nn-hourly-v5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:38.982008Z",
     "start_time": "2024-08-25T05:05:37.036984Z"
    }
   },
   "source": [
    "from unittest.mock import inplace\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "from keras.src.callbacks import EarlyStopping\n",
    "import random\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras.api.layers import Dense, Dropout\n",
    "from keras.api.models import Sequential\n",
    "from keras.api.regularizers import l1, l2\n",
    "from keras.api.optimizers import Adam"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:38.990048Z",
     "start_time": "2024-08-25T05:05:38.985536Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def set_seeds(seed = 100):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)\n",
    "    \n",
    "def cw(df):\n",
    "    c0, c1 = np.bincount(df[\"dir\"])\n",
    "    w0 = (1/c0) * (len(df)) / 2\n",
    "    w1 = (1/c1) * (len(df)) / 2\n",
    "    return {0:w0, 1:w1}"
   ],
   "id": "b782f57370576e45",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:39.112470Z",
     "start_time": "2024-08-25T05:05:39.109354Z"
    }
   },
   "cell_type": "code",
   "source": [
    "symbol = \"EURUSD=X\"\n",
    "#start = \"2022-08-27\"\n",
    "start = \"2022-08-28\"\n",
    "end = \"2024-08-22\"\n",
    "interval = '1h'\n",
    "ptc = 0.000059\n",
    "lags = 7"
   ],
   "id": "b9805ccb87d70066",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:39.780155Z",
     "start_time": "2024-08-25T05:05:39.123895Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = yf.download(symbol, start, end, interval=interval)\n",
    "#df.rename(columns={\"Close\": \"price\"}, inplace=True)\n",
    "df.drop(columns=['Adj Close', 'Volume'], inplace=True)\n",
    "#df.index = df.index.tz_convert('utc')\n",
    "df['returns'] = np.log(df['Close'].div(df['Close'].shift(1)))\n",
    "df['day_of_week'] = df.index.dayofweek\n",
    "df['dir'] = np.where(df['returns'] > 0, 1,0)\n",
    "df.dropna(inplace=True)\n",
    "features = ['Open', 'High', 'Low', 'Close', 'returns']"
   ],
   "id": "8cc6e61a6ac618cc",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:39.826716Z",
     "start_time": "2024-08-25T05:05:39.797884Z"
    }
   },
   "cell_type": "code",
   "source": [
    "copy = df.copy()\n",
    "cols = []\n",
    "for f in features:\n",
    "    for lag in range(1, lags + 1):\n",
    "        col = \"{}_lag_{}\".format(f, lag)\n",
    "        copy[col] = copy[f].shift(lag)\n",
    "        cols.append(col)\n",
    "copy.dropna(inplace=True)\n",
    "cols.append('day_of_week')\n",
    "split = int(len(copy)* 0.8)\n",
    "train = copy.iloc[:split].copy()\n",
    "test = copy.iloc[split:].copy()\n",
    "mu, std = train.mean(), train.std()\n",
    "train_standardized = (train-mu) / std"
   ],
   "id": "9262fad79b9be002",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:39.889771Z",
     "start_time": "2024-08-25T05:05:39.886115Z"
    }
   },
   "cell_type": "code",
   "source": [
    "### Parameters\n",
    "dropout = 0.3"
   ],
   "id": "b901ea99b98523e1",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:49.029386Z",
     "start_time": "2024-08-25T05:05:39.923778Z"
    }
   },
   "cell_type": "code",
   "source": [
    "optimizer = Adam(learning_rate = 0.0001)\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "set_seeds(100)\n",
    "model = Sequential()\n",
    "model.add(Dense(32, input_dim = len(cols), activity_regularizer = None ,activation = \"relu\", kernel_regularizer=l2(0.001), )) \n",
    "model.add(Dropout(dropout, seed = 100))\n",
    "model.add(Dense(32, activation = \"relu\", activity_regularizer = None, kernel_regularizer=l2(0.001),))\n",
    "model.add(Dense(32, activation = \"relu\", activity_regularizer = None, kernel_regularizer=l2(0.001),))    \n",
    "model.add(Dropout(dropout, seed = 100))        \n",
    "model.add(Dense(1, activation = \"sigmoid\")) \n",
    "model.compile(loss = \"binary_crossentropy\", optimizer = optimizer, metrics = [\"accuracy\"])\n",
    "\n",
    "model.fit(x=train_standardized[cols], y=train['dir'], epochs=30, verbose=False,\n",
    "          validation_split=0.2, shuffle=False, class_weight=cw(train), callbacks=[early_stopping] )\n"
   ],
   "id": "3d945ef08bf3221",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\code\\trading\\v1\\pythonProject\\.venv\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
      "C:\\code\\trading\\v1\\pythonProject\\.venv\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\data_adapter_utils.py:126: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  sample_weight[i] = class_weight.get(int(y[i]), 1.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x2409adb8c10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:49.779703Z",
