CTYPTO/freqtrade
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2026-02-13 09:40:29 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix ruff

Browse Source
This commit is contained in:
Joe Schr
2024-02-07 13:10:04 +01:00
parent 79074ea4b3
commit 634e81c7e0
1 changed files with 33 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files

61
freqtrade/data/converter/converter.py
View File

@@ -83,7 +83,11 @@ def _calculate_ohlcv_candle_start_and_end(df: DataFrame, timeframe: str):
df.drop(columns=['datetime'], inplace=True)
def populate_dataframe_with_trades(config: Config, dataframe: DataFrame, trades: DataFrame, *, pair: str) -> DataFrame:
def populate_dataframe_with_trades(config: Config,
dataframe: DataFrame,
trades: DataFrame,
*,
pair: str) -> DataFrame:
"""
Populates a dataframe with trades
:param dataframe: Dataframe to populate
@@ -122,26 +126,34 @@ def populate_dataframe_with_trades(config: Config, dataframe: DataFrame, trades:
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 not np.any((candle_next == df.candle_start)):
# 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 might be unfinished, because no finished trades at {candle_next}")
f"candle at {candle_start} with {len(trades_grouped_df)} trades might be unfinished, because no finished trades at {candle_next}") # noqa
# 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']))
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']))
lambda x: trades_orderflow_to_imbalances(x,
imbalance_ratio=config_orderflow['imbalance_ratio'],
imbalance_volume=config_orderflow['imbalance_volume']))
df.loc[is_between, 'stacked_imbalances_bid'] = df.loc[is_between,
'imbalances'].apply(lambda x: stacked_imbalance_bid(x, stacked_imbalance_range=config_orderflow['stacked_imbalance_range']))
'imbalances'].apply(
lambda x: stacked_imbalance_bid(x, # noqa: E501
stacked_imbalance_range=config_orderflow['stacked_imbalance_range']))
df.loc[is_between, 'stacked_imbalances_ask'] = df.loc[is_between,
'imbalances'].apply(lambda x: stacked_imbalance_ask(x, stacked_imbalance_range=config_orderflow['stacked_imbalance_range']))
'imbalances'].apply(
lambda x: stacked_imbalance_ask(x, # noqa: E501
stacked_imbalance_range=config_orderflow['stacked_imbalance_range']))
buy = df.loc[is_between, 'bid'].apply(lambda _: np.where(
trades_grouped_df['side'].str.contains('buy'), 0, trades_grouped_df['amount']))
@@ -188,9 +200,11 @@ def populate_dataframe_with_trades(config: Config, dataframe: DataFrame, trades:
return dataframe
# TODO: remove timeframe and pair
def public_trades_to_dataframe(trades: list, timeframe: str, pair: str, *,
fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
def public_trades_to_dataframe(trades: list,
timeframe: str,
pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Converts a list with candle (TRADES) data (in format returned by ccxt.fetch_trades)
to a Dataframe
@@ -278,12 +292,17 @@ def trades_orderflow_to_imbalances(df: DataFrame, imbalance_ratio: int, imbalanc
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)
{'bid_imbalance': bid_imbalance_filtered,
'ask_imbalance': ask_imbalance_filtered},
index=df.index)
return dataframe
def stacked_imbalance(df: DataFrame, label: str = "bid", stacked_imbalance_range: int = 3, should_reverse: bool = False):
def stacked_imbalance(df: DataFrame,
label: str = "bid",
stacked_imbalance_range: int = 3,
should_reverse: bool = False):
"""
y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
@@ -297,7 +316,6 @@ def stacked_imbalance(df: DataFrame, label: str = "bid", stacked_imbalance_range
stacked_imbalance_range]
stacked_imbalance_price = np.nan
if not max_stacked_imbalance_idx.empty:
# TODO: do better than just take first
idx = max_stacked_imbalance_idx[0] if not should_reverse else np.flipud(
max_stacked_imbalance_idx)[0]
stacked_imbalance_price = imbalance.index[idx]
@@ -393,16 +411,6 @@ def clean_duplicate_trades(trades: DataFrame, timeframe: str, pair: str, *,
df = pd.DataFrame(trades_df_remove_duplicates(
trades), columns=trades.columns)
#
# from freqtrade.exchange import timeframe_to_minutes
# timeframe_minutes = timeframe_to_minutes(timeframe)
# sum_dict = {}
# for col in ['amount']: # TODO: remove side,etc
# sum_dict[col] = 'sum'
# group by index and aggregate results to eliminate duplicate ticks
# df = data.groupby(
# by='date', as_index=False, sort=True).agg(sum_dict) # NOTE: sum doesn't make much sense for eliminating duplicates?
return df
@@ -417,10 +425,7 @@ def drop_incomplete_and_fill_missing_trades(data: DataFrame, timeframe: str, pai
data.drop(data.tail(1).index, inplace=True)
logger.debug('Dropping last trade')
if fill_missing:
return trades_fill_up_missing_data(data, timeframe, pair)
else:
return data
return data
def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame: