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to_explore/markov_variance_switching.ipynb
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874
to_explore/markov_variance_switching.ipynb
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@ -0,0 +1,874 @@
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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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"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:** 2024-09-18\n",
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"\n",
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"**Modified:** 2024-09-19\n",
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"\n",
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"## Changelog\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 ray\n",
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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 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": null,
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"metadata": {},
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"outputs": [],
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||||
"source": [
|
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"ray.init()"
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]
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},
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{
|
||||
"cell_type": "code",
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"execution_count": 3,
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"symbol = \"^GSPC\" # the S&P 500 ticker"
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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 = vbt.YFData.pull(\n",
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" symbol, start=\"50 years ago\", end=\"today\", timeframe=\"daily\", tz=\"UTC\"\n",
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") # 50 years of data\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[symbol]"
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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[symbol].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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"# The opens are corrupt...\n",
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"data.plot(yaxis=dict(type=\"log\")).show()"
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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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"# but the closes are fine.\n",
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"data.data[\"^GSPC\"][\"Close\"].vbt.plot(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.data[symbol][\"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.data[symbol][\"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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = data.remove_features([\"Dividends\", \"Stock Splits\"])"
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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.transform(lambda df: df.loc[\"April 19th 1982\" < 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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# data.plot(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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# len(data.index) / 365.25 # 30 years of data remaining"
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"# sr_open = data.get(\"Open\")\n",
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"# sr_high = data.get(\"High\")\n",
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"# sr_low = data.get(\"Low\")\n",
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"sr_close = data.get(\"Close\")"
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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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"# sr_log_open = np.log(sr_open)\n",
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"# sr_log_high = np.log(sr_high)\n",
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"# sr_log_low = np.log(sr_low)\n",
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"sr_log_close = np.log(sr_close)"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"sr_log_returns = data.log_returns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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||||
"sr_log_returns.vbt.plot().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": 20,
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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(), [symbol]),\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(sr_log_returns)).ravel(),\n",
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" index=sr_log_returns.index,\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": "code",
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"execution_count": 22,
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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)"
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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": [
|
||||
"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=\"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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||||
" \"Medium-variance\",\n",
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||||
" \"Low-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",
|
||||
" fig = results_kns.smoothed_marginal_probabilities[i].vbt.plot(\n",
|
||||
" add_trace_kwargs=dict(row=i + 1, col=1), fig=fig\n",
|
||||
" )\n",
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||||
"\n",
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"\n",
|
||||
"fig.update_layout(showlegend=False)\n",
