420 lines
10 KiB
Plaintext
420 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "aa29771a",
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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": "1ed22fa1",
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"metadata": {},
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"source": [
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"This code implements a trading algorithm using Zipline, simulating trades on Apple Inc. (AAPL) stock based on moving average crossovers. It initializes the trading environment, defines trading logic, and records relevant data. The algorithm is backtested over a specified time period, and performance metrics are analyzed and visualized using Pyfolio. This is useful for developing and evaluating trading strategies in a systematic manner."
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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": "5155ca1e",
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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 pandas_datareader.data as web"
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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": "d1b4290f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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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": "aef80e58",
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"metadata": {},
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"outputs": [],
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"source": [
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"from zipline import run_algorithm\n",
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"from zipline.api import order_target, record, symbol\n",
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"from zipline.finance import commission, slippage"
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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": "eec4cef3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pyfolio as pf"
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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": "e1fa431a",
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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 warnings\n",
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"warnings.filterwarnings('ignore')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "19f0166c",
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"metadata": {},
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"source": [
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"Load the Zipline extension for Jupyter notebooks"
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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": "5d952e31",
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext zipline"
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]
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},
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{
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"cell_type": "markdown",
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"id": "86ddb827",
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"metadata": {},
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"source": [
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"Ingest historical data from Quandl for Zipline to 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": "6416593a",
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"metadata": {},
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"outputs": [],
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"source": [
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"! zipline ingest -b quandl"
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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": "c578c2fc",
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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 initialize(context):\n",
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" \"\"\"Initialize the trading algorithm\n",
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"\n",
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" Sets up initial parameters and trading environment.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" context : zipline.assets.context\n",
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" The algorithm's context, including variables and state.\n",
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" \"\"\"\n",
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"\n",
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" context.i = 0\n",
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" context.asset = symbol(\"AAPL\")\n",
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"\n",
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" context.set_commission(commission.PerShare(cost=0.01))\n",
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" context.set_slippage(slippage.FixedSlippage(spread=0.01))"
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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": "ad255427",
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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 handle_data(context, data):\n",
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" \"\"\"Handle the trading logic\n",
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"\n",
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" Executes trading logic based on moving averages.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" context : zipline.assets.context\n",
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" The algorithm's context, including variables and state.\n",
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" data : zipline.protocol.BarData\n",
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" Contains current and historical market data.\n",
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" \"\"\"\n",
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"\n",
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" context.i += 1\n",
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" if context.i < 50:\n",
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" return\n",
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"\n",
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" short_mavg = data.history(\n",
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" context.asset, \n",
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" \"price\", \n",
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" bar_count=14,\n",
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" frequency=\"1d\"\n",
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" ).mean()\n",
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" \n",
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" long_mavg = data.history(\n",
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" context.asset,\n",
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" \"price\",\n",
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" bar_count=50,\n",
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" frequency=\"1d\"\n",
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" ).mean()\n",
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"\n",
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" if short_mavg > long_mavg:\n",
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" order_target(context.asset, 100)\n",
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" elif short_mavg < long_mavg:\n",
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" order_target(context.asset, 0)\n",
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"\n",
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" record(\n",
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" AAPL=data.current(context.asset, \"price\"),\n",
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" short_mavg=short_mavg,\n",
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" long_mavg=long_mavg,\n",
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" )"
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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": "c995758d",
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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 analyze(context, perf):\n",
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" \"\"\"Analyze the performance of the algorithm\n",
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"\n",
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" Visualizes the portfolio and stock performance.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" context : zipline.assets.context\n",
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" The algorithm's context, including variables and state.\n",
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" perf : pandas.DataFrame\n",
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" A dataframe containing the performance metrics.\n",
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" \"\"\"\n",
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"\n",
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" fig = plt.figure()\n",
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" ax1 = fig.add_subplot(211)\n",
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" perf.portfolio_value.plot(ax=ax1)\n",
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" ax1.set_ylabel(\"portfolio value in $\")\n",
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"\n",
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" ax2 = fig.add_subplot(212)\n",
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" perf[\"AAPL\"].plot(ax=ax2)\n",
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" perf[[\"short_mavg\", \"long_mavg\"]].plot(ax=ax2)\n",
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"\n",
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" perf_trans = perf.loc[\n",
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" [t != [] for t in perf.transactions]\n",
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" ]\n",
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" buys = perf_trans.loc[\n",
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" [t[0][\"amount\"] > 0 for t in perf_trans.transactions]\n",
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" ]\n",
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" sells = perf_trans.loc[\n",
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" [t[0][\"amount\"] < 0 for t in perf_trans.transactions]\n",
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" ]\n",
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" ax2.plot(\n",
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" buys.index,\n",
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" perf.short_mavg.loc[buys.index],\n",
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" \"^\",\n",
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" markersize=10,\n",
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" color=\"m\"\n",
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" )\n",
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" ax2.plot(\n",
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" sells.index,\n",
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" perf.short_mavg.loc[sells.index],\n",
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" \"v\",\n",
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" markersize=10,\n",
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" color=\"k\"\n",
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" )\n",
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" ax2.set_ylabel(\"price in $\")\n",
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" plt.legend(loc=0)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4bce6eb",
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"metadata": {},
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"source": [
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"Define the start and end dates for the backtesting period"
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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": "606aa860",
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"metadata": {},
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"outputs": [],
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"source": [
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"start = pd.Timestamp('2000')\n",
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"end = pd.Timestamp('2018')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "87ff84e5",
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"metadata": {},
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"source": [
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"Fetch the benchmark returns for the S&P 500 index from the Federal Reserve Economic Data (FRED)"
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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": "fc68568d",
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"metadata": {},
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"outputs": [],
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"source": [
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"sp500 = web.DataReader('SP500', 'fred', start, end).SP500\n",
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"benchmark_returns = sp500.pct_change()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3340b872",
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"metadata": {},
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"source": [
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"Run the algorithm with the specified parameters and historical data"
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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": "f81482a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"perf = run_algorithm(\n",
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" start=start,\n",
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" end=end,\n",
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" initialize=initialize,\n",
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" handle_data=handle_data,\n",
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" analyze=analyze,\n",
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" capital_base=100000,\n",
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" benchmark_returns=benchmark_returns,\n",
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" bundle=\"quandl\",\n",
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" data_frequency=\"daily\",\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": "8f8420c1",
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"metadata": {},
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"source": [
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"Display the performance metrics of the algorithm"
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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": "2c8fdd55",
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"metadata": {},
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"outputs": [],
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"source": [
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"perf.info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "01c3ebb3",
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"metadata": {},
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"source": [
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"Extract returns, positions, and transactions from the performance DataFrame for analysis"
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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": "2f2c6986",
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"metadata": {},
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"outputs": [],
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"source": [
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"returns, positions, transactions = \\\n",
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" pf.utils.extract_rets_pos_txn_from_zipline(perf)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c658ddfc",
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"metadata": {},
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"source": [
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"Create a full tear sheet using Pyfolio to analyze the trading strategy's performance"
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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": "4a92b98b",
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"metadata": {},
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"outputs": [],
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"source": [
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"pf.create_full_tear_sheet(\n",
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" returns,\n",
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" positions=positions,\n",
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" transactions=transactions,\n",
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" live_start_date=\"2016-01-01\",\n",
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" round_trips=True,\n",
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")"
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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": "5ad9c36f",
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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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"cell_type": "markdown",
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"id": "633aa53b",
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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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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.10.13"
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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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