forked from renat/ONNX.Price.Prediction
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
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# Copyright 2023, MetaQuotes Ltd.
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# https://www.mql5.com
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import MetaTrader5 as mt5
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import numpy as np
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import onnxruntime as ort
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import pandas as pd
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from sys import argv
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if not mt5.initialize():
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print("initialize() failed, error code =",mt5.last_error())
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quit()
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# you code here
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#
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data_path=argv[0]
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last_index=data_path.rfind("\\")+1
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data_path=data_path[0:last_index]
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print("data path to load onnx model",data_path)
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eurusd_rates = mt5.copy_rates_from_pos("EURUSD", mt5.TIMEFRAME_H1, 1, 10)
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#print(eurusd_rates)
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df = pd.DataFrame(eurusd_rates)
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X = df[['open', 'high', 'low', 'close']].values
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X = np.expand_dims(X, axis=0)
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#print(X)
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m = X.mean(axis=1, keepdims=True)
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#print(m)
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s = X.std(axis=1, keepdims=True)
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#print(s)
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X_norm = (X - m) / s
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#print(X_norm)
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model_path = data_path+"model2.onnx"
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ort_sess = ort.InferenceSession(model_path)
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output_names = [out.name for out in ort_sess.get_outputs()]
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outputs = ort_sess.run(output_names, {'lstm_input': X_norm.astype(np.float32)})
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y_pred_norm = outputs[0]
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print("raw output:",y_pred_norm)
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# --- y_pred ---{
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# y_pred = np.round(y_pred_norm.flatten() * ((s[:, 0, 1] - s[:, 0, 0]) - (s[:, 0, 0] - s[:, 0, 2])) + ((m[:, 0, 1] - m[:, 0, 0]) - (m[:, 0, 0] - m[:, 0, 2])), decimals=5)
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# ---
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y_pred = np.round(y_pred_norm.flatten(), decimals=5)
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# --- y_pred ---}
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print("predicted:",y_pred)
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mt5.shutdown()
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