diff --git a/.gitignore b/.gitignore index b6e4761..036833c 100644 --- a/.gitignore +++ b/.gitignore @@ -5,7 +5,8 @@ __pycache__/ # C extensions *.so - +.idea +.empty # Distribution / packaging .Python build/ @@ -127,3 +128,4 @@ dmypy.json # Pyre type checker .pyre/ +.vscode/ \ No newline at end of file diff --git a/FranzKrekeler/README.md b/FranzKrekeler/README.md new file mode 100644 index 0000000..6dc02ce --- /dev/null +++ b/FranzKrekeler/README.md @@ -0,0 +1,13 @@ +The vinecopulaslab is a basic implementation of the paper [Statistical Arbitrage with Vine Copulas [Stübinger, Mangold, Krauss (2016)]](https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf) + +Its purpose is to find correlated stock partners for statistical arbitrage + +For installation currently you can only use git clone + +Afterwards you can access the tutorial ipynb + +The requirements are mainly numpy, pandas. +The work was done in effort for the application of the march apprenticeship of Hudson & Thames + + +![uml diagram](UML_data.png) \ No newline at end of file diff --git a/FranzKrekeler/UML_data.png b/FranzKrekeler/UML_data.png new file mode 100644 index 0000000..dc56f48 Binary files /dev/null and b/FranzKrekeler/UML_data.png differ diff --git a/FranzKrekeler/submission_and_tutorial.ipynb b/FranzKrekeler/submission_and_tutorial.ipynb new file mode 100644 index 0000000..f6152db --- /dev/null +++ b/FranzKrekeler/submission_and_tutorial.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Statistical Arbitrage with Vine Copulas (March application)\n", + "Structure:\n", + "- Abstract\n", + "- Introduction\n", + "- Body\n", + "- Conclusion\n", + "- Learnings\n", + "\n", + "## Abstract\n", + "The paper [Statistical Arbitrage with Vine Copulas [Stübinger, Mangold, Krauss (2016)]](https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf) presents the use of vine copulas for statistical arbitrage. First the three most suitable partner stocks for a target stock get selected. Second a distribution function is fitted to the top 20 pairs. Third, the best copula is chosen by fitting them to the transformed returns. Fourth conditional distributions are derived and later transformed to daily mispricings. \n", + "\n", + "## Introduction\n", + "Pairs trading with statistical arbitrage is a popular research topic for quants since the beginning of algorithmic trading. \n", + "This notebook focuses on the partner selection process of section 3.1 of the mentioned paper.\n", + "The idea is to present a functional module for partner selection:\n", + "First the module for the partner selection will be imported. The framework is then used to build partners from historic SP500 data. Performance is evaluated.\n", + "Basic functionality is presented with examples.\n", + "The Python module *vinecopulaslab* provides both a stock ticker and a partner selection framework.\n", + "\n", + "## Body\n", + "\n", + "\n", + "### The framework\n", + "*vinecopulaslab* can simply be imported with:\n", + "\n", + "*import vinecopulaslab as vl*\n", + "\n", + "Or you can import the submodules directly. The framework is presented alongside code. Let's fetch the historic SP500 closing data. It usually takes around one minute. Luckily the request is getting cached.\n", + "\n", + "### The UniverseDownloader class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " A AAL AAP AAPL ABBV ABC \\\n", + "Date \n", + "2017-01-03 46.490002 46.299999 170.600006 29.037500 62.410000 82.610001 \n", + "2017-01-04 47.099998 46.700001 172.000000 29.004999 63.290001 84.660004 \n", + "\n", + " ABMD ABT ACN ADBE ... XLNX \\\n", + "Date ... \n", + "2017-01-03 112.360001 39.049999 116.459999 103.480003 ... 59.070000 \n", + "2017-01-04 115.739998 39.360001 116.739998 104.139999 ... 58.639999 \n", + "\n", + " XOM XRAY XRX XYL YUM ZBH \\\n", + "Date \n", + "2017-01-03 90.889999 58.619999 27.559999 49.650002 63.209999 103.330002 \n", + "2017-01-04 89.889999 59.099998 28.600000 50.389999 63.439999 104.279999 \n", + "\n", + " ZBRA ZION ZTS \n", + "Date \n", + "2017-01-03 86.250000 43.180000 53.590000 \n", + "2017-01-04 87.029999 43.799999 54.110001 \n", + "\n", + "[2 rows x 505 columns]" + ], + "text/html": "
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2017-01-0346.49000246.299999170.60000629.03750062.41000082.610001112.36000139.049999116.459999103.480003...59.07000090.88999958.61999927.55999949.65000263.209999103.33000286.25000043.18000053.590000
