{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from v2realbot.tools.loadbatch import load_batch\n",
    "from v2realbot.utils.utils import zoneNY\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import timedelta, datetime\n",
    "import vectorbtpro as vbt\n",
    "import os\n",
    "from itables import init_notebook_mode, show\n",
    "\n",
    "init_notebook_mode(all_interactive=True)\n",
    "\n",
    "vbt.settings.set_theme(\"dark\")\n",
    "vbt.settings['plotting']['layout']['width'] = 1280\n",
    "vbt.settings.plotting.auto_rangebreaks = True\n",
    "# Set the option to display with pagination\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "pd.set_option('display.max_rows', 10)  # Number of rows per page\n",
    "\n",
    "# Alpaca API credentials\n",
    "ALPACA_API_KEY = os.environ.get('ACCOUNT1_PAPER_API_KEY')\n",
    "ALPACA_API_SECRET = os.environ.get('ACCOUNT1_PAPER_SECRET_KEY')\n",
    "\n",
    "# Initialize Alpaca data client\n",
    "alpaca_data = vbt.AlpacaData.set_custom_settings(client_config=dict(\n",
    "        api_key=ALPACA_API_KEY,\n",
    "        secret_key=ALPACA_API_SECRET\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetch Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the symbol, start, and end dates for your data\n",
    "symbol = 'BAC'\n",
    "start_date = datetime.now() - timedelta(days=10)  # Last 30 days\n",
    "end_date = datetime.now() - timedelta(days=1)  # yesterday\n",
    "time_interval = '1T'  # 1-minute intervals '1 minute'\n",
    "\n",
    "basic_data = vbt.AlpacaData.pull([\"BAC\"], start=start_date, end=end_date, timeframe=time_interval, tz=\"America/New_York\")\n",
    "basic_data = basic_data.transform(lambda x: x.between_time(\"9:30\",\"16:00\"))\n",
    "#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# filter dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter na dny\n",
    "# dates_of_interest = pd.to_datetime(['2024-04-22', '2024-04-23']).tz_localize('US/Eastern')\n",
    "# filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
    "\n",
    "# df = filtered_df\n",
    "# df.info()\n",
    "\n",
    "basic_data.data[\"BAC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
    "\n",
    "m1_data.data[\"BAC\"]\n",
    "#m5_data = m1_data.resample(\"5T\")\n",
    "\n",
    "#m5_data.data[\"BAC\"].head(10)\n",
    "\n",
    "m15_data = m1_data.resample(\"15T\")\n",
    "\n",
    "m15_data = m15_data.transform(lambda x: x.between_time(\"9:30\",\"15:59\"))\n",
    "\n",
    "m15 = m15_data.data[\"BAC\"]\n",
    "\n",
    "m15.vbt.ohlcv.plot()\n",
    "\n",
    "m15\n",
    "\n",
    "# m1_data.wrapper.index\n",
    "\n",
    "# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
    "# m1_resampler.index_difference(reverse=True)\n",
    "\n",
    "\n",
    "# m5_resampler.prettify()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate VWAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#WWAP\n",
    "vbt.phelp(vbt.VWAP.run)\n",
    "close = m1_data.close\n",
    "high = m1_data.high\n",
    "low = m1_data.low\n",
    "volume = m1_data.volume\n",
    "vwapD = vbt.VWAP.run(high, low, close, volume, anchor=\"D\")\n",
    "# vwapT = vbt.VWAP.run(high, low, close, volume, anchor=\"T\")\n",
    "\n",
    "#vwap.vwap\n",
    "\n",
    "fig = m1_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
    "vwapD.vwap.vbt.plot(fig=fig)\n",
    "#vwapT.vwap.vbt.plot(fig=fig)\n",
    "fig.show()\n",
    "\n",
    "vwapD.vwap\n",
    "\n",
    "#vwap = vbt.VWAP.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1_data.data[\"BAC\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# defining ENTRY WINDOW and forced EXIT window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#m1_data.data[\"BAC\"].info()\n",
    "import datetime\n",
    "# Define the market open and close times\n",
    "market_open = datetime.time(9, 30)\n",
    "market_close = datetime.time(16, 0)\n",
    "entry_window_opens = 1\n",
    "entry_window_closes = 350\n",
    "\n",
    "forced_exit_start = 360\n",
    "forced_exit_end = 390\n",
    "\n",
    "forced_exit = m1_data.symbol_wrapper.fill(False)\n",
    "entry_window_open=  m1_data.symbol_wrapper.fill(False)\n",
    "\n",
    "# Calculate the time difference in minutes from market open for each timestamp\n",
    "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
    "\n",
    "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
    "\n",
    "#entry_window_open.info()\n",
    "# forced_exit.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "close = m1_data.close\n",
    "\n",
    "rsi = vbt.RSI.run(close, window=14)\n",
    "\n",
    "long_entries = (rsi.rsi.vbt.crossed_below(28) & entry_window_open)\n",
    "long_exits = (rsi.rsi.vbt.crossed_above(70) | forced_exit)\n",
    "#long_entries.info()\n",
