predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from Process import process, trade\n",
    "# import pandas as pd\n",
    "# from joblib import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7.1\n"
     ]
    }
   ],
   "source": [
    "import alpaca \n",
    "print(alpaca.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from joblib import load\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "import pymssql\n",
    "from datetime import datetime\n",
    "\n",
    "from alpaca.trading.client import TradingClient\n",
    "from alpaca.data import StockHistoricalDataClient\n",
    "from alpaca.data.requests import StockLatestQuoteRequest\n",
    "from alpaca.trading.requests import MarketOrderRequest, GetAssetsRequest\n",
    "from alpaca.trading.enums import OrderSide, TimeInForce, AssetClass\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "class Trade:\n",
    "    def __init__(self) -> None:\n",
    "        self.KEY = \"PKADLK3NVSMDQ7CZY6BE\"\n",
    "        self.SECRET_KEY = \"Mb8PUpwLhVV4DgVCDeaI0qGvHleZkgMg1KY179nH\"\n",
    "        self.trading_client = TradingClient(self.KEY, self.SECRET_KEY, paper=True)\n",
    "\n",
    "        self.qt = QuantileTransformer(output_distribution=\"normal\")\n",
    "        self.pipe = load(\"Finance_Project_RidgeReg.joblib\")\n",
    "        self.symbols = None\n",
    "        self.features = [\"acc\", \"agr\", \"beta\", \"bm\", \"ep\", \"gma\", \"lev\", \n",
    "                    \"mom12m\", \"mom1m\", \"operprof\", \"roic\", \"roaq\", \n",
    "                    \"retvol\", \"saleinv\", \"currat\"]\n",
    "\n",
    "    def set_features(self, features):\n",
    "        self.features = features\n",
    "\n",
    "    def set_pipe(self, pipe):\n",
    "        self.pipe = pipe\n",
    "\n",
    "    def set_keys(self, key, secret_key):\n",
    "        self.KEY = key\n",
    "        self.SECRET_KEY = secret_key\n",
    "        self.trading_client = TradingClient(self.KEY, self.SECRET_KEY, paper=True)\n",
    "\n",
    "    def get_today_table(self):\n",
    "        server = 'fs.rice.edu'\n",
    "        database = 'stocks'\n",
    "        username = 'stocks'\n",
    "        password = '6LAZH1'\n",
    "        string = \"mssql+pymssql://\" + username + \":\" + password + \"@\" + server + \"/\" + database \n",
    "        conn = create_engine(string).connect()\n",
    "\n",
    "        df = pd.read_sql(\n",
    "        \"\"\"\n",
    "        select ticker, date, mve, acc, agr, beta, bm, ep, gma, idiovol, lev, mom12m, mom1m, \n",
    "            operprof, roeq, roic, roaq, retvol, saleinv, currat\n",
    "        from today\n",
    "        where price > 5\n",
    "        \"\"\", \n",
    "        conn\n",
    "        )\n",
    "        conn.close()\n",
    "\n",
    "\n",
    "        df = df.dropna()\n",
    "        df = df.set_index(\"ticker\")\n",
    "        df = df.sort_values(by=\"mve\")\n",
    "        df = df.iloc[:-500]\n",
    "        return df\n",
    "\n",
    "    def get_account_equity(self):\n",
    "        account = self.trading_client.get_account()\n",
    "        cash = float(account.cash)\n",
    "        equity = float(account.equity)\n",
    "        print(f\"Equity {equity}, Cash {cash}\")\n",
    "        return equity, cash\n",
    "\n",
    "    def predict_and_rank(self, df, numstocks=100):\n",
    "        av_assets = self.trading_client.get_all_assets()\n",
    "\n",
    "        av_assets = [\n",
    "            x for x in av_assets \n",
    "            if (x.asset_class[:]=='us_equity') \n",
    "            and (x.status[:]=='active')\n",
    "        ]\n",
    "        pipe = self.pipe\n",
    "        self.symbols = [x.symbol for x in av_assets]\n",
    "        symbols = self.symbols\n",
    "        tradable = [x.tradable for x in av_assets]\n",
    "        shortable = [x.shortable for x in av_assets]\n",
    "        numstocks = numstocks #best stocks\n",
    "        numstocks2 = round(numstocks*30/130) #worst stocks\n",
    "\n",
    "        threshold_top = round(numstocks*4)\n",
    "        threshold_bot = round(numstocks*4)\n",
    "\n",
    "        trans_features = self.qt.fit_transform(df[self.features])\n",
    "        trans_features = pd.DataFrame(trans_features, columns=self.features)\n",
    "        df[\"predict\"] = pipe.predict(trans_features)\n",
    "        df[\"tradable\"] = pd.Series(tradable, index=symbols)\n",
    "        df[\"shortable\"] = pd.Series(shortable, index=symbols)\n",
    "        df = df.fillna(value=False, inplace=False)\n",
    "\n",
    "        data_client = StockHistoricalDataClient(self.KEY, self.SECRET_KEY)\n",
    "        params = StockLatestQuoteRequest(symbol_or_symbols=df.index.to_list())\n",
    "        quotes = data_client.get_stock_latest_quote(params)\n",
    "\n",
    "        df[\"ask\"] = [quotes[x].ask_price for x in df.index]\n",
    "        df[\"bid\"] = [quotes[x].bid_price for x in df.index]\n",
    "\n",
    "        #Get worst stocks\n",
    "        df[\"rnk\"] = df.predict.rank(method=\"first\")\n",
    "        worst = df[df.rnk<=threshold_bot]\n",
    "        worst = worst.sort_values(by='rnk')\n",
    "\n",
    "        #Get best stocks\n",
    "        df[\"rnk\"] = df.predict.rank(method=\"first\", ascending=False)\n",
    "        best = df[df.rnk<=threshold_top]\n",
    "        best = best.sort_values(by='rnk')\n",
    "\n",
    "        return df, best, worst\n",
    "\n",
    "    def position_retriever(self, tradable, shortable):\n",
    "        \"\"\"Get the postions out of the account and the nummber of long tickers and short tickers\n",
