├── .qrignore ├── README.md ├── first_last ├── Introduction.ipynb ├── first_last.py ├── Part1-SPY-Data-Collection.ipynb ├── Part3-VIX-Data-Collection.ipynb └── Part4-VIX-Research.ipynb └── LICENSE.txt /.qrignore: -------------------------------------------------------------------------------- 1 | README.md 2 | LICENSE.txt 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # first-last 2 | 3 | Intraday momentum strategy that buys (sells) the S&P 500 when the first half hour return and penultimate half hour return are both positive (negative). Uses VIX filter to restrict strategy to high volatility regimes. Uses 1-minute SPY data from QuantRocket and 30-minute VIX data from Interactive Brokers. Runs in Moonshot. 4 | 5 | ## Clone in QuantRocket 6 | 7 | CLI: 8 | 9 | ```shell 10 | quantrocket codeload clone 'first-last' 11 | ``` 12 | 13 | Python: 14 | 15 | ```python 16 | from quantrocket.codeload import clone 17 | clone("first-last") 18 | ``` 19 | 20 | ## Browse in GitHub 21 | 22 | Start here: [first_last/Introduction.ipynb](first_last/Introduction.ipynb) 23 | 24 | *** 25 | 26 | Find more code in QuantRocket's [Codeload Library](https://www.quantrocket.com/code/) 27 | -------------------------------------------------------------------------------- /first_last/Introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\"QuantRocket
\n", 8 | "Disclaimer" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "# First Half Hour Predicts Last Half Hour\n", 16 | "\n", 17 | "This tutorial demonstrates an intraday momentum strategy that buys (sells) the S&P 500 when the first half hour return and penultimate half hour return are both positive (negative). Positions are held from 3:30-4:00 PM. A filter is applied to disable the strategy whenever the VIX is below 20, reflecting that the strategy works best during high volatility regimes. The strategy uses 1-min data for SPY from QuantRocket's US Stock dataset and uses 30-minute data from Interactive Brokers for VIX. \n", 18 | "\n", 19 | "\n", 20 | "**Source paper**: Gao, Lei and Han, Yufeng and Li, Sophia Zhengzi and Zhou, Guofu, Intraday Momentum: The First Half-Hour Return Predicts the Last Half-Hour Return (June 28, 2015). Available at SSRN: https://ssrn.com/abstract=2552752 " 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "* Part 1: [SPY Data Collection](Part1-SPY-Data-Collection.ipynb)\n", 28 | "* Part 2: [Moonshot Backtest](Part2-Moonshot-Backtest.ipynb)\n", 29 | "* Part 3: [VIX Data Collection](Part3-VIX-Data-Collection.ipynb)\n", 30 | "* Part 4: [VIX Research](Part4-VIX-Research.ipynb)\n", 31 | "* Part 5: [Moonshot Backtest With VIX Filter](Part5-Moonshot-Backtest-With-VIX-Filter.ipynb)" 32 | ] 33 | } 34 | ], 35 | "metadata": { 36 | "kernelspec": { 37 | "display_name": "Python 3.9", 38 | "language": "python", 39 | "name": "python3" 40 | }, 41 | "language_info": { 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "file_extension": ".py", 47 | "mimetype": "text/x-python", 48 | "name": "python", 49 | "nbconvert_exporter": "python", 50 | "pygments_lexer": "ipython3", 51 | "version": "3.9.7" 52 | } 53 | }, 54 | "nbformat": 4, 55 | "nbformat_minor": 4 56 | } 57 | -------------------------------------------------------------------------------- /first_last/first_last.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 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 moonshot import Moonshot 17 | from moonshot.commission import PerShareCommission 18 | from quantrocket import get_prices 19 | 20 | class USStockCommission(PerShareCommission): 21 | BROKER_COMMISSION_PER_SHARE = 0.005 22 | 23 | class FirstHalfHourPredictsLastHalfHour(Moonshot): 24 | """ 25 | Intraday strategy that buys (sells) if the market is up (down) during the first 26 | and penultimate half-hour. 27 | """ 28 | 29 | CODE = 'first-last' 30 | DB = 'usstock-1min' 31 | DB_TIMES = ['10:00:00', '15:00:00', '15:30:00', '15:59:00'] 32 | DB_FIELDS = ['Open','Close'] 33 | SIDS = ["FIBBG000BDTBL9"] 34 | COMMISSION_CLASS = USStockCommission 35 | SLIPPAGE_BPS = 0.5 36 | MIN_VIX = None 37 | BENCHMARK = "FIBBG000BDTBL9" 38 | BENCHMARK_TIME = "15:59:00" 39 | 40 | def prices_to_signals(self, prices: pd.DataFrame): 41 | 42 | closes = prices.loc["Close"] 43 | opens = prices.loc["Open"] 44 | 45 | # Calculate first half-hour returns (including overnight return) 46 | prior_closes = closes.xs('15:59:00', level="Time").shift() 47 | ten_oclock_prices = opens.xs('10:00:00', level="Time") 48 | first_half_hour_returns = (ten_oclock_prices - prior_closes) / prior_closes 49 | 50 | # Calculate penultimate half-hour returns 51 | fifteen_oclock_prices = opens.xs('15:00:00', level="Time") 52 | fifteen_thirty_prices = opens.xs('15:30:00', level="Time") 53 | penultimate_half_hour_returns = (fifteen_thirty_prices - fifteen_oclock_prices) / fifteen_oclock_prices 54 | 55 | # long when both are positive, short when both are negative 56 | long_signals = (first_half_hour_returns > 0) & (penultimate_half_hour_returns > 0) 57 | short_signals = (first_half_hour_returns < 0) & (penultimate_half_hour_returns < 0) 58 | 59 | # Combine long and short signals 60 | signals = long_signals.astype(int).where(long_signals, -short_signals.astype(int)) 61 | 62 | # filter by VIX 63 | if self.MIN_VIX: 64 | # Query VIX at 15:30 NY time (= close of 14:00:00 bar because VIX is Chicago time) 65 | vix = get_prices("vix-30min", 66 | fields="Close", 67 | start_date=signals.index.min(), 68 | end_date=signals.index.max(), 69 | times="14:00:00") 70 | # extract VIX and squeeze single-column DataFrame to Series 71 | vix = vix.loc["Close"].xs("14:00:00", level="Time").squeeze() 72 | # reshape VIX like signals 73 | vix = signals.apply(lambda x: vix) 74 | signals = signals.where(vix >= self.MIN_VIX, 0) 75 | 76 | return signals 77 | 78 | def signals_to_target_weights(self, signals: pd.DataFrame, prices: pd.DataFrame): 79 | 80 | # only one instrument, so allocate all capital 81 | target_weights = signals.copy() 82 | return target_weights 83 | 84 | def target_weights_to_positions(self, target_weights: pd.DataFrame, prices: pd.DataFrame): 85 | 86 | # We enter on the same day as the signals/target_weights 87 | positions = target_weights.copy() 88 | return positions 89 | 90 | def positions_to_gross_returns(self, positions: pd.DataFrame, prices: pd.DataFrame): 91 | 92 | opens = prices.loc["Open"] 93 | closes = prices.loc["Close"] 94 | 95 | # Our signal came at 15:30 and we enter at 15:30 96 | entry_prices = opens.xs("15:30:00", level="Time") 97 | session_closes = closes.xs("15:59:00", level="Time") 98 | 99 | pct_changes = (session_closes - entry_prices) / entry_prices 100 | gross_returns = pct_changes * positions 101 | return gross_returns 102 | -------------------------------------------------------------------------------- /first_last/Part1-SPY-Data-Collection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\"QuantRocket
\n", 8 | "Disclaimer" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "***\n", 16 | "[First Half Hour Predicts Last Half Hour](Introduction.ipynb) › Part 1: Data Collection\n", 17 | "***" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "# SPY Data Collection\n", 25 | "\n", 26 | "We will backtest the trading strategy using the SPY ETF. The data collection process consists of collecting US Stocks listings, then collecting 1 minute bars for SPY." 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "## Collect SPY listing\n", 34 | "\n", 35 | "First, collect US Stock listings:" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 1, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "text/plain": [ 46 | "{'status': 'success', 'msg': 'successfully loaded US stock listings'}" 47 | ] 48 | }, 49 | "execution_count": 1, 50 | "metadata": {}, 51 | "output_type": "execute_result" 52 | } 53 | ], 54 | "source": [ 55 | "from quantrocket.master import collect_usstock_listings\n", 56 | "collect_usstock_listings()" 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "metadata": {}, 62 | "source": [ 63 | "## Lookup SPY\n", 64 | "\n", 65 | "Next, we look up the Sid (security ID) for SPY. We use the command line interface because less typing is required.\n", 66 | "\n", 67 | "> Prefixing a line with ! allows running terminal commands from inside a notebook." 