Repository: hardyqr/CNN-for-Stock-Market-Prediction-PyTorch
Branch: master
Commit: 36fea264057b
Files: 24
Total size: 1.9 MB

Directory structure:
gitextract_jvrn4z1d/

├── README.md
├── accuracy_records.csv
├── clean_log.sh
├── data/
│   ├── label_table_test.csv
│   ├── label_table_train.csv
│   ├── label_table_validation.csv
│   └── sample/
│       ├── label_table_test.csv
│       ├── label_table_train.csv
│       ├── label_table_validation.csv
│       └── sample_list.txt
├── script.md
├── source/
│   ├── 10-3-gen.py
│   ├── cnn.py
│   ├── cnn4matrix.py
│   ├── data_gen.py
│   ├── data_preprocess.py
│   ├── logger.py
│   ├── main-gen.py
│   ├── matrix_gen.py
│   └── utils.py
├── tensorboard.sh
├── trained_model/
│   ├── cnn.pkl
│   └── cnn4matrix.pkl
└── training_loss_records_4matrix.csv

================================================
FILE CONTENTS
================================================

================================================
FILE: README.md
================================================
# Can Machine Reads Like Analysts Do?

- Train a CNN to read candlestick graphs, predicting future trend.

- Training & testing Dataset from [Huge Stock Market Dataset-Full Historical Daily Price + Volume Data For All U.S. Stocks & ETFs](https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs).

- Pytorch implementation

update: 

Related paper: [Trading via Image Classification](https://arxiv.org/pdf/1907.10046.pdf) (by J.P. Morgan)


## Two Approaches
### Approach 1
- `cnn4matrix.py`

- Apply convolution on data matrices directly.

Input: (5\*n) matrices -> (Open,High,Low,Close,Volume)\*(d1,d2,...,dn)

Output: classification result

### Approach 2
- `cnn.py`

- Generate candlestick graphs first.
![sample](https://github.com/hardyqr/CNN-for-Stock-Market-Prediction/blob/master/screen_shots_logs/sample.png)

Input: candlestick graphs

Output: classification result


## Current results

11 layers + residual block
![prediction](https://github.com/hardyqr/Deep-Learning-for-Stock-Market-Prediction/blob/master/screen_shots_logs/sota/acc+loss.png)


================================================
FILE: accuracy_records.csv
================================================
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0,2810,80,0.55,0,0,0.55


================================================
FILE: clean_log.sh
================================================
#!/bin/bash

if [ -d "./logs" ]; then
	rm -r logs
fi

if [ -e "./training_loss_records.csv" ]; then
	rm training_loss_records.csv
fi

if [ $# -eq "1" ]; then
	git add .
	git commit -m $1+" version"
	git push
fi


================================================
FILE: data/label_table_test.csv
================================================



================================================
FILE: data/label_table_train.csv
================================================
 label_table_validation.csv


================================================
FILE: data/label_table_validation.csv
================================================

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================================================
FILE: data/sample/label_table_test.csv
================================================

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================================================
FILE: data/sample/label_table_train.csv
================================================
 label_table_validation.csv
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================================================
FILE: data/sample/label_table_validation.csv
================================================