|
||||
"fig.show()"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 27,
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_annotated_line(\n",
|
||||
" fig: go.Figure,\n",
|
||||
" x: pd.Series,\n",
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||||
" y: pd.Series,\n",
|
||||
" classes: pd.Series,\n",
|
||||
" dict_class_colours: dict,\n",
|
||||
" dict_class_labels: dict,\n",
|
||||
") -> go.Figure:\n",
|
||||
" \"\"\"Plot a line chart where each trace is coloured based on its class.\n",
|
||||
"\n",
|
||||
" Yes, plotly really doesn't support this out of the box.\n",
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"\n",
|
||||
" Args:\n",
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" fig: Figure.\n",
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" x: Indices.\n",
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" y: Close prices.\n",
|
||||
" classes: Regimes.\n",
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||||
" dict_class_colours: In the format {class: colour}\n",
|
||||
" dict_class_labels: In the format {class: label}\n",
|
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"\n",
|
||||
" Returns:\n",
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||||
" fig: The figure.\n",
|
||||
" \"\"\"\n",
|
||||
" # Plot each segment in its corresponding color.\n",
|
||||
" for i in range(len(x) - 1):\n",
|
||||
" fig.add_trace(\n",
|
||||
" go.Scatter(\n",
|
||||
" x=x[i : i + 2],\n",
|
||||
" y=y[i : i + 2],\n",
|
||||
" mode=\"lines\",\n",
|
||||
" line=dict(color=dict_class_colours[classes[i]], width=2),\n",
|
||||
" showlegend=False, # added manually\n",
|
||||
" )\n",
|
||||
" )\n",
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||||
"\n",
|
||||
" # Label each regime.\n",
|
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" for regime, colour in dict_class_colours.items():\n",
|
||||
" fig.add_trace(\n",
|
||||
" go.Scatter(\n",
|
||||
" x=[None],\n",
|
||||
" y=[None],\n",
|
||||
" mode=\"lines\",\n",
|
||||
" line=dict(color=colour, width=2),\n",
|
||||
" name=dict_class_labels[regime],\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" return fig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_variance_regime_forecasts = results_kns.filtered_marginal_probabilities.idxmax(\n",
|
||||
" axis=1\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"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": 30,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# order changes dependent on fit\n",
|
||||
"dict_variance_regime_labels = {\n",
|
||||
" 0: \"Medium\",\n",
|
||||
" 1: \"Low\",\n",
|
||||
" 2: \"High\",\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"dict_variance_regime_colours = {\n",
|
||||
" 0: \"orange\",\n",
|
||||
" 1: \"green\",\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",
|
||||
" sr_log_close,\n",
|
||||
" # sr_variance_regime_forecasts.rolling(5).mean().fillna(0).round(0),\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": [
|
||||
"## Backtest\n",
|
||||
"### Filtered marginal probabilities\n",
|
||||
"A backtest using filtered marginal probabilities.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# TODO - Double check me!\n",
|
||||
"# Assuming that you sell today if yesterday was in the high-variance regime.\n",
|
||||
"# entries = (sr_variance_regime_forecasts != 2).vbt.signals.fshift()\n",
|
||||
"# exits = (sr_variance_regime_forecasts == 2).vbt.signals.fshift()\n",
|
||||
"\n",
|
||||
"# Assuming that you sell today (at the close) if today was in the high-variance regime.\n",
|
||||
"entries = (sr_variance_regime_forecasts != 2)\n",
|
||||
"exits = (sr_variance_regime_forecasts == 2)\n",
|
||||
"\n",
|
||||
"# I haven't tested any additional logic.\n",
|
||||
"# entries = (sr_variance_regime_forecasts.rolling(5).mean().fillna(0).round(0) != 2)\n",
|
||||
"\n",
|
||||
"clean_entries, clean_exits = entries.vbt.signals.clean(exits)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = sr_variance_regime_forecasts.vbt.plot()\n",
|
||||
"\n",
|
||||
"clean_entries.vbt.signals.plot_as_entries(sr_variance_regime_forecasts, fig=fig)\n",
|
||||
"clean_exits.vbt.signals.plot_as_exits(sr_variance_regime_forecasts, fig=fig)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf = vbt.Portfolio.from_signals(\n",
|
||||
" close=sr_close,\n",
|
||||
" entries=clean_entries,\n",
|
||||
" exits=clean_exits,\n",
|
||||
" direction=\"both\",\n",
|
||||
" fees=0.001,\n",
|
||||
" size=1.0,\n",
|
||||
" size_type=vbt.pf_enums.SizeType.ValuePercent,\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": [
|
||||
"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": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Re-fitting Every Day\n",
|
||||
"A backtest with a single training and validation set, that's refit every day"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_days_validation = int(365.25 * 2) # 2 years of data held back for the validation set"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# slices_sr = vbt.Splitter.split_range(slice(None), new_split=-n_days_validation, index=data.index)\n",
|
||||
"splitter_fr = vbt.Splitter.from_rolling(\n",
|
||||
" data.index, length=len(data.index), split=len(data.index) - n_days_validation\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"splitter_fr.plot().show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define a Ray remote function for parallelization.\n",
|
||||
"@ray.remote\n",
|
||||
"def compute_smoothed_marginal_probabilities(sr):\n",
|
||||
" kns = sm.tsa.MarkovRegression(\n",
|
||||
" sr,\n",
|
||||
" k_regimes=k_regimes_kns,\n",
|
||||
" trend=\"n\",\n",
|
||||
" switching_variance=True,\n",
|
||||
" )\n",
|
||||
" results_kns = kns.fit()\n",
|
||||
"\n",
|
||||
