test_model.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b68d5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from finml.finml.data.handler import StandardNorm\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import polars as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eff8c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardNorm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0101c756",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler.fitted = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "027278db",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler.is_fitted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2973e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"scaler.pkl\",\"wb\") as f:\n",
    "    pickle.dump(scaler,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e1093a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"scaler.pkl\",\"rb\") as f:\n",
    "    scaler2= pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab050db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler2.is_fitted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa0b748a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler.fit_transform([1,2,3,4,5],[2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "731b5e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pl.read_parquet(\"example_data/ETHBUSD-PERP.BINANCE-250-VALUE_IMBALANCE-LAST-EXTERNAL.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16a77961",
   "metadata": {},
   "outputs": [],
   "source": [
    "from features.features import OrderBlockDetecter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1e0f3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "detecter = OrderBlockDetecter(5,0.001,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "953618c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_blocks_times = [] \n",
    "order_blocks_values = [] \n",
    "is_bulls = [] \n",
    "for i in range(2000):\n",
    "    detecter.update_raw(df[i,\"high\"],df[i,\"low\"],df[i,\"close\"],df[i,\"volume\"],int(df[i,\"datetime\"]))\n",
    "    if detecter.initialized():\n",
    "        if detecter.bull_blocks_num()>=1:\n",
    "            bull_info = detecter.get_bull_block(0)\n",
    "            if len(order_blocks_times)< 1 or bull_info[-1]!=order_blocks_times[-1]:\n",
    "                order_blocks_times.append(bull_info[-1])\n",
    "                order_blocks_values.append(bull_info[-2])\n",
    "                is_bulls.append(True)\n",
    "        if detecter.bear_blocks_num()>=1:\n",
    "            bear_info = detecter.get_bear_block(0)\n",
    "            if len(order_blocks_times)<1 or bear_info[-1]!=order_blocks_times[-1]:\n",
    "                order_blocks_times.append(bear_info[-1])\n",
    "                order_blocks_values.append(bear_info[-2])\n",
    "                is_bulls.append(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "29f867b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1183ce55",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c2f21ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({\"datetime\":order_blocks_times,\n",
    "                   \"values\":order_blocks_values,\n",
    "                   \"is_bull\":is_bulls})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8d028726",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "# create an empty figure\n",
    "fig = go.Figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ed7eaf5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2[df2[\"is_bull\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1c2a2600",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/clark/.cache/pypoetry/virtualenvs/nautilus-trader-hztl02LS-py3.10/lib/python3.10/site-packages/plotly/graph_objs/_deprecations.py:378: DeprecationWarning:\n",
      "\n",
      "plotly.graph_objs.Line is deprecated.\n",
      "Please replace it with one of the following more specific types\n",
      "  - plotly.graph_objs.scatter.Line\n",
      "  - plotly.graph_objs.layout.shape.Line\n",
      "  - etc.\n",
      "\n",
      "\n"
     ]
    },
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       "                            \n",
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       "            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",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "# create an empty figure\n",
    "fig = go.Figure()\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=[detecter.get_bear_block(0)[-1],detecter.get_bear_block(1)[-1]],\n",
    "        y=[detecter.get_bear_block(0)[-2],detecter.get_bear_block(1)[-2]])\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Line(\n",
    "        \n",
    "        x=sub_df[\"datetime\"],\n",
    "        y=sub_df[\"close\"]\n",
    "    )\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f9364422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>values</th>\n",
       "      <th>is_bull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1643922034976000000</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1644049608124999936</td>\n",
       "      <td>3037.00</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1644049608124999936</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1644049608124999936</td>\n",
       "      <td>3037.00</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1644049608124999936</td>\n",
       "      <td>3037.00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>1645467448379000064</td>\n",
       "      <td>2704.56</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>1645467448379000064</td>\n",
       "      <td>2704.56</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>1645467448379000064</td>\n",
       "      <td>2704.56</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>1645467448379000064</td>\n",
       "      <td>2704.56</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>1645488457684000000</td>\n",
       "      <td>2596.94</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>485 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                datetime   values  is_bull\n",
       "0    1643922034976000000  2635.11    False\n",
       "12   1644049608124999936  3037.00    False\n",
       "14   1644049608124999936  3037.00    False\n",
       "16   1644049608124999936  3037.00    False\n",
       "18   1644049608124999936  3037.00    False\n",
       "..                   ...      ...      ...\n",
       "955  1645467448379000064  2704.56    False\n",
       "957  1645467448379000064  2704.56    False\n",
       "959  1645467448379000064  2704.56    False\n",
       "961  1645467448379000064  2704.56    False\n",
       "962  1645488457684000000  2596.94    False\n",
       "\n",
       "[485 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b7eabb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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