    "\n",
    "        Args:\n",
    "            tradable (list): tradable stocks\n",
    "            shortable (list): shortable stocks\n",
    "            symbols (list): all the stocks\n",
    "\n",
    "        Returns:\n",
    "            tuple: positions(DF), long_ntickers(int), short_ntickers(int)\n",
    "        \"\"\"\n",
    "        symbols = self.symbols\n",
    "        all_positions = self.trading_client.get_all_positions()\n",
    "        \n",
    "        tick_val = []\n",
    "        qty_val = []\n",
    "\n",
    "        for x in all_positions:\n",
    "            tick_val.append(x.symbol)\n",
    "            qty_val.append(float(x.qty))\n",
    "\n",
    "        positions = pd.DataFrame({'Ticker':list(tick_val), 'Qty':list(qty_val)})\n",
    "        positions = positions.set_index('Ticker')\n",
    "        positions['Long'] = positions['Qty'] > 0\n",
    "        \n",
    "        positions[\"tradable\"] = pd.Series(tradable, index=symbols)\n",
    "        positions[\"shortable\"] = pd.Series(shortable, index=symbols)\n",
    "        positions = positions.fillna(value=False, inplace=False)\n",
    "        \n",
    "        sell = [True \n",
    "            if (positions.loc[ticker, 'Long']) \n",
    "            and (positions.loc[ticker, 'tradable'])\n",
    "            else False\n",
    "            for ticker in positions.index]\n",
    "\n",
    "        positions[\"sell\"] = pd.Series(sell, index=positions.index)\n",
    "\n",
    "        buy = [True \n",
    "                if (not positions.loc[ticker, 'Long'])\n",
    "                and (positions.loc[ticker, 'tradable'])\n",
    "                else False\n",
    "                for ticker in positions.index]\n",
    "        positions[\"buy\"] = pd.Series(buy, index=positions.index)\n",
    "\n",
    "        long_nticker = sum(positions['sell'])\n",
    "        short_nticker = sum(positions['buy'])\n",
    "\n",
    "\n",
    "        positions[positions['Qty']>0]\n",
    "\n",
    "        \n",
    "        return positions, long_nticker, short_nticker\n",
    "\n",
    "    def get_out_of_positions(self, df, positions):\n",
    "        sell_failed, buy_failed = 0, 0\n",
    "        for tick in positions.index: \n",
    "            if positions.loc[tick, \"sell\"]:\n",
    "                try:\n",
    "                    market_order_data = MarketOrderRequest(\n",
    "                        symbol=tick,\n",
    "                        #qty=-positions.loc[tick, \"trade\"],\n",
    "                        qty=positions.loc[tick, \"Qty\"],\n",
    "                        side=OrderSide.SELL,\n",
    "                        time_in_force=TimeInForce.DAY\n",
    "                    )\n",
    "                    market_order = self.trading_client.submit_order(\n",
    "                        order_data=market_order_data\n",
    "                    )\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    sell_failed += 1\n",
    "                    print(f\"sell order for {tick} failed\")\n",
    "            \n",
    "            if positions.loc[tick, \"buy\"]:\n",
    "                try:\n",
    "                    market_order_data = MarketOrderRequest(\n",
    "                        symbol=tick,\n",
    "                        qty=-positions.loc[tick, \"Qty\"],\n",
    "                        side=OrderSide.BUY,\n",
    "                        time_in_force=TimeInForce.DAY\n",
    "                    )\n",
    "                    market_order = self.trading_client.submit_order(\n",
    "                        order_data=market_order_data\n",
    "                    )\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    buy_failed += 1\n",
    "                    print(f\"buy order for {tick} failed\")\n",
    "                    #Buying back the shorted stocks might often fail, if we don't have many to invest\n",
    "        return sell_failed, buy_failed\n",
    "\n",
    "\n",
    "    def get_portfolio(self, best, worst, cash, long_nticker=0, short_nticker=0):\n",
    "        print(f\"long = {long_nticker}, short = {short_nticker}\")\n",
    "        if long_nticker != 0:\n",
    "            long_per_stock = 1.3*cash / long_nticker\n",
    "        else:\n",
    "            long_per_stock = 0\n",
    "            \n",
    "        if short_nticker != 0:\n",
    "            short_per_stock = 0.3*cash / short_nticker\n",
    "        else:\n",
    "            short_per_stock = 0  \n",
    "\n",
    "        worst = worst.sort_values(by=\"rnk\")\n",
    "        best = best.sort_values(by=\"rnk\")\n",
    "\n",
    "        try:\n",
    "            short_cutoff = worst[worst.shortable & (worst.bid>0)].rnk.iloc[short_nticker]\n",
    "            worst[\"target\"] = np.where(\n",
    "                worst.shortable & (worst.bid>0) & (worst.rnk<=short_cutoff),\n",
    "                -short_per_stock/worst.bid, \n",
    "                0\n",
    "            )\n",
    "            worst[\"target\"] = worst.target.astype(int)\n",
    "        except:\n",
    "            worst[\"target\"] = 0\n",
    "            \n",
    "        try:\n",
    "            long_cutoff = best[best.tradable & (best.ask>0)].rnk.iloc[long_nticker]\n",
    "            best[\"target\"] = np.where(\n",
    "                best.tradable & (best.ask>0) & (best.rnk<=long_cutoff), \n",
    "                long_per_stock/best.ask, \n",
    "                0\n",
    "            )\n",
    "            best[\"target\"] = best.target.astype(int)\n",
