random_forest_case.py

# Use the previous 10 bars' movements to predict the next movement.

# Use a random forest classifier. More here: http://scikit-learn.org/stable/user_guide.html
from sklearn.ensemble import RandomForestRegressor
import numpy as np

def initialize(context):
    context.security = sid(8554) # Trade SPY
    context.model = RandomForestRegressor()

    context.lookback = 3 # Look back 3 days
    context.history_range = 400 # Only consider the past 400 days' history

    # Generate a new model every week
    schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))

    # Trade at the start of every day
    schedule_function(trade, date_rules.every_day(), time_rules.market_open(minutes=1))

def create_model(context, data):
    # Get the relevant daily prices
    recent_prices = history(context.history_range, '1d', 'price')[context.security].values
    
    # Get the price changes
    price_changes = np.diff(recent_prices).tolist()

    X = [] # Independent, or input variables
    Y = [] # Dependent, or output variable
    
    # For each day in our history
    for i in range(context.history_range-context.lookback-1):
        X.append(price_changes[i:i+context.lookback]) # Store prior price changes
        Y.append(price_changes[i+context.lookback]) # Store the day's price change

    context.model.fit(X, Y) # Generate our model

def trade(context, data):
    if context.model: # Check if our model is generated
        
        # Get recent prices
        recent_prices = history(context.lookback+1, '1d', 'price')[context.security].values
        
        # Get the price changes
        price_changes = np.diff(recent_prices).tolist()
        
        # Predict using our model and the recent prices
        prediction = context.model.predict(price_changes)
        record(prediction = prediction)
        
        # Go long if we predict the price will rise, short otherwise
        if prediction > 0:
            order_target_percent(context.security, 1.0)
        else:
            order_target_percent(context.security, -1.0)

def handle_data(context, data):
    pass