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orderflow: ruff format

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This commit is contained in:
Joe Schr
2024-06-24 17:15:39 +02:00
parent ecd2118941
commit 390373cb9b
1 changed files with 22 additions and 8 deletions
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30
freqtrade/data/converter/orderflow.py
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@@ -58,7 +58,7 @@ def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
# Global cache dictionary
cache_size = 1000 # TODO move that in config
cached_grouped_trades = OrderedDict() # TODO move that where?
cached_grouped_trades = OrderedDict() # TODO move that where?
def populate_dataframe_with_trades(config, dataframe, trades):
@@ -89,7 +89,6 @@ def populate_dataframe_with_trades(config, dataframe, trades):
# group trades by candle start
trades_grouped_by_candle_start = trades.groupby("candle_start")
# Create Series to hold complex data
trades_series = pd.Series(index=dataframe.index, dtype=object)
orderflow_series = pd.Series(index=dataframe.index, dtype=object)
@@ -101,6 +100,7 @@ def populate_dataframe_with_trades(config, dataframe, trades):
is_between = candle_start == dataframe["date"]
if is_between.any():
from freqtrade.exchange import timeframe_to_next_date
candle_next = timeframe_to_next_date(timeframe, candle_start)
if candle_next not in trades_grouped_by_candle_start.groups:
logger.warning(
@@ -139,11 +139,21 @@ def populate_dataframe_with_trades(config, dataframe, trades):
imbalances_series.loc[indices] = [imbalances] * len(indices)
stacked_imb = config_orderflow["stacked_imbalance_range"]
stacked_imbalances_bid_series.loc[indices] = [stacked_imbalance_bid(imbalances, stacked_imbalance_range=stacked_imb)] * len(indices)
stacked_imbalances_ask_series.loc[indices] = [stacked_imbalance_ask(imbalances, stacked_imbalance_range=stacked_imb)] * len(indices)
stacked_imbalances_bid_series.loc[indices] = [
stacked_imbalance_bid(imbalances, stacked_imbalance_range=stacked_imb)
] * len(indices)
stacked_imbalances_ask_series.loc[indices] = [
stacked_imbalance_ask(imbalances, stacked_imbalance_range=stacked_imb)
] * len(indices)
bid = trades_grouped_df["side"].str.contains("sell").astype(int) * trades_grouped_df["amount"]
ask = trades_grouped_df["side"].str.contains("buy").astype(int) * trades_grouped_df["amount"]
bid = (
trades_grouped_df["side"].str.contains("sell").astype(int)
* trades_grouped_df["amount"]
)
ask = (
trades_grouped_df["side"].str.contains("buy").astype(int)
* trades_grouped_df["amount"]
)
deltas_per_trade = ask - bid
min_delta = deltas_per_trade.cumsum().min()
@@ -153,11 +163,15 @@ def populate_dataframe_with_trades(config, dataframe, trades):
dataframe.loc[indices, "bid"] = bid.sum()
dataframe.loc[indices, "ask"] = ask.sum()
dataframe.loc[indices, "delta"] = dataframe.loc[indices, "ask"] - dataframe.loc[indices, "bid"]
dataframe.loc[indices, "delta"] = (
dataframe.loc[indices, "ask"] - dataframe.loc[indices, "bid"]
)
dataframe.loc[indices, "total_trades"] = len(trades_grouped_df)
# Cache the result
cached_grouped_trades[(candle_start, candle_next)] = dataframe.loc[is_between].copy()
cached_grouped_trades[(candle_start, candle_next)] = dataframe.loc[
is_between
].copy()
# Maintain cache size
if len(cached_grouped_trades) > cache_size: