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Combine Different Machine Learning Methods

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:  
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier  
from sklearn import svm  
from collections import deque  
import numpy as np

def initialize(context): = 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[].price) # Update the recent prices  
    context.recent_volumes.append(data[].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.Y) # Generate the model1  
  , context.Y) # Generate the model2  
  , 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  
            order_target_percent(, position)

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

I did some backtest to tuning the SVM parameter for GLD specific security, it could have lower volatility then buy & hold strategy,FYI

Clone Algorithm
Backtest from to with initial capital
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 53849f6049c138070e3a778d
There was a runtime error.

Great post! I highly support ensemble learning. It would be interesting to add more features such as differently sized simple moving average windows, fundamental and technical indicators. And combine linear (e.g. Logistic Regression) and non-linear (e.g. Neural Networks, XGboost, Random Forest) algorithms to improve accuracy.