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" + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from vinecopulaslab.universe import UniverseDownloader\n", + "from vinecopulaslab.partnerselection import TraditionalSelection, ExtendedSelection, GeometricSelection, ExtremalSelection\n", + "\n", + "import seaborn as sns\n", + "sns.set()\n", + "\n", + "sp500_prices = UniverseDownloader(cache=True).historic_sp500_prices(start='2017-01-01',\n", + " end='2018-01-01')\n", + "sp500_prices.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Approaches from the paper\n", + "The paper compares four different selection approaches. Each selection approach will be implemented in a class\n", + "#### The TraditionalSelection class\n", + "\n", + "The TraditionalSelection is based on the subsection of the paper 3.1.1.. It is based on calculating the Spearmann correlation for the daily stock returns. Then the top 50 correlated stocks are filtered for a target stock to reduce the search space. Finally, it calculates the sum of the Spearman correlation for all possible quadruples of a target stock and picks the highest score.\n", + "\n", + "\n", + "Finding all quadruples for all the stocks in the SP500:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 3min 37s, sys: 2min 29s, total: 6min 7s\nWall time: 6min 6s\n" + ] + } + ], + "source": [ + "%%time\n", + "partners = TraditionalSelection().find_partners(sp500_prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "A [A, WAT, PKI, TMO]\n", + "AAL [AAL, LUV, DAL, UAL]\n", + "AAP [AAP, GPC, AZO, ORLY]\n", + "AAPL [AAPL, AMZN, GOOGL, GOOG]\n", + "ABBV [ABBV, PKI, A, TMO]\n", + "ABC [ABC, HSIC, MCK, CAH]\n", + "ABMD [ABMD, A, TMO, PKI]\n", + "ABT [ABT, PKI, A, TMO]\n", + "ACN [ACN, V, MA, MSFT]\n", + "ADBE [ADBE, MA, V, MSFT]\n", + "dtype: object" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "partners.head(10)" + ] + }, + { + "source": [ + "### Features\n", + "- Vectorization\n", + "- Selecting a subset of target stocks" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 1.41 s, sys: 350 ms, total: 1.76 s\nWall time: 1.73 s\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "MSFT [MSFT, AMZN, GOOGL, GOOG]\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "%%time\n", + "TraditionalSelection().find_partners(sp500_prices, [\"MSFT\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_partners = partners[\"ADBE\"]\n", + "sp500_prices[sample_partners].plot(figsize=(16,9));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The ExtendedSelection class\n", + "\n", + "The ExtendedSelection is extending the Spearman correlation by using the mean of three estimators. It is based on Schmid, F., Schmidt, R., 2007. Multivariate extensions of Spearman’s rho and related statistics. Statistics & Probability Letters 77 (4), 407–416." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 17min 27s, sys: 16min 33s, total: 34min 1s\n", + "Wall time: 34min 27s\n" + ] + } + ], + "source": [ + "%%time\n", + "partners_e = ExtendedSelection().find_partners(sp500_prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "A [A, WAT, PKI, TMO]\n", + "AAL [AAL, LUV, DAL, UAL]\n", + "AAP [AAP, GPC, AZO, ORLY]\n", + "AAPL [AAPL, AMZN, GOOGL, GOOG]\n", + "ABBV [ABBV, PKI, A, TMO]\n", + "ABC [ABC, HSIC, MCK, CAH]\n", + "ABMD [ABMD, A, TMO, PKI]\n", + "ABT [ABT, PKI, A, TMO]\n", + "ACN [ACN, V, MA, MSFT]\n", + "ADBE [ADBE, MA, V, MSFT]\n", + "dtype: object" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "partners_e.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 1.62 s, sys: 461 ms, total: 2.08 s\nWall time: 2.08 s\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "MSFT [MSFT, AMZN, GOOGL, GOOG]\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "%%time\n", + "ExtendedSelection().find_partners(sp500_prices, [\"MSFT\"])" + ] + }, + { + "source": [ + "#### The GeometricSelection class\n", + "\n", + "The GeometricSelection is transforming the prices to ranked returns and uses their percentile to create 4d space and measure the distance to hyperdimensional diagonal. A small example chart will follow" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 30s, sys: 39.4 s, total: 2min 10s\n", + "Wall time: 2min 11s\n" + ] + } + ], + "source": [ + "%%time\n", + "partners_g = GeometricSelection().find_partners(sp500_prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 1.25 s, sys: 164 ms, total: 1.42 s\nWall time: 1.41 s\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "MSFT [MSFT, FB, GOOGL, GOOG]\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "%%time\n", + "GeometricSelection().find_partners(sp500_prices, [\"MSFT\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import seaborn as sns\n", + "sns.set()\n", + "fig = plt.figure(figsize=(12, 9))\n", + "ax = Axes3D(fig)\n", + "sample = sp500_prices[sample_partners[:3]].pct_change(fill_method='ffill').dropna(how='all')\n", + "values = sample.rank(pct=True).values\n", + "y = np.linspace(0,1)\n", + "x = np.linspace(0,1)\n", + "z = np.linspace(0,1)\n", + "ax.scatter(values[:,0],values[:,0],values[:,0], label=\"Diagonal\",c=\"red\") \n", + "ax.scatter(values[:,0],values[:,1],values[:,2], label=\" \".join(sample_partners[:3]),c=\"blue\") \n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_partners = partners_g.tolist()[9]\n", + "sp500_prices[sample_partners].pct_change(fill_method='ffill').dropna(how='all').cumsum(axis=0).plot();\n", + "sp500_prices[sample_partners].