mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
299 lines
12 KiB
Python
299 lines
12 KiB
Python
"""
|
|
Functions to convert data from one format to another
|
|
"""
|
|
import itertools
|
|
import logging
|
|
from datetime import datetime, timezone
|
|
from operator import itemgetter
|
|
from typing import Any, Dict, List
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, to_datetime
|
|
|
|
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
|
|
|
|
|
|
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 = 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',
|
|
})
|
|
# 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" - {round(pct_missing * 100, 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 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':
|
|
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 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:
|
|
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: 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}")
|
|
if len(data) > 0:
|
|
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)
|