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