diff --git a/.github/workflows/branch.yml b/.github/workflows/branch.yml
index dbdad8a..c2ad613 100644
--- a/.github/workflows/branch.yml
+++ b/.github/workflows/branch.yml
@@ -38,7 +38,7 @@ jobs:
       - name: Run isort
         run: poetry run isort . --check-only --profile black
       - name: Run docformatter
-        run: poetry run docformatter . --recursive --check
+        run: poetry run docformatter . --recursive --check --diff --black --exclude _unittest_numpy_extensions.py
 #       - name: Run flake8
 #         run: poetry run flake8 .
 #       - name: Run bandit
diff --git a/README.md b/README.md
index aaf23c3..6646b7b 100644
--- a/README.md
+++ b/README.md
@@ -111,5 +111,5 @@ Before commit run following format commands in project folder:
 ```bash
 poetry run black .
 poetry run isort . --profile black
-poetry run docformatter . --recursive --in-place
+poetry run docformatter . --recursive --in-place --black --exclude _unittest_numpy_extensions.py
 ```
diff --git a/notebooks/Readme_Example.ipynb b/notebooks/Readme_Example.ipynb
index 28a3470..1bfd3c5 100644
--- a/notebooks/Readme_Example.ipynb
+++ b/notebooks/Readme_Example.ipynb
@@ -2,15 +2,21702 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+         "domain": [
+          0,
+          0.08181818181818182
+         ],
+         "showgrid": true,
+         "title": {
+          "text": "USD"
+         }
+        },
+        "yaxis6": {
+         "anchor": "x3",
+         "overlaying": "y5",
+         "showgrid": true,
+         "side": "right",
+         "title": {
+          "text": "USD"
+         }
+        }
+       }
+      }
      },
      "metadata": {},
      "output_type": "display_data"
@@ -20,7 +21707,7 @@
     "import pandas as pd\n",
     "\n",
     "from yabte.backtest import Strategy, StrategyRunner, Order, Book\n",
-    "from yabte.utilities.plot.matplotlib.strategy_runner import plot_strategy_runner\n",
+    "from yabte.utilities.plot.plotly.strategy_runner import plot_strategy_runner\n",
     "from yabte.utilities.strategy_helpers import crossover\n",
     "from yabte.tests._helpers import generate_nasdaq_dataset\n",
     "\n",
@@ -83,7 +21770,7 @@
     "bch = sr.book_history.loc[:, (slice(None), \"cash\")]\n",
     "\n",
     "# plot the trades against book value\n",
-    "plot_strategy_runner(sr);"
+    "plot_strategy_runner(sr)"
    ]
   },
   {
@@ -96,9 +21783,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "scratch-3.10",
+   "display_name": ".venv",
    "language": "python",
-   "name": "scratch-3.10"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -112,12 +21799,7 @@
    "pygments_lexer": "ipython3",
    "version": "3.10.6"
   },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "c90e5980e8b1d1eb9a7a62c4316604523bec4ee176d943f83fc4db9c74c8274b"
-   }
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/poetry.lock b/poetry.lock
index eee701d..6a35667 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,10 +1,9 @@
-# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
 [[package]]
 name = "alabaster"
 version = "0.7.13"
 description = "A configurable sidebar-enabled Sphinx theme"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -16,7 +15,6 @@ files = [
 name = "appnope"
 version = "0.1.3"
 description = "Disable App Nap on macOS >= 10.9"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -28,7 +26,6 @@ files = [
 name = "asttokens"
 version = "2.2.1"
 description = "Annotate AST trees with source code positions"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -44,43 +41,37 @@ test = ["astroid", "pytest"]
 
 [[package]]
 name = "attrs"
-version = "22.2.0"
+version = "23.1.0"
 description = "Classes Without Boilerplate"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "attrs-22.2.0-py3-none-any.whl", hash = "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836"},
-    {file = "attrs-22.2.0.tar.gz", hash = "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"},
+    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
+    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
-tests = ["attrs[tests-no-zope]", "zope.interface"]
-tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[docs,tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "babel"
-version = "2.11.0"
+version = "2.12.1"
 description = "Internationalization utilities"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "Babel-2.11.0-py3-none-any.whl", hash = "sha256:1ad3eca1c885218f6dce2ab67291178944f810a10a9b5f3cb8382a5a232b64fe"},
-    {file = "Babel-2.11.0.tar.gz", hash = "sha256:5ef4b3226b0180dedded4229651c8b0e1a3a6a2837d45a073272f313e4cf97f6"},
+    {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"},
+    {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"},
 ]
 
-[package.dependencies]
-pytz = ">=2015.7"
-
 [[package]]
 name = "backcall"
 version = "0.2.0"
 description = "Specifications for callback functions passed in to an API"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -90,14 +81,13 @@ files = [
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.11.2"
+version = "4.12.2"
 description = "Screen-scraping library"
-category = "dev"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.11.2-py3-none-any.whl", hash = "sha256:0e79446b10b3ecb499c1556f7e228a53e64a2bfcebd455f370d8927cb5b59e39"},
-    {file = "beautifulsoup4-4.11.2.tar.gz", hash = "sha256:bc4bdda6717de5a2987436fb8d72f45dc90dd856bdfd512a1314ce90349a0106"},
+    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
 ]
 
