notadamking/RLTrader

Allow user-defined prediction models to replace SARIMAX with LSTM, FB Prophet, etc.

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#15 opened on 2019年6月7日

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説明

Instead of using the SARIMAX model's predictions to be fed as a feature / observation into the agent at every time step, maybe using the predictions of a separately trained LSTM may perform better. I've found that LSTM's trained on stationary time series usually perform ok, in that their prediction "direction" (up or down) is generally correct. Also, the slope (change in log returns with respect to time) of the LSTM prediction can be used as an additional input feature as an indicator for the severity of the predicted change (will the price jump up or down suddenly?).

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技術スタック
python
領域
machine learning
Issue 種別
feature
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
4
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
over 1 week
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
needs investigation
前提条件
Python programmingReinforcement learning basicsTime series forecastingDeep learning (LSTM)
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
25
調査方針
Examine the existing SARIMAX integration in the RLTrader codebase to understand how predictions are fed to the agent. Review the two comments on this issue for additional context. Then design a generic interface for user defined prediction models, such as LSTM or FB Prophet, that can replace SARIMAX. Consider using libraries like PyTorch or TensorFlow for LSTM and ensure compatibility with the existing trading environment.
Allow user-defined prediction models to replace SARIMAX with LSTM, FB Prophet, etc. · notadamking/RLTrader#15 | Good First Issue