mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
cbe1c52e00
Rename all source files, scripts, tests, documentation, and configuration from Predix/predix to NexQuant/nexquant across the entire codebase.
154 lines
4.2 KiB
Python
154 lines
4.2 KiB
Python
"""
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Fallback RL implementation for users without stable-baselines3.
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Provides simple rule-based trading when RL library is not available.
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This ensures the NexQuant system works for all GitHub users, even
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without the optional stable-baselines3 dependency.
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The fallback implements a momentum-based strategy as a placeholder
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for proper RL algorithms.
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"""
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import numpy as np
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from typing import Optional, Any, Dict
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class SimpleRLFallback:
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"""
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Simple momentum-based trading as fallback when RL library unavailable.
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This is NOT a real RL algorithm. It provides basic functionality
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so the system doesn't break when stable-baselines3 is not installed.
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Strategy:
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- Positive momentum -> Long position
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- Negative momentum -> Short position
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- Zero momentum -> Hold
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Parameters
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----------
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window_size : int
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Lookback window for momentum calculation
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momentum_threshold : float
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Threshold for entering positions (absolute value)
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max_position : float
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Maximum position size (-1 to 1)
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"""
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def __init__(
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self,
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window_size: int = 20,
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momentum_threshold: float = 0.0,
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max_position: float = 1.0,
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) -> None:
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self.window_size = window_size
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self.momentum_threshold = momentum_threshold
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self.max_position = max_position
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self.algorithm = "FALLBACK" # Identify as fallback
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self.model = self # Self-reference for compatibility
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self.is_trained = True # Always "trained"
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def predict(
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self,
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observation: np.ndarray,
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deterministic: bool = True,
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) -> np.ndarray:
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"""
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Predict action from observation using momentum strategy.
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Parameters
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----------
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observation : np.ndarray
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Observation vector (first window_size elements are prices)
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deterministic : bool
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Ignored (for API compatibility)
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Returns
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-------
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np.ndarray
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Action (-1 to 1)
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"""
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# Extract prices from observation (first window_size elements)
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price_length = min(self.window_size, len(observation))
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prices = observation[:price_length]
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# Calculate momentum
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if len(prices) < 2 or prices[0] == 0:
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return np.array([0.0])
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momentum = (prices[-1] - prices[0]) / prices[0]
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# Apply threshold
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if abs(momentum) < self.momentum_threshold:
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return np.array([0.0])
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# Scale to position size
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position = np.clip(momentum, -self.max_position, self.max_position)
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return np.array([position])
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def learn(
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self,
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total_timesteps: int = 100000,
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*args: Any,
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**kwargs: Any,
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) -> None:
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"""
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No-op for compatibility with RLTradingAgent.train().
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The fallback doesn't actually train, it's a fixed strategy.
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"""
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pass
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def save(self, path: str) -> None:
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"""Save fallback config (no-op for compatibility)."""
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import json
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from pathlib import Path
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config = {
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"algorithm": "FALLBACK",
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"window_size": self.window_size,
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"momentum_threshold": self.momentum_threshold,
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"max_position": self.max_position,
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}
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path_obj = Path(path)
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path_obj.parent.mkdir(parents=True, exist_ok=True)
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# Save as JSON instead of model file
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with open(path_obj.with_suffix('.json'), 'w') as f:
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json.dump(config, f, indent=2)
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def load(self, path: str) -> None:
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"""Load fallback config (no-op for compatibility)."""
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# Fallback doesn't need to load anything
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pass
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@staticmethod
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def is_available() -> bool:
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"""
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Check if stable-baselines3 is available.
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Returns
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-------
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bool
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True if stable-baselines3 is installed
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"""
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try:
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import stable_baselines3 # noqa: F401
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return True
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except ImportError:
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return False
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@staticmethod
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def get_recommendation() -> str:
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"""
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Get recommendation for installing RL dependencies.
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Returns
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-------
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str
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Installation command
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"""
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return "pip install stable-baselines3[extra] gymnasium"
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