 [package.dependencies]
@@ -109,37 +99,36 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "23.1.0"
+version = "23.3.0"
 description = "The uncompromising code formatter."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "black-23.3.0.tar.gz", hash = "sha256:1c7b8d606e728a41ea1ccbd7264677e494e87cf630e399262ced92d4a8dac940"},
 ]
 
 [package.dependencies]
@@ -162,7 +151,6 @@ uvloop = ["uvloop (>=0.15.2)"]
 name = "bleach"
 version = "6.0.0"
 description = "An easy safelist-based HTML-sanitizing tool."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -179,21 +167,19 @@ css = ["tinycss2 (>=1.1.0,<1.2)"]
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"},
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+    {file = "certifi-2023.5.7-py3-none-any.whl", hash = "sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716"},
+    {file = "certifi-2023.5.7.tar.gz", hash = "sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7"},
 ]
 
 [[package]]
 name = "cffi"
 version = "1.15.1"
 description = "Foreign Function Interface for Python calling C code."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -268,24 +254,92 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "2.1.1"
+version = "3.1.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-category = "dev"
 optional = false
-python-versions = ">=3.6.0"
+python-versions = ">=3.7.0"
 files = [
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+    {file = "charset-normalizer-3.1.0.tar.gz", hash = "sha256:34e0a2f9c370eb95597aae63bf85eb5e96826d81e3dcf88b8886012906f509b5"},
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 ]
 
-[package.extras]
-unicode-backport = ["unicodedata2"]
-
 [[package]]
 name = "click"
 version = "8.1.3"
 description = "Composable command line interface toolkit"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -300,7 +354,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 name = "colorama"
 version = "0.4.6"
 description = "Cross-platform colored terminal text."
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
@@ -310,156 +363,148 @@ files = [
 
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 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
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 ]
 
 [package.dependencies]
 traitlets = ">=5.3"
 
 [package.extras]
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 test = ["pytest"]
+typing = ["mypy (>=0.990)"]
 
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 version = "0.11.0"
 description = "Composable style cycles"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
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 python-versions = ">=3.7"
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 [[package]]
 name = "decorator"
 version = "5.1.1"
 description = "Decorators for Humans"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -520,7 +563,6 @@ files = [
 name = "defusedxml"
 version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -530,29 +572,26 @@ files = [
 
 [[package]]
 name = "docformatter"
-version = "1.5.1"
+version = "1.7.3"
 description = "Formats docstrings to follow PEP 257"
-category = "dev"
 optional = false
-python-versions = ">=3.6,<4.0"
+python-versions = ">=3.7,<4.0"
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 [package.dependencies]
-charset_normalizer = ">=2.0.0,<3.0.0"
-tomli = {version = ">=2.0.0,<3.0.0", markers = "python_version >= \"3.7\""}
+charset_normalizer = ">=3.0.0,<4.0.0"
 untokenize = ">=0.1.1,<0.2.0"
 
 [package.extras]
-tomli = ["tomli (<2.0.0)"]
+tomli = ["tomli (>=2.0.0,<3.0.0)"]
 
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 version = "0.18.1"
 description = "Docutils -- Python Documentation Utilities"
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 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 description = "Get the currently executing AST node of a frame, and other information"
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 optional = false
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 files = [
@@ -577,14 +615,13 @@ tests = ["asttokens", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "fastjsonschema"
-version = "2.16.2"
+version = "2.17.1"
 description = "Fastest Python implementation of JSON schema"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -592,18 +629,49 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "fonttools"
-version = "4.38.0"
+version = "4.40.0"
 description = "Tools to manipulate font files"
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "fonttools-4.40.0-py3-none-any.whl", hash = "sha256:200729d12461e2038700d31f0d49ad5a7b55855dec7525074979a06b46f88505"},
+    {file = "fonttools-4.40.0.tar.gz", hash = "sha256:337b6e83d7ee73c40ea62407f2ce03b07c3459e213b6f332b94a69923b9e1cb9"},
 ]
 
 [package.extras]
-all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
 interpolatable = ["munkres", "scipy"]
 lxml = ["lxml (>=4.0,<5)"]
@@ -613,14 +681,13 @@ repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
 type1 = ["xattr"]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=14.0.0)"]
+unicode = ["unicodedata2 (>=15.0.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "idna"
 version = "3.4"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -632,7 +699,6 @@ files = [
 name = "imagesize"
 version = "1.4.1"
 description = "Getting image size from png/jpeg/jpeg2000/gif file"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -642,14 +708,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.21.1"
+version = "6.24.0"
 description = "IPython Kernel for Jupyter"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.21.1-py3-none-any.whl", hash = "sha256:1a04bb359212e23e46adc0116ec82ea128c1e5bd532fde4fbe679787ff36f0cf"},
-    {file = "ipykernel-6.21.1.tar.gz", hash = "sha256:a0f8eece39cab1ee352c9b59ec67bbe44d8299f8238e4c16ff7f4cf0052d3378"},
+    {file = "ipykernel-6.24.0-py3-none-any.whl", hash = "sha256:2f5fffc7ad8f1fd5aadb4e171ba9129d9668dbafa374732cf9511ada52d6547f"},
+    {file = "ipykernel-6.24.0.tar.gz", hash = "sha256:29cea0a716b1176d002a61d0b0c851f34536495bc4ef7dd0222c88b41b816123"},
 ]
 
