├── .qrignore
├── README.md
├── qmom
    ├── Introduction.ipynb
    ├── qmom.py
    └── Part1-Interactive-Strategy-Development.ipynb
└── 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 | # qmom
 2 | 
 3 | Long-only momentum strategy modeled on Alpha Architect's QMOM ETF, selecting stocks with the smoothest momentum and rebalancing the portfolio before quarter end to capture a window-dressing seasonality effect.
 4 | 
 5 | ## Clone in QuantRocket
 6 | 
 7 | CLI:
 8 | 
 9 | ```shell
10 | quantrocket codeload clone 'qmom'
11 | ```
12 | 
13 | Python:
14 | 
15 | ```python
16 | from quantrocket.codeload import clone
17 | clone("qmom")
18 | ```
19 | 
20 | ## Browse in GitHub
21 | 
22 | Start here: [qmom/Introduction.ipynb](qmom/Introduction.ipynb)
23 | 
24 | ***
25 | 
26 | Find more code in QuantRocket's [Codeload Library](https://www.quantrocket.com/code/)
27 | 


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/qmom/Introduction.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "<a href=\"https://www.quantrocket.com\"><img alt=\"QuantRocket logo\" src=\"https://www.quantrocket.com/assets/img/notebook-header-logo.png\"></a><br>\n",
 8 |     "<a href=\"https://www.quantrocket.com/disclaimer/\">Disclaimer</a>"
 9 |    ]
10 |   },
11 |   {
12 |    "cell_type": "markdown",
13 |    "metadata": {},
14 |    "source": [
15 |     "# QMOM: Quantitative Momentum\n",
16 |     "\n",
17 |     "This repository implements a long-only momentum strategy modeled on [Alpha Architect's QMOM ETF](https://etfsite.alphaarchitect.com/qmom/). QMOM provides a couple of enhancements to traditional momentum strategies, including selecting stocks with the smoothest momentum and rebalancing the portfolio before quarter end to capture a window-dressing seasonality effect.\n",
18 |     "\n",
19 |     "The rules of the QMOM strategy as outlined in the Alpha Architect white paper are:\n",
20 |     "\n",
21 |     "1. Universe selection\n",
22 |     "    1. Starting universe: all NYSE stocks\n",
23 |     "    2. Exclude financials, ADRs, REITs\n",
24 |     "    3. Liquidity screen: select top N percent of stocks by market cap (N=60)\n",
25 |     "2. Apply momentum screen: calculate 12-month returns, excluding most recent month, and select N percent of stocks with best return (N=10)\n",
26 |     "3. Filter by smoothness of momentum: of the momentum stocks, select the N percent with the smoothest momentum, as measured by the number of positive days in the last 12 months (N=50)\n",
27 |     "4. Apply equal weights    \n",
28 |     "5. Rebalance portfolio before quarter-end to capture window-dressing seasonality effect "
29 |    ]
30 |   },
31 |   {
32 |    "cell_type": "markdown",
33 |    "metadata": {},
34 |    "source": [
35 |     "## Prerequisites\n",
36 |     "\n",
37 |     "QVAL and QMOM make up the value and momentum portions, respectively, of the Alpha Architect VMOT (Value/Momentum/Trend) strategy. QMOM can be run independently of QVAL or VMOT. However, because the QMOM universe is identical to the QVAL universe, this tutorial relies on the data collection and universe selection of the QVAL tutorial. Please see that tutorial for help collecting the data and creating the universes.\n",
38 |     "\n",
39 |     "See also:\n",
40 |     "\n",
41 |     "* QVAL: `quantrocket codeload clone 'qval'`\n",
42 |     "* VMOT: `quantrocket codeload clone 'vmot'`"
43 |    ]
44 |   },
45 |   {
46 |    "cell_type": "markdown",
47 |    "metadata": {},
48 |    "source": [
49 |     "## Contents\n",
50 |     "\n",
51 |     "* Part 1: [Interactive Strategy Development](Part1-Interactive-Strategy-Development.ipynb)\n",
52 |     "* Part 2: [Moonshot Backtest](Part2-Moonshot-Backtest.ipynb)\n",
53 |     "* Part 3: [Parameter Scans](Part3-Parameter-Scans.ipynb)"
54 |    ]
55 |   }
56 |  ],
57 |  "metadata": {
58 |   "kernelspec": {
59 |    "display_name": "Python 3.9",
60 |    "language": "python",
61 |    "name": "python3"
62 |   },
63 |   "language_info": {
64 |    "codemirror_mode": {
65 |     "name": "ipython",
66 |     "version": 3
67 |    },
68 |    "file_extension": ".py",
69 |    "mimetype": "text/x-python",
70 |    "name": "python",
71 |    "nbconvert_exporter": "python",
72 |    "pygments_lexer": "ipython3",
73 |    "version": "3.9.7"
74 |   }
75 |  },
76 |  "nbformat": 4,
77 |  "nbformat_minor": 4
78 | }
79 | 


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/qmom/qmom.py:
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  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 | 
 19 | class USStockCommission(PerShareCommission):
 20 |     BROKER_COMMISSION_PER_SHARE = 0.005
 21 | 
 22 | class QuantitativeMomentum(Moonshot):
 23 |     """
 24 |     Momentum strategy modeled on Alpha Architect's QMOM ETF.
 25 | 
 26 |     Strategy rules:
 27 | 
 28 |     1. Universe selection
 29 |       a. Starting universe: all NYSE stocks
 30 |       b. Exclude financials, ADRs, REITs
 31 |       c. Liquidity screen: select top N percent of stocks by dollar
 32 |          volume (N=60)
 33 |     2. Apply momentum screen: calculate 12-month returns, excluding
 34 |        most recent month, and select N percent of stocks with best
 35 |        return (N=10)
 36 |     3. Filter by smoothness of momentum: of the momentum stocks, select
 37 |        the N percent with the smoothest momentum, as measured by the number
 38 |        of positive days in the last 12 months (N=50)
 39 |     4. Apply equal weights
 40 |     5. Rebalance portfolio before quarter-end to capture window-dressing seasonality effect
 41 |     """
 42 | 
 43 |     CODE = "qmom"
 44 |     DB = "sharadar-us-stk-1d"
 45 |     DB_FIELDS = ["Close", "Volume"]
 46 |     DOLLAR_VOLUME_TOP_N_PCT = 60
 47 |     DOLLAR_VOLUME_WINDOW = 90
 48 |     UNIVERSES = "nyse-stk"
 49 |     EXCLUDE_UNIVERSES = ["nyse-financials", "nyse-adrs", "nyse-reits"]
 50 |     MOMENTUM_WINDOW = 252
 51 |     MOMENTUM_EXCLUDE_MOST_RECENT_WINDOW = 22
 52 |     TOP_N_PCT = 10
 53 |     SMOOTHEST_TOP_N_PCT = 50
 54 |     REBALANCE_INTERVAL = "Q-NOV" #  = end of quarter, fiscal year ends in Nov (= Nov 30, Feb 28, May 31, Aug 31); https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#anchored-offsets
 55 |     COMMISSION_CLASS = USStockCommission
 56 | 
 57 |     def prices_to_signals(self, prices: pd.DataFrame):
 58 | 
 59 |         # Step 1.c: get a mask of stocks with adequate dollar volume
 60 |         closes = prices.loc["Close"]
 61 |         volumes = prices.loc["Volume"]
 62 |         avg_dollar_volumes = (closes * volumes).rolling(self.DOLLAR_VOLUME_WINDOW).mean()
 63 |         dollar_volume_ranks = avg_dollar_volumes.rank(axis=1, ascending=False, pct=True)
 64 |         have_adequate_dollar_volumes = dollar_volume_ranks <= (self.DOLLAR_VOLUME_TOP_N_PCT/100)
 65 | 
 66 |         # Step 2: apply momentum screen
 67 |         year_ago_closes = closes.shift(self.MOMENTUM_WINDOW)
 68 |         month_ago_closes = closes.shift(self.MOMENTUM_EXCLUDE_MOST_RECENT_WINDOW)
 69 |         returns = (month_ago_closes - year_ago_closes) / year_ago_closes.where(year_ago_closes != 0) # avoid DivisionByZero errors
 70 |         # Rank only among stocks with adequate dollar volume
 71 |         returns_ranks = returns.where(have_adequate_dollar_volumes).rank(axis=1, ascending=False, pct=True)
 72 |         have_momentum = returns_ranks <= (self.TOP_N_PCT / 100)
 73 | 
 74 |         # Step 3: Filter by smoothness of momentum
 75 |         are_positive_days = closes.pct_change() > 0
 76 |         positive_days_last_twelve_months = are_positive_days.astype(int).rolling(self.MOMENTUM_WINDOW).sum()
 77 |         positive_days_last_twelve_months_ranks = positive_days_last_twelve_months.where(have_momentum).rank(axis=1, ascending=False, pct=True)
 78 |         have_smooth_momentum = positive_days_last_twelve_months_ranks <= (self.SMOOTHEST_TOP_N_PCT/100)
 79 | 
 80 |         signals = have_smooth_momentum.astype(int)
 81 |         return signals
 82 | 
 83 |     def signals_to_target_weights(self, signals: pd.DataFrame, prices: pd.DataFrame):
 84 |         # Step 4: equal weights
 85 |         daily_signal_counts = signals.abs().sum(axis=1)
 86 |         weights = signals.div(daily_signal_counts, axis=0).fillna(0)
 87 | 
 88 |         # Step 5: Rebalance portfolio before quarter-end to capture window-dressing seasonality effect
 89 |         # Resample daily to REBALANCE_INTERVAL, taking the last day's signal
 90 |         # For pandas offset aliases, see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