    "        except:\n",
    "            best[\"target\"] = 0\n",
    "        \n",
    "        return best, worst\n",
    "\n",
    "    def make_trade(self, best, worst):\n",
    "        wfail = 0\n",
    "        bfail = 0\n",
    "        for tick in worst.index:\n",
    "            if worst.loc[tick, 'target'] != 0:\n",
    "                try:\n",
    "                    market_order_data = MarketOrderRequest(\n",
    "                        symbol=tick,\n",
    "                        qty=-worst.loc[tick, \"target\"],\n",
    "                        side=OrderSide.SELL,\n",
    "                        time_in_force=TimeInForce.DAY\n",
    "                    )\n",
    "                    market_order = self.trading_client.submit_order(\n",
    "                        order_data=market_order_data\n",
    "                    )\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    print(f\"sell order for {tick} failed\")\n",
    "                    wfail += 1\n",
    "\n",
    "        for tick in best.index:\n",
    "            if best.loc[tick, 'target'] != 0:\n",
    "                try:\n",
    "                    market_order_data = MarketOrderRequest(\n",
    "                        symbol=tick,\n",
    "                        qty=best.loc[tick, \"target\"],\n",
    "                        side=OrderSide.BUY,\n",
    "                        time_in_force=TimeInForce.DAY\n",
    "                    )\n",
    "                    market_order = self.trading_client.submit_order(\n",
    "                        order_data=market_order_data\n",
    "                    )\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    print(f\"buy order for {tick} failed\")\n",
    "                    bfail += 1\n",
    "        return wfail, bfail\n",
    "                \n",
    "    def save_results(self, best, worst, acc = 'google'):\n",
    "        trading_client = TradingClient(self.KEY, self.SECRET_KEY, paper=True)\n",
    "        today = datetime.today().strftime(\"%Y-%m-%d\")\n",
    "        worst['type'] = 'Worst'\n",
    "        best['type'] = 'Best'\n",
    "\n",
    "        worst[\"date\"] = today\n",
    "        best[\"date\"] = today\n",
    "        \n",
    "        month = today.split('-')[1]\n",
    "        day = today.split('-')[-1]\n",
    "        folder = f'{acc}/results_{month}_{day}'\n",
    "        self.folder = folder\n",
    "        if not os.path.isdir(folder):\n",
    "            os.makedirs(folder, exist_ok=True)\n",
    "\n",
    "        try:\n",
    "            df2 = pd.read_csv(f\"./{folder}/trade_data.csv\", index_col=\"ticker\")\n",
    "            df2 = df2[df2.date != today]\n",
    "            df2 = pd.concat((df2, pd.concat((worst, best))))\n",
    "            df2.to_csv(f\"./{folder}/trade_data.csv\")\n",
    "        except:\n",
    "            df2 = pd.concat((worst, best))\n",
    "            df2.to_csv(f\"./{folder}/trade_data.csv\")\n",
    "\n",
    "        account = trading_client.get_account()\n",
    "        account = dict(account)\n",
    "        account = pd.DataFrame(pd.Series(account)).T\n",
    "        account[\"date\"] = today\n",
    "\n",
    "        try:\n",
    "            d = pd.read_csv(f\"./{folder}/account.csv\")\n",
    "            d = d[d.date != today]\n",
    "            account = pd.concat((d, account))\n",
    "            account.to_csv(f\"./{folder}/account.csv\")\n",
    "        except:\n",
    "            account.to_csv(f\"./{folder}/account.csv\")\n",
    "\n",
    "        positions = trading_client.get_all_positions()\n",
    "        positions = {x.symbol: x.qty for x in positions}\n",
    "        positions = pd.DataFrame(pd.Series(positions))\n",
    "        positions[\"date\"] = today\n",
    "\n",
    "        try:\n",
    "            d = pd.read_csv(f\"./{folder}/positions.csv\")\n",
    "            d = d[d.date != today]\n",
    "            positions = pd.concat((d, positions))\n",
    "            positions.to_csv(f\"./{folder}/positions.csv\")\n",
    "        except:\n",
    "            positions.to_csv(f\"./{folder}/positions.csv\")\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_key_g = 'PKKSG3T90MT6D7ZR17KS'\n",
    "r_secret_key_g = 'cc6AO2GacTyPHtA9Wl2yMmDNXVtfhkOSUXVPTQ66'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_Key = 'PK4PPMHW7UPRSDK8NAPW'\n",
    "R_Secret_key = '9Hy6FR8I9htN1jUTISCmgV69fcLAZdvaF2igJ4xa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "meta = pd.read_csv('./stock_meta.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = Trade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rice Acc\n",
    "t.set_keys(R_Key, R_Secret_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.set_keys(r_key_g, r_secret_key_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('./today_table27feb.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.set_index('ticker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['agr',\n",
    " 'bm',\n",
    " 'retvol',\n",
    " 'mom1m',\n",
    " 'beta',\n",
    " 'operprof',\n",
    " 'roaq',\n",
    " 'currat',\n",
    " 'mve',\n",
    " 'roic',\n",
    " 'saleinv',\n",
    " 'lev',\n",
    " 'ep',\n",
    " 'roeq',\n",
    " 'gma',\n",
    " 'mom12m',\n",
    " 'acc',\n",
    " 'idiovol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.set_features(features)\n",
    "t.set_pipe(load('./models/xgb.joblib'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equity 98665.19, Cash 98665.19\n"
     ]
    }
   ],
   "source": [
    "equity, cash = t.get_account_equity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# long_nticker = 40\n",