 [package.dependencies]
@@ -658,12 +723,12 @@ comm = ">=0.1.1"
 debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
 jupyter-client = ">=6.1.12"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=17"
+pyzmq = ">=20"
 tornado = ">=6.1"
 traitlets = ">=5.4.0"
 
@@ -676,14 +741,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.9.0"
+version = "8.14.0"
 description = "IPython: Productive Interactive Computing"
-category = "dev"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.9.0-py3-none-any.whl", hash = "sha256:9c207b0ef2d276d1bfcfeb9a62804336abbe4b170574ea061500952319b1d78c"},
-    {file = "ipython-8.9.0.tar.gz", hash = "sha256:71618e82e6d59487bea059626e7c79fb4a5b760d1510d02fab1160db6fdfa1f7"},
+    {file = "ipython-8.14.0-py3-none-any.whl", hash = "sha256:248aca623f5c99a6635bc3857677b7320b9b8039f99f070ee0d20a5ca5a8e6bf"},
+    {file = "ipython-8.14.0.tar.gz", hash = "sha256:1d197b907b6ba441b692c48cf2a3a2de280dc0ac91a3405b39349a50272ca0a1"},
 ]
 
 [package.dependencies]
@@ -695,13 +759,13 @@ jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
 pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.1.0"
+prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.20)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
 doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
@@ -711,13 +775,12 @@ notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
 test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.20)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
 
 [[package]]
 name = "isort"
 version = "5.12.0"
 description = "A Python utility / library to sort Python imports."
-category = "dev"
 optional = false
 python-versions = ">=3.8.0"
 files = [
@@ -735,7 +798,6 @@ requirements-deprecated-finder = ["pip-api", "pipreqs"]
 name = "jedi"
 version = "0.18.2"
 description = "An autocompletion tool for Python that can be used for text editors."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -755,7 +817,6 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 name = "jinja2"
 version = "3.1.2"
 description = "A very fast and expressive template engine."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -773,7 +834,6 @@ i18n = ["Babel (>=2.7)"]
 name = "jsonschema"
 version = "4.17.3"
 description = "An implementation of JSON Schema validation for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -791,18 +851,17 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jupyter-client"
-version = "8.0.2"
+version = "8.3.0"
 description = "Jupyter protocol implementation and client libraries"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jupyter_client-8.3.0-py3-none-any.whl", hash = "sha256:7441af0c0672edc5d28035e92ba5e32fadcfa8a4e608a434c228836a89df6158"},
+    {file = "jupyter_client-8.3.0.tar.gz", hash = "sha256:3af69921fe99617be1670399a0b857ad67275eefcfa291e2c81a160b7b650f5f"},
 ]
 
 [package.dependencies]
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
 tornado = ">=6.2"
@@ -810,23 +869,22 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["codecov", "coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.2.0"
+version = "5.3.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jupyter_core-5.3.1-py3-none-any.whl", hash = "sha256:ae9036db959a71ec1cac33081eeb040a79e681f08ab68b0883e9a676c7a90dce"},
+    {file = "jupyter_core-5.3.1.tar.gz", hash = "sha256:5ba5c7938a7f97a6b0481463f7ff0dbac7c15ba48cf46fa4035ca6e838aa1aba"},
 ]
 
 [package.dependencies]
 platformdirs = ">=2.5"
-pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
 traitlets = ">=5.3"
 
 [package.extras]
@@ -837,7 +895,6 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 name = "jupyterlab-pygments"
 version = "0.2.2"
 description = "Pygments theme using JupyterLab CSS variables"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -849,7 +906,6 @@ files = [
 name = "kiwisolver"
 version = "1.4.4"
 description = "A fast implementation of the Cassowary constraint solver"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -925,113 +981,111 @@ files = [
 
 [[package]]
 name = "markupsafe"
-version = "2.1.2"
+version = "2.1.3"
 description = "Safely add untrusted strings to HTML/XML markup."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -1039,17 +1093,16 @@ contourpy = ">=1.0.1"
 cycler = ">=0.10"
 fonttools = ">=4.22.0"
 kiwisolver = ">=1.0.1"
-numpy = ">=1.19"
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 packaging = ">=20.0"
 pillow = ">=6.2.0"
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+pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
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 version = "0.1.6"
 description = "Inline Matplotlib backend for Jupyter"
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 optional = false
 python-versions = ">=3.5"
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-category = "dev"
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 [package.extras]
 dmypy = ["psutil (>=4.0)"]
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 python-versions = ">=3.5"
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 [[package]]
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-version = "23.0"
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 optional = false
 python-versions = ">=3.7"
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 python-versions = ">=3.8"
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@@ -1326,24 +1366,23 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 
 [[package]]
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 [package.dependencies]
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 types-pytz = ">=2022.1.1"
 
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 version = "0.7.5"
 description = "Tiny 'shelve'-like database with concurrency support"
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 optional = false
 python-versions = "*"
 files = [
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 [package.extras]
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 description = "Run a subprocess in a pseudo terminal"
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 python-versions = "*"
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 [package.extras]
@@ -1627,14 +1645,13 @@ plugins = ["importlib-metadata"]
 
