Python Share.get_historical方法代码示例

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def get_data(symbol='^GSPC'):
    symbol1 = symbol
    if symbol1[0] == '^':
        symbol1 = symbol[1:]
   
    fileName = 'c:/data/%s.csv' % symbol1
    keys = ['Date', 'Close']  # only save these fields
    
    gspc = Share(symbol)
    if os.path.isfile(fileName):
        result = read_data(fileName)
        oneday = timedelta(1)
        yesterday = datetime.today().date() - oneday
        latest = datetime.strptime(result[0]['Date'], '%Y-%m-%d').date()+oneday
        if latest < yesterday:
            print('Extending from %s to %s ...' % (str(latest), str(yesterday)))
            result = gspc.get_historical(str(latest), str(yesterday)) + result
            write_data(fileName, result, keys)
        else:
            print('No need to update')
    else:
        result = gspc.get_historical('1976-02-01', '2016-02-21')
        write_data(fileName, result, keys)

    return result

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
	def getDifferencePercentage(self):
	 	closing = []
	 	shareName = Share(self.company)
	 	self.getTimeStamp()
	 	startDate=''
	 	endDate=''
	 	hist=''
	 	for t in self.times:
	 		todayTimeStamp = t
	 		yesterdayTimeStamp = t-86400
	 		startDate = str(datetime.datetime.fromtimestamp(todayTimeStamp).strftime('%Y-%m-%d'))
	 		yesterdayDate=str(datetime.datetime.fromtimestamp(yesterdayTimeStamp).strftime('%Y-%m-%d'))
	 		todayHist = shareName.get_historical(startDate, startDate)
	 		yesterdayHist = shareName.get_historical(yesterdayDate,yesterdayDate)
	 		while(len(todayHist)==0):
	 			todayTimeStamp = todayTimeStamp+86400
	 			startDate = str(datetime.datetime.fromtimestamp(todayTimeStamp).strftime('%Y-%m-%d'))
	 			todayHist = shareName.get_historical(startDate, startDate)
	 		while(len(yesterdayHist)==0):
	 			yesterdayTimeStamp= yesterdayTimeStamp-86400
				yesterdayDate=str(datetime.datetime.fromtimestamp(yesterdayTimeStamp).strftime('%Y-%m-%d'))
	 			yesterdayHist = shareName.get_historical(yesterdayDate,yesterdayDate)
	 			
	 		closingPriceToday = float(todayHist[0]['Close'])
	 		closingPriceYesterday = float(yesterdayHist[0]['Close'])
	 		difference = (float(closingPriceYesterday) - float(closingPriceToday))*100.0/float(closingPriceYesterday)
	 		diff2 = float(format(difference, '.3f'))
	 		closing.append(diff2)
	 	self.differencePercentage = closing
	 	return closing

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def compare_stock(stock_1, stock_2, start_date, end_date):
    share_1 = Share( stock_1 )
    hist_1 = share_1.get_historical( start_date, end_date )
    
    share_2 = Share( stock_2 )
    hist_2 = share_2.get_historical( start_date, end_date )
    
    total_date = 0
    correct_prediction = 0
    
    len_1 = len( hist_1 )
    len_2 = len( hist_2 )
    
    length = min(len_1, len_2)

    i = 0
    j = 0
    
    # for i in range (0, length):
    while ( i < len_1 and j < len_2 ):
        x = hist_1[i]
        y = hist_2 [i];
    
        open = float( x['Open'])
        close = float( x['Close'])
        x_change = (close - open) * 100 / open
    
        open = float( y['Open'] )
        close = float( y['Close'] )
        y_change = (close - open) * 100 / open
    
        if x['Date'] != y['Date']:
            print "Error! DATE doesn't match"
            i += 1
            j += 1
            continue
    
        total_date += 1

    
        if y_change * x_change > 0:
            # print "Wow, They have Same performance"
            correct_prediction += 1
        else:
            print x['Date'] + ": " + stock_1 + " "+ str( x_change ) + " vs. " + stock_2 + " " + str( y_change )
            # print "Dam, they differs!"
    
    i += 1
    j += 1



    print "Total date is: " + str(total_date) + ", and correct prediction has " + str(correct_prediction ) 
    if total_date > 0:
        print "Correction rate is: " + str( correct_prediction * 1.0 / total_date )
    else:
        print "ERROR: Nothing compared!"

