diff --git a/notebooks/btcusdt_dataset.ipynb b/notebooks/btcusdt_dataset.ipynb
index 48626f8..1987161 100644
--- a/notebooks/btcusdt_dataset.ipynb
+++ b/notebooks/btcusdt_dataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/evaluate.ipynb b/notebooks/evaluate.ipynb
index 796d185..370cac2 100644
--- a/notebooks/evaluate.ipynb
+++ b/notebooks/evaluate.ipynb
@@ -1,5 +1,31 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wandb\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objs as go\n",
+    "from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer\n",
+    "from pytorch_forecasting.data.timeseries import TimeSeriesDataSet\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "from ml.model import load_model_from_wandb\n",
+    "from ml.data import get_dataset_from_wandb, get_train_validation_split, build_time_series_dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate model predicting price (with Quantiles)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -9,156 +35,52 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/anaconda3/envs/wnemsc/lib/python3.9/site-packages/pytorch_forecasting/models/base_model.py:30: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from tqdm.autonotebook import tqdm\n"
+      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 310.03MB. 10 files... \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   10 of 10 files downloaded.  \n",
+      "Done. 0:0:0.4\n",
+      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
+      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n"
      ]
     }
    ],
    "source": [
-    "import wandb\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "import plotly.graph_objs as go\n",
-    "from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer\n",
-    "from pytorch_forecasting.data.timeseries import TimeSeriesDataSet\n",
-    "import numpy as np\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_dataset_window(run, window=None):\n",
-    "    artifact_name = f\"{run.project}/{run.config['data']['dataset']}\"\n",
-    "    artifact = wandb.Api().artifact(artifact_name)\n",
-    "    base_path = artifact.download()\n",
+    "RUN_ID = \"filipstefaniuk/wne-masters-thesis-testing/x352wmas\"\n",
     "\n",
-    "    name = artifact.metadata['name']\n",
-    "    in_sample_name = f\"in-sample-{window or run.config['data']['sliding_window']}\"\n",
-    "    in_sample_data = pd.read_csv(os.path.join(\n",
-    "        base_path, name + '-' + in_sample_name + '.csv'))\n",
-    "    out_of_sample_name = f\"out-of-sample-{window or run.config['data']['sliding_window']}\"\n",
-    "    out_of_sample_data = pd.read_csv(os.path.join(\n",
-    "        base_path, name + '-' + out_of_sample_name + '.csv'))\n",
-    "\n",
-    "    return in_sample_data, out_of_sample_data\n",
-    "\n",
-    "def get_train_validation_split(config, in_sample_data):\n",
-    "    validation_part = config['data']['validation']\n",
-    "    train_data = in_sample_data.iloc[:int(len(in_sample_data) * (1 - validation_part))]\n",
-    "    val_data = in_sample_data.iloc[len(train_data) - config['past_window']:]\n",
-    "\n",
-    "    return train_data, val_data\n",
-    "\n",
-    "def build_time_series_dataset(config, data):\n",
-    "    data = data.copy()\n",
-    "    data['weekday'] = data['weekday'].astype('str')\n",
-    "    data['hour'] = data['hour'].astype('str')\n",
-    "\n",
-    "    time_series_dataset = TimeSeriesDataSet(\n",
-    "        data,\n",
-    "        time_idx=config['data']['fields']['time_index'],\n",
-    "        target=config['data']['fields']['target'],\n",
-    "        group_ids=config['data']['fields']['group_ids'],\n",
-    "        min_encoder_length=config['past_window'],\n",
-    "        max_encoder_length=config['past_window'],\n",
-    "        min_prediction_length=config['future_window'],\n",
-    "        max_prediction_length=config['future_window'],\n",
-    "        static_reals=config['data']['fields']['static_real'],\n",
-    "        static_categoricals=config['data']['fields']['static_cat'],\n",
-    "        time_varying_known_reals=config['data']['fields']['dynamic_known_real'],\n",
-    "        time_varying_known_categoricals=config['data']['fields']['dynamic_known_cat'],\n",
-    "        time_varying_unknown_reals=config['data']['fields']['dynamic_unknown_real'],\n",
-    "        time_varying_unknown_categoricals=config['data']['fields']['dynamic_unknown_cat'],\n",
-    "        randomize_length=False,\n",
-    "    )\n",
-    "\n",
-    "    return time_series_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: Maybe save all on cpu\n",
