├── .qrignore
├── README.md
├── pairs_pipeline
├── Introduction.ipynb
├── Part1-Data-Collection.ipynb
└── pairs.py
└── LICENSE.txt
/.qrignore:
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1 | README.md
2 | LICENSE.txt
3 |
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/README.md:
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1 | # pairs-pipeline
2 |
3 | Pairs trading strategy that includes a research pipeline for identifying and selecting pairs. Tests all possible pairs in a universe for cointegration using the Johansen test, then runs in-sample backtests on all cointegrating pairs, then runs an out-of-sample backtest on the 5 best performing pairs. Calculates daily hedge ratios using the Johansen test and times entries and exits using Bollinger Bands. Trading rules adapted from Ernie Chan's book *Algorithmic Trading*. Runs in Moonshot on the universe of US ETFs.
4 |
5 | ## Clone in QuantRocket
6 |
7 | CLI:
8 |
9 | ```shell
10 | quantrocket codeload clone 'pairs-pipeline'
11 | ```
12 |
13 | Python:
14 |
15 | ```python
16 | from quantrocket.codeload import clone
17 | clone("pairs-pipeline")
18 | ```
19 |
20 | ## Browse in GitHub
21 |
22 | Start here: [pairs_pipeline/Introduction.ipynb](pairs_pipeline/Introduction.ipynb)
23 |
24 | ***
25 |
26 | Find more code in QuantRocket's [Codeload Library](https://www.quantrocket.com/code/)
27 |
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/pairs_pipeline/Introduction.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "![\"QuantRocket](\"https://www.quantrocket.com/assets/img/notebook-header-logo.png\")
\n",
8 | "Disclaimer"
9 | ]
10 | },
11 | {
12 | "cell_type": "markdown",
13 | "metadata": {},
14 | "source": [
15 | "# Pairs Trading Strategy with Pairs Selection Pipeline\n",
16 | "\n",
17 | "This tutorial demonstrates a research pipeline for identifying and backtesting ETF or stock pairs using Moonshot. From a starting universe of stocks or ETFs, all pair combinations are tested for cointegration using the Johansen test. In-sample backtests are run on all cointegrating pairs. A portfolio of the 5 best performing pairs is then selected and backtested out-of-sample. \n",
18 | "\n",
19 | "The pairs trading strategy re-calculates daily hedge ratios using the Johansen test and times entries and exits using Bollinger Bands, as outlined in Ernie Chan's book *Algorithmic Trading: Winning Strategies and Their Rationale*, Wiley, 2013, chapter 4. \n",
20 | "\n",
21 | "The use of a pairs selection pipeline is aimed at mitigating the tendency of cointegrating pairs to eventually cease cointegrating. The tutorial runs on the universe of US ETFs but is designed to be easily adaptable to other universes of stocks or ETFs."
22 | ]
23 | },
24 | {
25 | "cell_type": "markdown",
26 | "metadata": {},
27 | "source": [
28 | "* Part 1: [Data Collection](Part1-Data-Collection.ipynb)\n",
29 | "* Part 2: [Moonshot Pairs Trading Strategy](Part2-Moonshot-Pairs-Strategy.ipynb)\n",
30 | "* Part 3: [Pairs Selection Pipeline](Part3-Pairs-Selection-Pipeline.ipynb)"
31 | ]
32 | }
33 | ],
34 | "metadata": {
35 | "kernelspec": {
36 | "display_name": "Python 3.11",
37 | "language": "python",
38 | "name": "python3"
39 | },
40 | "language_info": {
41 | "codemirror_mode": {
42 | "name": "ipython",
43 | "version": 3
44 | },
45 | "file_extension": ".py",
46 | "mimetype": "text/x-python",
47 | "name": "python",
48 | "nbconvert_exporter": "python",
49 | "pygments_lexer": "ipython3",
50 | "version": "3.11.0"
51 | }
52 | },
53 | "nbformat": 4,
54 | "nbformat_minor": 4
55 | }
56 |
--------------------------------------------------------------------------------
/pairs_pipeline/Part1-Data-Collection.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "
\n",
8 | "Disclaimer"
9 | ]
10 | },
11 | {
12 | "cell_type": "markdown",
13 | "metadata": {},
14 | "source": [
15 | "***\n",
16 | "[Pairs Trading](Introduction.ipynb) › Part 1: Data Collection\n",
17 | "***"
18 | ]
19 | },
20 | {
21 | "cell_type": "markdown",
22 | "metadata": {},
23 | "source": [
24 | "# Data Collection\n",
25 | "\n",
26 | "The data collection process consists of collecting 1-day bars for US stocks and creating a universe of ETFs."
