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feat: use dataframe.at directly to avoid intermediate series

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Matthias
2024-12-05 19:31:34 +01:00
parent 6584f86bce
commit 4879582896
1 changed files with 23 additions and 40 deletions
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freqtrade/data/converter/orderflow.py
+19 -36
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@@ -93,13 +93,6 @@ def populate_dataframe_with_trades(
trades = trades.loc[trades["candle_start"] >= start_date] trades = trades.loc[trades["candle_start"] >= start_date]
trades.reset_index(inplace=True, drop=True) trades.reset_index(inplace=True, drop=True)
# Create Series to hold complex data
trades_series = pd.Series(index=dataframe.index, dtype=object)
orderflow_series = pd.Series(index=dataframe.index, dtype=object)
imbalances_series = pd.Series(index=dataframe.index, dtype=object)
stacked_imbalances_bid_series = pd.Series(index=dataframe.index, dtype=object)
stacked_imbalances_ask_series = pd.Series(index=dataframe.index, dtype=object)
# group trades by candle start # group trades by candle start
trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False) trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False)
@@ -126,37 +119,34 @@ def populate_dataframe_with_trades(
) )
continue continue
indices = dataframe.index[is_between].tolist() # there can only be one row with the same date
# Add trades to each candle index = dataframe.index[is_between][0]
trades_series.loc[indices] = [ dataframe.at[index, "trades"] = trades_grouped_df.drop(
trades_grouped_df.drop(columns=["candle_start", "candle_end"]).to_dict( columns=["candle_start", "candle_end"]
orient="records" ).to_dict(orient="records")
)
]
# Calculate orderflow for each candle # Calculate orderflow for each candle
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.to_dict(orient="index")] dataframe.at[index, "orderflow"] = orderflow.to_dict(orient="index")
# orderflow_series.loc[[index]] = [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.to_dict(orient="index")] dataframe.at[index, "imbalances"] = 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] = [ dataframe.at[index, "stacked_imbalances_bid"] = stacked_imbalance_bid(
stacked_imbalance_bid(
imbalances, stacked_imbalance_range=stacked_imbalance_range imbalances, stacked_imbalance_range=stacked_imbalance_range
) )
]
stacked_imbalances_ask_series.loc[indices] = [ dataframe.at[index, "stacked_imbalances_ask"] = stacked_imbalance_ask(
stacked_imbalance_ask(
imbalances, stacked_imbalance_range=stacked_imbalance_range imbalances, stacked_imbalance_range=stacked_imbalance_range
) )
]
bid = np.where( bid = np.where(
trades_grouped_df["side"].str.contains("sell"), trades_grouped_df["amount"], 0 trades_grouped_df["side"].str.contains("sell"), trades_grouped_df["amount"], 0
@@ -168,15 +158,15 @@ def populate_dataframe_with_trades(
deltas_per_trade = ask - bid deltas_per_trade = ask - bid
min_delta = deltas_per_trade.cumsum().min() min_delta = deltas_per_trade.cumsum().min()
max_delta = deltas_per_trade.cumsum().max() max_delta = deltas_per_trade.cumsum().max()
dataframe.loc[indices, "max_delta"] = max_delta dataframe.loc[index, "max_delta"] = max_delta
dataframe.loc[indices, "min_delta"] = min_delta dataframe.loc[index, "min_delta"] = min_delta
dataframe.loc[indices, "bid"] = bid.sum() dataframe.loc[index, "bid"] = bid.sum()
dataframe.loc[indices, "ask"] = ask.sum() dataframe.loc[index, "ask"] = ask.sum()
dataframe.loc[indices, "delta"] = ( dataframe.loc[index, "delta"] = (
dataframe.loc[indices, "ask"] - dataframe.loc[indices, "bid"] dataframe.loc[index, "ask"] - dataframe.loc[index, "bid"]
) )
dataframe.loc[indices, "total_trades"] = len(trades_grouped_df) dataframe.loc[index, "total_trades"] = len(trades_grouped_df)
# Cache the result # Cache the result
cached_grouped_trades[(candle_start, candle_next)] = dataframe.loc[ cached_grouped_trades[(candle_start, candle_next)] = dataframe.loc[
@@ -193,13 +183,6 @@ def populate_dataframe_with_trades(
logger.debug(f"Found NO candles for trades starting with {candle_start}") logger.debug(f"Found NO candles for trades starting with {candle_start}")
logger.debug(f"trades.groups_keys in {time.time() - start_time} seconds") logger.debug(f"trades.groups_keys in {time.time() - start_time} seconds")
# Merge the complex data Series back into the DataFrame
dataframe["trades"] = trades_series
dataframe["orderflow"] = orderflow_series
dataframe["imbalances"] = imbalances_series
dataframe["stacked_imbalances_bid"] = stacked_imbalances_bid_series
dataframe["stacked_imbalances_ask"] = stacked_imbalances_ask_series
except Exception as e: except Exception as e:
logger.exception("Error populating dataframe with trades") logger.exception("Error populating dataframe with trades")
raise DependencyException(e) raise DependencyException(e)