trade

examples/trade/agent/basic.py

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+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)