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