File size: 5,082 Bytes
10477f4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import streamlit as st
import yfinance as yf
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
# Crear y entrenar el modelo
def train_model(x_train, y_train, input_size, prediction_days, dim_feedforward, epochs=100):
class Transformer(nn.Module):
def __init__(self, input_size, prediction_days, dim_feedforward):
super(Transformer, self).__init__()
self.input_size = input_size
self.fc1 = nn.Linear(input_size, dim_feedforward)
self.fc2 = nn.Linear(dim_feedforward, dim_feedforward * 2)
self.fc3 = nn.Linear(dim_feedforward * 2, dim_feedforward * 4)
self.fc4 = nn.Linear(dim_feedforward * 4, dim_feedforward * 8)
self.fc5 = nn.Linear(dim_feedforward * 8, dim_feedforward * 16)
self.fc6 = nn.Linear(dim_feedforward * 16, dim_feedforward * 32)
self.fc7 = nn.Linear(dim_feedforward * 32, dim_feedforward * 64)
self.fc8 = nn.Linear(dim_feedforward * 64, dim_feedforward * 128)
self.fc9 = nn.Linear(dim_feedforward * 128, dim_feedforward * 256)
self.fc10 = nn.Linear(dim_feedforward * 256, prediction_days)
self.dropout = nn.Dropout(0.2)
def forward(self, x):
x = x.reshape(-1, self.input_size)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc3(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc4(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc5(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc6(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc7(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc8(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc9(x)
x = nn.functional.relu(x)
x = self.dropout(x)
x = self.fc10(x)
return x
model = Transformer(input_size=input_size, prediction_days=1, dim_feedforward=dim_feedforward)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(epochs):
inputs = torch.from_numpy(x_train).float()
labels = torch.from_numpy(y_train).float().unsqueeze(1)
# Limpiando los gradientes
optimizer.zero_grad()
# Forward
outputs = model(inputs)
loss = criterion(outputs, labels)
# Backward y optimizaci贸n
loss.backward()
optimizer.step()
if (epoch + 1) % 10 == 0:
st.write("Epoch: {}/{} | Loss: {:.4f}".format(epoch + 1, epochs, loss.item()))
return model
# P谩gina principal
st.title("Stock Price Prediction")
# Interfaz para ingresar el ticket de la empresa
company = st.text_input("Enter the company ticket:")
# Interfaz para ingresar la cantidad de d铆as a predecir
prediction_days = st.slider("Enter the number of days to predict:", min_value=1, max_value=30, value=7)
# Bot贸n para iniciar el entrenamiento
if st.button("Start Training"):
# Descarga de datos hist贸ricos de la compa帽铆a deseada
ticker = yf.Ticker(company)
hist = ticker.history(start="2015-01-01", end=datetime.now())
# Escalando los datos
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(hist["Close"].values.reshape(-1, 1))
# Creando el conjunto de entrenamiento
x_train = []
y_train = []
for i in range(prediction_days, len(scaled_data)):
x_train.append(scaled_data[i - prediction_days : i, 0])
y_train.append(scaled_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], prediction_days))
# Entrenar el modelo
trained_model = train_model(x_train, y_train, input_size=x_train.shape[1], prediction_days=1, dim_feedforward=21)
# Predicci贸n
future_prediction = []
last_x = scaled_data[-prediction_days:]
for i in range(prediction_days):
future_input = torch.from_numpy(last_x).float().reshape(1, prediction_days)
future_price = trained_model(future_input)
future_prediction.append(future_price.detach().numpy()[0][0])
last_x = np.append(last_x[1:], future_price.detach().numpy().reshape(-1, 1))
# Desescalando los resultados
prediction = scaler.inverse_transform(np.array(future_prediction).reshape(-1, 1))
# Imprimiendo los resultados
st.subheader("Predictions:")
for i, price in enumerate(prediction):
st.write("Day {}: {:.2f}".format(i + 1, price[0]))
# Bot贸n de reinicio
if st.button("Reset"):
st.experimental_rerun()
|