chore(master): release 1.4.0 (#46)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

.release-please-manifest.json

@@ -1,3 +1,3 @@
 {
-  ".": "1.3.10"
+  ".": "1.4.0"
 }

CHANGELOG.md

@@ -1,5 +1,19 @@
 # Changelog
 
+## [1.4.0](https://github.com/TPTBusiness/Predix/compare/v1.3.11...v1.4.0) (2026-05-01)
+
+
+### Features
+
+* **optimizer:** add max_positions parameter to Optuna search space ([fdb4be3](https://github.com/TPTBusiness/Predix/commit/fdb4be3b3ebd93325e7821f4251148424184a40d))
+
+## [1.3.11](https://github.com/TPTBusiness/Predix/compare/v1.3.10...v1.3.11) (2026-05-01)
+
+
+### Bug Fixes
+
+* **ci:** lazy import logger in predix.py and cli.py to avoid ImportError in test env ([60763e8](https://github.com/TPTBusiness/Predix/commit/60763e8eae34f41865ba8e5e65bdfde13b564b4b))
+
 ## [1.3.10](https://github.com/TPTBusiness/Predix/compare/v1.3.9...v1.3.10) (2026-05-01)
 
 

predix.py

@@ -18,7 +18,12 @@ load_dotenv(Path(__file__).parent / ".env")
 import typer
 from rich.console import Console
 
-from rdagent.utils.env import logger
+try:
+    from rdagent.utils.env import logger
+except ImportError:
+    import logging
+
+    logger = logging.getLogger(__name__)
 
 app = typer.Typer(help="Predix - AI Quantitative Trading Agent")
 console = Console()

rdagent/app/cli.py

@@ -27,7 +27,12 @@ import typer
 from rich.console import Console
 from typing_extensions import Annotated
 
-from rdagent.utils.env import logger
+try:
+    from rdagent.utils.env import logger
+except ImportError:
+    import logging
+
+    logger = logging.getLogger(__name__)
 
 from rdagent.app.data_science.loop import main as data_science
 from rdagent.app.finetune.llm.loop import main as llm_finetune

rdagent/components/coder/optuna_optimizer.py

@@ -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.