diff --git a/.github/workflows/build-release.yml b/.github/workflows/build-release.yml
index 7c40db08b247..f190c65a959a 100644
--- a/.github/workflows/build-release.yml
+++ b/.github/workflows/build-release.yml
@@ -10,8 +10,7 @@ env:
on:
push:
branches:
- - release/*
- - main
+ - release/v3/*
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
diff --git a/.github/workflows/labels-PR.yml b/.github/workflows/labels-PR.yml
index 1f2f3f186b81..fa84744e432a 100644
--- a/.github/workflows/labels-PR.yml
+++ b/.github/workflows/labels-PR.yml
@@ -10,7 +10,7 @@ jobs:
with:
mode: minimum
count: 1
- labels: "guides, bug, build, do not merge, docker, docs, duplicate, enhancement, feat XS, feat S, feat M, feat L, feat XL, help wanted, refactor, tests, discord, dependencies, notebookreports, dashboards, analytics, portfolio, installer, SDK, release "
+ labels: "guides, bug, build, docker, docs, feat XS, feat S, feat M, feat L, feat XL, help wanted, refactor, tests, dependencies, release"
- uses: mheap/github-action-required-labels@v1
with:
mode: exactly
diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
index 588a8dfeb962..efc2fe5d9d8f 100644
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -3,8 +3,7 @@ name: Deploy to PyPI
on:
push:
branches:
- - release/*
- - main
+ - release/v3/*
workflow_dispatch:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 9b5d014d865c..6fa6be077afa 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -135,7 +135,7 @@ def get_price_targets(cls, symbol: str) -> pd.DataFrame:
url = f"https://financialmodelingprep.com/api/v4/price-target?symbol={symbol}&apikey={current_user.credentials.API_KEY_FINANCIALMODELINGPREP}"
response = request(url)
- # Check if response is valid
+ # Check if the response is valid
if response.status_code != 200 or "Error Message" in response.json():
message = f"Error, Status Code: {response.status_code}."
message = (
@@ -345,7 +345,7 @@ Now that we have the model and views, it is time to add to the controller.
- Call the function contained in a `_view.py` file with the arguments parsed by argparse.
Note that the function self.parse_known_args_and_warn() has some additional options we can add. If the function is showing a chart, but we want the option to show raw data, we can add the `raw=True` keyword and the resulting namespace will have the `raw` attribute.
-Same with limit, we can pass limit=10 to add the `-l` flag with default=10. Here we also specify the export, and whether it is data only, plots only or anything. This function also adds the `source` attribute to the namespace. In our example, this is important because we added an additional source.
+Same with limit, we can pass limit=10 to add the `-l` flag with default=10. Here we also specify the export, and whether it is data only, plots only or anything else. This function also adds the `source` attribute to the namespace. In our example, this is important because we added an additional source.
Our new function will be:
@@ -511,7 +511,7 @@ In order to add a command to the SDK, follow these steps:
### Add OpenBB Platform endpoint (V4)
-Refer to the documentation [here](openbb_platform\CONTRIBUTING.md).
+Refer to the documentation [here](https://docs.openbb.co/platform/development).
### Add Unit Tests
@@ -519,7 +519,7 @@ This is a vital part of the contribution process. We have a set of unit tests th
Unit tests minimize errors in code and quickly find errors when they do arise. Integration tests are standard usage examples, which are also used to identify errors.
-A thorough introduction on the usage of unit tests and integration tests in OpenBBTerminal can be found on the following page respectively:
+A thorough introduction on the usage of unit tests and integration tests in OpenBBTerminal can be found on the following page:
[Unit Test README](tests/README.md)
@@ -578,7 +578,7 @@ CLI :computer: → `_controller.py` :robot: → `_view.py` :art: &nbs
| **File ** | **Role** | **Description** |
| :------------------------- | :------------- | :----------------------------------------------------- |
| **_controller.py** :robot: | The router/input validator | The controller file should hold the least amount of logic possible. Its role is to be a stupid (no logic) router and redirect the command correctly while checking the input with argparser. |
-| **_view.py** :art: | The artist | The view file should only output or visualise the data it gets from the `_model` file! The `_view` can limit the data coming from the `_model`, otherwise the data object should be identical in the `_view` and the `_model` files. |
+| **_view.py** :art: | The artist | The view file should only output or visualize the data it gets from the `_model` file! The `_view` can limit the data coming from the `_model`, otherwise the data object should be identical in the `_view` and the `_model` files. |
| **_model.py** 🧠 |The brain | The model file is where everything fun happens. The data is gathered (external APIs), processed and returned here. |
| **sdk.py** 🏭 |The SDK Factory | The SDK file is where the callable functions are created for the SDK. There is only one SDK file in the openbb_terminal folder. |
diff --git a/README.md b/README.md
index 598a545fd634..021f5014a428 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,9 @@
+
+
+
The first financial terminal that is free and fully open source. With over 600 commands, the terminal has access to equity, options, crypto, forex, macro economy, fixed income, alternative datasets, and more.
diff --git a/build/README.md b/build/README.md
index 3f889704c52b..0d9dcc1b1b21 100644
--- a/build/README.md
+++ b/build/README.md
@@ -1,11 +1,11 @@
# Building Installers
-This guide serves to assist developers construct OpenBB Terminal installers on MacOs and Windows.
