daily update
This commit is contained in:
David Brazda
339
to_explore/pyquantnews/28_FlowEffectsBacktrader.ipynb
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339
to_explore/pyquantnews/28_FlowEffectsBacktrader.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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"id": "04574bc2",
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"metadata": {},
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"source": [
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"<div style=\"background-color:#000;\"><img src=\"pqn.png\"></img></div>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c027594",
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"metadata": {},
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"source": [
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"This notebook demonstrates backtesting a trading strategy using Backtrader and OpenBB SDK for data acquisition. It outlines creating a trading strategy and running backtests to simulate performance over historical data. The code includes setting up a backtest, downloading data, defining strategy logic, and assessing results with performance metrics. This is useful for evaluating trading strategies' robustness and optimizing them for better risk-adjusted returns."
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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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"id": "3bee1431",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"import datetime as dt\n",
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"import pandas as pd\n",
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"from openbb_terminal.sdk import openbb\n",
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"import quantstats as qs\n",
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"import backtrader as bt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c0e61059",
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"metadata": {},
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"source": [
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"Define a function to fetch stock data from OpenBB SDK and convert it for Backtrader use"
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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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"id": "132f6e66",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def openbb_data_to_bt_data(symbol, start_date, end_date):\n",
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" \"\"\"Fetch and convert stock data for Backtrader\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" symbol : str\n",
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" Stock symbol to fetch\n",
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" start_date : str\n",
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" Start date for data in 'YYYY-MM-DD' format\n",
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" end_date : str\n",
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" End date for data in 'YYYY-MM-DD' format\n",
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" \n",
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" Returns\n",
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" -------\n",
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" bt.feeds.YahooFinanceCSVData\n",
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" Formatted data for Backtrader\n",
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" \"\"\"\n",
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" \n",
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" # Fetch stock data from OpenBB SDK\n",
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" df = openbb.stocks.load(symbol, start_date=start_date, end_date=end_date)\n",
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"\n",
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" # Save data to a CSV file\n",
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" fn = f\"{symbol.lower()}.csv\"\n",
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" df.to_csv(fn)\n",
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" \n",
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" # Return data formatted for Backtrader\n",
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" return bt.feeds.YahooFinanceCSVData(\n",
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" dataname=fn,\n",
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" fromdate=dt.datetime.strptime(start_date, '%Y-%m-%d'),\n",
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" todate=dt.datetime.strptime(end_date, '%Y-%m-%d')\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6c3c0564",
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"metadata": {},
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"source": [
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"Determine the last day of a given month"
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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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"id": "cc17ebfc",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def last_day_of_month(any_day):\n",
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" \"\"\"Calculate the last day of the month\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" any_day : datetime.date\n",
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" Any date within the target month\n",
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" \n",
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" Returns\n",
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" -------\n",
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" int\n",
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" Last day of the month\n",
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" \"\"\"\n",
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" \n",
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" # Move to the next month and then back to the last day of the current month\n",
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" next_month = any_day.replace(day=28) + dt.timedelta(days=4)\n",
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" return (next_month - dt.timedelta(days=next_month.day)).day"
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]
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},
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{
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"cell_type": "markdown",
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"id": "64b04705",
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"metadata": {},
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"source": [
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"Define a trading strategy to execute monthly flows"
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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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"id": "0729cd4a",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"class MonthlyFlows(bt.Strategy):\n",
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" \"\"\"Strategy to trade based on monthly flows\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" end_of_month : int, optional\n",
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" Day of the month to start buying (default is 23)\n",
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" start_of_month : int, optional\n",
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" Day of the month to start selling (default is 7)\n",
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" \n",
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" Attributes\n",
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" ----------\n",
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" order : bt.Order or None\n",
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" Current order in the market\n",
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" dataclose : bt.LineSeries\n",
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" Closing prices of the data feed\n",
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" \"\"\"\n",
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" \n",
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" params = (\n",
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" (\"end_of_month\", 23),\n",
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" (\"start_of_month\", 7),\n",
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" )\n",
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" \n",
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" def __init__(self):\n",
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" self.order = None\n",
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" self.dataclose = self.datas[0].close\n",
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" \n",
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" def notify_order(self, order):\n",
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" \"\"\"Handle order notifications\"\"\"\n",
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" \n",
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" # No more orders\n",
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" self.order = None \n",