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0,irix.us_2743.png,1,1,1
0,irix.us_2873.png,-1,1,1
0,irix.us_3003.png,-1,-1,-1
0,pay.us_13.png,1,-1,1
0,pay.us_143.png,-1,-1,-1
0,pay.us_273.png,1,1,1
0,pay.us_403.png,-1,-1,1
0,pay.us_533.png,-1,-1,-1
0,pay.us_663.png,1,-1,-1
0,pay.us_793.png,-1,-1,-1
0,pay.us_923.png,-1,-1,-1
0,pay.us_1053.png,-1,-1,-1
0,pay.us_1183.png,-1,-1,1
0,pay.us_1313.png,1,1,1
0,pay.us_1443.png,-1,-1,-1
0,pay.us_1573.png,-1,-1,-1
0,pay.us_1703.png,-1,1,1
0,pay.us_1833.png,1,1,1
0,pay.us_1963.png,-1,-1,-1
0,pay.us_2093.png,-1,-1,-1
0,pay.us_2223.png,-1,-1,1
0,pay.us_2353.png,1,1,-1
0,pay.us_2483.png,1,-1,1
0,pay.us_2613.png,-1,-1,-1
0,pay.us_2743.png,1,-1,-1
0,pay.us_2873.png,1,1,1
0,pay.us_3003.png,-1,-1,-1
0,pay.us_3133.png,-1,1,-1
0,pfbx.us_13.png,-1,-1,-1
0,pfbx.us_143.png,-1,-1,-1
0,pfbx.us_273.png,1,1,1
0,pfbx.us_403.png,-1,-1,-1
0,pfbx.us_533.png,-1,-1,1
0,pfbx.us_663.png,1,1,1
0,pfbx.us_793.png,-1,-1,1
0,pfbx.us_923.png,1,-1,-1
0,pfbx.us_1053.png,-1,1,1
0,pfbx.us_1183.png,-1,1,1
0,pfbx.us_1313.png,1,1,1
0,pfbx.us_1443.png,-1,-1,-1
0,pfbx.us_1573.png,1,1,1
0,pfbx.us_1703.png,-1,-1,-1
0,pfbx.us_1833.png,-1,1,-1
0,pfbx.us_1963.png,1,-1,1
0,pfbx.us_2093.png,1,1,1
0,pfbx.us_2223.png,-1,-1,-1
0,pfbx.us_2353.png,1,1,1
0,pfbx.us_2483.png,-1,1,1
0,hyxu.us_13.png,1,1,1
0,hyxu.us_143.png,-1,-1,-1
0,hyxu.us_273.png,-1,-1,-1
0,hyxu.us_403.png,1,1,1
0,hyxu.us_533.png,-1,-1,-1
0,hyxu.us_663.png,1,-1,-1
0,hyxu.us_793.png,-1,-1,-1
0,hyxu.us_923.png,-1,1,1
0,hyxu.us_1053.png,-1,1,1
0,hyxu.us_1183.png,1,-1,-1
0,hyxu.us_1313.png,1,1,1
0,nlnk.us_13.png,1,1,1
0,nlnk.us_143.png,-1,1,1
0,nlnk.us_273.png,1,-1,1
0,nlnk.us_403.png,1,1,1
0,nlnk.us_533.png,1,-1,-1
0,nlnk.us_663.png,1,-1,-1
0,nlnk.us_793.png,1,1,1
0,nlnk.us_923.png,1,1,1
0,nlnk.us_1053.png,-1,-1,-1
0,nlnk.us_1183.png,1,1,-1
0,nlnk.us_1313.png,1,1,1
0,nlnk.us_1443.png,-1,-1,-1
0,ivc.us_13.png,1,1,1
0,ivc.us_143.png,-1,1,1
0,ivc.us_273.png,-1,1,-1
0,ivc.us_403.png,1,1,1
0,ivc.us_533.png,1,1,1
0,ivc.us_663.png,1,1,1
0,ivc.us_793.png,1,1,1
0,ivc.us_923.png,1,-1,-1
0,ivc.us_1053.png,1,1,-1
0,ivc.us_1183.png,-1,1,1
0,ivc.us_1313.png,1,1,1
0,ivc.us_1443.png,-1,-1,-1
0,ivc.us_1573.png,-1,-1,-1
0,ivc.us_1703.png,-1,1,1
0,ivc.us_1833.png,1,1,1
0,ivc.us_1963.png,1,1,1
0,ivc.us_2093.png,-1,-1,-1
0,ivc.us_2223.png,1,1,1
0,ivc.us_2353.png,-1,1,1
0,ivc.us_2483.png,-1,-1,-1
0,ivc.us_2613.png,1,1,1
0,ivc.us_2743.png,-1,-1,1
0,ivc.us_2873.png,-1,1,1
0,ivc.us_3003.png,-1,-1,-1
0,ivc.us_3133.png,1,-1,-1
0,mxe.us_13.png,-1,-1,-1
0,mxe.us_143.png,-1,1,1
0,mxe.us_273.png,-1,-1,-1
0,mxe.us_403.png,-1,-1,1
0,mxe.us_533.png,-1,1,1
0,mxe.us_663.png,1,1,1
0,mxe.us_793.png,1,1,1
0,mxe.us_923.png,-1,-1,-1
0,mxe.us_1053.png,-1,1,1
0,mxe.us_1183.png,-1,1,1
0,mxe.us_1313.png,1,1,1
0,mxe.us_1443.png,-1,-1,-1
0,mxe.us_1573.png,1,1,1
0,mxe.us_1703.png,1,1,1
0,mxe.us_1833.png,1,1,1
0,mxe.us_1963.png,1,-1,-1
0,mxe.us_2093.png,-1,-1,-1
0,mxe.us_2223.png,1,1,1
0,mxe.us_2353.png,-1,-1,-1
0,mxe.us_2483.png,-1,-1,1
0,mxe.us_2613.png,-1,-1,-1
0,mxe.us_2743.png,1,-1,-1
0,mxe.us_2873.png,1,1,1
0,mxe.us_3003.png,-1,-1,-1
0,mxe.us_3133.png,1,1,1


================================================
FILE: data/sample/sample_list.txt
================================================


================================================
FILE: script.md
================================================
# CNNs for financial time series prediction using candlesticks: several baselines


## Baselines

### AlexNet


### ResNet

Residual block

### GoogLeNet
Inception Module


## make a portfolio

Choose a state-of-the-art CNN. Make a portfolio based on prediction. Run it. Compare with SP500.


================================================
FILE: source/10-3-gen.py
================================================
# Freddy @Blair House, Waterloo, ON, Canada
# Nov 13, 2017

import numpy as np
import pandas as pd
import csv
import time
from datetime import datetime
import sys
import os
from tqdm import *
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.finance import candlestick_ohlc
import warnings
warnings.filterwarnings("ignore")
from data_gen import data_plot
from utils import one_hot


def main():
    #data = get_data(sys.argv[1], sys.argv[2], sys.argv[3])
    #print(data.head())
    data = pd.read_csv(sys.argv[1], parse_dates=True, dayfirst=True)# argv[1]: stock_symbol.txt
    # some preprocessing
    data[['Open','High','Low','Close']] = data[['Open','High','Low','Close']].astype('float32')     
    data["Date"] = data["Date"].apply(mdates.datestr2num)
    data_len = len(data)
    stock_symbol = sys.argv[1].split('/')[-1][:-4] # remove .txt
    counter = 0
    #tqdm
    for k in tqdm(range(0,int(data_len/13)+1)):
        if(counter + 13 >= data_len): break
        data_cur = data[counter:counter+13]
        data_close = data_cur['Close']
        labels = []
        for i in range(0,13):
            # draw day [0,1,...,9], predict day [10,11,12] 
            # positive label 1, negative label -1
            last_4 = data_close[9:13].values
            labels_raw = last_4[1:4] - last_4[0]
            labels = list(map(one_hot,labels_raw))
        counter = counter + 13
        
