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RL.py

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+import shift
+from RL_env import SHIFT_env as Env
+import torch
+
+from tianshou.data import Collector, VectorReplayBuffer
+from tianshou.env import DummyVectorEnv
+from tianshou.policy import PPOPolicy
+from tianshou.trainer import onpolicy_trainer
+from tianshou.utils import TensorboardLogger
+from tianshou.utils.net.common import ActorCritic, DataParallelNet, Net
+from tianshou.utils.net.discrete import Actor, Critic
+
+action_dim = 3
+state_dim = 4
+device = 'cpu'
+lr = 0.0003
+buffer_size = 20000
+epoch = 10,
+step_per_epoch = 50000
+repeat_per_collect = 10
+test_num = 100
+batch_size = 64
+
+# symbol = 'GS'
+# start = '2015-02-01'
+# end = '2017-01-03'
+# n_share = 100
+seed = 13
+
+trader = shift.Trader("test002")
+trader.disconnect()
+trader.connect("initiator.cfg", "password")
+trader.subAllOrderBook()
+
+env = Env(trader = trader,
+          t = 1,
+          nTimeStep=10,
+          ODBK_range=5,
+          symbol='CSCO',
+          target_price=100)
+
+
+np.random.seed(seed)
+torch.manual_seed(seed)
+train_envs.seed(seed)
+test_envs.seed(seed)
+
+net = Net(state_dim, hidden_sizes=(64, 64))
+actor = Actor(net, action_dim, device=device).to(device)
+critic = Critic(net, device=device).to(device)
+actor_critic = ActorCritic(actor, critic)
+optim = torch.optim.Adam(actor_critic.parameters(), lr=lr)
+dist = torch.distributions.Normal
+
+policy = PPOPolicy(
+        actor,
+        critic,
+        optim,
+        dist)
+
+train_collector = Collector(
+    policy, env, VectorReplayBuffer(buffer_size, 1)
+)
+test_collector = Collector(policy, env)
+
+result = onpolicy_trainer(
+        policy,
+        train_collector,
+        test_collector,
+        epoch,
+        step_per_epoch,
+        repeat_per_collect,
+        test_num,
+        batch_size
+    )
+
+env.save_to_csv()