 91 |         weights = weights.resample(self.REBALANCE_INTERVAL).last()
 92 |         # Reindex back to daily and fill forward
 93 |         weights = weights.reindex(prices.loc["Close"].index, method="ffill")
 94 | 
 95 |         return weights
 96 | 
 97 |     def target_weights_to_positions(self, weights: pd.DataFrame, prices: pd.DataFrame):
 98 |         # Enter the position the day after the signal
 99 |         return weights.shift()
100 | 
101 |     def positions_to_gross_returns(self, positions: pd.DataFrame, prices: pd.DataFrame):
102 |         closes = prices.loc["Close"]
103 |         position_ends = positions.shift()
104 | 
105 |         # The return is the security's percent change over the period,
106 |         # multiplied by the position.
107 |         gross_returns = closes.pct_change() * position_ends
108 | 
109 |         return gross_returns
110 | 
111 |     def order_stubs_to_orders(self, orders: pd.DataFrame, prices: pd.DataFrame):
112 |         orders["Exchange"] = "SMART"
113 |         orders["OrderType"] = "MOC"
114 |         orders["Tif"] = "DAY"
115 |         return orders
116 | 


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/LICENSE.txt:
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 1 | Apache License
 2 | Version 2.0, January 2004
 3 | http://www.apache.org/licenses/
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68 | 


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/qmom/Part1-Interactive-Strategy-Development.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "<a href=\"https://www.quantrocket.com\"><img alt=\"QuantRocket logo\" src=\"https://www.quantrocket.com/assets/img/notebook-header-logo.png\"></a><br>\n",
  8 |     "<a href=\"https://www.quantrocket.com/disclaimer/\">Disclaimer</a>"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "markdown",
 13 |    "metadata": {},
 14 |    "source": [
 15 |     "***\n",
 16 |     "[QMOM: Quantitative Momentum](Introduction.ipynb) › Part 1: Interactive Strategy Development\n",
 17 |     "***"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "markdown",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "# Interactive Strategy Development\n",
 25 |     "\n",
 26 |     "We'll develop our strategy logic interactively in a notebook, peeking at the DataFrames as we go, then transfer the code to a `.py` file for backtesting with Moonshot.\n",
 27 |     "\n",
 28 |     "As a reminder, the rules of the QMOM strategy as outlined in the Alpha Architect white paper are:\n",
 29 |     "\n",
 30 |     "1. Universe selection\n",
 31 |     "    1. Starting universe: all NYSE stocks\n",
 32 |     "    2. Exclude financials, ADRs, REITs\n",
 33 |     "    3. Liquidity screen: select top N percent of stocks by market cap (N=60)\n",
 34 |     "2. Apply momentum screen: calculate 12-month returns, excluding most recent month, and select N percent of stocks with best return (N=10)\n",
 35 |     "3. Filter by smoothness of momentum: of the momentum stocks, select the N percent with the smoothest momentum, as measured by the number of positive days in the last 12 months (N=50)\n",
 36 |     "4. Apply equal weights    \n",
 37 |     "5. Rebalance portfolio before quarter-end to capture window-dressing seasonality effect "
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "## Query historical prices\n",
 45 |     "\n",
 46 |     "Start by querying historical prices from your Sharadar history database. We specify our universe of NYSE stocks as well as the universes we wish to exclude. \n",
 47 |     "\n",
 48 |     "For now we limit ourselves to a couple years of data to make it easier to work with. Later we'll run a backtest using a larger date range."
