The agent is the RL model which takes the input features/state and decides the action to take. For example, the RL agent takes RSI and past 10 minutes returns as input and tells us whether we should go long on the Apple stock or square off the long position if we are already in a long position.
Let’s put everything together and see how it works.
Visit QuantInsti to see the animated graphic: https://blog.quantinsti.com/reinforcement-learning-trading/
State & Action: Suppose the Closing price of Apple was $92 on July 24, 2020. Based on the state (RSI and 10-days returns), the agent gave a buy signal.
Environment: For simplicity, we say that the order was placed at the open the next trading day, which is July 27. The order was filled at $92.Thus, the environment tells us that you are long one share of Apple at $92.
Reward: And no reward is given as we are still in the trade.
State & Action: You get the next state of the system created using the latest price data which is available. On the close of July 27, the price had reached $94. The agent would analyse the state and give the next action, say Sell to environment
Environment: A sell order will be placed which will square off the long position
Reward: A reward of 2.1% is given to the agent.
|Date||Closing price||Action||Reward (% returns)|
Great! We have understood how the different components of the RL model come together. Let us now try to understand the intuition of how the RL agent takes the action.
Stay tuned for the next installment in which Ishan will demonstrate the Q Table and Q Learning.
Visit QuantInsti to download practical code: https://blog.quantinsti.com/reinforcement-learning-trading/.
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