mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
442915f847
* feat(Data): create DataSource, DataCollection, added hourly crypto data * fix(Data): hourly data format, loading & config
48 lines
2.2 KiB
Python
48 lines
2.2 KiB
Python
# %%
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import requests
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import pandas as pd
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# %%
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def get_crypto_price_crypto_compare(symbol: str, exchange: str, days: int) -> pd.DataFrame:
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api_url = f'https://min-api.cryptocompare.com/data/v2/histoday?fsym={symbol}&tsym={exchange}&limit={days}&api_key={CC_API_KEY}'
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raw = requests.get(api_url).json()
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df = pd.DataFrame(raw['Data']['Data'])[['time', 'high', 'low', 'open', 'close']].set_index('time')
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df.index = pd.to_datetime(df.index, unit = 's')
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df.sort_index(inplace = True, ascending= True)
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return df
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ada = get_crypto_price_crypto_compare('ADA', 'USD', 1500)
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ada
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def get_crypto_price_av(symbol: str, exchange: str, start_date = None) -> pd.DataFrame:
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api_url = f'https://www.alphavantage.co/query?function=DIGITAL_CURRENCY_DAILY&symbol={symbol}&market={exchange}&apikey={AV_API_KEY}'
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raw_df = requests.get(api_url).json()
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df = pd.DataFrame(raw_df['Time Series (Digital Currency Daily)']).T
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df = df.rename(columns = {'1a. open (USD)': 'open', '2a. high (USD)': 'high', '3a. low (USD)': 'low', '4a. close (USD)': 'close', '5. volume': 'volume'})
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for i in df.columns:
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df[i] = df[i].astype(float)
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df.index = pd.to_datetime(df.index)
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df = df.iloc[::-1].drop(['1b. open (USD)', '2b. high (USD)', '3b. low (USD)', '4b. close (USD)', '6. market cap (USD)'], axis = 1)
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if start_date:
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df = df[df.index >= start_date]
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df.sort_index(inplace = True, ascending= True)
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return df
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def get_stock_price_av(symbol: str, start_date: str = None) -> pd.DataFrame:
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api_url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&outputsize=full&apikey={AV_API_KEY}'
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raw_df = requests.get(api_url).json()
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df = pd.DataFrame(raw_df['Time Series (Daily)']).T
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df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '5. adjusted close': 'close', '6. volume': 'volume'})
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for i in df.columns:
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df[i] = df[i].astype(float)
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df.index = pd.to_datetime(df.index)
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df = df.iloc[::-1].drop(['4. close', '7. dividend amount', '8. split coefficient'], axis = 1)
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if start_date:
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df = df[df.index >= start_date]
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df.sort_index(inplace = True, ascending= True)
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df = df.rename_axis('time')
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return df
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