freqtrade_origin/docs/freqai-reinforcement-learning.md

# Reinforcement Learning

!!! Note "Installation size"
    Reinforcement learning dependencies include large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]?".
    Users who prefer docker should ensure they use the docker image appended with `_freqairl`.

## Background and terminology

### What is RL and why does FreqAI need it?

Reinforcement learning involves two important components, the *agent* and the training *environment*. During agent training, the agent moves through historical data candle by candle, always making 1 of a set of actions: Long entry, long exit, short entry, short exit, neutral). During this training process, the environment tracks the performance of these actions and rewards the agent according to a custom user made `calculate_reward()` (here we offer a default reward for users to build on if they wish [details here](#creating-a-custom-reward-function)). The reward is used to train weights in a neural network.

A second important component of the FreqAI RL implementation is the use of *state* information. State information is fed into the network at each step, including current profit, current position, and current trade duration. These are used to train the agent in the training environment, and to reinforce the agent in dry/live (this functionality is not available in backtesting). *FreqAI + Freqtrade is a perfect match for this reinforcing mechanism since this information is readily available in live deployments.*

Reinforcement learning is a natural progression for FreqAI, since it adds a new layer of adaptivity and market reactivity that Classifiers and Regressors cannot match. However, Classifiers and Regressors have strengths that RL does not have such as robust predictions. Improperly trained RL agents may find "cheats" and "tricks" to maximize reward without actually winning any trades. For this reason, RL is more complex and demands a higher level of understanding than typical Classifiers and Regressors.

### The RL interface

With the current framework, we aim to expose the training environment via the common "prediction model" file, which is a user inherited `BaseReinforcementLearner` object (e.g. `freqai/prediction_models/ReinforcementLearner`). Inside this user class, the RL environment is available and customized via `MyRLEnv` as [shown below](#creating-a-custom-reward-function).

We envision the majority of users focusing their effort on creative design of the `calculate_reward()` function [details here](#creating-a-custom-reward-function), while leaving the rest of the environment untouched. Other users may not touch the environment at all, and they will only play with the configuration settings and the powerful feature engineering that already exists in FreqAI. Meanwhile, we enable advanced users to create their own model classes entirely.

The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.Env` which means that it is necessary to write an entirely new environment in order to switch to a different library.

### Important considerations

As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL training environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks like `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world.

## Running Reinforcement Learning

Setting up and running a Reinforcement Learning model is the same as running a Regressor or Classifier. The same two flags, `--freqaimodel` and `--strategy`, must be defined on the command line:

```bash
freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
```

where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:

```python
    def set_freqai_targets(self, dataframe, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        Required function to set the targets for the model.
        All targets must be prepended with `&` to be recognized by the FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/latest/freqai-feature-engineering

        :param df: strategy dataframe which will receive the targets
        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
        """
        # For RL, there are no direct targets to set. This is filler (neutral)
        # until the agent sends an action.
        dataframe["&-action"] = 0
        return dataframe
```

Most of the function remains the same as for typical Regressors, however, the function below shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:

```python
    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
        # The following features are necessary for RL models
        dataframe[f"%-raw_close"] = dataframe["close"]
        dataframe[f"%-raw_open"] = dataframe["open"]
        dataframe[f"%-raw_high"] = dataframe["high"]
        dataframe[f"%-raw_low"] = dataframe["low"]
    return dataframe
```

Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.

After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:

```python
    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]

        if enter_long_conditions:
            df.loc[
                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
            ] = (1, "long")

        enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]

        if enter_short_conditions:
            df.loc[
                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
            ] = (1, "short")

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
        if exit_long_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1

        exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
        if exit_short_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1

        return df
```

It is important to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short.

## Configuring the Reinforcement Learner

In order to configure the `Reinforcement Learner` the following dictionary must exist in the `freqai` config:

```json
        "rl_config": {
            "train_cycles": 25,
            "add_state_info": true,
            "max_trade_duration_candles": 300,
            "max_training_drawdown_pct": 0.02,
            "cpu_count": 8,
            "model_type": "PPO",
            "policy_type": "MlpPolicy",
            "model_reward_parameters": {
                "rr": 1,
                "profit_aim": 0.025
            }
        }
```

Parameter details can be found [here](freqai-parameter-table.md), but in general the `train_cycles` decides how many times the agent should cycle through the candle data in its artificial environment to train weights in the model. `model_type` is a string which selects one of the available models in [stable_baselines](https://stable-baselines3.readthedocs.io/en/master/)(external link).

!!! Note
    If you would like to experiment with `continual_learning`, then you should set that value to `true` in the main `freqai` configuration dictionary. This will tell the Reinforcement Learning library to continue training new models from the final state of previous models, instead of retraining new models from scratch each time a retrain is initiated.

