strategy-lab/to_explore/pyquantnews/35_TechnicalAnalysis.ipynb

This notebook demonstrates the use of technical indicators for stock analysis using OpenBB Terminal and TA-Lib. It loads historical stock prices for Apple (AAPL) and calculates Bollinger Bands (BBANDS), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD). The data is then visualized to provide insights into stock price trends and potential trading signals. This analysis is useful for investors and traders to make informed decisions based on technical analysis.

In [ ]:

import matplotlib.pyplot as plt
import pandas as pd
from talib import RSI, BBANDS, MACD

In [ ]:

from openbb_terminal.sdk import openbb
from openbb_terminal.sdk import TerminalStyle
theme = TerminalStyle("light", "light", "light")

Load historical stock data for Apple (AAPL) and rename 'Adj Close' to 'close'

In [ ]:

data = (
    openbb
    .stocks
    .load("AAPL", start_date="2020-01-01")
    .rename(columns={"Adj Close": "close"})
)

Calculate Bollinger Bands (BBANDS) for the close prices with a 21-day period

In [ ]:

up, mid, low = BBANDS(
    data.close, 
    timeperiod=21, 
    nbdevup=2, 
    nbdevdn=2, 
    matype=0
)

Calculate Relative Strength Index (RSI) for the close prices with a 14-day period

In [ ]:

rsi = RSI(data.close, timeperiod=14)

Calculate Moving Average Convergence Divergence (MACD) for the close prices with standard parameters

In [ ]:

macd, macdsignal, macdhist = MACD(
    data.close, 
    fastperiod=12, 
    slowperiod=26, 
    signalperiod=9
)

Create a DataFrame for the MACD components

In [ ]:

macd = pd.DataFrame(
    {
        "MACD": macd,
        "MACD Signal": macdsignal,
        "MACD History": macdhist,
    }
)

Create a DataFrame for the stock data and technical indicators

In [ ]:

data = pd.DataFrame(
    {
        "AAPL": data.close,
        "BB Up": up,
        "BB Mid": mid,
        "BB down": low,
        "RSI": rsi,
    }
)

Plot the Bollinger Bands, RSI, and MACD indicators in subplots

In [ ]:

fig, axes = plt.subplots(
    nrows=3,
    figsize=(15, 10),
    sharex=True
)

Plot Bollinger Bands and close prices

In [ ]:

data.drop(["RSI"], axis=1).plot(
    ax=axes[0],
    lw=1,
    title="BBANDS"
)

Plot RSI and add horizontal lines at 70 and 30 to indicate overbought and oversold levels

In [ ]:

data["RSI"].plot(
    ax=axes[1],
    lw=1,
    title="RSI"
)
axes[1].axhline(70, lw=1, ls="--", c="k")
axes[1].axhline(30, lw=1, ls="--", c="k")

Plot MACD components

In [ ]:

macd.plot(
    ax=axes[2],
    lw=1,
    title="MACD",
    rot=0
)
axes[2].set_xlabel("")
fig.tight_layout()

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