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Update .pre-commit-config.yaml #86

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Update .pre-commit-config.yaml
tschm Dec 30, 2024
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tschm Dec 31, 2024
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15 changes: 5 additions & 10 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
@@ -1,31 +1,26 @@
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.4.0
rev: v5.0.0
hooks:
- id: end-of-file-fixer
- id: trailing-whitespace
- id: fix-encoding-pragma

- repo: https://github.com/psf/black
rev: 23.7.0
hooks:
- id: black

- repo: https://github.com/charliermarsh/ruff-pre-commit
rev: 'v0.0.286'
rev: 'v0.8.4'
hooks:
- id: ruff
args: [ --fix, --exit-non-zero-on-fix ]
args: [ --fix, --exit-non-zero-on-fix, --ignore=F821]

- repo: https://github.com/igorshubovych/markdownlint-cli
rev: v0.35.0
rev: v0.43.0
hooks:
- id: markdownlint-fix
# MD013: line too long
args: [--disable, MD013, "--"]

- repo: https://github.com/python-jsonschema/check-jsonschema
rev: 0.26.3
rev: 0.30.0
hooks:
- id: check-dependabot
args: ["--verbose"]
Expand Down
4,896 changes: 4,658 additions & 238 deletions book/docs/applications/notebooks/portfolio_optimization.ipynb
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33 changes: 16 additions & 17 deletions book/docs/exercises/notebooks/13.20.ipynb
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Expand Up @@ -30,40 +30,43 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"$$\n",
"\\rho_i = \\frac{\\partial \\log R(x)}{\\partial \\log x_i} =\n",
"\\frac{\\partial R(x)}{R(x)} \\frac{x_i}{\\partial x_i}, \\quad i=1, \\ldots, n.\n",
"$$"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Thus $\\rho_i$ gives the fractional increase in risk per fractional increase\n",
"in investment $i$.\n",
"We can express the risk contributions as"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"$$\n",
"\\rho_i = \\frac{x_i (\\Sigma x)_i} {x^T\\Sigma x}, \\quad i=1, \\ldots, n,\n",
"$$"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"from which we see that $\\sum_{i=1}^n \\rho_i = 1$.\n",
"For general $x$, we can have $\\rho_i <0$, which means that a small increase\n",
Expand All @@ -88,10 +91,7 @@
"\n",
"*Hint.*\n",
"Minimize $(1/2)x^T\\Sigma x - \\sum_{i=1}^n \\rho_i^\\mathrm{des} \\log x_i$."
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
Expand All @@ -118,7 +118,6 @@
"outputs": [],
"source": [
"import numpy as np\n",
"import cvxpy as cp\n",
"\n",
"Sigma = np.array(\n",
" np.matrix(\n",
Expand Down
82 changes: 59 additions & 23 deletions book/docs/exercises/notebooks/13.21.ipynb
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Expand Up @@ -35,6 +35,12 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"$$\n",
" \\begin{array}{ll}\n",
Expand All @@ -43,13 +49,16 @@
" & \\mathbb{1}^Tw = 1,\n",
" \\end{array}\n",
"$$"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"where $w\\in \\mathbb{R}^n$ is the variable, $\\mu$ is the mean return,\n",
"$\\Sigma\\in \\mathbb{S}_{++}^n$ is the return covariance, and $\\gamma>0$ is the\n",
Expand All @@ -60,13 +69,16 @@
"portfolio by buying and selling assets.\n",
"We call the post-trade portfolio weights $\\tilde{w}_t$.\n",
"They are found by solving the (rebalancing) problem"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"$$\n",
" \\begin{array}{ll}\n",
Expand All @@ -75,13 +87,16 @@
" & \\mathbb{1}^Tw = 1,\n",
" \\end{array}\n",
"$$"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"with variable $w \\in \\mathbb{R}^n$,\n",
"where $\\kappa\\in\\mathbb{R}^n_+$ is the vector of (so-called linear) transaction costs\n",
Expand Down Expand Up @@ -112,10 +127,7 @@
"The standard model is that $r_t$ are IID random variables with\n",
"mean and covariance $\\mu$ and $\\Sigma$,\n",
"but this is not relevant in this problem."
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -166,16 +178,38 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"outputs": [
{
"ename": "NameError",
