├── borrow_fees_alpha
├── __init__.py
├── Introduction.ipynb
├── avoid-high-borrow.py
├── short-high-borrow.py
├── Part6-Zipline-Strategy-Code.ipynb
├── Part1-Data-Collection.ipynb
├── Part4-Moonshot-Strategy-Code.ipynb
└── Part2-EDA.ipynb
├── .qrignore
├── README.md
└── LICENSE.txt
/borrow_fees_alpha/__init__.py:
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1 |
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/.qrignore:
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1 | README.md
2 | LICENSE.txt
3 |
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/README.md:
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1 | # borrow-fees-alpha
2 |
3 | Introduction to QuantRocket's borrow fees dataset. Uses Alphalens to analyze borrow fees as an alpha source and uses Moonshot and Zipline to demonstrate ways to incorporate borrow fees into long or short strategies.
4 |
5 | ## Clone in QuantRocket
6 |
7 | CLI:
8 |
9 | ```shell
10 | quantrocket codeload clone 'borrow-fees-alpha'
11 | ```
12 |
13 | Python:
14 |
15 | ```python
16 | from quantrocket.codeload import clone
17 | clone("borrow-fees-alpha")
18 | ```
19 |
20 | ## Browse in GitHub
21 |
22 | Start here: [borrow_fees_alpha/Introduction.ipynb](borrow_fees_alpha/Introduction.ipynb)
23 |
24 | ***
25 |
26 | Find more code in QuantRocket's [Code Library](https://www.quantrocket.com/code/)
27 |
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/borrow_fees_alpha/Introduction.ipynb:
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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 | "# Borrow Fees as Alpha Factor\n",
16 | "\n",
17 | "This tutorial provides an overview of QuantRocket's borrow fees dataset. Borrow fees can be used to model the costs of short selling or as a source of alternative alpha.\n",
18 | "\n",
19 | "The borrow fees dataset is included with a paid software license. Data are sourced from Interactive Brokers, but an Interactive Brokers account is not required to access the data.\n",
20 | " \n",
21 | "**Data collection**\n",
22 | "\n",
23 | "* Part 1: [Data Collection](Part1-Data-Collection.ipynb)\n",
24 | "\n",
25 | "**Research: Borrow Fees as Alpha Factor**\n",
26 | "\n",
27 | "*Explore whether borrow fees predict forward returns*\n",
28 | "\n",
29 | "* Part 2: [Exploratory Data Analysis](Part2-EDA.ipynb)\n",
30 | "* Part 3: [Alphalens Analysis](Part3-Alphalens-Borrow-Fees.ipynb)\n",
31 | "\n",
32 | "**Short Strategy (Moonshot)**\n",
33 | "\n",
34 | "*Backtest a Moonshot strategy that short stocks with high borrow fees*\n",
35 | "\n",
36 | "* Part 4: [Moonshot Strategy Code](Part4-Moonshot-Strategy-Code.ipynb)\n",
37 | "* Part 5: [Moonshot Backtest](Part5-Moonshot-Backtest.ipynb)\n",
38 | "\n",
39 | "**Long Strategy (Zipline)**\n",
40 | "\n",
41 | "*Backtest a Zipline strategy that excludes stocks with high borrow fees from a long-only portfolio*\n",
42 | "\n",
43 | "* Part 6: [Zipline Strategy Code](Part6-Zipline-Strategy-Code.ipynb)\n",
44 | "* Part 7: [Zipline Parameter Scan](Part7-Zipline-Parameter-Scan.ipynb)"
45 | ]
46 | }
47 | ],
48 | "metadata": {
49 | "kernelspec": {
50 | "display_name": "Python 3.11",
51 | "language": "python",
52 | "name": "python3"
53 | },
54 | "language_info": {
55 | "codemirror_mode": {
56 | "name": "ipython",
57 | "version": 3
58 | },
59 | "file_extension": ".py",
60 | "mimetype": "text/x-python",
61 | "name": "python",
62 | "nbconvert_exporter": "python",
63 | "pygments_lexer": "ipython3",
64 | "version": "3.11.0"
65 | }
66 | },
67 | "nbformat": 4,
68 | "nbformat_minor": 4
69 | }
70 |
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/borrow_fees_alpha/avoid-high-borrow.py:
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1 | # Copyright 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 zipline.api as algo
16 | from zipline.pipeline import Pipeline, ibkr, master
17 | from zipline.pipeline.factors import AverageDollarVolume
18 | from zipline.finance.execution import MarketOrder
19 |
20 | BUNDLE = "usstock-1d-bundle"
21 | MAX_BORROW_FEE = 0.3
22 |
23 | def initialize(context: algo.Context):
24 | """
25 | Called once at the start of a backtest, and once per day in
26 | live trading.
