proof_of_concept_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0ad04cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from copy import copy\n",
    "from math import ceil\n",
    "\n",
    "path1 = \"./data/1/\"\n",
    "path2 = \"./data/2/\"\n",
    "path3 = \"./data/3/\"\n",
    "path4 = \"./data/4/\"\n",
    "path5 = \"./data/5/\"\n",
    "path6 = \"./data/UpToDate/\"\n",
    "\n",
    "\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2504bcf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def fix_dollars(apple2):\n",
    "    new_open = []\n",
    "    new_close = []\n",
    "    new_high = []\n",
    "    new_low = []\n",
    "    for i in range(len(apple2['Open'])):\n",
    "        new_open.append(float(apple2['Open'][i][1:]))\n",
    "        new_close.append(float(apple2['Close/Last'][i][1:]))\n",
    "        new_high.append(float(apple2['High'][i][1:]))\n",
    "        new_low.append(float(apple2['Low'][i][1:]))\n",
    "    \n",
    "\n",
    "    apple2.drop('Open',axis=1,inplace=True)\n",
    "    apple2.drop('Close/Last',axis=1,inplace=True)\n",
    "    apple2.drop('High',axis=1,inplace=True)\n",
    "    apple2.drop('Low',axis=1,inplace=True)\n",
    "    \n",
    "\n",
    "    apple2['Close'] = new_close\n",
    "    apple2['Open'] = new_open\n",
    "    apple2['High'] = new_high\n",
    "    apple2['Low'] = new_low\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56195792",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_date(apple2):\n",
    "    month = []\n",
    "    year = []\n",
    "    week_start = []\n",
    "    week_end = []\n",
    "    flag = False\n",
    "    for i in range(len(apple2['Date'])):\n",
    "        month.append(int(apple2['Date'][i][0:2]))\n",
    "        year.append(int(apple2['Date'][i][6:]))\n",
    "        today = int(apple2['Date'][i][3:5])\n",
    "        \n",
    "        if i == 0:\n",
    "            tomorrow = int(apple2['Date'][i + 1][3:5])\n",
    "            if int(apple2['Date'][i][3:5]) == int(apple2['Date'][i + 1][3:5]) - 1 == int(apple2['Date'][i + 2][3:5]) - 2 \\\n",
    "                    == int(apple2['Date'][i + 3][3:5]) - 3 == int(apple2['Date'][i + 4][3:5]) - 4:\n",
    "                week_start.append(True)\n",
    "                week_end.append(False)\n",
    "            elif today + 1 == tomorrow:\n",
    "                week_start.append(False)\n",
    "                week_end.append(False)\n",
    "            else:\n",
    "                week_start.append(False)\n",
    "                week_end.append(True)\n",
    "                flag = True\n",
    "                continue\n",
    "        else:\n",
    "            try:\n",
    "                tomorrow = int(apple2['Date'][i + 1][3:5])\n",
    "                if today + 1 == tomorrow:  # I should really be cross referencing with a calendar api, not hacking this together\n",
    "                    week_start.append(False)\n",
    "                    week_end.append(False)\n",
    "                else:\n",
    "                    week_start.append(False)\n",
    "                    week_end.append(True)\n",
    "                    flag = True\n",
    "                    continue\n",
    "                if flag:\n",
    "                    week_start[-1] = True\n",
    "                    flag = False\n",
    "            except:\n",
    "                yesterday = int(apple2['Date'][i - 1][3:5])\n",
    "                if int(apple2['Date'][i][3:5]) == int(apple2['Date'][i - 1][3:5]) + 1 == int(\n",
    "                        apple2['Date'][i - 2][3:5]) + 2 \\\n",
    "                        == int(apple2['Date'][i - 3][3:5]) + 3 == int(apple2['Date'][i - 4][3:5]) + 4:\n",
    "                    week_start.append(False)\n",
    "                    week_end.append(True)\n",
    "                elif yesterday + 1 == today:\n",
    "                    week_start.append(False)\n",
    "                    week_end.append(False)\n",
    "                else:\n",
    "                    week_start.append(True)\n",
    "                    week_end.append(False)\n",
    "\n",
