From 6c7fbe0ba47395c8d2d2139d593a6d94d4957786 Mon Sep 17 00:00:00 2001
From: Nitin Tony Paul <108007300+nitintonypaul@users.noreply.github.com>
Date: Mon, 26 Jan 2026 18:31:05 +0530
Subject: [PATCH] [FIX] backtester bug fix (#4)

* [ENH] Added `HierarchicalRiskParity`

- Added HRP optimizer
- fixed minor bug which returns portfolio weights reference instead of copying
- Improved documentation with minimal import syntax
- Updated README/Documentation

* [MNT] Improving documentation

Improving docstrings and markdown documentation website.

* [ENH] Improved `Backtester`

Decoupled `rebalance_freq` and `reopt_freq` enabling users to customize the portfolio style. Also refactored `Backtester` class for better readability.

Updated documentation and Readme with changes.

* [FIX] `backtest` bug in `Backtester`

Fixed backtesting bug where the return data was misaligned for computing drifted weights.
---
 opes/backtester.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/opes/backtester.py b/opes/backtester.py
index 0fa6209..330a305 100644
--- a/opes/backtester.py
+++ b/opes/backtester.py
@@ -274,6 +274,7 @@ def backtest(
             - `optimize` must output weights for the timestep.
 
         !!! note "Note"
+            - The backtest assumes portfolio weights are applied at the open of each timestep, with zero execution delay.
             - Re-optimization does not automatically imply rebalancing. When the portfolio is re-optimized at a given timestep, weights may or may not be updated depending on the value of `rebalance_freq`.
             - To ensure a coherent backtest, a common practice is to choose frequencies such that `reopt_freq % rebalance_freq == 0`. This guarantees that whenever optimization occurs, a rebalance is also performed.
             - Also note that within a given timestep, rebalancing, if it occurs, is performed after optimization when optimization is scheduled for that timestep.
@@ -401,8 +402,11 @@ def backtest(
             # ---------- REBALANCING BLOCK ----------
             # Computing drifted weights
             # This is necessary for turnover and slippage modelling
+            # NOTE: weights and returns of the previous timestep are passed in to compute drifted weights
+            # This is because weights[0], which is to be set on the beginning of the  zeroth day is separately computed
+            # Therefore, as the loop starts from 1, the return from the zeroth day will cause the first drifted weights on the start of the first day (end of zeroth day)
             drifted_weights = self._compute_drifted_weights(
-                weights[t - 1], test_data[t]
+                weights[t - 1], test_data[t - 1]
             )
 
             # Assigning computed weights to weight array