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Reduce unnecessary newlines

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This commit is contained in:
Matthias 2024-02-10 17:45:03 +01:00
parent 4b0383f197
commit fc15f98b80
1 changed files with 26 additions and 40 deletions
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66
freqtrade/data/converter/converter.py
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@ -112,8 +112,7 @@ def populate_dataframe_with_trades(config: Config,
trades.reset_index(inplace=True, drop=True)
# group trades by candle start
trades_grouped_by_candle_start = trades.groupby(
'candle_start', group_keys=False)
trades_grouped_by_candle_start = trades.groupby('candle_start', group_keys=False)
# repair 'date' datetime type (otherwise crashes on each compare)
if "date" in dataframe.columns:
dataframe['date'] = pd.to_datetime(dataframe['date'])
@ -122,10 +121,8 @@ def populate_dataframe_with_trades(config: Config,
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_frequency, timeframe_minutes) = _convert_timeframe_to_pandas_frequency(
timeframe)
candle_next = candle_start + \
pd.Timedelta(minutes=timeframe_minutes)
(_, 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:
@ -188,13 +185,10 @@ def populate_dataframe_with_trades(config: Config,
# 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"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")
logger.debug(f"trades.singleton_iterate in {time.time() - start_time} seconds")
except Exception as e:
logger.exception("Error populating dataframe with trades:", e)
@ -214,18 +208,15 @@ def public_trades_to_dataframe(trades: List, pair: str) -> DataFrame:
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
logger.debug(
f"Converting candle (TRADES) data to dataframe for pair {pair}.")
logger.debug(f"Converting candle (TRADES) data to dataframe for pair {pair}.")
cols = DEFAULT_TRADES_COLUMNS
df = DataFrame(trades, columns=cols)
df['date'] = pd.to_datetime(
df['timestamp'], unit='ms', utc=True)
df['date'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
# Some exchanges return int values for Volume and even for OHLC.
# Convert them since TA-LIB indicators used in the strategy assume floats
# and fail with exception...
df = df.astype(dtype={'amount': 'float', 'cost': 'float',
'price': 'float'})
df = df.astype(dtype={'amount': 'float', 'cost': 'float', 'price': 'float'})
return df
@ -237,18 +228,13 @@ def trades_to_volumeprofile_with_total_delta_bid_ask(trades: DataFrame, scale: f
"""
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)
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
df['price'] = ((trades['price'] / scale).round() * scale).astype('float64').values
if df.empty:
df['total'] = np.nan
df['delta'] = np.nan
@ -274,16 +260,14 @@ def trades_orderflow_to_imbalances(df: DataFrame, imbalance_ratio: int, imbalanc
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)
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,
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
@ -299,11 +283,13 @@ def stacked_imbalance(df: DataFrame,
"""
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)
stacked = (
int_series * (
int_series.groupby((int_series != int_series.shift()).cumsum()).cumcount() + 1
)
)
max_stacked_imbalance_idx = stacked.index[stacked >=
stacked_imbalance_range]
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(