mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-30 05:52:23 +00:00
963ca9b2b9
* Initial commit * Finalized functionality * update script * Allow using it without forecasting * Update gitignore * Update `sdk_audit.py` * Fixed issues, found more * Added fix for helper functions, and column for SDK type * Checked one more thing * Moved file * Move files ending with models/views * Added fix of name * Added file path fixes * Patch to fix sdk_audit for windows * fix Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
289 lines
8.7 KiB
Python
289 lines
8.7 KiB
Python
import os
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from datetime import datetime
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import logging
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import pyEX
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import requests
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import pandas as pd
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import yfinance as yf
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from alpha_vantage.timeseries import TimeSeries
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from openbb_terminal.decorators import check_api_key
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from openbb_terminal.rich_config import console
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from openbb_terminal import config_terminal as cfg
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# pylint: disable=unsupported-assignment-operation,no-member
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logger = logging.getLogger(__name__)
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def load_stock_av(
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symbol: str, start_date: datetime, end_date: datetime
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) -> pd.DataFrame:
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try:
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ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas")
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df_stock_candidate: pd.DataFrame = ts.get_daily_adjusted(
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symbol=symbol, outputsize="full"
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)[0]
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except Exception as e:
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console.print(e)
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return pd.DataFrame()
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df_stock_candidate.columns = [
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val.split(". ")[1].capitalize() for val in df_stock_candidate.columns
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]
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df_stock_candidate = df_stock_candidate.rename(
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columns={"Adjusted close": "Adj Close"}
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)
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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console.print("No data found.")
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return pd.DataFrame()
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df_stock_candidate.index = df_stock_candidate.index.tz_localize(None)
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df_stock_candidate.sort_index(ascending=True, inplace=True)
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# Slice dataframe from the starting date YYYY-MM-DD selected
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df_stock_candidate = df_stock_candidate[
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(df_stock_candidate.index >= start_date.strftime("%Y-%m-%d"))
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& (df_stock_candidate.index <= end_date.strftime("%Y-%m-%d"))
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]
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return df_stock_candidate
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def load_stock_yf(
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symbol: str, start_date: datetime, end_date: datetime, weekly: bool, monthly: bool
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) -> pd.DataFrame:
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# TODO: Better handling of interval with week/month
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int_ = "1d"
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int_string = "Daily"
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if weekly:
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int_ = "1wk"
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int_string = "Weekly"
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if monthly:
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int_ = "1mo"
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int_string = "Monthly"
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# Win10 version of mktime cannot cope with dates before 1970
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if os.name == "nt" and start_date < datetime(1970, 1, 1):
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start_date = datetime(
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1970, 1, 2
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) # 1 day buffer in case of timezone adjustments
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# Adding a dropna for weekly and monthly because these include weird NaN columns.
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df_stock_candidate = yf.download(
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symbol, start=start_date, end=end_date, progress=False, interval=int_
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).dropna(axis=0)
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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return pd.DataFrame()
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df_stock_candidate.index.name = "date", int_string
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return df_stock_candidate
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def load_stock_eodhd(
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symbol: str, start_date: datetime, end_date: datetime, weekly: bool, monthly: bool
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) -> pd.DataFrame:
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int_ = "d"
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if weekly:
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int_ = "w"
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elif monthly:
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int_ = "m"
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request_url = (
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f"https://eodhistoricaldata.com/api/eod/"
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f"{symbol.upper()}?"
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f"{start_date.strftime('%Y-%m-%d')}&"
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f"to={end_date.strftime('%Y-%m-%d')}&"
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f"period={int_}&"
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f"api_token={cfg.API_EODHD_KEY}&"
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f"fmt=json&"
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f"order=d"
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)
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r = requests.get(request_url)
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if r.status_code != 200:
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console.print("[red]Invalid API Key for eodhistoricaldata [/red]")
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console.print(
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"Get your Key here: https://eodhistoricaldata.com/r/?ref=869U7F4J"
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)
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return pd.DataFrame()
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r_json = r.json()
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df_stock_candidate = pd.DataFrame(r_json).dropna(axis=0)
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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console.print("No data found from End Of Day Historical Data.")
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return df_stock_candidate
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df_stock_candidate = df_stock_candidate[
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["date", "open", "high", "low", "close", "adjusted_close", "volume"]
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]
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df_stock_candidate = df_stock_candidate.rename(
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columns={
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"date": "Date",
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"close": "Close",
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"high": "High",
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"low": "Low",
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"open": "Open",
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"adjusted_close": "Adj Close",
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"volume": "Volume",
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}
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)
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df_stock_candidate["Date"] = pd.to_datetime(df_stock_candidate.Date)
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df_stock_candidate.set_index("Date", inplace=True)
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df_stock_candidate.sort_index(ascending=True, inplace=True)
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return df_stock_candidate
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@check_api_key(["API_IEX_TOKEN"])
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def load_stock_iex_cloud(symbol: str, iexrange: str) -> pd.DataFrame:
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df_stock_candidate = pd.DataFrame()
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try:
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client = pyEX.Client(api_token=cfg.API_IEX_TOKEN, version="v1")
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df_stock_candidate = client.chartDF(symbol, timeframe=iexrange)
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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console.print("No data found.")
