#!/usr/bin/env python
# coding: utf-8

# ## Imports

# In[1]:


import datetime
import requests
import math
from tqdm import tqdm
import pandas as pd
import zipfile


# ## Functions

# In[2]:


def unzip_data():
    #https://stackoverflow.com/a/3451150/993324
    zip_ref = zipfile.ZipFile("../output/omegacgl.zip", 'r')
    zip_ref.extractall("../output/stock_data/")
    zip_ref.close()


# In[3]:


def download_fresh_data():
    # https://stackoverflow.com/a/37573701/993324


    url = 'http://bossa.pl/pub/ciagle/omega/omegacgl.zip'
    r = requests.get(url, stream=True)

    total_size = int(r.headers.get('content-length', 0)); 
    block_size = 1024*1024
    wrote = 0 

    with open('../output/omegacgl.zip', 'wb') as f:
        for data in tqdm(r.iter_content(block_size), total=math.ceil(total_size//block_size),
                         unit='MB', unit_scale=True):
            wrote = wrote  + len(data)
            f.write(data)
    if total_size != 0 and wrote != total_size:
        print("ERROR, something went wrong")


# In[4]:


def prepare_data(company = "CDPROJEKT"):
    stock_data = pd.read_csv("../output/stock_data/{}.txt".format(company))
    stock_data["Date"] = pd.to_datetime(stock_data["Date"], format="%Y%m%d")
    stock_data.set_index(keys = "Date", drop = True, inplace = True)
    return stock_data


# In[5]:


def get_last_stock_day():
    yesterday = datetime.datetime.now() - datetime.timedelta(1)
    if yesterday.isoweekday() <= 5:
        last_stock_day = yesterday
    elif yesterday.isoweekday() == 6:
        last_stock_day = yesterday - datetime.timedelta(1)
    else:
        last_stock_day = yesterday - datetime.timedelta(2)
    return last_stock_day


# ## Getting data if we don't have it already

# In[6]:


# Kod 1
try:
    stock_data = prepare_data()
except FileNotFoundError:
    print("Stock data not found. Downloading and extracting.")
    download_fresh_data()
    unzip_data()
    stock_data = prepare_data()


# ## Getting most recent data if we don't have it already

# In[7]:


# Kod 2
last_stock_day = get_last_stock_day()
print(last_stock_day)

last_data = "{}-{:02d}-{:02d}".format(last_stock_day.year,last_stock_day.month,last_stock_day.day)
print("Looking for {}".format(last_data))
try:
    stock_data.loc[last_data]
    print("We have it!")
except KeyError:
    print("Fresh stock data not found. Downloading and extracting.")
    download_fresh_data()
    unzip_data()
    stock_data = prepare_data()


# ## Adding moving average over 30 days window

# In[8]:


# Kod 3
stock_data["SMA"] = stock_data["Close"].rolling(30).mean()


# In[9]:


stock_data.tail()


# ## Plotting *High*, *Low* and *SMA* from last year

# In[10]:


# Kod 4
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot

last_year = datetime.datetime.now() - datetime.timedelta(365)

init_notebook_mode()

data = [
    go.Scatter(
        x = stock_data[last_year:].index, 
        y = stock_data[last_year:]["SMA"],
        name = 'SMA'
    ),
    go.Scatter(
        x = stock_data[last_year:].index, 
        y = stock_data[last_year:]["Low"],
        name = 'Low'
    ),
    go.Scatter(
        x = stock_data[last_year:].index, 
        y = stock_data[last_year:]["High"],
        name = 'High'
    )
]

layout = dict(title = "Stock price",
              xaxis= dict(title= 'Date'))

fig = dict(data = data, layout = layout)
iplot(fig)


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