27 | """
28 | # Attach the pipeline to the algo
29 | algo.attach_pipeline(make_pipeline(), 'pipeline')
30 |
31 | # Rebalance monthly
32 | algo.schedule_function(
33 | rebalance,
34 | algo.date_rules.month_start(),
35 | )
36 |
37 | def make_pipeline():
38 | """
39 | Create a pipeline that filters by dollar volume and borrow fee.
40 | """
41 | # limit initial universe to common stocks
42 | universe = master.SecuritiesMaster.usstock_SecurityType2.latest.eq("Common Stock")
43 |
44 | screen = AverageDollarVolume(window_length=90).percentile_between(50, 75)
45 |
46 | if MAX_BORROW_FEE:
47 | screen &= (ibkr.BorrowFees.FeeRate.latest <= MAX_BORROW_FEE)
48 |
49 | pipeline = Pipeline(
50 | initial_universe=universe,
51 | screen=screen
52 | )
53 | return pipeline
54 |
55 | def rebalance(context: algo.Context, data: algo.BarData):
56 | """
57 | Execute orders according to our schedule_function() timing.
58 | """
59 | desired_stocks = algo.pipeline_output('pipeline').index
60 |
61 | positions = context.portfolio.positions
62 |
63 | # Exit positions we no longer want to hold
64 | for asset, position in positions.items():
65 | if asset not in desired_stocks and data.can_trade(asset):
66 | algo.order_target_value(asset, 0, style=MarketOrder())
67 |
68 | # Enter long positions
69 | for asset in desired_stocks:
70 |
71 | # allocate 1/Nth of capital per asset
72 | algo.order_target_percent(asset, 1/len(desired_stocks), style=MarketOrder())
73 |
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/borrow_fees_alpha/short-high-borrow.py:
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1 | # Copyright 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.slippage.borrowfee import IBKRBorrowFees
18 | from quantrocket.fundamental import get_ibkr_borrow_fees_reindexed_like
19 | from quantrocket.master import get_securities_reindexed_like
20 |
21 | class ShortHighBorrow(Moonshot):
22 | """
23 | Strategy that shorts stocks with the highest borrow fees.
24 | """
25 |
26 | CODE = "short-high-borrow"
27 | DB = "usstock-1d-bundle"
28 | DB_FIELDS = ["Open", "Close", "Volume"]
29 | BORROW_FEE_TOP_N_PCT = 10
30 | DOLLAR_VOLUME_TOP_N_PCT = 75
31 | SLIPPAGE_CLASSES = IBKRBorrowFees
32 |
33 | def prices_to_signals(self, prices: pd.DataFrame):
34 | closes = prices.loc["Close"]
35 | volumes = prices.loc["Volume"]
36 |
37 | # limit to common stocks...