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from vinecopulaslab.partnerselection import ExtremalSelection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extremal Selection (status: not complete)\n", + "Currently, the extremal selection approach is not fully implemented. It was hacked together in an effort make it vectorized. It is purely presented to research performance." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "EX = ExtremalSelection()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1h 26min 20s, sys: 30min 32s, total: 1h 56min 53s\n", + "Wall time: 1h 4min 27s\n" + ] + } + ], + "source": [ + "%%time\n", + "partners_e = EX.find_partners(sp500_prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:root:Extremal approach is still under construction\n", + "CPU times: user 12.5 s, sys: 5.25 s, total: 17.7 s\n", + "Wall time: 11.2 s\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "MSFT [MSFT, FB, GOOGL, GOOG]\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "%%time\n", + "EX.find_partners(sp500_prices, [\"MSFT\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:root:Extremal approach is still under construction\n", + "WARNING:root:Extremal approach is still under construction\n", + "WARNING:root:Extremal approach is still under construction\n", + "CPU times: user 34.6 s, sys: 15.1 s, total: 49.7 s\n", + "Wall time: 30.6 s\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TARGET_STOCK\n", + "BAC [BAC, FITB, HBAN, JPM]\n", + "MSFT [MSFT, FB, GOOGL, GOOG]\n", + "TMO [TMO, DHR, PKI, A]\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "%%time\n", + "EX.find_partners(sp500_prices, [\"MSFT\", \"BAC\", \"TMO\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance\n", + "For performance purposes it was tried as much as possible to use vectorized calculations.\n", + "For example the traditional approach took 30 seconds with only for loops for one target stock.\n", + "Later it got down to 2 seconds using the least for loops as possible.\n", + "This brings amazing result." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "The paper compares 4 different approaches to partner selections. The implementation for this project was mainly done in pure numpy and pandas. The main goal is to build a framework for partner selection. This is not fully completed yet. In the implementation it was mainly optimized for vectorized operation so no for-loops. For future research speed could further be improved with numba." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Learnings\n", + "- Speed vs readability\n", + "- Implementing a simple prototype and then making it more complex\n", + "- Learning about copulas and their use in finance\n", + "- Start with a clean implementation instead of hacking things together.\n", + "\n", + "Thanks for your attention,\n", + "Franz Krekeler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/FranzKrekeler/tests/unit/__init__.py b/FranzKrekeler/tests/unit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/FranzKrekeler/tests/unit/datafetchertest.py b/FranzKrekeler/tests/unit/datafetchertest.py new file mode 100644 index 0000000..d07a34b --- /dev/null +++ b/FranzKrekeler/tests/unit/datafetchertest.py @@ -0,0 +1,6 @@ +from unittest import TestCase + + +class TestDownload(TestCase): + def test_download_sample(self): + pass diff --git a/FranzKrekeler/tests/unit/geometrictest.py b/FranzKrekeler/tests/unit/geometrictest.py new file mode 100644 index 0000000..d808349 --- /dev/null +++ b/FranzKrekeler/tests/unit/geometrictest.py @@ -0,0 +1,18 @@ +import sys +sys.path.append("./../../") +import unittest +from unittest import TestCase +import numpy as np +from vinecopulaslab.partnerselection.geometric import GeometricSelection + +class TestDownload(TestCase): + def test_distance_to_diagonal(self): + # test template + # TODO: add tests + line = np.array([1, 1, 1]) + pts = np.array([[0, 0, 0]]) + self.assertEqual(GeometricSelection.distance_to_line(line, pts), 0, "Should be 0") + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/FranzKrekeler/tmp/sp500_2017-01-01_2018-01-01.p b/FranzKrekeler/tmp/sp500_2017-01-01_2018-01-01.p new file mode 100644 index 0000000..3b4b862 Binary files /dev/null and b/FranzKrekeler/tmp/sp500_2017-01-01_2018-01-01.p differ diff --git a/FranzKrekeler/vinecopulaslab/__init__.py b/FranzKrekeler/vinecopulaslab/__init__.py new file mode 100644 index 0000000..9f46c35 --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/__init__.py @@ -0,0 +1,3 @@ +from vinecopulaslab.partnerselection import TraditionalSelection, ExtendedSelection, GeometricSelection, \ + ExtremalSelection +from vinecopulaslab.universe.universe import