    "# short_nticker = 20\n",
    "# while cash > 500: \n",
    "#     df = pd.read_csv('./today_table27feb.csv')\n",
    "#     df = df.set_index('ticker')\n",
    "\n",
    "#     df, best, worst = t.predict_and_rank(df, 280)\n",
    "#     positions, long_nticker, short_nticker = t.position_retriever(df['tradable'], df['shortable'])\n",
    "#     long_nticker = 40\n",
    "#     short_nticker = 20\n",
    "#     best, worst = t.get_portfolio(best, worst, cash, long_nticker, short_nticker)\n",
    "#     t.make_trade(best, worst)\n",
    "#     equity, cash = t.get_account_equity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = t.get_today_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>mve</th>\n",
       "      <th>acc</th>\n",
       "      <th>agr</th>\n",
       "      <th>beta</th>\n",
       "      <th>bm</th>\n",
       "      <th>ep</th>\n",
       "      <th>gma</th>\n",
       "      <th>idiovol</th>\n",
       "      <th>lev</th>\n",
       "      <th>mom12m</th>\n",
       "      <th>mom1m</th>\n",
       "      <th>operprof</th>\n",
       "      <th>roeq</th>\n",
       "      <th>roic</th>\n",
       "      <th>roaq</th>\n",
       "      <th>retvol</th>\n",
       "      <th>saleinv</th>\n",
       "      <th>currat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WINT</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>1.526056</td>\n",
       "      <td>-0.455230</td>\n",
       "      <td>-0.337476</td>\n",
       "      <td>0.860127</td>\n",
       "      <td>1.290434</td>\n",
       "      <td>-2.083995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103584</td>\n",
       "      <td>1.013959</td>\n",
       "      <td>-0.844231</td>\n",
       "      <td>-0.238272</td>\n",
       "      <td>-0.209728</td>\n",
       "      <td>-0.211283</td>\n",
       "      <td>-0.925505</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>0.150928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RKDA</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>1.547563</td>\n",
       "      <td>0.183179</td>\n",
       "      <td>-0.072442</td>\n",
       "      <td>1.173936</td>\n",
       "      <td>1.000918</td>\n",
       "      <td>-0.471922</td>\n",
       "      <td>-0.040720</td>\n",
       "      <td>0.109365</td>\n",
       "      <td>0.409535</td>\n",
       "      <td>-0.737864</td>\n",
       "      <td>-0.262037</td>\n",
       "      <td>-0.897601</td>\n",
       "      <td>-0.106950</td>\n",
       "      <td>-0.787528</td>\n",
       "      <td>-0.081896</td>\n",
       "      <td>0.094295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INBS</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>1.686399</td>\n",
       "      <td>-0.324169</td>\n",
       "      <td>-0.434925</td>\n",
       "      <td>1.756426</td>\n",
       "      <td>1.134729</td>\n",
       "      <td>-1.422676</td>\n",
       "      <td>0.019998</td>\n",
       "      <td>0.246129</td>\n",
       "      <td>0.994531</td>\n",
       "      <td>0.099120</td>\n",
       "      <td>-0.604651</td>\n",
       "      <td>-0.298216</td>\n",
       "      <td>-0.079643</td>\n",
       "      <td>-0.775631</td>\n",
       "      <td>-0.041399</td>\n",
       "      <td>0.083448</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHSI</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>2.104134</td>\n",
       "      <td>-0.380940</td>\n",
       "      <td>1.379159</td>\n",
       "      <td>0.952000</td>\n",
       "      <td>1.567344</td>\n",
       "      <td>-1.739361</td>\n",
       "      <td>0.315431</td>\n",
       "      <td>0.152061</td>\n",
       "      <td>0.081738</td>\n",
       "      <td>-0.410788</td>\n",
       "      <td>-0.069014</td>\n",
       "      <td>-1.223570</td>\n",
       "      <td>-0.060271</td>\n",
       "      <td>-0.593954</td>\n",
       "      <td>-0.056850</td>\n",
       "      <td>0.026983</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUWE</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>2.116256</td>\n",
       "      <td>0.027364</td>\n",
       "      <td>-0.188842</td>\n",
       "      <td>1.132244</td>\n",
       "      <td>1.326212</td>\n",
       "      <td>-1.558892</td>\n",
       "      <td>0.156327</td>\n",
       "      <td>0.146456</td>\n",
       "      <td>1.321812</td>\n",
       "      <td>-0.913396</td>\n",
       "      <td>-0.086391</td>\n",
       "      <td>-0.850811</td>\n",
       "      <td>-0.135274</td>\n",
       "      <td>-0.410252</td>\n",
       "      <td>-0.102837</td>\n",
       "      <td>0.065547</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.857</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INGR</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>8.781356</td>\n",
       "      <td>0.045656</td>\n",
       "      <td>0.080297</td>\n",
       "      <td>0.656644</td>\n",
       "      <td>0.478774</td>\n",
       "      <td>0.074851</td>\n",
       "      <td>0.213459</td>\n",
       "      <td>0.031025</td>\n",
       "      <td>0.654035</td>\n",
       "      <td>0.198198</td>\n",
       "      <td>-0.021720</td>\n",
       "      <td>0.219355</td>\n",
       "      <td>0.037182</td>\n",
       "      <td>0.098071</td>\n",
       "      <td>0.015399</td>\n",
       "      <td>0.010902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AN</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>8.782354</td>\n",
       "      <td>-0.030487</td>\n",
       "      <td>0.124793</td>\n",
       "      <td>1.275084</td>\n",
       "      <td>0.277598</td>\n",
