{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c437407e",
   "metadata": {},
   "source": [
    "# Zipline Context 功能介紹"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7199a965",
   "metadata": {},
   "source": [
    "__請於閱讀本文前，預先閱讀 TSMC buy and hold strategy，以了解四大函式 (initialize, handel_data, analyze, run_algorithm) 使用方法。__\n",
    "\n",
    "在 Zipline 程式運行中的各種數據，會記錄在context並不斷更新，所以在程式運行中，可以隨時取得即時資訊。下面介紹幾個常用項目：\n",
    "    \n",
    "    context.asset\n",
    "    contexas.account\n",
    "    context.portfolio\n",
    "    context.blotter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c8d12f",
   "metadata": {},
   "source": [
    "# context.asset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c817a9",
   "metadata": {},
   "source": [
    "在 initialize 的階段可以加入\n",
    "\n",
    "    context.tickers = ['1216', '2330', '2327']\n",
    "    context.asset = [symbol(ticker) for ticker in context.tickers]  \n",
    "\n",
    "建立一個股票清單：[Equity(2 [1216]), Equity(14 [2330]), Equity(13 [2327])]，格式是 list，裡面資料型態是 zipline.asset.Assets，這樣在handle_data 階段就可以用來選取股票。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99247186",
   "metadata": {},
   "source": [
    "## context.account"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8016a5",
   "metadata": {},
   "source": [
    "account 裡面的資訊，大多都是來自 context.portfolio，共計有：\n",
    "    \n",
    "    account.settled_cash = portfolio.cash 帳上現金\n",
    "    account.accrued_interest = 0.0。股票不適用。\n",
    "    account.buying_power = np.inf （無限大）。股票不適用。\n",
    "    account.equity_with_loan = portfolio.portfolio_value 投資組合總市值\n",
    "    account.total_positions_value = portfolio.portfolio_value - portfolio.cash 投資組合總市值 - 現金\n",
    "    account.total_positions_exposure = portfolio.positions_exposure sum(股數 * 當天收盤價)，long為正值，short為負值\n",
    "    account.regt_equity = portfolio.cash 現金\n",
    "    account.regt_margin = np.inf （無限大）。股票不適用。\n",
    "    account.initial_margin_requirement = 0.0 必須保證金。股票不適用。\n",
    "    account.maintenance_margin_requirement = 0.0 維持保證金。股票不適用。\n",
    "    account.available_funds = portfolio.cash 現金\n",
    "    account.excess_liquidity = portfolio.cash 現金\n",
    "    account.cushion = portfolio.cash / portfolio.portfolio_value 現金 / 投資組合總市值，後者如果是 0 則回傳 np.inf （無限大）\n",
    "    account.day_trades_remaining = 剩餘交易日，預設np.inf （無限大）。股票不適用。\n",
    "    account.net_liquidation = portfolio.portfolio_value\n",
    "    account.gross_leverage = gross_exposure / portfolio_value 後者如果是 0 則回傳 np.inf （無限大）\n",
    "    account.net_leverage = net_exposure / portfolio_value 後者如果是 0 則回傳 np.inf （無限大）\n",
    "    account.leverage = account.gross_leverage\n",
    "    \n",
    "gross_exposure算法：sum(股數 * 當天收盤價)。其中，股數不管是long或short都是正值\n",
    "\n",
    "net_exposure算法：sum(股數 * 當天收盤價)。其中，股數long為正，short為負"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b7b427",
   "metadata": {},
   "source": [
    "## context.portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029ab99a",
   "metadata": {},
   "source": [
    "portfolio裡面的資訊，有一些也同時出現在回測產出的資料中，但是有些計算方式不太一樣\n",
    "    \n",
    "    cash_flow：從開始日到當天的累計現金流，流入正流出負\n",
    "    starting_cash：起始資金，不是當天開始時的現金\n",
    "    portfolio_value：positions_value + cash 投資組合總市值\n",
    "    pnl：累計的pnl，為 portfolio_value - 起始資金\n",
    "    returns：累計return，( 1 + 每日 pnl / 每日起始 portfolio_value) 的乘積 - 1\n",
    "    cash：回測資料中的 ending_cash\n",
    "    positions：帳上的股票部位\n",
    "    start_date：整個測試的開始日\n",
    "    positions_value：跟 net_exposure 算法一樣，sum(股數 * 當天收盤價)。其中，股數long為正，short為負\n",
