mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
Moves orderflow logic to it's own file
This commit is contained in:
Joe Schr
parent
64e9784d1f
commit
bff93e31c8
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@ -1,9 +1,8 @@
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from freqtrade.data.converter.converter import (clean_ohlcv_dataframe, convert_ohlcv_format,
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ohlcv_fill_up_missing_data, ohlcv_to_dataframe,
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order_book_to_dataframe,
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populate_dataframe_with_trades,
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reduce_dataframe_footprint, trim_dataframe,
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trim_dataframes)
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order_book_to_dataframe, reduce_dataframe_footprint,
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trim_dataframe, trim_dataframes)
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from freqtrade.data.converter.orderflow import populate_dataframe_with_trades
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from freqtrade.data.converter.trade_converter import (convert_trades_format,
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convert_trades_to_ohlcv, trades_convert_types,
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trades_df_remove_duplicates,
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@ -2,16 +2,14 @@
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Functions to convert data from one format to another
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"""
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import logging
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import time
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from typing import Dict
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import numpy as np
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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_ORDERFLOW_COLUMNS, Config
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from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, Config
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from freqtrade.enums import CandleType, TradingMode
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from freqtrade.exchange.exchange_utils import timeframe_to_resample_freq
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logger = logging.getLogger(__name__)
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@ -46,269 +44,6 @@ def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
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drop_incomplete=drop_incomplete)
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def _init_dataframe_with_trades_columns(dataframe: DataFrame):
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"""
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Populates a dataframe with trades columns
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:param dataframe: Dataframe to populate
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"""
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dataframe['trades'] = dataframe.apply(lambda _: [], axis=1)
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dataframe['orderflow'] = dataframe.apply(lambda _: {}, axis=1)
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dataframe['bid'] = np.nan
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dataframe['ask'] = np.nan
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dataframe['delta'] = np.nan
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dataframe['min_delta'] = np.nan
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dataframe['max_delta'] = np.nan
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dataframe['total_trades'] = np.nan
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dataframe['stacked_imbalances_bid'] = np.nan
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dataframe['stacked_imbalances_ask'] = np.nan
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def _convert_timeframe_to_pandas_frequency(timeframe: str):
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# convert timeframe to format usable by pandas
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from freqtrade.exchange import timeframe_to_minutes
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timeframe_minutes = timeframe_to_minutes(timeframe)
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timeframe_frequency = f'{timeframe_minutes}min'
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return (timeframe_frequency, timeframe_minutes)
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def _calculate_ohlcv_candle_start_and_end(df: DataFrame, timeframe: str):
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_, timeframe_minutes = _convert_timeframe_to_pandas_frequency(
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timeframe)
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timeframe_frequency = timeframe_to_resample_freq(timeframe)
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# calculate ohlcv candle start and end
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if df is not None and not df.empty:
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df['datetime'] = pd.to_datetime(df['date'], unit='ms')
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df['candle_start'] = df['datetime'].dt.floor(timeframe_frequency)
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df['candle_end'] = df['candle_start'] + \
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pd.Timedelta(minutes=timeframe_minutes)
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df.drop(columns=['datetime'], inplace=True)
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def populate_dataframe_with_trades(config: Config,
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dataframe: DataFrame,
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trades: DataFrame,
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*,
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pair: str) -> DataFrame:
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"""
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Populates a dataframe with trades
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:param dataframe: Dataframe to populate
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:param trades: Trades to populate with
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:return: Dataframe with trades populated
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"""
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config_orderflow = config['orderflow']
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timeframe = config['timeframe']
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# create columns for trades
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_init_dataframe_with_trades_columns(dataframe)
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df = dataframe.copy()
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try:
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start_time = time.time()
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# calculate ohlcv candle start and end
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# TODO: check if call is necessary for df.