27 | ]
28 | },
29 | {
30 | "cell_type": "markdown",
31 | "metadata": {},
32 | "source": [
33 | "## Collect historical data\n",
34 | "\n",
35 | "First, we create a database for collecting 1-day bars."
36 | ]
37 | },
38 | {
39 | "cell_type": "code",
40 | "execution_count": 1,
41 | "metadata": {},
42 | "outputs": [
43 | {
44 | "data": {
45 | "text/plain": [
46 | "{'status': 'successfully created quantrocket.v2.history.usstock-1d.sqlite'}"
47 | ]
48 | },
49 | "execution_count": 1,
50 | "metadata": {},
51 | "output_type": "execute_result"
52 | }
53 | ],
54 | "source": [
55 | "from quantrocket.history import create_usstock_db\n",
56 | "create_usstock_db(\"usstock-1d\", bar_size=\"1 day\")"
57 | ]
58 | },
59 | {
60 | "cell_type": "markdown",
61 | "metadata": {},
62 | "source": [
63 | "Then collect the data:"
64 | ]
65 | },
66 | {
67 | "cell_type": "code",
68 | "execution_count": 2,
69 | "metadata": {},
70 | "outputs": [
71 | {
72 | "data": {
73 | "text/plain": [
74 | "{'status': 'the historical data will be collected asynchronously'}"
75 | ]
76 | },
77 | "execution_count": 2,
78 | "metadata": {},
79 | "output_type": "execute_result"
80 | }
81 | ],
82 | "source": [
83 | "from quantrocket.history import collect_history\n",
84 | "collect_history(\"usstock-1d\")"
85 | ]
86 | },
87 | {
88 | "cell_type": "markdown",
89 | "metadata": {},
90 | "source": [
91 | "Monitor flightlog for completion:\n",
92 | "\n",
93 | "```\n",
94 | "quantrocket.history: INFO [usstock-1d] Collecting US history from 2007 to present\n",
95 | "quantrocket.history: INFO [usstock-1d] Collecting updated US securities listings\n",
96 | "quantrocket.history: INFO [usstock-1d] Collecting additional US history from 2020-04 to present\n",
97 | "quantrocket.history: INFO [usstock-1d] Collected 160 monthly files in quantrocket.v2.history.usstock-1d.sqlite\n",
98 | "```"
99 | ]
100 | },
101 | {
102 | "cell_type": "markdown",
103 | "metadata": {},
104 | "source": [
105 | "## Define universe of US ETFs \n",
106 | "\n",
107 | "Next we download a CSV of US ETFs:"
108 | ]
109 | },
110 | {
111 | "cell_type": "code",
112 | "execution_count": 3,
113 | "metadata": {},
114 | "outputs": [],
115 | "source": [
116 | "from quantrocket.master import download_master_file\n",
117 | "download_master_file(\"us_etfs.csv\", vendors=\"usstock\", sec_types=\"ETF\")"
118 | ]
119 | },
120 | {
121 | "cell_type": "markdown",
122 | "metadata": {},
123 | "source": [
124 | "Then upload the CSV to create the \"us-etf\" universe:"
125 | ]
126 | },
127 | {
128 | "cell_type": "code",
129 | "execution_count": 4,
130 | "metadata": {},
131 | "outputs": [
132 | {
133 | "data": {
134 | "text/plain": [
135 | "{'code': 'us-etf',\n",
136 | " 'provided': 5855,\n",
137 | " 'inserted': 5855,\n",
138 | " 'total_after_insert': 5855}"
139 | ]
140 | },
141 | "execution_count": 4,
142 | "metadata": {},
143 | "output_type": "execute_result"
144 | }
145 | ],
146 | "source": [
147 | "from quantrocket.master import create_universe\n",
148 | "create_universe(\"us-etf\", infilepath_or_buffer=\"us_etfs.csv\")"
149 | ]
150 | },
151 | {
152 | "cell_type": "markdown",
153 | "metadata": {},
154 | "source": [
155 | "***\n",
156 | "\n",
157 | "## *Next Up*\n",
158 | "\n",
159 | "Part 2: [Moonshot Pairs Strategy](Part2-Moonshot-Pairs-Strategy.ipynb)"
160 | ]
161 | }
162 | ],
