Support Vector Machines

Explanation & Rationale

The Support Vector Machine (SVM) Classifier strategy applies a machine learning model to classify future price movements based on historical data and technical indicators. By finding an optimal decision boundary, the SVM separates positive and negative price movement patterns, making it useful for identifying market trends. This approach is particularly effective in high-dimensional spaces and helps filter out noise, improving the reliability of trade signals.

Code

'''
Support Vector Machine (SVM) Classifier.

Trains a SVC using the past 30 days of data to predict the next day's action.
The model leverages basic technical indicators as features to make predictions.
Learn more @ docs.ubacktest.com/examples/machine-learning/svmclassifier
'''

import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler

def create_features(data, indicator_window=14):

    data[f'SMA_{indicator_window}'] = data['close'].rolling(window=indicator_window).mean()
    data[f'volume_{indicator_window}'] = data['volume'].rolling(window=indicator_window).mean()
    delta = data['close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=indicator_window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=indicator_window).mean()
    data[f'RSI_{indicator_window}'] = 100 - (100 / (1 + gain / loss))
    data[f'EMA_{indicator_window}'] = data['close'].ewm(span=indicator_window, adjust=False).mean()
    data['EMA_12'] = data['close'].ewm(span=12, adjust=False).mean()
    data['EMA_26'] = data['close'].ewm(span=26, adjust=False).mean()
    data['MACD'] = data['EMA_12'] - data['EMA_26']
    data['MACD_signal'] = data['MACD'].ewm(span=9, adjust=False).mean()  # Signal line for MACD
    data['bollinger_upper'] = data[f'SMA_{indicator_window}'] + (data['close'].rolling(window=indicator_window).std() * 2)
    data['bollinger_lower'] = data[f'SMA_{indicator_window}'] - (data['close'].rolling(window=indicator_window).std() * 2)

    return data

def svm_classifier(data, training_window=30, indicator_window=14, kernel='linear'):

    # Create features
    data = create_features(data, indicator_window)

    # Create the target variable: 1 if the next day's price is higher, -1 if it is lower
    data['target'] = (data['close'].shift(-1) > data['close']).astype(int) * 2 - 1

    features = [
        'close',
        'volume',
        f'SMA_{indicator_window}',
        f'volume_{indicator_window}',
        f'RSI_{indicator_window}',
        f'EMA_{indicator_window}',
        'MACD',
        'MACD_signal',
        'bollinger_upper',
        'bollinger_lower',
    ]

    scaler = StandardScaler()  # Standardize features for better SVM performance
    predictions = []

    for i in range(training_window+indicator_window, len(data)):
        train_data = data.iloc[i-training_window:i]  # Rolling window for training
        test_data = data.iloc[[i]]  # Single test point (next day)

        X_train, y_train = train_data[features], train_data['target']
        X_test = test_data[features]

        # Scale the data
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)

        # Train the model
        model = SVC(kernel=kernel)
        model.fit(X_train_scaled, y_train)

        # Make prediction
        pred = model.predict(X_test_scaled)[0]
        predictions.append(pred)

    # Assign predictions back to the data
    data.loc[data.index[training_window+indicator_window:], 'signal'] = predictions

    return data

def strategy(data):

    # Call the svm_classifier function to get signals
    data = svm_classifier(data)

    return data