From 7954a56ef5c5ae945dd77af6c9dff814cba2475d Mon Sep 17 00:00:00 2001
From: "Duc M. Nguyen" <56928903+kurone02@users.noreply.github.com>
Date: Fri, 15 Mar 2024 22:46:17 +0900
Subject: [PATCH] Fixing tuple, 'UTC has no key' errors and update
 alpaca_history to comply with the latest version of pandas in
 FinRL_PaperTrading_Demo.ipynb (#82)

* fix: tuple error and 'UTC has no key' error

* update: convert append to concat
---
 3-Practical/FinRL_PaperTrading_Demo.ipynb | 31 +++++++++++++----------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/3-Practical/FinRL_PaperTrading_Demo.ipynb b/3-Practical/FinRL_PaperTrading_Demo.ipynb
index 1aa61cf..5f4ccf7 100644
--- a/3-Practical/FinRL_PaperTrading_Demo.ipynb
+++ b/3-Practical/FinRL_PaperTrading_Demo.ipynb
@@ -305,9 +305,9 @@
     "            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",
@@ -399,11 +399,12 @@
     "        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",
@@ -412,7 +413,9 @@
     "\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",
@@ -490,14 +493,14 @@
     "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",
@@ -513,7 +516,7 @@
     "id": "9tzAw9k26nAC"
    },
    "source": [
-    "##DRL Agent Class"
+    "## DRL Agent Class"
    ]
   },
   {
@@ -622,7 +625,7 @@
     "\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",
@@ -632,7 +635,7 @@
     "                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",
@@ -1685,8 +1688,8 @@
    "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",
@@ -1698,7 +1701,7 @@
     "    trading_days = get_trading_days(start, end)\n",
     "    df = pd.DataFrame()\n",
     "    for day in trading_days:\n",
-    "        df = df.append(api.get_portfolio_history(date_start = day,timeframe='5Min').df.iloc[:78])\n",
+    "        df = pd.concat([df, api.get_portfolio_history(date_start = day,timeframe='5Min').df.iloc[:78]])\n",
     "    equities = df.equity.values\n",
     "    cumu_returns = equities/equities[0]\n",
     "    cumu_returns = cumu_returns[~np.isnan(cumu_returns)]\n",
@@ -1893,7 +1896,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.11"
   },
   "vscode": {
    "interpreter": {