Cách Xuất Python (PyTorch + Sklearn) sang ONNX và Sử dụng trong MT5

pip install MetaTrader5 numpy pandas scikit-learn
pip install onnx onnxruntime onnxmltools skl2onnx
pip install torch torchvision

import MetaTrader5 as mt5
import pandas as pd

mt5.initialize()
rates = mt5.copy_rates_from_pos("EURUSD", mt5.TIMEFRAME_H1, 0, 20000)
df = pd.DataFrame(rates)
df['time'] = pd.to_datetime(df['time'], unit='s')
df.to_csv("eurusd_h1.csv", index=False)
mt5.shutdown()

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType

X = np.load("X.npy")
y = np.load("y.npy")
split = int(len(X) * 0.8)

model = RandomForestClassifier(n_estimators=200, max_depth=8, random_state=42)
model.fit(X[:split], y[:split])
print(f"Accuracy: {model.score(X[split:], y[split:]):.3f}")

initial_type = [('input', FloatTensorType([None, 10]))]
onnx_model = convert_sklearn(model, initial_types=initial_type, target_opset=15)
with open("rf.onnx", "wb") as f:
    f.write(onnx_model.SerializeToString())

import torch
import torch.nn as nn

class LSTMTrader(nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(1, 32, 2, batch_first=True, dropout=0.2)
        self.fc = nn.Linear(32, 2)
    def forward(self, x):
        out, _ = self.lstm(x)
        return self.fc(out[:, -1, :])

model = LSTMTrader()
model.eval()
dummy_input = torch.randn(1, 10, 1)
torch.onnx.export(
    model, dummy_input, "lstm.onnx",
    input_names=['input'], output_names=['output'],
    dynamic_axes={'input': {0: 'batch'}, 'output': {0: 'batch'}},
    opset_version=15
)

long handle = OnnxCreate("rf.onnx", ONNX_DEFAULT);

const long input_shape[]  = {1, 10};
const long output_shape[] = {1};
OnnxSetInputShape(handle, 0, input_shape);
OnnxSetOutputShape(handle, 0, output_shape);

matrix input(1, 10);
for(int i = 0; i < 10; i++) {
    double c0 = iClose(_Symbol, PERIOD_CURRENT, i + 1);
    double c1 = iClose(_Symbol, PERIOD_CURRENT, i + 2);
    input[0][i] = (float)((c0 - c1) / c1);
}

vector output(1);
OnnxRun(handle, ONNX_DEFAULT, input, output);
int huong = (int)output[0];