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72 lines
1.8 KiB
Python
72 lines
1.8 KiB
Python
from tianshou.policy import BasePolicy
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from tianshou.data import Batch
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import numpy as np
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import torch
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from env import nan_weighted_avg
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class TWAP(BasePolicy):
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""" The TWAP strategy. """
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def __init__(self, config):
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super().__init__()
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self.max_step_num = config["max_step_num"]
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self.num_cpus = config["num_cpus"]
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# @njit(parallel=True)
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def forward(self, batch: Batch, state=None, **kwargs) -> Batch:
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act = [1] * len(batch.obs.private)
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return Batch(act=act, state=state)
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def learn(self, batch, batch_size, repeat):
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pass
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def process_fn(self, batch, buffer, indice):
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pass
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class VWAP(BasePolicy):
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""" The VWAP strategy."""
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def __init__(self, config):
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super().__init__()
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def forward(self, batch, state, **kwargs):
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obs = batch.obs
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r = np.stack(obs.prediction).reshape(-1)
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return Batch(act=r, state=state)
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def learn(self, batch, batch_size, repeat):
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pass
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def process_fn(self, batch, buffer, indice):
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pass
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class AC(VWAP):
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"""Almgren-Chriss strategy."""
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def __init__(self, config):
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super().__init__(config)
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self.T = config["max_step_num"]
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self.gamma = 0
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self.tau = 1
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self.lamb = config["lambda"]
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self.eps = 0.0625
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self.alpha = 0.02
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self.eta = 2.5e-6
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def forward(self, batch, state, **kwargs):
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obs = batch.obs
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sig = np.stack(obs.prediction).reshape(-1)
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sell = ~np.stack(obs.is_buy).astype(np.bool)
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data = np.stack(obs.private)
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t = data[:, 2]
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t = t + 1
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k_tild = self.lamb / self.eta * sig * sig
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k = np.arccosh(k_tild / 2 + 1)
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act = (np.sinh(k * (self.T - t)) - np.sinh(k * (self.T - t - 1))) / np.sinh(
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k * self.T
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)
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return Batch(act=act, state=state)
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