UniverseDownloader diff --git a/FranzKrekeler/vinecopulaslab/partnerselection/__init__.py b/FranzKrekeler/vinecopulaslab/partnerselection/__init__.py new file mode 100644 index 0000000..ad34ceb --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/partnerselection/__init__.py @@ -0,0 +1,8 @@ +""" +Classes from the partner selection module +""" + +from vinecopulaslab.partnerselection import TraditionalSelection, ExtendedSelection, GeometricSelection, \ + ExtremalSelection + +# TODO: Add PartnerSelection wrapper class diff --git a/FranzKrekeler/vinecopulaslab/partnerselection/base.py b/FranzKrekeler/vinecopulaslab/partnerselection/base.py new file mode 100644 index 0000000..3b12e8f --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/partnerselection/base.py @@ -0,0 +1,82 @@ +# Author: Franz Krekeler 2021 +from typing import List +import itertools +import numpy as np +import pandas as pd + + +class SelectionBase(object): + """The base class for the partner selection framework. + """ + + def __init__(self): + """Initialization + """ + self.corr_returns_top_n = None + + @staticmethod + def calculate_returns(prices: pd.DataFrame) -> pd.DataFrame: + """Calculate percentage based daily returns + :param: prices (pd.DataFrame): The columns must the closing prices for the stocks + :return: returns (pd.DataFrame) + """ + return prices.pct_change(fill_method='ffill').dropna(how='all') + + @staticmethod + def _ranked_correlation(returns: pd.DataFrame) -> pd.DataFrame: + """Given a df of returns calculated it's Spearman correlation matrix + :param: returns (pd.DataFrame): The input needs to be in percentage based returns + :return: returns_correlation (pd.DataFrame) + """ + return returns.corr("spearman") + + @staticmethod + def _rankings_pct(returns: pd.DataFrame): + """Calculate the rank of a given dataframe and then convert it to percentage based + :param: returns (pd.DataFrame) + :return: returns_ranked_percentile (pd.DataFrame) + """ + return returns.rank(pct=True) + + @staticmethod + def _top_n_correlations(corr_returns: pd.DataFrame, top_n: int = 50) -> pd.DataFrame: + """For correlation matrix return the top n correlations (default 50) + :param: corr_returns (pd.DataFrame): correlation matrix + :return: corr_returns_top_n pd.DataFrame shape is (n,n) + """ + # Filter self correlated and self correlated stocks + corr_returns_unstacked = corr_returns[corr_returns < 1].unstack().sort_values(ascending=False) + corr_returns_unstacked = corr_returns_unstacked.reset_index().dropna() + corr_returns_unstacked.columns = "TARGET_STOCK", "STOCK_PAIR", "CORRELATION" + # Note pandas was chosen here, but many ways lead to rome + corr_returns_top_n = corr_returns_unstacked.groupby("TARGET_STOCK").head(top_n) + return corr_returns_top_n.sort_values(['TARGET_STOCK', 'CORRELATION']) + + @staticmethod + def _prepare_combinations_of_partners(stock_selection: List[str]) -> pd.DataFrame: + """Helper function to calculate all combinations for a target stock and it's potential partners + :param: stock_selection (pd.DataFrame): the target stock has to be the first element of the array + :return: the possible combinations for the quadruples.Shape (19600,4) or + if the target stock is left out (19600,3) + """ + # We will convert the stock names into integers and then get a list of all combinations with a length of 3 + num_of_stocks = len(stock_selection) + # We turn our partner stocks into numerical indices so we can use them directly for indexing + partner_stocks_idx = np.arange(1, num_of_stocks) # basically exclude the target stock + partner_stocks_idx_combs = itertools.combinations(partner_stocks_idx, 3) + return np.array(list((0,) + comb for comb in partner_stocks_idx_combs)) + + def _find_partners(self, target_stocks: List[str] = []): + """ + Helper functions where we apply the approach to each stock. Optional a subset of target stocks can be chosen. + :param: return_target_stock (List[str]): the subset of target stocks to analyze (default []) + :return: (pd.DataFrame) + """ + assert self.corr_returns_top_n is not None + corr_returns_top_n = self.corr_returns_top_n.copy() + if len(target_stocks): + sublist = corr_returns_top_n.TARGET_STOCK.isin(target_stocks) + corr_returns_top_n = corr_returns_top_n[sublist] + target_stocks_partners_quadruples = corr_returns_top_n.groupby('TARGET_STOCK').apply( + self._partner_selection_approach) + return target_stocks_partners_quadruples diff --git a/FranzKrekeler/vinecopulaslab/partnerselection/extended.py b/FranzKrekeler/vinecopulaslab/partnerselection/extended.py new file mode 100644 index 0000000..c057c06 --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/partnerselection/extended.py @@ -0,0 +1,82 @@ +from typing import List +import numpy as np +import pandas as pd +import scipy.special +from statsmodels.distributions.empirical_distribution import ECDF +from vinecopulaslab.partnerselection.base import SelectionBase + + +class ExtendedSelection(SelectionBase): + """ + This class implements the extended approach for partner selection. Mentioned section 3.1 + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + It is an extension to the spearman correlation + """ + + def __init__(self): + """Initialization + """ + super().