mirror of
https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
7a443d93e4
* feat(Transformations): added robust scaler * fix(Config): set back default scaler to MinMax
75 lines
2.4 KiB
Python
75 lines
2.4 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(
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symbol: str, exchange: str, days: int
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) -> 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"])[
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["time", "high", "low", "open", "close"]
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].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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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(
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columns={
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"1a. open (USD)": "open",
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"2a. high (USD)": "high",
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"3a. low (USD)": "low",
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"4a. close (USD)": "close",
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"5. volume": "volume",
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}
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)
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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(
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[
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"1b. open (USD)",
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"2b. high (USD)",
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"3b. low (USD)",
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"4b. close (USD)",
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"6. market cap (USD)",
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],
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axis=1,
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)
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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(
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columns={
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"1. open": "open",
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"2. high": "high",
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"3. low": "low",
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"5. adjusted close": "close",
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"6. volume": "volume",
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}
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)
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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(
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["4. close", "7. dividend amount", "8. split coefficient"], axis=1
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)
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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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