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feat(optimizer): add max_positions parameter to Optuna search space

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Add max_positions (1-5) as an optimizable hyperparameter across all
three Optuna search stages (coarse, fine, very fine). The parameter
scales effective position size as min(position_size_pct × max_positions,
1.0), allowing the optimizer to discover pyramiding strategies.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
TPTBusiness
2026-05-01 15:58:01 +02:00
parent 7cb6531c2a
commit 9bc525a264
1 changed files with 14 additions and 0 deletions
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rdagent/components/coder/optuna_optimizer.py
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@@ -292,6 +292,7 @@ class OptunaOptimizer:
"volatility_lookback": trial.suggest_int("volatility_lookback", 5, 500, step=5),
"signal_bias": trial.suggest_float("signal_bias", -1.0, 1.0, step=0.05),
"max_hold_bars": trial.suggest_int("max_hold_bars", 5, 1000, step=5),
"max_positions": trial.suggest_int("max_positions", 1, 5, step=1),
}
# Parameters that are allowed to be negative (not clamped to 0).
@@ -308,6 +309,7 @@ class OptunaOptimizer:
"volatility_lookback": 1.0,
"signal_bias": -1.0,
"max_hold_bars": 1.0,
"max_positions": 1.0,
}
def _suggest_bounded(
@@ -357,6 +359,7 @@ class OptunaOptimizer:
"volatility_lookback": (center.get("volatility_lookback", 100), 30),
"signal_bias": (center.get("signal_bias", 0.0), 0.2),
"max_hold_bars": (center.get("max_hold_bars", 100), 50),
"max_positions": (center.get("max_positions", 1), 2),
}
return {key: self._suggest_bounded(trial, key, c, hw) for key, (c, hw) in ranges.items()}
@@ -388,6 +391,7 @@ class OptunaOptimizer:
"volatility_lookback": (center.get("volatility_lookback", 100), 10),
"signal_bias": (center.get("signal_bias", 0.0), 0.07),
"max_hold_bars": (center.get("max_hold_bars", 100), 17),
"max_positions": (center.get("max_positions", 1), 1),
}
return {key: self._suggest_bounded(trial, key, c, hw) for key, (c, hw) in ranges.items()}
@@ -467,6 +471,9 @@ class OptunaOptimizer:
# Max holding periods (in bars)
"max_hold_bars": trial.suggest_int("max_hold_bars", 10, 500, step=10),
# Max concurrent positions (1 = no pyramiding, 2-5 = scale-in)
"max_positions": trial.suggest_int("max_positions", 1, 5, step=1),
}
return params
@@ -597,6 +604,13 @@ class OptunaOptimizer:
if signal_bias != 0.0:
signal = (signal.astype(float) + signal_bias).round().astype(int).clip(-1, 1)
# Apply max_positions: scale signal by position_size_pct and cap exposure
max_positions = int(params.get("max_positions", 1))
position_size_pct = float(params.get("position_size_pct", 1.0))
# Each "position" is position_size_pct of equity; total exposure capped at max_positions × size
effective_size = min(position_size_pct * max_positions, 1.0)
signal = (signal.astype(float) * effective_size).clip(-1.0, 1.0)
# Build a synthetic close from the factor-mean so we can route
# through the same unified engine as every other backtest path.
# Backtest formulas must match the orchestrator's real-OHLCV path.