vectorized aggregator, minor changes (#198)
This commit is contained in:
David Brazda
GitHub
41438
research/basic.ipynb
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research/basic.ipynb
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research/indcross_parametrized.ipynb
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research/indcross_parametrized.ipynb
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research/loading_trades_aggregation.ipynb
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research/loading_trades_aggregation.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Loading trades and vectorized aggregation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from numba import jit\n",
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"from alpaca.data.historical import StockHistoricalDataClient\n",
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"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
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"from alpaca.data.requests import StockTradesRequest\n",
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"from v2realbot.enums.enums import BarType\n",
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"import time\n",
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"\n",
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"from datetime import datetime\n",
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"from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY, send_to_telegram, fetch_calendar_data\n",
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"import pyarrow\n",
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"from v2realbot.loader.aggregator_vectorized import fetch_daily_stock_trades, fetch_trades_parallel, generate_time_bars_nb, aggregate_trades\n",
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"import vectorbtpro as vbt\n",
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"\n",
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"vbt.settings.set_theme(\"dark\")\n",
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"vbt.settings['plotting']['layout']['width'] = 1280\n",
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"vbt.settings.plotting.auto_rangebreaks = True\n",
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"# Set the option to display with pagination\n",
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"pd.set_option('display.notebook_repr_html', True)\n",
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"pd.set_option('display.max_rows', 10) # Number of rows per page"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"symbol = \"SPY\"\n",
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"#datetime in zoneNY \n",
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"day_start = datetime(2024, 5, 15, 9, 30, 0)\n",
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"day_stop = datetime(2024, 5, 16, 16, 00, 0)\n",
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"day_start = zoneNY.localize(day_start)\n",
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"day_stop = zoneNY.localize(day_stop)\n",
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"#neslo by zrychlit, kdyz se zobrazuje pomalu Searching cache - nejaky bottle neck?\n",
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"df = fetch_trades_parallel(symbol, day_start, day_stop, minsize=50) #exclude_conditions=['C','O','4','B','7','V','P','W','U','Z','F'])\n",
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"ohlcv_df = aggregate_trades(symbol=symbol, trades_df=df, resolution=1, type=BarType.TIME)\n",
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"#df.info()\n",
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"ohlcv_df\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"basic_data = vbt.Data.from_data(vbt.symbol_dict({symbol: ohlcv_df}), tz_convert=zoneNY)\n",
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"vbt.settings['plotting']['auto_rangebreaks'] = True\n",
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"basic_data.ohlcv.plot()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
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"import gzip\n",
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"\n",
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"file_path = f\"{DATA_DIR}/tradecache/BAC-1709044200-1709067600.cache.gz\"\n",
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"\n",
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"with gzip.open(file_path, 'rb') as fp:\n",
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" tradesResponse = pickle.load(fp)\n",
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"\n",
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"tradesResponse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def convert_dict_to_multiindex_df(tradesResponse):\n",
