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orderflow: fixing typing

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This commit is contained in:
Joe Schr 2024-06-26 19:43:13 +02:00
parent 323274ecee
commit dad2cad525
1 changed files with 5 additions and 4 deletions
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freqtrade/data/converter/orderflow.py
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@ -4,6 +4,7 @@ Functions to convert orderflow data from public_trades
import logging
import time
import typing
from collections import OrderedDict
import numpy as np
@ -101,7 +102,7 @@ def populate_dataframe_with_trades(config, dataframe, trades):
if is_between.any():
from freqtrade.exchange import timeframe_to_next_date
candle_next = timeframe_to_next_date(timeframe, candle_start)
candle_next = timeframe_to_next_date(timeframe, typing.cast(datetime, candle_start))
if candle_next not in trades_grouped_by_candle_start.groups:
logger.warning(
f"candle at {candle_start} with {len(trades_grouped_df)} trades "
@ -113,7 +114,7 @@ def populate_dataframe_with_trades(config, dataframe, trades):
trades_series.loc[indices] = [trades_grouped_df]
# Use caching mechanism
if (candle_start, candle_next) in cached_grouped_trades:
cache_entry = cached_grouped_trades[(candle_start, candle_next)]
cache_entry = cached_grouped_trades[(typing.cast(datetime, candle_start), candle_next)]
# dataframe.loc[is_between] = cache_entry # doesn't take, so we need workaround:
# Create a dictionary of the column values to be assigned
update_dict = {c: cache_entry[c].iat[0] for c in cache_entry.columns}
@ -169,7 +170,7 @@ def populate_dataframe_with_trades(config, dataframe, trades):
dataframe.loc[indices, "total_trades"] = len(trades_grouped_df)
# Cache the result
cached_grouped_trades[(candle_start, candle_next)] = dataframe.loc[
cached_grouped_trades[(typing.cast(datetime, candle_start), candle_next)] = dataframe.loc[
is_between
].copy()
@ -194,7 +195,7 @@ def populate_dataframe_with_trades(config, dataframe, trades):
return dataframe
def trades_to_volumeprofile_with_total_delta_bid_ask(trades: pd.DataFrame, scale: float):
def trades_to_volumeprofile_with_total_delta_bid_ask(trades: pd.DataFrame, scale: float) -> pd.DataFrame:
"""
:param trades: dataframe
:param scale: scale aka bin size e.g. 0.5