forked from renat/ONNX.Price.Prediction
138 lines
4.5 KiB
Python
138 lines
4.5 KiB
Python
# Copyright 2023, MetaQuotes Ltd.
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# https://www.mql5.com
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from datetime import datetime
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import tensorflow as tf
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import MetaTrader5 as mt5
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import numpy as np
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import pandas as pd
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import tf2onnx
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from sklearn.model_selection import train_test_split
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from tqdm import tqdm
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from sys import argv
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# --- for Sequential ---{
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import LSTM, Dense, BatchNormalization, Dropout
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from tensorflow.keras.regularizers import l2
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# --- for Sequential ---}
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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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#terminal_info=mt5.terminal_info()
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#file_path=terminal_info.data_path+"\\MQL5\\Files\\"
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# you code here
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#
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# we will save generated onnx-file near the our script
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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 save onnx model",data_path)
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start_date = datetime(2013, 1, 1, 0)
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end_date = datetime(2023, 8, 8, 0)
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eurusd_rates = mt5.copy_rates_range("EURUSD", mt5.TIMEFRAME_H1, start_date, end_date)
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df = pd.DataFrame(eurusd_rates)
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def collect_dataset(df: pd.DataFrame, history_size: int):
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"""
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Collect dataset for the following regression problem:
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- input: history_size consecutive H1 bars;
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- output: close price for the next bar.
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:param df: H1 bars for a range of dates
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:param history_size: how many bars should be considered for making a prediction
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:return: features and labels
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"""
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n = len(df)
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xs = []
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ys = []
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for i in tqdm(range(n - history_size)):
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w = df.iloc[i: i + history_size + 1]
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start_ts = w.iloc[-1].time
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end_ts = w.iloc[0].time
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hours_elapsed = (start_ts - end_ts) // 3600
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if hours_elapsed > history_size:
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# skip non-consecutive H1 bars
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continue
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x = w[['open', 'high', 'low', 'close']].iloc[:-1].values
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# --- y ---{
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# y = w.iloc[-1]['close']
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# ---
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y = (w.iloc[-1]['high'] - w.iloc[-1]['open']) - (w.iloc[-1]['open'] - w.iloc[-1]['low']) # Updated line
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# ---
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# y = (w.iloc[-1]['high'] - w.iloc[-2]['high']) + (w.iloc[-1]['low'] - w.iloc[-2]['low']) # Updated line
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# --- y ---}
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xs.append(x)
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ys.append(y)
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X = np.array(xs)
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y = np.array(ys)
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return X, y
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X, y = collect_dataset(df, history_size=10)
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m = X.mean(axis=1, keepdims=True)
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s = X.std(axis=1, keepdims=True)
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X_norm = (X - m) / s
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# --- y_norm ---{
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y_norm = (y - ((m[:, 0, 1] - m[:, 0, 0]) - (m[:, 0, 0] - m[:, 0, 2]))) / ((s[:, 0, 1] - s[:, 0, 0]) - (s[:, 0, 0] - s[:, 0, 2]))
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# ---
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# y_mean = y.mean()
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# y_std = y.std()
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# y_norm = (y - y_mean) / y_std
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# ---
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# y_norm = y
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# --- y_norm ---}
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X_train, X_test, y_train, y_test = train_test_split(X_norm, y_norm, test_size=0.2, random_state=0)
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# --- Sequential ---{
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model = tf.keras.Sequential([
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tf.keras.layers.LSTM(64, input_shape=(10, 4)),
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tf.keras.layers.BatchNormalization(),
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tf.keras.layers.Dropout(0.1),
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tf.keras.layers.Dense(32, activation='relu'),
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tf.keras.layers.BatchNormalization(),
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tf.keras.layers.Dropout(0.1),
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tf.keras.layers.Dense(32, activation='relu'),
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tf.keras.layers.Dense(1)
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])
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# ---
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# model = tf.keras.Sequential([
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# tf.keras.layers.LSTM(128, input_shape=(10, 4), return_sequences=True), # Increased LSTM units for more complexity
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# tf.keras.layers.BatchNormalization(),
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# tf.keras.layers.Dropout(0.2), # Slightly increased dropout for regularization
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# tf.keras.layers.LSTM(64), # Add another LSTM layer
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# tf.keras.layers.BatchNormalization(),
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# tf.keras.layers.Dropout(0.2),
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# tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=l2(0.001)), # Added L2 regularization
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# tf.keras.layers.BatchNormalization(),
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# tf.keras.layers.Dropout(0.2),
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# tf.keras.layers.Dense(1)
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# ])
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# --- Sequential ---}
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model.compile(optimizer='adam', loss='mse', metrics=['mae'])
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lr_reduction = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, min_delta=1e-6)
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history = model.fit(X_train, y_train, epochs=32, verbose=2, validation_split=0.15, callbacks=[lr_reduction])
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test_loss, test_mae = model.evaluate(X_test, y_test)
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print(f"test_loss={test_loss:.3f}")
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print(f"test_mae={test_mae:.3f}")
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output_path = data_path+"model2.onnx"
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onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
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print(f"saved model to {output_path}")
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mt5.shutdown()
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