daily update
This commit is contained in:
David Brazda
855
to_explore/markov_variance_switching_2_leverage.ipynb
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855
to_explore/markov_variance_switching_2_leverage.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Markov Variance Switching\n",
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"\n",
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"# 2: Leveraged Exchange Traded Funds\n",
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"\n",
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"Kim, C., Nelson, C., and Startz, R. (1998). Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization. Journal of Empirical Finance, (5)2, pp.131-154.\n",
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"\n",
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"**Author:** shittles\n",
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"\n",
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"**Created:** 2024-10-17\n",
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"\n",
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"**Modified:** 2024-10-17\n",
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"\n",
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"## Sources\n",
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"- https://www.statsmodels.org/v0.11.1/examples/notebooks/generated/markov_autoregression.html\n",
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"- https://www.proshares.com/our-etfs/leveraged-and-inverse/upro\n",
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"- https://www.bogleheads.org/forum/viewtopic.php?t=272007\n",
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"- https://www.bogleheads.org/forum/viewtopic.php?t=288192\n",
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"- https://www.reddit.com/r/LETFs/comments/14lubaz/finally_an_accurate_backtesting_model/\n",
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"- https://www.reddit.com/r/mauerstrassenwetten/comments/sivtas/zahlgrafs_exzellente_abenteuer_teil_4/\n",
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"- https://code.launchpad.net/zgea\n",
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"\n",
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"## Changelog\n",
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"- Modified Markov variance switching notebook for portfolio optimisation (2h - 2024-10-17).\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as go\n",
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"import statsmodels.api as sm\n",
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"\n",
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"from pykalman import KalmanFilter\n",
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"from sklearn.compose import make_column_transformer\n",
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"from sklearn.preprocessing import RobustScaler\n",
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"\n",
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"from vectorbtpro import *"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"vbt.settings.set_theme(\"dark\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Ingestion\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use the fund, not the index, because its going to be part of a portfolio.\n",
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"data = vbt.YFData.pull(\n",
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" [\"SPY\", \"UPRO\"], start=\"50 years ago\", end=\"today\", timeframe=\"daily\", tz=\"UTC\"\n",
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")\n",
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"\n",
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"data.stats()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.data[\"SPY\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.data[\"UPRO\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.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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"metadata": {},
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"outputs": [],
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"source": [
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"data.plot(symbol=\"SPY\", yaxis=dict(type=\"log\")).show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.plot(symbol=\"UPRO\", yaxis=dict(type=\"log\")).show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Cleaning\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.features"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.get_feature(\"Dividends\").any()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.get_feature(\"Stock Splits\").any()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.get_feature(\"Capital Gains\").any()"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.remove_features([\"Capital Gains\"])"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"# A post global financial crisis backtest probably isn't long enough.\n",
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"data = data.transform(lambda df: df.loc[\"June 25th 2009\" < df.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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# I don't need to resample the data since it's sourced from the same exchange.\n",
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"# data = data.resample(\"daily\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Modelling\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.log_returns.vbt.plot().show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Pre-processing\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"column_transformer = make_column_transformer(\n",
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" (RobustScaler(), [\"SPY\"]),\n",
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")\n",
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"\n",
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"sr_log_returns_scaled = pd.Series(\n",
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" data=column_transformer.fit_transform(pd.DataFrame(data.log_returns[\"SPY\"])).ravel(),\n",
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" index=data.index,\n",
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" name=\"SPY\",\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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"metadata": {},
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"outputs": [],
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"source": [
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"sr_log_returns_scaled.vbt.plot().show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Markov Regression\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"k_regimes_kns = 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"kns = sm.tsa.MarkovRegression(\n",
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" sr_log_returns_scaled, k_regimes=k_regimes_kns, trend=\"n\", switching_variance=True\n",
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")\n",