__init__() + self.corr_returns_top_n = None + + def _partner_selection_approach(self, group) -> List[str]: + """ + Find the partners stocks for the groupby group of the data df.groupby("TARGET_STOCK").apply(...) + :param: group (pd.group) The group of n most correlated stocks + :return: (List[str]) returns a list of highest correlated quadruple + """ + target_stock = group.name + partner_stocks = group.STOCK_PAIR.tolist() + stock_selection = [target_stock] + partner_stocks + # We create a subset of our ecdf dataframe to increase lookup speed. + data_subset = self.ecdf_df[stock_selection].copy() + # We turn our partner stocks into numerical indices so we can use them directly for indexing + quadruples_combinations = self._prepare_combinations_of_partners(stock_selection) + # We can now use our list of possible quadruples as an index + quadruples_combinations_data = data_subset.values[:, quadruples_combinations] + # Now we can get closer to a vectorized calculation + # n is equal to the total number of returns d to the number of stocks + # we use lodash because we don't need the 19600 dimension + n, _, d = quadruples_combinations_data.shape + # We split up the given formula + # For reference: + # https://github.com/hudson-and-thames/march_applications_21/blob/main/Guide%20for%20the%20Extended%20Approach.pdf + hd = (d + 1) / (2 ** d - d - 1) + ecdf_df_product_1 = np.product(1-quadruples_combinations_data, axis=-1) + ecdf_df_product_2 = np.product(quadruples_combinations_data, axis=-1) + est1 = hd * (-1 + (2 ** d / n) * ecdf_df_product_1.sum(axis=0)) + est2 = hd * (-1 + (2 ** d / n) * ecdf_df_product_2.sum(axis=0)) + # here we create the index as we will use it on specific dimensions + idx = np.array([(k, l) for l in range(0, d) for k in range(0, l)]) + est3 = -3 + (12 / (n * scipy.special.comb(n, 2, exact=True))) * ( + (1 - quadruples_combinations_data[:, :, idx[:, 0]]) * ( + 1 - quadruples_combinations_data[:, :, idx[:, 1]])).sum(axis=(1, 2)) + quadruples_scores = (est1 + est2 + est3) / 3 + # The quadruple scores have the shape of (19600,1) now + max_index = np.argmax(quadruples_scores) + return data_subset.columns[list(quadruples_combinations[max_index])].tolist() + + def _preprocess(self, close: pd.DataFrame) -> pd.DataFrame: + """ + Helper function for preparing the data. Here we already prepare the ecdf + :param close: (pd.DataFrame) the closing prices + """ + close.sort_index(axis=1, inplace=True) + self.close_returns = self.calculate_returns(close) + self.ranked_correlation = self._ranked_correlation(self.close_returns) + self.corr_returns_top_n = self._top_n_correlations(self.ranked_correlation) + self.ecdf_df = self.close_returns.apply(lambda x: ECDF(x)(x), axis=0) + + def find_partners(self, close: pd.DataFrame, target_stocks: List[str] = []): + """ + Find partners based on an extension of the Spearmann correlation. Mentioned in section 3.1 + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + :param: close (pd.DataFrame) The close prices of the SP500 + :param: target_stocks (List[str]) A list of target stocks to analyze + :return: (List[str]) returns a list of highest correlated quadruple + """ + self._preprocess(close) + # find_partners could be moved to the base class but then it wouldn't have the right docstring... looking for best practice + return self._find_partners(target_stocks) diff --git a/FranzKrekeler/vinecopulaslab/partnerselection/extremal.py b/FranzKrekeler/vinecopulaslab/partnerselection/extremal.py new file mode 100644 index 0000000..0ddce5a --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/partnerselection/extremal.py @@ -0,0 +1,99 @@ +from typing import List +import itertools +import logging +import pandas as pd +import numpy as np +import scipy.special +import scipy.linalg +from vinecopulaslab.partnerselection.base import SelectionBase + + +class ExtremalSelection(SelectionBase): + """ + Class for partner selection based on "A multivariate linear rank test of independence + based on a multiparametric copula with cubic sections" + Mangold 2015 + """ + + def __init__(self): + """Initialization + """ + super().