" # the smoothing might not work properly out of sample\n",
|
||||
" return results_kns.smoothed_marginal_probabilities.iloc[-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The predict method doesn't support out of sample forecasts...\n",
|
||||
"# kns = sm.tsa.MarkovRegression(\n",
|
||||
"# sr_log_returns[splitter_fr.get_mask()[\"set_0\"]],\n",
|
||||
"# k_regimes=k_regimes_kns,\n",
|
||||
"# trend=\"n\",\n",
|
||||
"# switching_variance=True,\n",
|
||||
"# )\n",
|
||||
"# results_kns = kns.fit()\n",
|
||||
"\n",
|
||||
"# results_kns.summary()\n",
|
||||
"\n",
|
||||
"# results_kns.predict(sr_log_returns[splitter_fr.get_mask()[\"set_1\"]])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"https://github.com/statsmodels/statsmodels/issues/7982"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_train = sr_log_returns[splitter_fr.get_mask()[\"set_0\"]]\n",
|
||||
"sr_validate = sr_log_returns[splitter_fr.get_mask()[\"set_1\"]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ...so re-fit the model every timestep.\n",
|
||||
"futures = []\n",
|
||||
"\n",
|
||||
"sr_log_returns_to_date = sr_train.copy()\n",
|
||||
"\n",
|
||||
"# launch parallel tasks\n",
|
||||
"for i in range(len(sr_validate)):\n",
|
||||
" sr_log_returns_to_date = pd.concat(\n",
|
||||
" [\n",
|
||||
" sr_log_returns_to_date,\n",
|
||||
" pd.Series(sr_validate.iloc[i], index=[sr_validate.index[i]]),\n",
|
||||
" ]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" futures.append(\n",
|
||||
" compute_smoothed_marginal_probabilities.remote(sr_log_returns_to_date)\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"# collect results\n",
|
||||
"smoothed_marginal_probabilities = ray.get(futures)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"smoothed_marginal_probabilities = pd.concat(smoothed_marginal_probabilities, axis=1).T\n",
|
||||
"\n",
|
||||
"smoothed_marginal_probabilities.index.name = \"Date\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_variance_regime_predictions = smoothed_marginal_probabilities.idxmax(\n",
|
||||
" axis=1\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sr_close_validate = sr_close[splitter_fr.get_mask()[\"set_1\"]]\n",
|
||||
"\n",
|
||||
"sr_log_close_validate = sr_log_close[splitter_fr.get_mask()[\"set_1\"]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_figure()\n",
|
||||
"\n",
|
||||
"fig = plot_annotated_line(\n",
|
||||
" fig,\n",
|
||||
" data.index[splitter_fr.get_mask()[\"set_1\"]],\n",
|
||||
" sr_log_close_validate,\n",
|
||||
" sr_variance_regime_predictions,\n",
|
||||
" dict_variance_regime_colours,\n",
|
||||
" dict_variance_regime_labels,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.update_layout(\n",
|
||||
" title=\"Variance Regime Forecasts\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" yaxis_title=\"Log Close\",\n",
|
||||
" showlegend=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# TODO - Double check me!\n",
|
||||
"# Assuming that you sell today if yesterday was in the high-variance regime.\n",
|
||||
"# entries = (sr_variance_regime_forecasts != 2).vbt.signals.fshift()\n",
|
||||
"# exits = (sr_variance_regime_forecasts == 2).vbt.signals.fshift()\n",
|
||||
"\n",
|
||||
"# Assuming that you sell today (at the close) if today was in the high-variance regime.\n",
|
||||
"entries = (sr_variance_regime_forecasts != 2)\n",
|
||||
"exits = (sr_variance_regime_forecasts == 2)\n",
|
||||
"\n",
|
||||
"# I haven't tested any additional logic.\n",
|
||||
"# entries = (sr_variance_regime_forecasts.rolling(5).mean().fillna(0).round(0) != 2)\n",
|
||||
"\n",
|
||||
"clean_entries, clean_exits = entries.vbt.signals.clean(exits)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = sr_variance_regime_forecasts.vbt.plot()\n",
|
||||
"\n",
|
||||
"clean_entries.vbt.signals.plot_as_entries(sr_variance_regime_forecasts, fig=fig)\n",
|
||||
"clean_exits.vbt.signals.plot_as_exits(sr_variance_regime_forecasts, fig=fig)\n",
|
||||
"\n",
|
||||
"fig.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf = vbt.Portfolio.from_signals(\n",
|
||||
" close=sr_close_validate,\n",
|
||||
" entries=clean_entries,\n",
|
||||
" exits=clean_exits,\n",
|
||||
" direction=\"both\",\n",
|
||||
" fees=0.001,\n",
|
||||
" size=1.0,\n",
|
||||
" size_type=vbt.pf_enums.SizeType.ValuePercent,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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": [
|
||||
"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": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Observations\n",
|
||||
"- For this implementation you have to use the filtered probabilities to not introduce look-ahead bias.\n",
|
||||
"- If you backtest the smoothed probilities (which smoothes using all of the data) it only performs well after the great financial crisis. Why? No clue.\n",
|
||||
"- It looks like its better for labelling than it is as a strategy in its current state.\n",
|
||||
"- After slow recessions like the dot-com bubble, there can be a medium-variance decline which this simple strategy doesn't capture.\n",
|
||||
"- After fast recessions like covid-19, there can be a high-variance rebound which this simple strategy doesn't capture.\n",
|
||||
"- **It looks like you can safely leverage up during low-variance regimes.**\n",
|
||||
"- Maybe you could combine this strategy with other recession-leading indicators (e.g. manufacturing/services pmi, federal funds rate, 10y-2y yield curve, 1y-3mo yield curve) to help time the tops?\n",
|
||||
"- Maybe you could combine this strategy with another trend-following strategy (e.g. VWAP, EMA, BBANDS, ADX) to help time the bottoms?\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ray.shutdown()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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