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": 1,
 54 |    "metadata": {},
 55 |    "outputs": [],
 56 |    "source": [
 57 |     "from quantrocket import get_prices\n",
 58 |     "\n",
 59 |     "DB = \"sharadar-us-stk-1d\"\n",
 60 |     "UNIVERSES = \"nyse-stk\"\n",
 61 |     "EXCLUDE_UNIVERSES = [\"nyse-financials\", \"nyse-reits\", \"nyse-adrs\"]\n",
 62 |     "\n",
 63 |     "prices = get_prices(DB, \n",
 64 |     "                    start_date=\"2014-01-01\",\n",
 65 |     "                    end_date=\"2016-01-01\", \n",
 66 |     "                    universes=UNIVERSES,\n",
 67 |     "                    exclude_universes=EXCLUDE_UNIVERSES, \n",
 68 |     "                    fields=[\"Close\", \"Volume\"])"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "markdown",
 73 |    "metadata": {},
 74 |    "source": [
 75 |     "## Step 1.C: Filter by dollar volume\n",
 76 |     "\n",
 77 |     "The QMOM white paper calls for limiting the universe to the top 60% of stocks by market cap. We will use dollar volume as a proxy for market cap.\n",
 78 |     "\n",
 79 |     "The code below will compute daily ranks by dollar volume and give us a boolean mask indicating which stocks have adequate dollar volume."
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 2,
 85 |    "metadata": {},
 86 |    "outputs": [
 87 |     {
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157 |        "      <th>2015-12-24</th>\n",
158 |        "      <td>False</td>\n",
159 |        "      <td>False</td>\n",
160 |        "      <td>False</td>\n",
161 |        "      <td>False</td>\n",
162 |        "      <td>False</td>\n",
163 |        "      <td>False</td>\n",
164 |        "      <td>False</td>\n",
165 |        "      <td>False</td>\n",
166 |        "      <td>False</td>\n",
167 |        "      <td>False</td>\n",
168 |        "      <td>...</td>\n",
169 |        "      <td>False</td>\n",
170 |        "      <td>False</td>\n",
171 |        "      <td>False</td>\n",
172 |        "      <td>False</td>\n",
173 |        "      <td>False</td>\n",
174 |        "      <td>False</td>\n",
175 |        "      <td>False</td>\n",
176 |        "      <td>False</td>\n",
177 |        "      <td>False</td>\n",
178 |        "      <td>True</td>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>2015-12-28</th>\n",
182 |        "      <td>False</td>\n",
183 |        "      <td>False</td>\n",
184 |        "      <td>False</td>\n",
185 |        "      <td>False</td>\n",
186 |        "      <td>False</td>\n",
187 |        "      <td>False</td>\n",
188 |        "      <td>False</td>\n",
189 |        "      <td>False</td>\n",
190 |        "      <td>False</td>\n",
191 |        "      <td>False</td>\n",
192 |        "      <td>...</td>\n",
193 |        "      <td>False</td>\n",
194 |        "      <td>False</td>\n",
195 |        "      <td>False</td>\n",
196 |        "      <td>False</td>\n",
197 |        "      <td>False</td>\n",
198 |        "      <td>False</td>\n",
199 |        "      <td>False</td>\n",
200 |        "      <td>False</td>\n",
201 |        "      <td>False</td>\n",
202 |        "      <td>True</td>\n",
203 |        "    </tr>\n",
204 |        "    <tr>\n",
205 |        "      <th>2015-12-29</th>\n",
206 |        "      <td>False</td>\n",
207 |        "      <td>False</td>\n",
208 |        "      <td>False</td>\n",
209 |        "      <td>False</td>\n",
210 |        "      <td>False</td>\n",
211 |        "      <td>False</td>\n",
212 |        "      <td>False</td>\n",
213 |        "      <td>False</td>\n",
214 |        "      <td>False</td>\n",
215 |        "      <td>False</td>\n",
216 |        "      <td>...</td>\n",
217 |        "      <td>False</td>\n",
218 |        "      <td>False</td>\n",
219 |        "      <td>False</td>\n",
220 |        "      <td>False</td>\n",
221 |        "      <td>False</td>\n",
222 |        "      <td>False</td>\n",
223 |        "      <td>False</td>\n",
224 |        "      <td>False</td>\n",
225 |        "      <td>False</td>\n",
226 |        "      <td>True</td>\n",
227 |        "    </tr>\n",
228 |        "    <tr>\n",
229 |        "      <th>2015-12-30</th>\n",