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 2, 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "name": "stdout", 77 | "output_type": "stream", 78 | "text": [ 79 | "---\n", 80 | " - \n", 81 | " Sid: \"FIBBG000BDTBL9\"\n", 82 | " Symbol: \"SPY\"\n", 83 | " Exchange: \"ARCX\"\n", 84 | "\n" 85 | ] 86 | } 87 | ], 88 | "source": [ 89 | "!quantrocket master get --symbols 'SPY' --sec-types 'ETF' --fields 'Sid' 'Symbol' 'Exchange' --json | json2yaml" 90 | ] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": {}, 95 | "source": [ 96 | "## Collect historical data\n", 97 | "\n", 98 | "Next, we create a Zipline bundle for collecting 1-min bars for SPY:" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 3, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "data": { 108 | "text/plain": [ 109 | "{'status': 'success', 'msg': 'successfully created usstock-1min bundle'}" 110 | ] 111 | }, 112 | "execution_count": 3, 113 | "metadata": {}, 114 | "output_type": "execute_result" 115 | } 116 | ], 117 | "source": [ 118 | "from quantrocket.zipline import create_usstock_bundle\n", 119 | "create_usstock_bundle(\"usstock-1min\")" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "Then collect the data for SPY:" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 4, 132 | "metadata": {}, 133 | "outputs": [ 134 | { 135 | "data": { 136 | "text/plain": [ 137 | "{'status': 'the data will be ingested asynchronously'}" 138 | ] 139 | }, 140 | "execution_count": 4, 141 | "metadata": {}, 142 | "output_type": "execute_result" 143 | } 144 | ], 145 | "source": [ 146 | "from quantrocket.zipline import ingest_bundle\n", 147 | "ingest_bundle(\"usstock-1min\", sids=\"FIBBG000BDTBL9\")" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "Monitor flightlog for completion:\n", 155 | "\n", 156 | "```\n", 157 | "quantrocket.zipline: INFO [usstock-1min] Collecting minute bars for 1 securities in usstock-1min bundle\n", 158 | "quantrocket.zipline: INFO [usstock-1min] Collecting daily bars for usstock-1min bundle\n", 159 | "quantrocket.zipline: INFO [usstock-1min] Collecting adjustments for usstock-1min bundle\n", 160 | "quantrocket.zipline: INFO [usstock-1min] Collecting assets for usstock-1min bundle\n", 161 | "quantrocket.zipline: INFO [usstock-1min] Completed collecting data for 1 securities in usstock-1min bundle\n", 162 | "```" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "***\n", 170 | "\n", 171 | "## *Next Up*\n", 172 | "\n", 173 | "Part 2: [Moonshot Backtest](Part2-Moonshot-Backtest.ipynb)" 174 | ] 175 | } 176 | ], 177 | "metadata": { 178 | "kernelspec": { 179 | "display_name": "Python 3.9", 180 | "language": "python", 181 | "name": "python3" 182 | }, 183 | "language_info": { 184 | "codemirror_mode": { 185 | "name": "ipython", 186 | "version": 3 187 | }, 188 | "file_extension": ".py", 189 | "mimetype": "text/x-python", 190 | "name": "python", 191 | "nbconvert_exporter": "python", 192 | "pygments_lexer": "ipython3", 193 | "version": "3.9.7" 194 | } 195 | }, 196 | "nbformat": 4, 197 | "nbformat_minor": 4 198 | } 199 | -------------------------------------------------------------------------------- /first_last/Part3-VIX-Data-Collection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\"QuantRocket
\n", 8 | "Disclaimer" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "***\n", 16 | "[First Half Hour Predicts Last Half Hour](Introduction.ipynb) › Part 3: VIX Data Collection\n", 17 | "***" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "# VIX Data Collection\n", 25 | "\n", 26 | "The backtest revealed that this strategy was mainly profitable during the 2008 financial crisis and again in 2020 during the COVID-19 pandemic. In line with this observation, the authors of the source paper note that the predictive power of the first half hour is greater during periods of high volatility.\n", 27 | "\n", 28 | "We will use intraday VIX data to investigate this observation further. This data comes from Interactive Brokers." 