stable/freqtrade/data/converter.py
2018-12-31 07:12:54 +01:00

92 lines
3.2 KiB
Python

"""
Functions to convert data from one format to another
"""
import logging
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import TICKER_INTERVAL_MINUTES
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
: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
logger.debug('Dropping last candle')
return frame
def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: 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
"""
ohlc_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
}
tick_mins = TICKER_INTERVAL_MINUTES[ticker_interval]
# Resample to create "NAN" values
df = dataframe.resample(f'{tick_mins}min', on='date').agg(ohlc_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)
return df
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