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 [package.extras]
-dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
+dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "setuptools"
-version = "67.2.0"
-description = "Easily download, build, install, upgrade, and uninstall Python packages"
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "setuptools-67.2.0-py3-none-any.whl", hash = "sha256:16ccf598aab3b506593c17378473978908a2734d7336755a8769b480906bec1c"},
-    {file = "setuptools-67.2.0.tar.gz", hash = "sha256:b440ee5f7e607bb8c9de15259dba2583dd41a38879a7abc1d43a71c59524da48"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -1912,7 +1902,6 @@ files = [
 name = "snowballstemmer"
 version = "2.2.0"
 description = "This package provides 29 stemmers for 28 languages generated from Snowball algorithms."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1922,33 +1911,31 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.4"
+version = "2.4.1"
 description = "A modern CSS selector implementation for Beautiful Soup."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "soupsieve-2.4-py3-none-any.whl", hash = "sha256:49e5368c2cda80ee7e84da9dbe3e110b70a4575f196efb74e51b94549d921955"},
-    {file = "soupsieve-2.4.tar.gz", hash = "sha256:e28dba9ca6c7c00173e34e4ba57448f0688bb681b7c5e8bf4971daafc093d69a"},
+    {file = "soupsieve-2.4.1-py3-none-any.whl", hash = "sha256:1c1bfee6819544a3447586c889157365a27e10d88cde3ad3da0cf0ddf646feb8"},
+    {file = "soupsieve-2.4.1.tar.gz", hash = "sha256:89d12b2d5dfcd2c9e8c22326da9d9aa9cb3dfab0a83a024f05704076ee8d35ea"},
 ]
 
 [[package]]
 name = "sphinx"
-version = "6.1.3"
+version = "6.2.1"
 description = "Python documentation generator"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Sphinx-6.1.3.tar.gz", hash = "sha256:0dac3b698538ffef41716cf97ba26c1c7788dba73ce6f150c1ff5b4720786dd2"},
-    {file = "sphinx-6.1.3-py3-none-any.whl", hash = "sha256:807d1cb3d6be87eb78a381c3e70ebd8d346b9a25f3753e9947e866b2786865fc"},
+    {file = "Sphinx-6.2.1.tar.gz", hash = "sha256:6d56a34697bb749ffa0152feafc4b19836c755d90a7c59b72bc7dfd371b9cc6b"},
+    {file = "sphinx-6.2.1-py3-none-any.whl", hash = "sha256:97787ff1fa3256a3eef9eda523a63dbf299f7b47e053cfcf684a1c2a8380c912"},
 ]
 
 [package.dependencies]
 alabaster = ">=0.7,<0.8"
 babel = ">=2.9"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
-docutils = ">=0.18,<0.20"
+docutils = ">=0.18.1,<0.20"
 imagesize = ">=1.3"
 Jinja2 = ">=3.0"
 packaging = ">=21.0"
@@ -1965,13 +1952,12 @@ sphinxcontrib-serializinghtml = ">=1.1.5"
 [package.extras]
 docs = ["sphinxcontrib-websupport"]
 lint = ["docutils-stubs", "flake8 (>=3.5.0)", "flake8-simplify", "isort", "mypy (>=0.990)", "ruff", "sphinx-lint", "types-requests"]
-test = ["cython", "html5lib", "pytest (>=4.6)"]
+test = ["cython", "filelock", "html5lib", "pytest (>=4.6)"]
 
 [[package]]
 name = "sphinx-rtd-theme"
 version = "1.2.0rc4"
 description = "Read the Docs theme for Sphinx"
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
@@ -1991,7 +1977,6 @@ dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
 name = "sphinxcontrib-applehelp"
 version = "1.0.4"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2007,7 +1992,6 @@ test = ["pytest"]
 name = "sphinxcontrib-devhelp"
 version = "1.0.2"
 description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2023,7 +2007,6 @@ test = ["pytest"]
 name = "sphinxcontrib-htmlhelp"
 version = "2.0.1"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2037,24 +2020,22 @@ test = ["html5lib", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-jquery"
-version = "2.0.0"
+version = "4.1"
 description = "Extension to include jQuery on newer Sphinx releases"
-category = "dev"
 optional = false
 python-versions = ">=2.7"
 files = [
-    {file = "sphinxcontrib-jquery-2.0.0.tar.gz", hash = "sha256:8fb65f6dba84bf7bcd1aea1f02ab3955ac34611d838bcc95d4983b805b234daa"},
-    {file = "sphinxcontrib_jquery-2.0.0-py3-none-any.whl", hash = "sha256:ed47fa425c338ffebe3c37e1cdb56e30eb806116b85f01055b158c7057fdb995"},
+    {file = "sphinxcontrib-jquery-4.1.tar.gz", hash = "sha256:1620739f04e36a2c779f1a131a2dfd49b2fd07351bf1968ced074365933abc7a"},
+    {file = "sphinxcontrib_jquery-4.1-py2.py3-none-any.whl", hash = "sha256:f936030d7d0147dd026a4f2b5a57343d233f1fc7b363f68b3d4f1cb0993878ae"},
 ]
 
 [package.dependencies]
-setuptools = "*"
+Sphinx = ">=1.8"
 
