To analyze a company’s stock price data, time series analysis on daily stock data is essential. Sectors provides this daily stock data, enabling you to conduct such an analysis. One common method used in time series analysis is the Simple Moving Average (SMA). The SMA is a calculation that takes the arithmetic mean of a given set of prices over the specific number of days in the past; for example, over the previous 15, 30, 100, or 200 days. By using Sectors, you can effectively build your time series analysis using daily stock data.

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Install Required Libraries

  • Install the requests library to make HTTP Requests
  • Install pandas to do some data exploratory
  • In this recipe we will use altair to do the data visualization, if you are unfamiliar with altair, try to watch this playlist Altair Tutorial
  • This is not a library, but you will need your Sector API Keys for this recipe

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Data Exploration

In this recipe, we will use the Daily Transaction Data API from Sectors. Let’s get the Daily Data of BBCA from January 2024 to March 2024. 
import requests
import altair as alt
import pandas as pd

API_KEYS = "Your API Keys"

headers = {
    "Authorization": API_KEYS
}
response = requests.get("https://api.sectors.app/v1/daily/BBCA/?start=2024-01-01&end=2024-03-31", headers = headers)

if response.status_code == 200:
    daily_data = response.json()
else:
	# Handle error
	print("Error Status :",response.status_code)
If you try to print the daily_data it should look like the following: 
[
  {'symbol': 'BBCA.JK',
  'date': '2024-01-02',
  'close': 9425,
  'volume': 30545200,
  'market_cap': 1161866902503424},
 {'symbol': 'BBCA.JK',
  'date': '2024-01-03',
  'close': 9350,
  'volume': 34603300,
  'market_cap': 1152621314310144},
 {'symbol': 'BBCA.JK',
  'date': '2024-01-04',
  'close': 9475,
  'volume': 56501800,
  'market_cap': 1168030583226368},
  ...
]
As you can see, there are 5 properties we can get from the daily data, there are:
  • Symbol, the symbol that represent a company
  • Date, the date of transaction
  • Close, the closing price of that day
  • Volume, transaction volume of that day
  • Market Cap, the market cap of that day
Now using the closing price and date we can visualize it into a time series visualization.

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Data Visualization

Before visualize the data, let’s transform the data to Pandas DataFrame. 
df = pd.DataFrame(daily_data, columns=["symbol","date","close","volume","market_cap"])

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Time Series Visualization

Now let’s visualize the closing price data using altair: 
chart = alt.Chart(df).mark_line(size=3).encode(
    x=alt.X('date:T', axis=alt.Axis(title='Date')),
    y=alt.Y('close:Q', axis=alt.Axis(title='Close Price'), scale=alt.Scale(zero=False)),
    tooltip=['date:T', 'close:Q'],
    color=alt.value('darkgreen')
).properties(
    width=800,
    height=400
)

chart.display()
Daily Time Series Now we see the time series visualization on BBCA daily transaction data from January 2024 to March 2024.

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Simple Moving Average(SMA)

It’s good if you already familiar with SMA method for time series analysis, but no worries if you haven’t, take a look at this SMA Article. Let’s use 3-Day SMA to the chart: 
df['sma_3'] = round(df.iloc[:,2].rolling(window=3).mean(),2)

chart = alt.Chart(df)

close_price = chart.mark_line(size=3).encode(
    x=alt.X('date:T', axis=alt.Axis(title='Date')),
    y=alt.Y('close:Q', axis=alt.Axis(title='Close Price'), scale=alt.Scale(zero=False)),
    tooltip=['date:T', 'close:Q'],
    color=alt.value('darkgreen')
).properties(
    width=800,
    height=400
    )

sma_3 = chart.mark_line().encode(
    x=alt.X('date:T', axis=alt.Axis(title='Date')),
    y=alt.Y('sma_3:Q', axis=alt.Axis(title='SMA 3')),
    tooltip=['date:T', 'sma_3:Q'],
    color=alt.value('lightblue')
)

close_price + sma_3
Daily Time Series SMA 3 We’ve successfully incorporated a 3-day Simple Moving Average (SMA) into our chart, demonstrating the flexibility of our visualization tools. The Sectors Financial API simplifies the process of obtaining IDX Stocks data, providing daily transaction data that can be used to create a wide variety of visualizations. With this data, you can track trends, compare performance, and analyze market movements, tailoring your visualizations to meet your specific needs and objectives.