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b71abcfbf4
* quick econ fix * black * keys and feature flags * terminal name 👀 * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
147 lines
3.4 KiB
Python
147 lines
3.4 KiB
Python
"""Overlap Technical Analysis"""
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___docformat__ = "numpy"
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import logging
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import pandas as pd
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import pandas_ta as ta
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from openbb_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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WINDOW_LENGTHS = [20, 50]
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WINDOW_LENGTHS2 = [10, 20]
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@log_start_end(log=logger)
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def ema(values: pd.DataFrame, length: int, offset: int) -> pd.DataFrame:
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"""Gets exponential moving average (EMA) for stock
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Parameters
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----------
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values: pd.DataFrame
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Dataframe of dates and prices
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length: int
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Length of EMA window
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offset: int
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Length of offset
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Returns
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----------
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pd.DataFrame
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Dataframe containing prices and EMA
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"""
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return pd.DataFrame(ta.ema(values, length=length, offset=offset)).dropna()
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@log_start_end(log=logger)
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def sma(values: pd.DataFrame, length: int, offset: int) -> pd.DataFrame:
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"""Gets simple moving average (EMA) for stock
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Parameters
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----------
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values: pd.DataFrame
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Dataframe of dates and prices
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length: int
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Length of SMA window
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offset: int
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Length of offset
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Returns
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----------
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pd.DataFrame
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Dataframe containing prices and SMA
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"""
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return pd.DataFrame(ta.sma(values, length=length, offset=offset)).dropna()
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@log_start_end(log=logger)
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def wma(values: pd.DataFrame, length: int, offset: int) -> pd.DataFrame:
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"""Gets weighted moving average (WMA) for stock
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Parameters
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----------
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values: pd.DataFrame
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Dataframe of dates and prices
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length: int
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Length of SMA window
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offset: int
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Length of offset
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Returns
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----------
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df_ta: pd.DataFrame
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Dataframe containing prices and WMA
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"""
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return pd.DataFrame(ta.wma(values, length=length, offset=offset)).dropna()
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@log_start_end(log=logger)
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def hma(values: pd.DataFrame, length: int, offset: int) -> pd.DataFrame:
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"""Gets hull moving average (HMA) for stock
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Parameters
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----------
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values: pd.DataFrame
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Dataframe of dates and prices
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length: int
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Length of SMA window
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offset: int
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Length of offset
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Returns
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----------
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df_ta: pd.DataFrame
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Dataframe containing prices and HMA
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"""
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return pd.DataFrame(ta.hma(values, length=length, offset=offset)).dropna()
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@log_start_end(log=logger)
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def zlma(values: pd.DataFrame, length: int, offset: int) -> pd.DataFrame:
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"""Gets zero-lagged exponential moving average (ZLEMA) for stock
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Parameters
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----------
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data: pd.DataFrame
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Dataframe of dates and prices
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length: int
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Length of EMA window
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offset: int
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Length of offset
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Returns
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----------
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df_ta: pd.DataFrame
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Dataframe containing prices and EMA
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"""
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return pd.DataFrame(ta.zlma(values, length=length, offset=offset)).dropna()
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@log_start_end(log=logger)
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def vwap(day_df: pd.DataFrame, offset: int) -> pd.DataFrame:
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"""Gets volume weighted average price (VWAP)
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Parameters
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----------
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day_df: pd.DataFrame
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Dataframe of dates and prices for the last trading day
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offset: int
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Length of offset
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Returns
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----------
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df_vwap: pd.DataFrame
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Dataframe with VWAP data
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"""
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df_vwap = ta.vwap(
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high=day_df["High"],
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low=day_df["Low"],
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close=day_df["Close"],
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volume=day_df["Volume"],
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offset=offset,
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)
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return pd.DataFrame(df_vwap)
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