230 |        "      <td>False</td>\n",
231 |        "      <td>False</td>\n",
232 |        "      <td>False</td>\n",
233 |        "      <td>False</td>\n",
234 |        "      <td>False</td>\n",
235 |        "      <td>False</td>\n",
236 |        "      <td>False</td>\n",
237 |        "      <td>False</td>\n",
238 |        "      <td>False</td>\n",
239 |        "      <td>False</td>\n",
240 |        "      <td>...</td>\n",
241 |        "      <td>False</td>\n",
242 |        "      <td>False</td>\n",
243 |        "      <td>False</td>\n",
244 |        "      <td>False</td>\n",
245 |        "      <td>False</td>\n",
246 |        "      <td>False</td>\n",
247 |        "      <td>False</td>\n",
248 |        "      <td>False</td>\n",
249 |        "      <td>False</td>\n",
250 |        "      <td>True</td>\n",
251 |        "    </tr>\n",
252 |        "    <tr>\n",
253 |        "      <th>2015-12-31</th>\n",
254 |        "      <td>False</td>\n",
255 |        "      <td>False</td>\n",
256 |        "      <td>False</td>\n",
257 |        "      <td>False</td>\n",
258 |        "      <td>False</td>\n",
259 |        "      <td>False</td>\n",
260 |        "      <td>False</td>\n",
261 |        "      <td>False</td>\n",
262 |        "      <td>False</td>\n",
263 |        "      <td>False</td>\n",
264 |        "      <td>...</td>\n",
265 |        "      <td>False</td>\n",
266 |        "      <td>False</td>\n",
267 |        "      <td>False</td>\n",
268 |        "      <td>False</td>\n",
269 |        "      <td>False</td>\n",
270 |        "      <td>False</td>\n",
271 |        "      <td>False</td>\n",
272 |        "      <td>False</td>\n",
273 |        "      <td>False</td>\n",
274 |        "      <td>True</td>\n",
275 |        "    </tr>\n",
276 |        "  </tbody>\n",
277 |        "</table>\n",
278 |        "<p>5 rows × 1600 columns</p>\n",
279 |        "</div>"
280 |       ],
281 |       "text/plain": [
282 |        "Sid         FIBBG0000018G2  FIBBG000001J87  FIBBG000001JC2  FIBBG000001JD1  \\\n",
283 |        "Date                                                                         \n",
284 |        "2015-12-24           False           False           False           False   \n",
285 |        "2015-12-28           False           False           False           False   \n",
286 |        "2015-12-29           False           False           False           False   \n",
287 |        "2015-12-30           False           False           False           False   \n",
288 |        "2015-12-31           False           False           False           False   \n",
289 |        "\n",
290 |        "Sid         FIBBG000001NT5  FIBBG000001NV2  FIBBG000001SF9  FIBBG000002791  \\\n",
291 |        "Date                                                                         \n",
292 |        "2015-12-24           False           False           False           False   \n",
293 |        "2015-12-28           False           False           False           False   \n",
294 |        "2015-12-29           False           False           False           False   \n",
295 |        "2015-12-30           False           False           False           False   \n",
296 |        "2015-12-31           False           False           False           False   \n",
297 |        "\n",
298 |        "Sid         FIBBG0000027B8  FIBBG000002WJ5  ...  QA000000001978  \\\n",
299 |        "Date                                        ...                   \n",
300 |        "2015-12-24           False           False  ...           False   \n",
301 |        "2015-12-28           False           False  ...           False   \n",
302 |        "2015-12-29           False           False  ...           False   \n",
303 |        "2015-12-30           False           False  ...           False   \n",
304 |        "2015-12-31           False           False  ...           False   \n",
305 |        "\n",
306 |        "Sid         QA000000001981  QA000000001995  QA000000014708  QA000000014977  \\\n",
307 |        "Date                                                                         \n",
308 |        "2015-12-24           False           False           False           False   \n",