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"results_kns = kns.fit()\n",
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"\n",
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"results_kns.summary()"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"results_kns.filtered_marginal_probabilities # using data until time t (excluding time t+1, ..., T)\n",
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"# results_kns.smoothed_marginal_probabilities # using data until time T"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = vbt.make_subplots(\n",
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" rows=k_regimes_kns,\n",
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" cols=1,\n",
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" y_title=\"Filtered Marginal Variance Regime Probabilities\",\n",
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" # y_title=\"Smoothed Marginal Variance Regime Probabilities\",\n",
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" shared_xaxes=True,\n",
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" subplot_titles=[\n",
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" \"Low-variance\",\n",
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" \"Medium-variance\",\n",
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" \"High-variance\",\n",
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" ], # order changes dependent on fit\n",
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")\n",
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"\n",
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"for i in range(k_regimes_kns):\n",
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" fig = results_kns.filtered_marginal_probabilities[i].vbt.plot(\n",
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" # fig = results_kns.smoothed_marginal_probabilities[i].vbt.plot(\n",
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" add_trace_kwargs=dict(row=i + 1, col=1), fig=fig\n",
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" )\n",
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"\n",
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"fig.update_layout(showlegend=False)\n",
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"fig.show()"
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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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_annotated_line(\n",
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" fig: go.Figure,\n",
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" x: pd.Series,\n",
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" y: pd.Series,\n",
|
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" classes: pd.Series,\n",
|
||||
" dict_class_colours: dict,\n",
|
||||
" dict_class_labels: dict,\n",
|
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") -> go.Figure:\n",
|
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" \"\"\"Plot a line chart where each trace is coloured based on its class.\n",
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"\n",
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" Yes, plotly really doesn't support this out of the box.\n",
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"\n",
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" Args:\n",
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" fig: Figure.\n",
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" x: Indices.\n",
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" y: Close prices.\n",
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" classes: Regimes.\n",
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" dict_class_colours: In the format {class: colour}\n",
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" dict_class_labels: In the format {class: label}\n",
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"\n",
|
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" Returns:\n",
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" fig: The figure.\n",
|
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" \"\"\"\n",
|
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" # Plot each segment in its corresponding color.\n",
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" for i in range(len(x) - 1):\n",
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" fig.add_trace(\n",
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" go.Scatter(\n",
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" x=x[i : i + 2],\n",
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" y=y[i : i + 2],\n",
|
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" mode=\"lines\",\n",
|
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" line=dict(color=dict_class_colours[classes[i]], width=2),\n",
|
||||
" showlegend=False, # added manually\n",
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" )\n",
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" )\n",
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"\n",
|
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" # Label each regime.\n",
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" for regime, colour in dict_class_colours.items():\n",
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" fig.add_trace(\n",
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" go.Scatter(\n",
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" x=[None],\n",
|
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" y=[None],\n",
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" mode=\"lines\",\n",
|
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" line=dict(color=colour, width=2),\n",
|
||||
" name=dict_class_labels[regime],\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" return fig"
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||||
]
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},
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{
|
||||
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"sr_variance_regime_forecasts = results_kns.filtered_marginal_probabilities.idxmax(\n",
|
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" axis=1\n",
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")\n",
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"\n",
|
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"sr_variance_regime_predictions = results_kns.smoothed_marginal_probabilities.idxmax(\n",
|
||||
" axis=1\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_variance_regime_forecasts.vbt.plot().show()\n",
|
||||
"# sr_variance_regime_predictions.vbt.plot().show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# order changes dependent on fit\n",
|
||||
"dict_variance_regime_labels = {\n",
|
||||
" 0: \"Low\",\n",
|
||||
" 1: \"Medium\",\n",
|
||||
" 2: \"High\",\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"dict_variance_regime_colours = {\n",
|
||||
" 0: \"green\",\n",
|
||||
" 1: \"orange\",\n",
|
||||
" 2: \"red\",\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"fig = plot_annotated_line(\n",
|
||||
" fig,\n",
|
||||
" data.index,\n",
|