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## Collect CBOE index listings\n", 36 | "\n", 37 | "First, start IB Gateway:" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 1, 43 | "metadata": {}, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/plain": [ 48 | "{'ibg1': {'status': 'running'}}" 49 | ] 50 | }, 51 | "execution_count": 1, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | } 55 | ], 56 | "source": [ 57 | "from quantrocket.ibg import start_gateways\n", 58 | "start_gateways(wait=True)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "Then, collect all index listings from CBOE:" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 2, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "data": { 75 | "text/plain": [ 76 | "{'status': 'the IBKR listing details will be collected asynchronously'}" 77 | ] 78 | }, 79 | "execution_count": 2, 80 | "metadata": {}, 81 | "output_type": "execute_result" 82 | } 83 | ], 84 | "source": [ 85 | "from quantrocket.master import collect_ibkr_listings\n", 86 | "collect_ibkr_listings(exchanges=\"CBOE\", sec_types=\"IND\")" 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "metadata": {}, 92 | "source": [ 93 | "Monitor flightlog for a completion message:\n", 94 | "\n", 95 | "```\n", 96 | "quantrocket.master: INFO Collecting CBOE IND listings from IBKR website\n", 97 | "quantrocket.master: INFO Requesting details for 206 CBOE listings found on IBKR website\n", 98 | "quantrocket.master: INFO Saved 206 CBOE listings to securities master database\n", 99 | "```" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "metadata": {}, 105 | "source": [ 106 | "## Lookup VIX\n", 107 | "\n", 108 | "Next, we look up the Sid for VIX." 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 3, 114 | "metadata": {}, 115 | "outputs": [ 116 | { 117 | "name": "stdout", 118 | "output_type": "stream", 119 | "text": [ 120 | "---\n", 121 | " - \n", 122 | " Sid: \"IB13455763\"\n", 123 | " Symbol: \"VIX\"\n", 124 | "\n" 125 | ] 126 | } 127 | ], 128 | "source": [ 129 | "!quantrocket master get --symbols 'VIX' --exchanges 'CBOE' --sec-types 'IND' --fields 'Sid' 'Symbol' --json | json2yaml" 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "## Collect historical data\n", 137 | "\n", 138 | "Next, we create a database for collecting 30-min bars for VIX:" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 4, 144 | "metadata": {}, 145 | "outputs": [ 146 | { 147 | "data": { 148 | "text/plain": [ 149 | "{'status': 'successfully created quantrocket.v2.history.vix-30min.sqlite'}" 150 | ] 151 | }, 152 | "execution_count": 4, 153 | "metadata": {}, 154 | "output_type": "execute_result" 155 | } 156 | ], 157 | "source": [ 158 | "from quantrocket.history import create_ibkr_db\n", 159 | "create_ibkr_db(\"vix-30min\", sids=\"IB13455763\", bar_size=\"30 mins\", shard=\"off\")" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "Then collect the data:" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 5, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "data": { 176 | "text/plain": [ 177 | "{'status': 'the historical data will be collected asynchronously'}" 178 | ] 179 | }, 180 | "execution_count": 5, 181 | "metadata": {}, 182 | "output_type": "execute_result" 183 | } 184 | ], 185 | "source": [ 186 | "from quantrocket.history import collect_history\n", 187 | "collect_history(\"vix-30min\")" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "Monitor flightlog for completion:\n", 195 | "\n", 196 | "```\n", 197 | "quantrocket.history: INFO [vix-30min] Collecting history from IBKR for 1 securities in vix-30min\n", 198 | "quantrocket.history: INFO [vix-30min] Saved 53788 total records for 1 total securities to quantrocket.v2.history.vix-30min.sqlite\n", 199 | "```" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "***\n", 207 | "\n", 208 | "## *Next Up*\n", 209 | "\n", 210 | "Part 4: [VIX Research](Part4-VIX-Research.ipynb)" 211 | ] 212 | } 213 | ], 214 | "metadata": { 215 | "kernelspec": { 216 | "display_name": "Python 3.9", 217 | "language": "python", 218 | "name": "python3" 219 | }, 220 | "language_info": { 221 | "codemirror_mode": { 222 | "name": "ipython", 223 | "version": 3 224 | }, 225 | "file_extension": ".py", 226 | "mimetype": "text/x-python", 227 | "name": "python", 228 | "nbconvert_exporter": "python", 229 | "pygments_lexer": "ipython3", 230 | "version": "3.9.7" 231 | } 232 | }, 233 | "nbformat": 4, 234 | "nbformat_minor": 4 235 | } 236 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Accepting Warranty or Additional Liability. 64 | 65 | While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. 66 | 67 | END OF TERMS AND CONDITIONS 68 | -------------------------------------------------------------------------------- /first_last/Part4-VIX-Research.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\"QuantRocket