     "start_time": "2024-08-25T05:05:49.060334Z"
    }
   },
   "cell_type": "code",
   "source": [
    "loss_train, accuracy_train =  model.evaluate(train_standardized[cols], train['dir'])\n",
    "predict = model.predict(train_standardized[cols])\n",
    "\n",
    "test_standardized = (test-mu) / std\n",
    "loss_test, accuracy_test =  model.evaluate(test_standardized[cols], test['dir'])\n",
    "test['probability'] = model.predict(test_standardized[cols])\n",
    "threshold = 0.53\n",
    "test['position'] = np.where(test.probability < (1-threshold), -1, np.nan)\n",
    "test['position'] = np.where(test.probability > threshold, 1, test.position)\n",
    "test['position'] = test.position.ffill()\n",
    "test['strategy'] = test['position'] * test['returns']\n",
    "test['creturns'] = test['returns'].cumsum().apply(np.exp)\n",
    "test['cstrategy'] = test['strategy'].cumsum().apply(np.exp)\n",
    "test['trades'] = test.position.diff().abs()\n",
    "test['strategy_net'] = test.strategy - test.trades * ptc\n",
    "test['cstrategy_net'] = test['strategy_net'].cumsum().apply(np.exp)"
   ],
   "id": "f391aaecdae9bb04",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m308/308\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 529us/step - accuracy: 0.5104 - loss: 0.7338\n",
      "\u001B[1m308/308\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 545us/step\n",
      "\u001B[1m77/77\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 580us/step - accuracy: 0.5346 - loss: 0.7346\n",
      "\u001B[1m77/77\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 490us/step\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:49.856518Z",
     "start_time": "2024-08-25T05:05:49.852556Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(f\"Loss: {loss_train}\")\n",
    "print(f\"Accuracy: {accuracy_train}\")"
   ],
   "id": "4683f4b4dd7cfbf0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.7336093187332153\n",
      "Accuracy: 0.5286397337913513\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:49.916618Z",
     "start_time": "2024-08-25T05:05:49.912596Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(f\"Loss: {loss_test}\")\n",
    "print(f\"Accuracy: {accuracy_test}\")"
   ],
   "id": "6f575fa58fa73cd3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.7342873811721802\n",
      "Accuracy: 0.5423108339309692\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:50.048477Z",
     "start_time": "2024-08-25T05:05:49.927653Z"
    }
   },
   "cell_type": "code",
   "source": "test.probability.hist()",
   "id": "e62c05c5d7a3e94d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:50.094107Z",
     "start_time": "2024-08-25T05:05:50.087569Z"
    }
   },
   "cell_type": "code",
   "source": "test.creturns.corr(test.cstrategy)",
   "id": "2e77923ccf6611cf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999245998700397"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:50.141053Z",
     "start_time": "2024-08-25T05:05:50.135417Z"
    }
   },
   "cell_type": "code",
   "source": "test.position.value_counts()",
   "id": "49142fff6b0af84",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "position\n",
       " 1.0    2264\n",
       "-1.0       7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.181181Z",
     "start_time": "2024-08-25T05:05:50.202701Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train['probability'] = model.predict(train_standardized[cols])\n",
    "threshold = 0.53\n",
    "train['position'] = np.where(train.probability < (1-threshold), -1, np.nan)\n",
    "train['position'] = np.where(train.probability > threshold, 1, train.position)\n",
    "train['position'] = train.position.ffill()\n",
    "train['strategy'] = train['position'] * train['returns']\n",
    "train['creturns'] = train['returns'].cumsum().apply(np.exp)\n",
    "train['cstrategy'] = train['strategy'].cumsum().apply(np.exp)\n",
    "train['trades'] = train.position.diff().abs()\n",
    "train['strategy_net'] = train.strategy - train.trades * ptc\n",
    "train['cstrategy_net'] = train['strategy_net'].cumsum().apply(np.exp)\n",
    "train[['cstrategy_net', 'cstrategy', 'creturns']].plot()"
   ],
   "id": "35503ebb2054d35",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m308/308\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 644us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Datetime'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.459949Z",
     "start_time": "2024-08-25T05:05:51.211005Z"
    }
   },
   "cell_type": "code",
   "source": "test[['cstrategy_net', 'cstrategy', 'creturns']].plot()",