309 |        "2015-12-28           False           False           False           False   \n",
310 |        "2015-12-29           False           False           False           False   \n",
311 |        "2015-12-30           False           False           False           False   \n",
312 |        "2015-12-31           False           False           False           False   \n",
313 |        "\n",
314 |        "Sid         QA000000017129  QA000000018169  QA000000020127  QA000000021599  \\\n",
315 |        "Date                                                                         \n",
316 |        "2015-12-24           False           False           False           False   \n",
317 |        "2015-12-28           False           False           False           False   \n",
318 |        "2015-12-29           False           False           False           False   \n",
319 |        "2015-12-30           False           False           False           False   \n",
320 |        "2015-12-31           False           False           False           False   \n",
321 |        "\n",
322 |        "Sid         QA000000021660  \n",
323 |        "Date                        \n",
324 |        "2015-12-24            True  \n",
325 |        "2015-12-28            True  \n",
326 |        "2015-12-29            True  \n",
327 |        "2015-12-30            True  \n",
328 |        "2015-12-31            True  \n",
329 |        "\n",
330 |        "[5 rows x 1600 columns]"
331 |       ]
332 |      },
333 |      "execution_count": 2,
334 |      "metadata": {},
335 |      "output_type": "execute_result"
336 |     }
337 |    ],
338 |    "source": [
339 |     "closes = prices.loc[\"Close\"]\n",
340 |     "volumes = prices.loc[\"Volume\"]\n",
341 |     "\n",
342 |     "# calculate 90 day average dollar volume\n",
343 |     "avg_dollar_volumes = (closes * volumes).rolling(90).mean()\n",
344 |     "\n",
345 |     "# rank biggest to smallest; pct=True gives percentile ranks between 0-1\n",
346 |     "dollar_volume_ranks = avg_dollar_volumes.rank(axis=1, ascending=False, pct=True)\n",
347 |     "        \n",
348 |     "have_adequate_dollar_volumes = dollar_volume_ranks <= (0.60)\n",
349 |     "have_adequate_dollar_volumes.tail()"
350 |    ]
351 |   },
352 |   {
353 |    "cell_type": "markdown",
354 |    "metadata": {},
355 |    "source": [
356 |     "We'll use this filter in the next step."
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "markdown",
361 |    "metadata": {},
362 |    "source": [
363 |     "## Step 2: Apply momentum screen\n",
364 |     "\n",
365 |     "Next, we identify the 10% of stocks with the strongest 12-month momentum, excluding the most recent month. First calculate the returns:"
366 |    ]
367 |   },
368 |   {
369 |    "cell_type": "code",
370 |    "execution_count": 3,
371 |    "metadata": {},
372 |    "outputs": [],
373 |    "source": [
374 |     "TRADING_DAYS_PER_YEAR = 252\n",
375 |     "TRADING_DAYS_PER_MONTH = 22\n",
376 |     "year_ago_closes = closes.shift(TRADING_DAYS_PER_YEAR)\n",
377 |     "month_ago_closes = closes.shift(TRADING_DAYS_PER_MONTH)\n",
378 |     "returns = (month_ago_closes - year_ago_closes) / year_ago_closes.where(year_ago_closes != 0) # avoid DivisionByZero errors"
379 |    ]
380 |   },
381 |   {
382 |    "cell_type": "markdown",
383 |    "metadata": {},
384 |    "source": [
385 |     "We identify momentum stocks by ranking on returns, but we only apply the rankings to stocks with adequate dollar volume:"
386 |    ]
387 |   },
388 |   {
389 |    "cell_type": "code",
390 |    "execution_count": 4,
391 |    "metadata": {},
392 |    "outputs": [],
393 |    "source": [
394 |     "returns_ranks = returns.where(have_adequate_dollar_volumes).rank(axis=1, ascending=False, pct=True)\n",
395 |     "have_momentum = returns_ranks <= 0.10"
396 |    ]
397 |   },
398 |   {
399 |    "cell_type": "markdown",
400 |    "metadata": {},
401 |    "source": [
402 |     "## Step 3: Filter by smoothness of momentum\n",
403 |     "\n",