 [[package]]
 name = "sphinxcontrib-jsmath"
 version = "1.0.1"
 description = "A sphinx extension which renders display math in HTML via JavaScript"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2069,7 +2050,6 @@ test = ["flake8", "mypy", "pytest"]
 name = "sphinxcontrib-qthelp"
 version = "1.0.3"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp document."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2085,7 +2065,6 @@ test = ["pytest"]
 name = "sphinxcontrib-serializinghtml"
 version = "1.1.5"
 description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2101,7 +2080,6 @@ test = ["pytest"]
 name = "stack-data"
 version = "0.6.2"
 description = "Extract data from python stack frames and tracebacks for informative displays"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2117,11 +2095,24 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "tenacity"
+version = "8.2.2"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "tenacity-8.2.2-py3-none-any.whl", hash = "sha256:2f277afb21b851637e8f52e6a613ff08734c347dc19ade928e519d7d2d8569b0"},
+    {file = "tenacity-8.2.2.tar.gz", hash = "sha256:43af037822bd0029025877f3b2d97cc4d7bb0c2991000a3d59d71517c5c969e0"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx", "tornado (>=4.5)"]
+
 [[package]]
 name = "tinycss2"
 version = "1.2.1"
 description = "A tiny CSS parser"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2138,21 +2129,19 @@ test = ["flake8", "isort", "pytest"]
 
 [[package]]
 name = "tokenize-rt"
-version = "5.0.0"
+version = "5.1.0"
 description = "A wrapper around the stdlib `tokenize` which roundtrips."
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "tokenize_rt-5.0.0-py2.py3-none-any.whl", hash = "sha256:c67772c662c6b3dc65edf66808577968fb10badfc2042e3027196bed4daf9e5a"},
-    {file = "tokenize_rt-5.0.0.tar.gz", hash = "sha256:3160bc0c3e8491312d0485171dea861fc160a240f5f5766b72a1165408d10740"},
+    {file = "tokenize_rt-5.1.0-py2.py3-none-any.whl", hash = "sha256:9b7bb843e77dd6ed0be5564bfaaba200083911e0497841cd3e9235a6a9794d74"},
+    {file = "tokenize_rt-5.1.0.tar.gz", hash = "sha256:08f0c2daa94c4052e53c2fcaa8e32585e6ae9bdfc800974092d031401694e002"},
 ]
 
 [[package]]
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2162,30 +2151,28 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.2"
+version = "6.3.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
-category = "dev"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">= 3.8"
 files = [
-    {file = "tornado-6.2-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:20f638fd8cc85f3cbae3c732326e96addff0a15e22d80f049e00121651e82e72"},
-    {file = "tornado-6.2-cp37-abi3-macosx_10_9_x86_64.whl", hash = "sha256:87dcafae3e884462f90c90ecc200defe5e580a7fbbb4365eda7c7c1eb809ebc9"},
-    {file = "tornado-6.2-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac"},
-    {file = "tornado-6.2-cp37-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b8150f721c101abdef99073bf66d3903e292d851bee51910839831caba341a75"},
-    {file = "tornado-6.2-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e"},
-    {file = "tornado-6.2-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:5f8c52d219d4995388119af7ccaa0bcec289535747620116a58d830e7c25d8a8"},
-    {file = "tornado-6.2-cp37-abi3-musllinux_1_1_i686.whl", hash = "sha256:6fdfabffd8dfcb6cf887428849d30cf19a3ea34c2c248461e1f7d718ad30b66b"},
-    {file = "tornado-6.2-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:1d54d13ab8414ed44de07efecb97d4ef7c39f7438cf5e976ccd356bebb1b5fca"},
-    {file = "tornado-6.2-cp37-abi3-win32.whl", hash = "sha256:5c87076709343557ef8032934ce5f637dbb552efa7b21d08e89ae7619ed0eb23"},
-    {file = "tornado-6.2-cp37-abi3-win_amd64.whl", hash = "sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b"},
-    {file = "tornado-6.2.tar.gz", hash = "sha256:9b630419bde84ec666bfd7ea0a4cb2a8a651c2d5cccdbdd1972a0c859dfc3c13"},
+    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:c367ab6c0393d71171123ca5515c61ff62fe09024fa6bf299cd1339dc9456829"},
+    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b46a6ab20f5c7c1cb949c72c1994a4585d2eaa0be4853f50a03b5031e964fc7c"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c2de14066c4a38b4ecbbcd55c5cc4b5340eb04f1c5e81da7451ef555859c833f"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4"},
+    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b17b1cf5f8354efa3d37c6e28fdfd9c1c1e5122f2cb56dac121ac61baa47cbe"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:29e71c847a35f6e10ca3b5c2990a52ce38b233019d8e858b755ea6ce4dcdd19d"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:834ae7540ad3a83199a8da8f9f2d383e3c3d5130a328889e4cc991acc81e87a0"},
+    {file = "tornado-6.3.2-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:6a0848f1aea0d196a7c4f6772197cbe2abc4266f836b0aac76947872cd29b411"},
+    {file = "tornado-6.3.2-cp38-abi3-win32.whl", hash = "sha256:7efcbcc30b7c654eb6a8c9c9da787a851c18f8ccd4a5a3a95b05c7accfa068d2"},
+    {file = "tornado-6.3.2-cp38-abi3-win_amd64.whl", hash = "sha256:0c325e66c8123c606eea33084976c832aa4e766b7dff8aedd7587ea44a604cdf"},
+    {file = "tornado-6.3.2.tar.gz", hash = "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba"},
 ]
 