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_calculate_ohlcv_candle_start_and_end(df, timeframe)
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_calculate_ohlcv_candle_start_and_end(trades, timeframe)
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# slice of trades that are before current ohlcv candles to make groupby faster
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# TODO: maybe use df.date instead of df.candle_start at comparision below
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trades = trades.loc[trades.candle_start >= df.candle_start[0]]
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trades.reset_index(inplace=True, drop=True)
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# group trades by candle start
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trades_grouped_by_candle_start = trades.groupby(
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'candle_start', group_keys=False)
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for candle_start in trades_grouped_by_candle_start.groups:
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trades_grouped_df = trades[candle_start == trades['candle_start']]
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is_between = (candle_start == df['candle_start'])
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if np.any(is_between == True): # noqa: E712
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(_, timeframe_minutes) = _convert_timeframe_to_pandas_frequency(timeframe)
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candle_next = candle_start + \
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pd.Timedelta(minutes=timeframe_minutes)
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# skip if there are no trades at next candle
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# because that this candle isn't finished yet
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if candle_next not in trades_grouped_by_candle_start.groups:
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logger.warning(
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f"candle at {candle_start} with {len(trades_grouped_df)} trades "
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f"might be unfinished, because no finished trades at {candle_next}")
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# add trades to each candle
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df.loc[is_between, 'trades'] = df.loc[is_between,
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'trades'].apply(lambda _: trades_grouped_df)
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# calculate orderflow for each candle
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df.loc[is_between, 'orderflow'] = df.loc[is_between, 'orderflow'].apply(
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lambda _: trades_to_volumeprofile_with_total_delta_bid_ask(
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pd.DataFrame(trades_grouped_df),
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scale=config_orderflow['scale']))
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# calculate imbalances for each candle's orderflow
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df.loc[is_between, 'imbalances'] = df.loc[is_between, 'orderflow'].apply(
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lambda x: trades_orderflow_to_imbalances(
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x,
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imbalance_ratio=config_orderflow['imbalance_ratio'],
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imbalance_volume=config_orderflow['imbalance_volume']))
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_stacked_imb = config_orderflow['stacked_imbalance_range']
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df.loc[is_between, 'stacked_imbalances_bid'] = df.loc[
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is_between, 'imbalances'].apply(lambda x: stacked_imbalance_bid(
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x,
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stacked_imbalance_range=_stacked_imb))
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df.loc[is_between, 'stacked_imbalances_ask'] = df.loc[
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is_between, 'imbalances'].apply(
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lambda x: stacked_imbalance_ask(x, stacked_imbalance_range=_stacked_imb))
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# TODO: maybe use simple np.where instead
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buy = df.loc[is_between, 'bid'].apply(lambda _: np.where(
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trades_grouped_df['side'].str.contains('buy'), 0, trades_grouped_df['amount']))
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sell = df.loc[is_between, 'ask'].apply(lambda _: np.where(
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trades_grouped_df['side'].str.contains('sell'), 0, trades_grouped_df['amount']))
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deltas_per_trade = sell - buy
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min_delta = 0
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max_delta = 0
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delta = 0
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for deltas in deltas_per_trade:
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for d in deltas:
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delta += d
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if delta > max_delta:
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max_delta = delta
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if delta < min_delta:
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min_delta = delta
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df.loc[is_between, 'max_delta'] = max_delta
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df.loc[is_between, 'min_delta'] = min_delta
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df.loc[is_between, 'bid'] = np.where(trades_grouped_df['side'].str.contains(
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'buy'), 0, trades_grouped_df['amount']).sum()
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df.loc[is_between, 'ask'] = np.where(trades_grouped_df['side'].str.contains(
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'sell'), 0, trades_grouped_df['amount']).sum()