38 | sec_types = get_securities_reindexed_like(closes, "usstock_SecurityType2").loc["usstock_SecurityType2"]
39 | are_common_stocks = sec_types == "Common Stock"
40 |
41 | # ...in the top 75% by dollar volume
42 | avg_dollar_volumes = (closes * volumes).rolling(30).mean()
43 | dollar_volume_pct_ranks = avg_dollar_volumes.where(are_common_stocks).rank(axis=1, ascending=False, pct=True)
44 | have_adequate_dollar_volumes = dollar_volume_pct_ranks <= (self.DOLLAR_VOLUME_TOP_N_PCT / 100)
45 |
46 | # rank by borrow fee and short the 10% with the highest fees
47 | borrow_fees = get_ibkr_borrow_fees_reindexed_like(closes)
48 | borrow_fee_ranks = borrow_fees.where(have_adequate_dollar_volumes).rank(axis=1, ascending=False, pct=True)
49 | short_signals = borrow_fee_ranks <= (self.BORROW_FEE_TOP_N_PCT / 100)
50 |
51 | return -short_signals.astype(int)
52 |
53 | def signals_to_target_weights(self, signals: pd.DataFrame, prices: pd.DataFrame):
54 | weights = self.allocate_equal_weights(signals)
55 | return weights
56 |
57 | def target_weights_to_positions(self, weights: pd.DataFrame, prices: pd.DataFrame):
58 | # Enter the position the day after the signal
59 | return weights.shift()
60 |
61 | def positions_to_gross_returns(self, positions: pd.DataFrame, prices: pd.DataFrame):
62 | # We'll enter on the open, so our return is today's open to
63 | # tomorrow's open
64 | opens = prices.loc["Open"]
65 | # The return is the security's percent change over the period,
66 | # multiplied by the position.
67 | gross_returns = opens.pct_change() * positions.shift()
68 | return gross_returns
69 |
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/borrow_fees_alpha/Part6-Zipline-Strategy-Code.ipynb:
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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 | "[Borrow Fees Alpha](Introduction.ipynb) › Part 6: Zipline Code\n",
17 | "***"
18 | ]
19 | },
20 | {
21 | "cell_type": "markdown",
22 | "metadata": {},
23 | "source": [
24 | "# Zipline Strategy Code"
25 | ]
26 | },
27 | {
28 | "cell_type": "markdown",
29 | "metadata": {},
30 | "source": [
31 | "Since high borrow fee stocks tend to decline but the high borrow fees destroy the profitability of shorting them, perhaps a more appealing way to use borrow fee data is to exclude stocks with high borrow fees from long strategies. Next, we use Zipline to test this approach."
32 | ]
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "metadata": {},
37 | "source": [
38 | "## Install strategy file\n",
39 | "\n",
40 | "The strategy is implemented in [avoid-high-borrow.py](avoid-high-borrow.py).\n",
41 | "\n",
42 | "Execute the following cell to move the strategy file to the `/codeload/zipline` directory:"
43 | ]
44 | },
45 | {
46 | "cell_type": "code",
47 | "execution_count": 1,
48 | "metadata": {},
49 | "outputs": [],
50 | "source": [
51 | "# make directory if doesn't exist\n",
52 | "!mkdir -p /codeload/zipline\n",
53 | "\n",
54 | "!mv avoid-high-borrow.py /codeload/zipline/"
55 | ]
56 | },
57 | {
58 | "cell_type": "markdown",
59 | "metadata": {},
60 | "source": [
61 | "## Strategy Highlights\n",
62 | "\n",
63 | "Using similar code as the Alphalens analysis earlier, the Zipline strategy creates a Pipeline of midcap stocks (defined as stocks in the 50th-75th percentile by dollar volume) that excludes stocks with borrow fees above 0.3%: \n",