+This guide serves to assist developers construct OpenBB Terminal installers on macOS and Windows.
## Overview
-Building an installer takes source code as input and produces deployable software as an output. For MacOS the output is a DMG while for windows it is an EXE. This process utilizes two major modules: Pyinstaller & Create-dmg. Windows additionally recruits the service of NSIS. There are two ways to build an installer: locally or through Github.
+Building an installer takes source code as input and produces deployable software as an output. For MacOS the output is a DMG while for Windows it is an EXE. This process utilizes two major modules: Pyinstaller & Create-dmg. Windows additionally recruits the service of NSIS. There are two ways to build an installer: locally or through Github.
## Local
-In this section we detail the steps necessary to construct an installer locally on a windows machine and mac machine. These steps can be performed on either one of these operating systems so long as they have the OpenBBTerminal repository codebase. This process assumes you already have a working conda environment. Building locally usually takes anywhere from 10-20 minutes.
+In this section we detail the steps necessary to construct an installer locally on a Windows machine and Mac machine. These steps can be performed on either one of these operating systems so long as they have the OpenBBTerminal repository codebase. This process assumes you already have a working conda environment. Building locally usually takes anywhere from 10-20 minutes.
### MacOS Steps
1. `brew install create-dmg`
diff --git a/build/pyinstaller/macOS/darwin/Resources/conclusion.html b/build/pyinstaller/macOS/darwin/Resources/conclusion.html
index b6fc0e8b2574..b8e904734691 100644
--- a/build/pyinstaller/macOS/darwin/Resources/conclusion.html
+++ b/build/pyinstaller/macOS/darwin/Resources/conclusion.html
@@ -9,7 +9,7 @@
Thank you for installing WSO2 API MAnager.
Open a new terminal and run the following command to get started with APIM server:
diff --git a/build/pyinstaller/macOS/darwin/Resources/uninstall.sh b/build/pyinstaller/macOS/darwin/Resources/uninstall.sh
index 27873ddf9f73..09e364e9019d 100644
--- a/build/pyinstaller/macOS/darwin/Resources/uninstall.sh
+++ b/build/pyinstaller/macOS/darwin/Resources/uninstall.sh
@@ -42,7 +42,7 @@ VERSION=__VERSION__
PRODUCT=__PRODUCT__
echo "Application uninstalling process started"
-# remove link to shorcut file
+# remove link to shortcut file
find "/usr/local/bin/" -name "__PRODUCT__-__VERSION__" | xargs rm
if [ $? -eq 0 ]
then
diff --git a/build/pypi/openbb_platform/nightly.py b/build/pypi/openbb_platform/nightly.py
index 6212f80e8825..fc002f5cb8aa 100644
--- a/build/pypi/openbb_platform/nightly.py
+++ b/build/pypi/openbb_platform/nightly.py
@@ -10,7 +10,10 @@
SUB_PACKAGES = {}
DEPENDENCIES: Dict[str, Any] = {}
-PLUGINS = {"openbb_core_extension": {}, "openbb_provider_extension": {}}
+PLUGINS: Dict[str, Dict] = {
+ "openbb_core_extension": {},
+ "openbb_provider_extension": {},
+}
CMD = [sys.executable, "-m", "poetry", "build"]
@@ -44,7 +47,7 @@ def gather_metadata(sub_path: str):
def build():
"""Build the Platform package."""
- for sub_path in ["platform", "providers", "extensions"]:
+ for sub_path in ["core", "providers", "extensions"]:
gather_metadata(sub_path)
# need to pop these from the dependencies
diff --git a/build/pypi/openbb_platform/publish.py b/build/pypi/openbb_platform/publish.py
index c2955986b4a6..cfb856c56380 100644
--- a/build/pypi/openbb_platform/publish.py
+++ b/build/pypi/openbb_platform/publish.py
@@ -69,7 +69,7 @@ def publish(dry_run: bool = False, core: bool = False, extensions: bool = False)
for path in PLATFORM_PATH.rglob(f"{sub_path}/**/pyproject.toml"):
try:
# Update dependencies
- if is_extension:
+ if is_extension and "devtools" not in str(path):
update_extension_dependencies(path)
# Bump version
bump_version(path)
diff --git a/examples/README.md b/examples/README.md
index bf63622353d1..9c3be4b32834 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -1,76 +1,49 @@
-# Jupyter Notebook Examples Using the OpenBB SDK
+# Jupyter Notebook Examples Using the OpenBB Platform
-This folder is a collection of example notebooks that demonstrate some of the ways to get started using the OpenBB SDK. To run them, ensure that the active kernel selected is the same Python virtual environment where OpenBB was installed.
+This folder is a collection of example notebooks that demonstrate some of the ways to get started with using the OpenBB Platform. To run them, ensure that the active kernel selected is the same Python virtual environment where OpenBB was installed.