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df.loc[is_between, 'delta'] = df.loc[is_between,
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'ask'] - df.loc[is_between, 'bid']
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min_delta = np.min(deltas_per_trade)
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max_delta = np.max(deltas_per_trade)
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df.loc[is_between, 'total_trades'] = len(trades_grouped_df)
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# copy to avoid memory leaks
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dataframe.loc[is_between] = df.loc[is_between].copy()
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else:
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logger.debug(
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f"Found NO candles for trades starting with {candle_start}")
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logger.debug(
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f"trades.groups_keys in {time.time() - start_time} seconds")
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logger.debug(
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f"trades.singleton_iterate in {time.time() - start_time} seconds")
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except Exception as e:
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logger.exception("Error populating dataframe with trades:", e)
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return dataframe
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def trades_to_volumeprofile_with_total_delta_bid_ask(trades: DataFrame, scale: float):
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"""
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:param trades: dataframe
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:param scale: scale aka bin size e.g. 0.5
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:return: trades binned to levels according to scale aka orderflow
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"""
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df = pd.DataFrame([], columns=DEFAULT_ORDERFLOW_COLUMNS)
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# create bid, ask where side is sell or buy
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df['bid_amount'] = np.where(
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trades['side'].str.contains('buy'), 0, trades['amount'])
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df['ask_amount'] = np.where(
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trades['side'].str.contains('sell'), 0, trades['amount'])
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df['bid'] = np.where(trades['side'].str.contains('buy'), 0, 1)
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df['ask'] = np.where(trades['side'].str.contains('sell'), 0, 1)
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# round the prices to the nearest multiple of the scale
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df['price'] = ((trades['price'] / scale).round()
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* scale).astype('float64').values
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if df.empty:
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df['total'] = np.nan
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df['delta'] = np.nan
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return df
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df['delta'] = df['ask_amount'] - df['bid_amount']
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df['total_volume'] = df['ask_amount'] + df['bid_amount']
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df['total_trades'] = df['ask'] + df['bid']
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# group to bins aka apply scale
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df = df.groupby('price').sum(numeric_only=True)
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return df
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def trades_orderflow_to_imbalances(df: DataFrame, imbalance_ratio: int, imbalance_volume: int):
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"""
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:param df: dataframes with bid and ask
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:param imbalance_ratio: imbalance_ratio e.g. 300
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:param imbalance_volume: imbalance volume e.g. 3)
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:return: dataframe with bid and ask imbalance
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"""
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bid = df.bid
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ask = df.ask.shift(-1)
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bid_imbalance = (bid / ask) > (imbalance_ratio / 100)
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# overwrite bid_imbalance with False if volume is not big enough
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bid_imbalance_filtered = np.where(
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df.total_volume < imbalance_volume, False, bid_imbalance)
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ask_imbalance = (ask / bid) > (imbalance_ratio / 100)
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# overwrite ask_imbalance with False if volume is not big enough
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ask_imbalance_filtered = np.where(
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df.total_volume < imbalance_volume, False, ask_imbalance)
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dataframe = DataFrame({
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"bid_imbalance": bid_imbalance_filtered,
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"ask_imbalance": ask_imbalance_filtered
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}, index=df.index,
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)
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return dataframe
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def stacked_imbalance(df: DataFrame,
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label: str,
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stacked_imbalance_range: int,
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should_reverse: bool):
