Combining cryptofeed with AI and ML Libraries for Real-Time Analysis

In the constantly evolving world of cryptocurrency trading, real-time data analysis is crucial for making informed decisions. By combining cryptofeed, a cryptocurrency market data feed streaming library, with AI and ML libraries, traders can gain deeper insights and predictive power. This article will guide you through setting up an environment to analyze real-time crypto data using Python.

Setting Up the Environment

Before getting started, ensure you have Python installed on your system. You can download Python from the official Python website. It's also recommended to use a virtual environment for isolating dependencies.

# Create virtual environment
your-computer:~$ python3 -m venv cryptoanalytica

# Activate virtual environment
your-computer:~$ source cryptoanalytica/bin/activate

# Install cryptofeed, an AI, and ML library
your-computer:~$ pip install cryptofeed numpy scikit-learn pandas matplotlib

Initial Setup with Cryptofeed

Cryptofeed allows you to collect real-time data from various exchanges. Here's a basic setup to get the data stream running:

from cryptofeed import FeedHandler
from cryptofeed.exchanges import Coinbase

async def trade_callback(feed, symbol, order_id, timestamp, side, amount, price, receipt_timestamp):
    print(f'Trade: {timestamp} {symbol} {side} {amount}@{price}')

feed = FeedHandler()
feed.add_feed(Coinbase(symbols=['BTC-USD'], channels=['trades'], callbacks={
    'trades': trade_callback
}))
feed.run()

This code sets up a feed handler for the Coinbase exchange, streaming real-time trade data for the BTC-USD trading pair. Each trade is printed out on the console.

Integrating Machine Learning

Leveraging machine learning, you can begin analyzing the trading patterns. Let's start by storing data received from our callback into a data structure that Scikit-learn can work with:

import pandas as pd

trades = []

def trade_callback(feed, symbol, order_id, timestamp, side, amount, price, receipt_timestamp):
    trades.append({
        'timestamp': timestamp,
        'symbol': symbol,
        'side': side,
        'amount': amount,
        'price': price
    })

# Convert to DataFrame after collecting sufficient data
df = pd.DataFrame(trades)

Data Preprocessing

Before applying any machine learning algorithms, it’s important to preprocess the data. This includes handling missing values, normalizing data, and splitting into training/test sets:

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Assume df has been populated

# Handle missing data
df.fillna(method='ffill', inplace=True)

# Normalize price and amount
scaler = StandardScaler()
df[['amount', 'price']] = scaler.fit_transform(df[['amount', 'price']])

# Prepare features and target label
X = df[['amount', 'price']]
y = df['side']

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Applying a Machine Learning Model

With prepared data, you can apply various ML models. For demonstration, we'll implement a simple logistic regression to classify the trades:

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

model = LogisticRegression()

# Train the model
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate the model
print(classification_report(y_test, predictions))

The output will provide metrics such as precision, recall, and f1-score, helping you gauge the effectiveness of your model on predicting the trade side.

Visualizing Results

To gain a better intuitive understanding of your work, visualizing the output can be very helpful. Here's a simple way to plot some of the trade data using matplotlib:

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.scatter(df['timestamp'], df['price'], c=(df['side'] == 'sell'), cmap='cool', label='Sell', marker='o')
plt.scatter(df['timestamp'], df['price'], c=(df['side'] == 'buy'), cmap='coolwarm', label='Buy', marker='x')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.title('BTC-USD Trade Prices')
plt.show()

This example shows how you can develop both operational and strategic insights by combining cryptofeed with powerful AI/ML tools. Adjust and expand upon this setup to incorporate more complex models or additional exchanges and trading pairs.