__init__() + self.corr_returns_top_n = None + + def _partner_selection_approach(self, group): + """ + Approach function Partner selection based on "A multivariate linear rank test of independence based on + a multiparametric copula with cubic sections" + for df.groupby("TARGET_STOCK").apply(...) + This has only been implemented for performance testing. + References: + https://www.researchgate.net/publication/309408947_A_multivariate_linear_rank_test_of_independence_based_on_a_multiparametric_copula_with_cubic_sections + https://pypi.org/project/Independence-test/ + :param group: (group) The group of 50 most correlated stocks + :return: (List[str]) returns a list of highest correlated quadruple + """ + logging.warning("Extremal approach is still under construction") + target_stock = group.name + partner_stocks = group.STOCK_PAIR.tolist() + stock_selection = [target_stock] + partner_stocks + # We create a subset of our ranked returns dataframe to increase lookup speed. + data_subset = self.ranked_returns[stock_selection].copy() + # We turn our partner stocks into numerical indices so we can use them directly for indexing + quadruples_combinations = self._prepare_combinations_of_partners(stock_selection) + # We can now use our list of possible quadruples as an index + quadruples_combinations_data = data_subset.values[:, quadruples_combinations] + # Now we can get closer to a vectorized calculation + # n is equal to the total number of returns d to the number of stocks + # we use lodash because we don't need the 19600 dimension + n, _, d = quadruples_combinations_data.shape + # Here the math from the Mangold 2015 paper begins + permut_mat = np.array(list(itertools.product([-1, 1], repeat=d)), dtype=np.int8) + sub_mat = permut_mat @ permut_mat.T + F = (d + sub_mat) / 2 + D = (d - sub_mat) / 2 + cov_mat = ((2 / 15) ** F) * ((1 / 30) ** D) + cov_mat_inv = scipy.linalg.inv(cov_mat) + rank_df_norm = quadruples_combinations_data / (n + 1) + pos_rank_df = (rank_df_norm - 1) * (3 * rank_df_norm - 1) + neg_rank_df = rank_df_norm * (2 - 3 * rank_df_norm) + # performance here is still lagging + # Proposition 3.3. from the paper + pos_neg_combined = np.add(np.einsum('ijk,lmk->jmik', pos_rank_df, np.expand_dims(permut_mat > 0, axis=0)), + np.einsum('ijk,lmk->jmik', neg_rank_df, np.expand_dims(permut_mat < 0, axis=0))) + TNP = pos_neg_combined.prod(axis=-1).mean(-1) + # Incomplete: Still not documented + # performance here is also not optimal here + T = ((np.expand_dims(TNP, axis=1) @ np.expand_dims(cov_mat_inv, axis=0)) @ TNP.T) + T_results = np.diag(T[:, 0, :]) * n + max_index = np.argmax(T_results) + partners = data_subset.columns[list(quadruples_combinations[max_index])].tolist() + # Please take this with a grain of salt, I was too obsessed with a proof of concept. + return partners + + def _preprocess(self, close: pd.DataFrame) -> pd.DataFrame: + """ + Helper function for preparing the data. + :param close: (pd.DataFrame) the closing prices + """ + close.sort_index(axis=1, inplace=True) + self.close_returns = self.calculate_returns(close) + self.ranked_correlation = self._ranked_correlation(self.close_returns) + self.corr_returns_top_n = self._top_n_correlations(self.ranked_correlation) + self.ranked_returns = self.close_returns.rank() + + def find_partners(self, close: pd.DataFrame, target_stocks: List[str] = []): + """ + Find partners based on the extremal approach mentioned in section 3.1 + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + Based on the paper Class for partner selection based on "A multivariate linear rank test of independence + based on a multiparametric copula with cubic sections" Mangold 2015 + :param: close (pd.DataFrame) The close prices of the SP500 + :param: target_stocks (List[str]) A list of target stocks to analyze + :return: (List[str]) returns a list of highest correlated quadruple + """ + self._preprocess(close) + # find_partners could be moved to the base class but then it wouldn't have the right docstring... + # looking for best practice + return self._find_partners(target_stocks) diff --git a/FranzKrekeler/vinecopulaslab/partnerselection/geometric.py b/FranzKrekeler/vinecopulaslab/partnerselection/geometric.py new file mode 100644 index 0000000..d3e7900 --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/partnerselection/geometric.py @@ -0,0 +1,87 @@ +from typing import List +import numpy as np +import pandas as pd +from vinecopulaslab.partnerselection.base import SelectionBase + + +class GeometricSelection(SelectionBase): + """ + This class implements the geometric approach for partner selection. Mentioned section 3.1 + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + """ + + def __init__(self): + """Initialization + """ + super().