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"""
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y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
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https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
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"""
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imbalance = df[f'{label}_imbalance']
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int_series = pd.Series(np.where(imbalance, 1, 0))
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stacked = (
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int_series * (
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int_series.groupby(
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(int_series != int_series.shift()).cumsum()).cumcount() + 1
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)
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)
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max_stacked_imbalance_idx = stacked.index[stacked >=
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stacked_imbalance_range]
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stacked_imbalance_price = np.nan
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if not max_stacked_imbalance_idx.empty:
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idx = max_stacked_imbalance_idx[0] if not should_reverse else np.flipud(
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max_stacked_imbalance_idx)[0]
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stacked_imbalance_price = imbalance.index[idx]
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return stacked_imbalance_price
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def stacked_imbalance_bid(df: DataFrame, stacked_imbalance_range: int):
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return stacked_imbalance(df, 'bid', stacked_imbalance_range, should_reverse=False)
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def stacked_imbalance_ask(df: DataFrame, stacked_imbalance_range: int):
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return stacked_imbalance(df, 'ask', stacked_imbalance_range, should_reverse=True)
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def orderflow_to_volume_profile(df: DataFrame):
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"""
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:param orderflow: dataframe
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:return: volume profile dataframe
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"""
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bid = df.groupby('level').bid.sum()
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ask = df.groupby('level').ask.sum()
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df.groupby('level')['level'].sum()
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delta = df.groupby('level').ask.sum() - df.groupby('level').bid.sum()
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df = pd.DataFrame({'bid': bid, 'ask': ask, 'delta': delta})
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return df
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def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
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fill_missing: bool, drop_incomplete: bool) -> DataFrame:
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"""
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280
freqtrade/data/converter/orderflow.py
Normal file
280
freqtrade/data/converter/orderflow.py
Normal file
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@ -0,0 +1,280 @@
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"""
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Functions to convert orderflow data from public_trades
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"""
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import logging
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import time
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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS, Config
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from freqtrade.exchange.exchange_utils import timeframe_to_resample_freq
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logger = logging.getLogger(__name__)
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def _init_dataframe_with_trades_columns(dataframe: DataFrame):
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"""
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Populates a dataframe with trades columns
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:param dataframe: Dataframe to populate
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"""
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dataframe['trades'] = dataframe.apply(lambda _: [], axis=1)
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dataframe['orderflow'] = dataframe.apply(lambda _: {}, axis=1)
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dataframe['bid'] = np.nan
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dataframe['ask'] = np.nan
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dataframe['delta'] = np.nan
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dataframe['min_delta'] = np.nan
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dataframe['max_delta'] = np.nan
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dataframe['total_trades'] = np.nan
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dataframe['stacked_imbalances_bid'] = np.nan
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dataframe['stacked_imbalances_ask'] = np.nan
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def _convert_timeframe_to_pandas_frequency(timeframe: str):
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# convert timeframe to format usable by pandas
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from freqtrade.exchange import timeframe_to_minutes
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timeframe_minutes = timeframe_to_minutes(timeframe)
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timeframe_frequency = f'{timeframe_minutes}min'
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return (timeframe_frequency, timeframe_minutes)
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def _calculate_ohlcv_candle_start_and_end(df: DataFrame, timeframe: str):