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" # Create a DataFrame for each key and add the key as part of the MultiIndex\n",
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" dfs = []\n",
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" for key, values in tradesResponse.items():\n",
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" df = pd.DataFrame(values)\n",
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" # Rename columns\n",
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" # Select and order columns explicitly\n",
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" #print(df)\n",
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" df = df[['t', 'x', 'p', 's', 'i', 'c','z']]\n",
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" df.rename(columns={'t': 'timestamp', 'c': 'conditions', 'p': 'price', 's': 'size', 'x': 'exchange', 'z':'tape', 'i':'id'}, inplace=True)\n",
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" df['symbol'] = key # Add ticker as a column\n",
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" df['timestamp'] = pd.to_datetime(df['timestamp']) # Convert 't' from string to datetime before setting it as an index\n",
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" df.set_index(['symbol', 'timestamp'], inplace=True) # Set the multi-level index using both 'ticker' and 't'\n",
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" df = df.tz_convert(zoneNY, level='timestamp')\n",
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" dfs.append(df)\n",
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"\n",
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" # Concatenate all DataFrames into a single DataFrame with MultiIndex\n",
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" final_df = pd.concat(dfs)\n",
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"\n",
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" return final_df\n",
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"\n",
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"# Convert and print the DataFrame\n",
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"df = convert_dict_to_multiindex_df(tradesResponse)\n",
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"df\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ohlcv_df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ohlcv_df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ohlcv_df = aggregate_trades(symbol=symbol, trades_df=df, resolution=1000, type=\"dollar\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ohlcv_df.index.strftime('%Y-%m-%d %H').unique()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#ohlcv_df.groupby(ohlcv_df.index.date).size()\n",
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"ohlcv_df.head(100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#access just BCA\n",
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"df_filtered = df.loc[\"BAC\"]\n",
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"\n",
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"df_filtered.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_filtered= df_filtered.reset_index()\n",
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"ticks = df_filtered[['timestamp', 'price', 'size']].to_numpy()\n",
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"ticks\n",
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"timestamps = ticks[:, 0]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_filtered= df_filtered.reset_index()\n",
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"ticks = df_filtered[['timestamp', 'price', 'size']].to_numpy()\n",
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"\n",
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"#timestamp to integer\n",
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"# Extract the timestamps column (assuming it's the first column)\n",
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"timestamps = ticks[:, 0]\n",
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"\n",
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"# Convert the timestamps to Unix timestamps in seconds with microsecond precision\n",
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"unix_timestamps_s = np.array([ts.timestamp() for ts in timestamps], dtype='float64')\n",
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"\n",
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"# Replace the original timestamps in the NumPy array with the converted Unix timestamps\n",
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"ticks[:, 0] = unix_timestamps_s\n",
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"\n",
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"#ticks[:, 0] = pd.to_datetime(ticks[:, 0]).astype('int64') // 1_000_000_000 # Convert to Unix timestamp\n",