64 | "\n",
65 | "```python\n",
66 | "\n",
67 | "MAX_BORROW_FEE = 0.3\n",
68 | "\n",
69 | "def make_pipeline():\n",
70 | " \"\"\"\n",
71 | " Create a pipeline that filters by dollar volume and borrow fee.\n",
72 | " \"\"\"\n",
73 | "\n",
74 | " # limit initial universe to common stocks\n",
75 | " universe = master.SecuritiesMaster.usstock_SecurityType2.latest.eq(\"Common Stock\")\n",
76 | "\n",
77 | " screen = AverageDollarVolume(window_length=90).percentile_between(50, 75)\n",
78 | " \n",
79 | " if MAX_BORROW_FEE:\n",
80 | " screen &= (ibkr.BorrowFees.FeeRate.latest <= MAX_BORROW_FEE)\n",
81 | " \n",
82 | " pipeline = Pipeline(\n",
83 | " initial_universe=universe,\n",
84 | " screen=screen\n",
85 | " )\n",
86 | " ...\n",
87 | "```\n",
88 | "\n",
89 | "The results of the Pipeline screen are purchased in an equal-weighted, long-only portfolio and rebalanced monthly. "
90 | ]
91 | },
92 | {
93 | "cell_type": "markdown",
94 | "metadata": {},
95 | "source": [
96 | "***\n",
97 | "\n",
98 | "## *Next Up*\n",
99 | "\n",
100 | "Part 7: [Zipline Parameter Scan](Part7-Zipline-Parameter-Scan.ipynb)"
101 | ]
102 | }
103 | ],
104 | "metadata": {
105 | "kernelspec": {
106 | "display_name": "Python 3.11",
107 | "language": "python",
108 | "name": "python3"
109 | },
110 | "language_info": {
111 | "codemirror_mode": {
112 | "name": "ipython",
113 | "version": 3
114 | },
115 | "file_extension": ".py",
116 | "mimetype": "text/x-python",
117 | "name": "python",
118 | "nbconvert_exporter": "python",
119 | "pygments_lexer": "ipython3",
120 | "version": "3.11.0"
121 | }
122 | },
123 | "nbformat": 4,
124 | "nbformat_minor": 4
125 | }
126 |
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/borrow_fees_alpha/Part1-Data-Collection.ipynb:
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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 | "[Borrow Fees Alpha](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 | "\n",
27 | "## Collect US Stock Daily Bundle\n",
28 | "\n",
29 | "This tutorial uses the US Stock daily bundle. First create the bundle:"
30 | ]
31 | },
32 | {
33 | "cell_type": "code",
34 | "execution_count": 1,
35 | "metadata": {},
36 | "outputs": [
37 | {
38 | "data": {
39 | "text/plain": [
40 | "{'status': 'success', 'msg': 'successfully created usstock-1d-bundle bundle'}"
41 | ]
42 | },
43 | "execution_count": 1,
44 | "metadata": {},
45 | "output_type": "execute_result"
46 | }
47 | ],
48 | "source": [
49 | "from quantrocket.zipline import create_usstock_bundle\n",
50 | "create_usstock_bundle(\"usstock-1d-bundle\", data_frequency=\"daily\")"
51 | ]
52 | },
53 | {
54 | "cell_type": "markdown",
55 | "metadata": {},
56 | "source": [
57 | "Then ingest the data:"
58 | ]
59 | },
60 | {
61 | "cell_type": "code",
62 | "execution_count": 2,
63 | "metadata": {},
64 | "outputs": [
65 | {
66 | "data": {
67 | "text/plain": [
68 | "{'status': 'the data will be ingested asynchronously'}"
69 | ]
70 | },
71 | "execution_count": 2,
72 | "metadata": {},
73 | "output_type": "execute_result"
74 | }
75 | ],
76 | "source": [
77 | "from quantrocket.zipline import ingest_bundle\n",
78 | "ingest_bundle(\"usstock-1d-bundle\")"
79 | ]
80 | },
81 | {
82 | "cell_type": "markdown",