## Table of Contents
-### loadHistoricalPriceData
-
-This notebook walks through collecting historical price data using a variety of methods and sources.
-
-- Loading data with different intervals, and changing sources.
-- A brief explanation of ticker symbology.
-- Using other functions and modules to load data with besides `openbb.stocks.load()`.
-- Gang-loading a list of tickers' price data in a single call.
-- Drawing candle and line charts.
-
-### copperToGoldRatio
-
-This notebook explains how to calculate and plot the Copper-to-Gold ratio.
-
-- Loading historical front-month futures prices.
-- Getting the historical series from FRED for the 10-year constant maturity US treasury bill.
-- Performing basic DataFrame operations.
-- Creating OpenBB Figure objects, and plotting on two y-axis.
-
-### realizedVolatilityModels
+### googleColab
-This notebook demonstrates the six, realized volatility models with in the Technical Analysis module.
+This notebook installs the OpenBB Platform in a Google Colab environment with examples for:
-- Explore differences between:
- - Standard Deviation
- - Parkinson
- - Hodges-Tompkins
- - Garman-Klass
- - Rogers-Satchell
- - Yang-Zhang
-- Creating and plotting realized volatility cones.
-- Overlaying multiple time series.
-- Use the calculated outputs as inputs to a forecast model.
+- Logging into OpenBB Hub
+- Setting the output preference
+- Fetching options and company fundamentals data
+- Creating bar chart visualizations
-### usdLiquidityIndex
+### findSymbols
-This notebook demonstrates how to query the Federal Reserve Economic Database and recreate the USD Liquidity Index.
+This notebook provides an introduction to discovering, finding, and searching ticker symbols.
-- Search FRED for series IDs.
-- Load multiple series as a single call.
-- Unpacking the data response from the FRED query.
-- Perform arithmetic operations on a DataFrame.
-- Normalization methods for a series or DataFrame.
-- Simple processes for creating charts.
+- Search
+- Find company and institutional filings
+- Screen stocks by region and metrics
-### optionsChains
+### loadHistoricalPriceData
-This notebook demonstrates how to fetch and work with options chains data.
+This notebook walks through collecting historical price data, at different intervals, using a variety of sources.
-- Use the new method for loading options chains data.
-- Examine the differences of the data, between sources.
-- Calculate and visualize volume and open interest by strike and expiration.
-- Calculate and visualize the Net Delta Exposure, in dollars, by strike and expiration.
-- Visualize the term structure of the implied volatility skew.
-- Get tables with multiple single-leg options strategies.
+- Loading data with different intervals, and changing sources
+- A brief explanation of ticker symbology
+- Resampling a time series index
+- Some differences between providers, and comparing outputs
-### googleColabInstallation
+### financialStatements
-This notebook demonstrates how to install the OpenBB SDK within Google Colab.
+This set of examples introduces financial statements in the OpenBB Platform and compares the free cash flow yields of large-cap retail industry companies.
+- Financial statements
+- What to expect with data from different sources
+- Financial attributes
+- Ratios and other metrics
-### volumeAtPrice
+### copperToGoldRatio
-Volume-at-price is a chart used by technical analysts as a visual gauge to where the trading levels are concentrated, relative to price and volume. In short, it is the sum of volume at different price levels. This notebook utilizes individual components of the OpenBB SDK and combines them to build a new view.
+This notebook explains how to calculate and plot the Copper-to-Gold ratio.
-- Price
-- Volume
-- VWAP
-- SMA
-- OpenBBFigure
+- Loading historical front-month futures prices.
+- Getting the historical series from FRED for the 10-year constant maturity US treasury bill.
+- Performing basic DataFrame operations.
+- Creating charts with Plotly Graph Objects.
diff --git a/examples/copperToGoldRatio.ipynb b/examples/copperToGoldRatio.ipynb
index 45f5a7f7860d..e2ccf423114b 100644
--- a/examples/copperToGoldRatio.ipynb
+++ b/examples/copperToGoldRatio.ipynb
@@ -9,21 +9,20 @@
"\n",
"The copper-to-gold ratio is known as a leading economic indicator. It is most commonly paired as a time series with the ten-year US Treasury yield. The notable events are the divergences in directional movement, signaling a fundamental regime change that will unfold over months and years. Not something to go YOLO into, but a metric to shape a long-term view of global economic conditions.\n",
"\n",
- "The ratio is defined as dividing the spot price of one ounce of copper by an ounce gold. How much copper is bought with one ouce of gold. Sounds simple enough, divide the price of copper by the price of gold, done. The OpenBB SDK can make quick work out of this task, really quick. Let's explore."
+ "The ratio is defined as dividing the spot price of one ounce of copper by an ounce gold. How much copper is bought with one ouce of gold. Sounds simple enough, divide the price of copper by the price of gold, done. The OpenBB Platform can make quick work out of this task, really quick. Let's explore."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
- "# Import the SDK and Pandas for some calculations.\n",
- "from datetime import datetime\n",
- "import pandas as pd\n",
- "from openbb_terminal.sdk import openbb\n",
+ "# Import the Platform and Pandas for some calculations.\n",
"\n",
- "from openbb_terminal import OpenBBFigure"
+ "import pandas as pd\n",
+ "import plotly.graph_objects as go\n",
+ "from openbb import obb"
]
},
{
@@ -31,24 +30,27 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The most accessible data is going to be the continuous front-month futures contracts for physical delivery, listed on the CME. We'll create a Pandas Series for each asset, requesting all the daily historical data that is available."
