I have seen Gus Gordon's algorithm which uses machine learning method--Random Forest.
Simple Machine Learning Example

I have tried some other machine learning methods, including svm and AdaBoost. It turns out that svm performs the best in the same case of the single security--Boeing. Also, I attempted to adjust the parameters involved in the algo, including the number of training samples and the length of features.

I think only use prices as features may not be enough, and I add the volumes to the feature vector.

Inspired by the idea of ensemble learning, which enhances the performance of weak classifiers, I combined three classifiers together, gave them different voting weights. In addition, the weights of classifiers are parameters that can be learned further, but it would be a little bit complicated.

Fortunately, the algo performs somewhat better than before. However, In my experiments, it seems that the performance of the algo depends on the security I choose to a great extent, which means it's not as stable as the benchmark. Maybe a portfolio of more securities would be better.

This is the first time I post my idea. If anything wrong, please just tell me. Thanks!

# Use three machine learning methods. More here: http://scikit-learn.org/stable/user_guide.html  
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier  
from sklearn import svm  
from collections import deque  
import numpy as np

def initialize(context):  
    context.security = sid(698) # Boeing  
    context.window_length = 10 # Amount of prior bars to study

    context.clf1 = RandomForestClassifier(n_estimators=20)  
    context.clf2 = AdaBoostClassifier(n_estimators=10)  
    context.clf3 = svm.SVC()

    # deques are lists with a maximum length where old entries are shifted out  
    context.recent_prices = deque(maxlen=context.window_length+2) # Stores recent prices  
    context.recent_volumes = deque(maxlen=context.window_length+2) # Stores recent volumes  
    context.X = deque(maxlen=500) # Independent, or input variables  
    context.Y = deque(maxlen=500) # Dependent, or output variable

    context.prediction1 = 0  
    context.prediction2 = 0  
    context.prediction3 = 0

def handle_data(context, data):  
    context.recent_prices.append(data[context.security].price) # Update the recent prices  
    context.recent_volumes.append(data[context.security].volume) # Update the recent volumes

    if len(context.recent_prices) == context.window_length+2: # If there's enough recent price data  
        # Make a list of 1's and 0's, 1 when the price increased from the prior bar  
        price_changes = np.diff(context.recent_prices) > 0  
        volume_changes = np.diff(context.recent_volumes) > 0  
        feature = np.append(price_changes[:-1], volume_changes[:-1])

        context.X.append(feature) # Add independent variables, the prior changes  
        context.Y.append(price_changes[-1]) # Add dependent variable, the final change

        if len(context.Y) >= 50: # There needs to be enough data points to make a good model  
            context.clf1.fit(context.X, context.Y) # Generate the model1  
            context.clf2.fit(context.X, context.Y) # Generate the model2  
            context.clf3.fit(context.X, context.Y) # Generate the model3

            target_feature = np.append(price_changes[1:], volume_changes[1:])  
            context.prediction1 = context.clf1.predict(target_feature)  
            context.prediction2 = context.clf2.predict(target_feature)  
            context.prediction3 = context.clf3.predict(target_feature)

            # use weighted voting to get position percentage  
            position = 0  
            if context.prediction1:  
                position += 0.1  
            if context.prediction2:  
                position += 0.1  
            if context.prediction3:  
                position += 0.8  
            log.info(position)  
            order_target_percent(context.security, position)

            record(prediction1=int(context.prediction1), prediction2 = int(context.prediction2), prediction3=int(context.prediction3))