feature/heikin-ashi-candles: Adds a flag for Heikin Ashi Candles on stocks/candle

[deeleeramone]

Description

This PR adds a flag for showing Heikin Ashi candles from the /stocks/candle command.

• Summary of the change / bug fix.

Updates files:

• stocks_helper.py
• stocks_controller.py

Minor details are updated, such as this description.

  -p, --prepost         Pre/After market hours. Only works for 'yf' source, and intraday data (default: False)

(🦋) /stocks/ $ candle --help

usage: candle [-t TICKER] [-p] [--sort {open,high,low,close,adjclose,volume,dividends,stock_splits}] [-r] [--raw] [--trend] [--ma MOV_AVG] [--ha] [--log] [-h] [--export EXPORT]
              [--sheet-name SHEET_NAME [SHEET_NAME ...]] [-l LIMIT]

Shows historic price and volume for the asset.

options:
  -t TICKER, --ticker TICKER
                        Ticker to analyze. (default: None)
  -p, --prepost         Pre/After market hours. Only works for intraday data. (default: False)
  --sort {open,high,low,close,adjclose,volume,dividends,stock_splits}
                        Choose a column to sort by. Only works when raw data is displayed. (default: )
  -r, --reverse         Data is sorted in descending order by default. Reverse flag will sort it in an ascending way. Only works when raw data is displayed. (default: False)
  --raw                 Shows raw data instead of a chart. (default: False)
  --trend               Flag to add high and low trends to candle. (default: False)
  --ma MOV_AVG          Add moving average in number of days to plot and separate by a comma. Value for ma (moving average) keyword needs to be greater than 1. (default: None)
  --ha                  Flag to show Heikin Ashi candles. (default: False)
  --log                 Plot with y axis on log scale (default: False)
  -h, --help            show this help message (default: False)
  --export EXPORT       Export raw data into csv, json, xlsx and figure into png, jpg, pdf, svg (default: )
  --sheet-name SHEET_NAME [SHEET_NAME ...]
                        Name of excel sheet to save data to. Only valid for .xlsx files. (default: None)
  -l LIMIT, --limit LIMIT
                        Number of entries to show in data. (default: 20)

For more information and examples, use 'about candle' to access the related guide.

Also touched is the default title, removing the arbitrary default label, "Stock".

• Screenshot of the feature or the bug before/after fix, if applicable.

Regular:

/stocks/load AAPL -i 15 --start 2023-05-01/candle

Heikin Ashi:

candle --ha

• Relevant motivation and context.

• An option to smooth noise in a candle chart.

• This could become a synced preference in Hub that aligns with the Bot.

How has this been tested?

• Make sure affected commands still run in terminal
• Ensure the SDK still works

Signature:     
openbb.stocks.candle(
    symbol: str,
    data: Optional[pandas.core.frame.DataFrame] = None,
    add_trend: bool = False,
    ma: Optional[Iterable[int]] = None,
    asset_type: str = '',
    start_date: Union[datetime.datetime, str, NoneType] = None,
    interval: int = 1440,
    end_date: Union[datetime.datetime, str, NoneType] = None,
    prepost: bool = False,
    source: str = 'YahooFinance',
    weekly: bool = False,
    monthly: bool = False,
    ha: Optional[bool] = False,
    external_axes: bool = False,
    raw: bool = False,
    yscale: str = 'linear',
) -> Optional[openbb_terminal.core.plots.plotly_helper.OpenBBFigure]
Call signature: openbb.stocks.candle(*args: Any, **kwargs: Any) -> Any
Type:           display_candle
String form:    <openbb_terminal.stocks.stocks_helper.Operation object at 0x10559f340>
File:           ~/GitHub/OpenBBTerminal/openbb_terminal/stocks/stocks_helper.py
Docstring:     
Show candle plot of loaded ticker.

Checklist:

• I have adhered to the GitFlow naming convention and my branch name is in the format of feature/feature-name or hotfix/hotfix-name.
• Update our documentation following these guidelines. Update any user guides that are affected by the changes.
• Make sure you are following our CONTRIBUTING guidelines.
• If a feature was added make sure to add it to the corresponding integration test script.

Others

• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.

[tehcoderer]

if you want to use the code from bot you can do this

def heikin_ashi(df_data: pd.DataFrame) -> pd.DataFrame:
    """Attempts to calaculate heikin ashi based on given stock ticker data frame."""

    HA_df = df_data.copy()

    # Close column
    HA_df["Close"] = round(
        ((df_data["Open"] + df_data["High"] + df_data["Low"] + df_data["Close"]) / 4), 2
    )
    HA_df.iat[0, 0] = round(
        ((df_data["Open"].iloc[0] + df_data["Close"].iloc[0]) / 2), 2
    )

    # Open column
    for i in range(1, len(df_data)):
        HA_df.iat[i, 0] = round(((HA_df.iat[i - 1, 0] + HA_df.iat[i - 1, 3]) / 2), 2)

    # High and Low column
    HA_df["High"] = HA_df.loc[:, ["Open", "Close"]].join(df_data["High"]).max(axis=1)
    HA_df["Low"] = HA_df.loc[:, ["Open", "Close"]].join(df_data["Low"]).min(axis=1)

    return HA_df

has easy return

    data = heikin_ashi(data)

[deeleeramone]

if you want to use the code from bot you can do this

Pretty much the same thing as what pandas-ta does, except you are dividing, and he is multiplying:

def ha(open_, high, low, close, offset=None, **kwargs):
    """Candle Type: Heikin Ashi"""
    # Validate Arguments
    open_ = verify_series(open_)
    high = verify_series(high)
    low = verify_series(low)
    close = verify_series(close)
    offset = get_offset(offset)

    # Calculate Result
    m = close.size
    df = DataFrame({
        "HA_open": 0.5 * (open_.iloc[0] + close.iloc[0]),
        "HA_high": high,
        "HA_low": low,
        "HA_close": 0.25 * (open_ + high + low + close),
    })

    for i in range(1, m):
        df["HA_open"][i] = 0.5 * (df["HA_open"][i - 1] + df["HA_close"][i - 1])

    df["HA_high"] = df[["HA_open", "HA_high", "HA_close"]].max(axis=1)
    df["HA_low"] = df[["HA_open", "HA_low", "HA_close"]].min(axis=1)

    # Offset
    if offset != 0:
        df = df.shift(offset)

    # Handle fills
    if "fillna" in kwargs:
        df.fillna(kwargs["fillna"], inplace=True)
    if "fill_method" in kwargs:
        df.fillna(method=kwargs["fill_method"], inplace=True)

    # Name and Categorize it
    df.name = "Heikin-Ashi"
    df.category = "candles"

    return df