+ "The most accessible data is going to be the continuous front-month futures contracts for physical delivery, listed on the CME. We'll create a Pandas Series for each asset, requesting weekly historical data using the `openbb-yfinance` data extension."
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
- "data = pd.DataFrame(\n",
- " openbb.futures.historical(\n",
- " symbols = [\"GC\", \"HG\"],\n",
- " start_date=\"2000-01-01\",\n",
- " end_date = str(datetime.now().date())\n",
- " )[\"Close\"]\n",
- ").rename(\n",
- " columns={\"GC\": \"Gold\", \"HG\": \"Copper\"}\n",
- ")"
+ "data = pd.DataFrame()\n",
+ "cols_dict = {\"GC\": \"Gold\", \"HG\": \"Copper\"}\n",
+ "for symbol in [\"GC\", \"HG\"]:\n",
+ " data[symbol] = (\n",
+ " obb.derivatives.futures.historical(\n",
+ " symbol,\n",
+ " start_date=\"2000-01-01\",\n",
+ " interval=\"1wk\",\n",
+ " ).to_df()[\"close\"].rename(symbol)\n",
+ " )\n",
+ "data.columns = cols_dict.values()\n",
+ "data.index = data.index.strftime(\"%Y-%m-%d\")"
]
},
{
@@ -61,7 +63,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
@@ -89,21 +91,21 @@
"
Copper | \n",
" \n",
"
\n",
- " Date | \n",
+ " date | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
" \n",
- " 2000-08-30 | \n",
- " 273.899994 | \n",
- " 0.885 | \n",
+ " 2000-08-28 | \n",
+ " 277.000000 | \n",
+ " 0.889 | \n",
"
\n",
" \n",
- " 2000-08-31 | \n",
- " 278.299988 | \n",
- " 0.885 | \n",
+ " 2000-09-04 | \n",
+ " 273.299988 | \n",
+ " 0.912 | \n",
"
\n",
" \n",
"\n",
@@ -111,12 +113,12 @@
],
"text/plain": [
" Gold Copper\n",
- "Date \n",
- "2000-08-30 273.899994 0.885\n",
- "2000-08-31 278.299988 0.885"
+ "date \n",
+ "2000-08-28 277.000000 0.889\n",
+ "2000-09-04 273.299988 0.912"
]
},
- "execution_count": 13,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -135,7 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
@@ -164,7 +166,7 @@
"
Copper/Gold Ratio | \n",
" \n",
"
\n",
- " Date | \n",
+ " date | \n",
" | \n",
" | \n",
" | \n",
@@ -172,16 +174,16 @@
" \n",
"
\n",
" \n",
- " 2023-05-24 | \n",
- " 1962.800049 | \n",
- " 3.5535 | \n",
- " 0.001810 | \n",
+ " 2023-11-20 | \n",
+ " 2002.199951 | \n",
+ " 3.7965 | \n",
+ " 0.001896 | \n",
"
\n",
" \n",
- " 2023-05-25 | \n",
- " 1943.099976 | \n",
- " 3.5805 | \n",
- " 0.001843 | \n",
+ " 2023-11-27 | \n",
+ " 2011.800049 | \n",
+ " 3.7625 | \n",
+ " 0.001870 | \n",
"
\n",
" \n",
"\n",
@@ -189,18 +191,18 @@
],
"text/plain": [
" Gold Copper Copper/Gold Ratio\n",
- "Date \n",
- "2023-05-24 1962.800049 3.5535 0.001810\n",
- "2023-05-25 1943.099976 3.5805 0.001843"
+ "date \n",
+ "2023-11-20 2002.199951 3.7965 0.001896\n",
+ "2023-11-27 2011.800049 3.7625 0.001870"
]
},
- "execution_count": 14,
+ "execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "data[\"Copper/Gold Ratio\"] = data['Copper']/data['Gold']\n",
+ "data[\"Copper/Gold Ratio\"] = data[\"Copper\"]/data[\"Gold\"]\n",
"\n",
"data.tail(2)"
]
@@ -215,7 +217,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 65,
"metadata": {},
"outputs": [
{
@@ -244,7 +246,7 @@
"
Copper/Gold Ratio | \n",
" \n",
"
\n",
- " Date | \n",
+ " date | \n",
" | \n",
" | \n",
" | \n",
@@ -252,16 +254,16 @@
" \n",
"
\n",
" \n",
- " 2023-05-24 | \n",
- " 1962.800049 | \n",
- " 3.5535 | \n",
- " 1.810424 | \n",
+ " 2023-11-20 | \n",
+ " 2002.199951 | \n",
+ " 3.7965 | \n",
+ " 1.896164 | \n",
"
\n",
" \n",
- " 2023-05-25 | \n",
- " 1943.099976 | \n",
- " 3.5805 | \n",
- " 1.842674 | \n",
+ " 2023-11-27 | \n",
+ " 2011.800049 | \n",
+ " 3.7625 | \n",
+ " 1.870216 | \n",
"
\n",
" \n",
"\n",
@@ -269,18 +271,18 @@
],
"text/plain": [
" Gold Copper Copper/Gold Ratio\n",
- "Date \n",
- "2023-05-24 1962.800049 3.5535 1.810424\n",
- "2023-05-25 1943.099976 3.5805 1.842674"
+ "date \n",
+ "2023-11-20 2002.199951 3.7965 1.896164\n",
+ "2023-11-27 2011.800049 3.7625 1.870216"
]
},
- "execution_count": 15,
+ "execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "data[\"Copper/Gold Ratio\"] = (data['Copper']/data['Gold']) * 1000\n",
+ "data[\"Copper/Gold Ratio\"] = (data[\"Copper\"]/data[\"Gold\"]) * 1000\n",
"\n",
"data.tail(2)"
]
@@ -290,12 +292,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now let's add a column for the daily 10 Year US Treasury Yield. This can be requested using the `fred` function within the `economy` module. The first line in the block below requests the data, the second assigns it to a column in the target DataFrame."