    "    positions_exposure：買賣股票時 value = exposure，跟上面一樣 參考~\\zipline\\finance\\_finance_ext.pyx\n",
    "    \n",
    "context.portfolio.positions 回傳內容範例：\n",
    "\n",
    "{Equity(0 [1101]): Position({'asset': Equity(0 [1101]), 'amount': 1000, 'cost_basis': 45.56483750129291, 'last_sale_price': 45.1, 'last_sale_date': Timestamp('2018-07-25 05:30:00+0000', tz='UTC')}), Equity(14 [2330]): Position({'asset': Equity(14 [2330]), 'amount': -1000, 'cost_basis': 240.65657496810346, 'last_sale_price': 240.5, 'last_sale_date': Timestamp('2018-07-25 05:30:00+0000', tz='UTC')})}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0daed4b4",
   "metadata": {},
   "source": [
    "## context.blotter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237579c6",
   "metadata": {},
   "source": [
    "blotter主要紀錄訂單相關的資訊\n",
    "\n",
    "1. slippage_models 紀錄這次模擬時，股票和期貨所用的slippage model，範例：\n",
    "\n",
    "    {<class 'zipline.assets._assets.Equity'>: VolumeShareSlippage(\n",
    "    volume_limit=0.025,\n",
    "    price_impact=0.1), <class 'zipline.assets._assets.Future'>: VolatilityVolumeShare(volume_limit=0.05, eta=<varies>)}\n",
    "\n",
    "    \n",
    "2. commissions_models 概念相同，範例：\n",
    "\n",
    "     {<class 'zipline.assets._assets.Equity'>: PerDollar(cost_per_dollar=0.001425), <class 'zipline.assets._assets.Future'>: \n",
    "    PerContract(cost_per_contract=0.85, exchange_fee=<varies>, min_trade_cost=0)}\n",
    "\n",
    "    \n",
    "3. open_orders：回傳字典，key是asset (例如 symbol('2327'))，對應list，list裡面是該股票的open orders：\n",
    "    \n",
    "    Order({'id': 'c201d801586349febfd88f3b97b35738', 'dt': Timestamp('2022-10-20 05:30:00+0000', tz='UTC'), 'reason': None, \n",
    "    'created': Timestamp('2022-10-20 05:30:00+0000', tz='UTC'), 'amount': 1000, 'filled': 0, 'commission': 0, 'stop': None, \n",
    "    'limit': None, 'stop_reached': False, 'limit_reached': False, 'sid': Equity(11 [2327]), 'status': <ORDER_STATUS.OPEN: 0>})\n",
    "    \n",
    "\n",
    "4. orders：目前為止所有的訂單，包括open, canceled, filled，回傳格式是字典，key是order id，對應的是和上面一樣的 <class 'zipline.finance.order.Order'>\n",
    "\n",
    "\n",
    "5. new_orders：當天創立的訂單，回傳一個清單，裡面物件和上面一樣，<class 'zipline.finance.order.Order'>\n",
    "\n",
    "\n",
    "6. current_dt： 當日日期"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431a09c5",
   "metadata": {},
   "source": [
    "## context.sim_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9538d252",
   "metadata": {},
   "source": [
    "sim_params紀錄各種模擬的參數，範例：\n",
    "\n",
    "    SimulationParameters(\n",
    "    start_session=2022-12-05 00:00:00+00:00,\n",
    "    end_session=2022-12-23 00:00:00+00:00,\n",
    "    capital_base=1000000.0,\n",
    "    data_frequency=daily,\n",
    "    emission_rate=daily,\n",
    "    first_open=2022-12-05 01:01:00+00:00,\n",
    "    last_close=2022-12-23 05:30:00+00:00,\n",
    "    trading_calendar=<exchange_calendars.exchange_calendar_tejxtai.TEJ_XTAIExchangeCalendar object at 0x000001B76D7682E0>\n",
    "    )\n",
    "\n",
    "1. start_session：開始日，UTC午夜時間\n",
    "2. end_session：結束日，UTC午夜時間\n",
    "3. capital_base：起始資金\n",
    "4. data_frequency：資料頻率，目前僅支援日頻率\n",
    "5. emission_rate：計算頻率，也是每日\n",
    "6. first_open：第一個開始交易時間\n",
    "7. last_close：最後一個收盤時間\n",
    "8. trading_calendar：使用的交易日曆(TEJ_XTAI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26ee15ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:zipline-tej] *",
   "language": "python",
   "name": "conda-env-zipline-tej-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}