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