83 | "metadata": {},
84 | "source": [
85 | "Use flightlog to monitor the progress:\n",
86 | "\n",
87 | "```\n",
88 | "quantrocket.zipline: INFO [usstock-1d-bundle] Ingesting daily bars for usstock-1d-bundle bundle\n",
89 | "quantrocket.zipline: INFO [usstock-1d-bundle] Ingesting adjustments for usstock-1d-bundle bundle\n",
90 | "quantrocket.zipline: INFO [usstock-1d-bundle] Ingesting assets for usstock-1d-bundle bundle\n",
91 | "quantrocket.zipline: INFO [usstock-1d-bundle] Completed ingesting data for 24749 securities in usstock-1d-bundle bundle\n",
92 | "```"
93 | ]
94 | },
95 | {
96 | "cell_type": "markdown",
97 | "metadata": {},
98 | "source": [
99 | "## Collect Borrow Fees\n",
100 | "\n",
101 | "Next, collect borrow fees, which are organized by country:"
102 | ]
103 | },
104 | {
105 | "cell_type": "code",
106 | "execution_count": 3,
107 | "metadata": {},
108 | "outputs": [
109 | {
110 | "data": {
111 | "text/plain": [
112 | "{'status': 'the borrow fees data will be collected asynchronously'}"
113 | ]
114 | },
115 | "execution_count": 3,
116 | "metadata": {},
117 | "output_type": "execute_result"
118 | }
119 | ],
120 | "source": [
121 | "from quantrocket.fundamental import collect_ibkr_borrow_fees\n",
122 | "collect_ibkr_borrow_fees(countries=\"usa\")"
123 | ]
124 | },
125 | {
126 | "cell_type": "markdown",
127 | "metadata": {},
128 | "source": [
129 | "Use flightlog to monitor for completion:\n",
130 | "\n",
131 | "```\n",
132 | "quantrocket.fundamental: INFO Collecting ibkr usa borrow fees data from 2024-05-01 to present\n",
133 | "quantrocket.fundamental: INFO Saved 2104318 total ibkr borrow fees records to quantrocket.v2.fundamental.ibkr.stockloan.fees.sqlite\n",
134 | "```"
135 | ]
136 | },
137 | {
138 | "cell_type": "markdown",
139 | "metadata": {},
140 | "source": [
141 | "***\n",
142 | "\n",
143 | "## *Next Up*\n",
144 | "\n",
145 | "Part 2: [Exploratory Data Analysis](Part2-EDA.ipynb)"
146 | ]
147 | }
148 | ],
149 | "metadata": {
150 | "kernelspec": {
151 | "display_name": "Python 3.11",
152 | "language": "python",
153 | "name": "python3"
154 | },
155 | "language_info": {
156 | "codemirror_mode": {
157 | "name": "ipython",
158 | "version": 3
159 | },
160 | "file_extension": ".py",
161 | "mimetype": "text/x-python",
162 | "name": "python",
163 | "nbconvert_exporter": "python",
164 | "pygments_lexer": "ipython3",
165 | "version": "3.11.0"
166 | }
167 | },
168 | "nbformat": 4,
169 | "nbformat_minor": 4
170 | }
171 |
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/borrow_fees_alpha/Part4-Moonshot-Strategy-Code.ipynb:
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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 | "[Borrow Fees Alpha](Introduction.ipynb) › Part 4: Moonshot Code\n",
17 | "***"
18 | ]
19 | },
20 | {
21 | "cell_type": "markdown",
22 | "metadata": {},
23 | "source": [
24 | "# Moonshot Strategy Code"
25 | ]
26 | },
27 | {
28 | "cell_type": "markdown",
29 | "metadata": {},
30 | "source": [
31 | "The previous notebook revealed that stocks with high borrow fees tend to decline. On the one hand, this tendency makes such stocks attractive to short. On the other hand, their high borrow fees make them unattractive to short. In the next two notebooks, we pit these competing forces against each other by using Moonshot to backtest a strategy that shorts the top decile of stocks by borrow fee, while also incurring the borrowing costs of those short positions. "