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"ticks\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ticks = ticks.astype(np.float64)\n",
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"ticks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"resolution = 1 # Example resolution of 60 seconds\n",
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"ohlcv_bars = generate_time_bars_nb(ticks, resolution)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ohlcv_bars"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert the resulting array back to a DataFrame\n",
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"columns = ['time', 'open', 'high', 'low', 'close', 'volume', 'trades']\n",
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"ohlcv_df = pd.DataFrame(ohlcv_bars, columns=columns)\n",
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"ohlcv_df['time'] = pd.to_datetime(ohlcv_df['time'], unit='s')\n",
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"ohlcv_df.set_index('time', inplace=True)\n",
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"ohlcv_df.index = ohlcv_df.index.tz_localize('UTC').tz_convert(zoneNY)\n",
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"#ohlcv_df = ohlcv_df.loc[\"2024-03-1 15:50:00\":\"2024-03-28 13:40:00\"]\n",
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"#ohlcv_df.index.strftime('%Y-%m-%d %H').unique()\n",
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"\n",
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"ohlcv_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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26673
research/rsi_alpaca.ipynb
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research/rsi_alpaca.ipynb
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research/strat1/strat1_v1_MULTI.ipynb
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research/strat1/strat1_v1_MULTI.ipynb
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research/strat1/strat1_v1_SINGLE.ipynb
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research/strat1/strat1_v1_SINGLE.ipynb
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research/test1sbars.ipynb
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research/test1sbars.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from v2realbot.tools.loadbatch import load_batch\n",
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"from v2realbot.utils.utils import zoneNY\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import vectorbtpro as vbt\n",
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"from itables import init_notebook_mode, show\n",
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"\n",
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"init_notebook_mode(all_interactive=True)\n",
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"\n",
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"vbt.settings.set_theme(\"dark\")\n",
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"vbt.settings['plotting']['layout']['width'] = 1280\n",
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"vbt.settings.plotting.auto_rangebreaks = True\n",
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"# Set the option to display with pagination\n",
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"pd.set_option('display.notebook_repr_html', True)\n",
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"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
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"\n",
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"res, df = load_batch(batch_id=\"0fb5043a\", #46 days 1.3 - 6.5.\n",
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" space_resolution_evenly=False,\n",
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" indicators_columns=[\"Rsi14\"],\n",
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" main_session_only=True,\n",
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" verbose = False)\n",
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"if res < 0:\n",
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" print(\"Error\" + str(res) + str(df))\n",
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"df = df[\"bars\"]\n",
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"\n",
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# filter dates"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#filter na dny\n",
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"# dates_of_interest = pd.to_datetime(['2024-04-22', '2024-04-23']).tz_localize('US/Eastern')\n",
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"# filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
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"\n",
