Technical Analysis with Python & Ploty:  Moving Averages

import yfinance as yf import pandas as pd

# Download stock data for BRIS.JK from Yahoo Finance
df = yf.download("BRIS.JK", start="2022-05-22", end="2024-05-22")

# Display the first few rows of the dataframe to understand its structure
print(df.head())

The yfinance library is a popular tool for downloading historical market data from Yahoo Finance. The yf.download function fetches the data for a specified ticker symbol and date range, and returns it as a Pandas DataFrame. A Pandas DataFrame is a powerful data structure that allows easy manipulation and analysis of the data.

Creating a Candlestick Chart

A candlestick chart is a popular way to visualize stock data. Here’s how to create one using Plotly:

import plotly.graph_objects as go 
# Create the candlestick chart 
fig = go.Figure(data=[go.Candlestick( 
    x=df.index, # Dates for the x-axis 
    open=df['Open'], # Opening prices 
    high=df['High'], # Highest prices 
    low=df['Low'], # Lowest prices 
   close=df['Close'] # Closing prices 
)]) 

# Update the layout of the chart 
fig.update_layout( title='BRIS.JK Stock Candlestick Chart', xaxis_title='Date', yaxis_title='Price (IDR)', autosize=True, height=600) 
# Display the chart 
fig.show()

Adding Technical Indicators

To enhance our analysis, we can add technical indicators like moving averages. Here’s how to add the 5-day (SMA5) and 20-day (SMA20) Simple Moving Averages:

# Calculate the 5-day and 20-day SMAs
df['SMA5'] = df['Close'].rolling(window=5).mean()
df['SMA20'] = df['Close'].rolling(window=20).mean()

# Add the SMAs to the chart
fig.add_trace(go.Scatter(x=df.index, y=df['SMA5'], mode='lines', name='5-Day SMA'))
fig.add_trace(go.Scatter(x=df.index, y=df['SMA20'], mode='lines', name='20-Day SMA'))

# Display the updated chart
fig.show()

We can identify potential buy / sell signals based on the crossover of SMA5 and SMA20.

Specifically, we can buy the stocks when the fast moving average (5-day SMA) crosses up the slow moving average (20-day SMA), and we can sell the stocks when the 5-day SMA crosses down the 20-day SMA.

# Highlight the buy signals
cross_buy = df[(df['SMA5'] > df['SMA20']) & (df['SMA5'].shift(1) <= df['SMA20'].shift(1))]

fig.add_trace(go.Scatter(
    x=cross_buy.index,
    y=cross_buy['Low'] - 75,  # Move marker to bottom with padding
    mode='markers+text',
    marker=dict(color='green', size=10, symbol='triangle-up'),
    text=["Buy Here"] * len(cross_buy),  # Add text annotations
    textposition="bottom center",
    textfont=dict(size=12),
    name='Buy Signal'
))

# Highlight the sell signals
cross_sell = df[(df['SMA5'] < df['SMA20']) & (df['SMA5'].shift(1) >= df['SMA20'].shift(1))]

fig.add_trace(go.Scatter(
    x=cross_sell.index,
    y=cross_sell['High'] + 75,  # Move marker to top with padding
    mode='markers+text',
    marker=dict(color='red', size=10, symbol='triangle-down'),
    text=["Sell Here"] * len(cross_sell),  # Add text annotations
    textposition="top center",
    textfont=dict(size=12),
    name='Sell Signal'
))

# Update the layout with the new traces
fig.update_layout(
    title='BRIS.JK Stock Candlestick Chart with 5 and 20-Day SMA Buy/Sell Signals',
    xaxis_title='Date',
    yaxis_title='Price (IDR)',
    autosize=True,
    height=800,
)

# Show the chart with buy signals
fig.show()