Ash
1e16b161c4
This commit includes the initial project files for the MT5 trading bot. It includes: - MQL5 scripts for exporting data and for the trading EAs. - Python scripts for model development and for creating a benchmark model. - ONNX models for the trading EAs. - A file with all the code concatenated. - A directory with the separate code files.
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
import pandas as pd
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from sklearn.linear_model import LogisticRegression
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import skl2onnx
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from skl2onnx.common.data_types import FloatTensorType
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# Load the data
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raw_price_df = pd.read_csv("raw_price_data.csv")
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# Feature Engineering
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raw_price_df["feature_price_change"] = raw_price_df["Close"].diff()
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# Target Engineering
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raw_price_df["y_target_direction"] = (raw_price_df["Close"].shift(-1) > raw_price_df["Close"]).astype(int)
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# Drop rows with NaN values
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raw_price_df.dropna(inplace=True)
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# Separate features and target
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X = raw_price_df[['feature_price_change']]
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y = raw_price_df['y_target_direction']
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# Create and train the logistic regression model
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log_reg_model = LogisticRegression()
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log_reg_model.fit(X, y)
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# Convert the model to ONNX format
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initial_type = [('float_input', FloatTensorType([None, 1]))]
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onnx_model = skl2onnx.convert_sklearn(log_reg_model, initial_types=initial_type)
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# Save the ONNX model
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with open("benchmark_logistic_model.onnx", "wb") as f:
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f.write(onnx_model.SerializeToString())
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print("Model successfully exported to benchmark_logistic_model.onnx") |