 [[package]]
 name = "traitlets"
 version = "5.9.0"
 description = "Traitlets Python configuration system"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2201,7 +2188,6 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 name = "types-pytz"
 version = "2023.3.0.0"
 description = "Typing stubs for pytz"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2211,21 +2197,19 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.5.0"
+version = "4.7.1"
 description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"},
-    {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"},
+    {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
+    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
 ]
 
 [[package]]
 name = "untokenize"
 version = "0.1.1"
 description = "Transforms tokens into original source code (while preserving whitespace)."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2234,26 +2218,25 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "1.26.14"
+version = "2.0.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = ">=3.7"
 files = [
-    {file = "urllib3-1.26.14-py2.py3-none-any.whl", hash = "sha256:75edcdc2f7d85b137124a6c3c9fc3933cdeaa12ecb9a6a959f22797a0feca7e1"},
-    {file = "urllib3-1.26.14.tar.gz", hash = "sha256:076907bf8fd355cde77728471316625a4d2f7e713c125f51953bb5b3eecf4f72"},
+    {file = "urllib3-2.0.3-py3-none-any.whl", hash = "sha256:48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1"},
+    {file = "urllib3-2.0.3.tar.gz", hash = "sha256:bee28b5e56addb8226c96f7f13ac28cb4c301dd5ea8a6ca179c0b9835e032825"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "wcwidth"
 version = "0.2.6"
 description = "Measures the displayed width of unicode strings in a terminal"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2265,7 +2248,6 @@ files = [
 name = "webencodings"
 version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2276,4 +2258,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10,<3.12"
-content-hash = "2fba1356f4ba8e6000188d2998c64c5a0bbfbe5fd12f7aa7f269b9cdd35be7e2"
+content-hash = "e7f96f37ea6db05fba7982dc91a7a265756b6e5822d299fb79b4c249055b299d"
diff --git a/pyproject.toml b/pyproject.toml
index 94f1fd1..0e2fe73 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "yabte"
-version = "0.3.5"
+version = "0.3.6"
 description = "Yet another backtesting engine"
 authors = ["Blair Azzopardi <blairuk@gmail.com>"]
 license = "MIT"
@@ -39,6 +39,7 @@ optional = true
 
 [tool.poetry.group.notebooks.dependencies]
 matplotlib = "^3.6.2"
+plotly = "^5.10.0"
 ipykernel = "^6.20.2"
 pyfeng = "^0.2.5"
 nbconvert = "^7.2.9"
diff --git a/readme_image.png b/readme_image.png
index ea4da18..b7f4602 100644
Binary files a/readme_image.png and b/readme_image.png differ
diff --git a/yabte/backtest/asset.py b/yabte/backtest/asset.py
index 1df6250..9eac99f 100644
--- a/yabte/backtest/asset.py
+++ b/yabte/backtest/asset.py
@@ -65,8 +65,8 @@ def check_and_fix_data(self, data: pd.DataFrame) -> pd.DataFrame:
 @mypyc_attr(allow_interpreted_subclasses=True)
 @dataclass(kw_only=True)
 class Asset(AssetBase):
-    """Assets whose price history is represented by High, Low, Open, Close and
-    Volume fields."""
+    """Assets whose price history is represented by High, Low, Open, Close and Volume
+    fields."""
 
     @property
     def fields_available_at_open(self) -> Sequence[str]:
diff --git a/yabte/backtest/order.py b/yabte/backtest/order.py
index baf7921..11c22ef 100644
--- a/yabte/backtest/order.py
+++ b/yabte/backtest/order.py
@@ -74,8 +74,8 @@ class OrderBase:
     key: Optional[str] = None
     """Unique key for this order.
 
-    If a key is set then only the newest order with this key is kept.
-    Older orders with the same key will be removed.
+    If a key is set then only the newest order with this key is kept. Older orders with
+    the same key will be removed.
     """
 
     def __post_init__(self):
@@ -94,8 +94,7 @@ def _book_trades(self, trades):
     def post_complete(self, trades: List[Trade]):
         """Called after and with trades that have been successfully booked.
 
-        It can append new orders to suborders for execution in the
-        following timestep.
+        It can append new orders to suborders for execution in the following timestep.
         """
         pass
 
@@ -149,8 +148,8 @@ def _calc_quantity_price(self, day_data, asset_map) -> Tuple[Decimal, Decimal]:
     def pre_execute_check(
         self, ts: pd.Timestamp, trade_price: Decimal
     ) -> Optional[OrderStatus]:
-        """Called with the current timestep and calculated trade price before
-        the trade is executed.
+        """Called with the current timestep and calculated trade price before the trade
+        is executed.
 
         If it returns `None`, the trade is executed as normal. It can
         return `OrderStatus.CANCELLED` to indicate the trade should be
diff --git a/yabte/backtest/strategy.py b/yabte/backtest/strategy.py
index 5f40033..252260b 100644
--- a/yabte/backtest/strategy.py
+++ b/yabte/backtest/strategy.py
@@ -49,8 +49,7 @@ def sort_by_priority(self):
     def remove_duplicate_keys(self) -> List[OrderBase]:
         """Remove older orders with same key.
 