32 | ]
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "metadata": {},
37 | "source": [
38 | "## Install strategy file\n",
39 | "\n",
40 | "The strategy is implemented in [short-high-borrow.py](short-high-borrow.py).\n",
41 | "\n",
42 | "Execute the following cell to move the strategy file to the `/codeload/moonshot` directory:"
43 | ]
44 | },
45 | {
46 | "cell_type": "code",
47 | "execution_count": 1,
48 | "metadata": {},
49 | "outputs": [],
50 | "source": [
51 | "# make directory if doesn't exist\n",
52 | "!mkdir -p /codeload/moonshot\n",
53 | "\n",
54 | "!mv short-high-borrow.py /codeload/moonshot/"
55 | ]
56 | },
57 | {
58 | "cell_type": "markdown",
59 | "metadata": {},
60 | "source": [
61 | "## Strategy Highlights\n",
62 | "\n",
63 | "The `prices_to_signals` method of the Moonshot strategy replicates much of the logic from our Pipeline. \n",
64 | "\n",
65 | "* Near the end of the `prices_to_signals` method, we use `get_ibkr_borrow_fees_reindexed_like(...)` to pull borrow fees into the strategy. We then use `rank(axis=1...)` to rank stocks daily by borrow fee, then select the top decile.\n",
66 | "* Before that, we limit the universe to common stocks in the top 75% by dollar volume. This is done by creating two boolean DataFrames (`are_common_stocks` and `have_adequate_dollar_volumes`). These DataFrames are passed to Pandas' `where()` method before ranking (`are_common_stocks` is applied before ranking by dollar volume, and `have_adequate_dollar_volumes` is applied before ranking by borrow fees). This approach of using `where()`, which is typical in Moonshot, sets the dollar volumes or borrow fees, respectively, to `NaN` for stocks that don't belong in the universe and thereby excludes such stocks from the ranking, as `rank(...)` ignores `NaN`s. \n",
67 | "\n",
68 | "```python\n",
69 | "\n",
70 | "from quantrocket.fundamental import get_ibkr_borrow_fees_reindexed_like\n",
71 | "from quantrocket.master import get_securities_reindexed_like\n",
72 | "...\n",
73 | "\n",
74 | "class ShortHighBorrow(Moonshot):\n",
75 | "\n",
76 | " ...\n",
77 | "\n",
78 | " def prices_to_signals(self, prices: pd.DataFrame):\n",
79 | " closes = prices.loc[\"Close\"]\n",
80 | " volumes = prices.loc[\"Volume\"]\n",
81 | "\n",
82 | " # limit to common stocks...\n",
83 | " sec_types = get_securities_reindexed_like(closes, \"usstock_SecurityType2\").loc[\"usstock_SecurityType2\"]\n",
84 | " are_common_stocks = sec_types == \"Common Stock\"\n",
85 | "\n",
86 | " # ...in the top 75% by dollar volume\n",
87 | " avg_dollar_volumes = (closes * volumes).rolling(30).mean()\n",
88 | " dollar_volume_pct_ranks = avg_dollar_volumes.where(are_common_stocks).rank(axis=1, ascending=False, pct=True)\n",
89 | " have_adequate_dollar_volumes = dollar_volume_pct_ranks <= 0.75\n",
90 | "\n",
91 | " # rank by borrow fee and short the 10% with the highest fees\n",
92 | " borrow_fees = get_ibkr_borrow_fees_reindexed_like(closes)\n",
93 | " borrow_fee_ranks = borrow_fees.where(have_adequate_dollar_volumes).rank(axis=1, ascending=False, pct=True)\n",
94 | " short_signals = borrow_fee_ranks <= 0.10\n",