404 |     "The next step is to rank the momentum stocks by the smoothness of their momentum and select the top 50%. To calculate \"smoothness,\" we count the number of days with a positive return over the last 12 months. The basic idea as explained in the white paper is that a stock which was mediocre for most of the year but made giant gains over a short period is not as appealing as a stock which rose more steadily over the course of the year. \n",
405 |     "\n",
406 |     "First, get a rolling count of positive days in the last year:"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "code",
411 |    "execution_count": 5,
412 |    "metadata": {},
413 |    "outputs": [],
414 |    "source": [
415 |     "are_positive_days = closes.pct_change() > 0\n",
416 |     "positive_days_last_twelve_months = are_positive_days.astype(int).rolling(TRADING_DAYS_PER_YEAR).sum()"
417 |    ]
418 |   },
419 |   {
420 |    "cell_type": "markdown",
421 |    "metadata": {},
422 |    "source": [
423 |     "Then, rank and filter to select the stocks with smoothest momentum:"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "code",
428 |    "execution_count": 6,
429 |    "metadata": {},
430 |    "outputs": [],
431 |    "source": [
432 |     "positive_days_last_twelve_months_ranks = positive_days_last_twelve_months.where(have_momentum).rank(axis=1, ascending=False, pct=True)\n",
433 |     "have_smooth_momentum = positive_days_last_twelve_months_ranks <= 0.50"
434 |    ]
435 |   },
436 |   {
437 |    "cell_type": "markdown",
438 |    "metadata": {},
439 |    "source": [
440 |     "These stocks are our long signals: "
441 |    ]
442 |   },
443 |   {
444 |    "cell_type": "code",
445 |    "execution_count": 7,
446 |    "metadata": {},
447 |    "outputs": [],
448 |    "source": [
449 |     "long_signals = have_smooth_momentum.astype(int)"
450 |    ]
451 |   },
452 |   {
453 |    "cell_type": "markdown",
454 |    "metadata": {},
455 |    "source": [
456 |     "## Step 4: Apply equal weights\n",
457 |     "\n",
458 |     "The QMOM strategy trades an equal-weighted portfolio. By convention, for an unlevered strategy the daily weights should add up to 1 (=100% invested), so we divide each day's signals by the number of signals to get the individual position weights:  "
459 |    ]
460 |   },
461 |   {
462 |    "cell_type": "code",
463 |    "execution_count": 8,
464 |    "metadata": {},
465 |    "outputs": [
466 |     {
467 |      "data": {
468 |       "text/plain": [
469 |        "Date\n",
470 |        "2015-12-24    39\n",
471 |        "2015-12-28    42\n",
472 |        "2015-12-29    40\n",
473 |        "2015-12-30    43\n",
474 |        "2015-12-31    44\n",
475 |        "dtype: int64"
476 |       ]
477 |      },
478 |      "execution_count": 8,
479 |      "metadata": {},
480 |      "output_type": "execute_result"
481 |     }
482 |    ],
483 |    "source": [
484 |     "daily_signal_counts = long_signals.abs().sum(axis=1)\n",
485 |     "daily_signal_counts.tail()"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "code",
490 |    "execution_count": 9,
491 |    "metadata": {},
492 |    "outputs": [
493 |     {
494 |      "data": {
495 |       "text/plain": [
496 |        "Date        Sid           \n",
497 |        "2015-12-31  FIBBG0027Y18M0    0.022727\n",
498 |        "            FIBBG002832GV8    0.022727\n",
499 |        "            FIBBG002WMH2F2    0.022727\n",
500 |        "            FIBBG00449JPX5    0.022727\n",
501 |        "            FIBBG00KXRCDP0    0.022727\n",
502 |        "dtype: float64"
503 |       ]
504 |      },
505 |      "execution_count": 9,
506 |      "metadata": {},
507 |      "output_type": "execute_result"
508 |     }
509 |    ],
510 |    "source": [
511 |     "weights = long_signals.div(daily_signal_counts, axis=0).fillna(0)\n",
512 |     "weights.where(weights!=0).stack().tail()"
513 |    ]
514 |   },
515 |   {
516 |    "cell_type": "markdown",
517 |    "metadata": {},
518 |    "source": [
519 |     "## Step 5: Rebalance before quarter-end\n",
520 |     "\n",
521 |     "The Alpha Architect white paper outlines a technique to potentially enhance momentum returns by rebalancing the portfolio a month or so before quarter-end. The intention is to benefit from window dressing behavior by portfolio managers who bid up the strongest performing stocks in the last month of the quarter in order to include them in their quarterly statements.  \n",