-        Returns a list of orders than were removed with status set to
-        REPLACED.
+        Returns a list of orders than were removed with status set to REPLACED.
         """
         removed = []
         cntr = Counter(o.key for o in self.deque if o.key is not None)
@@ -210,11 +209,11 @@ class StrategyRunner:
     """
 
     data: pd.DataFrame = field()
-    """Dataframe of price data including columns High, Low, Open, Close, Volume
-    for each asset.
+    """Dataframe of price data including columns High, Low, Open, Close, Volume for each
+    asset.
 
-    Both asset name and field make a multiindex column. The index should
-    consist of order pandas timestamps.
+    Both asset name and field make a multiindex column. The index should consist of
+    order pandas timestamps.
     """
 
     assets: List[Asset]
@@ -232,8 +231,7 @@ class StrategyRunner:
     books: List[Book] = field(default_factory=list)
     """Books available to strategies.
 
-    If not supplied will be populated with single book named 'Main'
-    denominated in USD.
+    If not supplied will be populated with single book named 'Main' denominated in USD.
     """
 
     @property
diff --git a/yabte/tests/_unittest_numpy_extensions.py b/yabte/tests/_unittest_numpy_extensions.py
index 3ed455f..91651db 100644
--- a/yabte/tests/_unittest_numpy_extensions.py
+++ b/yabte/tests/_unittest_numpy_extensions.py
@@ -6,13 +6,13 @@
 
 
 class NumpyTestCase(unittest.TestCase):
-    """Specialized TestCase which includes numpy test assertion functions and
-    maps them from assert_func_name to self.numpyAssertFuncName.
+    """Specialized TestCase which includes numpy test assertion functions and maps them
+    from assert_func_name to self.numpyAssertFuncName.
 
     class NumpyTest(NumpyTestCase):
-    def test_allclose_example(self):
-        a1 = np.array([1.,2.,3.])
-        self.numpyAssertAllclose(a1, np.array([1.,2.,3.1]))
+        def test_allclose_example(self):
+            a1 = np.array([1.,2.,3.])
+            self.numpyAssertAllclose(a1, np.array([1.,2.,3.1]))
     """
 
 
diff --git a/yabte/utilities/plot/matplotlib/strategy_runner.py b/yabte/utilities/plot/matplotlib/strategy_runner.py
index 8438ae2..ccbf1b0 100644
--- a/yabte/utilities/plot/matplotlib/strategy_runner.py
+++ b/yabte/utilities/plot/matplotlib/strategy_runner.py
@@ -13,10 +13,9 @@
 def plot_strategy_runner(sr: StrategyRunner, settings: dict[str, Any] | None = None):
     """Display the results of a strategy run using matplotlib.
 