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"# df = filtered_df\n",
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"# df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import plotly.io as pio\n",
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"pio.renderers.default = 'notebook'\n",
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"\n",
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"#naloadujeme do vbt symbol as column\n",
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"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
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"start_date = pd.Timestamp('2024-03-12 09:30', tz=zoneNY)\n",
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"end_date = pd.Timestamp('2024-03-13 16:00', tz=zoneNY)\n",
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"\n",
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"#basic_data = basic_data.transform(lambda df: df[df.index.date == start_date.date()])\n",
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"#basic_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
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"#basic_data.data[\"BAC\"].info()\n",
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"\n",
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"# fig = basic_data.plot(plot_volume=False)\n",
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"# pivot_info = basic_data.run(\"pivotinfo\", up_th=0.003, down_th=0.002)\n",
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"# #pivot_info.plot()\n",
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"# pivot_info.plot(fig=fig, conf_value_trace_kwargs=dict(visible=True))\n",
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"# fig.show()\n",
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"\n",
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"\n",
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"# rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n",
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"\n",
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"# rsi14.vbt.plot().show()\n",
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"#basic_data.xloc[\"09:30\":\"10:00\"].data[\"BAC\"].vbt.ohlcv.plot().show()\n",
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"\n",
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"vbt.settings.plotting.auto_rangebreaks = True\n",
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"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
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"\n",
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"#basic_data.data[\"BAC\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
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"\n",
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"m1_data.data[\"BAC\"]\n",
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"#m5_data = m1_data.resample(\"5T\")\n",
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"\n",
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"#m5_data.data[\"BAC\"].head(10)\n",
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"\n",
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"# m15_data = m1_data.resample(\"15T\")\n",
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"\n",
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"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\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": [
|
||||
"# 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 = 380\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(20) & 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": [
|
||||
"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(rsi.rsi, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(rsi.rsi, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(rsi, close, long_entries, long_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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, 0.2/100, 0.02/100).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, sl_stop=sl_stop, tp_stop = sl_stop, exits=long_exits,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[(0.0015,0.0013)].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(50, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013)].plot()\n",
|
||||
"\n",
|
||||
"#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()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
1639
research/test1sbars_roc.ipynb
Normal file
1639
research/test1sbars_roc.ipynb
Normal file
File diff suppressed because one or more lines are too long
@ -1,620 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import pyarrow\n",
|
||||
"import numpy as np\n",
|
||||
"from numba import jit\n",
|
||||
"import v2realbot.utils.config_handler as cfh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Další info k pokračování je zde https://blog.quantinsti.com/tick-tick-ohlc-data-pandas-tutorial/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"DatetimeIndex: 190261 entries, 2024-04-22 13:30:00.267711+00:00 to 2024-04-22 19:59:59.987614+00:00\n",
|
||||
"Data columns (total 6 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 exchange 190261 non-null object \n",
|
||||
" 1 price 190261 non-null float64\n",
|
||||
" 2 size 190261 non-null float64\n",
|
||||
" 3 id 190261 non-null int64 \n",
|
||||
" 4 conditions 190261 non-null object \n",
|
||||
" 5 tape 190261 non-null object \n",
|
||||
"dtypes: float64(2), int64(1), object(3)\n",
|
||||
"memory usage: 10.2+ MB\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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>exchange</th>\n",
|
||||
" <th>price</th>\n",
|
||||
" <th>size</th>\n",
|
||||
" <th>id</th>\n",
|
||||
" <th>conditions</th>\n",
|
||||
" <th>tape</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>timestamp</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>2024-04-22 13:30:00.267711+00:00</th>\n",
|
||||
" <td>K</td>\n",
|
||||
" <td>36.890</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>52983525037630</td>\n",
|
||||
" <td>[ , F, I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.300501+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241117014</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.305439+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241117496</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.314520+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241118034</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.335201+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241121369</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</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>2024-04-22 19:59:59.902614+00:00</th>\n",
|
||||
" <td>V</td>\n",
|
||||
" <td>37.750</td>\n",
|
||||
" <td>1100.0</td>\n",
|
||||
" <td>56480705310575</td>\n",
|
||||
" <td>[ ]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.977134+00:00</th>\n",
|
||||
" <td>N</td>\n",
|
||||
" <td>37.745</td>\n",
|
||||
" <td>300.0</td>\n",
|
||||
" <td>52983559963478</td>\n",
|
||||
" <td>[ ]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.977137+00:00</th>\n",
|
||||
" <td>N</td>\n",
|
||||
" <td>37.740</td>\n",
|
||||
" <td>7300.0</td>\n",
|
||||
" <td>52983559963696</td>\n",
|
||||
" <td>[ ]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.978626+00:00</th>\n",
|
||||
" <td>V</td>\n",
|
||||
" <td>37.750</td>\n",
|
||||
" <td>16.0</td>\n",
|
||||
" <td>56480706886228</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.987614+00:00</th>\n",
|
||||
" <td>N</td>\n",
|
||||
" <td>37.745</td>\n",
|
||||
" <td>30.0</td>\n",
|
||||
" <td>52983559963958</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>190261 rows × 6 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" exchange price size id \\\n",
|
||||
"timestamp \n",
|
||||
"2024-04-22 13:30:00.267711+00:00 K 36.890 5.0 52983525037630 \n",
|
||||
"2024-04-22 13:30:00.300501+00:00 D 37.005 1.0 71675241117014 \n",
|
||||
"2024-04-22 13:30:00.305439+00:00 D 37.005 1.0 71675241117496 \n",
|
||||
"2024-04-22 13:30:00.314520+00:00 D 37.005 1.0 71675241118034 \n",
|
||||
"2024-04-22 13:30:00.335201+00:00 D 37.005 1.0 71675241121369 \n",
|
||||
"... ... ... ... ... \n",
|
||||
"2024-04-22 19:59:59.902614+00:00 V 37.750 1100.0 56480705310575 \n",
|
||||
"2024-04-22 19:59:59.977134+00:00 N 37.745 300.0 52983559963478 \n",
|
||||
"2024-04-22 19:59:59.977137+00:00 N 37.740 7300.0 52983559963696 \n",
|
||||
"2024-04-22 19:59:59.978626+00:00 V 37.750 16.0 56480706886228 \n",
|
||||
"2024-04-22 19:59:59.987614+00:00 N 37.745 30.0 52983559963958 \n",
|
||||
"\n",
|
||||
" conditions tape \n",
|
||||
"timestamp \n",
|
||||
"2024-04-22 13:30:00.267711+00:00 [ , F, I] A \n",
|
||||
"2024-04-22 13:30:00.300501+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.305439+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.314520+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.335201+00:00 [ , I] A \n",
|
||||
"... ... ... \n",
|
||||
"2024-04-22 19:59:59.902614+00:00 [ ] A \n",
|
||||
"2024-04-22 19:59:59.977134+00:00 [ ] A \n",
|
||||
"2024-04-22 19:59:59.977137+00:00 [ ] A \n",
|
||||
"2024-04-22 19:59:59.978626+00:00 [ , I] A \n",
|
||||
"2024-04-22 19:59:59.987614+00:00 [ , I] A \n",
|
||||
"\n",
|
||||
"[190261 rows x 6 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tdf=pd.read_parquet('trades_bac.parquet',engine='pyarrow')\n",
|
||||
"#print(df)\n",
|
||||
"df = tdf.loc['BAC']\n",
|
||||
"df.info()\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@jit(nopython=True)\n",
|
||||
"def ohlcv_bars(ticks, start_time, end_time, resolution):\n",
|
||||
" \"\"\"\n",
