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Stable Baselines

Introduction

This page explains how to build, train, deploy and store stable baselines 3 models.

Import Libraries

Import the gym and stable_baselines3 libraries.

from AlgorithmImports import *
import gym
from stable_baselines3 import DQN

Create Subscriptions

In the Initialize method, subscribe to some data so you can train the stable_baselines model and make predictions.

self.symbol = self.add_equity("SPY", Resolution.DAILY).symbol

Build Models

In this example, create a gym environment to initialize the training environment, agent, and reward. Then, create a reinforcement learning model by a single-asset deep Q-network learning algorithm using the following observations and rewards:

Data CategoryDescription
ObservationsThe 5-day open, high, low, close, and volume (OHLCV) of the SPY
RewardsMaximum portfolio return

Follow these steps to create a method to build the model:

  1. Create a custom gym environment class.
  2. In this example, create a custom environment with the previous 5 OHLCV log-return data as observation and the highest portfolio value as reward.

    class TradingEnv(gym.Env):
        FLAT = 0
        LONG = 1
        SHORT = 2
    
        def __init__(self, ohlcv, ret):
            super(TradingEnv, self).__init__()
            
            self.ohlcv = ohlcv
            self.ret = ret
            self.trading_cost = 0.01
            self.reward = 1
            
            # The number of step the training has taken, starts at 5 since we're using the previous 5 data for observation.
            self.current_step = 5
            # The last action
            self.last_action = 0
    
            # Define action and observation space
            # Example when using discrete actions, we have 3: LONG, SHORT and FLAT.
            n_actions = 3
            self.action_space = gym.spaces.Discrete(n_actions)
            # The observation will be the coordinate of the agent, shape for (5 previous data poionts, OHLCV)
            self.observation_space = gym.spaces.Box(low=-2, high=2, shape=(5, 5, 5), dtype=np.float64)
    
        def reset(self):
            # Reset the number of step the training has taken
            self.current_step = 5
            # Reset the last action
            self.last_action = 0
            # must return np.array type
            return self.ohlcv[self.current_step-5:self.current_step].astype(np.float32)
    
        def step(self, action):
            if action == self.LONG:
                self.reward *= 1 + self.ret[self.current_step] - (self.trading_cost if self.last_action != action else 0)
            elif action == self.SHORT:
                self.reward *= 1 + -1 * self.ret[self.current_step] - (self.trading_cost if self.last_action != action else 0)
            elif action == self.FLAT:
                    self.reward *= 1 - (self.trading_cost if self.last_action != action else 0)
            else:
                raise ValueError("Received invalid action={} which is not part of the action space".format(action))
                
            self.last_action = action
            self.current_step += 1
    
            # Have we iterate all data points?
            done = (self.current_step == self.ret.shape[0]-1)
    
            # Reward as return
            return self.ohlcv[self.current_step-5:self.current_step].astype(np.float32), self.reward, done, {}
  3. Get the processed training data.
  4. obs, rewards = self.get_observations_and_rewards()
  5. Initialize the environment with the observations and results.
  6. self.env = TradingEnv(obs, rewards)
  7. Call the DQN constructor with the learning policy and the gym environment.
  8. self.model = DQN(MlpPolicy, env)

Train Models

You can train the model at the beginning of your algorithm and you can periodically re-train it as the algorithm executes.

Warm Up Training Data

You need historical data to initially train the model at the start of your algorithm. To get the initial training data, in the Initialize method, make a history request.

training_length = 252*2
self.training_data = RollingWindow[TradeBar](training_length)
history = self.history[TradeBar](self.spy, training_length, Resolution.DAILY)
for trade_bar in history:
    self.training_data.add(trade_bar)

Define a Training Method

To train the model, define a method that fits the model with the training data.

def get_observations_and_rewards(self, n_step=5):
    training_df = self.pandas_converter.get_data_frame[TradeBar](list(self.training_data)[::-1])
    daily_pct_change = training_df['close'].pct_change().dropna()

    obs = []
    rewards = []
    for i in range(len(daily_pct_change)-n_step):
        obs.append(training_df.iloc[i:i+n_step].values)
        rewards.append(float(daily_pct_change.iloc[i+n_step]))
    obs = np.array(obs)
    rewards = np.array(rewards)

    return obs, rewards

def my_training_method(self):
    obs, rewards = self.get_observations_and_rewards()
    self.env = TradingEnv(obs, rewards)
    self.model = DQN("MlpPolicy", self.env)
    self.model.learn(total_timesteps=500)

Set Training Schedule

To train the model at the beginning of your algorithm, in the Initialize method, call the Train method.

self.train(self.my_training_method)

To periodically re-train the model as your algorithm executes, in the Initialize method, call the Train method as a Scheduled Event.

# Train the model every Sunday at 8:00 AM
self.train(self.date_rules.every(DayOfWeek.sunday), self.time_rules.at(8, 0), self.my_training_method)

Update Training Data

To update the training data as the algorithm executes, in the OnData method, add the current TradeBar to the RollingWindow that holds the training data.

def OnData(self, slice: Slice) -> None:
    if self.symbol in slice.Bars:
        self.training_data.Add(slice.Bars[self.symbol])

Predict Labels

To predict the labels of new data, in the OnData method, get the most recent set of features and then call the predict method.

features, _ = self.get_observations_and_rewards()
action, _ = self.model.predict(features[-5:], deterministic=True)
_, _, _, _ = self.env.step(action)

You can use the label prediction to place orders.

if action == 0:
    self.liquidate(self.spy)
elif action == 1:
    self.set_holdings(self.spy, 1)
elif action == 2:
    self.set_holdings(self.spy, -1)

Save Models

Follow these steps to save stable_baselines models into the Object Store:

  1. Set the key name of the model to be stored in the Object Store.
  2. model_key = "model"
  3. Call the GetFilePath method with the key.
  4. file_name = self.object_store.get_file_path(model_key)

    This method returns the file path where the model will be stored.

  5. Call the save method the file path.
  6. self.model.save(file_name)

Load Models

You can load and trade with pre-trained keras models that you saved in the Object Store. To load a keras model from the Object Store, in the Initialize method, get the file path to the saved model and then call the load_model method.

def initialize(self) -> None:
    if self.object_store.contains_key(model_key):
        file_name = self.object_store.get_file_path(model_key)
        self.model = DQN.load(file_name, env=env)

The ContainsKey method returns a boolean that represents if the model_key is in the Object Store. If the Object Store doesn't contain the model_key, save the model using the model_key before you proceed.

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