    "    apple2.drop('Date', axis=1, inplace=True)\n",
    "\n",
    "    apple2['Month'] = month\n",
    "    apple2['Year'] = year\n",
    "    apple2['WeekStart'] = week_start\n",
    "    apple2['WeekEnd'] = week_end\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "591bd473",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_targets(df,KF):\n",
    "    target_percent_inc = []\n",
    "    target_best_day = []\n",
    "    target_worst_sell = []\n",
    "    target_day_high = []\n",
    "    target_day_low = []\n",
    "    options = ['t--'+str(i)+'Low' for i in range(1,KF+1)]\n",
    "    for i in range(len(df['t-0Low'])):\n",
    "        today = df['t-0High'][i]\n",
    "        target_day_high.append(today)\n",
    "        today_worst = df['t-0Low'][i]\n",
    "        target_day_low.append(today_worst)\n",
    "        best = np.max([df['t--'+str(j)+'Low'][i] for j in range(1,KF+1)])\n",
    "        worst = np.max([df['t--'+str(j)+'High'][i] for j in range(1,KF+1)])\n",
    "        best_day = np.argmax([df['t--'+str(j)+'Low'][i] for j in range(1,KF+1)])+1\n",
    "        target_percent_inc.append(round((best-today)*100/today,0))\n",
    "        target_worst_sell.append(round((worst-today_worst)*100/today,0))\n",
    "        target_best_day.append(best_day)\n",
    "    return target_percent_inc, target_best_day, target_worst_sell,target_day_high,target_day_low\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9dc603be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_shift(apple2,KP,KF,updated=False):\n",
    "\n",
    "    names = ['Date','Volume','Close','Open','High','Low'] \n",
    "    cases = pd.DataFrame(apple2.values)\n",
    "    df = pd.concat([cases.shift(i) for i in range(KP-int(updated),-KF-1,-1)],axis=1)\n",
    "    df.columns = ['t-'+str(i+int(updated))+x for i in range(KP-int(updated),-KF-1,-1) for x in names]\n",
    "    return df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0955521",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_normalize(apple2,df):\n",
    "    for apple_col in list(apple2):\n",
    "        if apple_col in ['WeekStart','WeekEnd']:\n",
    "            continue\n",
    "        MEAN = apple2[apple_col].mean()\n",
    "        STD = apple2[apple_col].std()\n",
    "        for df_col in list(df):\n",
    "            if apple_col in df_col:\n",
    "                if apple_col =='Month' or apple_col == 'Year':\n",
    "                    df = df.astype({df_col: 'int32'})\n",
    "                else:\n",
    "                    df[df_col]=round((df[df_col]-MEAN)/STD,0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cb40f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_future(df,KF):\n",
    "    for i in range(0,-KF-1,-1):\n",
    "        for x in ['Month','Year','Volume','Close','Open','High','Low']:\n",
    "            df.drop('t-'+str(i)+x,axis=1,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65e689f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_reverse(apple2):\n",
    "    return apple2.loc[::-1].reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ffcc9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbd99332",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_null(df):\n",
    "    df.dropna(inplace=True)\n",
    "    return df.reset_index(drop=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82541f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mult_money_percs(days,multiplier):\n",
    "    percs = {}\n",
    "    for period_length in range(2,days+1):\n",
    "        percs[period_length]=(-1+multiplier**(period_length/250))\n",
    "    return percs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "618116cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2: 0.0055605803984681845, 3: 0.008352454894595018, 4: 0.011152080851304147, 5: 0.013959479790029095, 6: 0.016774673291956255, 7: 0.019597682998190535, 8: 0.022428530609922337, 9: 0.025267237888593863, 10: 0.028113826656066543, 11: 0.030968318794788674, 12: 0.0338307362479644, 13: 0.03670110101972068, 14: 0.03957943517527873, 15: 0.042465760841121414, 16: 0.0453601002051649, 17: 0.04826247551692875, 18: 0.05117290908770644, 19: 0.054091423290737906, 20: 0.05701804056138027, 21: 0.05995278339728172, 22: 0.06289567435855314, 23: 0.06584673606794245, 24: 0.06880599121100794, 25: 0.07177346253629313}\n"
