Predicting Stock Price Direction using Support Vector Machines Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Predicting stock price direction is a key goal for traders and analysts. Support Vector Machines (SVM) is a machine learning algorithm that can help classify whether a stock's price will rise or fall. In this article, we'll demonstrate how to apply SVM to predict stock price movements using historical data, covering data preparation, model training, and evaluation.Step-by-Step ImplementationStep 1: Import the librariesHere we will import pandas, scikit learn and matplotlib. Python # Machine learning from sklearn.svm import SVC from sklearn.metrics import accuracy_score # For data manipulation import pandas as pd import numpy as np # To plot import matplotlib.pyplot as plt # To ignore warnings import warnings warnings.filterwarnings("ignore") Step 2: Read Stock dataWe will Read the Stock Data Downloaded From Yahoo Finance Website. You can download dataset from here. Python # Read the csv file using read_csv # method of pandas df = pd.read_csv('RELIANCE.csv') df Output:datasetStep 3: Data Preparation The data needed to be processed before use such that the date column should act as an index to do that and will drop date column. Python # Changes The Date column as index columns df.index = pd.to_datetime(df['Date']) df # drop The original date column df = df.drop(['Date'], axis='columns') df Output:Data Preparation Step 4: Define the explanatory variablesExplanatory or independent variables are used to predict the value response variable. The X is a dataset that holds the variables which are used for prediction. The X consists of variables such as 'Open - Close' and 'High - Low'. These can be understood as indicators based on which the algorithm will predict tomorrow's trend. Feel free to add more indicators and see the performance. Python # Create predictor variables df['Open-Close'] = df.Open - df.Close df['High-Low'] = df.High - df.Low # Store all predictor variables in a variable X X = df[['Open-Close', 'High-Low']] X.head() Output:Explanatory variablesStep 5: Define the target variableThe target variable is the outcome which the machine learning model will predict based on the explanatory variables. If tomorrow's price is greater than today's price then we will buy the particular Stock else we will have no position in the. We will store +1 for a buy signal and 0 for a no position in y. We will use where() function from NumPy to do this. Python # Target variables y = np.where(df['Close'].shift(-1) > df['Close'], 1, 0) y Output:array([1, 1, 0, ..., 1, 0, 0])Step 6: Split the data into train and testWe will split data into training and test data sets. This is done so that we can evaluate the effectiveness of the model in the test dataset. We will split 80% data for training and 20% for testing. Python split_percentage = 0.8 split = int(split_percentage*len(df)) # Train data set X_train = X[:split] y_train = y[:split] # Test data set X_test = X[split:] y_test = y[split:] Step 7: Support Vector Classifier (SVC)We will use Support Vector Machines by SVC() function from sklearn.svm.SVC library to create our classifier model using the fit() method on the training data set. Python # Support vector classifier cls = SVC().fit(X_train, y_train) Step 8: Classifier accuracyThis code calculates and prints the accuracy of your model on both the training and testing data which were split 80/20 to check for overfitting. Python print("The data was split into training and testing sets using an 80/20 split.") # Calculate training accuracy train_accuracy = accuracy_score(y_train, cls.predict(X_train)) # Calculate testing accuracy test_accuracy = accuracy_score(y_test, cls.predict(X_test)) print(f"Training Accuracy: {train_accuracy}") print(f"Testing Accuracy: {test_accuracy}") Output:Training and testing accuracyStep 9: Different KernelsThis code trains and tests SVC models with different kernels like linear, polynomial, RBF and sigmoid to see how the kernel affects prediction accuracy on the test data. It then prints the accuracy for each kernel. Python from sklearn.metrics import accuracy_score # Linear kernel cls_linear = SVC(kernel='linear').fit(X_train, y_train) y_pred_linear = cls_linear.predict(X_test) accuracy_linear = accuracy_score(y_test, y_pred_linear) print(f"Accuracy with Linear Kernel: {accuracy_linear}") # Polynomial kernel cls_poly = SVC(kernel='poly', degree=3).fit(X_train, y_train) y_pred_poly = cls_poly.predict(X_test) accuracy_poly = accuracy_score(y_test, y_pred_poly) print(f"Accuracy with Polynomial Kernel (degree=3): {accuracy_poly}") # RBF kernel (default) cls_rbf = SVC(kernel='rbf').fit(X_train, y_train) y_pred_rbf = cls_rbf.predict(X_test) accuracy_rbf = accuracy_score(y_test, y_pred_rbf) print(f"Accuracy with RBF Kernel: {accuracy_rbf}") # Sigmoid kernel cls_sigmoid = SVC(kernel='sigmoid').fit(X_train, y_train) y_pred_sigmoid = cls_sigmoid.predict(X_test) accuracy_sigmoid = accuracy_score(y_test, y_pred_sigmoid) print(f"Accuracy with Sigmoid Kernel: {accuracy_sigmoid}") Output:Accuracy with different KernelsStep 10: Strategy implementationWe will predict the signal (buy or sell) using the cls.predict() function. Python df['Predicted_Signal'] = cls.predict(X) Calculate Daily returns Python # Calculate daily returns df['Return'] = df.Close.pct_change() Calculate Strategy Returns Python # Calculate strategy returns df['Strategy_Return'] = df.Return *df.Predicted_Signal.shift(1) Calculate Cumulative Returns Python # Calculate Cumulutive returns df['Cum_Ret'] = df['Return'].cumsum() df Output :Calculate Cumulative ReturnsCalculate Strategy Cumulative Returns Python # Plot Strategy Cumulative returns df['Cum_Strategy'] = df['Strategy_Return'].cumsum() df OutputCalculate Strategy Cumulative ReturnsStep 11: Visualizing Strategy Returns vs Original Returns Python import matplotlib.pyplot as plt %matplotlib inline plt.plot(Df['Cum_Ret'],color='red') plt.plot(Df['Cum_Strategy'],color='blue') Output:Plot Strategy Returns vs Original ReturnsAs You Can See Our Strategy Seem to be Totally Outperforming the Performance of The Reliance Stock. Our Strategy (Blue Line) Provided the return of 18.87 % in the last 1 year whereas the stock of Reliance Industries (Red Line) Provides the Return of just 5.97% in the last 1 year. We can further fine tune our model for better accuracy.Step 12: Let's see our model result on different stocks1. TCSStock Return Over Last 1 year - 48% Strategy result - 48.9 %2. ICICI BANKStock Return Over Last 1 year - 48% Strategy result - 48.9 %Get the complete notebook link from here:Notebook link : click here. Comment More infoAdvertise with us Next Article Introduction to Machine Learning N nirvikarnayan Follow Improve Article Tags : Machine Learning Blogathon AI-ML-DS Blogathon-2021 python Python-projects +2 More Practice Tags : Machine Learningpython Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. 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