-    "def get_model(run):\n",
-    "    model_name = run.config['model']['name']\n",
-    "    model_path = f\"{run.project}/model-{run.id}:best\"\n",
-    "    model_artifact = wandb.Api().artifact(model_path)\n",
-    "\n",
-    "    if model_name == 'TemporalFusionTransformer':\n",
-    "        return TemporalFusionTransformer.load_from_checkpoint(model_artifact.file())\n",
-    "\n",
-    "    raise ValueError(\"Invalid model name\")"
+    "run = wandb.Api().run(RUN_ID)\n",
+    "in_sample, out_of_sample = get_dataset_from_wandb(run)\n",
+    "test_data = out_of_sample\n",
+    "train_data, valid_data = get_train_validation_split(run.config, in_sample)\n",
+    "train = build_time_series_dataset(run.config, train_data)\n",
+    "valid = build_time_series_dataset(run.config, valid_data)\n",
+    "test = build_time_series_dataset(run.config, test_data)\n",
+    "model = load_model_from_wandb(run)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "RUN_ID = \"filipstefaniuk/wne-masters-thesis-testing/r1gtdlsf\"\n",
-    "PROJECT = \"wne-masters-thesis-testing\"\n",
-    "run = wandb.Api().run(RUN_ID)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 310.03MB. 10 files... \n",
-      "\u001b[34m\u001b[1mwandb\u001b[0m:   10 of 10 files downloaded.  \n",
-      "Done. 0:0:0.5\n",
-      "/usr/local/anaconda3/envs/wnemsc/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
-      "/usr/local/anaconda3/envs/wnemsc/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "in_sample, _ = get_dataset_window(run)\n",
-    "train_data, valid_data = get_train_validation_split(run.config, in_sample)\n",
-    "train = build_time_series_dataset(run.config, train_data)\n",
-    "valid = build_time_series_dataset(run.config, valid_data)\n",
-    "model = get_model(run)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: False, used: False\n",
+      "GPU available: True (cuda), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/anaconda3/envs/wnemsc/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
+      "HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: PossibleUserWarning:\n",
+      "\n",
+      "The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n",
+      "\n"
      ]
     }
    ],
    "source": [
     "predictions = model.predict(\n",
     "        valid.to_dataloader(train=False, batch_size=64),\n",
+    "        # test.to_dataloader(train=False, batch_size=64),\n",
     "        mode=\"raw\",\n",
     "        return_index=True,\n",
     "        trainer_kwargs={\n",
@@ -168,15 +90,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Baseline returns 0.23008643692049624\n",
-      "Strategy returns 0.09137899364187074\n"
+      "Baseline returns 0.22730028053392815\n",
+      "Strategy returns 0.15714438080714976\n"
      ]
     },
     {
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('748aa109-8016-4005-bdb8-c09d5c8cf8cd');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from strategy.strategy import BuyAndHoldStrategy, PriceQuantilePredictionStrategy\n",
+    "from strategy.evaluation import evaluate_strategy\n",
+    "\n",
+    "model_predictions = pd.DataFrame(predictions.index)\n",
+    "model_predictions['prediction'] = list(torch.squeeze(predictions.output.prediction).cpu().numpy())\n",
+    "\n",
+    "result_baseline = evaluate_strategy(valid_data, BuyAndHoldStrategy())\n",
+    "result_strategy = evaluate_strategy(valid_data, PriceQuantilePredictionStrategy(model_predictions), exchange_fee=0.001)\n",
+    "\n",
+    "print('Baseline returns', result_baseline['total_return'])\n",
+    "print('Strategy returns', result_strategy['total_return'])\n",
+    "\n",
+    "go.Figure([\n",
+    "    go.Scatter(y=result_baseline['portfolio_value']),\n",
+    "    go.Scatter(y=result_strategy['portfolio_value'])]).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate model predicting returns (with GMADL loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 310.03MB. 10 files... \n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m:   10 of 10 files downloaded.  \n",
+      "Done. 0:0:0.4\n",
+      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: UserWarning:\n",
+      "\n",
+      "Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
+      "\n",
+      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: UserWarning:\n",
+      "\n",
+      "Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "RUN_ID = \"filipstefaniuk/wne-masters-thesis-testing/m6zetdhf\"\n",
+    "\n",
+    "run = wandb.Api().run(RUN_ID)\n",
+    "in_sample, out_of_sample = get_dataset_from_wandb(run)\n",
