Initial commit: Mad Turtle v2.0 ML EA for XAUUSD H1 with Python inference server and MQL5 EA

2026-06-13 15:27:41 +01:00
commit a5bfac82e4
21 changed files with 13055 additions and 0 deletions
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+"""
+Build a minimal ONNX ensemble model using raw ONNX ops (no sklearn needed).
+Creates a simple linear classifier + softmax as a demo.
+Replace this with a real trained model later (Python 3.11/3.12 + sklearn + skl2onnx).
+"""
+
+import json
+import numpy as np
+import onnx
+from onnx import helper, TensorProto, numpy_helper
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+MODELS_DIR = ROOT / "models"
+MODELS_DIR.mkdir(exist_ok=True)
+
+FEATURES = [
+    "returns_1", "returns_3", "returns_6",
+    "sma_10", "sma_20", "sma_50",
+    "macd", "macd_signal",
+    "rsi_14", "atr_14", "atr_pct",
+    "vol_ratio", "high_low_range", "dist_sma20",
+]
+
+# Dummy weights for demo — replace with real trained weights
+rng = np.random.default_rng(123)
+W = rng.standard_normal((14, 3)).astype(np.float32) * 0.1
+b = np.zeros(3, dtype=np.float32)
+
+# Normalize weights roughly
+W = W / np.maximum(np.abs(W).sum(axis=0, keepdims=True), 1e-6)
+
+scale = np.array([
+    1000.0, 1000.0, 1000.0,
+    1.0, 1.0, 1.0,
+    10.0, 10.0,
+    1.0, 10.0, 1.0,
+    1.0, 1.0, 1.0,
+], dtype=np.float32).reshape(1, 14)
+
+bias = np.zeros((1, 14), dtype=np.float32)
+
+
+def build_model():
+    # Input
+    x = helper.make_tensor_value_info("float_input", TensorProto.FLOAT, [None, 14])
+
+    # Scale nodes: x_scaled = x * scale + bias
+    scale_tensor = numpy_helper.from_array(scale, name="scale")
+    bias_tensor = numpy_helper.from_array(bias, name="bias")
+    mul_node = helper.make_node("Mul", ["float_input", "scale"], ["x_scaled"])
+    add_node = helper.make_node("Add", ["x_scaled", "bias"], ["x_scaled_centered"])
+
+    # Linear layer: logits = x_scaled @ W + b
+    W_tensor = numpy_helper.from_array(W, name="W")
+    b_tensor = numpy_helper.from_array(b, name="b")
+    matmul_node = helper.make_node("MatMul", ["x_scaled_centered", "W"], ["logits"])
+    add_bias_node = helper.make_node("Add", ["logits", "b"], ["logits_biased"])
+
+    # Softmax
+    softmax_node = helper.make_node("Softmax", ["logits_biased"], ["probs"], axis=1)
+
+    # Output
+    y = helper.make_tensor_value_info("probs", TensorProto.FLOAT, [None, 3])
+
+    graph = helper.make_graph(
+        [mul_node, add_node, matmul_node, add_bias_node, softmax_node],
+        "mad_turtle_ensemble",
+        [x],
+        [y],
+        [scale_tensor, bias_tensor, W_tensor, b_tensor],
+    )
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 15)])
+    model.ir_version = 8
+    onnx.checker.check_model(model)
+    out = MODELS_DIR / "xauusd_h1_ensemble.onnx"
+    onnx.save(model, str(out))
+    print(f"Saved demo ONNX model -> {out}")
+
+
+def save_metadata():
+    meta = {
+        "symbol": "XAUUSD",
+        "timeframe": "H1",
+        "features": FEATURES,
+        "target_horizon": 3,
+        "built_at": __import__('datetime').datetime.utcnow().isoformat(),
+        "models": {
+            "ensemble": {
+                "features": FEATURES,
+                "path": "models/xauusd_h1_ensemble.onnx",
+                "note": "Demo model with random weights. Replace with real trained model.",
+            }
+        },
+    }
+    out = MODELS_DIR / "metadata.json"
+    with open(out, "w") as f:
+        json.dump(meta, f, indent=2)
+    print(f"Saved metadata -> {out}")
+
+
+if __name__ == "__main__":
+    build_model()
+    save_metadata()