       "      <td>0.186719</td>\n",
       "      <td>0.588723</td>\n",
       "      <td>0.055731</td>\n",
       "      <td>1.086087</td>\n",
       "      <td>0.084870</td>\n",
       "      <td>0.081154</td>\n",
       "      <td>0.867859</td>\n",
       "      <td>0.126995</td>\n",
       "      <td>0.221938</td>\n",
       "      <td>0.030419</td>\n",
       "      <td>0.035155</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RRC</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>8.784591</td>\n",
       "      <td>-0.101999</td>\n",
       "      <td>-0.005247</td>\n",
       "      <td>0.875431</td>\n",
       "      <td>0.463100</td>\n",
       "      <td>0.190549</td>\n",
       "      <td>0.359211</td>\n",
       "      <td>0.106733</td>\n",
       "      <td>0.603761</td>\n",
       "      <td>-0.028859</td>\n",
       "      <td>0.147554</td>\n",
       "      <td>0.987359</td>\n",
       "      <td>0.376354</td>\n",
       "      <td>0.215135</td>\n",
       "      <td>0.117668</td>\n",
       "      <td>0.044828</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SITE</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>8.785891</td>\n",
       "      <td>0.011094</td>\n",
       "      <td>0.197439</td>\n",
       "      <td>1.741537</td>\n",
       "      <td>0.195816</td>\n",
       "      <td>0.036882</td>\n",
       "      <td>0.671755</td>\n",
       "      <td>0.044455</td>\n",
       "      <td>0.185010</td>\n",
       "      <td>-0.113927</td>\n",
       "      <td>-0.034191</td>\n",
       "      <td>0.306798</td>\n",
       "      <td>-0.000680</td>\n",
       "      <td>0.103002</td>\n",
       "      <td>-0.000342</td>\n",
       "      <td>0.020184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PII</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>8.790284</td>\n",
       "      <td>-0.011896</td>\n",
       "      <td>0.033698</td>\n",
       "      <td>1.456957</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>0.065661</td>\n",
       "      <td>0.388189</td>\n",
       "      <td>0.050146</td>\n",
       "      <td>0.602684</td>\n",
       "      <td>-0.009456</td>\n",
       "      <td>-0.011707</td>\n",
       "      <td>0.858531</td>\n",
       "      <td>0.192251</td>\n",
       "      <td>0.172585</td>\n",
       "      <td>0.038383</td>\n",
       "      <td>0.010715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1282 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date       mve       acc       agr      beta        bm  \\\n",
       "ticker                                                                 \n",
       "WINT    2023-03-06  1.526056 -0.455230 -0.337476  0.860127  1.290434   \n",
       "RKDA    2023-03-06  1.547563  0.183179 -0.072442  1.173936  1.000918   \n",
       "INBS    2023-03-06  1.686399 -0.324169 -0.434925  1.756426  1.134729   \n",
       "GHSI    2023-03-06  2.104134 -0.380940  1.379159  0.952000  1.567344   \n",
       "NUWE    2023-03-06  2.116256  0.027364 -0.188842  1.132244  1.326212   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "INGR    2023-03-06  8.781356  0.045656  0.080297  0.656644  0.478774   \n",
       "AN      2023-03-06  8.782354 -0.030487  0.124793  1.275084  0.277598   \n",
       "RRC     2023-03-06  8.784591 -0.101999 -0.005247  0.875431  0.463100   \n",
       "SITE    2023-03-06  8.785891  0.011094  0.197439  1.741537  0.195816   \n",
       "PII     2023-03-06  8.790284 -0.011896  0.033698  1.456957  0.161399   \n",
       "\n",
       "              ep       gma   idiovol       lev    mom12m     mom1m  operprof  \\\n",
       "ticker                                                                         \n",
       "WINT   -2.083995  0.000000  0.103584  1.013959 -0.844231 -0.238272 -0.209728   \n",
       "RKDA   -0.471922 -0.040720  0.109365  0.409535 -0.737864 -0.262037 -0.897601   \n",
       "INBS   -1.422676  0.019998  0.246129  0.994531  0.099120 -0.604651 -0.298216   \n",
       "GHSI   -1.739361  0.315431  0.152061  0.081738 -0.410788 -0.069014 -1.223570   \n",
       "NUWE   -1.558892  0.156327  0.146456  1.321812 -0.913396 -0.086391 -0.850811   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "INGR    0.074851  0.213459  0.031025  0.654035  0.198198 -0.021720  0.219355   \n",
       "AN      0.186719  0.588723  0.055731  1.086087  0.084870  0.081154  0.867859   \n",
       "RRC     0.190549  0.359211  0.106733  0.603761 -0.028859  0.147554  0.987359   \n",
       "SITE    0.036882  0.671755  0.044455  0.185010 -0.113927 -0.034191  0.306798   \n",
       "PII     0.065661  0.388189  0.050146  0.602684 -0.009456 -0.011707  0.858531   \n",
       "\n",
       "            roeq      roic      roaq    retvol  saleinv  currat  \n",
       "ticker                                                           \n",
       "WINT   -0.211283 -0.925505 -0.078282  0.150928      1.0   4.772  \n",
       "RKDA   -0.106950 -0.787528 -0.081896  0.094295      1.0   7.021  \n",
       "INBS   -0.079643 -0.775631 -0.041399  0.083448      1.0   2.331  \n",
       "GHSI   -0.060271 -0.593954 -0.056850  0.026983      1.0  10.413  \n",
       "NUWE   -0.135274 -0.410252 -0.102837  0.065547      1.0   4.857  \n",
       "...          ...       ...       ...       ...      ...     ...  \n",
       "INGR    0.037182  0.098071  0.015399  0.010902      1.0   1.758  \n",
       "AN      0.126995  0.221938  0.030419  0.035155      1.0   0.921  \n",
       "RRC     0.376354  0.215135  0.117668  0.044828      1.0   0.529  \n",
       "SITE   -0.000680  0.103002 -0.000342  0.020184      1.0   2.357  \n",