   "id": "2e536b11d081a21",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Datetime'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.470454Z",
     "start_time": "2024-08-25T05:05:51.465011Z"
    }
   },
   "cell_type": "code",
   "source": [
    "analysis = test.copy()\n",
    "analysis.drop(columns = cols, inplace = True)\n",
    "analysis.dropna(inplace = True)"
   ],
   "id": "c8ff124b6f163697",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.514590Z",
     "start_time": "2024-08-25T05:05:51.510254Z"
    }
   },
   "cell_type": "code",
   "source": "analysis.cstrategy_net.iloc[-1]",
   "id": "4835fdad07820181",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0347199729198437"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.572634Z",
     "start_time": "2024-08-25T05:05:51.557795Z"
    }
   },
   "cell_type": "code",
   "source": "analysis",
   "id": "eb4a028e141da00a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                               Open      High       Low     Close   returns  \\\n",
       "Datetime                                                                      \n",
       "2024-04-10 15:00:00+01:00  1.075384  1.076890  1.075038  1.075616 -0.000108   \n",
       "2024-04-10 16:00:00+01:00  1.075732  1.075732  1.073768  1.073883 -0.001612   \n",
       "2024-04-10 17:00:00+01:00  1.073998  1.073998  1.073192  1.073883  0.000000   \n",
       "2024-04-10 18:00:00+01:00  1.073883  1.075500  1.073192  1.075269  0.001290   \n",
       "2024-04-10 19:00:00+01:00  1.075384  1.075616  1.073883  1.073883 -0.001290   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "2024-08-21 19:00:00+01:00  1.116321  1.117693  1.116321  1.116445  0.000447   \n",
       "2024-08-21 20:00:00+01:00  1.116321  1.116321  1.115449  1.115573 -0.000781   \n",
       "2024-08-21 21:00:00+01:00  1.115573  1.115822  1.115076  1.115324 -0.000223   \n",
       "2024-08-21 22:00:00+01:00  1.115324  1.115822  1.115200  1.115324  0.000000   \n",
       "2024-08-21 23:00:00+01:00  1.115573  1.115947  1.115573  1.115947  0.000558   \n",
       "\n",
       "                           dir  probability  position  strategy  creturns  \\\n",
       "Datetime                                                                    \n",
       "2024-04-10 15:00:00+01:00    0     0.527297       1.0 -0.000108  0.996558   \n",
       "2024-04-10 16:00:00+01:00    0     0.504584       1.0 -0.001612  0.994953   \n",
       "2024-04-10 17:00:00+01:00    0     0.512319       1.0  0.000000  0.994953   \n",
       "2024-04-10 18:00:00+01:00    1     0.501237       1.0  0.001290  0.996237   \n",
       "2024-04-10 19:00:00+01:00    0     0.509258       1.0 -0.001290  0.994953   \n",
       "...                        ...          ...       ...       ...       ...   \n",
       "2024-08-21 19:00:00+01:00    1     0.475612      -1.0 -0.000447  1.034387   \n",
       "2024-08-21 20:00:00+01:00    0     0.481678      -1.0  0.000781  1.033579   \n",
       "2024-08-21 21:00:00+01:00    0     0.495468      -1.0  0.000223  1.033348   \n",
       "2024-08-21 22:00:00+01:00    0     0.486050      -1.0 -0.000000  1.033348   \n",
       "2024-08-21 23:00:00+01:00    1     0.486294      -1.0 -0.000558  1.033925   \n",
       "\n",
       "                           cstrategy  trades  strategy_net  cstrategy_net  \n",
       "Datetime                                                                   \n",
       "2024-04-10 15:00:00+01:00   0.997418     0.0     -0.000108       0.999892  \n",
       "2024-04-10 16:00:00+01:00   0.995812     0.0     -0.001612       0.998282  \n",
       "2024-04-10 17:00:00+01:00   0.995812     0.0      0.000000       0.998282  \n",
       "2024-04-10 18:00:00+01:00   0.997097     0.0      0.001290       0.999570  \n",
       "2024-04-10 19:00:00+01:00   0.995812     0.0     -0.001290       0.998282  \n",
       "...                              ...     ...           ...            ...  \n",
       "2024-08-21 19:00:00+01:00   1.031821     0.0     -0.000447       1.034258  \n",
       "2024-08-21 20:00:00+01:00   1.032627     0.0      0.000781       1.035066  \n",
       "2024-08-21 21:00:00+01:00   1.032858     0.0      0.000223       1.035297  \n",
       "2024-08-21 22:00:00+01:00   1.032858     0.0     -0.000000       1.035297  \n",
       "2024-08-21 23:00:00+01:00   1.032282     0.0     -0.000558       1.034720  \n",
       "\n",
       "[2270 rows x 14 columns]"
      ],
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>probability</th>\n",
       "      <th>position</th>\n",
       "      <th>strategy</th>\n",
       "      <th>creturns</th>\n",
       "      <th>cstrategy</th>\n",
       "      <th>trades</th>\n",
       "      <th>strategy_net</th>\n",
