Exploratory Data Analysis of Bitcoin Prices with Visualizations

Jul 25, 2025, 3:51 PM

btcoin.csv.csv

Missing file

Your EDA project should cover:

✅ Summary statistics
✅ Missing values
✅ Price trends
✅ Correlation matrix
✅ Volume vs. price
✅ Moving averages (MA, EMA)
✅ Volatility (e.g., using standard deviation or Bollinger Bands)

Exploratory Data Analysis for btcoin.csv.csv

1. Summary statistics

MetricOpenHighLowClose
Mean110,014.6116,237.6108,570.2113,915.6
Std8,006.76,448.07,565.15,660.0
Min98,924108,30398,261107,285
Max118,079123,166116,226118,429
(Full descriptive table was created in the notebook above.)

2. Missing-value audit

There are no missing values in any of the OHLC columns.

3. Price trend

The Close-price line chart (see above) shows a steady up-trend from late June to late July 2025, rising roughly 10 % in less than a month.

4. Correlation matrix

All OHLC price columns are very strongly and positively correlated (> 0.97). This is expected because they are derived from the same trading sessions.

5. Volume vs. price

No volume field is present in this dataset, so this analysis was skipped.

6. Moving averages

A 3-period SMA and EMA were overlaid on the Close price:
  • Both lag slightly behind raw prices but capture the up-trend.
  • The EMA reacts faster to new information than the SMA (closer to the latest Close).

7. Volatility – Bollinger Bands

Using the 3-period SMA ± 2 × rolling σ:
  • Bands widen after early July, indicating higher volatility.
  • Latest Close (23 Jul) sits near the mid-band, suggesting price is not currently stretched.
Key insight: Over the observable period Bitcoin showed a strong upward drift with mild, increasing volatility; no extreme over-bought or over-sold signals are evident right now.
Feel free to explore a different window length, add volume data, or compute other technical indicators like RSI or MACD.

you download option file and edit opation add in donloda project ok python lounguese use pands numpy

Could you please clarify what you would like me to do?
From your request it sounds like you want to:
  1. Load or download an option file,
  2. Edit it in some way, and
  3. Provide it back to you for download – all using Python (pandas / NumPy).
To proceed I need to know:
  • The exact file you’d like me to load (please upload it or provide its name/path).
  • What kind of edits or additions you want applied to that file.
  • The desired output format (CSV, Excel, etc.).
Once I have that information I can write the code, run it, and give you a download link.
Let me know and we’ll get started!
I need a bit more detail to make sure I do exactly what you want.
From your last message it sounds like you’d like me to:
  1. Download an “option” file (perhaps a CSV or Excel sheet that isn’t in the notebook yet),
  2. Edit or enrich that file in some way, and then
  3. Add the resulting file to your downloadable project folder.
To proceed, please clarify:
  • Where should I download the option file from?
    • Provide a URL, upload the file here, or tell me the exact filename if it’s already in the workspace.
  • What specific edits or additions do you need?
    • For example: add new columns, calculate indicators, filter rows, merge with the Bitcoin data, etc.
  • What final file format would you like (CSV, Excel, Parquet, …)?
Once you give me those details I’ll write the Python code (using pandas & NumPy) to perform the steps and save the edited file for you to download.
Looking forward to your instructions!
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