     ]
    }
   ],
   "source": [
    "print(get_mult_money_percs(25,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a1767fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68461e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mult_money_op_days(df,days,mult,off_by=0):\n",
    "    percs = get_mult_money_percs(days,mult)\n",
    "    wins = []\n",
    "    for i in range(len(df)):\n",
    "        win = False\n",
    "        today_low = df['t-0High'][i]\n",
    "        for j in percs.keys():\n",
    "            required_perc = percs[j]\n",
    "            high = df['t--'+str(j-1)+'High'][i]\n",
    "            perc_inc = (high-((today_low*(1+off_by))))/today_low\n",
    "            if perc_inc>=required_perc:\n",
    "                win = True\n",
    "                wins.append(1)\n",
    "                break\n",
    "        if not win:\n",
    "            wins.append(0)\n",
    "    return wins\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b379f703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Close</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/16/2013</td>\n",
       "      <td>602950359</td>\n",
       "      <td>15.5206</td>\n",
       "      <td>15.1157</td>\n",
       "      <td>15.6375</td>\n",
       "      <td>14.9607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>427864749</td>\n",
       "      <td>15.4736</td>\n",
       "      <td>15.6804</td>\n",
       "      <td>15.7175</td>\n",
       "      <td>15.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>450118020</td>\n",
       "      <td>15.8189</td>\n",
       "      <td>15.4254</td>\n",
       "      <td>15.9214</td>\n",
       "      <td>15.3607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>455670978</td>\n",
       "      <td>15.7021</td>\n",
       "      <td>15.6482</td>\n",
       "      <td>15.9100</td>\n",
       "      <td>15.5071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>442730783</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</td>\n",
       "      <td>16.0125</td>\n",
       "      <td>15.6507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.7700</td>\n",
       "      <td>173.0500</td>\n",
       "      <td>173.5400</td>\n",
       "      <td>171.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>53724500</td>\n",
       "      <td>173.5550</td>\n",
       "      <td>173.0200</td>\n",
       "      <td>174.0300</td>\n",
       "      <td>171.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>49514680</td>\n",
       "      <td>173.7500</td>\n",
       "      <td>173.8500</td>\n",
       "      <td>174.5900</td>\n",
       "      <td>172.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>45533140</td>\n",
       "      <td>172.5700</td>\n",
       "      <td>173.6200</td>\n",
       "      <td>174.0600</td>\n",
       "      <td>171.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>05/15/2023</td>\n",
       "      <td>37266660</td>\n",
       "      <td>172.0700</td>\n",
       "      <td>173.1600</td>\n",
       "      <td>173.2100</td>\n",
       "      <td>171.4700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2517 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date     Volume     Close      Open      High       Low\n",
       "0     05/16/2013  602950359   15.5206   15.1157   15.6375   14.9607\n",
       "1     05/17/2013  427864749   15.4736   15.6804   15.7175   15.3932\n",
       "2     05/20/2013  450118020   15.8189   15.4254   15.9214   15.3607\n",
       "3     05/21/2013  455670978   15.7021   15.6482   15.9100   15.5071\n",
       "4     05/22/2013  442730783   15.7626   15.8589   16.0125   15.6507\n",
       "...          ...        ...       ...       ...       ...       ...\n",
       "2512  05/09/2023   45326870  171.7700  173.0500  173.5400  171.6000\n",
       "2513  05/10/2023   53724500  173.5550  173.0200  174.0300  171.9000\n",
       "2514  05/11/2023   49514680  173.7500  173.8500  174.5900  172.1700\n",
