AI on the Bitcoin blockchain with Python

There are a few different ways you could use AI on the Bitcoin blockchain. One can use a machine learning algorithm to analyze Bitcoin transaction data and make predictions about future trends. This would require training a machine learning model on a dataset of historical Bitcoin transactions, and then using that model to make predictions about future transactions. The model could be hosted on a server or in the cloud, and could interact with the blockchain via APIs.

AI and Machine Learning

Machine learning is a subfield of artificial intelligence (AI). AI refers to the broader field of computer science that is concerned with creating machines that can perform tasks that would typically require human intelligence, such as perception, reasoning, and decision-making. Machine learning is a specific approach to building AI systems that involves using statistical algorithms and models to enable machines to learn from data, without being explicitly programmed. In other words, machine learning algorithms are designed to learn from patterns and trends in data, rather than following a fixed set of rules.

Machine Learning on Bitcoin

In this tutorial we will build an AI machine learning program that analyzes the Bitcoin blockchain and predicts transactions. The program collects data on Bitcoin transactions from a public API and preprocesses the data to extract relevant features. It then performs feature engineering to create additional features, such as the hour of the day and day of the week when a transaction occurred. The program trains a linear regression model on the data and evaluates its performance using mean squared error. Finally, it uses the trained model to make predictions on new data, such as the total output value of a Bitcoin transaction given the number of input and output addresses and the time of day and day of the week when the transaction occurred.

Why Machine Learning on the Bitcoin blockchain

Someone might use an AI machine learning program to gain insights into the behavior of Bitcoin users and make predictions about future transactions using the blockchain. For example, an analyst at a cryptocurrency exchange might use this program to identify patterns in Bitcoin transactions and anticipate changes in market demand. Similarly, a researcher studying the economics of Bitcoin might use this program to test hypotheses about how different factors affect transaction volume and value. Overall, this program provides a way to apply machine learning techniques to the analysis of Bitcoin data, which can help us better understand this emerging technology and its impact on the financial world.

To learn more about Python and Bitcoin check out Getting started with Python and To learn Python basics visit Getting Started with Python.

Machine Learning, Bitcoin blockchain, and Python

Below is an example of a machine learning program that could analyze the Bitcoin blockchain and make predictions about future transactions:

Step 1: Collect data from the Bitcoin blockchain

The first step in building a machine learning model is to collect data. In this case, we would need to collect data on past Bitcoin transactions. One way to do this is to use a blockchain explorer API, which allows us to access data on past transactions on the Bitcoin network.

import requests

# define the API endpoint to get transaction data
api_endpoint = ""

# specify the Bitcoin address we want to analyze
address = "1HLoD9E4SDFFPDiYfNYnkBLQ85Y51J3Zb1"

# make a request to the API to get transaction data for the address
response = requests.get(api_endpoint + address)

# parse the JSON data into a Python dictionary
transaction_data = response.json()

Step 2: Preprocess Bitcoin data

Once we have collected the data, we need to preprocess it to make it suitable for machine learning. This might involve cleaning the data, removing duplicates or outliers, and transforming the data into a format that can be used by our machine learning algorithms.

import pandas as pd

# convert the transaction data into a Pandas DataFrame
df = pd.DataFrame(transaction_data["txs"])

# clean the data by removing unnecessary columns
df = df[["time", "inputs", "out"]]

# convert the timestamps to datetime objects
df["time"] = pd.to_datetime(df["time"], unit="s")

# create a new column to track the total transaction volume
df["total_output_value"] = df["out"].apply(lambda x: sum([o["value"] for o in x]))

# remove any rows where the total transaction volume is 0
df = df[df["total_output_value"] > 0]

Step 3: Feature engineering

Next, we need to engineer features that will help our machine learning model make predictions. For example, we might create features that capture the volume of Bitcoin transactions, the number of unique addresses sending and receiving Bitcoin, or the time of day or day of the week when transactions occur.

# create a new column to track the number of input addresses
df["num_input_addresses"] = df["inputs"].apply(lambda x: len([i["prev_out"]["addr"] for i in x]))

# create a new column to track the number of output addresses
df["num_output_addresses"] = df["out"].apply(lambda x: len([o["addr"] for o in x]))

# create a new column to track the hour of the day when the transaction occurred
df["hour_of_day"] = df["time"].apply(lambda x: x.hour)

# create a new column to track the day of the week when the transaction occurred
df["day_of_week"] = df["time"].apply(lambda x: x.weekday())

Step 4: AI Machine Learning model training

With our preprocessed data and engineered features, we can now train a machine learning model. There are many different algorithms we could use for this, including linear regression, decision trees, or neural networks. The choice of algorithm will depend on the specific problem we are trying to solve and the characteristics of our data.

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# define our features and target variable
X = df[["num_input_addresses", "num_output_addresses", "hour_of_day", "day_of_week"]]
y = df["total_output_value"]

# split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# train a linear regression model on the training data
model = LinearRegression(), y_train)

# make predictions on the testing data and evaluate the model's performance
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

Step 5: Machine Learning model evaluation and tuning

Once we have trained our model, we need to evaluate its performance and tune its parameters to improve its accuracy. We might do this by splitting our data into training and testing sets, using cross-validation to evaluate our model’s performance, or tweaking our model’s hyperparameters to improve its performance.

from sklearn.model_selection import GridSearchCV

# define a grid of hyperparameters to search over
param_grid = {"fit_intercept": [True, False], "normalize": [True, False]}

# perform a grid search over the hyperparameters using cross-validation
grid_search = GridSearchCV(model, param_grid, cv=5), y_train)

# select the best model from the grid search and evaluate its performance on the testing data
best_model = grid_search.best_estimator_
y_pred = best_model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Best Model Mean Squared Error:", mse)

Step 6: Prediction

Finally, we can use our trained machine learning model to make predictions about future Bitcoin transactions. We might do this by feeding in new data as it becomes available, or by using our model to make predictions on historical data to see how well it performs.

# use the trained model to make predictions on new data
new_data = pd.DataFrame({
    "num_input_addresses": [3],
    "num_output_addresses": [5],
    "hour_of_day": [10],
    "day_of_week": [2]
prediction = best_model.predict(new_data)[0]
print("Predicted Total Output Value:", prediction)

This program uses a linear regression model to predict the total output value of a Bitcoin transaction based on the number of input addresses, number of output addresses, hour of the day, and day of the week when the transaction occurred. You can modify the feature engineering and model training steps to experiment with different features and algorithms.


Overall, building a machine learning program to analyze the Bitcoin blockchain and predict transactions is not that complex but requires a good understanding of machine learning algorithms, data preprocessing techniques, and the Bitcoin network itself. However, with careful planning and a solid understanding of the underlying technology, it is certainly possible to build a predictive model that can help us better understand Bitcoin transaction patterns.