+    "train_data, valid_data = get_train_validation_split(run.config, in_sample)\n",
+    "train = build_time_series_dataset(run.config, train_data)\n",
+    "valid = build_time_series_dataset(run.config, valid_data)\n",
+    "test = build_time_series_dataset(run.config, out_of_sample)\n",
+    "model = load_model_from_wandb(run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: PossibleUserWarning:\n",
+      "\n",
+      "The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = model.predict(\n",
+    "        valid.to_dataloader(train=False, batch_size=64),\n",
+    "        # test.to_dataloader(train=False, batch_size=64),\n",
+    "        mode=\"raw\",\n",
+    "        return_index=True,\n",
+    "        trainer_kwargs={\n",
+    "            'logger': False\n",
+    "        })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline returns 0.2240889082270856\n",
+      "Strategy returns -0.3902088328046026\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
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+       "                            \n",
+       "var gd = document.getElementById('dad05062-a768-45f2-803d-b0a8c1cf697c');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
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+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -42543,13 +85131,13 @@
    ],
    "source": [
     "from strategy.evaluation import evaluate_strategy\n",
-    "from strategy.strategy import BuyAndHoldStrategy, ModelReturnsPredictionStrategy\n",
+    "from strategy.strategy import BuyAndHoldStrategy, ReturnsPredictionStrategy\n",
     "\n",
     "model_predictions = pd.DataFrame(predictions.index)\n",
-    "model_predictions['prediction'] = predictions.output.prediction.reshape(-1).numpy()\n",
+    "model_predictions['prediction'] = predictions.output.prediction.reshape(-1).cpu().numpy()\n",
     "\n",
     "result_baseline = evaluate_strategy(valid_data, BuyAndHoldStrategy())\n",
-    "result_strategy = evaluate_strategy(valid_data, ModelReturnsPredictionStrategy(model_predictions))\n",
+    "result_strategy = evaluate_strategy(valid_data, ReturnsPredictionStrategy(model_predictions, threshold=0.001))\n",
     "\n",
     "print('Baseline returns', result_baseline['total_return'])\n",
     "print('Strategy returns', result_strategy['total_return'])\n",
@@ -42561,7 +85149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
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      "output_type": "display_data"
diff --git a/src/ml/data.py b/src/ml/data.py
new file mode 100644
index 0000000..2edabf7
--- /dev/null
+++ b/src/ml/data.py
@@ -0,0 +1,54 @@
+import os
+
+import pandas as pd
+import wandb
+from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
+
+
+def get_dataset_from_wandb(run, window=None):
+    artifact_name = f"{run.project}/{run.config['data']['dataset']}"
+    artifact = wandb.Api().artifact(artifact_name)
+    base_path = artifact.download()
+
+    name = artifact.metadata['name']
+    in_sample_name = f"in-sample-{window or run.config['data']['sliding_window']}"
+    in_sample_data = pd.read_csv(os.path.join(
+        base_path, name + '-' + in_sample_name + '.csv'))
+    out_of_sample_name = f"out-of-sample-{window or run.config['data']['sliding_window']}"
+    out_of_sample_data = pd.read_csv(os.path.join(
+        base_path, name + '-' + out_of_sample_name + '.csv'))
+
+    return in_sample_data, out_of_sample_data
+
+def get_train_validation_split(config, in_sample_data):
+    validation_part = config['data']['validation']
+    train_data = in_sample_data.iloc[:int(len(in_sample_data) * (1 - validation_part))]
+    val_data = in_sample_data.iloc[len(train_data) - config['past_window']:]
+
+    return train_data, val_data
+
+
+def build_time_series_dataset(config, data):
+    data = data.copy()
+    data['weekday'] = data['weekday'].astype('str')
+    data['hour'] = data['hour'].astype('str')
+
+    time_series_dataset = TimeSeriesDataSet(
+        data,
+        time_idx=config['data']['fields']['time_index'],
+        target=config['data']['fields']['target'],
+        group_ids=config['data']['fields']['group_ids'],
+        min_encoder_length=config['past_window'],
+        max_encoder_length=config['past_window'],
+        min_prediction_length=config['future_window'],
+        max_prediction_length=config['future_window'],
+        static_reals=config['data']['fields']['static_real'],
+        static_categoricals=config['data']['fields']['static_cat'],
+        time_varying_known_reals=config['data']['fields']['dynamic_known_real'],
+        time_varying_known_categoricals=config['data']['fields']['dynamic_known_cat'],