       "      <th>cstrategy_net</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-04-10 15:00:00+01:00</th>\n",
       "      <td>1.075384</td>\n",
       "      <td>1.076890</td>\n",
       "      <td>1.075038</td>\n",
       "      <td>1.075616</td>\n",
       "      <td>-0.000108</td>\n",
       "      <td>0</td>\n",
       "      <td>0.527297</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000108</td>\n",
       "      <td>0.996558</td>\n",
       "      <td>0.997418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000108</td>\n",
       "      <td>0.999892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-10 16:00:00+01:00</th>\n",
       "      <td>1.075732</td>\n",
       "      <td>1.075732</td>\n",
       "      <td>1.073768</td>\n",
       "      <td>1.073883</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>0</td>\n",
       "      <td>0.504584</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>0.994953</td>\n",
       "      <td>0.995812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>0.998282</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-10 17:00:00+01:00</th>\n",
       "      <td>1.073998</td>\n",
       "      <td>1.073998</td>\n",
       "      <td>1.073192</td>\n",
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       "      <td>0.512319</td>\n",
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       "      <td>0.994953</td>\n",
       "      <td>0.995812</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-10 18:00:00+01:00</th>\n",
       "      <td>1.073883</td>\n",
       "      <td>1.075500</td>\n",
       "      <td>1.073192</td>\n",
       "      <td>1.075269</td>\n",
       "      <td>0.001290</td>\n",
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       "      <td>0.501237</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001290</td>\n",
       "      <td>0.996237</td>\n",
       "      <td>0.997097</td>\n",
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       "      <td>0.001290</td>\n",
       "      <td>0.999570</td>\n",
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       "    <tr>\n",
       "      <th>2024-04-10 19:00:00+01:00</th>\n",
       "      <td>1.075384</td>\n",
       "      <td>1.075616</td>\n",
       "      <td>1.073883</td>\n",
       "      <td>1.073883</td>\n",
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       "    <tr>\n",
       "      <th>2024-08-21 19:00:00+01:00</th>\n",
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       "      <th>2024-08-21 20:00:00+01:00</th>\n",
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       "    <tr>\n",
       "      <th>2024-08-21 21:00:00+01:00</th>\n",
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       "    <tr>\n",
       "      <th>2024-08-21 22:00:00+01:00</th>\n",
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       "    <tr>\n",
       "      <th>2024-08-21 23:00:00+01:00</th>\n",
       "      <td>1.115573</td>\n",
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       "<p>2270 rows × 14 columns</p>\n",
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     },
     "execution_count": 18,
     "metadata": {},
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    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
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   "cell_type": "code",
   "source": "analysis['dir_position'] = np.where(analysis['probability'] < 0.47, 0, np.where(analysis['probability'] > 0.53, 1, np.nan))",
   "id": "29af9d26df620cc0",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.698889Z",
     "start_time": "2024-08-25T05:05:51.685999Z"
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   "source": "analysis[(analysis.dir_position != analysis.dir) & (pd.notna(analysis.dir_position))]",
   "id": "b0365f8e052d0a3b",
   "outputs": [
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       "\n",
       "                           dir  probability  position  strategy  creturns  \\\n",
       "Datetime                                                                    \n",
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       "\n",
       "                           cstrategy  trades  strategy_net  cstrategy_net  \\\n",
       "Datetime                                                                    \n",
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       "2024-05-02 00:00:00+01:00   0.994104     0.0      0.000000       0.996569   \n",
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       "2024-08-21 17:00:00+01:00   1.033203     2.0     -0.000452       1.035644   \n",
       "\n",
       "                           dir_position  \n",
       "Datetime                                 \n",
