feat: Add MT5 trading bot project files

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.

separate_codes/create_benchmark_model.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+import skl2onnx
+from skl2onnx.common.data_types import FloatTensorType
+
+# Load the data
+raw_price_df = pd.read_csv("raw_price_data.csv")
+
+# Feature Engineering
+raw_price_df["feature_price_change"] = raw_price_df["Close"].diff()
+
+# Target Engineering
+raw_price_df["y_target_direction"] = (raw_price_df["Close"].shift(-1) > raw_price_df["Close"]).astype(int)
+
+# Drop rows with NaN values
+raw_price_df.dropna(inplace=True)
+
+# Separate features and target
+X = raw_price_df[['feature_price_change']]
+y = raw_price_df['y_target_direction']
+
+# Create and train the logistic regression model
+log_reg_model = LogisticRegression()
+log_reg_model.fit(X, y)
+
+# Convert the model to ONNX format
+initial_type = [('float_input', FloatTensorType([None, 1]))]
+onnx_model = skl2onnx.convert_sklearn(log_reg_model, initial_types=initial_type)
+
+# Save the ONNX model
+with open("benchmark_logistic_model.onnx", "wb") as f:
+    f.write(onnx_model.SerializeToString())
+
+print("Model successfully exported to benchmark_logistic_model.onnx")