95 | " \n",
96 | " return -short_signals.astype(int)\n",
97 | "```\n",
98 | "\n",
99 | "Modeling borrowing costs for short positions is simple in Moonshot. Import the `IBKRBorrowFees` slippage class and assign it to the `SLIPPAGE_CLASSES` attribute of the Moonshot strategy:\n",
100 | "\n",
101 | "```python\n",
102 | "from moonshot.slippage.borrowfee import IBKRBorrowFees\n",
103 | "\n",
104 | "class ShortHighBorrow(Moonshot):\n",
105 | "\n",
106 | " ...\n",
107 | " SLIPPAGE_CLASSES = IBKRBorrowFees\n",
108 | "```\n"
109 | ]
110 | },
111 | {
112 | "cell_type": "markdown",
113 | "metadata": {},
114 | "source": [
115 | "***\n",
116 | "\n",
117 | "## *Next Up*\n",
118 | "\n",
119 | "Part 5: [Moonshot Backtest](Part5-Moonshot-Backtest.ipynb)"
120 | ]
121 | }
122 | ],
123 | "metadata": {
124 | "kernelspec": {
125 | "display_name": "Python 3.11",
126 | "language": "python",
127 | "name": "python3"
128 | },
129 | "language_info": {
130 | "codemirror_mode": {
131 | "name": "ipython",
132 | "version": 3
133 | },
134 | "file_extension": ".py",
135 | "mimetype": "text/x-python",
136 | "name": "python",
137 | "nbconvert_exporter": "python",
138 | "pygments_lexer": "ipython3",
139 | "version": "3.11.0"
140 | }
141 | },
142 | "nbformat": 4,
143 | "nbformat_minor": 4
144 | }
145 |
--------------------------------------------------------------------------------
/LICENSE.txt:
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/borrow_fees_alpha/Part2-EDA.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "id": "740c90f9-14cb-428a-8630-4e0339e0a74c",
6 | "metadata": {},
7 | "source": [
8 | " \n",
9 | "Disclaimer"
10 | ]
11 | },
12 | {
13 | "cell_type": "markdown",
14 | "id": "7476da07-78d4-4928-8131-c50f7a80f9ae",
15 | "metadata": {},
16 | "source": [
17 | "***\n",
18 | "[Borrow Fees Alpha](Introduction.ipynb) › Part 2: Exploratory Data Analysis\n",
19 | "***"
20 | ]
21 | },
22 | {
23 | "cell_type": "markdown",
24 | "id": "869f3181-bd1f-475d-bb84-24027cf2ac54",
25 | "metadata": {},
26 | "source": [
27 | "# Exploratory Data Analysis\n",
28 | "\n",
29 | "With an alternative data source like borrow fees, exploratory data analysis (EDA) is a good first step. In this notebook, we will run a Pipeline to get an overview of the distribution of borrow fees."
30 | ]
31 | },
32 | {
33 | "cell_type": "markdown",
34 | "id": "59efa3a8-e807-4b35-b10f-cc2acb1efcc4",
35 | "metadata": {},
36 | "source": [
37 | "The Pipeline has one column, the borrow fee. For this tutorial, we choose to limit our universe to common stocks."
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "execution_count": 1,
43 | "id": "3686fe19-69af-4a45-9ff3-2e131cef11f6",
44 | "metadata": {
45 | "tags": []
46 | },
47 | "outputs": [],
48 | "source": [
49 | "from zipline.pipeline import Pipeline, ibkr, master\n",
50 | "\n",
51 | "universe = master.SecuritiesMaster.usstock_SecurityType2.latest.eq(\"Common Stock\")\n",
52 | "\n",
53 | "pipeline = Pipeline(\n",
54 | " columns={\n",
55 | " \"borrow_fee\": ibkr.BorrowFees.FeeRate.latest,\n",
56 | " },\n",
57 | " initial_universe=universe\n",
58 | ")"
59 | ]
60 | },
61 | {
62 | "cell_type": "markdown",
63 | "id": "59357694-ca10-44e7-aa20-c23b40066966",
64 | "metadata": {},
65 | "source": [
66 | "Next, we run the pipeline for a single date."