163 | "metadata": {
164 | "kernelspec": {
165 | "display_name": "Python 3.11",
166 | "language": "python",
167 | "name": "python3"
168 | },
169 | "language_info": {
170 | "codemirror_mode": {
171 | "name": "ipython",
172 | "version": 3
173 | },
174 | "file_extension": ".py",
175 | "mimetype": "text/x-python",
176 | "name": "python",
177 | "nbconvert_exporter": "python",
178 | "pygments_lexer": "ipython3",
179 | "version": "3.11.0"
180 | }
181 | },
182 | "nbformat": 4,
183 | "nbformat_minor": 4
184 | }
185 |
--------------------------------------------------------------------------------
/pairs_pipeline/pairs.py:
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1 | # Copyright 2019-2024 QuantRocket LLC - All Rights Reserved
2 | #
3 | # Licensed under the Apache License, Version 2.0 (the "License");
4 | # you may not use this file except in compliance with the License.
5 | # You may obtain a copy of the License at
6 | #
7 | # http://www.apache.org/licenses/LICENSE-2.0
8 | #
9 | # Unless required by applicable law or agreed to in writing, software
10 | # distributed under the License is distributed on an "AS IS" BASIS,
11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 | # See the License for the specific language governing permissions and
13 | # limitations under the License.
14 |
15 | import pandas as pd
16 | from statsmodels.tsa.vector_ar.vecm import coint_johansen
17 | from moonshot import Moonshot
18 | from moonshot.commission import PerShareCommission
19 |
20 | class USStockCommission(PerShareCommission):
21 | BROKER_COMMISSION_PER_SHARE = 0.005
22 |
23 | class PairsStrategy(Moonshot):
24 | """
25 | Pairs trading strategy that uses the Johansen test to re-calculate
26 | hedge ratios daily and uses Bollinger Bands to time entries and exits.
27 | Buys (sells) the spread when it crosses below (above) its lower (upper)
28 | Bollinger Band and exits when it crosses its moving average.
29 |
30 | To use the strategy, subclass this base class and define the appropriate
31 | DB and SIDS.
32 | """
33 |
34 | CODE = "pairs"
35 | DB = None
36 | DB_FIELDS = ["Close", "Open"]
37 | SIDS = []
38 | LOOKBACK_WINDOW = 20 # Calculate hedge ratios and Bollinger Bands using this lookback
39 | BBAND_STD = 1 # Set Bollinger Bands this many standard deviations away from mean
40 | COMMISSION_CLASS = USStockCommission
41 |
42 | def get_hedge_ratio(self, pair_prices: pd.DataFrame):
43 | """
44 | Helper function that uses the Johansen test to calculate hedge ratio. This is applied
45 | to the pair prices on a rolling basis in prices_to_signals.
46 | """
47 | pair_prices = pair_prices.dropna()
48 |
49 | # Skip if we don't have at least 75% of the expected observations
50 | if len(pair_prices) < self.LOOKBACK_WINDOW * 0.75:
51 | return pd.Series(0, index=pair_prices.columns)
52 |
53 | # The second and third parameters indicate constant term, with a lag of 1.
54 | # See Chan, Algorithmic Trading, chapter 2.
55 | result = coint_johansen(pair_prices, 0, 1)
56 |
57 | # The first column of eigenvectors contains the best weights
58 | weights = list(result.evec[:, 0])
59 |
60 | return pd.Series(weights, index=pair_prices.columns)
61 |
62 | def prices_to_signals(self, prices: pd.DataFrame):
63 | """
64 | Generates a DataFrame of signals indicating whether to long or short the
65 | spread.