+ "Now let's add a column for the daily 10 Year US Treasury Yield. This can be requested using the `fred_series` function within the `economy` module. The first line in the block below requests the data, the second assigns it to a column in the target DataFrame."
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
@@ -325,7 +327,7 @@
"
US 10-Year Constant Maturity | \n",
" \n",
"
\n",
- " Date | \n",
+ " date | \n",
" | \n",
" | \n",
" | \n",
@@ -334,18 +336,18 @@
" \n",
"
\n",
" \n",
- " 2000-08-30 | \n",
- " 273.899994 | \n",
- " 0.885 | \n",
- " 3.231106 | \n",
- " 5.81 | \n",
+ " 2000-08-28 | \n",
+ " 277.000000 | \n",
+ " 0.889 | \n",
+ " 3.209386 | \n",
+ " 5.78 | \n",
"
\n",
" \n",
- " 2000-08-31 | \n",
- " 278.299988 | \n",
- " 0.885 | \n",
- " 3.180022 | \n",
- " 5.73 | \n",
+ " 2000-09-04 | \n",
+ " 273.299988 | \n",
+ " 0.912 | \n",
+ " 3.336992 | \n",
+ " 5.68 | \n",
"
\n",
" \n",
"\n",
@@ -353,24 +355,31 @@
],
"text/plain": [
" Gold Copper Copper/Gold Ratio \\\n",
- "Date \n",
- "2000-08-30 273.899994 0.885 3.231106 \n",
- "2000-08-31 278.299988 0.885 3.180022 \n",
+ "date \n",
+ "2000-08-28 277.000000 0.889 3.209386 \n",
+ "2000-09-04 273.299988 0.912 3.336992 \n",
"\n",
" US 10-Year Constant Maturity \n",
- "Date \n",
- "2000-08-30 5.81 \n",
- "2000-08-31 5.73 "
+ "date \n",
+ "2000-08-28 5.78 \n",
+ "2000-09-04 5.68 "
]
},
- "execution_count": 16,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "us10year = openbb.economy.fred(['DGS10'])[0]\n",
- "data['US 10-Year Constant Maturity'] = us10year[\"2000-08-30\":]\n",
+ "us10year = obb.economy.fred_series(\n",
+ " \"DGS10\",\n",
+ " frequency=\"wem\",\n",
+ " start_date=\"2000-08-28\",\n",
+ " end_date=\"2023-11-27\"\n",
+ ").to_df()[[\"DGS10\"]]\n",
+ "\n",
+ "us10year.index = us10year.index.strftime(\"%Y-%m-%d\")\n",
+ "data[\"US 10-Year Constant Maturity\"] = us10year[\"DGS10\"]\n",
"\n",
"data.head(2)"
]
@@ -385,7 +394,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
@@ -399,22863 +408,4871 @@
"name": "Copper/Gold Ratio (x1000) %",
"type": "scatter",
"x": [
- "2000-08-30T00:00:00",
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+ "2002-09-02",
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- 4.37,
- 4.28,
- 4.35,
- 4.27,
4.28,
- 4.29,
- 4.2,
- 4.19,
- 4.17,
4.26,
- 4.24,
- 4.16,
- 4.12,
- 4.04,
4.09,
- 3.96,
- 3.96,
- 4.03,
- 4.21,
4.17,
- 4.27,
- 4.27,
- 4.32,