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" \n",
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" def next(self):\n",
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" \"\"\"Execute strategy logic for each step in the backtest\"\"\"\n",
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" \n",
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" # Get today's date, day of month, and last day of current month\n",
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" dt_ = self.datas[0].datetime.date(0)\n",
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" dom = dt_.day\n",
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" ldm = last_day_of_month(dt_)\n",
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" \n",
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" # If an order is pending, exit\n",
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" if self.order:\n",
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" return\n",
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" \n",
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" # Check if we are in the market\n",
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" if not self.position:\n",
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" \n",
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" # We're in the first week of the month, sell\n",
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" if dom <= self.params.start_of_month:\n",
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" self.order = self.order_target_percent(target=-1)\n",
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" print(f\"Created SELL of {self.order.size} at {self.data_close[0]} on day {dom}\")\n",
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" \n",
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" # We're in the last week of the month, buy\n",
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" if dom >= self.params.end_of_month:\n",
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" self.order = self.order_target_percent(target=1)\n",
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" print(f\"Created BUY of {self.order.size} {self.data_close[0]} on day {dom}\")\n",
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" \n",
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" # We are not in the market\n",
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" else:\n",
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" \n",
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" # If we're long\n",
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" if self.position.size > 0:\n",
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" if not self.params.end_of_month <= dom <= ldm:\n",
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" print(f\"Created CLOSE of {self.position.size} at {self.data_close[0]} on day {dom}\")\n",
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" self.order = self.order_target_percent(target=0.0)\n",
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" \n",
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" # If we're short\n",
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" if self.position.size < 0:\n",
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" if not 1 <= dom <= self.params.start_of_month:\n",
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" print(f\"Created CLOSE of {self.position.size} at {self.data_close[0]} on day {dom}\")\n",
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" self.order = self.order_target_percent(target=0.0)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8fbbc83",
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"metadata": {},
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"source": [
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"Run the strategy using Backtrader"
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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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"id": "b47c7a71",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = openbb_data_to_bt_data(\"TLT\", start_date=\"2002-01-01\", end_date=\"2022-06-30\")"
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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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"id": "d53cbfee",
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"metadata": {},
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"outputs": [],
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"source": [
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"cerebro = bt.Cerebro(stdstats=False)\n",
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"cerebro.adddata(data)\n",
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"cerebro.broker.setcash(1000.0)\n",
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"cerebro.addstrategy(MonthlyFlows)\n",
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"cerebro.addobserver(bt.observers.Value)\n",
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"cerebro.addanalyzer(bt.analyzers.Returns, _name=\"returns\")\n",
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"cerebro.addanalyzer(bt.analyzers.TimeReturn, _name=\"time_return\")"
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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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"id": "94ea3665",
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"metadata": {},
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"outputs": [],
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"source": [
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"backtest_result = cerebro.run()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "257cc57c",
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"metadata": {},
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"source": [
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"Convert backtest results into a pandas DataFrame"
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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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"id": "27a1ec64",
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"metadata": {},
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"outputs": [],
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"source": [
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"returns_dict = backtest_result[0].analyzers.time_return.get_analysis()\n",
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"returns_df = (\n",
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" pd.DataFrame(\n",
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" list(returns_dict.items()),\n",
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" columns=[\"date\", \"return\"]\n",
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" )\n",
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" .set_index(\"date\")\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b6e5887",
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"metadata": {},
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"source": [
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"Fetch benchmark data for comparison"
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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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"id": "86c540be",
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"metadata": {},
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"outputs": [],
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"source": [
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"bench = openbb.stocks.load(\"TLT\", start_date=\"2002-01-01\", end_date=\"2022-06-30\")[\"Adj Close\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c39d605",
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"metadata": {},
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"source": [
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"Assess results using QuantStats metrics"
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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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"id": "2dec6aa2",
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"metadata": {},
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"outputs": [],
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"source": [
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"qs.reports.metrics(\n",
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" returns_df,\n",
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" benchmark=bench,\n",
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" mode=\"full\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5b590090",
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"metadata": {},
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"source": [
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"<a href=\"https://pyquantnews.com/\">PyQuant News</a> is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to <a href=\"https://gettingstartedwithpythonforquantfinance.com/\">get started with Python for quant finance</a>. For educational purposes. Not investment advise. Use at your own risk."
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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