284 lines
7.3 KiB
Plaintext
284 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab3c3685",
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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": "1873e801",
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"metadata": {},
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"source": [
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"This code downloads historical price data for the TLT ETF and generates trading signals based on calendar rules. It sets up short and long entry and exit signals, such as entering a short position on the first day of each month and exiting five days later. The code also defines a function to simulate a trading strategy and calculate performance metrics, specifically the Sharpe ratio. Finally, it runs this strategy on shuffled data to generate a distribution of Sharpe ratios for comparison. This approach aids in evaluating the robustness and statistical significance of the trading strategy."
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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": "11c2ac72",
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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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"import matplotlib.pyplot as plt\n",
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"import vectorbt as vbt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "baa26e68",
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"metadata": {},
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"source": [
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"Download historical price data for TLT ETF from Yahoo Finance and extract the closing prices"
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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": "ecf3f8f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"tlt = vbt.YFData.download(\n",
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" \"TLT\", \n",
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" start=\"2004-01-01\"\n",
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").get(\"Close\").to_frame()\n",
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"close = tlt.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": "0192959f",
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"metadata": {},
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"source": [
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"Set up empty dataframes to hold trading signals for short and long positions"
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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": "c5aa36b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"short_entries = pd.DataFrame.vbt.signals.empty_like(close)\n",
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"short_exits = pd.DataFrame.vbt.signals.empty_like(close)\n",
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"long_entries = pd.DataFrame.vbt.signals.empty_like(close)\n",
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"long_exits = pd.DataFrame.vbt.signals.empty_like(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": "b5404f63",
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"metadata": {},
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"source": [
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"Generate short entry signals on the first day of each new 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": "a37dc3c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"short_entry_mask = ~tlt.index.tz_convert(None).to_period(\"M\").duplicated()\n",
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"short_entries.iloc[short_entry_mask] = True"
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]
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},
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{
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"cell_type": "markdown",
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"id": "79e3c619",
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"metadata": {},
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"source": [
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"Generate short exit signals five days after short entry"
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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": "6fb2d80b",
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"metadata": {},
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"outputs": [],
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"source": [
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"short_exit_mask = short_entries.shift(5).fillna(False)\n",
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"short_exits.iloc[short_exit_mask] = True"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d014794e",
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"metadata": {},
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"source": [
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"Generate long entry signals seven days before the end of each 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": "ebf6e5e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"long_entry_mask = short_entries.shift(-7).fillna(False)\n",
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"long_entries.iloc[long_entry_mask] = True"
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]
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},
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{
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"cell_type": "markdown",
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"id": "16a2dce9",
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"metadata": {},
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"source": [
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"Generate long exit signals one day before the end of each 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": "22160cf9",
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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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"long_exit_mask = short_entries.shift(-1).fillna(False)\n",
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"long_exits.iloc[long_exit_mask] = True"
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]
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},
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{
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"cell_type": "markdown",
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"id": "52de0bee",
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"metadata": {},
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"source": [
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"Define a function to simulate the trading strategy and calculate performance 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": "2df090a8",
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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 run_sim(close):\n",
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" \"\"\"Simulate trading strategy\n",
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"\n",
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" This function simulates a trading strategy based on given entry and exit signals.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" close : pd.Series\n",
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" The closing prices of the asset.\n",
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"\n",
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" Returns\n",
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" -------\n",
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" pf : vbt.Portfolio\n",
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" The simulated portfolio.\n",
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" \"\"\"\n",
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" \n",
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" return vbt.Portfolio.from_signals(\n",
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" close=close,\n",
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" entries=long_entries,\n",
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" exits=long_exits,\n",
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" short_entries=short_entries,\n",
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" short_exits=short_exits,\n",
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" freq=\"1d\"\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": "7afb4498",
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"metadata": {},
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"source": [
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"Run the simulation and calculate the Sharpe ratio for the trading strategy"
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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": "241a38f9",
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"metadata": {},
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"outputs": [],
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"source": [
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"pf = run_sim(close)\n",
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"sr = pf.sharpe_ratio()\n",
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"pf.stats()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ff347e51",
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"metadata": {},
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"source": [
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"Run the simulation on shuffled data 1000 times to generate a distribution of Sharpe ratios"
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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": "7761864b",
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"metadata": {},
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"outputs": [],
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"source": [
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"sharpes = []\n",
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"for i in range(1000):\n",
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" shuffled_close = close.sample(frac=1)\n",
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" shuffled_close.index = close.index\n",
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" shuffled_sharpe = run_sim(shuffled_close).sharpe_ratio()\n",
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" sharpes.append(shuffled_sharpe)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1e7613a8",
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"metadata": {},
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"source": [
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"Plot the distribution of simulated Sharpe ratios and mark the original strategy's Sharpe ratio"
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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": "ff9c8df9",
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"metadata": {},
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"outputs": [],
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"source": [
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"pd.DataFrame(\n",
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" sharpes, \n",
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" columns=[\"Simulated Sharpe Ratios\"]\n",
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").hist(\n",
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" bins=np.histogram_bin_edges(sharpes, bins=\"fd\")\n",
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")\n",
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"plt.axvline(sr, linestyle=\"dashed\", linewidth=1)\n",
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"min_ylim, max_ylim = plt.ylim()\n",
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"plt.text(sr * -0.01, max_ylim * 0.9, f\"Strat Sharpe: {sr:.1f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c8c6d66",
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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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