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_, timeframe_minutes = _convert_timeframe_to_pandas_frequency(
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timeframe)
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timeframe_frequency = timeframe_to_resample_freq(timeframe)
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# calculate ohlcv candle start and end
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if df is not None and not df.empty:
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df['datetime'] = pd.to_datetime(df['date'], unit='ms')
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df['candle_start'] = df['datetime'].dt.floor(timeframe_frequency)
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# used in _now_is_time_to_refresh_trades
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df['candle_end'] = df['candle_start'] + \
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pd.Timedelta(minutes=timeframe_minutes)
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df.drop(columns=['datetime'], inplace=True)
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def populate_dataframe_with_trades(config: Config,
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dataframe: DataFrame,
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trades: DataFrame,
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*,
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pair: str) -> DataFrame:
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"""
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Populates a dataframe with trades
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:param dataframe: Dataframe to populate
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:param trades: Trades to populate with
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:return: Dataframe with trades populated
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"""
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config_orderflow = config['orderflow']
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timeframe = config['timeframe']
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# create columns for trades
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_init_dataframe_with_trades_columns(dataframe)
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df = dataframe.copy()
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try:
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start_time = time.time()
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# calculate ohlcv candle start and end
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# TODO: check if call is necessary for df.
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_calculate_ohlcv_candle_start_and_end(df, timeframe)
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_calculate_ohlcv_candle_start_and_end(trades, timeframe)
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# slice of trades that are before current ohlcv candles to make groupby faster
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# TODO: maybe use df.date instead of df.candle_start at comparision below
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trades = trades.loc[trades.candle_start >= df.candle_start[0]]
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trades.reset_index(inplace=True, drop=True)
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# group trades by candle start
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trades_grouped_by_candle_start = trades.groupby(
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'candle_start', group_keys=False)
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for candle_start in trades_grouped_by_candle_start.groups:
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trades_grouped_df = trades[candle_start == trades['candle_start']]
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is_between = (candle_start == df['candle_start'])
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if np.any(is_between == True): # noqa: E712
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(_, timeframe_minutes) = _convert_timeframe_to_pandas_frequency(timeframe)
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candle_next = candle_start + \
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pd.Timedelta(minutes=timeframe_minutes)
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# skip if there are no trades at next candle
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# because that this candle isn't finished yet
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if candle_next not in trades_grouped_by_candle_start.groups:
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logger.warning(
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f"candle at {candle_start} with {len(trades_grouped_df)} trades "
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f"might be unfinished, because no finished trades at {candle_next}")
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# add trades to each candle
|
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df.loc[is_between, 'trades'] = df.loc[is_between,
|
||||
'trades'].apply(lambda _: trades_grouped_df)
|
||||
# calculate orderflow for each candle
|
||||
df.loc[is_between, 'orderflow'] = df.loc[is_between, 'orderflow'].apply(
|
||||
lambda _: trades_to_volumeprofile_with_total_delta_bid_ask(
|
||||
pd.DataFrame(trades_grouped_df),
|
||||
scale=config_orderflow['scale']))
|
||||
# calculate imbalances for each candle's orderflow
|
||||
df.loc[is_between, 'imbalances'] = df.loc[is_between, 'orderflow'].apply(
|
||||
lambda x: trades_orderflow_to_imbalances(
|
||||
x,
|
||||
imbalance_ratio=config_orderflow['imbalance_ratio'],
|
||||
imbalance_volume=config_orderflow['imbalance_volume']))
|
||||
|
||||
_stacked_imb = config_orderflow['stacked_imbalance_range']
|
||||
df.loc[is_between, 'stacked_imbalances_bid'] = df.loc[
|
||||
is_between, 'imbalances'].apply(lambda x: stacked_imbalance_bid(
|
||||
x,
|
||||
stacked_imbalance_range=_stacked_imb))
|
||||
df.loc[is_between, 'stacked_imbalances_ask'] = df.loc[
|
||||
is_between, 'imbalances'].apply(
|
||||
lambda x: stacked_imbalance_ask(x, stacked_imbalance_range=_stacked_imb))
|
||||
|
||||
# TODO: maybe use simple np.where instead
|
||||
buy = df.loc[is_between, 'bid'].apply(lambda _: np.where(
|
||||
trades_grouped_df['side'].str.contains('buy'), 0, trades_grouped_df['amount']))
