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feat: use dicts instead of nested dataframes
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@ -115,7 +115,7 @@ def populate_dataframe_with_trades(
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indices = dataframe.index[is_between].tolist()
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# Add trades to each candle
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trades_series.loc[indices] = [trades_grouped_df]
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trades_series.loc[indices] = [trades_grouped_df.to_dict(orient="records")]
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# Use caching mechanism
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if (candle_start, candle_next) in cached_grouped_trades:
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cache_entry = cached_grouped_trades[
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@ -134,14 +134,14 @@ def populate_dataframe_with_trades(
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orderflow = trades_to_volumeprofile_with_total_delta_bid_ask(
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trades_grouped_df, scale=config_orderflow["scale"]
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)
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orderflow_series.loc[indices] = [orderflow]
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orderflow_series.loc[indices] = [orderflow.to_dict(orient="index")]
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# Calculate imbalances for each candle's orderflow
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imbalances = trades_orderflow_to_imbalances(
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orderflow,
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imbalance_ratio=config_orderflow["imbalance_ratio"],
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imbalance_volume=config_orderflow["imbalance_volume"],
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)
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imbalances_series.loc[indices] = [imbalances]
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imbalances_series.loc[indices] = [imbalances.to_dict(orient="index")]
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stacked_imbalance_range = config_orderflow["stacked_imbalance_range"]
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stacked_imbalances_bid_series.loc[indices] = [
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@ -1317,10 +1317,6 @@ class RPC:
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# replace NaT with `None`
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dataframe[date_column] = dataframe[date_column].astype(object).replace({NaT: None})
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# TODO: Temporary workaround for orderflow: drop orderflow columns
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dataframe = dataframe.drop(
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["orderflow", "trades", "imbalances"], axis=1, errors="ignore"
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)
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dataframe = dataframe.replace({inf: None, -inf: None, nan: None})
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res = {
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