stable/freqtrade/data/converter.py

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"""
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Functions to convert data from one format to another
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"""
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import itertools
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import logging
from datetime import datetime, timezone
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from operator import itemgetter
from typing import Any, Dict, List
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import pandas as pd
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from pandas import DataFrame, to_datetime
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from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
DEFAULT_TRADES_COLUMNS)
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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)
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:param fill_missing: fill up missing candles with 0 candles
(see ohlcv_fill_up_missing_data for details)
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: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.
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# Convert them since TA-LIB indicators used in the strategy assume floats
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# 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 = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Clense 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',
})
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# eliminate partial candle
if drop_incomplete:
data.drop(data.tail(1).index, inplace=True)
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logger.debug('Dropping last candle')
if fill_missing:
return ohlcv_fill_up_missing_data(data, timeframe, pair)
else:
return data
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def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
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"""
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 = {
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'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
}
timeframe_minutes = timeframe_to_minutes(timeframe)
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# Resample to create "NAN" values
df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
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# 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)
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len_before = len(dataframe)
len_after = len(df)
if len_before != len_after:
logger.info(f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}")
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return df
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def trim_dataframe(df: DataFrame, 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
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def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
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"""
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TODO: This should get a dedicated test
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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']
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bids_frame = DataFrame(bids, columns=cols)
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# add cumulative sum column
bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
cols2 = ['asks', 'a_size']
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asks_frame = DataFrame(asks, columns=cols2)
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# 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
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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]) -> List[List]:
"""
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: List, timeframe: str) -> DataFrame:
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"""
Converts trades list to OHLCV list
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TODO: This should get a dedicated test
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:param trades: List of trades, as returned by ccxt.fetch_trades.
:param timeframe: Timeframe to resample data to
:return: OHLCV Dataframe.
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"""
from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe)
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df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
utc=True,)
df = df.set_index('timestamp')
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df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
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df_new['date'] = df_new.index
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# Drop 0 volume rows
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df_new = df_new.dropna()
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return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
def convert_trades_format(config: Dict[str, Any], 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 souce 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: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert OHLCV from one format to another
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce 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)
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))
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)
logger.info(f"Converting {len(data)} candles for {pair}")
trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")
src.ohlcv_purge(pair=pair, timeframe=timeframe)