|
||||
" Generate OHLCV bars from tick data, skipping intervals without trading activity.\n",
|
||||
" \n",
|
||||
" Parameters:\n",
|
||||
" - ticks: numpy array with columns [timestamp, price, size]\n",
|
||||
" - start_time: the start timestamp for bars (Unix timestamp)\n",
|
||||
" - end_time: the end timestamp for bars (Unix timestamp)\n",
|
||||
" - resolution: time resolution in seconds\n",
|
||||
" \n",
|
||||
" Returns:\n",
|
||||
" - OHLCV bars as a numpy array\n",
|
||||
" \"\"\"\n",
|
||||
" num_bars = (end_time - start_time) // resolution + 1\n",
|
||||
" bar_list = []\n",
|
||||
"\n",
|
||||
" for i in range(num_bars):\n",
|
||||
" bar_start_time = start_time + i * resolution\n",
|
||||
" bar_end_time = bar_start_time + resolution\n",
|
||||
" bar_ticks = ticks[(ticks[:, 0] >= bar_start_time) & (ticks[:, 0] < bar_end_time)]\n",
|
||||
" \n",
|
||||
" if bar_ticks.shape[0] == 0:\n",
|
||||
" continue # Skip this bar as there are no ticks\n",
|
||||
"\n",
|
||||
" # Calculate OHLCV values\n",
|
||||
" open_price = bar_ticks[0, 1] # open\n",
|
||||
" high_price = np.max(bar_ticks[:, 1]) # high\n",
|
||||
" low_price = np.min(bar_ticks[:, 1]) # low\n",
|
||||
" close_price = bar_ticks[-1, 1] # close\n",
|
||||
" volume = np.sum(bar_ticks[:, 2]) # volume\n",
|
||||
" bar_time = bar_start_time # timestamp for the bar\n",
|
||||
"\n",
|
||||
" bar_list.append([open_price, high_price, low_price, close_price, volume, bar_time])\n",
|
||||
"\n",
|
||||
" # Convert list to numpy array\n",
|
||||
" if bar_list:\n",
|
||||
" ohlcv = np.array(bar_list)\n",
|
||||
" else:\n",
|
||||
" ohlcv = np.empty((0, 6)) # return an empty array if no bars were created\n",
|
||||
"\n",
|
||||
" return ohlcv\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"DatetimeIndex: 190261 entries, 2024-04-22 13:30:00.267711+00:00 to 2024-04-22 19:59:59.987614+00:00\n",
|
||||
"Data columns (total 6 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 exchange 190261 non-null object \n",
|
||||
" 1 price 190261 non-null float64\n",
|
||||
" 2 size 190261 non-null float64\n",
|
||||
" 3 id 190261 non-null int64 \n",
|
||||
" 4 conditions 190261 non-null object \n",
|
||||
" 5 tape 190261 non-null object \n",
|
||||
"dtypes: float64(2), int64(1), object(3)\n",
|
||||
"memory usage: 10.2+ MB\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['C', 'O', '4', 'B', '7', 'V', 'P', 'W', 'U', 'Z', 'F']\n",
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"DatetimeIndex: 143751 entries, 2024-04-22 13:30:00.300501+00:00 to 2024-04-22 19:59:59.987614+00:00\n",
|
||||
"Data columns (total 6 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 exchange 143751 non-null object \n",
|
||||
" 1 price 143751 non-null float64\n",
|
||||
" 2 size 143751 non-null float64\n",
|
||||
" 3 id 143751 non-null int64 \n",
|
||||
" 4 conditions 143751 non-null object \n",
|
||||
" 5 tape 143751 non-null object \n",
|
||||
"dtypes: float64(2), int64(1), object(3)\n",
|
||||
"memory usage: 7.7+ MB\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"</style>\n",
|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>exchange</th>\n",
|
||||
" <th>price</th>\n",
|
||||
" <th>size</th>\n",
|
||||
" <th>id</th>\n",
|
||||
" <th>conditions</th>\n",
|
||||
" <th>tape</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
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|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.300501+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241117014</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.305439+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241117496</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.314520+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241118034</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.335201+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241121369</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 13:30:00.346219+00:00</th>\n",
|
||||
" <td>D</td>\n",
|
||||
" <td>37.005</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>71675241122389</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</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>2024-04-22 19:59:59.902614+00:00</th>\n",
|
||||
" <td>V</td>\n",
|
||||
" <td>37.750</td>\n",
|
||||
" <td>1100.0</td>\n",
|
||||
" <td>56480705310575</td>\n",
|
||||
" <td>[ ]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.977134+00:00</th>\n",
|
||||
" <td>N</td>\n",
|
||||
" <td>37.745</td>\n",
|
||||
" <td>300.0</td>\n",
|
||||
" <td>52983559963478</td>\n",
|
||||
" <td>[ ]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.977137+00:00</th>\n",
|
||||
" <td>N</td>\n",
|
||||
" <td>37.740</td>\n",
|
||||
" <td>7300.0</td>\n",
|
||||
" <td>52983559963696</td>\n",
|
||||
" <td>[ ]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.978626+00:00</th>\n",
