mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-15 04:33:57 +00:00
273 lines
11 KiB
Python
273 lines
11 KiB
Python
"""
|
|
Functions to convert orderflow data from public_trades
|
|
"""
|
|
import logging
|
|
import time
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS, Config
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def _init_dataframe_with_trades_columns(dataframe: pd.DataFrame):
|
|
"""
|
|
Populates a dataframe with trades columns
|
|
:param dataframe: Dataframe to populate
|
|
"""
|
|
dataframe['trades'] = dataframe.apply(lambda _: [], axis=1)
|
|
dataframe['orderflow'] = dataframe.apply(lambda _: {}, axis=1)
|
|
dataframe['bid'] = np.nan
|
|
dataframe['ask'] = np.nan
|
|
dataframe['delta'] = np.nan
|
|
dataframe['min_delta'] = np.nan
|
|
dataframe['max_delta'] = np.nan
|
|
dataframe['total_trades'] = np.nan
|
|
dataframe['stacked_imbalances_bid'] = np.nan
|
|
dataframe['stacked_imbalances_ask'] = np.nan
|
|
|
|
|
|
def _convert_timeframe_to_pandas_frequency(timeframe: str):
|
|
# convert timeframe to format usable by pandas
|
|
from freqtrade.exchange import timeframe_to_minutes
|
|
timeframe_minutes = timeframe_to_minutes(timeframe)
|
|
timeframe_frequency = f'{timeframe_minutes}min'
|
|
return (timeframe_frequency, timeframe_minutes)
|
|
|
|
|
|
def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
|
|
from freqtrade.exchange import timeframe_to_resample_freq
|
|
_, timeframe_minutes = _convert_timeframe_to_pandas_frequency(
|
|
timeframe)
|
|
|
|
timeframe_frequency = timeframe_to_resample_freq(timeframe)
|
|
# calculate ohlcv candle start and end
|
|
if df is not None and not df.empty:
|
|
df['datetime'] = pd.to_datetime(df['date'], unit='ms')
|
|
df['candle_start'] = df['datetime'].dt.floor(timeframe_frequency)
|
|
# used in _now_is_time_to_refresh_trades
|
|
df['candle_end'] = df['candle_start'] + pd.Timedelta(minutes=timeframe_minutes)
|
|
df.drop(columns=['datetime'], inplace=True)
|
|
|
|
|
|
def populate_dataframe_with_trades(config: Config,
|
|
dataframe: pd.DataFrame,
|
|
trades: pd.DataFrame) -> pd.DataFrame:
|
|
"""
|
|
Populates a dataframe with trades
|
|
:param dataframe: Dataframe to populate
|
|
:param trades: Trades to populate with
|
|
:return: Dataframe with trades populated
|
|
"""
|
|
config_orderflow = config['orderflow']
|
|
timeframe = config['timeframe']
|
|
|
|
# create columns for trades
|
|
_init_dataframe_with_trades_columns(dataframe)
|
|
df = dataframe.copy()
|
|
|
|
try:
|
|
start_time = time.time()
|
|
# calculate ohlcv candle start and end
|
|
_calculate_ohlcv_candle_start_and_end(df, timeframe)
|
|
_calculate_ohlcv_candle_start_and_end(trades, timeframe)
|
|
|
|
# slice of trades that are before current ohlcv candles to make groupby faster
|
|
trades = trades.loc[trades.candle_start >= df.candle_start[0]]
|
|
trades.reset_index(inplace=True, drop=True)
|
|
|
|
# group trades by candle start
|
|
trades_grouped_by_candle_start = trades.groupby(
|
|
'candle_start', group_keys=False)
|
|
|
|
for candle_start in trades_grouped_by_candle_start.groups:
|
|
trades_grouped_df = trades[candle_start == trades['candle_start']]
|
|
is_between = (candle_start == df['candle_start'])
|
|
if np.any(is_between == True): # noqa: E712
|
|
(_, timeframe_minutes) = _convert_timeframe_to_pandas_frequency(timeframe)
|
|
candle_next = candle_start + pd.Timedelta(minutes=timeframe_minutes)
|
|
# skip if there are no trades at next candle
|
|
# because that this candle isn't finished yet
|
|
if candle_next not in trades_grouped_by_candle_start.groups:
|
|
logger.warning(
|
|
f"candle at {candle_start} with {len(trades_grouped_df)} trades "
|
|
f"might be unfinished, because no finished trades at {candle_next}")
|
|
|
|
# add trades to each candle
|
|
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))
|
|
|
|
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)
|
|
raise e
|
|
|
|
return dataframe
|
|
|
|
|
|
def trades_to_volumeprofile_with_total_delta_bid_ask(trades: pd.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: pd.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 = pd.DataFrame({
|
|
"bid_imbalance": bid_imbalance_filtered,
|
|
"ask_imbalance": ask_imbalance_filtered
|
|
}, index=df.index,
|
|
)
|
|
|
|
return dataframe
|
|
|
|
|
|
def stacked_imbalance(df: pd.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: pd.DataFrame, stacked_imbalance_range: int):
|
|
return stacked_imbalance(df, 'bid', stacked_imbalance_range, should_reverse=False)
|
|
|
|
|
|
def stacked_imbalance_ask(df: pd.DataFrame, stacked_imbalance_range: int):
|
|
return stacked_imbalance(df, 'ask', stacked_imbalance_range, should_reverse=True)
|
|
|
|
|
|
def orderflow_to_volume_profile(df: pd.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
|