       "PII     0.192251  0.172585  0.038383  0.010715      1.0   1.189  \n",
       "\n",
       "[1282 rows x 19 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df, best, worst = t.predict_and_rank(df, 280)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "positions, long_nticker, short_nticker = t.position_retriever(df['tradable'], df['shortable'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>Qty</th>\n",
       "      <th>Long</th>\n",
       "      <th>tradable</th>\n",
       "      <th>shortable</th>\n",
       "      <th>sell</th>\n",
       "      <th>buy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</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>EVKG</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FNHCQ</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSRDQ</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACHHY</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACUT</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>JLL</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MFD</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MFDX</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLRCR</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MFEM</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11228 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Qty   Long  tradable  shortable   sell    buy\n",
       "Ticker                                                 \n",
       "EVKG    False  False     False      False  False  False\n",
       "FNHCQ   False  False     False      False  False  False\n",
       "FSRDQ   False  False     False      False  False  False\n",
       "ACHHY   False  False     False      False  False  False\n",
       "ACUT    False  False     False      False  False  False\n",
       "...       ...    ...       ...        ...    ...    ...\n",
       "JLL     False  False     False      False  False  False\n",
       "MFD     False  False     False      False  False  False\n",
       "MFDX    False  False     False      False  False  False\n",
       "CLRCR   False  False     False      False  False  False\n",
       "MFEM    False  False     False      False  False  False\n",
       "\n",
       "[11228 rows x 6 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "sell_failed, buy_failed = t.get_out_of_positions(df, positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sell Failed: 0\n",
      "Buy Failed: 0\n"
     ]
    }
   ],
   "source": [
    "print(f'Sell Failed: {sell_failed}\\nBuy Failed: {buy_failed}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_nticker = 130\n",
    "short_nticker = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# google account\n",
    "long_nticker = 40\n",
    "short_nticker = 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long = 130, short = 30\n"
     ]
    }
   ],
   "source": [
    "best, worst = t.get_portfolio(best, worst, cash, long_nticker, short_nticker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Best Portoflio 2023-03-07 14:49:54.825372'}, ylabel='sector'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.merge(best.reset_index(), meta)['sector'].value_counts().plot.pie(autopct='%1.1f%%', title = f'Best Portoflio {datetime.today()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Short Portoflio 2023-03-07 14:49:55.465777'}, ylabel='sector'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.merge(worst.reset_index(), meta)['sector'].value_counts().plot.pie(autopct='%1.1f%%', title = f'Short Portoflio {datetime.today()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long = 130, short = 30\n"
     ]
    }
   ],
   "source": [
    "best, worst = t.get_portfolio(best, worst, cash, long_nticker, short_nticker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>date</th>\n",
       "      <th>mve</th>\n",
       "      <th>acc</th>\n",
       "      <th>agr</th>\n",
       "      <th>beta</th>\n",
       "      <th>bm</th>\n",
       "      <th>ep</th>\n",
       "      <th>gma</th>\n",
       "      <th>idiovol</th>\n",
       "      <th>lev</th>\n",
       "      <th>...</th>\n",
       "      <th>retvol</th>\n",
       "      <th>saleinv</th>\n",
       "      <th>currat</th>\n",
       "      <th>predict</th>\n",
       "      <th>tradable</th>\n",
       "      <th>shortable</th>\n",
       "      <th>ask</th>\n",
       "      <th>bid</th>\n",
       "      <th>rnk</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticker</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",
       "      <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>ACCO</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>6.233234</td>\n",
       "      <td>-0.030740</td>\n",
       "      <td>-0.095947</td>\n",
       "      <td>1.577400</td>\n",
       "      <td>1.590232</td>\n",
       "      <td>-0.025912</td>\n",
       "      <td>0.178663</td>\n",
       "      <td>0.056350</td>\n",
       "      <td>3.895783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032336</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.498</td>\n",
       "      <td>2.000155</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.43</td>\n",
       "      <td>5.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCTT</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>7.231359</td>\n",
       "      <td>-0.003454</td>\n",
       "      <td>-0.031846</td>\n",
       "      <td>2.044939</td>\n",
       "      <td>0.622264</td>\n",
       "      <td>0.028313</td>\n",
       "      <td>0.229584</td>\n",
       "      <td>0.063525</td>\n",