|
||||
" <td>V</td>\n",
|
||||
" <td>37.750</td>\n",
|
||||
" <td>16.0</td>\n",
|
||||
" <td>56480706886228</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2024-04-22 19:59:59.987614+00:00</th>\n",
|
||||
" <td>N</td>\n",
|
||||
" <td>37.745</td>\n",
|
||||
" <td>30.0</td>\n",
|
||||
" <td>52983559963958</td>\n",
|
||||
" <td>[ , I]</td>\n",
|
||||
" <td>A</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>143751 rows × 6 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" exchange price size id \\\n",
|
||||
"timestamp \n",
|
||||
"2024-04-22 13:30:00.300501+00:00 D 37.005 1.0 71675241117014 \n",
|
||||
"2024-04-22 13:30:00.305439+00:00 D 37.005 1.0 71675241117496 \n",
|
||||
"2024-04-22 13:30:00.314520+00:00 D 37.005 1.0 71675241118034 \n",
|
||||
"2024-04-22 13:30:00.335201+00:00 D 37.005 1.0 71675241121369 \n",
|
||||
"2024-04-22 13:30:00.346219+00:00 D 37.005 1.0 71675241122389 \n",
|
||||
"... ... ... ... ... \n",
|
||||
"2024-04-22 19:59:59.902614+00:00 V 37.750 1100.0 56480705310575 \n",
|
||||
"2024-04-22 19:59:59.977134+00:00 N 37.745 300.0 52983559963478 \n",
|
||||
"2024-04-22 19:59:59.977137+00:00 N 37.740 7300.0 52983559963696 \n",
|
||||
"2024-04-22 19:59:59.978626+00:00 V 37.750 16.0 56480706886228 \n",
|
||||
"2024-04-22 19:59:59.987614+00:00 N 37.745 30.0 52983559963958 \n",
|
||||
"\n",
|
||||
" conditions tape \n",
|
||||
"timestamp \n",
|
||||
"2024-04-22 13:30:00.300501+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.305439+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.314520+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.335201+00:00 [ , I] A \n",
|
||||
"2024-04-22 13:30:00.346219+00:00 [ , I] A \n",
|
||||
"... ... ... \n",
|
||||
"2024-04-22 19:59:59.902614+00:00 [ ] A \n",
|
||||
"2024-04-22 19:59:59.977134+00:00 [ ] A \n",
|
||||
"2024-04-22 19:59:59.977137+00:00 [ ] A \n",
|
||||
"2024-04-22 19:59:59.978626+00:00 [ , I] A \n",
|
||||
"2024-04-22 19:59:59.987614+00:00 [ , I] A \n",
|
||||
"\n",
|
||||
"[143751 rows x 6 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"excludes = cfh.config_handler.get_val('AGG_EXCLUDED_TRADES')\n",
|
||||
"print(excludes)\n",
|
||||
"#excludes = [\"F\", \"I\"]\n",
|
||||
"# FILTER EXCLUDED TRADES\n",
|
||||
"# Filter rows to exclude those where 'conditions' contains 'F' or 'I'\n",
|
||||
"# This simplifies the logic by directly using ~ (bitwise not operator) with np.isin\n",
|
||||
"df = df[~df['conditions'].apply(lambda x: np.isin(x, excludes).any())]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/var/folders/8p/dwqnp65s0s77jdbm4_6z4vp80000gn/T/ipykernel_52602/3341929382.py:2: DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future\n",
|
||||
" structured_array = np.array(list(zip(df.index, df['price'], df['size'])),\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[('2024-04-22T13:30:00.300501000', 37.005, 1.0e+00)\n",
|
||||
" ('2024-04-22T13:30:00.305439000', 37.005, 1.0e+00)\n",
|
||||
" ('2024-04-22T13:30:00.314520000', 37.005, 1.0e+00) ...\n",
|
||||
" ('2024-04-22T19:59:59.977137000', 37.74 , 7.3e+03)\n",
|
||||
" ('2024-04-22T19:59:59.978626000', 37.75 , 1.6e+01)\n",
|
||||
" ('2024-04-22T19:59:59.987614000', 37.745, 3.0e+01)]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([('2024-04-22T13:30:00.300501000', 37.005, 1.0e+00),\n",
|
||||
" ('2024-04-22T13:30:00.305439000', 37.005, 1.0e+00),\n",
|
||||
" ('2024-04-22T13:30:00.314520000', 37.005, 1.0e+00), ...,\n",
|
||||
" ('2024-04-22T19:59:59.977137000', 37.74 , 7.3e+03),\n",
|
||||
" ('2024-04-22T19:59:59.978626000', 37.75 , 1.6e+01),\n",
|
||||
" ('2024-04-22T19:59:59.987614000', 37.745, 3.0e+01)],\n",
|
||||
" dtype=[('timestamp', '<M8[ns]'), ('price', '<f8'), ('size', '<f8')])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Creating a structured array with the timestamp as the first element\n",
|
||||
"structured_array = np.array(list(zip(df.index, df['price'], df['size'])),\n",
|
||||
" dtype=[('timestamp', 'datetime64[ns]'), ('price', 'float'), ('size', 'float')])\n",
|
||||
"\n",
|
||||
"print(structured_array)\n",
|
||||
"structured_array\n",
|
||||
"\n",
|
||||
"# ticks = df[['index', 'price', 'size']].to_numpy()\n",
|
||||
"# # ticks[:, 0] = pd.to_datetime(ticks[:, 0]).astype('int64') // 1_000_000_000 # \n",
|
||||
"# ticks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"resolution_seconds = 1 # 1 second resolution\n",
|
||||
"ohlcv_data = ohlcv_bars(structured_array, resolution_seconds)\n",
|
||||
"\n",
|
||||
"# Converting the result back to DataFrame for better usability\n",
|
||||
"ohlcv_df = pd.DataFrame(ohlcv_data, columns=['Open', 'High', 'Low', 'Close', 'Volume', 'Time'])\n",
|
||||
"ohlcv_df['Time'] = pd.to_datetime(ohlcv_df['Time'], unit='s') # Convert timestamps back to datetime\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user