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feat: use dicts instead of nested dataframes

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Matthias
2024-07-15 20:08:55 +02:00
parent b163f8fcf4
commit 8922f0cdf3
2 changed files with 3 additions and 7 deletions
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freqtrade/data/converter/orderflow.py
+3 -3
View File
@@ -115,7 +115,7 @@ def populate_dataframe_with_trades(
indices = dataframe.index[is_between].tolist() indices = dataframe.index[is_between].tolist()
# Add trades to each candle # Add trades to each candle
trades_series.loc[indices] = [trades_grouped_df] trades_series.loc[indices] = [trades_grouped_df.to_dict(orient="records")]
# Use caching mechanism # Use caching mechanism
if (candle_start, candle_next) in cached_grouped_trades: if (candle_start, candle_next) in cached_grouped_trades:
cache_entry = cached_grouped_trades[ cache_entry = cached_grouped_trades[
@@ -134,14 +134,14 @@ def populate_dataframe_with_trades(
orderflow = trades_to_volumeprofile_with_total_delta_bid_ask( orderflow = trades_to_volumeprofile_with_total_delta_bid_ask(
trades_grouped_df, scale=config_orderflow["scale"] trades_grouped_df, scale=config_orderflow["scale"]
) )
orderflow_series.loc[indices] = [orderflow] orderflow_series.loc[indices] = [orderflow.to_dict(orient="index")]
# Calculate imbalances for each candle's orderflow # Calculate imbalances for each candle's orderflow
imbalances = trades_orderflow_to_imbalances( imbalances = trades_orderflow_to_imbalances(
orderflow, orderflow,
imbalance_ratio=config_orderflow["imbalance_ratio"], imbalance_ratio=config_orderflow["imbalance_ratio"],
imbalance_volume=config_orderflow["imbalance_volume"], imbalance_volume=config_orderflow["imbalance_volume"],
) )
imbalances_series.loc[indices] = [imbalances] imbalances_series.loc[indices] = [imbalances.to_dict(orient="index")]
stacked_imbalance_range = config_orderflow["stacked_imbalance_range"] stacked_imbalance_range = config_orderflow["stacked_imbalance_range"]
stacked_imbalances_bid_series.loc[indices] = [ stacked_imbalances_bid_series.loc[indices] = [
freqtrade/rpc/rpc.py
-4
View File
@@ -1317,10 +1317,6 @@ class RPC:
# replace NaT with `None` # replace NaT with `None`
dataframe[date_column] = dataframe[date_column].astype(object).replace({NaT: None}) dataframe[date_column] = dataframe[date_column].astype(object).replace({NaT: None})
# TODO: Temporary workaround for orderflow: drop orderflow columns
dataframe = dataframe.drop(
["orderflow", "trades", "imbalances"], axis=1, errors="ignore"
)
dataframe = dataframe.replace({inf: None, -inf: None, nan: None}) dataframe = dataframe.replace({inf: None, -inf: None, nan: None})
res = { res = {