feat: 24/7 synthetic rollover calibration, stop-limit orders, MT5 wave model, and open prices mode fixes

python/edge_engine/__init__.py

@@ -7,7 +7,7 @@ General-Purpose MetaTrader 5 Strategy Tester Replica (Python Interface).
 from __future__ import annotations
 
 import edge_tester as tester_api
-from .symbol_spec import make_symbol_spec
+from .symbol_spec import make_symbol_spec, create_symbol_spec
 from .optimizer import StrategyOptimizer
 
 # Expose core tester classes directly at edge_engine top level
@@ -22,6 +22,9 @@ LatencyMode = tester_api.LatencyMode
 OptimizationCriterion = tester_api.OptimizationCriterion
 OptimizationAlgorithm = tester_api.OptimizationAlgorithm
 
+INIT_SUCCEEDED = tester_api.INIT_SUCCEEDED
+INIT_FAILED = tester_api.INIT_FAILED
+
 __version__ = "2.0.0"
 __all__ = [
     "Strategy",
@@ -36,5 +39,8 @@ __all__ = [
     "OptimizationAlgorithm",
     "StrategyOptimizer",
     "make_symbol_spec",
+    "create_symbol_spec",
+    "INIT_SUCCEEDED",
+    "INIT_FAILED",
     "tester_api",
 ]

python/edge_engine/optimizer.py

@@ -39,6 +39,16 @@ def _eval_pass(
         if hasattr(spec, k):
             setattr(spec, k, v)
 
+    # Optional preparation hook for strategies that precalculate buffers
+    if hasattr(strat, "prepare"):
+        strat.prepare(
+            bars_dict["open"],
+            bars_dict["high"],
+            bars_dict["low"],
+            bars_dict["close"],
+            spec
+        )
+
     # Build config
     cfg = et.TesterConfig()
     for k, v in cfg_dict.items():
@@ -129,7 +139,9 @@ class StrategyOptimizer:
             "high": df["high"].to_numpy(np.float64),
             "low": df["low"].to_numpy(np.float64),
             "close": df["close"].to_numpy(np.float64),
-            "time": df["time"].to_numpy(np.int64) if "time" in df.columns else np.zeros(n, np.int64),
+            "time": (df["time"].astype("int64") // 10**9).to_numpy(np.int64)
+            if pd.api.types.is_datetime64_any_dtype(df["time"])
+            else df["time"].to_numpy(np.int64) if "time" in df.columns else np.zeros(n, np.int64),
             "volume": df["tick_volume"].to_numpy(np.int64) if "tick_volume" in df.columns else np.zeros(n, np.int64),
             "spread": df["spread"].to_numpy(np.int32) if "spread" in df.columns else np.zeros(n, np.int32),
         }

python/edge_engine/symbol_spec.py

@@ -70,3 +70,26 @@ def make_symbol_spec(name: str, try_mt5: bool = False) -> Dict[str, Any]:
         except Exception:
             pass
     return spec
+
+
+def create_symbol_spec(name: str, try_mt5: bool = False):
+    """Instantiate and populate a C++ SymbolSpec object for the given instrument."""
+    import edge_tester as et
+    d = make_symbol_spec(name, try_mt5=try_mt5)
+    sym = et.SymbolSpec()
+    sym.name = name
+    sym.point = float(d.get("point", 0.01))
+    sym.digits = int(d.get("digits", 2))
+    sym.spread_val = float(d.get("spread_val", 0.0))
+    sym.trade_tick_size = float(d.get("trade_tick_size", 0.01))
+    sym.trade_tick_value = float(d.get("trade_tick_value", 0.01))
+    sym.volume_min = float(d.get("volume_min", 0.01))
+    sym.volume_max = float(d.get("volume_max", 100.0))
+    sym.volume_step = float(d.get("volume_step", 0.01))
+    sym.commission_per_lot = float(d.get("commission_per_lot", 0.0))
+    sym.trade_stops_level = float(d.get("trade_stops_level", 0.0))
+    sym.trade_freeze_level = float(d.get("trade_freeze_level", 0.0))
+    sym.swap_long = float(d.get("swap_long", 0.0))
+    sym.swap_short = float(d.get("swap_short", 0.0))
+    return sym
+