        # plot
        fig = data_plot(data_cur[0:10]) # only plot the first 10 days
        #fig.set_size_inches(8,8);
        #fig.show()
        file_name = stock_symbol+'_'+str(counter)+'.png'
        mode = 'train' # 70% training set
        if(k%10 == 0): mode = 'validation' # 10% validation set
        elif(k%10 == 4 or k%10 == 7): mode = 'test' # 20% testing set
        #fig.savefig('./data/'+ mode +'/'+ file_name, dpi=40)
        fig.savefig('./data/sample/'+ mode +'/'+ file_name, dpi=40)
        # save label
        #label_table = pd.read_csv('./data/label_table.csv',header = None, delim_whitespace=False)
        new_data = [[file_name, labels[0],labels[1],labels[2]]]
        df = pd.DataFrame(new_data, columns=['file_name', 'pred_1', 'pred_2', 'pred_3'])
        #label_table.append(df)
        #df.to_csv('./data/label_table_'+mode+'.csv', mode='a', header=False)
        df.to_csv('./data/sample/label_table_'+mode+'.csv', mode='a', header=False)
        #label_table.to_csv('./data/label_table.csv')


if __name__ == '__main__':
    main()




================================================
FILE: source/cnn.py
================================================
# Freddy @DC, uWaterloo, ON, Canada
# Nov 13, 2017

import torch 
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
import numpy as np
import pandas as pd
import sys
import math
import time
from tqdm import *

from data_preprocess import *
from utils import *
from logger import Logger

# Hyper Parameters
num_epochs = 12
batch_size = 256
learning_rate = 2e-4

# argv
data_dir = sys.argv[1]
debug = False
load_prev_model = False
direct_test = False
use_gpu = False

if(sys.argv[2] == '1'):
    debug = True
if(sys.argv[3] == '1'):
    load_prev_model = True
if(sys.argv[4] == '1'):
    direct_test = True

if(torch.cuda.is_available()):
    use_gpu = True



''' Data ''' 

# stock Datase
train_set = stock_img_dataset(csv_file=data_dir+'/sample/label_table_train.csv',
        root_dir=data_dir+'/sample/train',
        transform=transforms.Compose([
            Rescale(64),
            ToTensor()
            ]))
test_set = stock_img_dataset(csv_file=data_dir+'/sample/label_table_test.csv',
        root_dir=data_dir+'/sample/test',
        transform=transforms.Compose([
            Rescale(64),
            ToTensor()
            ]))
validation_set = stock_img_dataset(csv_file=data_dir+'/sample/label_table_validation.csv',
        root_dir=data_dir+'/sample/validation',
        transform=transforms.Compose([
            Rescale(64),
            ToTensor()
            ]))


# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_set,
                                           batch_size=batch_size, 
                                           shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_set,
                                          batch_size=batch_size, 
                                          shuffle=False)
val_loader = torch.utils.data.DataLoader(dataset=validation_set,
                                          batch_size=batch_size, 
                                          shuffle=False)


''' Models '''


# Residual CNN
class res_cnn(nn.Module):
    def __init__(self):
        super(res_cnn, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(4, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU())
        self.layer3 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer4 = nn.Sequential(
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU())
        self.layer6 = nn.Sequential(
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU())
        self.layer7 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.MaxPool2d(2))
        self.layer8 = nn.Sequential(
            nn.Conv2d(64, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU())
        self.layer9 = nn.Sequential(
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer10 = nn.Sequential(
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU())
        self.layer5 = nn.Sequential(
            nn.MaxPool2d(2),
            nn.MaxPool2d(2))
        #self.fc = nn.Linear(4*4*256, 3)
        self.fc = nn.Linear(4*4*256, 1)
        
    def forward(self, x):
        out1 = self.layer1(x)
        out2 = self.layer2(out1)
        out3 = self.layer3(out2)
        out4 = self.layer4(out3)
        out = self.layer6(out4)
        out = self.layer7(out)
        #re_layer_0 = self.layer
        res_layer_1 = self.layer5(self.layer8(out1))
        #print(res_layer.size())
        #print(out.size())
        out5 = res_layer_1+out
        #out5 = out4
        out = self.layer9(out5)
        out = self.layer10(out)
        #out = self.layer10(out)
        out6 = out.view(out.size(0), -1)
        #print(out6.size())
        out7 = self.fc(out6)
        return out7


'''
# GoogLeNet
class google_net(nn.Module):
    def __init__(self):
        super(google_net, self).__init__()
        
        self.conv2d_1x1_a = nn.Conv2d(4,64,kernel_size=1),
        self.conv2d_3x3_a = nn.Conv2d(4,64,kernel_size=3,padding=1),
        self.conv2d_5x5_a = nn.Conv2d(4,64,kernel_size=5,padding=2),
        
        self.conv2d_1x1_b = nn.Conv2d(64,128,kernel_size=1)
        self.conv2d_3x3_b = nn.Conv2d(64,128,kernel_size=3,padding=1)
        self.conv2d_5x5_b = nn.Conv2d(64,128,kernel_size=5,padding=2)
        self.max_pool = nn.MaxPool2d(kernel_size=3,stride=1,padding=1)
    def forward(self, x):
        # inception 1


        # inception 2
    return out7
'''



''' train and test ''' 

cnn = res_cnn().double()
if(use_gpu):
    cnn.cuda()

if(load_prev_model):
    print('Loading previous model...')
    cnn.load_state_dict(torch.load('cnn.pkl'))

# Loss and Optimizer
#criterion = nn.CrossEntropyLoss()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)

logger = Logger('./logs')


def test_module(train_size, epoch, data_loader, write=False):
    # Test the Model
    cnn.eval()  # Change model to 'eval' mode (BN uses moving mean/var).
    
    correct_d1 = 0
    correct_d2 = 0
    correct_d3 = 0
    total = 0
    counter = 0
    
    for sample in tqdm(data_loader):
        if(debug and counter >= 3): break
        counter+=1
        
        images = Variable(sample['image'])
        labels = Variable(sample['labels'])
        if(use_gpu):
            images = images.cuda()
            labels = labels.cuda()
        outputs = cnn(images)
        #_, predicted = torch.max(outputs.data, 1)
        