__init__() + self.corr_returns_top_n = None + + def _partner_selection_approach(self, group): + """ + Find the partners stocks for the groupby group of the data df.groupby("TARGET_STOCK").apply(...) + :param: group (pd.group) The group of n most correlated stocks + :return: (List[str]) returns a list of highest correlated quadruple + """ + target_stock = group.name + partner_stocks = group.STOCK_PAIR.tolist() + stock_selection = [target_stock] + partner_stocks + # We create a subset of our rank transformed dataframe to increase lookup speed. + data_subset = self.ranked_returns_pct[stock_selection].copy() + combinations_quadruples = self._prepare_combinations_of_partners(stock_selection) + # We can now use our list of possible quadruples as an index + quadruples_combinations_data = data_subset.values[:, combinations_quadruples] + # n is equal to the total number of returns d to the number of stocks + # we use lodash because we don't need the 19600 dimension + n, _, d = quadruples_combinations_data.shape + # Now we will create a diagonal for our distance calculation. + # Please refer to the paper + line = np.ones(d) + print(quadruples_combinations_data.shape) + # Einsum is great for specifying which dimension to multiply together + # this extends the distance method for all 19600 combinations + pp = (np.einsum("ijk,k->ji", quadruples_combinations_data, line) / np.linalg.norm(line)) + pn = np.sqrt(np.einsum('ijk,ijk->ji', quadruples_combinations_data, quadruples_combinations_data)) + distance_scores = np.sqrt(pn ** 2 - pp ** 2).sum(axis=1) + min_index = np.argmin(distance_scores) + partners = data_subset.columns[list(combinations_quadruples[min_index])].tolist() + return partners + + @staticmethod + def distance_to_line(line, pts): + """ + original helper function + reference: https://stackoverflow.com/a/50398213 + :param line: the line endpoint assuming it starts at point zero. For example np.array([1,1,1]) for a 3d line + :param pts: the points to measure the distance to the line + :return: float np.array with distances + """ + dp = np.dot(pts, line) + pp = dp / np.linalg.norm(line) + pn = np.linalg.norm(pts, axis=1) + return np.sqrt(pn ** 2 - pp ** 2) + + def _preprocess(self, close: pd.DataFrame) -> pd.DataFrame: + """ + Helper function for preparing the data. + :param close: (pd.DataFrame) the closing prices + """ + close.sort_index(axis=1, inplace=True) + self.close_returns = self.calculate_returns(close) + self.ranked_correlation = self._ranked_correlation(self.close_returns) + self.corr_returns_top_n = self._top_n_correlations(self.ranked_correlation) + self.ranked_returns_pct = self._rankings_pct(self.close_returns) + + def find_partners(self, close: pd.DataFrame, target_stocks: List[str] = []): + """ + Find partners based on the geometric mentioned in section 3.1 + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + :param: close (pd.DataFrame) The close prices of the SP500 + :param: target_stocks (List[str]) A list of target stocks to analyze + :return: (List[str]) returns a list of highest correlated quadruple + """ + self._preprocess(close) + # find_partners could be moved to the base class but then it wouldn't have the right docstring... + # looking for best practice + return self._find_partners(target_stocks) diff --git a/FranzKrekeler/vinecopulaslab/partnerselection/traditional.py b/FranzKrekeler/vinecopulaslab/partnerselection/traditional.py new file mode 100644 index 0000000..cc7f042 --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/partnerselection/traditional.py @@ -0,0 +1,71 @@ +from typing import List +import pandas as pd +import numpy as np +from vinecopulaslab.partnerselection.base import SelectionBase + + +class TraditionalSelection(SelectionBase): + """ + This class implements the traditional approach for partner selection. Mentioned section 3.1 + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + """ + + def __init__(self): + """Initialization + """ + self.corr_returns_top_n = None + + def _preprocess(self, close: pd.DataFrame) -> pd.DataFrame: + """ + Helper function for preparing the data. + :param close: (pd.DataFrame) the closing prices + """ + close.sort_index(axis=1, inplace=True) + self.close_returns = self.calculate_returns(close) + self.ranked_correlation = self._ranked_correlation(self.close_returns) + self.corr_returns_top_n = self._top_n_correlations(self.ranked_correlation) + + def _partner_selection_approach(self, group: pd.DataFrame): + """ + Find the partners stocks for the groupby group of the data df.groupby("TARGET_STOCK").apply(...) + :param: group (pd.group) The group of n most correlated stocks + :return: (List[str]) returns a list of highest correlated quadruple + """ + target_stock = group.name + potential_partners = group.STOCK_PAIR.tolist() + stock_selection = [target_stock] + potential_partners + # we convert our stocks symbols into indices and then return the combinations of the indices + # For example A,AAPL,... -> 0,1,... + # these are the quadruples we are going to use for our calculation + all_possible_combinations = self._prepare_combinations_of_partners(stock_selection) + df_subset = self.ranked_correlation.loc[stock_selection, stock_selection].copy() + # Here the magic happens: + # We use the combinations as an index + corr_matrix_a = df_subset.values[:, all_possible_combinations] + # corr_matrix_a