+        time_varying_unknown_reals=config['data']['fields']['dynamic_unknown_real'],
+        time_varying_unknown_categoricals=config['data']['fields']['dynamic_unknown_cat'],
+        randomize_length=False,
+    )
+
+    return time_series_dataset
\ No newline at end of file
diff --git a/src/ml/model.py b/src/ml/model.py
new file mode 100644
index 0000000..b4a0caa
--- /dev/null
+++ b/src/ml/model.py
@@ -0,0 +1,13 @@
+import wandb
+from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer
+
+# TODO: Maybe save all models on cpu
+def load_model_from_wandb(run):
+    model_name = run.config['model']['name']
+    model_path = f"{run.project}/model-{run.id}:best"
+    model_artifact = wandb.Api().artifact(model_path)
+
+    if model_name == 'TemporalFusionTransformer':
+        return TemporalFusionTransformer.load_from_checkpoint(model_artifact.file())
+
+    raise ValueError("Invalid model name")
\ No newline at end of file
diff --git a/src/strategy/strategy.py b/src/strategy/strategy.py
index 215315f..3aa454b 100644
--- a/src/strategy/strategy.py
+++ b/src/strategy/strategy.py
@@ -34,7 +34,36 @@ class BuyAndHoldStrategy(StrategyBase):
             dtype=np.int32)
 
 
-class ModelReturnsPredictionStrategy(StrategyBase):
+class ModelPredictionsStrategyBase(StrategyBase):
+    """Base class for strategies based on model predictions."""
+    def __init__(self,
+                 predictions,
+                 name: str = None):
+        self.predictions = predictions
+        assert 'time_index' in self.predictions.columns
+        assert 'group_id' in self.predictions.columns
+        assert 'prediction' in self.predictions.columns
+
+        self.name = name
+
+
+    def info(self):
+        return {'strategy_name': self.name or 'Unknown model'}
+
+    def run(self, data):
+        # Adds predictions to data, if prediction is unknown for a given
+        # item it will be nan.
+        merged_data = pd.merge(
+            data, self.predictions, on=['time_index', 'group_id'],
+            how='left')
+        
+        return self.get_positions(merged_data)
+
+    def get_positions(self, data):
+        raise NotImplementedError()
+
+
+class ReturnsPredictionStrategy(ModelPredictionsStrategyBase):
     """Strategy that selects position based on returns predictions."""
 
     def __init__(
@@ -42,22 +71,11 @@ class ModelReturnsPredictionStrategy(StrategyBase):
             predictions,
             threshold=0.001,
             name=None):
-        self.predictions = predictions
-        assert 'time_index' in self.predictions.columns
-        assert 'group_id' in self.predictions.columns
-        assert 'prediction' in self.predictions.columns
-
-        self.name = name or "ML Returns prediction"
+        super().__init__(predictions, name=name)
         self.threshold = threshold
 
-    def info(self) -> Dict[str, Any]:
-        return {'strategy_name': self.name}
-
-    def run(self, data):
-        arr = pd.merge(
-            data, self.predictions, on=['time_index', 'group_id'],
-            how='left')['prediction'].to_numpy()
-
+    def get_positions(self, data):
+        arr = data['prediction']
         positions = []
         for i in range(len(arr)):
             if arr[i] > self.threshold:
@@ -70,3 +88,39 @@ class ModelReturnsPredictionStrategy(StrategyBase):
                 positions.append(positions[-1])
 
         return np.array(positions, dtype=np.int32)
+
+
+class PriceQuantilePredictionStrategy(ModelPredictionsStrategyBase):
+    def __init__(
+        self,
+        predictions,
+        name=None):
+        super().__init__(predictions, name=name)
+
+    def info(self):
+        return {'strategy_name': self.name}
+
+    def get_positions(self, data):
+
+        arr_preds = data['prediction'].to_numpy()
+        arr_close_price = data['close_price'].to_numpy()
+
+        positions = []
+        for i in range(len(arr_preds)):
+            if not np.isnan(arr_preds[i]).any():
+                price = arr_close_price[i]
+                pred_low = arr_preds[i][0]
+                pred_high = arr_preds[i][-1]
+                if (pred_low - price) / price > 0.001:
+                    positions.append(LONG_POSITION)
+                    continue
+                elif (pred_high - price) / price < -0.001:
+                    positions.append(EXIT_POSITION)
+                    continue
+            
+            if not len(positions):
+                positions.append(EXIT_POSITION)
+            else:
+                positions.append(positions[-1])
+
+        return np.array(positions, dtype=np.int32)
\ No newline at end of file