       "2515  05/12/2023   45533140  172.5700  173.6200  174.0600  171.0000\n",
       "2516  05/15/2023   37266660  172.0700  173.1600  173.2100  171.4700\n",
       "\n",
       "[2517 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KP = 0\n",
    "KF = 24\n",
    "apple = pd.read_csv(path5+\"aapl_historical.csv\")\n",
    "#apple = apple.iloc[:200]\n",
    "apple2 = apple.copy(deep=True)\n",
    "fix_dollars(apple2)\n",
    "apple2 = fix_reverse(apple2)\n",
    "apple2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "860dc6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t-0Date</th>\n",
       "      <th>t-0Volume</th>\n",
       "      <th>t-0Close</th>\n",
       "      <th>t-0Open</th>\n",
       "      <th>t-0High</th>\n",
       "      <th>t-0Low</th>\n",
       "      <th>t--1Date</th>\n",
       "      <th>t--1Volume</th>\n",
       "      <th>t--1Close</th>\n",
       "      <th>t--1Open</th>\n",
       "      <th>...</th>\n",
       "      <th>t--23Close</th>\n",
       "      <th>t--23Open</th>\n",
       "      <th>t--23High</th>\n",
       "      <th>t--23Low</th>\n",
       "      <th>t--24Date</th>\n",
       "      <th>t--24Volume</th>\n",
       "      <th>t--24Close</th>\n",
       "      <th>t--24Open</th>\n",
       "      <th>t--24High</th>\n",
       "      <th>t--24Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/16/2013</td>\n",
       "      <td>602950359</td>\n",
       "      <td>15.5206</td>\n",
       "      <td>15.1157</td>\n",
       "      <td>15.6375</td>\n",
       "      <td>14.9607</td>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>427864749</td>\n",
       "      <td>15.4736</td>\n",
       "      <td>15.6804</td>\n",
       "      <td>...</td>\n",
       "      <td>15.1071</td>\n",
       "      <td>15.4071</td>\n",
       "      <td>15.4164</td>\n",
       "      <td>15.1071</td>\n",
       "      <td>06/20/2013</td>\n",
       "      <td>357285177</td>\n",
       "      <td>14.8871</td>\n",
       "      <td>14.975</td>\n",
       "      <td>15.2136</td>\n",
       "      <td>14.8275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>427864749</td>\n",
       "      <td>15.4736</td>\n",
       "      <td>15.6804</td>\n",
       "      <td>15.7175</td>\n",
       "      <td>15.3932</td>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>450118020</td>\n",
       "      <td>15.8189</td>\n",
       "      <td>15.4254</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8871</td>\n",
       "      <td>14.975</td>\n",
       "      <td>15.2136</td>\n",
       "      <td>14.8275</td>\n",
       "      <td>06/21/2013</td>\n",
       "      <td>480849968</td>\n",
       "      <td>14.7679</td>\n",
       "      <td>14.9461</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>450118020</td>\n",
       "      <td>15.8189</td>\n",
       "      <td>15.4254</td>\n",
       "      <td>15.9214</td>\n",
       "      <td>15.3607</td>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>455670978</td>\n",
       "      <td>15.7021</td>\n",
       "      <td>15.6482</td>\n",
       "      <td>...</td>\n",
       "      <td>14.7679</td>\n",
       "      <td>14.9461</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.575</td>\n",
       "      <td>06/24/2013</td>\n",
       "      <td>480187768</td>\n",
       "      <td>14.3764</td>\n",
       "      <td>14.55</td>\n",
       "      <td>14.595</td>\n",
       "      <td>14.2161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>455670978</td>\n",
       "      <td>15.7021</td>\n",
       "      <td>15.6482</td>\n",
       "      <td>15.91</td>\n",
       "      <td>15.5071</td>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>442730783</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</td>\n",
       "      <td>...</td>\n",
       "      <td>14.3764</td>\n",
       "      <td>14.55</td>\n",
       "      <td>14.595</td>\n",
       "      <td>14.2161</td>\n",
       "      <td>06/25/2013</td>\n",
       "      <td>314132994</td>\n",
       "      <td>14.3796</td>\n",
       "      <td>14.4893</td>\n",
       "      <td>14.5639</td>\n",