66 | """
67 | closes = prices.loc["Close"]
68 |
69 | # Calculate hedge ratios on a rolling basis. Unfortunately, pandas
70 | # rolling apply() won't work here, so we have to loop through each day
71 | all_hedge_ratios = []
72 | for idx in range(len(closes)):
73 | start_idx = idx - self.LOOKBACK_WINDOW
74 | some_closes = closes.iloc[start_idx:idx]
75 | hedge_ratio = self.get_hedge_ratio(some_closes)
76 | hedge_ratio = pd.Series(hedge_ratio).to_frame().T
77 | all_hedge_ratios.append(hedge_ratio)
78 |
79 | hedge_ratios = pd.concat(all_hedge_ratios)
80 | hedge_ratios.index = closes.index
81 |
82 | # Compute spread and Bollinger Bands (spreads and everything derived
83 | # from it is a Series, which we later broadcast back to a DataFrame)
84 | spreads = (closes * hedge_ratios).sum(axis=1)
85 | means = spreads.ffill().rolling(self.LOOKBACK_WINDOW).mean()
86 | stds = spreads.ffill().rolling(self.LOOKBACK_WINDOW).std()
87 | upper_bands = means + self.BBAND_STD * stds
88 | lower_bands = means - self.BBAND_STD * stds
89 |
90 | # Long (short) the spread when it crosses below (above) the lower (upper)
91 | # band, then exit when it crosses the mean
92 | long_entries = spreads < lower_bands
93 | long_exits = spreads >= means
94 | short_entries = spreads > upper_bands
95 | short_exits = spreads <= means
96 |
97 | # Combine entries and exits
98 | ones = pd.Series(1, index=spreads.index)
99 | zeros = pd.Series(0, index=spreads.index)
100 | minus_ones = pd.Series(-1, index=spreads.index)
101 | long_signals = ones.where(long_entries).fillna(zeros.where(long_exits)).ffill()
102 | short_signals = minus_ones.where(short_entries).fillna(zeros.where(short_exits)).ffill()
103 | signals = long_signals + short_signals
104 |
105 | # Broadcast Series of signals to DataFrame
106 | signals = closes.apply(lambda x: signals)
107 |
108 | # Save hedge_ratios for signals_to_target_weights
109 | self.hedge_ratios = hedge_ratios
110 |
111 | return signals
112 |
113 | def signals_to_target_weights(self, signals: pd.DataFrame, prices: pd.DataFrame):
114 | """
115 | Converts the DataFrame of integer signals, indicating whether to long
116 | or short the spread, into the corresponding weight of each instrument
117 | to hold.
118 | """
119 | # hedge_ratios represents ratios of shares, multiply by price to get ratios
120 | # of weights
121 | hedge_ratio_weights = self.hedge_ratios * prices.loc["Close"]
122 |
123 | # Multiply weight ratios by signals to get target weights, then reduce to
124 | # 1X total allocation
125 | weights = signals * hedge_ratio_weights
126 | total_weights= weights.abs().sum(axis=1)
127 | weights = weights.div(total_weights, axis=0)
128 | return weights
129 |
130 | def target_weights_to_positions(self, weights: pd.DataFrame, prices: pd.DataFrame):
131 | # we'll enter in the period after the signal
132 | positions = weights.shift()
133 | return positions
134 |
135 | def positions_to_gross_returns(self, positions: pd.DataFrame, prices: pd.DataFrame):
136 | # Enter and exit on the open
137 | opens = prices.loc["Open"]
138 | gross_returns = opens.pct_change() * positions.shift()
139 | return gross_returns
140 |
141 | class GDX_GLD_Pair(PairsStrategy):
142 |
143 | CODE = "pairs-gdx-gld"
144 | DB = "usstock-1d"
145 | SIDS = [
146 | "FIBBG000PLNQN7", # GDX
147 | "FIBBG000CRF6Q8", # GLD
148 | ]
149 |
--------------------------------------------------------------------------------
/LICENSE.txt:
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