!!! Note
    Remember that the general `model_training_parameters` dictionary should contain all the model hyperparameter customizations for the particular `model_type`. For example, `PPO` parameters can be found [here](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html).

## Creating a custom reward function

!!! danger "Not for production"
    Warning!
    The reward function provided with the Freqtrade source code is a showcase of functionality designed to show/test as many possible environment control features as possible. It is also designed to run quickly on small computers. This is a benchmark, it is *not* for live production. Please beware that you will need to create your own custom_reward() function or use a template built by other users outside of the Freqtrade source code.

As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but this is *not* designed for production. Users *must* create their own custom reinforcement learning model class or use a pre-built one from outside the Freqtrade source code and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:

!!! note "Hint"
    The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occuring at a single point in time.

```python
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions


class MyCoolRLModel(ReinforcementLearner):
    """
    User created RL prediction model.

    Save this file to `freqtrade/user_data/freqaimodels`

    then use it with:

    freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat

    Here the users can override any of the functions
    available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
    is where the user overrides `MyRLEnv` (see below), to define custom
    `calculate_reward()` function, or to override any other parts of the environment.

    This class also allows users to override any other part of the IFreqaiModel tree.
    For example, the user can override `def fit()` or `def train()` or `def predict()`
    to take fine-tuned control over these processes.

    Another common override may be `def data_cleaning_predict()` where the user can
    take fine-tuned control over the data handling pipeline.
    """
    class MyRLEnv(Base5ActionRLEnv):
        """
        User made custom environment. This class inherits from BaseEnvironment and gym.Env.
        Users can override any functions from those parent classes. Here is an example
        of a user customized `calculate_reward()` function.

        Warning!
        This is function is a showcase of functionality designed to show as many possible
        environment control features as possible. It is also designed to run quickly
        on small computers. This is a benchmark, it is *not* for live production.
        """
        def calculate_reward(self, action: int) -> float:
            # first, penalize if the action is not valid
            if not self._is_valid(action):
                return -2
            pnl = self.get_unrealized_profit()

            factor = 100

            pair = self.pair.replace(':', '')

            # you can use feature values from dataframe
            # Assumes the shifted RSI indicator has been generated in the strategy.
            rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
                            f"{self.config['timeframe']}"].iloc[self._current_tick]

            # reward agent for entering trades
            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
                    and self._position == Positions.Neutral):
                if rsi_now < 40:
                    factor = 40 / rsi_now
                else:
                    factor = 1
                return 25 * factor

            # discourage agent from not entering trades
            if action == Actions.Neutral.value and self._position == Positions.Neutral:
                return -1
            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
            trade_duration = self._current_tick - self._last_trade_tick
            if trade_duration <= max_trade_duration:
                factor *= 1.5
            elif trade_duration > max_trade_duration:
                factor *= 0.5
            # discourage sitting in position
            if self._position in (Positions.Short, Positions.Long) and \
            action == Actions.Neutral.value:
                return -1 * trade_duration / max_trade_duration
            # close long
            if action == Actions.Long_exit.value and self._position == Positions.Long:
                if pnl > self.profit_aim * self.rr:
                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
                return float(pnl * factor)
            # close short
            if action == Actions.Short_exit.value and self._position == Positions.Short:
                if pnl > self.profit_aim * self.rr:
                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
                return float(pnl * factor)
            return 0.
```

## Using Tensorboard

Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:

```bash
tensorboard --logdir user_data/models/unique-id
```

where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell to view the output in the browser at 127.0.0.1:6006 (6006 is the default port used by Tensorboard).

![tensorboard](assets/tensorboard.jpg)

## Custom logging

FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.

`self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:

```python
    class MyRLEnv(Base5ActionRLEnv):
        """
        User made custom environment. This class inherits from BaseEnvironment and gym.Env.
        Users can override any functions from those parent classes. Here is an example
        of a user customized `calculate_reward()` function.
        """
        def calculate_reward(self, action: int) -> float:
            if not self._is_valid(action):
                self.tensorboard_log("invalid")
                return -2

```

!!! Note
    The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.

## Choosing a base environment

FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:

* the actions available in the `calculate_reward`
* the actions consumed by the user strategy

All of the FreqAI provided environments inherit from an action/position agnostic environment object called the `BaseEnvironment`, which contains all shared logic. The architecture is designed to be easily customized. The simplest customization is the `calculate_reward()` (see details [here](#creating-a-custom-reward-function)). However, the customizations can be further extended into any of the functions inside the environment. You can do this by simply overriding those functions inside your `MyRLEnv` in the prediction model file. Or for more advanced customizations, it is encouraged to create an entirely new environment inherited from `BaseEnvironment`.

!!! Note
    Only the `Base3ActionRLEnv` can do long-only training/trading (set the user strategy attribute `can_short = False`).