-    Plots a grid of charts with each column representing a book and rows
-    representing each asset's price series along with long/short
-    positioning and volumne series. A bottom row shows the value of each
-    book as a price series.
+    Plots a grid of charts with each column representing a book and rows representing
+    each asset's price series along with long/short positioning and volumne series. A
+    bottom row shows the value of each book as a price series.
     """
     default_settings = {
         "candle_body_width": 0.8,
diff --git a/yabte/utilities/plot/plotly/__init__.py b/yabte/utilities/plot/plotly/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/yabte/utilities/plot/plotly/strategy_runner.py b/yabte/utilities/plot/plotly/strategy_runner.py
new file mode 100644
index 0000000..2bd6b84
--- /dev/null
+++ b/yabte/utilities/plot/plotly/strategy_runner.py
@@ -0,0 +1,175 @@
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+from ....backtest import StrategyRunner
+
+
+def plot_strategy_runner(sr: StrategyRunner, settings: dict[str, Any] | None = None):
+    """Display the results of a strategy run using plotly.
+
+    Plots a grid of charts with each column representing a book and rows representing
+    each asset's price series along with long/short positioning and volumne series. A
+    bottom row shows the value of each book as a price series.
+    """
+    default_settings: dict[str, Any] = {}
+
+    if isinstance(settings, dict):
+        default_settings = default_settings | settings
+    s = pd.Series(default_settings, dtype=object)
+
+    traded_assets = [
+        a for a in sr.assets if a.name in sr.transaction_history.asset_name.unique()
+    ]
+
+    dpi = 100
+    col_width = 8 * dpi
+    row_unit_height = 3 * dpi
+    ncols = len(sr.books)
+    nrows = 1 + len(traded_assets)
+
+    subplot_titles = [a.name for a in (traded_assets)] + ["Book Value"]
+    row_heights = [10 for a in (traded_assets)] + [2]
+    specs = [[{"secondary_y": True} for c in range(ncols)] for r in range(nrows)]
+
+    fig = make_subplots(
+        rows=nrows,
+        cols=ncols,
+        shared_xaxes=True,
+        subplot_titles=subplot_titles,
+        row_heights=row_heights,
+        start_cell="top-left",
+        specs=specs,
+        vertical_spacing=0.05,
+    )
+
+    for col, book in enumerate(sr.books, start=1):
+        for row, asset in enumerate(traded_assets, start=1):
+            prices = sr.data[asset.data_label]
+
+            fig.add_trace(
+                go.Candlestick(
+                    x=prices.index,
+                    open=prices.Open,
+                    high=prices.High,
+                    low=prices.Low,
+                    close=prices.Close,
+                ),
+                row=row,
+                col=col,
+                secondary_y=True,
+            )
+
+            fig.add_trace(
+                go.Bar(
+                    x=prices.index,
+                    y=prices.Volume,
+                    marker_color="lightgrey",
+                ),
+                row=row,
+                col=col,
+                secondary_y=False,
+            )
+
+            fig.update_xaxes(rangeslider_visible=False, row=row, col=col)
+            fig.update_yaxes(
+                title=asset.denom, secondary_y=True, showgrid=True, row=row, col=col
+            )
+            fig.update_yaxes(
+                title="Volume", secondary_y=False, showgrid=False, row=row, col=col
+            )
+
+            # add some range selector buttons
+            fig.update_xaxes(
+                rangeselector=dict(
+                    buttons=[
+                        dict(count=1, label="1m", step="month", stepmode="backward"),
+                        dict(count=6, label="6m", step="month", stepmode="backward"),
+                        dict(count=1, label="YTD", step="year", stepmode="todate"),
+                        dict(count=1, label="1y", step="year", stepmode="backward"),
+                        dict(step="all"),
+                    ]
+                ),
+                row=1,
+                col=col,
+            )
+
+            trans_hist = sr.transaction_history.query(
+                "asset_name==@asset.name and book==@book.name"
+            )
+            pos_hist = (
+                trans_hist.groupby("ts")
+                .agg(
+                    quantity=("quantity", np.sum),
+                    labels=(
+                        "order_label",
+                        lambda L: " ".join(l for l in L if l is not None),
+                    ),
+                )
+                .reindex(prices.index)
+            )
+
+            shorts = prices[pos_hist.eval("quantity < 0")][["Low"]].join(
+                pos_hist.labels
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=shorts.index,
+                    y=shorts.Low,
+                    customdata=shorts.labels,
+                    mode="markers",
+                    marker_symbol="arrow-down",
+                    marker_color="red",
+                    marker_size=10,
+                    hovertemplate="%{x}<br>%{y}<br>%{customdata}<extra></extra>",
+                ),
+                row=row,
+                col=col,
+                secondary_y=True,
+            )
+
+            longs = prices[pos_hist.eval("quantity > 0")][["High"]].join(
+                pos_hist.labels
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=longs.index,
+                    y=longs.High,
+                    customdata=shorts.labels,
+                    mode="markers",
+                    marker_symbol="arrow-up",
+                    marker_color="green",
+                    marker_size=10,
+                    hovertemplate="%{x}<br>%{y}<br>%{customdata}<extra></extra>",
+                ),
+                row=row,
+                col=col,
+                secondary_y=True,
+            )
+
+    row = nrows
+    bh = sr.book_history.loc[:, book.name]
+    fig.add_trace(
+        go.Scatter(
+            x=bh.index,
+            y=bh.total,
+        ),
+        row=row,
+        col=col,
+    )
+
+    fig.update_xaxes(rangeslider_visible=True, row=row, col=col)
+    fig.update_yaxes(title=book.denom, showgrid=True, row=row, col=col)
+    fig.update_xaxes(title="Date", row=row, col=col)
+
+    fig.update_layout(
+        height=nrows * row_unit_height,
+        width=ncols * col_width,
+        showlegend=False,
+        title_text="Strategy Runner Report",
+    )
+
+    return fig
diff --git a/yabte/utilities/portopt/hierarchical_risk_parity.py b/yabte/utilities/portopt/hierarchical_risk_parity.py
index bdaa14b..be217bc 100644
--- a/yabte/utilities/portopt/hierarchical_risk_parity.py
+++ b/yabte/utilities/portopt/hierarchical_risk_parity.py
@@ -57,8 +57,8 @@ def _getRecBipart(cov, sortIx):
 
 
 def hrp(corr: pd.DataFrame, sigma: np.ndarray) -> np.ndarray:
-    """Calculate weights using hierarchical risk parity and scipy's
-    linkage/to_tree functions."""
+    """Calculate weights using hierarchical risk parity and scipy's linkage/to_tree
+    functions."""
     cov = np.diag(sigma) @ corr @ np.diag(sigma)
     cov.index, cov.columns = corr.index, corr.columns
     rho = corr.values
diff --git a/yabte/utilities/portopt/minimum_variance.py b/yabte/utilities/portopt/minimum_variance.py
index e90cd9e..d31e40d 100644
--- a/yabte/utilities/portopt/minimum_variance.py
+++ b/yabte/utilities/portopt/minimum_variance.py
@@ -33,8 +33,8 @@ def minimum_variance(Sigma: np.ndarray, mu: np.ndarray, r: float) -> np.ndarray:
 
 
 def minimum_variance_numeric(Sigma: np.ndarray, mu: np.ndarray, r: float) -> np.ndarray:
-    """Calculate weights using Lagrangian multipliers and numeric solution
-    (using scipy's root function)."""
+    """Calculate weights using Lagrangian multipliers and numeric solution (using
+    scipy's root function)."""
     m = len(mu)
     ones = np.ones(m)
 
@@ -52,8 +52,8 @@ def minimum_variance_numeric(Sigma: np.ndarray, mu: np.ndarray, r: float) -> np.
 def minimum_variance_numeric_slsqp(
     Sigma: np.ndarray, mu: np.ndarray, r: float
 ) -> np.ndarray:
-    """Calculate weights using Lagrangian multipliers and numeric solution
-    (using scipy's minimize function)."""
+    """Calculate weights using Lagrangian multipliers and numeric solution (using
+    scipy's minimize function)."""
     from scipy.optimize import minimize
 
     m = len(mu)