{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ORDER Imbalance\n",
    "\n",
    "* introduced buyvolume and sellvolume on bar level.\n",
    "* calculated order imbalance ratio (buyvolume-sellvolume/totalvolume)\n",
    "* calculated on multiple timeframes\n",
    "* entry based on confluences imbalances\n",
    "\n",
    "## Note\n",
    "\n",
    "The order imbalance does not necessarily cause a price change (i.e., there can be an order imbalance on the buy side, but the price does not have to go up, and vice versa). However, if there is a prolonged imbalance without a price change, it could indicate something.\n",
    "\n",
    "Create a cumulative imbalance — accumulators that will build up when there are strong imbalances without a price change. This accumulator will charge up with the imbalance and discharge with the corresponding price change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "#as V2realbot is client , load env variables here\n",
    "env_file = \"/Users/davidbrazda/Documents/Development/python/.env\"\n",
    "# Load the .env file\n",
    "load_dotenv(env_file)\n",
    "\n",
    "from v2realbot.utils.utils import zoneNY\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import vectorbtpro as vbt\n",
    "# from itables import init_notebook_mode, show\n",
    "import datetime\n",
    "from itertools import product\n",
    "from v2realbot.config import DATA_DIR\n",
    "from lightweight_charts import JupyterChart, chart, Panel\n",
    "from IPython.display import display\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trades_df-BAC-2024-01-01T09_30_00-2024-05-14T16_00_00-CO4B7VPWUZF-100.parquet\n",
      "trades_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet\n",
      "trades_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\n",
      "trades_df-BAC-2023-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
      "ohlcv_df-BAC-2024-01-11T09:30:00-2024-01-12T16:00:00.parquet\n",
      "trades_df-BAC-2024-05-15T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
      "ohlcv_df-BAC-2024-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
      "ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\n",
      "ohlcv_df-BAC-2024-01-01T09_30_00-2024-05-14T16_00_00-CO4B7VPWUZF-100.parquet\n",
      "ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T16_00_00-47BCFOPUVWZ-100.parquet\n",
      "ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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 = 370\n",
    "forced_exit_start = 380\n",
    "forced_exit_end = 390\n",
    "\n",
    "#LOAD FROM PARQUET\n",
    "#list all files is dir directory with parquet extension\n",
    "dir = DATA_DIR + \"/notebooks/\"\n",
    "import os\n",
    "files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
    "print('\\n'.join(map(str, files)))\n",
    "file_name = \"ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\"\n",
    "ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
    "#filter ohlcv_df to certain date range (assuming datetime index)\n",
    "#ohlcv_df = ohlcv_df.loc[\"2024-02-12 9:30\":\"2024-02-14 16:00\"]\n",
    "\n",
    "#add vwap column to ohlcv_df\n",
    "#ohlcv_df[\"hlcc4\"] = (ohlcv_df[\"close\"] + ohlcv_df[\"high\"] + ohlcv_df[\"low\"] + ohlcv_df[\"close\"]) / 4\n",
    "\n",
    "basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": ohlcv_df}), tz_convert=zoneNY)\n",
    "ohlcv_df= None\n",
    "basic_data.wrapper.index.normalize().nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_data.data[\"BAC\"].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add resample function to custom columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig\n",
    "from vectorbtpro import _typing as tp\n",
    "from vectorbtpro.generic import nb as generic_nb\n",
    "\n",
    "_feature_config: tp.ClassVar[Config] = HybridConfig(\n",
    "    {\n",
    "        \"buyvolume\": dict(\n",
    "            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
    "                resampler,\n",
    "                generic_nb.sum_reduce_nb,\n",
    "            )\n",
    "        ),\n",
    "        \"sellvolume\": dict(\n",
    "            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
    "                resampler,\n",
    "                generic_nb.sum_reduce_nb,\n",
    "            )\n",
    "        ),\n",
    "        \"trades\": dict(\n",
    "            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
    "                resampler,\n",
    "                generic_nb.sum_reduce_nb,\n",
    "            )\n",
    "        )\n",
    "    }\n",
    ")\n",
    "\n",
    "basic_data._feature_config = _feature_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample(\"1T\")\n",
    "t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "t1data.data[\"BAC\"].info()\n",
    "\n",
    "m30data  = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample(\"30T\")\n",
    "m30data = m30data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "m30data.data[\"BAC\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbands = vbt.talib(\"BBANDS\").run(\n",
    "        t1data.get(\"Close\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "supertrend = vbt.SUPERTREND.run(t1data.high, t1data.low, t1data.close, period=14, multiplier=3)\n",
    "direction_series = supertrend.direction\n",
    "uptrend= pd.Series(False, index=direction_series.index)\n",
    "downtrend= pd.Series(False, index=direction_series.index)\n",
    "\n",
    "# -1 na 1\n",
    "uptrend[1:] = (direction_series[1:] == 1) & (direction_series.shift(1)[1:] == -1)\n",
    "# 1 na -1\n",
    "downtrend[1:] = (direction_series[1:] == -1) & (direction_series.shift(1)[1:] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "supertrendm30 = vbt.SUPERTREND.run(m30data.high, m30data.low, m30data.close, period=14, multiplier=3)\n",
    "direction_series = supertrendm30.direction\n",
    "uptrend_m30= pd.Series(False, index=direction_series.index)\n",
    "downtrend_m30= pd.Series(False, index=direction_series.index)\n",
    "# -1 na 1\n",
    "uptrend_m30[1:] = (direction_series[1:] == 1) & (direction_series.shift(1)[1:] == -1)\n",
    "# 1 na -1\n",
    "downtrend_m30[1:] = (direction_series[1:] == -1) & (direction_series.shift(1)[1:] == 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "macd = vbt.talib(\"MACD\").run(m30data.close)\n",
    "macd.macd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pane1 = Panel(\n",
    "    ohlcv=(t1data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
    "    histogram=[], # [(series, name, \"rgba(53, 94, 59, 0.6), opacity\")]\n",
    "    right=[#(bbands,), #[(series, name, entries, exits, other_markers)]\n",
    "           (t1data.data[\"BAC\"].vwap, \"vwap\", uptrend, downtrend),\n",
    "           (supertrend.trend,\"STtrend\"),\n",
    "           (supertrend.long,\"STlong\"),\n",
    "           (supertrend.short,\"STshort\")\n",
    "           ],\n",
    "    left = [(supertrend.direction,\"STdirection\")],\n",
    "    # right=[(bbands.upperband, \"upperband\",),\n",
    "    #       (bbands.lowerband, \"lowerband\",),\n",
    "    #       (bbands.middleband, \"middleband\",)\n",
    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
    "    middle1=[],\n",
    "    middle2=[],\n",
    ")\n",
    "\n",
    "pane2 = Panel(\n",
    "    ohlcv=(m30data.data[\"BAC\"],uptrend_m30, downtrend_m30), #(series, entries, exits, other_markers)\n",
    "    histogram=[], # [(series, name, \"rgba(53, 94, 59, 0.6), opacity\")]\n",
    "    right=[#(bbands,), #[(series, name, entries, exits, other_markers)]\n",
    "           (supertrendm30.trend,\"STtrend30\"),\n",
    "           (supertrendm30.long,\"STlong30\"),\n",
    "           (supertrendm30.short,\"STshort30\")\n",
    "           ],\n",
    "    left = [(supertrendm30.direction,\"STdirection30\")],\n",
    "    # right=[(bbands.upperband, \"upperband\",),\n",
    "    #       (bbands.lowerband, \"lowerband\",),\n",
    "    #       (bbands.middleband, \"middleband\",)\n",
    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
    "    middle1=[],\n",
    "    middle2=[],\n",
    "    title = \"30m\")\n",
    "\n",
    "pane3 = Panel(\n",
    "    ohlcv=(m30data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
    "    histogram=[(macd.macdhist,\"macdhist30\",None,0.5)], # [(series, name, \"rgba(53, 94, 59, 0.6), opacity\")]\n",
    "    # right=[#(bbands,), #[(series, name, entries, exits, other_markers)]\n",
    "    #        (supertrendm30.trend,\"STtrend30\"),\n",
    "    #        (supertrendm30.long,\"STlong30\"),\n",
    "    #        (supertrendm30.short,\"STshort30\")\n",
    "    #        ],\n",
    "    left = [(macd.macd,\"macd30\"),\n",
    "            (macd.macdsignal,\"macdsignal30\")\n",
    "            ],\n",
    "    # right=[(bbands.upperband, \"upperband\",),\n",
    "    #       (bbands.lowerband, \"lowerband\",),\n",
    "    #       (bbands.middleband, \"middleband\",)\n",
    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
    "    middle1=[],\n",
    "    middle2=[],\n",
    "    title = \"30m_macd\")\n",
    "\n",
    "ch = chart([pane1, pane2, pane3], sync=False, size=\"l\", xloc=slice(\"2024-02-12 09:30\",\"2024-03-12\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "buyvolume = t1data.data[\"BAC\"].buyvolume\n",
    "sellvolume = t1data.data[\"BAC\"].sellvolume\n",
    "totalvolume = buyvolume + sellvolume\n",
    "\n",
    "#adjust to minimal value to avoid division by zero\n",
    "sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
    "oibratio = buyvolume / sellvolume\n",
    "\n",
    "#cumulative order flow (net difference)\n",
    "cof = buyvolume - sellvolume\n",
    "\n",
    "# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
    "order_imbalance = cof / totalvolume\n",
    "order_imbalance = order_imbalance.fillna(0) #nan nahradime 0\n",
    "\n",
    "order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume\n",
    "\n",
    "order_imbalance_sma = vbt.indicator(\"talib:EMA\").run(order_imbalance, timeperiod=5)\n",
    "short_signals = order_imbalance.vbt < -0.5\n",
    "#short_entries = oibratio.vbt < 0.01\n",
    "short_signals.value_counts()\n",
    "short_signals.name = \"short_entries\"\n",
    "#.fillna(False)\n",
    "short_exits = short_signals.shift(-2).fillna(False).astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pane1 = Panel(\n",
    "    ohlcv=(t1data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
    "    histogram=[(order_imbalance_allvolume, \"oib_allvolume\", \"rgba(53, 94, 59, 0.6)\",0.5),\n",
    "               (t1data.data[\"BAC\"].trades, \"trades\",None,0.4),\n",
    "               ], # [(series, name, \"rgba(53, 94, 59, 0.6)\", opacity)]\n",
    "    # right=[\n",
    "    #        (supertrend.trend,\"STtrend\"),\n",
    "    #        (supertrend.long,\"STlong\"),\n",
    "    #        (supertrend.short,\"STshort\")\n",
    "    #        ],\n",
    "    # left = [(supertrend.direction,\"STdirection\")],\n",
    "    # right=[(bbands.upperband, \"upperband\",),\n",
    "    #       (bbands.lowerband, \"lowerband\",),\n",
    "    #       (bbands.middleband, \"middleband\",)\n",
    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
    "    middle1=[],\n",
    "    middle2=[],\n",
    ")\n",
    "\n",
    "pane2 = Panel(\n",
    "    ohlcv=(basic_data.data[\"BAC\"],), #(series, entries, exits, other_markers)\n",
    "    left=[(basic_data.data[\"BAC\"].trades, \"trades\")],\n",
    "    histogram=[(basic_data.data[\"BAC\"].trades, \"trades_hist\", \"white\", 0.5)], #\"rgba(53, 94, 59, 0.6)\"\n",
    "    #           ], # [(series, name, \"rgba(53, 94, 59, 0.6)\")]\n",
    "    # right=[\n",
    "    #        (supertrend.trend,\"STtrend\"),\n",
    "    #        (supertrend.long,\"STlong\"),\n",
    "    #        (supertrend.short,\"STshort\")\n",
    "    #        ],\n",
    "    # left = [(supertrend.direction,\"STdirection\")],\n",
    "    # right=[(bbands.upperband, \"upperband\",),\n",
    "    #       (bbands.lowerband, \"lowerband\",),\n",
    "    #       (bbands.middleband, \"middleband\",)\n",
    "    #       ], #[(series, name, entries, exits, other_markers)]\n",
    "    middle1=[],\n",
    "    middle2=[],\n",
    ")\n",
    "\n",
    "\n",
    "ch = chart([pane1, pane2], size=\"m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##z tohoto si udelat plot funkci (i pro entries,exits)\n",
    "#t1data = t1data[[\"open\", \"high\", \"low\", \"close\", \"volume\"]]\n",
    "chart = JupyterChart(width=1000, height=600, inner_width=1, inner_height=0.5, leftScale=True)\n",
    "#set resolution\n",
    "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")\n",
    "t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "chart.set(t1data.data[\"BAC\"])\n",
    "line_vwap = chart.create_line(name=\"vwap\")#, color=\"blue\")\n",
    "line_vwap.set(t1data.vwap)\n",
    "\n",
    "\n",
    "chart.topbar.textbox(\"title\",\"Nadpis\")\n",
    "chart2 = chart.create_subchart(position='right', width=1, height=0.5, sync=True, leftScale=True)\n",
    "t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"5T\")\n",
    "t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "\n",
    "\n",
    "#5min close realigned to 1T\n",
    "close_realigned = t2data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
    "#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
    "line1 = chart.create_line(name=\"5minclose\")#, color=\"green\")\n",
    "line1.set(close_realigned)\n",
    "\n",
    "#sma z realigned dat\n",
    "sma_tp = 20\n",
    "sma_t2 = vbt.indicator(\"talib:EMA\").run(close_realigned, timeperiod=sma_tp)\n",
    "smaline = chart.create_line(name=f\"sma{sma_tp}\")#, color=\"blue\")\n",
    "smaline.set(sma_t2)\n",
    "\n",
    "\n",
    "#sma z puvodnich resamplovanych dat plus navic realign, melo by byt stejne \n",
    "sma_real = vbt.indicator(\"talib:EMA\").run(t2data.close, timeperiod=sma_tp)\n",
    "sma_real_value = sma_real.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "sma_real_value = sma_real_value[sma_real_value.index.dayofweek < 5]\n",
    "smaline_real = chart.create_line(name=f\"smareal{sma_tp}\", color=\"yellow\")\n",
    "smaline_real.set(sma_real_value)\n",
    "\n",
    "#resample 15T\n",
    "t15data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"15T\")\n",
    "t15data = t15data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "\n",
    "#5min close realigned to 1T\n",
    "close_15realigned = t15data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "close_15realigned = close_15realigned[close_15realigned.index.dayofweek < 5]\n",
    "#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
    "line2 = chart.create_line(name=\"15minclose\")#, color=\"pink\")\n",
    "line2.set(close_15realigned)\n",
    "\n",
    "\n",
    "chart.legend(True)\n",
    "hst = chart2.create_histogram(name=\"buyvolume\", color=\"rgba(53, 94, 59, 0.6)\") #green transparent\n",
    "hst1 = chart2.create_histogram(name=\"sellvolume\", color=\"rgba(165, 42, 42, 0.6)\") #red transparent\n",
    "hst.set(t1data.data[\"BAC\"])\n",
    "hst1.set(t1data.data[\"BAC\"])\n",
    "line2 = chart2.create_line(name=\"5minclose\")#, color=\"green\")\n",
    "line2.set(close_realigned)\n",
    "\n",
    "lineoib = chart2.create_line(name=\"oib\", priceScaleId=\"left\") #color=\"violet\", \n",
    "#lineoib.scale(0.7,0)\n",
    "lineoib.set(order_imbalance_allvolume)\n",
    "\n",
    "lineoib_sma = chart2.create_line(name=\"oibsma5\", priceScaleId=\"left\") #, color=\"blue\", \n",
    "lineoib_sma.set(order_imbalance_sma)\n",
    "\n",
    "chart.fit()\n",
    "chart2.legend(True)\n",
    "#\n",
    "line2.markers_set(short_signals, \"entries\")\n",
    "# TODO jelikoz se davaji do jednoho pole je treba zajistit spravne sortovani\n",
    "# domyslet jak to pojmout iterativni doplnovani markeru\n",
    "line2.markers_set(short_exits, \"exits\")\n",
    "\n",
    "\n",
    "chart2.fit()\n",
    "chart.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_signals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vbt.IF.list_indicators(\"*ma\")\n",
    "vbt.phelp(vbt.indicator(\"talib:EMA\").run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sma = vbt.indicator(\"talib:EMA\").run(t1data.close, timeperiod=20)\n",
    "sma.real.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = vbt.RSI.run(t1data.close)\n",
    "type(rr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "buyvolume = t1data.data[\"BAC\"].buyvolume\n",
    "sellvolume = t1data.data[\"BAC\"].sellvolume\n",
    "totalvolume = buyvolume + sellvolume\n",
    "\n",
    "#adjust to minimal value to avoid division by zero\n",
    "sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
    "oibratio = buyvolume / sellvolume\n",
    "\n",
    "#cumulative order flow (net difference)\n",
    "cof = buyvolume - sellvolume\n",
    "\n",
    "# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
    "order_imbalance = cof / totalvolume\n",
    "order_imbalance = order_imbalance.fillna(0) #nan nahradime 0\n",
    "\n",
    "order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_imbalance_sma = vbt.indicator(\"talib:EMA\").run(order_imbalance, timeperiod=5)\n",
    "short_signals = order_imbalance.vbt < -0.5\n",
    "#short_entries = oibratio.vbt < 0.01\n",
    "short_signals.value_counts()\n",
    "short_signals.name = \"short_entries\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_signals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_imbalance.fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_imbalance.vbt.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chartN = JupyterChart(width=500, height=300, inner_width=1, inner_height=0.3, leftScale=True)\n",
    "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")\n",
    "t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "chartN.set(t1data.data[\"BAC\"])\n",
    "line_sma = chartN.create_line(name=\"sma\", priceScaleId=\"right\")#, color=\"blue\")\n",
    "line_sma.set(sma)\n",
    "# line_sma.markers_set(short_signals, \"entries\")\n",
    "# line_sma.markers_set(short_exits, \"exits\")\n",
    "# hst = chartN.create_histogram(name=\"oivol\")\n",
    "# hst.set(order_imbalance_allvolume)\n",
    "# chartN.legend(True)\n",
    "# chartN.fit()\n",
    "\n",
    "# subchart = chartN.create_subchart(position='right', width=1, height=0.5, sync=False, leftScale=True)\n",
    "# # subchart.set(t1data.data[\"BAC\"])\n",
    "# line_sma1 = subchart.create_line(name=\"smao\", priceScaleId=\"left\")#, color=\"blue\")\n",
    "# line_sma1.set(sma)\n",
    "# # line_sma1.markers_set(short_signals, \"entries\")\n",
    "# # line_sma1.markers_set(short_exits, \"exits\")\n",
    "# hsto = subchart.create_histogram(name=\"oivolo\")\n",
    "# hsto.set(order_imbalance_sma)\n",
    "\n",
    "chart2 = chartN.create_subchart(position='left', width=1, height=0.5, sync=True, leftScale=True, toolbox=True)\n",
    "# hst = chart2.create_histogram(name=\"buyvolume\", color=\"rgba(53, 94, 59, 0.6)\") #green transparent\n",
    "# hst1 = chart2.create_histogram(name=\"sellvolume\", color=\"rgba(165, 42, 42, 0.6)\") #red transparent\n",
    "# hst.set(t1data.data[\"BAC\"])\n",
    "# hst1.set(t1data.data[\"BAC\"])\n",
    "line2 = chart2.create_line(name=\"sma\")#, color=\"green\")\n",
    "line2.set(sma)\n",
    "chart2.topbar.textbox(\"title\",\"Nadpis\")\n",
    "# chartN.topbar.textbox(\"title\",\"NadpisT\")\n",
    "\n",
    "# subchart.legend(True)\n",
    "# subchart.fit()\n",
    "chartN.load()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##z tohoto si udelat plot funkci (i pro entries,exits)\n",
    "#t1data = t1data[[\"open\", \"high\", \"low\", \"close\", \"volume\"]]\n",
    "chart = JupyterChart(width=1000, height=600, inner_width=1, inner_height=0.5, leftScale=True)\n",
    "#set resolution\n",
    "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")\n",
    "t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "chart.set(t1data.data[\"BAC\"])\n",
    "line_vwap = chart.create_line(name=\"vwap\")#, color=\"blue\")\n",
    "line_vwap.set(t1data.vwap)\n",
    "\n",
    "\n",
    "chart.topbar.textbox(\"title\",\"Nadpis\")\n",
    "chart2 = chart.create_subchart(position='right', width=1, height=0.5, sync=True, leftScale=True)\n",
    "t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"5T\")\n",
    "t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "\n",
    "\n",
    "#5min close realigned to 1T\n",
    "close_realigned = t2data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
    "#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
    "line1 = chart.create_line(name=\"5minclose\")#, color=\"green\")\n",
    "line1.set(close_realigned)\n",
    "\n",
    "#sma z realigned dat\n",
    "sma_tp = 20\n",
    "sma_t2 = vbt.indicator(\"talib:EMA\").run(close_realigned, timeperiod=sma_tp)\n",
    "smaline = chart.create_line(name=f\"sma{sma_tp}\")#, color=\"blue\")\n",
    "smaline.set(sma_t2)\n",
    "\n",
    "\n",
    "#sma z puvodnich resamplovanych dat plus navic realign, melo by byt stejne \n",
    "sma_real = vbt.indicator(\"talib:EMA\").run(t2data.close, timeperiod=sma_tp)\n",
    "sma_real_value = sma_real.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "sma_real_value = sma_real_value[sma_real_value.index.dayofweek < 5]\n",
    "smaline_real = chart.create_line(name=f\"smareal{sma_tp}\", color=\"yellow\")\n",
    "smaline_real.set(sma_real_value)\n",
    "\n",
    "#resample 15T\n",
    "t15data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"15T\")\n",
    "t15data = t15data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "\n",
    "#5min close realigned to 1T\n",
    "close_15realigned = t15data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "close_15realigned = close_15realigned[close_15realigned.index.dayofweek < 5]\n",
    "#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
    "line2 = chart.create_line(name=\"15minclose\")#, color=\"pink\")\n",
    "line2.set(close_15realigned)\n",
    "\n",
    "\n",
    "chart.legend(True)\n",
    "hst = chart2.create_histogram(name=\"buyvolume\", color=\"rgba(53, 94, 59, 0.6)\") #green transparent\n",
    "hst1 = chart2.create_histogram(name=\"sellvolume\", color=\"rgba(165, 42, 42, 0.6)\") #red transparent\n",
    "hst.set(t1data.data[\"BAC\"])\n",
    "hst1.set(t1data.data[\"BAC\"])\n",
    "line2 = chart2.create_line(name=\"5minclose\")#, color=\"green\")\n",
    "line2.set(close_realigned)\n",
    "\n",
    "lineoib = chart2.create_line(name=\"oib\", priceScaleId=\"left\") #color=\"violet\", \n",
    "#lineoib.scale(0.7,0)\n",
    "lineoib.set(order_imbalance_allvolume)\n",
    "\n",
    "lineoib_sma = chart2.create_line(name=\"oibsma5\", priceScaleId=\"left\") #, color=\"blue\", \n",
    "lineoib_sma.set(order_imbalance_sma)\n",
    "\n",
    "chart.fit()\n",
    "chart2.legend(True)\n",
    "#\n",
    "line2.markers_set(short_signals, \"entries\")\n",
    "# TODO jelikoz se davaji do jednoho pole je treba zajistit spravne sortovani\n",
    "# domyslet jak to pojmout iterativni doplnovani markeru\n",
    "line2.markers_set(short_exits, \"exits\")\n",
    "\n",
    "\n",
    "chart2.fit()\n",
    "chart.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sma.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#priminds list (same Y as price), secinds list (secondary Y napr. rsi), close, voluminds (volume based) list\n",
    "def plot_2y_close(priminds, secinds, close, volumeinds, ohlcv=None):\n",
    "    fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, \n",
    "                            specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
    "                            vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
    "\n",
    "    if ohlcv is not None:\n",
    "        ohlcv.vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(row=1, col=1))\n",
    "\n",
    "    # Plotting the close price\n",
    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
    "    \n",
    "    # Plotting primary indicators on the first row\n",
    "    for ind in priminds:\n",
    "        if isinstance(ind, pd.Series):\n",
    "            #if series has no name, make the name same as the variable name\n",
    "            \n",
    "            ind = ind.vbt\n",
    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
    "    \n",
    "    # Plotting secondary indicators on the first row\n",
    "    for ind in secinds:\n",
    "        #ind = ind.rename(str(ind.name))\n",
    "        if isinstance(ind, pd.Series):\n",
    "            ind = ind.vbt\n",
    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1), trace_kwargs=dict(line=dict(color=\"rgba(255, 0, 0, 0.4)\")))\n",
    "    \n",
    "    for indvolume in volumeinds:\n",
    "        # Plotting the volume on the second row\n",
    "        indvolume.rename(str(indvolume.name)).vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
    "        #vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
    "    \n",
    "    return fig\n",
    "\n",
    "fig = plot_2y_close([sma], [order_imbalance.rename(\"order_imbalance_norm\"),order_imbalance_sma.real.rename(\"oib_sma\")], t1data.close, [t1data.data[\"BAC\"].buyvolume, t1data.data[\"BAC\"].sellvolume, t1data.volume], t1data.data[\"BAC\"])\n",
    "fig.update_yaxes(range=[33,34], secondary_y=False, row=1, col=1) #update y axis range\n",
    "fig.update_yaxes(range=[-1,1], secondary_y=True, row=1, col=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "t0data = basic_data\n",
    "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"1T\")\n",
    "t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"15T\")\n",
    "t3data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"30T\")\n",
    "t4data = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample(\"D\").dropna()\n",
    "\n",
    "t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "t3data = t3data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "\n",
    "#30min data to daily\n",
    "# t4data = t3data.resample(\"D\").dropna()\n",
    "\n",
    "#t4data = t4data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
    "#m1data.data[\"SPY\"].info()\n",
    "\n",
    "#m1data.data[\"SPY\"].vbt.ohlcv.plot()\n",
    "#h2data.data[\"SPY\"].vbt.ohlcv.plot()\n",
    "#ddata.data[\"SPY\"]\n",
    "t2data.data[\"BAC\"].vbt.ohlcv.plot().show()\n",
    "\n",
    "\n",
    "#t4data.data[\"BAC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t2data.close\n",
    "\n",
    "#in df remove rows with nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#realign na 1T = t1data + oriznout main session\n",
    "t2data_vwap = t2data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "t3data_vwap = t3data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "t4data_vwap = t4data.vwap.vbt.realign_closing(\"1T\").dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t2data_vwap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_2y_close(priminds, secinds, close):\n",
    "    fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
    "    close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
    "    for ind in priminds:\n",
    "        if isinstance(ind, pd.Series):\n",
    "            ind = ind.vbt\n",
    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
    "    for ind in secinds:\n",
    "        if isinstance(ind, pd.Series):\n",
    "            ind = ind.vbt\n",
    "        ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t4data.clos.vbt \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obvind = vbt.indicator.obv.run(t1data.close, t1data.volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1_lengtgh = 15\n",
    "t2_length = 15\n",
    "t3_length = 15\n",
    "t4_length = 5\n",
    "t1_th = 0.1\n",
    "t2_th = 0.1\n",
    "t3_th = 0.1\n",
    "t4_th = 0.1\n",
    "\n",
    "\n",
    "\n",
    "#minute\n",
    "t1slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t1data.close, timeperiod=t1_lengtgh) # -0.09, 0.09\n",
    "t2slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t2data.vwap, timeperiod=t2_length) #   -0.08 , 0.079\n",
    "t3slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t3data.vwap, timeperiod=t3_length) #   -0.08, 0.08\n",
    "#daily\n",
    "t4slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t4data.vwap, timeperiod=t4_length) #   -0.1, 0.09\n",
    "\n",
    "plot_2y_close(priminds=[], secinds=[t1slope, t2slope, t3slope, t4slope], close=t1data.close).show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#thirtymin_slope = thirtymin_slope.real.rename(\"30min\") #timto se prejmenuje real na 30min\n",
    "t3slope = t3slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "##filter daily_slope_to_compare to only monday to friday\n",
    "t3slope = t3slope[t3slope.index.dayofweek < 5]\n",
    "\n",
    "#t3slope.info()\n",
    "\n",
    "t2slope = t2slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
    "##filter daily_slope_to_compare to only monday to friday\n",
    "t2slope = t2slope[t2slope.index.dayofweek < 5]\n",
    "\n",
    "t2slope.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oibratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#\n",
    "short_signals = order_imbalance.vbt < -0.3\n",
    "#short_entries = oibratio.vbt < 0.01\n",
    "short_signals.value_counts()\n",
    "\n",
    "long_signals = order_imbalance.vbt > 0.3\n",
    "#entries = oibratio.vbt > 10\n",
    "long_signals.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = vbt.make_subplots(rows=3, cols=1, shared_xaxes=True, \n",
    "                        specs=[[{\"secondary_y\": True}], [{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
    "                        vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
    "t1data.data[\"BAC\"].vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
    "#oibratio.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
    "order_imbalance.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
    "long_signals.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
    "                                                                                    line=dict(color=\"#ffe476\"),\n",
    "                                                                                    marker=dict(color=\"limegreen\"),\n",
    "                                                                                    fill=None,\n",
    "                                                                                    connectgaps=True,\n",
    "                                                                                    ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
    "\n",
    "short_signals.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
    "                                                                                    line=dict(color=\"#ffe476\"),\n",
    "                                                                                    marker=dict(color=\"red\"),\n",
    "                                                                                    fill=None,\n",
    "                                                                                    connectgaps=True,\n",
    "                                                                                    ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# thirtymin_slope_to_compare.vbt.xloc[\"04-16-2024\"].get()\n",
    "thirty_down_signal.vbt.xloc[\"04-16-2024\"].get()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)\n",
    "#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)\n",
    "\n",
    "#test na daily s reversem  crossed 0\n",
    "short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01  #min value of threshold\n",
    "long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01  #min\n",
    "\n",
    "# thirty_up_signal = t3slope.vbt.crossed_above(0.01)\n",
    "# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)\n",
    "\n",
    "fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close)\n",
    "#short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig)\n",
    "\n",
    "short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
    "                                                                                    line=dict(color=\"#ffe476\"),\n",
    "                                                                                    marker=dict(color=\"red\", symbol=\"triangle-down\"),\n",
    "                                                                                    fill=None,\n",
    "                                                                                    connectgaps=True,\n",
    "                                                                                    ))\n",
    "long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
    "                                                                                    line=dict(color=\"#ffe476\"),\n",
    "                                                                                    marker=dict(color=\"limegreen\"),\n",
    "                                                                                    fill=None,\n",
    "                                                                                    connectgaps=True,\n",
    "                                                                                    ))\n",
    "\n",
    "# thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"DOWN30\",\n",
    "#                                                                                     line=dict(color=\"#ffe476\"),\n",
    "#                                                                                     marker=dict(color=\"yellow\", symbol=\"triangle-down\"),\n",
    "#                                                                                     fill=None,\n",
    "#                                                                                     connectgaps=True,\n",
    "#                                                                                     ))\n",
    "# thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"UP30\",\n",
    "#                                                                                     line=dict(color=\"#ffe476\"),\n",
    "#                                                                                     marker=dict(color=\"grey\"),\n",
    "#                                                                                     fill=None,\n",
    "#                                                                                     connectgaps=True,\n",
    "#                                                                                     ))\n",
    "\n",
    "# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name=\"30min slope\",\n",
    "#                                                                                         line=dict(color=\"yellow\"),                                                                                     \n",
    "#                                                                                         fill=None,\n",
    "#                                                                                         connectgaps=True,\n",
    "#                                                                                         ))\n",
    "\n",
    "fig.show()\n",
    "# print(\"short signal\")\n",
    "# print(short_signal.value_counts())\n",
    "\n",
    "#forced_exit = pd.Series(False, index=close.index)\n",
    "forced_exit = basic_data.symbol_wrapper.fill(False)\n",
    "#entry_window_open = pd.Series(False, index=close.index)\n",
    "entry_window_open=  basic_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",
    "\n",
    "#print(entry_window_open.value_counts())\n",
    "\n",
    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
    "short_entries = (short_signal & entry_window_open)\n",
    "short_exits = forced_exit\n",
    "\n",
    "entries = (long_signal & entry_window_open)\n",
    "exits = forced_exit\n",
    "#long_entries.info()\n",
    "#number of trues and falses in long_entries\n",
    "# print(short_exits.value_counts())\n",
    "# print(short_entries.value_counts())\n",
    "\n",
    "#fig = plot_2y_close([],[momshort, rocp], close)\n",
    "#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
    "#print(sl_stop)\n",
    "#short_entries=short_entries, short_exits=short_exits,\n",
    "# pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
    "\n",
    "# pf.stats()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forced_exit = t1data.symbol_wrapper.fill(False)\n",
    "#entry_window_open = pd.Series(False, index=close.index)\n",
    "entry_window_open=  t1data.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",
    "\n",
    "#print(entry_window_open.value_counts())\n",
    "\n",
    "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
    "short_entries = (short_signals & entry_window_open)\n",
    "short_exits = forced_exit\n",
    "\n",
    "entries = (long_signals & entry_window_open)\n",
    "exits = forced_exit\n",
    "\n",
    "pf = vbt.Portfolio.from_signals(close=t1data, entries=entries, exits=exits, short_entries=short_entries, short_exits=exits,\n",
    "td_stop=2, time_delta_format=\"rows\",\n",
    "tsl_stop=0.005, tp_stop = 0.005, 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.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.get_drawdowns().records_readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.orders.records_readable"
   ]
  }
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