4.29,
- null,
- 4.37,
- 4.43,
- 4.47,
4.41,
- 4.41,
- 4.38,
- 4.29,
- 4.34,
- 4.24,
4.3,
- 4.23,
- 4.31,
- 4.36,
- 4.33,
4.4,
- 4.33,
- 4.38,
- 4.45,
- 4.48,
4.49,
- null,
- 4.44,
- 4.3,
- 4.22,
4.18,
- 4.17,
- 4.24,
- 4.16,
- 4.15,
4.23,
- 4.19,
- 4.25,
4.4,
- 4.38,
- 4.41,
- 4.38,
- 4.23,
4.29,
- 4.32,
- 4.3,
- 4.27,
- 4.26,
4.28,
- 4.24,
- 4.19,
- 4.16,
- 4.15,
4.18,
- 4.28,
- 4.2,
4.24,
- 4.29,
- 4.27,
4.41,
- 4.29,
- 4.27,
- 4.27,
4.11,
- 4.11,
- 4.05,
- 4.01,
- 3.99,
4.04,
- 4.08,
- 4.05,
- 3.99,
- 4.09,
- 4.16,
- 4.11,
- 4.22,
- 4.22,
4.16,
4.18,
- 4.13,
- 4.15,
- 4.2,
- 4.12,
4.09,
- 4.13,
- 4.05,
- 4.1,
- 4.05,
- 4.05,
4.05,
4.05,
- 4.1,
- 4.05,
- 4.04,
- 4.02,
- 4.05,
- 3.99,
4,
- 4.05,
- 4.07,
- 4.04,
- 3.85,
- 3.78,
- 3.73,
- 3.74,
- 3.74,
3.78,
3.78,
- 3.7,
- 3.71,
- 3.76,
- 3.8,
3.74,
- 3.73,
- 3.73,
- 3.75,
- 3.85,
- 3.91,
- 3.91,
- 3.86,
3.91,
- 4.15,
4.24,
- 4.19,
- 4.19,
- 4.21,
4.25,
- 4.35,
- 4.4,
- 4.42,
- 4.37,
4.39,
- 4.43,
- 4.45,
- 4.4,
- 4.48,
4.46,
- 4.43,
- 4.5,
- 4.55,
- 4.53,
4.53,
- 4.56,
- 4.61,
- 4.63,
- 4.79,
4.81,
- 4.79,
- 4.83,
- 4.85,
- 4.79,
4.7,
- 4.74,
- 4.79,
- 4.72,
- 4.76,
4.75,
- 4.73,
- 4.67,
- 4.6,
4.66,
- 4.71,
- 4.74,
- 4.71,
- 4.78,
- 4.78,
4.78,
- 4.82,
- 4.81,
4.89,
- 4.69,
- 4.74,
- 4.71,
- 4.72,
4.7,
- 4.72,
- 4.71,
- 4.66,
- 4.66,
4.76,
- 4.7,
- 4.62,
- 4.57,
4.48,
- 4.49,
- 4.5,
- 4.49,
- 4.49,
4.46,
- 4.5,
- 4.5,
- 4.5,
4.38,
- 4.38,
- 4.47,
- 4.5,
- 4.48,
- 4.45,
4.49,
- 4.62,
- 4.61,
- 4.6,
- 4.5,
4.48,
- 4.45,
- 4.45,
- 4.43,
- 4.24,
4.28,
- 4.32,
- 4.3,
- 4.27,
- 4.22,
4.26,
- 4.21,
- 4.23,
- 4.22,
- 4.24,
- 4.28,
4.28,
- 4.26,
- 4.22,
- 4.23,
4.19,
- 4.13,
- 4.13,
- 4.2,
4.3,
- 4.26,
- 4.18,
- 4.22,
- 4.19,
4.16,
- 4.15,
- 4.18,
- 4.08,
- 4.14,
4.07,
- 4.05,
- 4,
- 4.02,
- 4.04,
4.01,
- 4.02,
- 4.1,
- 4.14,
- 4.21,
4.19,
- 4.18,
- 4.23,
- 4.26,
4.15,
- null,
- 4.12,
- 4.09,
- 4.03,
- 4.07,
4.07,
- 4.07,
- 4.01,
- 4.01,
- 4,
3.99,
- 4.01,
- 4.11,
- 4.09,
- 4.05,
4.11,
- 4.1,
- 4.09,
- 4.1,
- 4.21,
4.22,
- 4.22,
- 4.25,
- null,
- 4.2,
- 4.2,
- 4.21,
- 4.14,
- 4.12,
4.2,
4.18,
- 4.19,
- 4.2,
4.34,
- 4.36,
- 4.38,
- 4.4,
- 4.27,
4.24,
- 4.23,
- 4.14,
- 4.19,
- 4.16,
4.16,
- 4.14,
- 4.09,
- 4.19,
- 4.21,
- 4.21,
- 4.18,
4.21,
- 4.23,
4.3,
- 4.31,
- 4.33,
- 4.27,
4.23,
4.29,
- 4.29,
- 4.29,
- 4.29,
- 4.29,
- 4.26,
- 4.25,
- 4.2,
4.23,
- 4.21,
- 4.2,
- 4.17,
- 4.16,
4.14,
- 4.2,
- 4.21,
- 4.22,