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       "      <th>2024-06-07 17:00:00+01:00</th>\n",
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       "      <th>2024-06-12 18:00:00+01:00</th>\n",
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     "execution_count": 20,
     "metadata": {},
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   "execution_count": 20
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   "metadata": {
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   "source": "analysis[(analysis.dir_position != analysis.dir) & (pd.notna(analysis.dir_position))].probability.describe()",
   "id": "860fd132ce4a40ad",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.000000\n",
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "source": "analysis[analysis.dir_position == analysis.dir]",
   "id": "d64ea190fc8a8ea",
   "outputs": [
    {
     "data": {
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       "                               Open      High       Low     Close   returns  \\\n",
       "Datetime                                                                      \n",
       "2024-05-03 18:00:00+01:00  1.076774  1.077122  1.076195  1.077006  0.000323   \n",
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       "2024-08-05 13:00:00+01:00  1.096491  1.100958  1.096491  1.099384  0.002525   \n",
       "\n",
       "                           dir  probability  position  strategy  creturns  \\\n",
       "Datetime                                                                    \n",
       "2024-05-03 18:00:00+01:00    1     0.542091       1.0  0.000323  0.997846   \n",
       "2024-05-15 18:00:00+01:00    1     0.546979       1.0  0.000653  1.008271   \n",
       "2024-08-05 13:00:00+01:00    1     0.541049       1.0  0.002525  1.018580   \n",
       "\n",
       "                           cstrategy  trades  strategy_net  cstrategy_net  \\\n",
       "Datetime                                                                    \n",
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       "\n",
       "                           dir_position  \n",
       "Datetime                                 \n",
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       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>probability</th>\n",
       "      <th>position</th>\n",
       "      <th>strategy</th>\n",
       "      <th>creturns</th>\n",
       "      <th>cstrategy</th>\n",
       "      <th>trades</th>\n",
       "      <th>strategy_net</th>\n",
       "      <th>cstrategy_net</th>\n",
       "      <th>dir_position</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-05-03 18:00:00+01:00</th>\n",
       "      <td>1.076774</td>\n",
       "      <td>1.077122</td>\n",
       "      <td>1.076195</td>\n",
       "      <td>1.077006</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>1</td>\n",
       "      <td>0.542091</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.997846</td>\n",
       "      <td>0.998708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>1.001185</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-15 18:00:00+01:00</th>\n",
       "      <td>1.087548</td>\n",
       "      <td>1.088258</td>\n",
       "      <td>1.087548</td>\n",
       "      <td>1.088258</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>1</td>\n",
       "      <td>0.546979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>1.008271</td>\n",
       "      <td>1.009141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>1.011644</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-05 13:00:00+01:00</th>\n",
       "      <td>1.096491</td>\n",
       "      <td>1.100958</td>\n",
       "      <td>1.096491</td>\n",
       "      <td>1.099384</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>1</td>\n",
       "      <td>0.541049</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>1.018580</td>\n",
       "      <td>1.019459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>1.021988</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-25T05:05:51.962832Z",
     "start_time": "2024-08-25T05:05:51.953648Z"
    }
   },
   "cell_type": "code",
   "source": "analysis[analysis.dir_position == analysis.dir].probability.describe()",
   "id": "cc98b2a43ca0462d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.000000\n",
       "mean     0.543373\n",
       "std      0.003166\n",
       "min      0.541049\n",
       "25%      0.541570\n",
       "50%      0.542091\n",
       "75%      0.544535\n",
       "max      0.546979\n",
       "Name: probability, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  }
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