mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-01 01:51:18 +08:00
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
from joblib import Parallel, delayed
|
|
from numba import njit, prange
|
|
from tianshou.policy import BasePolicy
|
|
from tianshou.data import Batch
|
|
import numpy as np
|
|
import torch
|
|
from env import nan_weighted_avg
|
|
|
|
|
|
class TWAP(BasePolicy):
|
|
""" The TWAP strategy. """
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.max_step_num = config["max_step_num"]
|
|
self.num_cpus = config["num_cpus"]
|
|
|
|
# @njit(parallel=True)
|
|
def forward(self, batch: Batch, state=None, **kwargs) -> Batch:
|
|
act = [1] * len(batch.obs.private)
|
|
return Batch(act=act, state=state)
|
|
|
|
def learn(self, batch, batch_size, repeat):
|
|
pass
|
|
|
|
def process_fn(self, batch, buffer, indice):
|
|
pass
|
|
|
|
|
|
class VWAP(BasePolicy):
|
|
""" The VWAP strategy."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
def forward(self, batch, state, **kwargs):
|
|
obs = batch.obs
|
|
r = np.stack(obs.prediction).reshape(-1)
|
|
return Batch(act=r, state=state)
|
|
|
|
def learn(self, batch, batch_size, repeat):
|
|
pass
|
|
|
|
def process_fn(self, batch, buffer, indice):
|
|
pass
|
|
|
|
|
|
class AC(VWAP):
|
|
"""Almgren-Chriss strategy."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.T = config["max_step_num"]
|
|
self.gamma = 0
|
|
self.tau = 1
|
|
self.lamb = config["lambda"]
|
|
self.eps = 0.0625
|
|
self.alpha = 0.02
|
|
self.eta = 2.5e-6
|
|
|
|
def forward(self, batch, state, **kwargs):
|
|
obs = batch.obs
|
|
sig = np.stack(obs.prediction).reshape(-1)
|
|
sell = ~np.stack(obs.is_buy).astype(np.bool)
|
|
data = np.stack(obs.private)
|
|
t = data[:, 2]
|
|
t = t + 1
|
|
k_tild = self.lamb / self.eta * sig * sig
|
|
k = np.arccosh(k_tild / 2 + 1)
|
|
act = (np.sinh(k * (self.T - t)) - np.sinh(k * (self.T - t - 1))) / np.sinh(k * self.T)
|
|
return Batch(act=act, state=state)
|