Bitcoin Data

Bitcoin Data

This post describes how to use Bitcoin Data to forecast Bitcoin Price through AI learning.

Table of Contents

  1. Data Introduction
  2. Data Visualization with Machbase Neo
  3. Table Creation and Data Upload in Machbase Neo
  4. Experimental Methodology
  5. Experiment Code
  6. Experimental Results

1. Data Introduction


  • DataHub Serial Number: 2024-17.
  • Data Name: Bitcoin Data.
  • Data Collection Methods: Collected Bitcoin data from the Bithumb API to Machbase Neo at 1-second intervals(Resample at 1-minute intervals thereafter).
  • Data Source: Link
  • Raw data size and format: 7.69MB, CSV.
  • Number of tags: 18.
Tag Description
BTC-acc_trade_price Cumulative trading volume (KST 0:00)
BTC-acc_trade_price_24h 24-hour cumulative trading volume
BTC-acc_trade_volume Cumulative trading volume (KST 0:00)
BTC-acc_trade_volume_24h 24-hour cumulative trading volume
BTC-change_price Absolute value of price change
BTC-change_rate Absolute value of price change rate
BTC-high_price Highest price
BTC-highest_52_week_price Highest price in 52 weeks
BTC-low_price Lowest price
BTC-lowest_52_week_price Lowest price in 52 weeks
BTC-opening_price Opening price
BTC-prev_closing_price Previous day's closing price (KST 0:00)
BTC-signed_change_price Signed price change
BTC-signed_change_rate Signed price change rate
BTC-trace_volumn Most recent trading volume
BTC-trade_price Cumulative trading volume (KST 0:00)
BTC-trade_volume Most recent trading volume
KRW-BTC Closing price (current price)

2. Data Visualization with Machbase Neo


  • Data visualization is possible through the Tag Analyzer in Machbase Neo.
  • Select desired tag names and visualize them in various types of graphs.
  • Below, access the 2024-17 DataHub in real-time, select the desired tag names from the data of 18 tags, visualize them, and preview the data patterns.
DataHub Viewer

3. Table Creation and Data Upload in Machbase Neo


  • In the DataHub directory, use setup.wrk located in the Bitcoin Dataset folder to create tables and load data, as illustrated in the image below.

1) Table Creation

  • The table is created immediately upon pressing the "Run" button in the menu.
  • If the Bitcoin table exists, execute the first line and then the second. If it does not exist, start from the second line.

2) Data Upload


  • Loading tables in two different ways.
Method 1) Table loading method using TQL in Machbase Neo (since machbase-neo v8.0.29-rc1

  • Pros

    • Markbase Neo loads as soon as you hit the launch button.
  • Cons

    • Slower table loading speed compared to other method.
Method 2) Loading tables using commands

  • Pros

    • Fast table loading speed.
  • Cons

    • The table loading process is cumbersome.
    • Run cmd window - Change machbase-neo path - Enter command in cmd window.
  • If run the below script from the command shell, the data will be entered at high speed into the Bitcoin table.
curl http://data.yotahub.com/2024-17/datahub-2024-17-Bitcoin.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns bitcoin
  • If specify a separate username and password, use the --user and --password options (if not sys/manager) and add the options as shown below.
curl http://data.yotahub.com/2024-17/datahub-2024-17-Bitcoin.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns bitcoin --user USERNAME --password PASSWORD

4. Experimental Methodology


  • Model Objective: Bitcoin Price Forecasting.
  • Tags Used: KRW-BTC.
  • Model Configuration: SparseTSF.
  • Goal: Forecasting the Bitcoin Price for the next 1 minute using data from the past 10 minutes.
  • Learning Method: supervised Learning.
    • Train: Model Training.
    • Test: Model Performance Evaluation Based on Bitcoin Price Forecasting.
  • Model Optimizer: Adam.
  • Model Loss Function: Mean Squared Error.
  • Model Performance Metric: Mean Squared Error & R2 Score.
  • Data Loading Method
    • Loading the Entire Dataset.
    • Loading the Fetch Dataset.
  • Data Preprocessing
    • MinMax Scaling.

5. Experiment Code


Bitcoin Data Exploratory Data Analysis


  • Composed of three methods.
  • Data Information: Outputs general information about the data.
  • Visual Information: correlation heatmap, plot, Decomposition about the data.
  • Statistical Test: ADF Test, KPSS Test, PP Test, ljung box Test, Arch Test, VIF Test about the data.

Bitcoin Price Forecasting


  • Below is the code for each of the two ways to get data from the database.
  • If all the data can be loaded and trained at once without causing memory errors, then method 1 is the fastest and simplest.
  • If the data is too large, causing memory errors, then the batch loading method proposed in method 2 is the most efficient.

Method 1) Loading the Entire Dataset


  • The code below is implemented in a way that loads all the data needed for training from the database all at once.
  • It is exactly the same as loading all CSV files (The only difference is that the data is loaded from Machbase Neo).
  • Pros
    • Can use the same code that was previously utilizing CSVs (Only the loading process is different).
  • Cons
    • Unable to train if trainable data size exceeds memory size.

Method 2) Loading the Fetch Dataset


  • Method for loading data from the Machbase Neo for a buffer size.
  • Pros
    • It is possible to train the model regardless of the data size, no matter how large it is.
  • Cons
    • It takes longer to train compared to method 1.

6. Experimental Results


Method 1) Loading the Entire Dataset Result


Method 2) Loading the Fetch Dataset Result


  • The R2 score shows high performance above 0.9 in both methods.
  • Since the original values are very large, the MSE values are also high in both methods.
  • Reducing the MSE value will be a future task.

※ Various datasets and tutorial codes can be found in the GitHub repository below.

datahub/dataset/2024 at main · machbase/datahub
All Industrial IoT DataHub with data visualization and AI source - machbase/datahub

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