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Use logger for debug output in cross-sectional backtest #102

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Merged
Likio3000 merged 1 commit into from
Sep 1, 2025
Merged

Use logger for debug output in cross-sectional backtest #102

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28 changes: 22 additions & 6 deletions backtesting_components/core_logic.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,14 @@
from __future__ import annotations

import logging
import numpy as np
import pandas as pd # For DataFrame rolling in hold period
from typing import TYPE_CHECKING, Dict, List, Any, OrderedDict as OrderedDictType

from evolution_components.data_handling import get_sector_groups, get_features_at_time

logger = logging.getLogger(__name__)

if TYPE_CHECKING:
from alpha_framework import AlphaProgram # Use the actual class from the framework

Expand Down Expand Up @@ -241,9 +245,16 @@ def backtest_cross_sectional_alpha(
signal_matrix = np.array(raw_signals_over_time)

if debug_prints:
print(f"Debug (core_logic): Raw signal_matrix σ_cross_sectional (first 5): {signal_matrix.std(axis=1)[:5]}")
print(">>> signal_matrix[:5,:5] std:", np.std(signal_matrix[:5,:], axis=1, ddof=0))
print(">>> first few rows of signal_matrix:", signal_matrix[:3,:4])
logger.debug(
f"Debug (core_logic): Raw signal_matrix σ_cross_sectional (first 5): {signal_matrix.std(axis=1)[:5]}"
)
if debug_prints:
logger.debug(
">>> signal_matrix[:5,:5] std: %s",
np.std(signal_matrix[:5, :], axis=1, ddof=0),
)
if debug_prints:
logger.debug(">>> first few rows of signal_matrix: %s", signal_matrix[:3, :4])

target_positions_matrix = np.zeros_like(signal_matrix)
for t in range(signal_matrix.shape[0]):
Expand Down Expand Up @@ -275,7 +286,9 @@ def backtest_cross_sectional_alpha(
target_positions_matrix[t, :] = neutralized_signal_t

if debug_prints:
print(f"Debug (core_logic): Target_positions_matrix σ_cross_sectional (first 5): {target_positions_matrix.std(axis=1)[:5]}")
logger.debug(
f"Debug (core_logic): Target_positions_matrix σ_cross_sectional (first 5): {target_positions_matrix.std(axis=1)[:5]}"
)

if hold > 1:
df_target_pos = pd.DataFrame(target_positions_matrix)
Expand Down Expand Up @@ -408,10 +421,13 @@ def backtest_cross_sectional_alpha(
"Error": "No trades executed",
}

if debug_prints and len(daily_portfolio_returns_net) > 0:
if len(daily_portfolio_returns_net) > 0:
mean_ret_calc = np.mean(daily_portfolio_returns_net)
std_ret_calc = np.std(daily_portfolio_returns_net, ddof=0)
print(f"DEBUG (core_logic): PnL mean {mean_ret_calc:.6e} std {std_ret_calc:.6e}")
if debug_prints:
logger.debug(
f"DEBUG (core_logic): PnL mean {mean_ret_calc:.6e} std {std_ret_calc:.6e}"
)

if len(daily_portfolio_returns_net) < 2:
return {"Sharpe": 0.0, "AnnReturn": 0.0, "AnnVol": 0.0, "MaxDD": 0.0, "Turnover": 0.0, "Bars": len(daily_portfolio_returns_net)}
Expand Down