        #labels = to_np(labels.view(-1,1))
        #predicted = np.sign(to_np(outputs.view(-1,1)))
        #print(labels.shape, predicted.shape)

        #=======cpu computation=======#
        # can we migrate it to the GPU?
        labels = to_np(labels)
        outputs = to_np(outputs)
        labels_d1 = labels[:,0]
        #labels_d2 = labels[:,1]
        #labels_d3 = labels[:,2]

        # if output>=0: predict=1, else: predict=-1
        predicted_d1 = np.sign(outputs[:,0])
        #predicted_d2 = np.sign(outputs[:,1])
        #predicted_d3 = np.sign(outputs[:,2])

        # only conduct a prediction when abs(output) > 0.5
        #predicted_high_d1 = np.sign(outputs[:,0])
        #predicted_high_d2 = np.sign(outputs[:,1])
        #predicted_high_d3 = np.sign(outputs[:,2])


        correct_d1 += (predicted_d1 == labels_d1).sum()
        #correct_d2 += (predicted_d2 == labels_d2).sum()
        #correct_d3 += (predicted_d3 == labels_d3).sum()
        
        total += labels.shape[0]
        
        
    acc1 = correct_d1 / total
    #acc2 = correct_d2 / total
    #acc3 = correct_d3 / total
    #acc_total = (correct_d3+correct_d2+correct_d3) / ( total * 3)
    acc2 = 0
    acc3 = 0
    acc_total = acc1
    print('Test Accuracy of the model on the %d test images, Day 11: %.4f %%' % (total, 100 * acc1))
    #print('Test Accuracy of the model on the %d test images, Day 12: %.4f %%' % (total, 100 * acc2))
    #print('Test Accuracy of the model on the %d test images, Day 13: %.4f %%' % (total, 100 * acc3))
    #print('Test Accuracy of the model on the %d test images, total: %.4f %%' % (total, 100 * acc_total))

    test_size = total
    if(write):
        df = pd.DataFrame([[train_size,test_size,acc1,acc2,acc3,acc_total]])
        df.to_csv('./accuracy_records.csv', mode='a',header=False)
    

    #============ TensorBoard logging ============#
    # log validation acc
    if(epoch != -1):
        info = {
                'acc_d1': acc1*100,
                #'acc_d2': acc2*100,
                #'acc_d3': acc3*100,
                #'acc_avg': acc_total*100
                }
        
        for tag, value in info.items():
            logger.scalar_summary(tag, value, epoch)


counter = 0
total = 0
# Train the Model
for epoch in range(num_epochs):
    if(direct_test):
        print('direc _test...')
        test_module(-1, -1,test_loader, False)
        break

    if(debug and counter>=3):
        break
    prev_i = len(train_loader)*epoch

    if(epoch == 0):
        test_module(total, epoch, val_loader,False)

    for i, sample in enumerate(train_loader):
        if(debug and counter>=3):break
        counter+=1
        #if(i == 0): continue
        #print(images,labels)
        images = Variable(sample['image'])
        labels = Variable(sample['labels']).float()
        if(use_gpu):
            images = images.cuda()
            labels = labels.cuda()
        #print(images.size(), labels.size())
        
        # Forward + Backward + Optimize
        optimizer.zero_grad()
        outputs = cnn(images).float()
        #print(outputs.size(), labels.size())
        #print(outputs)
        #print(labels)
        labels = labels[:,0]
        outputs = outputs[:,0]
        loss = criterion(outputs.float(), labels)
        # costly? change this step?
        df = pd.DataFrame([[i+1+prev_i ,to_np(loss)[0]]])
        df.to_csv('./training_loss_records.csv', mode='a',header=False)
        loss.backward()
        optimizer.step()
        
        # costly? change this step?
        total += to_np(labels).shape[0]
        #total += labels.shape[0] # test

        if (i+1) % 1 == 0:
            print ('Epoch [%d/%d], Batch [%d/%d] Loss: %.4f' 
                   %(epoch+1, num_epochs,i+1, math.ceil(len(train_set)/batch_size),loss.data[0]))
            
            #============ TensorBoard logging ============#
            # (1) Log the scalar values
            info = {
            'loss': loss.data[0]
            }

            for tag, value in info.items():
                logger.scalar_summary(tag, value, i+1+prev_i)

            # (2) Log values and gradients of the parameters (histogram)
            for tag, value in cnn.named_parameters():
                tag = tag.replace('.', '/')
                logger.histo_summary(tag, to_np(value), i+1+prev_i)
                logger.histo_summary(tag+'/grad', to_np(value.grad), i+1+prev_i)
    
    # test at the end of every epoch
    if(epoch + 1 == num_epochs ): 
        # last epoch ends
        print('final test:')
        test_module(total, -1, test_loader, True)
        print('Traine data size: ' + str(total))
    else:
        # during training
        test_module(total, epoch+1,val_loader,False)
    
    # Save the Trained Model
    if(not debug):
        torch.save(cnn.state_dict(), 'cnn.pkl')
    
    # rest 20min for every n epochs
    #rest_time = 1200 #20min
    #n = 10
    #if((epoch+1) % n == 0 and (epoch+1) != num_epochs):
    #    print('Having a rest...')
    #    time.sleep(rest_time)



