"
83 | ]
84 | },
85 | "execution_count": 1,
86 | "metadata": {},
87 | "output_type": "execute_result"
88 | }
89 | ],
90 | "source": [
91 | "from IPython.display import VimeoVideo\n",
92 | "VimeoVideo('925534381', width=700, height=394)"
93 | ]
94 | },
95 | {
96 | "cell_type": "markdown",
97 | "metadata": {},
98 | "source": [
99 | " ⚠️ Due to an open issue in the current version of JupyterLab used in QuantRocket, please use Ctrl + Enter to copy code from the file editor to the console, not Shift + Enter as stated in the video.
"
100 | ]
101 | },
102 | {
103 | "cell_type": "markdown",
104 | "metadata": {},
105 | "source": [
106 | "***\n",
107 | "\n",
108 | "[Back to Introduction](Introduction.ipynb)"
109 | ]
110 | }
111 | ],
112 | "metadata": {
113 | "kernelspec": {
114 | "display_name": "Python 3.11",
115 | "language": "python",
116 | "name": "python3"
117 | },
118 | "language_info": {
119 | "codemirror_mode": {
120 | "name": "ipython",
121 | "version": 3
122 | },
123 | "file_extension": ".py",
124 | "mimetype": "text/x-python",
125 | "name": "python",
126 | "nbconvert_exporter": "python",
127 | "pygments_lexer": "ipython3",
128 | "version": "3.11.0"
129 | }
130 | },
131 | "nbformat": 4,
132 | "nbformat_minor": 4
133 | }
134 |
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/intro_moonshot/__init__.py:
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https://raw.githubusercontent.com/quantrocket-codeload/moonshot-intro/8717e87e279e3615bdd4c7787ce746b92d0713d7/intro_moonshot/__init__.py
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/intro_moonshot/umd.py:
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1 | # Copyright 2020-2024 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 moonshot import Moonshot
17 | from moonshot.commission import PerShareCommission
18 |
19 | class UpMinusDown(Moonshot):
20 | """
21 | Strategy that buys recent winners and sells recent losers.
22 |
23 | Specifically:
24 |
25 | - rank stocks by their performance over the past MOMENTUM_WINDOW days
26 | - ignore very recent performance by excluding the last RANKING_PERIOD_GAP
27 | days from the ranking window (as commonly recommended for UMD)
28 | - buy the TOP_N_PCT percent of highest performing stocks and short the TOP_N_PCT
29 | percent of lowest performing stocks
30 | - rebalance the portfolio according to REBALANCE_INTERVAL
31 | """
32 |
33 | CODE = "umd"
34 | MOMENTUM_WINDOW = 252 # rank by twelve-month returns
35 | RANKING_PERIOD_GAP = 22 # but exclude most recent 1 month performance
36 | TOP_N_PCT = 50 # Buy/sell the top/bottom 50%
37 | REBALANCE_INTERVAL = "M" # M = monthly; see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
38 |
39 | def prices_to_signals(self, prices: pd.DataFrame):
40 | """
41 | This method receives a DataFrame of prices and should return a
42 | DataFrame of integer signals, where 1=long, -1=short, and 0=cash.
43 | """
44 | closes = prices.loc["Close"]
45 |
46 | # Calculate the returns
47 | returns = closes.shift(self.RANKING_PERIOD_GAP)/closes.shift(self.MOMENTUM_WINDOW) - 1
48 |
49 | # Rank the best and worst
50 | top_ranks = returns.rank(axis=1, ascending=False, pct=True)
51 | bottom_ranks = returns.rank(axis=1, ascending=True, pct=True)
52 |
53 | top_n_pct = self.TOP_N_PCT / 100
54 |
55 | # Get long and short signals and convert to 1, 0, -1
56 | longs = (top_ranks <= top_n_pct)
57 | shorts = (bottom_ranks <= top_n_pct)
58 |
59 | longs = longs.astype(int)
60 | shorts = -shorts.astype(int)
61 |
62 | # Combine long and short signals
63 | signals = longs.where(longs == 1, shorts)
64 |
65 | # Resample using the rebalancing interval.
66 | # Keep only the last signal of the month, then fill it forward
67 | signals = signals.resample(self.REBALANCE_INTERVAL).last()
68 | signals = signals.reindex(closes.index, method="ffill")
69 |
70 | return signals
71 |
72 | def signals_to_target_weights(self, signals: pd.DataFrame, prices: pd.DataFrame):
73 | """
74 | This method receives a DataFrame of integer signals (-1, 0, 1) and
75 | should return a DataFrame indicating how much capital to allocate to
76 | the signals, expressed as a percentage of the total capital allocated
77 | to the strategy (for example, -0.25, 0, 0.1 to indicate 25% short,
78 | cash, 10% long).
79 | """
80 | weights = self.allocate_equal_weights(signals)
81 | return weights
82 |
83 | def target_weights_to_positions(self, weights: pd.DataFrame, prices: pd.DataFrame):
84 | """
85 | This method receives a DataFrame of allocations and should return a
86 | DataFrame of positions. This allows for modeling the delay between
87 | when the signal occurs and when the position is entered, and can also
88 | be used to model non-fills.
89 | """
90 | # Enter the position in the period/day after the signal
91 | return weights.shift()
92 |
93 | def positions_to_gross_returns(self, positions: pd.DataFrame, prices: pd.DataFrame):
94 | """
95 | This method receives a DataFrame of positions and a DataFrame of
96 | prices, and should return a DataFrame of percentage returns before
97 | commissions and slippage.
98 | """
99 | # We'll enter on the open, so our return is today's open to
100 | # tomorrow's open
101 | opens = prices.loc["Open"]
102 | # The return is the security's percent change over the period,
103 | # multiplied by the position.
104 | gross_returns = opens.pct_change() * positions.shift()
105 | return gross_returns
106 |
107 | class USStockCommission(PerShareCommission):
108 | BROKER_COMMISSION_PER_SHARE = 0.005
109 |
110 | class UpMinusDownDemo(UpMinusDown):
111 |
112 | CODE = "umd-demo"
113 | DB = "usstock-free-1d"
114 | UNIVERSES = "usstock-free"
115 | TOP_N_PCT = 50
116 | COMMISSION_CLASS = USStockCommission
117 |
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