       "      <td>0.717576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.823</td>\n",
       "      <td>1.698725</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>36.60</td>\n",
       "      <td>24.37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAAS</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>5.403578</td>\n",
       "      <td>-0.024600</td>\n",
       "      <td>0.013005</td>\n",
       "      <td>0.981239</td>\n",
       "      <td>3.367537</td>\n",
       "      <td>0.115911</td>\n",
       "      <td>0.101870</td>\n",
       "      <td>0.117213</td>\n",
       "      <td>3.978948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055838</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.425</td>\n",
       "      <td>1.646077</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>8.68</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBGI</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>6.988874</td>\n",
       "      <td>0.280545</td>\n",
       "      <td>-0.614047</td>\n",
       "      <td>1.525964</td>\n",
       "      <td>0.673299</td>\n",
       "      <td>2.387150</td>\n",
       "      <td>0.065055</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>5.246870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024895</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.768</td>\n",
       "      <td>1.590701</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>15.53</td>\n",
       "      <td>12.46</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAND</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>6.017620</td>\n",
       "      <td>-0.015274</td>\n",
       "      <td>-0.127842</td>\n",
       "      <td>1.052270</td>\n",
       "      <td>0.614816</td>\n",
       "      <td>0.044206</td>\n",
       "      <td>0.223692</td>\n",
       "      <td>0.091018</td>\n",
       "      <td>1.484404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.453</td>\n",
       "      <td>1.582530</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>15.72</td>\n",
       "      <td>15.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>62</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>RNA</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>7.366824</td>\n",
       "      <td>-0.078957</td>\n",
       "      <td>0.493989</td>\n",
       "      <td>1.012457</td>\n",
       "      <td>0.344472</td>\n",
       "      <td>-0.103683</td>\n",
       "      <td>0.021573</td>\n",
       "      <td>0.101924</td>\n",
       "      <td>0.036186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043894</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.001</td>\n",
       "      <td>0.647043</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>22.70</td>\n",
       "      <td>18.11</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>6.910252</td>\n",
       "      <td>-0.030430</td>\n",
       "      <td>-0.037484</td>\n",
       "      <td>2.022563</td>\n",
       "      <td>0.397098</td>\n",
       "      <td>-0.073366</td>\n",
       "      <td>0.100538</td>\n",
       "      <td>0.071035</td>\n",
       "      <td>0.507667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.302</td>\n",
       "      <td>0.644544</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>42.94</td>\n",
       "      <td>28.54</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUNG</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>6.093795</td>\n",
       "      <td>-0.066582</td>\n",
       "      <td>-0.176453</td>\n",
       "      <td>1.985743</td>\n",
       "      <td>0.356329</td>\n",
       "      <td>-0.136331</td>\n",
       "      <td>0.169514</td>\n",
       "      <td>0.095923</td>\n",
       "      <td>0.091783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.137</td>\n",
       "      <td>0.643500</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>14.23</td>\n",
       "      <td>11.83</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSPN</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>6.568078</td>\n",
       "      <td>-0.026974</td>\n",
       "      <td>-0.021004</td>\n",
       "      <td>0.791045</td>\n",
       "      <td>0.375932</td>\n",
       "      <td>-0.026689</td>\n",
       "      <td>0.434071</td>\n",
       "      <td>0.071098</td>\n",
       "      <td>0.243651</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060630</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.783</td>\n",
       "      <td>0.643261</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>21.42</td>\n",
       "      <td>14.27</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCEL</th>\n",
       "      <td>2023-03-06</td>\n",
       "      <td>5.884714</td>\n",
       "      <td>-0.074382</td>\n",
       "      <td>-0.153006</td>\n",
       "      <td>1.839760</td>\n",
       "      <td>0.369749</td>\n",
       "      <td>-0.116348</td>\n",
       "      <td>0.272335</td>\n",
       "      <td>0.083567</td>\n",
       "      <td>0.056579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083445</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.179</td>\n",
       "      <td>0.643216</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>14.12</td>\n",
       "      <td>11.30</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1120 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date       mve       acc       agr      beta        bm  \\\n",
       "ticker                                                                 \n",
       "ACCO    2023-03-06  6.233234 -0.030740 -0.095947  1.577400  1.590232   \n",
