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import utility
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import model_selection
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def runner(model_fit=None, close_prices_vecs=None, stock_name_time_stamps=None, rsi_vecs=None, mfi_vecs=None,
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           ema_vecs=None, so_vecs=None, macd_vecs=None, sl_vecs=None, scaler=None):
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    for i in range(0, len(close_prices_vecs)):
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        # set data for svm
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        close_len = len(close_prices_vecs[i])
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        date_len = len(stock_name_time_stamps[i][1])
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        rsi_len = len(rsi_vecs[i])
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        mfi_len = len(mfi_vecs[i])
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        ema_len = len(ema_vecs[i])
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        so_len = len(so_vecs[i])
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        macd_len = len(macd_vecs[i])
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        sl_len = len(sl_vecs[i])
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        shared_data_len = min(close_len, rsi_len, mfi_len, ema_len, so_len, macd_len, sl_len)
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        # define matrix X for SVM
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        x = np.column_stack((rsi_vecs[i][rsi_len - shared_data_len:], mfi_vecs[i][mfi_len - shared_data_len:],
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                             ema_vecs[i][ema_len - shared_data_len:], so_vecs[i][so_len - shared_data_len:],
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                             macd_vecs[i][macd_len - shared_data_len:], sl_vecs[i][sl_len - shared_data_len:]))
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        # define y for SVM
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        y = close_prices_vecs[i][close_len - shared_data_len:]
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        print("Analysis for: " + stock_name_time_stamps[i][0])
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        x_len = len(x)
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        y_len = len(y)
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        x_train, x_test, y_train, y_test = x[:int(x_len*0.75)], x[int(x_len*0.75):], y[:int(y_len*0.75)], y[int(y_len*0.75):]
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        x_train = scaler.fit_transform(x_train)
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        x_test = scaler.fit_transform(x_test)
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        y_pred = utility.test_regression(x_train, y_train, x_test, y_test, model_fit)
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        y_test_len, y_pred_len = len(y_test), len(y_pred)
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        shared_data_len = min(date_len, y_test_len, y_pred_len)
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        plt.figure(figsize=(20, 5))
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        plt.title(stock_name_time_stamps[i][0])
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        plt.xlabel("date")
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        plt.ylabel("close_price")
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        plt.plot(stock_name_time_stamps[i][1][date_len - shared_data_len:], y_test, 'ro')
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        plt.plot(stock_name_time_stamps[i][1][date_len - shared_data_len:], y_pred)
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        plt.show()
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        print("Done for: " + stock_name_time_stamps[i][0])
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        print("====================================================================")
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    print("All analysis done")
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    print()
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if __name__ == '__main__':
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    runner()
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