       "      <td>14.2439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>442730783</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</td>\n",
       "      <td>16.0125</td>\n",
       "      <td>15.6507</td>\n",
       "      <td>05/23/2013</td>\n",
       "      <td>352959179</td>\n",
       "      <td>15.7907</td>\n",
       "      <td>15.5696</td>\n",
       "      <td>...</td>\n",
       "      <td>14.3796</td>\n",
       "      <td>14.4893</td>\n",
       "      <td>14.5639</td>\n",
       "      <td>14.2439</td>\n",
       "      <td>06/26/2013</td>\n",
       "      <td>367647133</td>\n",
       "      <td>14.2168</td>\n",
       "      <td>14.425</td>\n",
       "      <td>14.4568</td>\n",
       "      <td>14.1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>04/04/2023</td>\n",
       "      <td>46278300</td>\n",
       "      <td>165.63</td>\n",
       "      <td>166.595</td>\n",
       "      <td>166.84</td>\n",
       "      <td>165.11</td>\n",
       "      <td>04/05/2023</td>\n",
       "      <td>51511740</td>\n",
       "      <td>163.76</td>\n",
       "      <td>164.74</td>\n",
       "      <td>...</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>173.85</td>\n",
       "      <td>172.11</td>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>04/05/2023</td>\n",
       "      <td>51511740</td>\n",
       "      <td>163.76</td>\n",
       "      <td>164.74</td>\n",
       "      <td>165.05</td>\n",
       "      <td>161.8</td>\n",
       "      <td>04/06/2023</td>\n",
       "      <td>45390120</td>\n",
       "      <td>164.66</td>\n",
       "      <td>162.43</td>\n",
       "      <td>...</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>53724500</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>174.03</td>\n",
       "      <td>171.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>04/06/2023</td>\n",
       "      <td>45390120</td>\n",
       "      <td>164.66</td>\n",
       "      <td>162.43</td>\n",
       "      <td>164.9584</td>\n",
       "      <td>162.0</td>\n",
       "      <td>04/10/2023</td>\n",
       "      <td>47716880</td>\n",
       "      <td>162.03</td>\n",
       "      <td>161.42</td>\n",
       "      <td>...</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>174.03</td>\n",
       "      <td>171.9</td>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>49514680</td>\n",
       "      <td>173.75</td>\n",
       "      <td>173.85</td>\n",
       "      <td>174.59</td>\n",
       "      <td>172.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>04/10/2023</td>\n",
       "      <td>47716880</td>\n",
       "      <td>162.03</td>\n",
       "      <td>161.42</td>\n",
       "      <td>162.03</td>\n",
       "      <td>160.08</td>\n",
       "      <td>04/11/2023</td>\n",
       "      <td>47644220</td>\n",
       "      <td>160.8</td>\n",
       "      <td>162.35</td>\n",
       "      <td>...</td>\n",
       "      <td>173.75</td>\n",
       "      <td>173.85</td>\n",
       "      <td>174.59</td>\n",
       "      <td>172.17</td>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>45533140</td>\n",
       "      <td>172.57</td>\n",
       "      <td>173.62</td>\n",
       "      <td>174.06</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>04/11/2023</td>\n",
       "      <td>47644220</td>\n",
       "      <td>160.8</td>\n",
       "      <td>162.35</td>\n",
       "      <td>162.36</td>\n",
       "      <td>160.51</td>\n",
       "      <td>04/12/2023</td>\n",
       "      <td>50133060</td>\n",
       "      <td>160.1</td>\n",
       "      <td>161.22</td>\n",
       "      <td>...</td>\n",
       "      <td>172.57</td>\n",
       "      <td>173.62</td>\n",
       "      <td>174.06</td>\n",
       "      <td>171.0</td>\n",
       "      <td>05/15/2023</td>\n",
       "      <td>37266660</td>\n",
       "      <td>172.07</td>\n",
       "      <td>173.16</td>\n",
       "      <td>173.21</td>\n",
       "      <td>171.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2493 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         t-0Date  t-0Volume t-0Close  t-0Open   t-0High   t-0Low    t--1Date  \\\n",