    "#number of trues and falses in long_entries\n",
    "#long_entries.value_counts()\n",
    "long_exits.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_rsi(rsi, close, entries, exits):\n",
    "    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
    "    rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
    "    entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=True))  \n",
    "    exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=True))  \n",
    "    return fig\n",
    "\n",
    "plot_rsi(rsi, close, long_entries, long_exits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsi.rsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vbt.phelp(vbt.Portfolio.from_signals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sl_stop = np.arange(0.03/100, 0.4/100, 0.05/100).tolist()\n",
    "# Using the round function\n",
    "sl_stop = [round(val, 4) for val in sl_stop]\n",
    "print(sl_stop)\n",
    "sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
    "\n",
    "pf = vbt.Portfolio.from_signals(close=close, entries=long_entries, exits=long_exits, sl_stop=sl_stop, tp_stop = sl_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
    "\n",
    "pf.stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf[(0.0003,0.0018)].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf[0.03].plot_trade_signals()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pristup k pf jako multi index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pf[0.03].plot()\n",
    "#pf.order_records\n",
    "pf[(0.03)].stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zgrupovane statistiky\n",
    "stats_df = pf.stats([\n",
    "    'total_return',\n",
    "    'total_trades',\n",
    "    'win_rate',\n",
    "    'expectancy'\n",
    "], agg_func=None)\n",
    "stats_df\n",
    "\n",
    "\n",
    "stats_df.nlargest(10, 'Total Return [%]')\n",
    "#stats_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf[(0.0011,0.0013000000000000002)].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import DateOffset\n",
    "\n",
    "temp_data = basic_data['2024-4-22']\n",
    "temp_data\n",
    "res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
    "\n",
    "# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
    "custom_offset = DateOffset(hours=4, minutes=30)\n",
    "\n",
    "# res1m = res1m.get().resample(\"4H\").agg({  \n",
    "#     \"Open\": \"first\",\n",
    "#     \"High\": \"max\",\n",
    "#     \"Low\": \"min\",\n",
    "#     \"Close\": \"last\",\n",
    "#     \"Volume\": \"sum\"\n",
    "# })\n",
    "\n",
    "res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
    "\n",
    "res4h.data\n",
    "\n",
    "res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
    "\n",
    "res15m.data[\"BAC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@vbt.njit\n",
    "def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
    "    market_open_minutes = 570  # 9 hours * 60 minutes + 30 minutes\n",
    "\n",
    "    for out_i in range(len(c.out)):\n",
    "        i = c.from_i + out_i\n",
    "\n",
    "        current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
    "        #print(\"current_minutes\", current_minutes)\n",
    "        # Calculate elapsed minutes since market open at 9:30 AM\n",
    "        elapsed_from_open = current_minutes - market_open_minutes\n",
    "        elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
    "        #print( \"elapsed_from_open\", elapsed_from_open)\n",
    "\n",
    "        #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
    "        in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
    "        #print(\"in_window\", in_window)\n",
    "        # if in_window:\n",
    "        #     print(\"in window\")\n",
    "\n",
    "        if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9:  # (4)!\n",
    "            return out_i\n",
    "    return -1\n",
    "\n",
    "@vbt.njit\n",
    "def long_exit_place_func_nb(c, high, close, time_index, tp, sl):  # (5)!\n",
    "    entry_i = c.from_i - c.wait\n",
    "    entry_price = close[entry_i, c.col]\n",
    "    hit_price = entry_price * (1 + tp)\n",
    "    stop_price = entry_price * (1 - sl)\n",
    "    for out_i in range(len(c.out)):\n",
    "        i = c.from_i + out_i\n",
    "        last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
    "\n",
    "        #print(next_day)\n",
    "        if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
    "            print(\"ted\",out_i)\n",
    "            return out_i\n",
    "        if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
    "            return out_i\n",
    "    return -1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sum()"
   ]
  }
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