|
||||
sell = df.loc[is_between, 'ask'].apply(lambda _: np.where(
|
||||
trades_grouped_df['side'].str.contains('sell'), 0, trades_grouped_df['amount']))
|
||||
deltas_per_trade = sell - buy
|
||||
min_delta = 0
|
||||
max_delta = 0
|
||||
delta = 0
|
||||
for deltas in deltas_per_trade:
|
||||
for d in deltas:
|
||||
delta += d
|
||||
if delta > max_delta:
|
||||
max_delta = delta
|
||||
if delta < min_delta:
|
||||
min_delta = delta
|
||||
df.loc[is_between, 'max_delta'] = max_delta
|
||||
df.loc[is_between, 'min_delta'] = min_delta
|
||||
|
||||
df.loc[is_between, 'bid'] = np.where(trades_grouped_df['side'].str.contains(
|
||||
'buy'), 0, trades_grouped_df['amount']).sum()
|
||||
df.loc[is_between, 'ask'] = np.where(trades_grouped_df['side'].str.contains(
|
||||
'sell'), 0, trades_grouped_df['amount']).sum()
|
||||
df.loc[is_between, 'delta'] = df.loc[is_between,
|
||||
'ask'] - df.loc[is_between, 'bid']
|
||||
min_delta = np.min(deltas_per_trade)
|
||||
max_delta = np.max(deltas_per_trade)
|
||||
|
||||
df.loc[is_between, 'total_trades'] = len(trades_grouped_df)
|
||||
# copy to avoid memory leaks
|
||||
dataframe.loc[is_between] = df.loc[is_between].copy()
|
||||
else:
|
||||
logger.debug(
|
||||
f"Found NO candles for trades starting with {candle_start}")
|
||||
logger.debug(
|
||||
f"trades.groups_keys in {time.time() - start_time} seconds")
|
||||
|
||||
logger.debug(
|
||||
f"trades.singleton_iterate in {time.time() - start_time} seconds")
|
||||
|
||||
except Exception as e:
|
||||
logger.exception("Error populating dataframe with trades:", e)
|
||||
|
||||
return dataframe
|
||||
|
||||
|
||||
def trades_to_volumeprofile_with_total_delta_bid_ask(trades: DataFrame, scale: float):
|
||||
"""
|
||||
:param trades: dataframe
|
||||
:param scale: scale aka bin size e.g. 0.5
|
||||
:return: trades binned to levels according to scale aka orderflow
|
||||
"""
|
||||
df = pd.DataFrame([], columns=DEFAULT_ORDERFLOW_COLUMNS)
|
||||
# create bid, ask where side is sell or buy
|
||||
df['bid_amount'] = np.where(
|
||||
trades['side'].str.contains('buy'), 0, trades['amount'])
|
||||
df['ask_amount'] = np.where(
|
||||
trades['side'].str.contains('sell'), 0, trades['amount'])
|
||||
df['bid'] = np.where(trades['side'].str.contains('buy'), 0, 1)
|
||||
df['ask'] = np.where(trades['side'].str.contains('sell'), 0, 1)
|
||||
|
||||
# round the prices to the nearest multiple of the scale
|
||||
df['price'] = ((trades['price'] / scale).round()
|
||||
* scale).astype('float64').values
|
||||
if df.empty:
|
||||
df['total'] = np.nan
|
||||
df['delta'] = np.nan
|
||||
return df
|
||||
|
||||
df['delta'] = df['ask_amount'] - df['bid_amount']
|
||||
df['total_volume'] = df['ask_amount'] + df['bid_amount']
|
||||
df['total_trades'] = df['ask'] + df['bid']
|
||||
|
||||
# group to bins aka apply scale
|
||||
df = df.groupby('price').sum(numeric_only=True)
|
||||
return df
|
||||
|
||||
|
||||
def trades_orderflow_to_imbalances(df: DataFrame, imbalance_ratio: int, imbalance_volume: int):
|
||||
"""
|
||||
:param df: dataframes with bid and ask
|
||||
:param imbalance_ratio: imbalance_ratio e.g. 300
|
||||
:param imbalance_volume: imbalance volume e.g. 3)
|
||||
:return: dataframe with bid and ask imbalance
|
||||
"""
|
||||
bid = df.bid
|
||||
ask = df.ask.shift(-1)
|
||||
bid_imbalance = (bid / ask) > (imbalance_ratio / 100)
|
||||
# overwrite bid_imbalance with False if volume is not big enough
|
||||
bid_imbalance_filtered = np.where(
|
||||
df.total_volume < imbalance_volume, False, bid_imbalance)
|
||||
ask_imbalance = (ask / bid) > (imbalance_ratio / 100)
|
||||
# overwrite ask_imbalance with False if volume is not big enough
|
||||
ask_imbalance_filtered = np.where(
|
||||
df.total_volume < imbalance_volume, False, ask_imbalance)
|
||||
dataframe = DataFrame({
|
||||
"bid_imbalance": bid_imbalance_filtered,
|
||||
"ask_imbalance": ask_imbalance_filtered
|
||||
}, index=df.index,
|
||||
)
|
||||
|
||||
return dataframe
|
||||
|
||||
|
||||
def stacked_imbalance(df: DataFrame,
|
||||
label: str,
|
||||
stacked_imbalance_range: int,
|
||||
should_reverse: bool):
|
||||
"""
|
||||
y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
|
||||
https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
|
||||
"""
|
||||
imbalance = df[f'{label}_imbalance']
|
||||
int_series = pd.Series(np.where(imbalance, 1, 0))
|
||||
stacked = (
|
||||
int_series * (
|
||||
int_series.groupby(
|
||||
(int_series != int_series.shift()).cumsum()).cumcount() + 1
|
||||
)
|
||||
)
|
||||
|
||||
max_stacked_imbalance_idx = stacked.index[stacked >=
|
||||
stacked_imbalance_range]
|
||||
stacked_imbalance_price = np.nan
|
||||
if not max_stacked_imbalance_idx.empty:
|
||||
idx = max_stacked_imbalance_idx[0] if not should_reverse else np.flipud(
|
||||
max_stacked_imbalance_idx)[0]
|
||||
stacked_imbalance_price = imbalance.index[idx]
|
||||
return stacked_imbalance_price
|
||||
|
||||
|
||||
def stacked_imbalance_bid(df: DataFrame, stacked_imbalance_range: int):
|
||||
return stacked_imbalance(df, 'bid', stacked_imbalance_range, should_reverse=False)
|
||||
|
||||
|
||||
def stacked_imbalance_ask(df: DataFrame, stacked_imbalance_range: int):
|
||||
return stacked_imbalance(df, 'ask', stacked_imbalance_range, should_reverse=True)
|
||||
|
||||
|
||||
def orderflow_to_volume_profile(df: DataFrame):
|
||||
"""
|
||||
:param orderflow: dataframe
|
||||
:return: volume profile dataframe
|
||||
"""
|
||||
bid = df.groupby('level').bid.sum()
|
||||
ask = df.groupby('level').ask.sum()
|
||||
df.groupby('level')['level'].sum()
|
||||
delta = df.groupby('level').ask.sum() - df.groupby('level').bid.sum()
|
||||
df = pd.DataFrame({'bid': bid, 'ask': ask, 'delta': delta})
|
||||
return df
|
||||
|
|
@ -25,7 +25,7 @@ from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, DEFAULT_TRADES_
|
|||
ExchangeConfig, ListPairsWithTimeframes, MakerTaker, OBLiteral,
|
||||
PairWithTimeframe)
|
||||
from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
|
||||
from freqtrade.data.converter.converter import _calculate_ohlcv_candle_start_and_end
|
||||
from freqtrade.data.converter.orderflow import _calculate_ohlcv_candle_start_and_end
|
||||
from freqtrade.data.converter.trade_converter import trades_df_remove_duplicates, trades_list_to_df
|
||||
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, PriceType, RunMode, TradingMode
|
||||
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user