       "UCTT    2023-03-06  7.231359 -0.003454 -0.031846  2.044939  0.622264   \n",
       "CAAS    2023-03-06  5.403578 -0.024600  0.013005  0.981239  3.367537   \n",
       "SBGI    2023-03-06  6.988874  0.280545 -0.614047  1.525964  0.673299   \n",
       "BAND    2023-03-06  6.017620 -0.015274 -0.127842  1.052270  0.614816   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "RNA     2023-03-06  7.366824 -0.078957  0.493989  1.012457  0.344472   \n",
       "PAR     2023-03-06  6.910252 -0.030430 -0.037484  2.022563  0.397098   \n",
       "LUNG    2023-03-06  6.093795 -0.066582 -0.176453  1.985743  0.356329   \n",
       "OSPN    2023-03-06  6.568078 -0.026974 -0.021004  0.791045  0.375932   \n",
       "RCEL    2023-03-06  5.884714 -0.074382 -0.153006  1.839760  0.369749   \n",
       "\n",
       "              ep       gma   idiovol       lev  ...    retvol  saleinv  \\\n",
       "ticker                                          ...                      \n",
       "ACCO   -0.025912  0.178663  0.056350  3.895783  ...  0.032336      1.0   \n",
       "UCTT    0.028313  0.229584  0.063525  0.717576  ...  0.027075      1.0   \n",
       "CAAS    0.115911  0.101870  0.117213  3.978948  ...  0.055838      1.0   \n",
       "SBGI    2.387150  0.065055  0.069316  5.246870  ...  0.024895      1.0   \n",
       "BAND    0.044206  0.223692  0.091018  1.484404  ...  0.073001      1.0   \n",
       "...          ...       ...       ...       ...  ...       ...      ...   \n",
       "RNA    -0.103683  0.021573  0.101924  0.036186  ...  0.043894      1.0   \n",
       "PAR    -0.073366  0.100538  0.071035  0.507667  ...  0.030715      1.0   \n",
       "LUNG   -0.136331  0.169514  0.095923  0.091783  ...  0.052032      1.0   \n",
       "OSPN   -0.026689  0.434071  0.071098  0.243651  ...  0.060630      1.0   \n",
       "RCEL   -0.116348  0.272335  0.083567  0.056579  ...  0.083445      1.0   \n",
       "\n",
       "        currat   predict  tradable  shortable    ask    bid     rnk  target  \n",
       "ticker                                                                       \n",
       "ACCO     1.498  2.000155      True       True   6.43   5.36     1.0     153  \n",
       "UCTT     2.823  1.698725      True       True  36.60  24.37     2.0      26  \n",
       "CAAS     1.425  1.646077      True      False   8.68   5.76     3.0     113  \n",
       "SBGI     2.768  1.590701      True       True  15.53  12.46     4.0      63  \n",
       "BAND     2.453  1.582530      True       True  15.72  15.70     5.0      62  \n",
       "...        ...       ...       ...        ...    ...    ...     ...     ...  \n",
       "RNA     12.001  0.647043      True       True  22.70  18.11  1116.0       0  \n",
       "PAR      3.302  0.644544      True       True  42.94  28.54  1117.0       0  \n",
       "LUNG     9.137  0.643500      True       True  14.23  11.83  1118.0       0  \n",
       "OSPN     1.783  0.643261      True       True  21.42  14.27  1119.0       0  \n",
       "RCEL     8.179  0.643216      True       True  14.12  11.30  1120.0       0  \n",
       "\n",
       "[1120 rows x 26 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.make_trade(best, worst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rice acc\n",
    "t.save_results(best, worst, acc='rice')\n",
    "pd.merge(best.reset_index(), meta)['sector'].value_counts().plot.pie(autopct='%1.1f%%', title = f'Best Portoflio {datetime.today()}').figure.savefig(t.folder + 'long.png')\n",
    "pd.merge(worst.reset_index(), meta)['sector'].value_counts().plot.pie(autopct='%1.1f%%', title = f'Short Portoflio {datetime.today()}').figure.savefig(t.folder + 'short.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trading_client = TradingClient(R_Key, R_Secret_key, paper=True)\n",
    "# today = datetime.today().strftime(\"%Y-%m-%d\")\n",
    "# worst['type'] = 'Worst'\n",
    "# best['type'] = 'Best'\n",
    "\n",
    "# worst[\"date\"] = today\n",
    "# best[\"date\"] = today\n",
    "\n",
    "# try:\n",
    "#     df2 = pd.read_csv(f\"./results_2/trade_data.csv\", index_col=\"ticker\")\n",
    "#     df2 = df2[df2.date != today]\n",
    "#     df2 = pd.concat((df2, pd.concat((worst, best))))\n",
    "#     df2.to_csv(f\"./results_2/trade_data.csv\")\n",
    "# except:\n",
    "#     df2 = pd.concat((worst, best))\n",
    "#     df2.to_csv(\"./results_2/trade_data.csv\")\n",
    "\n",
    "# account = trading_client.get_account()\n",
    "# account = dict(account)\n",
    "# account = pd.DataFrame(pd.Series(account)).T\n",
    "# account[\"date\"] = today\n",
    "\n",
    "# try:\n",
    "#     d = pd.read_csv(\"./results_2/account.csv\")\n",
    "#     d = d[d.date != today]\n",
    "#     account = pd.concat((d, account))\n",
    "#     account.to_csv(\"./results_2/account.csv\")\n",
    "# except:\n",
    "#     account.to_csv(\"./results_2/account.csv\")\n",
    "\n",
    "# positions = trading_client.get_all_positions()\n",
    "# positions = {x.symbol: x.qty for x in positions}\n",
    "# positions = pd.DataFrame(pd.Series(positions))\n",
    "# positions[\"date\"] = today\n",
    "\n",
    "# try:\n",
    "#     d = pd.read_csv(\"./results_2/positions.csv\")\n",
    "#     d = d[d.date != today]\n",
    "#     positions = pd.concat((d, positions))\n",
    "#     positions.to_csv(\"./results_2/positions.csv\")\n",
    "# except:\n",
    "#     positions.to_csv(\"./results_2/positions.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "09ac0f642c4809c63ace70dc888cbd5b35159287af3895fb76989324c78b2f31"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}