mirror of
https://github.com/OpenBB-finance/OpenBB.git
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dcf3bfe1f7
* Handle no adj close * Added fixes for bad data handling * Finished switching model layout * Fixed all the bugs * Big fixes * Handle a lot more bugs * A couple more enhancements * Fixed issue Co-authored-by: Henrique Joaquim <h.joaquim@campus.fct.unl.pt> Co-authored-by: James Maslek <jmaslek11@gmail.com>
95 lines
2.6 KiB
Python
95 lines
2.6 KiB
Python
"""Custom Indicator Models"""
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__docformat__ = "numpy"
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import logging
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from typing import Any, Tuple
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import pandas as pd
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.rich_config import console
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from openbb_terminal.common.technical_analysis import ta_helpers
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def calculate_fib_levels(
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data: pd.DataFrame,
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limit: int = 120,
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start_date: Any = None,
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end_date: Any = None,
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) -> Tuple[pd.DataFrame, pd.Timestamp, pd.Timestamp, float, float]:
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"""Calculate Fibonacci levels
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Parameters
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----------
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data : pd.DataFrame
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Dataframe of prices
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limit : int
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Days to look back for retracement
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start_date : Any
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Custom start date for retracement
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end_date : Any
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Custom end date for retracement
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Returns
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-------
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df : pd.DataFrame
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Dataframe of fib levels
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min_date: pd.Timestamp
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Date of min point
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max_date: pd.Timestamp:
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Date of max point
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min_pr: float
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Price at min point
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max_pr: float
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Price at max point
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"""
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close_col = ta_helpers.check_columns(data, high=False, low=False)
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if close_col is None:
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return pd.DataFrame(), pd.Timestamp(), pd.Timestamp(), 0, 0
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if start_date and end_date:
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if start_date not in data.index:
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date0 = data.index[data.index.get_loc(start_date, method="nearest")]
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console.print(f"Start date not in data. Using nearest: {date0}")
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else:
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date0 = start_date
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if end_date not in data.index:
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date1 = data.index[data.index.get_loc(end_date, method="nearest")]
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console.print(f"End date not in data. Using nearest: {date1}")
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else:
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date1 = end_date
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data0 = data.loc[date0, close_col]
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data1 = data.loc[date1, close_col]
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min_pr = min(data0, data1)
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max_pr = max(data0, data1)
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if min_pr == data0:
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min_date = date0
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max_date = date1
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else:
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min_date = date1
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max_date = date0
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else:
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data_to_use = data.iloc[limit:][close_col]
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min_pr = data_to_use.min()
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min_date = data_to_use.idxmin()
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max_pr = data_to_use.max()
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max_date = data_to_use.idxmax()
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fib_levels = [0, 0.235, 0.382, 0.5, 0.618, 0.65, 1]
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price_dif = max_pr - min_pr
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levels = [round(max_pr - price_dif * f_lev, 2) for f_lev in fib_levels]
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df = pd.DataFrame()
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df["Level"] = fib_levels
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df["Level"] = df["Level"].apply(lambda x: str(x * 100) + "%")
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df["Price"] = levels
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return df, min_date, max_date, min_pr, max_pr
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