Merge pull request #1 from AI4Finance-Foundation/master

Update fork

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ ci:
     skip: [flake8]  # remove this eventually
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: check-docstring-first
       - id: check-yaml
@@ -16,12 +16,12 @@ repos:
       - id: reorder-python-imports
         args: [--py37-plus, --add-import, "from __future__ import annotations"]
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.1
+    rev: v3.15.2
     hooks:
       - id: pyupgrade
         args: [--py37-plus]
   - repo: https://github.com/psf/black
-    rev: 24.2.0
+    rev: 24.3.0
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/flake8

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2020 AI4Finance Foundation
+Copyright (c) 2024 AI4Finance Foundation Inc.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

examples/FinRL_PaperTrading_Demo.ipynb

@@ -317,9 +317,9 @@
     "            action, logprob = [t.squeeze(0) for t in get_action(state.unsqueeze(0))[:2]]\n",
     "\n",
     "            ary_action = convert(action).detach().cpu().numpy()\n",
-    "            ary_state, reward, done, _ = env.step(ary_action)\n",
+    "            ary_state, reward, done, _, _ = env.step(ary_action)\n",
     "            if done:\n",
-    "                ary_state = env.reset()\n",
+    "                ary_state, _ = env.reset()\n",
     "\n",
     "            states[i] = state\n",
     "            actions[i] = action\n",
@@ -411,11 +411,12 @@
     "        self.if_discrete = False  # discrete action or continuous action\n",
     "\n",
     "    def reset(self) -> np.ndarray:  # reset the agent in env\n",
-    "        return self.env.reset()\n",
+    "        resetted_env, _ = self.env.reset()\n",
+    "        return resetted_env\n",
     "\n",
     "    def step(self, action: np.ndarray) -> (np.ndarray, float, bool, dict):  # agent interacts in env\n",
     "        # We suggest that adjust action space to (-1, +1) when designing a custom env.\n",
-    "        state, reward, done, info_dict = self.env.step(action * 2)\n",
+    "        state, reward, done, info_dict, _ = self.env.step(action * 2)\n",
     "        return state.reshape(self.state_dim), float(reward), done, info_dict\n",
     "\n",
     "    \n",
@@ -424,7 +425,9 @@
     "\n",
     "    env = build_env(args.env_class, args.env_args)\n",
     "    agent = args.agent_class(args.net_dims, args.state_dim, args.action_dim, gpu_id=args.gpu_id, args=args)\n",
-    "    agent.states = env.reset()[np.newaxis, :]\n",
+    "\n",
+    "    new_env, _ = env.reset()\n",
+    "    agent.states = new_env[np.newaxis, :]\n",
     "\n",
     "    evaluator = Evaluator(eval_env=build_env(args.env_class, args.env_args),\n",
     "                          eval_per_step=args.eval_per_step,\n",
@@ -502,14 +505,14 @@
     "def get_rewards_and_steps(env, actor, if_render: bool = False) -> (float, int):  # cumulative_rewards and episode_steps\n",
     "    device = next(actor.parameters()).device  # net.parameters() is a Python generator.\n",
     "\n",
-    "    state = env.reset()\n",
+    "    state, _ = env.reset()\n",
     "    episode_steps = 0\n",
     "    cumulative_returns = 0.0  # sum of rewards in an episode\n",
     "    for episode_steps in range(12345):\n",
     "        tensor_state = torch.as_tensor(state, dtype=torch.float32, device=device).unsqueeze(0)\n",
     "        tensor_action = actor(tensor_state)\n",
     "        action = tensor_action.detach().cpu().numpy()[0]  # not need detach(), because using torch.no_grad() outside\n",
-    "        state, reward, done, _ = env.step(action)\n",
+    "        state, reward, done, _, _ = env.step(action)\n",
     "        cumulative_returns += reward\n",
     "\n",
     "        if if_render:\n",
@@ -525,7 +528,7 @@
     "id": "9tzAw9k26nAC"
    },
    "source": [
-    "##DRL Agent Class"
+    "## DRL Agent Class"
    ]
   },
   {
@@ -634,7 +637,7 @@
     "\n",
     "        # test on the testing env\n",
     "        _torch = torch\n",
-    "        state = environment.reset()\n",
+    "        state, _ = environment.reset()\n",
     "        episode_returns = []  # the cumulative_return / initial_account\n",
     "        episode_total_assets = [environment.initial_total_asset]\n",
     "        with _torch.no_grad():\n",
@@ -644,7 +647,7 @@
     "                action = (\n",
     "                    a_tensor.detach().cpu().numpy()[0]\n",
     "                )  # not need detach(), because with torch.no_grad() outside\n",
-    "                state, reward, done, _ = environment.step(action)\n",
+    "                state, reward, done, _, _ = environment.step(action)\n",
     "\n",
     "                total_asset = (\n",
     "                    environment.amount\n",
@@ -1693,8 +1696,8 @@
    "source": [
     "def get_trading_days(start, end):\n",
     "    nyse = tc.get_calendar('NYSE')\n",
-    "    df = nyse.sessions_in_range(pd.Timestamp(start,tz=pytz.UTC),\n",
-    "                                pd.Timestamp(end,tz=pytz.UTC))\n",
+    "    df = nyse.sessions_in_range(pd.Timestamp(start),\n",
+    "                                pd.Timestamp(end))\n",
     "    trading_days = []\n",
     "    for day in df:\n",
     "        trading_days.append(str(day)[:10])\n",
@@ -1889,7 +1892,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.11"
   },
   "vscode": {
    "interpreter": {