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#711 Fix date ambiguity and pandas compatibility in estimate_intraday #723

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#711 Fix date ambiguity and pandas compatibility in estimate_intraday #723

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40 changes: 35 additions & 5 deletions pyfolio/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -339,9 +339,37 @@ def estimate_intraday(returns, positions, transactions, EOD_hour=23):
Daily net position values, resampled for intraday behavior.
"""

# Construct DataFrame of transaction amounts
txn_val = transactions.copy()
# Ensure datetime index
txn_val.index = pd.to_datetime(txn_val.index, errors="coerce")

# Remove invalid timestamps
txn_val = txn_val[txn_val.index.notnull()]

# Remove timezone if present
if getattr(txn_val.index, "tz", None) is not None:
txn_val.index = txn_val.index.tz_localize(None)

# Ensure sorted time index
txn_val = txn_val.sort_index()

# Remove duplicate columns
txn_val = txn_val.loc[:, ~txn_val.columns.duplicated()]

# Set index name safely
txn_val.index.names = ['date']

# Avoid duplicate 'date' column
if 'date' in txn_val.columns:
txn_val.drop(columns=['date'], inplace=True)

# Create date column
txn_val['date'] = txn_val.index.date

# Prevent crashes if no transactions
if txn_val.empty:
return positions

# Calculate transaction values
txn_val['value'] = txn_val.amount * txn_val.price
txn_val = txn_val.reset_index().pivot_table(
index='date', values='value',
Expand All @@ -361,8 +389,10 @@ def estimate_intraday(returns, positions, transactions, EOD_hour=23):

# Shift EOD positions to positions at start of next trading day
positions_shifted = positions.copy().shift(1).fillna(0)
starting_capital = positions.iloc[0].sum() / (1 + returns[0])
positions_shifted.cash[0] = starting_capital
starting_capital = positions.iloc[0].sum() / (1 + returns.iloc[0])
positions_shifted.loc[
positions_shifted.index[0], "cash"
] = starting_capital

# Format and add start positions to intraday position changes
txn_val.index = txn_val.index.normalize()
Expand Down Expand Up @@ -538,4 +568,4 @@ def sample_colormap(cmap_name, n_samples):
for i in np.linspace(0, 1, n_samples):
colors.append(colormap(i))

return colors
return colors