67 | ]
68 | },
69 | {
70 | "cell_type": "code",
71 | "execution_count": 2,
72 | "id": "4e6a281e-c387-4843-baef-196131dedbc4",
73 | "metadata": {
74 | "tags": []
75 | },
76 | "outputs": [],
77 | "source": [
78 | "from zipline.research import run_pipeline, use_bundle\n",
79 | "\n",
80 | "use_bundle(\"usstock-1d-bundle\")\n",
81 | "\n",
82 | "results = run_pipeline(pipeline, start_date=\"2024-01-05\", end_date=\"2024-01-05\")"
83 | ]
84 | },
85 | {
86 | "cell_type": "markdown",
87 | "id": "f95ffd00-a912-4839-8d9b-ef8c97e80ce9",
88 | "metadata": {},
89 | "source": [
90 | "Let's peek at the results. Borrow fees are quoted as the annualized interest rate that is charged to the borrower of the shares. For example, 0.25 corresponds to an annualized interest rate of 0.25%; 5.7 corresponds to an annualized interest rate of 5.7%."
91 | ]
92 | },
93 | {
94 | "cell_type": "code",
95 | "execution_count": 3,
96 | "id": "09ab8c07-74a3-4b8d-b648-8ee72c799704",
97 | "metadata": {},
98 | "outputs": [
99 | {
100 | "data": {
101 | "text/html": [
102 | "
\n",
103 | "\n",
116 | "
\n",
117 | " \n",
118 | "
\n",
119 | "
\n",
120 | "
\n",
121 | "
borrow_fee
\n",
122 | "
\n",
123 | "
\n",
124 | "
date
\n",
125 | "
asset
\n",
126 | "
\n",
127 | "
\n",
128 | " \n",
129 | " \n",
130 | "
\n",
131 | "
2024-01-05
\n",
132 | "
Equity(FIBBG000C2V3D6 [A])
\n",
133 | "
0.2918
\n",
134 | "
\n",
135 | "
\n",
136 | "
Equity(FIBBG00B3T3HD3 [AA])
\n",
137 | "
0.2843
\n",
138 | "
\n",
139 | "
\n",
140 | "
Equity(FIBBG00VR487K7 [ORGN])
\n",
141 | "
0.5998
\n",
142 | "
\n",
143 | "
\n",
144 | "
Equity(FIBBG005P7Q881 [AAL])
\n",
145 | "
0.2526
\n",
146 | "
\n",
147 | "
\n",
148 | "
Equity(FIBBG003PNL136 [AAMC])
\n",
149 | "
5.7020
\n",
150 | "
\n",
151 | " \n",
152 | "
\n",
153 | "
"
154 | ],
155 | "text/plain": [
156 | " borrow_fee\n",
157 | "date asset \n",
158 | "2024-01-05 Equity(FIBBG000C2V3D6 [A]) 0.2918\n",
159 | " Equity(FIBBG00B3T3HD3 [AA]) 0.2843\n",
160 | " Equity(FIBBG00VR487K7 [ORGN]) 0.5998\n",
161 | " Equity(FIBBG005P7Q881 [AAL]) 0.2526\n",
162 | " Equity(FIBBG003PNL136 [AAMC]) 5.7020"
163 | ]
164 | },
165 | "execution_count": 3,
166 | "metadata": {},
167 | "output_type": "execute_result"
168 | }
169 | ],
170 | "source": [
171 | "results.head()"
172 | ]
173 | },
174 | {
175 | "cell_type": "markdown",
176 | "id": "f4af2f86-0007-43f0-8e85-e8fc271ad8c3",
177 | "metadata": {},
178 | "source": [
179 | "Next we use pandas' `describe()` method to look at the distribution. Most borrow fees are low: the minimum borrow fee is 0.25%, and the median is below 1%. However, at the extreme end of the distribution, borrow fees can be very high: the maximum on this date is 860%. "
180 | ]
181 | },
182 | {
183 | "cell_type": "code",
184 | "execution_count": 4,
185 | "id": "d0376969-67aa-4818-8016-71922b08d739",
186 | "metadata": {
187 | "tags": []
188 | },
189 | "outputs": [
190 | {
191 | "data": {
192 | "text/html": [
193 | "
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