#Imports
import pandas as pd
import numpy as np
import requests
import time
from datetime import date
 
#Global variables
hideoutput = False
 
#Helper functions
def getStockValues(symbol,name,apikey): 
 
    #Load data from alphavantage
    json = requests.get(url='https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol='+symbol+'&outputsize=full&apikey='+apikey).json()
 
    if("Error Message" in json):
        outputError(json["Error Message"] + " This is likely due to the fact that the stock symbol was not found.")
        exit()
    if("Note" in json):
        outputError(json["Note"] + " This is likely due to the fact that you set premium key to True but did not supply a premium key.")
        exit()
 
    json = json["Time Series (Daily)"]
    df = pd.DataFrame.from_dict(json, orient='index')
    df.reset_index(level=0, inplace=True)
 
    #Rename columns and assign the right data types
    df.columns = ['Date', name+'Open',name+'High',name+'Low',name+'CloseNonAdjusted',name+'Close',name+'Volume',name+'Dividend',name+'Coefficient']
    df['Date']=df['Date'].astype("datetime64")
    df[name+"Open"] = pd.to_numeric(df[name+"Open"])
    df[name+"High"] = pd.to_numeric(df[name+"High"])
    df[name+"Low"] = pd.to_numeric(df[name+"Low"])
    df[name+"CloseNonAdjusted"] = pd.to_numeric(df[name+"CloseNonAdjusted"])
    df[name+"Close"] = pd.to_numeric(df[name+"Close"])
    df[name+"Volume"] = pd.to_numeric(df[name+"Volume"])
    df[name+"Dividend"] = pd.to_numeric(df[name+"Dividend"])
    df[name+"Coefficient"] = pd.to_numeric(df[name+"Coefficient"])
 
    #Delete unnecessary columns
    df = df.drop(name+"CloseNonAdjusted", axis=1)
 
    return df
 
def output(message):
    global hideoutput
 
    #Output status messages
    if(not hideoutput):
        print(message)
 
def outputError(message):
    print("> Error: " + message)
 
def download(stocklist,apikey,premiumkey,suppressoutput,startdate,enddate):
 
    #Set the output
    global hideoutput
    hideoutput = suppressoutput
 
    #Initialize variables
    fulldata = ""
    stockdata = ""
    counter = 0
    if(premiumkey):
        sleeptime = 0
    else:
        sleeptime = 15
    key = apikey
 
    #Check if the arguments are valid
    if(not apikey):
        outputError("No apikey found.")
        exit()
    if(np.datetime64(startdate)>np.datetime64(enddate)):
        outputError("The enddate has to be later than the startdate.")
        exit()
 
    output("\nArguments passed")
    output("> Key: " + str(key))
    output("> Stocks: ")
    output(stocklist)
 
    #Download the stock data
    output("\nDownloading new stock data")
    output("> This will take about " + str( round((len(stocklist) * sleeptime + len(stocklist * 5)) / 60))+ " minute(s)")
 
    #Loop through all stock symbols
    for index, row in stocklist.iterrows():
 
        #Download new stock data and merge it with existing data
        output("> Downloading: "+str(row['Name'])+ " (" +str(counter+1) +" of "+ str(len(stocklist.index) )+ ")")
        stockdata = getStockValues(row['Symbol'],row['Name'],apikey)
        if(counter == 0):
            fulldata = stockdata
        else:
            fulldata = fulldata.merge(stockdata, on="Date", how="outer")
        counter = counter +1;
        output("> Merging: " + row["Name"])
 
        if(not(counter == len(stocklist.index))):
            if(sleeptime > 0):
                output("> Sleeping for "+ str(sleeptime)+" seconds to not exceed the API limit.")
                time.sleep(sleeptime)
 
    #Filter for start and enddate
    output("> Filtering timeframe from " + startdate + " to "+ enddate)
    if(startdate):
        fulldata = fulldata[(fulldata['Date'] >=  np.datetime64(startdate) )]
    if(enddate):
        fulldata = fulldata[(fulldata['Date'] <=  np.datetime64(enddate) )]
 
    output("> All stocks were fetched.")
    return fulldata
 
 
#Public functions
def downloadFromDict(stockdict,apikey,premiumkey,suppressoutput,startdate,enddate):
 
    #Transform the dict
    try: 
        stocklist = pd.DataFrame(stockdict.items(), columns=['Symbol', 'Name'])
    except:
        outputError("The dict was malformated. Please use the format {Symbol:Name,Symbol:Name}")
        exit()
 
    results = download(stocklist,apikey,premiumkey,suppressoutput,startdate,enddate)
    return results
 
def downloadFromCSV(stockcsv,apikey,premiumkey,suppressoutput,startdate,enddate):
 
    #Transform the CSV
    try: 
        stocklist = pd.read_csv(stockcsv)
    except:
        outputError("The csv was malformated. Please use the format \nSymbol,Name\nSymbol,Name")
        exit()
 
    results = download(stocklist,apikey,premiumkey,suppressoutput,startdate,enddate)
    return results