- 4.16,
4.14,
- 4.15,
- 4.15,
- 4.18,
- 4.09,
- 4.07,
- 4.05,
- 4,
4.07,
- 4.1,
4.08,
- 4.1,
- 4.16,
- 4.19,
- 4.27,
- 4.29,
- 4.27,
- 4.29,
4.27,
4.36,
- 4.38,
- 4.38,
- 4.39,
- 4.32,
4.31,
- 4.38,
- 4.52,
- 4.48,
- 4.56,
- 4.52,
- 4.54,
4.52,
- 4.47,
- 4.51,
4.53,
- 4.63,
- 4.61,
- 4.6,
4.64,
- 4.6,
- 4.5,
- 4.46,
4.47,
- 4.48,
- 4.44,
- 4.49,
- 4.5,
4.45,
- 4.38,
- 4.38,
- 4.37,
4.27,
- 4.27,
- 4.21,
- 4.22,
- 4.32,
4.26,
- 4.26,
- 4.28,
- 4.25,
- 4.21,
4.21,
- 4.21,
- 4.2,
- 4.19,
- 4.28,
4.29,
- 4.23,
- 4.21,
- 4.18,
- 4.12,
- 4.13,
- 4.12,
- 4.07,
- 4.11,
4.13,
4.07,
- 4.04,
- 4.08,
4.08,
- 4.08,
- 4,
- 3.91,
- 3.89,
- 3.98,
3.96,
- 3.92,
- 3.95,
- 3.98,
- 4.05,
- 4.09,
- 4.13,
- 4.12,
- 4.09,
4.09,
4.11,
- 4.06,
- 3.95,
- 3.96,
- 3.92,
3.9,
- 3.97,
- 3.99,
- 3.94,
4.06,
4.11,
- 4.08,
- 4.05,
- 4.11,
- 4.11,
- 4.15,
- 4.17,
- 4.19,
- 4.18,
4.22,
- 4.2,
- 4.17,
- 4.28,
- 4.23,
4.25,
- 4.24,
- 4.27,
- 4.2,
- 4.28,
- 4.32,
- 4.34,
- 4.3,
4.32,
- 4.4,
4.42,
- 4.41,
- 4.4,
- 4.32,
- 4.24,
4.27,
- 4.23,
- 4.28,
- 4.21,
- 4.21,
4.22,
4.2,
- 4.19,
- 4.18,
- 4.2,
- 4.2,
- 4.16,
- 4.02,
- 4.02,
4.03,
- 4.09,
- 4.15,
- 4.15,
- 4.14,
4.18,
- 4.14,
- 4.17,
- 4.22,
- 4.26,
- 4.25,
- 4.26,
- 4.19,
- 4.19,
4.25,
4.3,
- 4.3,
- 4.26,
- 4.29,
- 4.34,
4.39,
- 4.38,
- 4.36,
- 4.37,
4.35,
- null,
- 4.39,
- 4.45,
- 4.48,
- 4.48,
4.5,
- 4.49,
- 4.47,
- 4.46,
- 4.39,
4.45,
- 4.54,
- 4.6,
- 4.57,
- 4.58,
4.57,
- 4.58,
- 4.61,
- 4.65,
- 4.66,
4.65,
- 4.57,
- 4.64,
- 4.55,
- null,
4.61,
- 4.56,
- 4.49,
4.46,
- 4.5,
- 4.46,
- 4.43,
- 4.47,
4.41,
- 4.48,
- 4.49,
- 4.52,
- 4.52,
4.57,
- 4.49,
- 4.52,
- 4.47,
- 4.54,
4.56,
- 4.54,
- 4.45,
- 4.47,
- 4.45,
4.45,
- 4.47,
- 4.49,
- 4.44,
- 4.38,
- 4.34,
4.38,
- 4.37,
4.39,
- 4.37,
- 4.36,
- 4.36,
4.38,
- 4.38,
- 4.43,
- 4.46,
- 4.42,
4.36,
- 4.34,
- 4.34,
- 4.38,
- 4.37,
4.36,
- 4.4,
- 4.49,
- 4.53,
- 4.52,
- 4.54,
- 4.53,
- 4.57,
- 4.57,
4.54,
4.55,
- 4.57,
- 4.56,
- 4.54,
- 4.59,
4.58,
- 4.62,
- 4.61,
- 4.59,
4.54,
- 4.57,
- 4.53,
- 4.56,
- 4.58,
4.59,
- 4.55,
- 4.59,
- 4.64,
- 4.68,
4.74,
- 4.74,
- 4.73,
- 4.74,
- 4.76,
4.77,
- 4.71,
- 4.73,
- 4.65,
- 4.68,
4.66,
- 4.71,
- 4.7,
- 4.73,
- 4.67,
4.7,
- 4.79,
- 4.81,
- 4.86,
- 4.86,
4.88,
- 4.87,
- 4.84,
- 4.9,
- 4.97,
4.97,
- 4.93,
- 4.98,
- 5.05,
- 5.01,
- 4.99,
- 5.04,
- 5.04,
5.01,
4.99,
- 5.07,
- 5.12,
- 5.09,
- 5.07,
5.14,
5.12,
5.15,
- 5.16,
- 5.12,
- 5.12,
- 5.13,
- 5.13,