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
 def refresh_data(self, s_date=None, e_date=None):
     if s_date and e_date:
         try:
             share=Share(self.name)
             self.begin=s_date
             self.end=e_date
             share.get_historical(s_date, e_date)
             self.log.refresh("{} data collected".format(self.name))
         except:
             self.log.error("platform is offline or not connecting")

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def readLast5DaysStockPrice():
    with open('stock_features.csv', 'w') as csvfile:
        fieldnames = ['Stock', 'Adj_Close_1','High_1', 'Low_1'
                      ,'Adj_Close_2','High_2', 'Low_2','VolChg2_1','IndexChg2_1'
                      ,'Adj_Close_3','High_3', 'Low_3','VolChg3_2','IndexChg3_2'
                      ,'Adj_Close_4','High_4', 'Low_4','VolChg4_3','IndexChg4_3'
                      ,'Adj_Close_5','High_5', 'Low_5','VolChg5_4','IndexChg5_4']
        writer = csv.DictWriter(csvfile, lineterminator='\n', fieldnames=fieldnames)
        writer.writeheader()
    
        dow_code = 'BCB/UDJIAD1'
        dji_index = []
        j = 0
        while (j < 10):
            ticker = Share(stock_dict.get(j))
            i = 0
            stock_price = []
            current_date = date.today()
            fmt = '%Y-%m-%d'
            while( i < 5):
                if (ticker.get_historical(current_date.strftime(fmt), current_date.strftime(fmt)) == []):
                    current_date = current_date - timedelta(days=1)
                else:
                    stock_price += ticker.get_historical(current_date.strftime(fmt), current_date.strftime(fmt))
                    if(j == 0):
                        if(i == 0 and Quandl.get(dow_code, trim_start=current_date, trim_end=current_date, authtoken="T246AaoCUiwSyz1C4Vfe").values.tolist() == []):
                            dji_index.append(get_latest_dji())
                        else:
                            dji_index.append(Quandl.get(dow_code, trim_start=current_date, trim_end=current_date, authtoken="T246AaoCUiwSyz1C4Vfe").values.tolist()[0][0])
                                
                    current_date = current_date - timedelta(days=1)
                    i = i + 1    
            AbVolChg2_1 = int(stock_price[1].get('Volume')) - int(stock_price[0].get('Volume'))  
            VolChg2_1 = log(AbVolChg2_1,2)  if AbVolChg2_1 > 0 else -1*log(fabs(AbVolChg2_1),2)
            AbVolChg3_2 = int(stock_price[2].get('Volume')) - int(stock_price[1].get('Volume'))  
            VolChg3_2 = log(AbVolChg3_2,2)  if AbVolChg3_2 > 0 else -1*log(fabs(AbVolChg3_2),2)                        
            AbVolChg4_3 = int(stock_price[3].get('Volume')) - int(stock_price[2].get('Volume'))  
            VolChg4_3 = log(AbVolChg4_3,2)  if AbVolChg4_3 > 0 else -1*log(fabs(AbVolChg4_3),2)                        
            AbVolChg5_4 = int(stock_price[4].get('Volume')) - int(stock_price[3].get('Volume'))  
            VolChg5_4 = log(AbVolChg5_4,2)  if AbVolChg5_4 > 0 else -1*log(fabs(AbVolChg5_4),2)        
            writer.writerow({'Stock': stock_dict.get(j)
                         ,'Adj_Close_1' : stock_price[0].get('Adj_Close'),'High_1' : stock_price[0].get('High'),'Low_1' : stock_price[0].get('Low') 
                         ,'Adj_Close_2' : stock_price[1].get('Adj_Close'),'High_2' : stock_price[1].get('High'),'Low_2' : stock_price[1].get('Low')
                         ,'VolChg2_1': VolChg2_1,'IndexChg2_1': (float(dji_index[1]) - float(dji_index[0])) 
                         ,'Adj_Close_3' : stock_price[2].get('Adj_Close'),'High_3' : stock_price[2].get('High'),'Low_3' : stock_price[2].get('Low')
                         ,'VolChg3_2': VolChg3_2,'IndexChg3_2': (dji_index[2] - dji_index[1]) 
                         ,'Adj_Close_4' : stock_price[3].get('Adj_Close'),'High_4' : stock_price[3].get('High'),'Low_4' : stock_price[3].get('Low')
                         ,'VolChg4_3': VolChg4_3,'IndexChg4_3': (dji_index[3] - dji_index[2]) 
                         ,'Adj_Close_5' : stock_price[4].get('Adj_Close'),'High_5' : stock_price[4].get('High'),'Low_5' : stock_price[4].get('Low')
                         ,'VolChg5_4': VolChg5_4,'IndexChg5_4': (dji_index[4] - dji_index[3]) 
                         }) 
    
