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75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
"""FDScanner model"""
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__docformat__ = "numpy"
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import numpy as np
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import pandas as pd
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import requests
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def unusual_options(num: int, sort_column: str, ascending: bool):
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"""Get unusual option activity from fdscanner.com
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Parameters
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----------
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num: int
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Number to show
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sort_column: str
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Column to sort data by
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ascending: bool
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Flag to sort by ascending
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Returns
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-------
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df: pd.DataFrame
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Dataframe containing options information
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last_updated: pd.Timestamp
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Timestamp indicated when data was updated from website
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"""
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pages = np.arange(0, num // 20 + 1)
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data_list = []
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for page_num in pages:
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r = requests.get(
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f"https://app.fdscanner.com/api2/unusualvolume?p=0&page_size=20&page={int(page_num)}",
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headers={"User-Agent": "Mozilla/5.0"},
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)
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if r.status_code != 200:
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print("Error in fdscanner request")
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return pd.DataFrame(), "request error"
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data_list.append(r.json())
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ticker, expiry, option, ask, bid, oi, vol, voi = [], [], [], [], [], [], [], []
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for data in data_list:
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for entry in data["data"]:
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ticker.append(entry["tk"])
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expiry.append(entry["expiry"])
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option.append(str(entry["s"]) + " " + entry["t"])
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ask.append(entry["a"])
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bid.append(entry["b"])
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oi.append(entry["oi"])
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vol.append(entry["v"])
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voi.append(entry["vol/oi"])
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# Subtract an hour to align with NYSE timezone
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last_updated = pd.to_datetime(
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data_list[-1]["last_updated"], unit="s"
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) - pd.Timedelta(hours=1)
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df = pd.DataFrame(
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{
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"Ticker": ticker,
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"Exp": expiry,
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"Option": option,
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"Vol/OI": voi,
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"Vol": vol,
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"OI": oi,
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"Bid": bid,
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"Ask": ask,
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}
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)
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df = df.sort_values(by=sort_column, ascending=ascending)
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return df, last_updated
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