has now the shape of (51, 19600, 4) + # We now use take along axis to get the shape (4,19600,4), then we can sum the first and the last dimension + corr_sums = np.sum(np.take_along_axis(corr_matrix_a, all_possible_combinations.T[..., np.newaxis], axis=0), + axis=(0, 2)) + # this returns the shape of + # (19600,1) + # Afterwards we return the maximum index for the sums + max_index = np.argmax( + corr_sums) + # Finally convert the index to the list of stocks and return the column names + return [target_stock] + df_subset.columns[list(all_possible_combinations[max_index])].tolist() + + def find_partners(self, close: pd.DataFrame, target_stocks: List[str] = []): + """ + Find partners based on the traditional approach mentioned in section 3.1. + Returns quadruples of highest scoring sum of correlated stock (spearman) method + of the paper "Statistical arbitrage with vine copulas" + https://www.econstor.eu/bitstream/10419/147450/1/870932616.pdf + :param: close (pd.DataFrame) The close prices of the SP500 + :param: target_stocks (List[str]) A list of target stocks to analyze + :return: (List[str]) returns a list of highest correlated quadruple + """ + self._preprocess(close) + # find_partners could be moved to the base class but then it wouldn't have the right docstring... + # looking for best practice + return self._find_partners(target_stocks) diff --git a/FranzKrekeler/vinecopulaslab/requirements.txt b/FranzKrekeler/vinecopulaslab/requirements.txt new file mode 100644 index 0000000..257e4c5 --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/requirements.txt @@ -0,0 +1,6 @@ +numpy +requests +pandas +seaborn +yfinance +scipy \ No newline at end of file diff --git a/FranzKrekeler/vinecopulaslab/universe/__init__.py b/FranzKrekeler/vinecopulaslab/universe/__init__.py new file mode 100644 index 0000000..14e985b --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/universe/__init__.py @@ -0,0 +1 @@ +from vinecopulaslab.universe.universe import UniverseDownloader diff --git a/FranzKrekeler/vinecopulaslab/universe/universe.py b/FranzKrekeler/vinecopulaslab/universe/universe.py new file mode 100644 index 0000000..650b99a --- /dev/null +++ b/FranzKrekeler/vinecopulaslab/universe/universe.py @@ -0,0 +1,69 @@ +""" +Fetch historical stock data from Yahoo Finance +""" +import os +from __future__ import print_function +from typing import List +import requests +import pandas as pd +import yfinance as yf + + + +class UniverseDownloader: + """ + The class UniverseDownloader fetches a part of the SP500 symbols and + returns the historic closing prices. + """ + + def __init__(self, cache=False, cachepath="./tmp"): + """ + :param cache: (bool) Cache downloaded data as pickle (False by default) + :param cache_path: (str) Path where to cache data (./tmp by default) + """ + self.cache = cache + self.cachepath = cachepath + + @staticmethod + def fetch_sp500symbols() -> List[str]: + """ + Fetches constituents symbols of the SP500 from GitHub repository and returns it as a list. + inspired from previous application: + reference: https://github.com/hudson-and-thames/oct_applications/blob/fe5a48188f5d022d5cbdc4f3c5bf475b8894513e/locnguyen/download_data.py#L15 + :return: (List[str]) returns a list of SP500 symbols + """ + url = "https://raw.githubusercontent.com/datasets/s-and-p-500-companies/master/data/constituents_symbols.txt" + r = requests.get(url) + symbols = r.text.split("\n")[:-1] + return symbols + + def _fetch_historic_sp500_data(self, start="2015-01-01", end="2020-01-01") -> pd.DataFrame: + """ + :param start: (str) start date ('2015-01-01' by default) + :param end: (str) end date ('2020-01-01' default) + :return: (DataFrame) returns dataframe of SP500 historic data (including open,close) + """ + sp500symbols = self.fetch_sp500symbols() + sp500prices = yf.download(" ".join(sp500symbols), start=start, end=end) + return sp500prices + + def historic_sp500_prices(self, start="2015-01-01", end="2020-01-01") -> pd.DataFrame: + """ + :param start: (str) start date ('2015-01-01' by default) + :param end: (str) end date ('2020-01-01' default) + :return: (List[str]) returns a list of SP500 symbols + """ + if self.cache: + cache_pickle_path = f"{self.cachepath}/sp500_{start}_{end}.p" + try: + return pd.read_pickle(cache_pickle_path) + except FileNotFoundError: + print("File could not be loaded from cache") + sp500_prices = self._fetch_historic_sp500_data( + start=start, end=end) + sp500_closeprices = sp500_prices['Close'].dropna(how='all') + if self.cache: + if not os.path.exists(self.cachepath): + os.makedirs(self.cachepath) + sp500_closeprices.to_pickle(cache_pickle_path) + return sp500_closeprices diff --git a/FranzKrekeler/writeup.pdf b/FranzKrekeler/writeup.pdf new file mode 100644 index 0000000..e4edd85 Binary files /dev/null and b/FranzKrekeler/writeup.pdf differ