            j = j+1

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def prepare_stock_graph(symbol, start, end):

    stock = Share(symbol)
    stringprices = list(pd.DataFrame(stock.get_historical(start, end))["Adj_Close"])
    stringdates = list(pd.DataFrame(stock.get_historical(start, end))["Date"])

    prices = [float(p) for p in stringprices]
    dates = []

    for d in stringdates:
        year, month, day = d.split("-")
        d = datetime.date(int(year), int(month), int(day))
        dates.append(d)

    return prices, dates

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def trailing_stop(ticker, start, end, percentage=0.15):
    ticker = Share(ticker)

    while True:
        try:
            historical = sorted(ticker.get_historical(start, end), key=itemgetter("Date"))
        except ValueError as e:
            raise e
        except YQLQueryError as e:
            continue
        break

    if len(historical) <= 1:
        return None

    initial_price = float(historical[0]["Adj_Close"])
    curr_price = initial_price
    stop_price = initial_price * (1 - percentage)
    historical = historical[1:]

    while len(historical) > 0 and stop_price < curr_price:
        curr_price = float(historical[0]["Adj_Close"])
        if curr_price > stop_price / (1 - percentage):
            stop_price = curr_price * (1 - percentage)
        historical = historical[1:]

    out = (curr_price - initial_price) / initial_price
    return out

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def main():
    start_date = '2007-01-01'
    end_date = '2015-08-31'
    plotly_name = 'username'
    plotly_api_key = 'api-key'

    # get_historical is part of the yahoo-finance module
    nflx = Share('nflx')
    nflx_prices = nflx.get_historical(start_date, end_date)

    # how you can just extract the dates only
    nflx_dates = [x['Date'] for x in nflx_prices]

    # nflx_prices is currently sorted by dates because thats how the module gave them
    sorted_by_price = sorted(nflx_prices, key=lambda x: x['Adj_Close'], reverse=True)

    # say you wanted multiple stock prices
    ticker_symbols = ['hrl', 'tsn', 'gis', 'k']
    foods = {}

    if do_plot:
        plot_stock(plotly_name, plotly_api_key, nflx_prices, 'nflx')

    for symbol in ticker_symbols:
        foods[symbol] = Share(symbol).get_historical(start_date, end_date)
        foods[symbol].sort(key=lambda x: x['Date'])
        dates = [x['Date'] for x in foods[symbol]]
        prices = [x['Adj_Close'] for x in foods[symbol]]
        if do_plot:
            add_trace(dates, prices, symbol)

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def fetch_data(dt_from, dt_to, sym):
    min_dt_from = dt.datetime(2014, 1, 1)
    assert(dt_from >= min_dt_from)
    r = TStockHistory.find_one(
        {'Symbol': sym}, projection={'Date': 1}, sort=[('Date', pymongo.DESCENDING)])
    if not r:
        fetch_dt_from = min_dt_from
    elif r['Date'] < dt_to:
        fetch_dt_from = r['Date'] + dt.timedelta(days=1)
        if fetch_dt_from > dt_to:
            fetch_dt_from = None
    else:
        fetch_dt_from = None
    if fetch_dt_from:
        f = '%d-%d-%d' % (
            fetch_dt_from.year, fetch_dt_from.month, fetch_dt_from.day)
        t = '%d-%d-%d' % (dt_to.year, dt_to.month, dt_to.day)
        print('fetch %s from network...' % (sym))
        share = Share(sym)
        docs = share.get_historical(f, t)
        if docs:
            for r in docs:
                for k in ['Adj_Close', 'High', 'Low', 'Close', 'Volume']:
                    r[k] = np.float(r[k])
                r['Date'] = dateutil.parser.parse(r['Date'])
            TStockHistory.insert_many(docs)
    data = TStockHistory.find(
        {'Symbol': sym, 'Date': {'$gte': dt_from, '$lte': dt_to}})
    rs = filter(lambda x: 'placeholder' not in x, [u for u in data])
    return rs

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def getStockData(theTicker):
    global startDate
    print("Getting Data for ... " + theTicker)
    stock = Share(theTicker)
    print(startDate)
    data = stock.get_historical(startDate, DateNow)
    for d in data:
        tmp = []
        volume = int(d['Volume'])
        adjclose = float(d['Adj_Close'])
        high = float(d['High'])
        low = float(d['Low'])
        close = float(d['Close'])
        date = d['Date']
        open = float(d['Open'])
        # newDate = datetime.strptime(date, "%Y-%m-%d")
        tmp.append(date)
        tmp.append(open)
        tmp.append(high)
        tmp.append(low)
        tmp.append(close)
        tmp.append(adjclose)
        tmp.append(volume)
        givenStock.append(tmp)
    return givenStock

