mohammadian7/freqtrade
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

use timeframe_to_next_date and date to calculate candle_start/candle_end

Browse Source
This commit is contained in:
Joe Schr
2024-05-08 15:11:24 +02:00
parent 56b07386ae
commit 10cbc76482
1 changed files with 11 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files
freqtrade/data/converter/orderflow.py
+11 -11
View File
@@ -39,9 +39,7 @@ def _convert_timeframe_to_pandas_frequency(timeframe: str):
def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str): def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
from freqtrade.exchange import timeframe_to_resample_freq from freqtrade.exchange import timeframe_to_resample_freq, timeframe_to_next_date
_, timeframe_minutes = _convert_timeframe_to_pandas_frequency(
timeframe)
timeframe_frequency = timeframe_to_resample_freq(timeframe) timeframe_frequency = timeframe_to_resample_freq(timeframe)
# calculate ohlcv candle start and end # calculate ohlcv candle start and end
@@ -49,8 +47,10 @@ def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
df['datetime'] = pd.to_datetime(df['date'], unit='ms') df['datetime'] = pd.to_datetime(df['date'], unit='ms')
df['candle_start'] = df['datetime'].dt.floor(timeframe_frequency) df['candle_start'] = df['datetime'].dt.floor(timeframe_frequency)
# used in _now_is_time_to_refresh_trades # used in _now_is_time_to_refresh_trades
df['candle_end'] = df['candle_start'] + pd.Timedelta(minutes=timeframe_minutes) df['candle_end'] = df['candle_start'].apply(
df.drop(columns=['datetime'], inplace=True) lambda candle_start: timeframe_to_next_date(timeframe, candle_start)
)
df.drop(columns=["datetime"], inplace=True)
def populate_dataframe_with_trades(config: Config, def populate_dataframe_with_trades(config: Config,
@@ -72,11 +72,10 @@ def populate_dataframe_with_trades(config: Config,
try: try:
start_time = time.time() start_time = time.time()
# calculate ohlcv candle start and end # calculate ohlcv candle start and end
_calculate_ohlcv_candle_start_and_end(df, timeframe)
_calculate_ohlcv_candle_start_and_end(trades, timeframe) _calculate_ohlcv_candle_start_and_end(trades, timeframe)
# slice of trades that are before current ohlcv candles to make groupby faster # slice of trades that are before current ohlcv candles to make groupby faster
trades = trades.loc[trades.candle_start >= df.candle_start[0]] trades = trades.loc[trades.candle_start >= df.date[0]]
trades.reset_index(inplace=True, drop=True) trades.reset_index(inplace=True, drop=True)
# group trades by candle start # group trades by candle start
@@ -84,11 +83,12 @@ def populate_dataframe_with_trades(config: Config,
'candle_start', group_keys=False) 'candle_start', group_keys=False)
for candle_start in trades_grouped_by_candle_start.groups: for candle_start in trades_grouped_by_candle_start.groups:
trades_grouped_df = trades[candle_start == trades['candle_start']] trades_grouped_df = trades[candle_start == trades["candle_start"]]
is_between = (candle_start == df['candle_start']) is_between = candle_start == df["date"]
if np.any(is_between == True): # noqa: E712 if np.any(is_between == True): # noqa: E712
(_, timeframe_minutes) = _convert_timeframe_to_pandas_frequency(timeframe) from freqtrade.exchange import timeframe_to_next_date
candle_next = candle_start + pd.Timedelta(minutes=timeframe_minutes)
candle_next = timeframe_to_next_date(timeframe, candle_start)
# skip if there are no trades at next candle # skip if there are no trades at next candle
# because that this candle isn't finished yet # because that this candle isn't finished yet
if candle_next not in trades_grouped_by_candle_start.groups: if candle_next not in trades_grouped_by_candle_start.groups: