{ "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", "import vectorbtpro as vbt\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", "res, df = load_batch(batch_id=\"e44a5075\",\n", " space_resolution_evenly=False,\n", " indicators_columns=[\"Rsi14\"],\n", " main_session_only=True)\n", "if res < 0:\n", " print(\"Error\" + str(res) + str(df))\n", "df = df[\"bars\"]\n", "\n", "#df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# filter dates" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#naloadujeme do vbt symbol as column\n", "basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n", "start_date = pd.Timestamp('2024-03-12 09:30', tz=zoneNY)\n", "end_date = pd.Timestamp('2024-03-13 16:00', tz=zoneNY)\n", "\n", "#basic_data = basic_data.transform(lambda df: df[df.index.date == start_date.date()])\n", "basic_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n", "#basic_data.data[\"BAC\"].info()\n", "\n", "# fig = basic_data.plot(plot_volume=False)\n", "# pivot_info = basic_data.run(\"pivotinfo\", up_th=0.003, down_th=0.002)\n", "# #pivot_info.plot()\n", "# pivot_info.plot(fig=fig, conf_value_trace_kwargs=dict(visible=True))\n", "# fig.show()\n", "\n", "\n", "# rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n", "\n", "# rsi14.vbt.plot().show()\n", "# basic_data.data[\"BAC\"].vbt.ohlcv.plot().show()" ] }, { "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()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.10.11" } }, "nbformat": 4, "nbformat_minor": 2 }