * (start_mem - end_mem) / start_mem))
    
    return df

def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
                   startup_candles: int = 0) -> 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
    :param startup_candles: When not 0, is used instead the timerange start date
    :return: trimmed dataframe
    """
    if startup_candles:
        # Trim candles instead of timeframe in case of given startup_candle count
        df = df.iloc[startup_candles:, :]
    else:
        if timerange.starttype == 'date':
            df = df.loc[df[df_date_col] >= timerange.startdt, :]
    if timerange.stoptype == 'date':
        df = df.loc[df[df_date_col] <= timerange.stopdt, :]
    return df


def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
                    startup_candles: int) -> Dict[str, DataFrame]:
    """
    Trim startup period from analyzed dataframes
    :param preprocessed: Dict of pair: dataframe
    :param timerange: timerange (use start and end date if available)
    :param startup_candles: Startup-candles that should be removed
    :return: Dict of trimmed dataframes
    """
    processed: Dict[str, DataFrame] = {}
    for pair, df in preprocessed.items():
        trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
        if not trimed_df.empty:
            # start_mem = trimed_df.memory_usage().sum() / 1024**2
            # logger.info(f"Memory usage of dataframe for {pair} before reduced is {start_mem:.2f} MB")
            trimed_df = reduce_mem_usage(pair, trimed_df)
            # end_mem = trimed_df.memory_usage().sum() / 1024**2
            # logger.info(f"Memory usage of dataframe for {pair} after reduced is {end_mem:.2f} MB")
            processed[pair] = trimed_df
        else:
            logger.warning(f'{pair} has no data left after adjusting for startup candles, '
                           f'skipping.')
    return processed


def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
    """
    TODO: This should get a dedicated test
    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


def trades_remove_duplicates(trades: List[List]) -> List[List]:
    """
    Removes duplicates from the trades list.
    Uses itertools.groupby to avoid converting to pandas.
    Tests show it as being pretty efficient on lists of 4M Lists.
    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
    :return: same format as above, but with duplicates removed
    """
    return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]


def trades_dict_to_list(trades: List[Dict]) -> TradeList:
    """
    Convert fetch_trades result into a List (to be more memory efficient).
    :param trades: List of trades, as returned by ccxt.fetch_trades.
    :return: List of Lists, with constants.DEFAULT_TRADES_COLUMNS as columns
    """
    return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]


def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
    """
    Converts trades list to OHLCV list
    :param trades: List of trades, as returned by ccxt.fetch_trades.
    :param timeframe: Timeframe to resample data to
    :return: OHLCV Dataframe.
    :raises: ValueError if no trades are provided
    """
    from freqtrade.exchange import timeframe_to_minutes
    timeframe_minutes = timeframe_to_minutes(timeframe)
    if not trades:
        raise ValueError('Trade-list empty.')
    df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
                                     utc=True,)
    df = df.set_index('timestamp')

    df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
    df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
    df_new['date'] = df_new.index
    # Drop 0 volume rows
    df_new = df_new.dropna()
    return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]


def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
    """
    Convert trades from one format to another format.
    :param config: Config dictionary
    :param convert_from: Source format
    :param convert_to: Target format
    :param erase: Erase source data (does not apply if source and target format are identical)
    """
    from freqtrade.data.history.idatahandler import get_datahandler
    src = get_datahandler(config['datadir'], convert_from)
    trg = get_datahandler(config['datadir'], convert_to)

    if 'pairs' not in config:
        config['pairs'] = src.trades_get_pairs(config['datadir'])
    logger.info(f"Converting trades for {config['pairs']}")

    for pair in config['pairs']:
        data = src.trades_load(pair=pair)
        logger.info(f"Converting {len(data)} trades for {pair}")
        trg.trades_store(pair, data)
        if erase and convert_from != convert_to:
            logger.info(f"Deleting source Trade data for {pair}.")
            src.trades_purge(pair=pair)


def convert_ohlcv_format(
    config: Config,
    convert_from: str,
    convert_to: str,
    erase: bool,
    candle_type: CandleType
):
    """
    Convert OHLCV from one format to another
    :param config: Config dictionary
    :param convert_from: Source format
    :param convert_to: Target format
    :param erase: Erase source data (does not apply if source and target format are identical)
    :param candle_type: Any of the enum CandleType (must match trading mode!)
    """
    from freqtrade.data.history.idatahandler import get_datahandler
    src = get_datahandler(config['datadir'], convert_from)
    trg = get_datahandler(config['datadir'], convert_to)
    timeframes = config.get('timeframes', [config.get('timeframe')])
    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")

    if 'pairs' not in config:
        config['pairs'] = []
        # Check timeframes or fall back to timeframe.
        for timeframe in timeframes:
            config['pairs'].extend(src.ohlcv_get_pairs(
                config['datadir'],
                timeframe,
                candle_type=candle_type
            ))
        config['pairs'] = sorted(set(config['pairs']))
    logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")

    for timeframe in timeframes:
        for pair in config['pairs']:
            data = src.ohlcv_load(pair=pair, timeframe=timeframe,
                                  timerange=None,
                                  fill_missing=False,
                                  drop_incomplete=False,
                                  startup_candles=0,
                                  candle_type=candle_type)
            logger.info(f"Converting {len(data)} {timeframe} {candle_type} candles for {pair}")
            if len(data) > 0:
                trg.ohlcv_store(
                    pair=pair,
                    timeframe=timeframe,
                    data=data,
                    candle_type=candle_type
                )
                if erase and convert_from != convert_to:
                    logger.info(f"Deleting source data for {pair} / {timeframe}")
                    src.ohlcv_purge(pair=pair, timeframe=timeframe, candle_type=candle_type)


def reduce_dataframe_footprint(df: DataFrame) -> DataFrame:
    """
    Ensure all values are float32 in the incoming dataframe.
    :param df: Dataframe to be converted to float/int 32s
    :return: Dataframe converted to float/int 32s
    """

    logger.debug(f"Memory usage of dataframe is "
                 f"{df.memory_usage().sum() / 1024**2:.2f} MB")

    df_dtypes = df.dtypes
    for column, dtype in df_dtypes.items():
        if column in ['open', 'high', 'low', 'close', 'volume']:
            continue
        if dtype == np.float64:
            df_dtypes[column] = np.float32
        elif dtype == np.int64:
            df_dtypes[column] = np.int32
    df = df.astype(df_dtypes)

    logger.debug(f"Memory usage after optimization is: "
                 f"{df.memory_usage().sum() / 1024**2:.2f} MB")

    return df