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mplfinance

7.1.2

mplfinance

Last updated 2020-02-12 Read time 3 min

mplfinance is an extension to matplotlib for visualising financial data. In this article we create graphs from financial data using mplfinance. The basic usage is explained in the Tutorials section of the mplfinance repository.

Loading Data #

Pandas-datareader | Remote Data Access

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import os
import time
import pandas as pd
import pandas_datareader.data as web
from pandas_datareader._utils import RemoteDataError

Yahoo Terms of Service

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def get_finance_data(
    ticker_symbol: str, start="2021-01-01", end="2021-06-30", savedir="data"
) -> pd.DataFrame:
    """Retrieves data recording stock prices

    Args:
        ticker_symbol (str): Description of param1
        start (str): Date of beginning of period, optional.
        end (str): Date of end of period, optional.

    Returns:
        res: Stock Price Data

    """
    res = None
    filepath = os.path.join(savedir, f"{ticker_symbol}_{start}_{end}_historical.csv")
    os.makedirs(savedir, exist_ok=True)

    if not os.path.exists(filepath):
        try:
            time.sleep(5.0)
            res = web.DataReader(ticker_symbol, "yahoo", start=start, end=end)
            res.to_csv(filepath, encoding="utf-8-sig")
        except (RemoteDataError, KeyError):
            print(f"ticker_symbol ${ticker_symbol} が正しいか確認してください。")
    else:
        res = pd.read_csv(filepath, index_col="Date")
        res.index = pd.to_datetime(res.index)

    assert res is not None, "データ取得に失敗しました"
    return res

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# ticker symbol, period, and destination file
ticker_symbol = "NVDA"
start = "2021-01-01"
end = "2021-06-30"

df = get_finance_data(ticker_symbol, start=start, end=end, savedir="../data")
df.head()

HighLowOpenCloseVolumeAdj Close
Date
2020-12-31131.509995129.149994131.365005130.55000319242400.0130.413864
2021-01-04136.524994129.625000131.042496131.13499556064000.0130.998245
2021-01-05134.434998130.869995130.997498134.04750132276000.0133.907700
2021-01-06132.449997125.860001132.225006126.14499758042400.0126.013443
2021-01-07133.777496128.865005129.675003133.44000246148000.0133.300842

Plotting OHLC #

OHLC is the opening price, high, low, and close, and is one of the graphs we usually look at most often. Let’s plot it as a candlestick and as a line.

mplfinance | matplotlib utilities for the visualization, and visual analysis, of financial data

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import mplfinance as mpf

mpf.plot(df, type="candle", style="starsandstripes", figsize=(12, 4))
mpf.plot(df, type="line", style="starsandstripes", figsize=(12, 4))

Plotting OHLC figure

Plotting OHLC figure

Moving Average #

One of the indicators used in technical analysis of stock prices and foreign exchange is the moving average. In current technical analysis, there are many examples where three moving averages (short, medium, and long term) are displayed simultaneously. In daily charts, the 5-day, 25-day, and 75-day moving averages are often used.

Specify the mav option to plot the 5/25/75 day moving average.

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mpf.plot(df, type="candle", style="starsandstripes", figsize=(12, 4), mav=[5, 25, 75])

Specify the mav option to plot the 5/25/75 day moving averag… figure

Show legend #

Add a legend because it is difficult to tell which line represents a moving average over what number of days.

matplotlib.pyplot.legend

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import japanize_matplotlib
import matplotlib.patches as mpatches

fig, axes = mpf.plot(
    df,
    type="candle",
    style="starsandstripes",
    figsize=(12, 4),
    mav=[5, 25, 75],
    returnfig=True,
)
fig.legend(
    [f"{days} days" for days in [5, 25, 75]], bbox_to_anchor=(0.0, 0.78, 0.28, 0.102)
)

<matplotlib.legend.Legend at 0x7f8be96e5b80>

Show legend figure

Displaying Volume #

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fig, axes = mpf.plot(
    df,
    title="NVDA 2021/1/B~2021/6/E",
    type="candle",
    style="starsandstripes",
    figsize=(12, 4),
    mav=[5, 25, 75],
    volume=True,
    datetime_format="%Y/%m/%d",
    returnfig=True,
)
fig.legend(
    [f"{days} days" for days in [5, 25, 75]], bbox_to_anchor=(0.0, 0.78, 0.28, 0.102)
)

Displaying Volume figure

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fig, axes = mpf.plot(
    df,
    title="NVDA 2021/1/B~2021/6/E",
    type="candle",
    style="yahoo",
    figsize=(12, 4),
    mav=[5, 25, 75],
    volume=True,
    datetime_format="%Y/%m/%d",
    returnfig=True,
)
fig.legend(
    [f"{days} days" for days in [5, 25, 75]], bbox_to_anchor=(0.0, 0.78, 0.28, 0.102)
)

Displaying Volume figure