forecast_tomorrow.py

from market import EquityData
from models.lstm import split_multivariate
from utils import create_time_steps
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np


features_considered = ['Close', 'Volume']

e = EquityData('data/SPY.csv', 'SPY')
features = e.data[features_considered]
features.index = e.date()

step = 1
history_size = 90
target_distance = 14

dataset = features.values
train_split = int(len(dataset) * 0.7)

data_mean = dataset[:train_split].mean(axis=0)
data_std = dataset[:train_split].std(axis=0)
data_no_transform = dataset[-history_size:]
data_no_transform = np.reshape(data_no_transform, (1, data_no_transform.shape[0], data_no_transform.shape[1]))
dataset = (dataset-data_mean)/data_std
data = dataset[-history_size:]
data = np.reshape(data, (1, data.shape[0], data.shape[1]))

# model = tf.keras.models.load_model('saved_models/multivariate_multi_model')
model = tf.keras.models.load_model('checkpoints/multivariate_multi_model')
y = model.predict(data)
y_no_transform = (y * data_std[0]) + data_mean[0]


plt.figure(figsize=(12, 6))
num_in = create_time_steps(data.shape[1])
num_out = target_distance
data = np.reshape(data, (data.shape[1], data.shape[2]))

# data = data_no_transform
# y = y_no_transform

print(data)
print(y)
print(y_no_transform)
plt.plot(num_in, np.array(data[:, 0]), label='History')
plt.plot(np.arange(num_out)/step, np.array(y[0]), '-',
            label='Predicted Future')
plt.legend(loc='upper left')
plt.grid(True)
plt.show()