"evalue": "name 'ws' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 38\u001b[0m\n\u001b[1;32m 36\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m13\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n):\n\u001b[0;32m---> 38\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;28mrange\u001b[39m(T), \u001b[43mws\u001b[49m[:, j], colors[j])\n\u001b[1;32m 39\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;28mrange\u001b[39m(T), [w_star[j]] \u001b[38;5;241m*\u001b[39m T, colors[j] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 40\u001b[0m non_zero_trades \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mabs\u001b[39m(us[:, j]) \u001b[38;5;241m>\u001b[39m threshold\n",
"\u001b[0;31mNameError\u001b[0m: name 'ws' is not defined"
]
},
{
"data": {
"text/plain": [
"<Figure size 1300x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# data and code for multiperiod portfolio rebalancing problem\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"T = 100\n",
"n = 5\n",
Expand All @@ -196,7 +230,7 @@
"\n",
"## Generate returns\n",
"# call this function to generate a vector r of market returns\n",
"generateReturns = lambda: np.random.multivariate_normal(mu, Sigma)\n",
"# generateReturns = np.random.multivariate_normal(mu, Sigma)\n",
"\n",
"## Plotting code\n",
"# You must provide three objects:\n",
Expand All @@ -206,7 +240,6 @@
"# the trades at each period: w_t_tilde - w_t;\n",
"# - w_star: np.array of size n,\n",
"# the \"target\" solution w_star.\n",
"import matplotlib.pyplot as plt\n",
"\n",
"colors = [\"b\", \"r\", \"g\", \"c\", \"m\"]\n",
"plt.figure(figsize=(13, 5))\n",
Expand All @@ -224,6 +257,9 @@
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"pycharm": {
"name": "#%%\n"
}
Expand All @@ -234,7 +270,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -248,9 +284,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}
20 changes: 12 additions & 8 deletions book/docs/exercises/notebooks/17.3.ipynb
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Expand Up @@ -13,6 +13,12 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"Flux balance analysis is based on a very simple model of\n",
"the reactions going on in a cell, keeping track only of the gross rate of consumption and production\n",
Expand Down Expand Up @@ -50,10 +56,7 @@
"$$\n",
"Sv = 0, \\quad v \\succeq 0, \\quad v \\preceq v^\\mathrm{max}.\n",
"$$"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -175,13 +178,14 @@
},
"outputs": [],
"source": [
"# flake8: noqa: F821\n",
"# Check essential genes and synthetic lethals\n",
"G = np.zeros((n, n))\n",
"for i in range(n):\n",
" for j in range(i, n):\n",
" # Taking out genes i and j (i can equal j)\n",
" # TODO: your code here\n",
"\n",
" # TODO: replace pass with your code here, \n",
" pass\n",
"\n",
"print(G < 0.2*Gstar)"
]
Expand All @@ -203,9 +207,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}
16 changes: 9 additions & 7 deletions book/docs/exercises/notebooks/3.20.ipynb
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Expand Up @@ -13,6 +13,12 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"A vehicle (say, an airplane) travels along a fixed path of $n$\n",
"segments, between $n+1$ waypoints labeled $0, \\ldots, n$. Segment $i$ starts at waypoint $i-1$ and\n",
Expand All @@ -28,10 +34,7 @@
"You are given the data $d$ (segment travel distances), $s^\\mathrm{min}$ and $s^\\mathrm{max}$ (speed bounds), $\\tau^\\mathrm{min}$ and $\\tau^\\mathrm{max}$\n",
"(waypoint arrival time bounds), and the fuel use function $\\Phi: \\mathbf{R} \\rightarrow \\mathbf{R}$. You are to choose the speeds\n",
"$s_1, \\ldots, s_n$ so as to minimize the total fuel consumed in kg."
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -618,7 +621,6 @@
},
"outputs": [],
"source": [
"import cvxpy as cp\n",
"\n",
"# TODO: your code here\n",
"# store the optimal speeds in s\n"
Expand Down Expand Up @@ -660,9 +662,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}
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