       "0     05/16/2013  602950359  15.5206  15.1157   15.6375  14.9607  05/17/2013   \n",
       "1     05/17/2013  427864749  15.4736  15.6804   15.7175  15.3932  05/20/2013   \n",
       "2     05/20/2013  450118020  15.8189  15.4254   15.9214  15.3607  05/21/2013   \n",
       "3     05/21/2013  455670978  15.7021  15.6482     15.91  15.5071  05/22/2013   \n",
       "4     05/22/2013  442730783  15.7626  15.8589   16.0125  15.6507  05/23/2013   \n",
       "...          ...        ...      ...      ...       ...      ...         ...   \n",
       "2488  04/04/2023   46278300   165.63  166.595    166.84   165.11  04/05/2023   \n",
       "2489  04/05/2023   51511740   163.76   164.74    165.05    161.8  04/06/2023   \n",
       "2490  04/06/2023   45390120   164.66   162.43  164.9584    162.0  04/10/2023   \n",
       "2491  04/10/2023   47716880   162.03   161.42    162.03   160.08  04/11/2023   \n",
       "2492  04/11/2023   47644220    160.8   162.35    162.36   160.51  04/12/2023   \n",
       "\n",
       "     t--1Volume t--1Close t--1Open  ... t--23Close t--23Open t--23High  \\\n",
       "0     427864749   15.4736  15.6804  ...    15.1071   15.4071   15.4164   \n",
       "1     450118020   15.8189  15.4254  ...    14.8871    14.975   15.2136   \n",
       "2     455670978   15.7021  15.6482  ...    14.7679   14.9461      15.0   \n",
       "3     442730783   15.7626  15.8589  ...    14.3764     14.55    14.595   \n",
       "4     352959179   15.7907  15.5696  ...    14.3796   14.4893   14.5639   \n",
       "...         ...       ...      ...  ...        ...       ...       ...   \n",
       "2488   51511740    163.76   164.74  ...      173.5    172.48    173.85   \n",
       "2489   45390120    164.66   162.43  ...     171.77    173.05    173.54   \n",
       "2490   47716880    162.03   161.42  ...    173.555    173.02    174.03   \n",
       "2491   47644220     160.8   162.35  ...     173.75    173.85    174.59   \n",
       "2492   50133060     160.1   161.22  ...     172.57    173.62    174.06   \n",
       "\n",
       "     t--23Low   t--24Date t--24Volume t--24Close t--24Open t--24High t--24Low  \n",
       "0     15.1071  06/20/2013   357285177    14.8871    14.975   15.2136  14.8275  \n",
       "1     14.8275  06/21/2013   480849968    14.7679   14.9461      15.0   14.575  \n",
       "2      14.575  06/24/2013   480187768    14.3764     14.55    14.595  14.2161  \n",
       "3     14.2161  06/25/2013   314132994    14.3796   14.4893   14.5639  14.2439  \n",
       "4     14.2439  06/26/2013   367647133    14.2168    14.425   14.4568  14.1307  \n",
       "...       ...         ...         ...        ...       ...       ...      ...  \n",
       "2488   172.11  05/09/2023    45326870     171.77    173.05    173.54    171.6  \n",
       "2489    171.6  05/10/2023    53724500    173.555    173.02    174.03    171.9  \n",
       "2490    171.9  05/11/2023    49514680     173.75    173.85    174.59   172.17  \n",
       "2491   172.17  05/12/2023    45533140     172.57    173.62    174.06    171.0  \n",
       "2492    171.0  05/15/2023    37266660     172.07    173.16    173.21   171.47  \n",
       "\n",
       "[2493 rows x 150 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fix_date(apple2)\n",
    "apple2\n",
    "\n",
    "df = fix_shift(apple2,KP,KF)\n",
    "df = fix_null(df)\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5f6f949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8182912154031288"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins = mult_money_op_days(df,KF,1.5,.000)\n",
    "np.average(wins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef52af22",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7212bf0a",
   "metadata": {},
   "source": [
    "target_percent_inc, target_best_day,target_worst_sell,target_day_high,target_day_low = get_targets(df,KF)\n",
    "np.average(target_day_high)\n",
    "\n",
    "np.average(target_day_low)\n",
    "\n",
    "fix_normalize(apple2,df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a89faa85",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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