||||
" np.log(data.data[\"SPY\"][\"Close\"]),\n",
|
||||
" sr_variance_regime_forecasts,\n",
|
||||
" # sr_variance_regime_predictions,\n",
|
||||
" dict_variance_regime_colours,\n",
|
||||
" dict_variance_regime_labels,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.update_layout(\n",
|
||||
" title=\"Filtered Variance Regime Labels\",\n",
|
||||
" # title=\"Smoothed Variance Regime Labels\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" yaxis_title=\"Log Close\",\n",
|
||||
" showlegend=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Kalman Filter\n",
|
||||
"Experiment with smoothing the regime probabilities.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def kalman_smooth(column: np.array) -> np.array:\n",
|
||||
" \"\"\"Apply a Kalman filter to the column.\n",
|
||||
" \n",
|
||||
" The Kalman filter class cannot handle the NaNs created by aligning symbols\n",
|
||||
" indices, so only apply it to the relevent slice of the array.\n",
|
||||
" \"\"\"\n",
|
||||
" # index = column.index\n",
|
||||
" # column = column.loc[column.first_valid_index() : column.last_valid_index()]\n",
|
||||
"\n",
|
||||
" # Filter out NaNs at the start and end of the column.\n",
|
||||
" valid_mask = ~np.isnan(column)\n",
|
||||
"\n",
|
||||
" if not valid_mask.any():\n",
|
||||
" # If all values are NaN, return an array of NaNs with the same length.\n",
|
||||
" return np.full_like(column, np.nan)\n",
|
||||
"\n",
|
||||
" # Get the index of the first occurrence of the maximum value in the array.\n",
|
||||
" first_valid = valid_mask.argmax()\n",
|
||||
" # Reverse the array to find the index of the last occurence.\n",
|
||||
" last_valid = len(valid_mask) - valid_mask[::-1].argmax()\n",
|
||||
"\n",
|
||||
" column = column[first_valid:last_valid]\n",
|
||||
"\n",
|
||||
" kf = KalmanFilter(initial_state_mean=0, n_dim_obs=1)\n",
|
||||
" kf = kf.em(column, n_iter=5)\n",
|
||||
"\n",
|
||||
" smoothed_state_means, _ = kf.smooth(column)\n",
|
||||
"\n",
|
||||
" # return pd.Series(\n",
|
||||
" # data=smoothed_state_means.ravel(),\n",
|
||||
" # index=column.index,\n",
|
||||
" # name=column.name,\n",
|
||||
" # ).reindex(index)\n",
|
||||
" result = np.full(len(valid_mask), np.nan)\n",
|
||||
" result[first_valid:last_valid] = smoothed_state_means.ravel()\n",
|
||||
"\n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Kalman = vbt.IF(\n",
|
||||
" class_name=\"Kalman\",\n",
|
||||
" short_name=\"kf\",\n",
|
||||
" input_names=[\"column\"],\n",
|
||||
" output_names=[\"smoothed_state_means\"],\n",
|
||||
").with_apply_func(\n",
|
||||
" kalman_smooth,\n",
|
||||
" takes_1d=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ssm = Kalman.run(results_kns.filtered_marginal_probabilities)\n",
|
||||
"\n",
|
||||
"ssm.kf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_subplots(\n",
|
||||
" rows=k_regimes_kns,\n",
|
||||
" cols=1,\n",
|
||||
" y_title=\"Kalman Filtered Marginal Variance Regime (Not) Probabilities\",\n",
|
||||
" shared_xaxes=True,\n",
|
||||
" subplot_titles=[\n",
|
||||
" \"Low-variance\",\n",
|
||||
" \"Medium-variance\",\n",
|
||||
" \"High-variance\",\n",
|
||||
" ], # order changes dependent on fit\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"for i in range(k_regimes_kns):\n",
|
||||
" fig = ssm.kf[i].vbt.plot(\n",
|
||||
" add_trace_kwargs=dict(row=i + 1, col=1), fig=fig\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"fig.update_layout(showlegend=False)\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_variance_regime_kalman_forecasts = ssm.kf.idxmax(axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_variance_regime_kalman_forecasts.vbt.plot().show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"fig = plot_annotated_line(\n",
|
||||
" fig,\n",
|
||||
" data.index,\n",
|
||||
" np.log(data.data[\"SPY\"][\"Close\"]),\n",
|
||||
" sr_variance_regime_kalman_forecasts,\n",
|
||||
" dict_variance_regime_colours,\n",
|
||||
" dict_variance_regime_labels,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.update_layout(\n",
|
||||
" title=\"Kalman Filtered Variance Regime Labels\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" yaxis_title=\"Log Close\",\n",
|
||||
" showlegend=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Backtest\n",
|
||||
"\n",
|
||||
"### 100% SPX Allocation\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf = vbt.Portfolio.from_holding(close=data.data[\"SPY\"][\"Close\"])\n",
|
||||
"\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot(yaxis=dict(type=\"log\")).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"pf.drawdowns.plot(yaxis=dict(type=\"log\"), fig=fig)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"pf.plot_underwater(pct_scale=True, fig=fig)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 100% UPRO Allocation\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf = vbt.Portfolio.from_holding(\n",
|
||||
" close=data.data[\"UPRO\"][\"Close\"], bm_close=data.data[\"SPY\"][\"Close\"]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot(yaxis=dict(type=\"log\")).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"pf.drawdowns.plot(yaxis=dict(type=\"log\"), fig=fig)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Nice!\n",
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"pf.plot_underwater(pct_scale=True, fig=fig)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Filtered Marginal Probability Allocation\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws_points = data.wrapper.get_index_points(every=\"W\")\n",
|
||||
"\n",
|
||||
"ws_points"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws_timestamps = data.wrapper.index[ws_points]\n",
|
||||
"\n",
|
||||
"ws_timestamps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"symbol_wrapper = data.get_symbol_wrapper(freq=\"1D\") \n",
|
||||
"\n",
|
||||
"allocations = symbol_wrapper.fill()\n",
|
||||
"\n",
|
||||
"allocations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"allocations[\"UPRO\"] = results_kns.filtered_marginal_probabilities[0]\n",
|
||||
"allocations[\"SPY\"] = 1 - allocations[\"UPRO\"]\n",
|
||||
"\n",
|
||||
"allocations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf = vbt.Portfolio.from_orders(\n",
|
||||
" close=data.get(\"Close\"),\n",
|
||||
" bm_close=data.data[\"SPY\"][\"Close\"],\n",
|
||||
" size=allocations,\n",
|
||||
" size_type=\"targetpercent\",\n",
|
||||
" group_by=True, \n",
|
||||
" cash_sharing=True,\n",
|
||||
" call_seq=\"auto\" \n",
|
||||
")\n",
|
||||
"\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# sim_alloc = pf.get_asset_value(group_by=False).vbt / pf.value\n",
|
||||
"\n",
|
||||
"# sim_alloc.vbt.plot(\n",
|
||||
"# trace_kwargs=dict(stackgroup=\"one\"),\n",
|
||||
"# use_gl=False\n",
|
||||
"# ).show()\n",
|
||||
"\n",
|
||||
"pf.plot_allocations().show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot(yaxis=dict(type=\"log\")).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.drawdowns.plot(yaxis=dict(type=\"log\")).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot_underwater(pct_scale=True).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user