294 lines
7.1 KiB
Plaintext
294 lines
7.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "daa349e7",
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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": "47f62676",
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"metadata": {},
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"source": [
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"This code fetches historical futures data for specific indices, calculates their percentage changes, and creates two different portfolios. It then computes the annualized return and Calmar ratio for each portfolio. The annualized return provides a measure of the portfolio's performance over time. The Calmar ratio assesses the risk-adjusted return by comparing the annualized return to the maximum drawdown. This analysis helps in understanding and comparing the performance and risk of different investment portfolios."
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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": "4d4b6bf1",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openbb_terminal.sdk import openbb"
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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": "b74585c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5bb635c",
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"metadata": {},
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"source": [
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"Fetch historical futures data for specified indices from OpenBB"
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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": "a9449129",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = openbb.futures.historical(\n",
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" [\"ES\", \"YM\", \"NQ\"], \n",
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" start_date=\"2020-01-01\", \n",
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" end_date=\"2022-12-31\"\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": "b15bfced",
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"metadata": {},
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"source": [
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"Calculate percentage changes of adjusted closing prices and drop NaN values"
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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": "388d9c35",
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"metadata": {},
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"outputs": [],
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"source": [
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"futs = data['Adj Close'].pct_change().dropna()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fb9dbb0",
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"metadata": {},
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"source": [
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"Create first portfolio with specified weights for each index"
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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": "5e04783d",
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"metadata": {},
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"outputs": [],
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"source": [
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"port_1 = futs.ES * 0.60 + futs.YM * 0.10 + futs.NQ * 0.10"
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]
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},
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{
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"cell_type": "markdown",
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"id": "edbeec72",
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"metadata": {},
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"source": [
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"Create second portfolio with different weights for each index"
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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": "d1166c30",
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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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"port_2 = futs.ES * 0.90 + futs.YM * 0.15 + futs.NQ * 0.15"
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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": "d94715de",
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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 ann_return(returns):\n",
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" \"\"\"Calculate annualized return of a portfolio\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" returns : pd.Series\n",
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" Daily returns of the portfolio\n",
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" \n",
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" Returns\n",
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" -------\n",
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" ann_return : float\n",
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" Annualized return of the portfolio\n",
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" \n",
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" Notes\n",
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" -----\n",
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" This method computes the annualized return \n",
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" using the geometric mean of daily returns.\n",
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" \"\"\"\n",
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" \n",
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" # Compute the ending value of the investment\n",
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" ending_value = (returns + 1).prod()\n",
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" \n",
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" # Calculate the number of years based on daily returns\n",
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" num_years = len(returns) / 252\n",
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" \n",
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" # Calculate annualized return\n",
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" ann_return = ending_value ** (1/num_years) - 1\n",
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" \n",
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" return ann_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": "bfa077cb",
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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 calmar_ratio(returns):\n",
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" \"\"\"Calculate Calmar ratio of a portfolio\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" returns : pd.Series\n",
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" Daily returns of the portfolio\n",
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" \n",
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" Returns\n",
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" -------\n",
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" calmar_ratio : float\n",
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" Calmar ratio of the portfolio\n",
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" \n",
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" Notes\n",
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" -----\n",
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" This method computes the Calmar ratio by \n",
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" dividing the annualized return by the \n",
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" absolute value of maximum drawdown.\n",
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" \"\"\"\n",
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" \n",
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" # Compute cumulative returns\n",
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" cumulative_returns = (returns + 1).cumprod() * 100\n",
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" \n",
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" # Compute maximum drawdown\n",
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" max_return = np.fmax.accumulate(cumulative_returns)\n",
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" max_dd = ((cumulative_returns - max_return) / max_return).min()\n",
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" \n",
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" # Calculate annualized return\n",
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" ann_ret = ann_return(returns)\n",
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" \n",
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" return ann_ret / abs(max_dd)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "be187227",
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"metadata": {},
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"source": [
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"Calculate annualized return for the first portfolio and print it"
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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": "a34d0895",
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"metadata": {},
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"outputs": [],
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"source": [
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"ret = ann_return(port_1)\n",
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"print(ret)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1cb6f61",
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"metadata": {},
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"source": [
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"Calculate Calmar ratio for the first portfolio and print it"
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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": "5a4b583b",
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"metadata": {},
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"outputs": [],
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"source": [
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"p1 = calmar_ratio(port_1)\n",
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"print(p1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "07af7202",
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"metadata": {},
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"source": [
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"Calculate annualized return for the second portfolio and print it"
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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": "dae931cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"ret = ann_return(port_2)\n",
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"print(ret)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "33e5bf30",
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"metadata": {},
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"source": [
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"Calculate Calmar ratio for the second portfolio and print it"
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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": "21ca7fd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"p2 = calmar_ratio(port_2)\n",
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"print(p2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0850471f",
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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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