from alpaca.data.historical import CryptoHistoricalDataClient, StockHistoricalDataClient from alpaca.data.requests import CryptoLatestTradeRequest, StockLatestTradeRequest, StockLatestBarRequest, StockTradesRequest, StockBarsRequest from alpaca.data.enums import DataFeed from config import API_KEY, SECRET_KEY, MAX_BATCH_SIZE import datetime import time from alpaca.data import Quote, Trade, Snapshot, Bar from alpaca.data.models import BarSet, QuoteSet, TradeSet from alpaca.data.timeframe import TimeFrame # import mplfinance as mpf import pandas as pd from v2realbot.utils.utils import zoneNY from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY parametry = {} # no keys required #client = CryptoHistoricalDataClient() client = StockHistoricalDataClient(ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, raw_data=False) datetime_object_from = datetime.datetime(2023, 2, 27, 18, 51, 38, tzinfo=datetime.timezone.utc) datetime_object_to = datetime.datetime(2023, 2, 27, 21, 51, 39, tzinfo=datetime.timezone.utc) bar_request = StockBarsRequest(symbol_or_symbols="BAC",timeframe=TimeFrame.Minute, start=datetime_object_from, end=datetime_object_to, feed=DataFeed.SIP) # bars = client.get_stock_bars(bar_request).df bars = client.get_stock_bars(bar_request) #bars = bars.drop(['symbol']) #print(bars.df.close) #bars = bars.tz_convert('America/New_York') print(bars.data["BAC"]) #print(bars.df.columns) #Index(['open', 'high', 'low', 'close', 'volume', 'trade_count', 'vwap'], dtype='object') # bars.df.set_index('timestamp', inplace=True) # mpf.plot(bars.df, # the dataframe containing the OHLC (Open, High, Low and Close) data # type='candle', # use candlesticks # volume=True, # also show the volume # mav=(3,6,9), # use three different moving averages # figratio=(3,1), # set the ratio of the figure # style='yahoo', # choose the yahoo style # title='Prvni chart'); # #vracĂ­ se list od dict # print(bars["BAC"]) # # k nemu muzeme pristupovat s # dict = bars["BAC"] # print(type(dict)) # print(dict[2].timestamp) # print(dict[2].close) # print(dict[].close)