================================================
FILE: source/cnn4matrix.py
================================================
# Freddy @BH
# Dec 20, 2017

import torch 
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
import numpy as np
import pandas as pd
import sys
import math
import time
from tqdm import *

from data_preprocess import *
from utils import *
from logger import Logger

# Hyper Parameters
num_epochs = 10
batch_size = 256
#learning_rate = 1e-3
learning_rate = 1e-4

# argv
#data_dir = sys.argv[1]

debug = False
load_prev_model = False
direct_test = False
use_gpu = False

if(sys.argv[2] == '1'):
    debug = True
if(sys.argv[3] == '1'):
    load_prev_model = True
if(sys.argv[4] == '1'):
    direct_test = True

if(torch.cuda.is_available()):
    use_gpu = True



''' Data ''' 

class DigitDataset(Dataset):

    def __init__(self, matrix_csv_path, label_csv_path, dtype):
        matrix_data = pd.read_csv(matrix_csv_path,header=None)
        label_data = pd.read_csv(label_csv_path,header=None)
        self.dtype = dtype
        
        #labels = np.ravel(label_data.ix[:,1:3]) # np.ravel: return a flattened array
        labels = label_data.ix[:,1:3]
        pixels = np.array(matrix_data.ix[:,1:])

        self.N = int(pixels.shape[0]/5) # N: num of train samples
        if(self.N != labels.shape[0]):
            print('matrix & label dimension mismatch')
        pixels = pixels.reshape(self.N,5,1,20)
        pixels = pixels.transpose(0,2,3,1)

        #self.pixels_train = np.array(pixels).reshape([self.N,1,20,5])
        self.pixels_train = pixels
        self.labels_train = np.array(labels).reshape(self.N,3)
        
    def __getitem__(self,index):
        label = torch.from_numpy(self.labels_train[index]).type(self.dtype)
        img = torch.from_numpy(self.pixels_train[index]).type(self.dtype)
        return img, label

    def __len__(self):
        return self.N


data_root = sys.argv[1]

train_path = data_root+'/matrix_train.csv'
train_label_path = data_root+'/label_train.csv'
test_path = data_root+'/matrix_test.csv'
test_label_path = data_root+'/label_test.csv'
val_path = data_root+'/matrix_validation.csv'
val_label_path = data_root+'/label_validation.csv'

dtype = torch.FloatTensor

training_dataset = DigitDataset(train_path, train_label_path,dtype)
train_loader = DataLoader(training_dataset, batch_size = batch_size, shuffle=True)

test_dataset = DigitDataset(test_path, test_label_path, dtype)
test_loader = DataLoader(test_dataset)

val_dataset = DigitDataset(val_path, val_label_path,dtype)
val_loader = DataLoader(val_dataset)

print(len(training_dataset))
print(len(test_dataset))
print(len(val_dataset))


''' Models '''

def helper(x):
    if(x>=0.5):
        return 1.
    else: 
        return 0.


# CNN

# reproduction of CLEAR-Trade:
# https://arxiv.org/pdf/1709.01574.pdf
class res_cnn(nn.Module):
    def __init__(self):
        super(res_cnn, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, padding=1),
            nn.LeakyReLU(0.3))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 8, kernel_size=3, padding=1),
            nn.LeakyReLU(0.3))
        self.layer3 = nn.Sequential(
            nn.Conv2d(8, 32, kernel_size=1),
            nn.LeakyReLU(0.3))
        self.layer4 = nn.Sequential(
            nn.Conv2d(32, 2,kernel_size=1),
            nn.LeakyReLU(0.3))
        self.pl = nn.AvgPool2d((20,5))
        self.sm = nn.Softmax()

    def forward(self, x):
        #print(x.size())
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        #out = self.layer35(out)
        out = self.layer4(out)
        #print(out.size())
        out = self.pl(out)
        #print(out.size())
        out = out.view(out.size(0),-1)
        #print(out.size())
        out = self.sm(out)
        return out


''' train and test ''' 

cnn = res_cnn().double()
if(use_gpu):
    cnn.cuda()

if(load_prev_model):
    print('Loading previous model...')
    cnn.load_state_dict(torch.load('cnn4matrix.pkl'))

# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
#criterion = nn.MSELoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)

logger = Logger('./logs')


def test_module(train_size, epoch, data_loader, write=False):
    # Test the Model
    cnn.eval()  # Change model to 'eval' mode (BN uses moving mean/var).
    
    correct_d1 = 0
    correct_d2 = 0
    correct_d3 = 0
    total = 0
    counter = 0
    
    for sample in tqdm(data_loader):
        if(debug and counter >= 3): break
        counter+=1
        

        images = Variable(sample[0]).double()
        labels = Variable(sample[1]).float()

        if(use_gpu):
            images = images.cuda()
            labels = labels.cuda()
        outputs = cnn(images)
        #_, predicted = torch.max(outputs.data, 1)
        
        #labels = to_np(labels.view(-1,1))
        #predicted = np.sign(to_np(outputs.view(-1,1)))
        #print(labels.shape, predicted.shape)

        #=======cpu computation=======#
        # can we migrate it to the GPU?
        labels = to_np(labels)
        scores = to_np(outputs)
        labels_d1 = labels[:,0]
        #labels_d1_onehot[np.arrage(labels_d1.shape[0]), labels_d1] = 1
        #labels_d2 = labels[:,1]
        #labels_d3 = labels[:,2]


        # if output>=0: predict=1, else: predict=-1
        #predicted_d1 = list(map(helper,outputs[:,0]))
        predicted_d1 = scores.argmax()
        #predicted_d2 = np.sign(outputs[:,1])
        #predicted_d3 = np.sign(outputs[:,2])

