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Emre 2022-11-27 22:23:00 +03:00 committed by GitHub
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docs/freqai-reinforcement-learning.md
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@ -95,7 +95,7 @@ Most of the function remains the same as for typical Regressors, however, the fu
informative[f"%-{pair}raw_low"] = informative["low"] informative[f"%-{pair}raw_low"] = informative["low"]
``` ```
Finally, there is no explicit "label" to make - instead the you need 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. Finally, there is no explicit "label" to make - instead the user need 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: 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: