How to analyze ABAT stock price and volume with Python

How to Analyze ABAT Stock Using Python

ABAT stock is straightforward to analyze with Python if you want to inspect its price history and trading volume yourself. ABAT is American Battery Technology Company, and it has attracted many individual investors interested in the U.S. battery materials business. This guide shows you how to download ABAT data in Python and turn it into charts and simple measurements. If you're new to Python for market analysis, the course Getting Started with Python for Quant Finance explains the basics before you try the examples here. You can also read this guide on how to download and clean free market data for more detail on getting price data into Python.

You don't need a paid data subscription. The yfinance library downloads historical price data for free from Yahoo Finance. Before you run any code, install the libraries you'll need.

pip install yfinance pandas plotly

Pulling ABAT Stock Data with Python

First, download the historical price data. The code below pulls two years of daily ABAT data. That includes the opening price, highest price, lowest price, closing price, and the number of shares traded each day.

import yfinance as yf
import pandas as pd

abat = yf.download("ABAT", period="2y", interval="1d")

print(abat.tail())

This returns a pandas DataFrame, which is a table with labeled rows and columns, similar to a spreadsheet. Each row represents one trading day. The Close column is the price when the market closed that day, and the Volume column shows how many shares changed hands.

One thing to watch for is stock splits, where the company divides existing shares into more shares at a lower price. When that happens, raw historical close prices can look misleading. Check whether your downloaded data includes an Adj Close column, which adjusts past prices so they're comparable to current ones. Use that column when it's available.

Large jumps in trading volume often happen on days when the stock price moves sharply. That's common in smaller public companies like ABAT. Check trading volume next to price. If the stock jumps on very low volume, fewer shares changed hands, so the move may be less reliable than a jump backed by heavy trading.

Visualizing the ABAT Stock Price History

A chart shows patterns that a table of numbers can't. You can build an interactive price chart with the plotly library in a few lines.

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=abat.index,
    y=abat["Close"],
    mode="lines",
    name="ABAT Close Price"
))

fig.update_layout(
    title="ABAT Stock Price (2-Year History)",
    xaxis_title="Date",
    yaxis_title="Price (USD)"
)

fig.show()

This produces a chart you can zoom and pan. ABAT often has large price swings. Zoom in on dates around earnings reports or industry news if you want to see whether those events lined up with major price moves.

Calculating Rolling Averages and Recent Price Changes for ABAT Stock

A rolling average reduces the effect of daily price swings by averaging the closing price over a fixed number of past trading days. A 20-day rolling average, for example, gives you the average close over the last 20 sessions. When the current price stays above that average, recent price movement has been strong. When it drops below, that strength may be fading.

You can also measure how much the stock has moved over the last 20 days as a percentage. This tells you the recent direction and size of the move.

abat["MA_20"] = abat["Close"].rolling(window=20).mean()
abat["MA_50"] = abat["Close"].rolling(window=50).mean()
abat["Return_20D"] = abat["Close"].pct_change(20)

print(abat[["Close", "MA_20", "MA_50", "Return_20D"]].tail(10))

Here's how to read one row of that output. If Close is above both MA_20 and MA_50, the stock is trading higher than its recent averages, which suggests upward movement. If Close is below both, the opposite is true. The Return_20D column shows the percentage change over 20 trading days, so a value of -0.15 means the stock fell 15% in that period.

Comparing the 20-day average with the 50-day average shows whether recent price moves are stronger or weaker than the stock's behavior over a longer period. ABAT has had fast price increases followed by long declines, so this context helps before you make any decision.

Now plot the price alongside both averages so you can see the relationship visually.

fig = go.Figure()

fig.add_trace(go.Scatter(x=abat.index, y=abat["Close"], mode="lines", name="Close"))
fig.add_trace(go.Scatter(x=abat.index, y=abat["MA_20"], mode="lines", name="20-Day Avg"))
fig.add_trace(go.Scatter(x=abat.index, y=abat["MA_50"], mode="lines", name="50-Day Avg"))

fig.update_layout(title="ABAT Price with Rolling Averages", xaxis_title="Date", yaxis_title="Price (USD)")
fig.show()

Measuring How Much ABAT Moves Around

You can also calculate how much the stock's daily returns vary. First compute the daily percentage change, then take a 20-day rolling standard deviation, which is a measure of how spread out those daily moves are.

abat["Daily_Return"] = abat["Close"].pct_change()
abat["Volatility_20D"] = abat["Daily_Return"].rolling(window=20).std()

print(abat[["Close", "Daily_Return", "Volatility_20D"]].tail(10))

A higher number in the Volatility_20D column means the stock has been swinging more on a daily basis. For ABAT, this number can spike during periods of heavy news coverage or earnings reports.

What This Analysis Actually Tells You About ABAT Stock

These tools don't tell you where ABAT will trade tomorrow. They show how much the stock has moved, whether recent moves were unusually large, and how current prices compare with recent averages. That's a better starting point than relying on social media posts or headlines alone.

Specifically, you now know how to check whether trading volume backed up a price move, whether the stock is above or below its recent averages, how much it gained or lost over the past 20 days, and how much daily prices have been swinging. None of that guarantees a profitable trade, but it gives you real data instead of guesses.

Further Reading

If you want to repeat this process for other stocks, the Python market data analysis guide walks through each step from data download to charting. For a faster way to build price charts, see how to create stock price charts with one line of code. For a broader look at working with historical prices, this resource on analyzing historical stock price data covers additional techniques. The yfinance documentation on PyPI explains additional parameters for handling stock splits and dividends.