        # only conduct a prediction when abs(output) > 0.5
        #predicted_high_d1 = np.sign(outputs[:,0])
        #predicted_high_d2 = np.sign(outputs[:,1])
        #predicted_high_d3 = np.sign(outputs[:,2])
        
        correct_d1 += (predicted_d1 == labels_d1).sum()
        #correct_d2 += (predicted_d2 == labels_d2).sum()
        #correct_d3 += (predicted_d3 == labels_d3).sum()
        
        total += labels.shape[0]
        
        
    acc1 = correct_d1 / total
    #acc2 = correct_d2 / total
    #acc3 = correct_d3 / total
    #acc_total = (correct_d3+correct_d2+correct_d3) / ( total * 3)
    acc2 = 0
    acc3 = 0
    acc_total = acc1
    print('Test Accuracy of the model on the %d test images, Day 21: %.4f %%' % (total, 100 * acc1))
    #print('Test Accuracy of the model on the %d test images, Day 22: %.4f %%' % (total, 100 * acc2))
    #print('Test Accuracy of the model on the %d test images, Day 23: %.4f %%' % (total, 100 * acc3))
    #print('Test Accuracy of the model on the %d test images, total: %.4f %%' % (total, 100 * acc_total))

    test_size = total
    if(write):
        df = pd.DataFrame([[train_size,test_size,acc1,acc2,acc3,acc_total]])
        df.to_csv('./accuracy_records.csv', mode='a',header=False)
    

    #============ TensorBoard logging ============#
    # log validation acc
    if(epoch != -1):
        info = {
                'acc_d1': acc1*100,
                #'acc_d2': acc2*100,
                #'acc_d3': acc3*100,
                #'acc_avg': acc_total*100
                }
        
        for tag, value in info.items():
            logger.scalar_summary(tag, value, epoch)


counter = 0
total = 0
# Train the Model
for epoch in range(num_epochs):
    if(direct_test):
        print('direc _test...')
        test_module(-1, -1,test_loader, False)
        break

    if(debug and counter>=3):
        break
    prev_i = len(train_loader)*epoch

    if(epoch == 0):
        test_module(total, epoch, val_loader,False)

    for i, sample in enumerate(train_loader):
        if(debug and counter>=3):break
        counter+=1
        #if(i == 0): continue
        #print(images,labels)
        images = Variable(sample[0]).double()
        labels = Variable(sample[1]).float()
        if(use_gpu):
            images = images.cuda()
            labels = labels.cuda()
        #print(images.size(), labels.size())
        
        # Forward + Backward + Optimize
        optimizer.zero_grad()
        score = cnn(images).float()
        labels = labels[:,0]
        #outputs = outputs[:,0]
        
        loss = criterion(score, labels.long())
        #df = pd.DataFrame([[i+1+prev_i ,to_np(loss)[0]]])
        #df.to_csv('./training_loss_records_4matrix.csv', mode='a',header=False)
        loss.backward()
        optimizer.step()
        
        # costly? change this step?
        total += to_np(labels).shape[0]
        #total += labels.shape[0] # test


        #============ TensorBoard logging ============#
        # (1) Log the scalar values
        info = {
            'loss': loss.data[0]
        }

        for tag, value in info.items():
            logger.scalar_summary(tag, value, i+1+prev_i)

        '''
        # (2) Log values and gradients of the parameters (histogram)
        for tag, value in cnn.named_parameters():
            tag = tag.replace('.', '/')
            logger.histo_summary(tag, to_np(value), i+1+prev_i)
            logger.histo_summary(tag+'/grad', to_np(value.grad), i+1+prev_i)
        '''
        if (i+1) % 100 == 0:
            print ('Epoch [%d/%d], Batch [%d/%d] Loss: %.4f' 
                   %(epoch+1, num_epochs,i+1, math.ceil(len(training_dataset)/batch_size),loss.data[0]))

    # test at the end of every epoch
    if(epoch + 1 == num_epochs ): 
        # last epoch ends
        print('final test:')
        test_module(total, -1, test_loader, True)
        print('Traine data size: ' + str(total))
    else:
        # during training
        test_module(total, epoch+1,val_loader,False)
    
    # Save the Trained Model
    if(not debug):
        torch.save(cnn.state_dict(), 'cnn4matrix.pkl')


================================================
FILE: source/data_gen.py
================================================
# Freddy @DC, uWaterloo, ON, Canada
# Nov 12, 2017

import numpy as np
import pandas as pd
import csv
import time
from datetime import datetime
import sys
import os
# plot
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mtick
from matplotlib.finance import candlestick_ohlc

# yahoo data
#from pandas_datareader import data as pdr
#import fix_yahoo_finance as yf


#yf.pdr_override() # <== that's all it takes :-)


'''
def get_data(abbv, start, end):
    data = pdr.get_data_yahoo(abbv, start=start, end=end)
    #data['Date'] = data.index
    data.reset_index(inplace=True,drop=False)
    df = data[['Date', 'Open', 'High', 'Low', 'Close']]
    df["Date"] = df["Date"].apply(mdates.datestr2num)
    return df
'''

def data_plot(data):
    fig, ax = plt.subplots()
    #plt.subplot2grid((6, 4), (1, 0), rowspan=6, colspan=4, axisbg='#07000d')
    candlestick_ohlc(ax, data.values, width=.6, colorup='#53c156', colordown='#ff1717')
    #ax.xaxis_date()
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m-%d'))
    fig.autofmt_xdate()
    plt.xticks(rotation=45)
    plt.ylabel('Stock Price')
    plt.xlabel('Date')
    fig.set_size_inches(8,8);
    #plt.close()
    return fig

def main():
    data = get_data(sys.argv[1], sys.argv[2], sys.argv[3])
    #print(data.head())
    fig = data_plot(data)
    #fig.show()
    #os.system('sleep 3s')
    fig.savefig('./data/imgs/' + sys.argv[1]+'_'+sys.argv[2]+'_'+sys.argv[3], dpi=200)


if __name__ == '__main__':
    main()




================================================
FILE: source/data_preprocess.py
================================================
# Freddy @Blair House
# Nov. 19, 2017

# reference: http://pytorch.org/tutorials/beginner/data_loading_tutorial.html

from __future__ import print_function, division
import os
from tqdm import *
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils

# Ignore warnings
import warnings
warnings.filterwarnings("ignore")

plt.ion()   # interactive mode

#label_table = pd.read_csv('./data/label_table.csv')
#img_name = label_table.ix[,0]


class stock_img_dataset(Dataset):
    """stock canslestick graph dataset."""

    def __init__(self, csv_file, root_dir, transform=None):
        """
        Args:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all the images.
            transform (callable, optional): Optional transform to be applied
                on a sample.
        """
        self.label_table = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.label_table)

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.label_table.ix[idx, 1])
        image = io.imread(img_name)
        labels = self.label_table.ix[idx, 2:].as_matrix().astype('int')
        #labels = labels.reshape(-1, 2)
        sample = {'image': image, 'labels': labels}

        if self.transform:
            sample = self.transform(sample)

        return sample

def show_imgs(image, labels):
    """Show image with landmarks"""
    plt.imshow(image)
    #plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')
    plt.pause(2)  # pause a bit so that plots are updated

class Rescale(object):
    """Rescale the image in a sample to a given size.

    Args:
        output_size (tuple or tuple): Desired output size. If tuple, output is
            matched to output_size. If int, smaller of image edges is matched
            to output_size keeping aspect ratio the same.
    """

    def __init__(self, output_size):
        assert isinstance(output_size, (int, tuple))
        self.output_size = output_size

    def __call__(self, sample):
        image, labels = sample['image'], sample['labels']

        h, w = image.shape[:2]
        if isinstance(self.output_size, int):
            if h > w:
                new_h, new_w = self.output_size * h / w, self.output_size
            else:
                new_h, new_w = self.output_size, self.output_size * w / h
        else:
            new_h, new_w = self.output_size

        new_h, new_w = int(new_h), int(new_w)

        img = transform.resize(image, (new_h, new_w))

        # h and w are swapped for landmarks because for images,
        # x and y axes are axis 1 and 0 respectively
        #labels = labels * [new_w / w, new_h / h]

        return {'image': img, 'labels': labels}

class ToTensor(object):
    """Convert ndarrays in sample to Tensors."""

    def __call__(self, sample):
        image, labels = sample['image'], sample['labels']

        # swap color axis because
        # numpy image: H x W x C
        # torch image: C X H X W
        image = image.transpose((2, 0, 1))
        return {'image': torch.from_numpy(image),
                'labels': torch.from_numpy(labels)}


'''test script'''
'''
data = stock_img_dataset('./data/label_table.csv', './data/imgs')
fig = plt.figure()

for i in range(len(data)):
    sample = data[i]

    print(i, sample['image'].shape, sample['labels'].shape)

    ax = plt.subplot(1, 4, i + 1)
    plt.tight_layout()
    ax.set_title('Sample #{}'.format(i))
    ax.axis('off')
    show_imgs(**sample)

    #plt.show()
    if i == 3:
        plt.show()
        #os.system('sleep 2s')
        break



scale = Rescale(256)
composed = transforms.Compose([Rescale(256)])


# Apply each of the above transforms on sample.
fig = plt.figure()
sample = data[10]
for i, tsfrm in enumerate([scale, composed]):
    transformed_sample = tsfrm(sample)

    ax = plt.subplot(1, 3, i + 1)
    plt.tight_layout()
    ax.set_title(type(tsfrm).__name__)
    show_imgs(**transformed_sample)

plt.show()
'''
'''data to tensor, and save tensor'''
'''
transformed_dataset = stock_img_dataset(csv_file='./data/label_table.csv',
                                           root_dir='./data/imgs',
                                           transform=transforms.Compose([
                                               Rescale(256),
                                               ToTensor()
                                           ]))

for i in range(len(transformed_dataset)):
    sample = transformed_dataset[i]

    print(i, sample['image'].size(), sample['labels'].size())

    if i == 3:
        break
'''


================================================
FILE: source/logger.py
================================================
# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import tensorflow as tf
import numpy as np
import scipy.misc 
try:
    from StringIO import StringIO  # Python 2.7
except ImportError:
    from io import BytesIO         # Python 3.x


class Logger(object):
    
    def __init__(self, log_dir):
        """Create a summary writer logging to log_dir."""
        self.writer = tf.summary.FileWriter(log_dir)

    def scalar_summary(self, tag, value, step):
        """Log a scalar variable."""
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
        self.writer.add_summary(summary, step)

    def image_summary(self, tag, images, step):
        """Log a list of images."""

        img_summaries = []
        for i, img in enumerate(images):
            # Write the image to a string
            try:
                s = StringIO()
            except:
                s = BytesIO()
            scipy.misc.toimage(img).save(s, format="png")

            # Create an Image object
            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                                       height=img.shape[0],
                                       width=img.shape[1])
            # Create a Summary value
            img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))

        # Create and write Summary
        summary = tf.Summary(value=img_summaries)
        self.writer.add_summary(summary, step)
        
    def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the histogram proto
        hist = tf.HistogramProto()
        hist.min = float(np.min(values))
        hist.max = float(np.max(values))
        hist.num = int(np.prod(values.shape))
        hist.sum = float(np.sum(values))
        hist.sum_squares = float(np.sum(values**2))

        # Drop the start of the first bin
        bin_edges = bin_edges[1:]

        # Add bin edges and counts
        for edge in bin_edges:
            hist.bucket_limit.append(edge)
        for c in counts:
            hist.bucket.append(c)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()