\n", 8 | "Disclaimer" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "***\n", 16 | "[First Half Hour Predicts Last Half Hour](Introduction.ipynb) › Part 4: VIX Research\n", 17 | "***" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "# VIX Research\n", 25 | "\n", 26 | "To quantify the effect of volatility on our trading strategy, we will subdivide the backtest returns based on whether the VIX was above or below 20 at the time of the trading signal.\n", 27 | "\n", 28 | "To begin, load the backtest results from earlier and extract the returns:" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "from quantrocket.moonshot import read_moonshot_csv\n", 38 | "\n", 39 | "results = read_moonshot_csv(\"first_last.csv\")\n", 40 | "returns = results.loc[\"Return\"]\n", 41 | "\n", 42 | "# Squeeze single-column DataFrame to Series\n", 43 | "returns = returns.squeeze()" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "Next, we query the VIX values as of 3:30 PM each day NY time. Since the VIX is provided by CBOE which is located in Chicago, we need the close of the 14:00:00 bar: " 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 2, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "from quantrocket import get_prices\n", 60 | "vix = get_prices(\"vix-30min\", \n", 61 | " fields=\"Close\", \n", 62 | " start_date=returns.index.min(), \n", 63 | " end_date=returns.index.max(), \n", 64 | " times=\"14:00:00\")\n", 65 | "\n", 66 | "# extract VIX and squeeze single-column DataFrame to Series\n", 67 | "vix = vix.loc[\"Close\"].xs(\"14:00:00\", level=\"Time\").squeeze()" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "Next, we subdivide the returns based on the VIX:" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 3, 80 | "metadata": {}, 81 | "outputs": [], 82 | "source": [ 83 | "returns_hivol = returns.where(vix >= 20, 0)\n", 84 | "returns_lowvol = returns.where(vix < 20, 0)\n", 85 | "\n", 86 | "import pandas as pd\n", 87 | "returns = pd.concat({\"VIX > 20\": returns_hivol, \"VIX < 20\": returns_lowvol}, axis=1)" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "Then we plot the cumulative returns to compare:" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 4, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "data": { 104 | "text/plain": [ 105 | "" 106 | ] 107 | }, 108 | "execution_count": 4, 109 | "metadata": {}, 110 | "output_type": "execute_result" 111 | }, 112 | { 113 | "data": { 114 | "image/png": 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\n", 115 | "text/plain": [ 116 | "
" 117 | ] 118 | }, 119 | "metadata": {}, 120 | "output_type": "display_data" 121 | } 122 | ], 123 | "source": [ 124 | "from moonchart import DailyPerformance\n", 125 | "perf = DailyPerformance(returns)\n", 126 | "perf.cum_returns.plot(title=\"Cumulative Returns by VIX level\")" 127 | ] 128 | }, 129 | { 130 | "cell_type": "markdown", 131 | "metadata": {}, 132 | "source": [ 133 | "Limiting to times when the VIX was above 20, the strategy has continued to move higher over time. When the VIX is under 20, the strategy loses money. " 134 | ] 135 | }, 136 | { 137 | "cell_type": "markdown", 138 | "metadata": {}, 139 | "source": [ 140 | "***\n", 141 | "\n", 142 | "## *Next Up*\n", 143 | "\n", 144 | "Part 5: [Moonshot Backtest With VIX Filter](Part5-Moonshot-Backtest-With-VIX-Filter.ipynb)" 145 | ] 146 | } 147 | ], 148 | "metadata": { 149 | "kernelspec": { 150 | "display_name": "Python 3.9", 151 | "language": "python", 152 | "name": "python3" 153 | }, 154 | "language_info": { 155 | "codemirror_mode": { 156 | "name": "ipython", 157 | "version": 3 158 | }, 159 | "file_extension": ".py", 160 | "mimetype": "text/x-python", 161 | "name": "python", 162 | "nbconvert_exporter": "python", 163 | "pygments_lexer": "ipython3", 164 | "version": "3.9.7" 165 | } 166 | }, 167 | "nbformat": 4, 168 | "nbformat_minor": 4 169 | } 170 | --------------------------------------------------------------------------------