mirror of
https://github.com/freqtrade/freqtrade.git
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279 lines
11 KiB
Python
279 lines
11 KiB
Python
"""
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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) -> 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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