================================================
FILE: source/main-gen.py
================================================

import os
import sys
from tqdm import *


# commandformat
# python3 main-gen.py -m ../Desktop/Stocks 10 ~/Desktop/stock_matrix_data


flags = sys.argv[1]

files = os.listdir(sys.argv[2])

iter_num = int(sys.argv[3])




def ten_three_img_gen():
    counter = 0
    for f in tqdm(files):
        if('.txt' not in f): continue
        if((len(sys.argv) == 3) and (counter > iter_num)): break
        os.system('python3 ./10-3-gen.py '+sys.argv[1]+'/'+f)
        print('generating: %s, iteration: %d' % (f, counter))
        counter = counter + 1

def twenty_three_matrix_gen():
    counter = 0
    for f in tqdm(files):
        if('.txt' not in f): continue
        if(counter > iter_num): break
        os.system('python3 ./matrix_gen.py '+sys.argv[2]+'/'+f+' '+sys.argv[4]) 
        # argv3 output root dir
        print('generating: %s, iteration: %d' % (f, counter))
        counter = counter + 1


if('-i' in flags):
    ten_three_img_gen()
elif('-m' in flags):
    twenty_three_matrix_gen()


================================================
FILE: source/matrix_gen.py
================================================
# Freddy @Blair House, Waterloo, ON, Canada
# Dec 20, 2017

import numpy as np
import pandas as pd
import csv
import time
from datetime import datetime
import sys
import os
from tqdm import *
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.finance import candlestick_ohlc
import warnings
warnings.filterwarnings("ignore")
from data_gen import data_plot
from utils import *



def main():
    data = pd.read_csv(sys.argv[1], parse_dates=True, dayfirst=True)# argv[1]: stock_symbol.txt
    # some preprocessing
    data = data[['Open','High','Low','Close','Volume']].astype('float32')     
    
    data_len = len(data)
    counter = 0
    
    for k in range(0,int(data_len/23)+1):
        if(counter + 23 >= data_len): break
        data_cur = data[counter:counter+23]
        data_close = data_cur['Close']
        labels = []
        for i in range(0,23):
            # draw day [0,1,...,19], predict day [20,21,22] 
            # positive label 1, negative label 0
            last_4 = data_close[19:23].values
            labels_raw = last_4[1:4] - last_4[0]
            labels = list(map(one_hot2,labels_raw))
        counter = counter + 23
        
        # save data
        
        data_cur = data_cur[0:20].transpose() # only save the first 20 days
        
        output_dir = sys.argv[2] # root dir of the output data

        mode = 'train' # 70% training set
        if(k%10 == 0): mode = 'validation' # 10% validation set
        elif(k%10 == 4 or k%10 == 7): mode = 'test' # 20% testing set
        
        matrix_file_name = output_dir + '/matrix_'+mode+'.csv'
        
        new_label = [[labels[0],labels[1],labels[2]]]
        df = pd.DataFrame(new_label, columns=['pred_1', 'pred_2', 'pred_3'])
        label_file_name = output_dir+'/label_'+mode+'.csv'

        #write
        if(df.shape[0]==1 and df.shape[1]==3 and data_cur.shape[0]==5 and data_cur.shape[1]==20):
            df.to_csv(label_file_name, mode='a', header=False)
            data_cur.to_csv(matrix_file_name, mode='a', header=False)
        else:
            print('data shape error:')
            print(data_cur.shape)
            print(df.shape)

if __name__ == '__main__':
    main()




================================================
FILE: source/utils.py
================================================
# Freddy @DC, uWaterloo, ON, Canada
# Nov 16, 2017

def one_hot(x):
    if(x>=0): return 1
    else: return -1

def one_hot2(x):
    if(x>=0): return 1
    else: return 0

def dataset_printer(object):
    for i in range(len(object)):
        sample = object[i]
        print(i, sample['image'].size(), sample['labels'].size())
        
        if i == 3:break


def to_np(x):
    return x.data.cpu().numpy()


================================================
FILE: tensorboard.sh
================================================
#!/bin/bash

tensorboard --logdir='./logs' --port=6006


================================================
FILE: training_loss_records_4matrix.csv
================================================
0,1,0.8239462971687317
0,2,0.7468798756599426
0,3,0.7995794415473938
0,4,0.8167116641998291
0,5,0.766636312007904
0,6,0.8174178004264832
0,7,0.7702108025550842
0,8,0.8080196380615234
0,9,0.773985743522644
0,10,0.8258000016212463
0,11,0.8564041256904602
0,12,0.7835349440574646
0,13,0.8006752133369446
0,14,0.8009995222091675
0,15,0.7849965691566467
0,16,0.8621325492858887
0,17,0.8206315636634827
0,18,0.8108331561088562
0,19,0.7688201665878296
0,20,0.7678539156913757
0,21,0.7179438471794128
0,22,0.7902442812919617
0,23,0.8231701850891113
0,24,0.7462820410728455
0,25,0.8633847832679749
0,26,0.8301745653152466
0,27,0.7508311867713928
0,28,0.8679159283638
0,29,0.8138713240623474
0,30,0.79303377866745
0,31,0.8598165512084961
0,32,0.7680348753929138
0,33,0.7887089848518372
0,34,0.7624881863594055
0,35,0.8019254207611084
0,36,0.8445202112197876
0,37,0.8349438905715942
0,38,0.7507615089416504
0,39,0.7859309315681458
0,40,0.8366753458976746
0,41,0.8365954756736755
0,42,0.7857361435890198
0,43,0.8484177589416504
0,44,0.7778297662734985
0,45,0.8366873860359192
0,46,0.7936280965805054
0,47,0.7585654258728027
0,48,0.8054482936859131
0,49,0.8055921196937561
0,50,0.7704899311065674
0,51,0.7860273122787476
0,52,0.770311713218689
0,53,0.8330877423286438
0,54,0.813261866569519
0,55,0.8249776363372803
0,56,0.8015325665473938
0,57,0.8406051993370056
0,58,0.8366989493370056
0,59,0.8562303185462952
0,60,0.8366990089416504
0,61,0.817168116569519
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