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def stock_Prices():
    # exit if the output already existed
    # if os.path.isfile('./input/stockPrices.json'):
    #     sys.exit("Prices data already existed!")

    priceSet = {}
    fin = open('./input/tickerList.csv')
    for num, line in enumerate(fin):
        line = line.strip().split(',')
        ticker, name, exchange, MarketCap = line
        if 1:
            print(num, ticker)
            yahoo = Share(ticker)
            time.sleep(np.random.poisson(3))
            prices = yahoo.get_historical('2005-01-01', '2020-01-01')
            priceDt = {}
            for i in range(len(prices)):
                date = ''.join(prices[i]['Date'].split('-'))
                priceDt[date] = round(log(float(prices[i]['Close']) / float(prices[i]['Open'])), 6)
            priceSet[ticker] = priceDt
        #except:
            #continue

    with open('./input/stockPrices.json', 'w') as outfile:
        json.dump(priceSet, outfile, indent=4)

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
    def generateData(self):
        global StartingDate
        global DataSize
        i = datetime.datetime.now()
        EndingDate = '%s-%s-%s' % (i.year,i.month,i.day)
        stock = Share(Stock_name)
        data = stock.get_historical(StartingDate,EndingDate)
        file = open('stock_data', 'w')
        closes = [c['Close'] for c in data]
        opens = [o['Open'] for o in data]
        oArray = []
        cArray = []

        for c in closes:
            cArray.append(c)

        for o in opens:
            oArray.append(o)

        for x in range(len(data)-2):
            #  %Difference, Next Day %Difference, Money Made Holding for a Day
            file.write(str((float(cArray[x])-float(oArray[x+1]))/100) + ' ' + str((float(cArray[x+1]) - float(oArray[x+2]))/100) + ' ' + str((float(oArray[x]) - float(oArray[x+1]))) + '\n')

            self.dayChange.append((float(cArray[x])-float(oArray[x+1]))/100)
            self.nextDayChange.append((float(cArray[x+1]) - float(oArray[x+2]))/100)
            self.profit.append(float(oArray[x]) - float(oArray[x+1]))
        #Makes sure the population size is
        DataSize = len(self.dayChange)
        file.close()

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
    def calculateValue(self, date, prices):
        value = 0.
        date = date.strftime('%Y-%m-%d')
        for sym,qty in self.holdings.items():
            if sym == 'cash':
                value += qty
            else:
                if sym not in prices:
                    stock = Share(sym)
                    # get prices until today
                    string_today = datetime.date.today().strftime('%Y-%m-%d')
                    print(string_today)
                    price_list = stock.get_historical(date, string_today)
                    # pprint.PrettyPrinter(depth = 6).pprint(price_list)
                    # Prices is a dictionary of dictionaries; each inner dictionary consists of (date, value) pairs for a ticker
                    prices[sym] = {item['Date']: float(item['Close']) for item in price_list}

                # find price for the date of interest
                if date in prices[sym]:
                    close_price = prices[sym][date]
                    value += close_price * qty
                else:
                    print(date, sym)
                    return (None, None)
        return (value, prices)

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def getStock(id):
    stock = Share(str(id)+'.TW')
    today = datetime.date.today()
    data = stock.get_historical('2017-04-20', str(today))
#    data = stock.get_historical('2017-04-20', '2017-04-20')
#    data = stock.get_historical(str(today), str(today))
    return data

# 需要导入模块: from yahoo_finance import Share [as 别名]
# 或者: from yahoo_finance.Share import get_historical [as 别名]
def getStockData(theTicker, startDate):
    stock = Share(theTicker)
    print("Getting Data for ... " + theTicker)
    now = datetime.now()
    DateNow = str(now.year) + "-" + str(now.month) + "-" + str(now.day)
    data = stock.get_historical(startDate, DateNow)
    stockData = []
    for d in data:
        tmp = []
        volume = int(d['Volume'])
        adjclose = float(d['Adj_Close'])
        high = float(d['High'])
        low = float(d['Low'])
        close = float(d['Close'])
        date = d['Date']
        open = float(d['Open'])
        tmp.append(date)
        tmp.append(open)
        tmp.append(high)
        tmp.append(low)
        tmp.append(close)
        tmp.append(adjclose)
        tmp.append(volume)
        stockData.append(tmp)
    return stockData