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return df_stock_candidate
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except Exception as e:
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if "The API key provided is not valid" in str(e):
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console.print("[red]Invalid API Key[/red]")
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else:
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console.print(e)
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return df_stock_candidate
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df_stock_candidate = df_stock_candidate[
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["close", "fHigh", "fLow", "fOpen", "fClose", "volume"]
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]
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df_stock_candidate = df_stock_candidate.rename(
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columns={
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"close": "Close",
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"fHigh": "High",
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"fLow": "Low",
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"fOpen": "Open",
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"fClose": "Adj Close",
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"volume": "Volume",
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}
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)
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df_stock_candidate.sort_index(ascending=True, inplace=True)
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return df_stock_candidate
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@check_api_key(["API_POLYGON_KEY"])
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def load_stock_polygon(
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symbol: str, start_date: datetime, end_date: datetime, weekly: bool, monthly: bool
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) -> pd.DataFrame:
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# Polygon allows: day, minute, hour, day, week, month, quarter, year
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timespan = "day"
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if weekly or monthly:
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timespan = "week" if weekly else "month"
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request_url = (
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f"https://api.polygon.io/v2/aggs/ticker/"
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f"{symbol.upper()}/range/1/{timespan}/"
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f"{start_date.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}?adjusted=true"
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f"&sort=desc&limit=49999&apiKey={cfg.API_POLYGON_KEY}"
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)
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r = requests.get(request_url)
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if r.status_code != 200:
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console.print("[red]Error in polygon request[/red]")
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return pd.DataFrame()
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r_json = r.json()
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if "results" not in r_json.keys():
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console.print("[red]No results found in polygon reply.[/red]")
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return pd.DataFrame()
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df_stock_candidate = pd.DataFrame(r_json["results"])
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df_stock_candidate = df_stock_candidate.rename(
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columns={
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"o": "Open",
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"c": "Adj Close",
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"h": "High",
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"l": "Low",
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"t": "date",
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"v": "Volume",
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"n": "Transactions",
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}
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)
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df_stock_candidate["date"] = pd.to_datetime(df_stock_candidate.date, unit="ms")
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# TODO: Clean up Close vs Adj Close throughout
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df_stock_candidate["Close"] = df_stock_candidate["Adj Close"]
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df_stock_candidate = df_stock_candidate.sort_values(by="date")
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df_stock_candidate = df_stock_candidate.set_index("date")
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return df_stock_candidate
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def load_quote(symbol: str) -> pd.DataFrame:
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"""Ticker quote. [Source: YahooFinance]
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Parameters
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----------
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symbol : str
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Ticker
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"""
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ticker = yf.Ticker(symbol)
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try:
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quote_df = pd.DataFrame(
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[
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{
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"Symbol": ticker.info["symbol"],
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"Name": ticker.info["shortName"],
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"Price": ticker.info["regularMarketPrice"],
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"Open": ticker.info["regularMarketOpen"],
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"High": ticker.info["dayHigh"],
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"Low": ticker.info["dayLow"],
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"Previous Close": ticker.info["previousClose"],
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"Volume": ticker.info["volume"],
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"52 Week High": ticker.info["fiftyTwoWeekHigh"],
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"52 Week Low": ticker.info["fiftyTwoWeekLow"],
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}
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]
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)
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quote_df["Change"] = quote_df["Price"] - quote_df["Previous Close"]
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quote_df["Change %"] = quote_df.apply(
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lambda x: f'{((x["Change"] / x["Previous Close"]) * 100):.2f}%',
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axis="columns",
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)
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for c in [
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"Price",
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"Open",
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"High",
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"Low",
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"Previous Close",
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"52 Week High",
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"52 Week Low",
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"Change",
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]:
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quote_df[c] = quote_df[c].apply(lambda x: f"{x:.2f}")
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quote_df["Volume"] = quote_df["Volume"].apply(lambda x: f"{x:,}")
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quote_df = quote_df.set_index("Symbol")
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quote_data = quote_df.T
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return quote_data
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except KeyError:
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logger.exception("Invalid stock ticker")
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console.print(f"Invalid stock ticker: {symbol}")
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return pd.DataFrame()
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