522 |     "\n",
523 |     "To accomplish this with pandas, we can resample the DataFrame of daily weights to quarterly using the `Q-NOV` frequency. `Q-NOV` is a quarterly frequency with a fiscal year ending November 30. We can use pandas' `date_range` function to see some sample dates:"
524 |    ]
525 |   },
526 |   {
527 |    "cell_type": "code",
528 |    "execution_count": 10,
529 |    "metadata": {},
530 |    "outputs": [
531 |     {
532 |      "data": {
533 |       "text/plain": [
534 |        "DatetimeIndex(['2018-02-28', '2018-05-31', '2018-08-31', '2018-11-30'], dtype='datetime64[ns]', freq='Q-NOV')"
535 |       ]
536 |      },
537 |      "execution_count": 10,
538 |      "metadata": {},
539 |      "output_type": "execute_result"
540 |     }
541 |    ],
542 |    "source": [
543 |     "import pandas as pd\n",
544 |     "pd.date_range(start=\"2018-01-01\", freq=\"Q-NOV\", periods=4)"
545 |    ]
546 |   },
547 |   {
548 |    "cell_type": "markdown",
549 |    "metadata": {},
550 |    "source": [
551 |     "Rebalancing on these dates will allow us to benefit from quarter-end window dressing. After resampling to `Q-NOV`, we take the last signal of the modified quarter, then reindex back to daily and fill forward:"
552 |    ]
553 |   },
554 |   {
555 |    "cell_type": "code",
556 |    "execution_count": 11,
557 |    "metadata": {},
558 |    "outputs": [],
559 |    "source": [
560 |     "# Resample daily to Q-NOV, taking the last day's signal\n",
561 |     "# For pandas offset aliases, see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases\n",
562 |     "weights = weights.resample(\"Q-NOV\").last()\n",
563 |     "\n",
564 |     "# Reindex back to daily and fill forward\n",
565 |     "weights = weights.reindex(closes.index, method=\"ffill\")"
566 |    ]
567 |   },
568 |   {
569 |    "cell_type": "markdown",
570 |    "metadata": {},
571 |    "source": [
572 |     "## Step 6: Positions and returns\n",
573 |     "\n",
574 |     "The DataFrame of weights represents what we want to own, as calculated at the end of the day. Assuming we enter positions the next day, we simply shift the weights forward to simulate our positions:"
575 |    ]
576 |   },
577 |   {
578 |    "cell_type": "code",
579 |    "execution_count": 12,
580 |    "metadata": {},
581 |    "outputs": [],
582 |    "source": [
583 |     "positions = weights.shift()"
584 |    ]
585 |   },
586 |   {
587 |    "cell_type": "markdown",
588 |    "metadata": {},
589 |    "source": [
590 |     "To calculate the return (before costs), we multiply the security's percent change over the period by the size of the position.\n",
591 |     "\n",
592 |     "Since `positions` represents when we enter the position, we must shift `positions` forward  to get the \"end\" of the position, since that is when we collect the percent change, not when we first enter the position."
593 |    ]
594 |   },
595 |   {
596 |    "cell_type": "code",
597 |    "execution_count": 13,
598 |    "metadata": {},
599 |    "outputs": [],
600 |    "source": [
601 |     "position_ends = positions.shift()\n",
602 |     "gross_returns = closes.pct_change() * position_ends"
603 |    ]
604 |   },
605 |   {
606 |    "cell_type": "markdown",
607 |    "metadata": {},
608 |    "source": [
609 |     "***\n",
610 |     "\n",
611 |     "## *Next Up*\n",
612 |     "\n",
613 |     "Part 2: [Moonshot Backtest](Part2-Moonshot-Backtest.ipynb)"
614 |    ]
615 |   }
616 |  ],
617 |  "metadata": {
618 |   "kernelspec": {
619 |    "display_name": "Python 3.9",
620 |    "language": "python",
621 |    "name": "python3"
622 |   },
623 |   "language_info": {
624 |    "codemirror_mode": {
625 |     "name": "ipython",
626 |     "version": 3
627 |    },
628 |    "file_extension": ".py",
629 |    "mimetype": "text/x-python",
630 |    "name": "python",
631 |    "nbconvert_exporter": "python",
632 |    "pygments_lexer": "ipython3",
633 |    "version": "3.9.7"
634 |   }
635 |  },
636 |  "nbformat": 4,
637 |  "nbformat_minor": 4
638 | }
639 | 


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