mirror of
https://github.com/OpenBB-finance/OpenBB.git
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237 lines
6.9 KiB
Python
237 lines
6.9 KiB
Python
"""TA Overlap View"""
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__docformat__ = "numpy"
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import logging
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import os
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from typing import List, Optional
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import matplotlib.pyplot as plt
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import mplfinance as mpf
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import pandas as pd
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from pandas.plotting import register_matplotlib_converters
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from openbb_terminal.config_terminal import theme
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from openbb_terminal.common.technical_analysis import overlap_model
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from openbb_terminal.config_plot import PLOT_DPI
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.helper_funcs import (
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export_data,
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plot_autoscale,
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reindex_dates,
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lambda_long_number_format_y_axis,
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is_valid_axes_count,
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)
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from openbb_terminal.rich_config import console
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logger = logging.getLogger(__name__)
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register_matplotlib_converters()
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@log_start_end(log=logger)
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def view_ma(
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data: pd.Series,
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window: List[int] = None,
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offset: int = 0,
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ma_type: str = "EMA",
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symbol: str = "",
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export: str = "",
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external_axes: Optional[List[plt.Axes]] = None,
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) -> None:
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"""Plots MA technical indicator
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Parameters
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----------
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data: pd.Series
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Series of prices
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window: List[int]
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Length of EMA window
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offset: int
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Offset variable
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ma_type: str
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Type of moving average. Either "EMA" "ZLMA" or "SMA"
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symbol: str
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Ticker
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export: str
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Format to export data
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external_axes: Optional[List[plt.Axes]], optional
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External axes (1 axis is expected in the list), by default None
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Examples
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--------
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>>> from openbb_terminal.sdk import openbb
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>>> df = openbb.stocks.load("AAPL")
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>>> openbb.ta.ma_chart(data=df["Adj Close"], symbol="AAPL", ma_type="EMA", window=[20, 50, 100])
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>>> from openbb_terminal.sdk import openbb
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>>> spuk_index = openbb.economy.index(indices = ["^SPUK"])
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>>> openbb.ta.ma_chart(data = spuk_index["^SPUK"], symbol = "S&P UK Index", ma_type = "EMA", window = [20, 50, 100])
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"""
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# Define a dataframe for adding EMA series to it
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price_df = pd.DataFrame(data)
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price_df.index.name = "date"
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l_legend = [symbol]
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if not window:
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window = [20, 50]
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for win in window:
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if ma_type == "EMA":
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df_ta = overlap_model.ema(data, win, offset)
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l_legend.append(f"EMA {win}")
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elif ma_type == "SMA":
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df_ta = overlap_model.sma(data, win, offset)
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l_legend.append(f"SMA {win}")
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elif ma_type == "WMA":
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df_ta = overlap_model.wma(data, win, offset)
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l_legend.append(f"WMA {win}")
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elif ma_type == "HMA":
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df_ta = overlap_model.hma(data, win, offset)
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l_legend.append(f"HMA {win}")
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elif ma_type == "ZLMA":
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df_ta = overlap_model.zlma(data, win, offset)
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l_legend.append(f"ZLMA {win}")
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price_df = price_df.join(df_ta)
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plot_data = reindex_dates(price_df)
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# This plot has 1 axis
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if external_axes is None:
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_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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elif is_valid_axes_count(external_axes, 1):
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(ax,) = external_axes
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else:
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return
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ax.plot(plot_data.index, plot_data.iloc[:, 1].values)
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ax.set_xlim([plot_data.index[0], plot_data.index[-1]])
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ax.set_ylabel(f"{symbol} Price")
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for idx in range(2, plot_data.shape[1]):
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ax.plot(plot_data.iloc[:, idx])
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ax.set_title(f"{symbol} {ma_type.upper()}")
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ax.legend(l_legend)
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theme.style_primary_axis(
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ax,
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data_index=plot_data.index.to_list(),
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tick_labels=plot_data["date"].to_list(),
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)
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if external_axes is None:
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theme.visualize_output()
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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f"{ma_type.lower()}{'_'.join([str(win) for win in window])}",
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price_df,
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)
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@log_start_end(log=logger)
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def view_vwap(
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data: pd.DataFrame,
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symbol: str = "",
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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offset: int = 0,
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interval: str = "",
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export: str = "",
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external_axes: Optional[List[plt.Axes]] = None,
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):
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"""Plots VWMA technical indicator
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Parameters
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----------
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data : pd.DataFrame
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Dataframe of OHLC prices
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symbol : str
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Ticker
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offset : int
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Offset variable
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start_date: Optional[str]
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Initial date, format YYYY-MM-DD
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end_date: Optional[str]
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Final date, format YYYY-MM-DD
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interval : str
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Interval of data
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export : str
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Format to export data
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external_axes : Optional[List[plt.Axes]], optional
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External axes (3 axes are expected in the list), by default None
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"""
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data.index = data.index.tz_localize(None)
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if start_date is None:
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start = data.index[0].date()
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console.print(f"No start date specified. Start date: {start}")
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else:
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start = start_date
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if end_date is None:
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end = data.index[-1].date()
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console.print(f"No end date specified. End date: {end}")
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else:
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end = end_date
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day_df = data[(start <= data.index.date) & (data.index.date <= end)]
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if len(day_df) == 0:
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console.print(
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f"[red]No data found between {start.strftime('%Y-%m-%d')} and {end.strftime('%Y-%m-%d')}\n[/red]"
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)
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return
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df_vwap = overlap_model.vwap(day_df, offset)
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candle_chart_kwargs = {
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"type": "candle",
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"style": theme.mpf_style,
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"volume": True,
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"xrotation": theme.xticks_rotation,
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"scale_padding": {"left": 0.3, "right": 1.2, "top": 0.8, "bottom": 0.8},
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"update_width_config": {
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"candle_linewidth": 0.6,
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"candle_width": 0.8,
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"volume_linewidth": 0.8,
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"volume_width": 0.8,
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},
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"warn_too_much_data": 10000,
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}
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# This plot has 2 axes
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if external_axes is None:
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candle_chart_kwargs["returnfig"] = True
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candle_chart_kwargs["figratio"] = (10, 7)
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candle_chart_kwargs["figscale"] = 1.10
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candle_chart_kwargs["figsize"] = plot_autoscale()
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candle_chart_kwargs["addplot"] = mpf.make_addplot(
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df_vwap, width=theme.line_width
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)
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fig, ax = mpf.plot(day_df, **candle_chart_kwargs)
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fig.suptitle(
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f"{symbol} {interval} VWAP",
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x=0.055,
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y=0.965,
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horizontalalignment="left",
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)
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lambda_long_number_format_y_axis(day_df, "Volume", ax)
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theme.visualize_output(force_tight_layout=False)
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elif is_valid_axes_count(external_axes, 3):
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(ax1, ax2, ax3) = external_axes
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candle_chart_kwargs["ax"] = ax1
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candle_chart_kwargs["volume"] = ax2
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candle_chart_kwargs["addplot"] = mpf.make_addplot(
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df_vwap, width=theme.line_width, ax=ax3
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)
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mpf.plot(day_df, **candle_chart_kwargs)
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else:
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return
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
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"VWAP",
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df_vwap,
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)
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