- 5.14,
- 5.19,
- 5.15,
- 5.1,
- 5.16,
- 5.08,
- 5.05,
5.04,
- 5.07,
- 5.03,
- 5.07,
5.06,
- 5.09,
- 5.12,
- 5.11,
- 5,
5.02,
- 5.01,
- 5.02,
- 5,
- 4.98,
4.99,
- 4.97,
- 5.05,
- 5.1,
- 5.13,
5.14,
- 5.15,
- 5.16,
- 5.2,
- 5.23,
5.25,
- 5.21,
- 5.25,
- 5.22,
5.15,
- 5.23,
- 5.19,
- 5.14,
5.13,
- 5.1,
- 5.1,
- 5.08,
- 5.07,
5.07,
- 5.13,
- 5.06,
- 5.03,
5.05,
- 5.05,
- 5.07,
- 5.04,
- 5.07,
- 5,
- 4.99,
4.99,
- 4.96,
- 4.96,
- 4.91,
- 4.93,
- 4.93,
- 4.92,
4.93,
- 4.97,
5,
- 4.93,
- 4.87,
- 4.87,
- 4.84,
- 4.82,
4.82,
- 4.82,
- 4.81,
- 4.79,
4.8,
- 4.79,
- 4.76,
- 4.74,
4.73,
- null,
- 4.78,
- 4.8,
- 4.8,
- 4.78,
- 4.8,
- 4.78,
- 4.77,
- 4.79,
4.8,
4.81,
- 4.74,
- 4.73,
- 4.65,
- 4.6,
4.56,
- 4.59,
- 4.6,
- 4.63,
- 4.64,
4.62,
- 4.62,
- 4.57,
- 4.61,
4.7,
- null,
- 4.75,
- 4.78,
- 4.79,
- 4.81,
- 4.79,
- 4.78,
- 4.77,
4.79,
- 4.79,
- 4.83,
4.83,
- 4.78,
- 4.73,
4.68,
- 4.68,
- 4.61,
- 4.57,
- 4.6,
- 4.72,
4.71,
- 4.66,
- 4.64,
- 4.62,
- 4.59,
- 4.61,
- 4.57,
- 4.61,
- 4.66,
4.61,
4.6,
- 4.58,
- 4.57,
- 4.55,
4.54,
- 4.51,
- 4.52,
- 4.46,
- 4.43,
4.43,
- 4.45,
- 4.48,
- 4.49,
- 4.56,
4.52,
- 4.49,
- 4.58,
- 4.6,
- 4.6,
- 4.6,
- 4.6,
4.6,
- 4.55,
4.63,
- null,
- 4.61,
- 4.66,
- 4.7,
4.71,
- null,
- 4.68,
- 4.67,
- 4.62,
- 4.65,
4.66,
- 4.66,
- 4.69,
- 4.74,
4.77,
- 4.75,
- 4.79,
- 4.75,
- 4.78,
4.76,
- 4.81,
- 4.81,
- 4.87,
- 4.88,
4.9,
- 4.88,
- 4.83,
- 4.84,
- 4.83,
4.81,
- 4.77,
- 4.74,
- 4.73,
- 4.79,
4.8,
- 4.82,
- 4.74,
- 4.7,
- 4.69,
- 4.68,
4.69,
- 4.73,
- 4.68,
4.63,
- 4.5,
- 4.56,
- 4.56,
- 4.52,
- 4.51,
- 4.53,
- 4.5,
4.51,
- 4.59,
4.56,
- 4.5,
- 4.53,
- 4.54,
- 4.55,
4.58,
- 4.56,
- 4.53,
4.6,
- 4.62,
- 4.6,
- 4.62,
- 4.62,
- 4.64,
- 4.65,
4.65,
- 4.67,
- 4.66,
- 4.68,
4.75,
- 4.73,
- 4.74,
- 4.74,
- 4.76,
4.74,
- 4.69,
- 4.66,
- 4.68,
- 4.68,
- 4.66,
- 4.63,
4.66,
- 4.69,
- 4.71,
4.63,
4.64,
- 4.65,
- 4.68,
- 4.65,
- 4.64,
- 4.63,
- 4.67,
- 4.65,
- 4.67,
4.69,
- 4.71,
- 4.71,
- 4.76,
- 4.81,
4.79,
- 4.83,
- 4.86,
- 4.86,
4.86,
- 4.88,
- 4.88,
- 4.9,
- 4.95,
4.93,
- 4.98,
- 4.97,
- 5.11,
- 5.12,
5.14,
- 5.26,
- 5.2,
- 5.23,
- 5.16,
5.15,
5.09,
- 5.14,
- 5.16,
- 5.14,
- 5.09,
- 5.1,
- 5.09,
- 5.12,
- 5.03,
5,
- 5.05,
- 5.16,
- 5.19,
5.16,
- 5.03,
- 5.09,
- 5.13,
- 5.11,
5.05,
- 5.08,
- 5.02,
- 5.04,
- 4.96,
4.97,
- 4.94,
- 4.92,
- 4.79,
- 4.8,
4.82,
- 4.78,
- 4.76,
- 4.77,
- 4.71,
4.72,
- 4.77,
- 4.84,
- 4.79,
- 4.81,
4.78,
- 4.73,
- 4.69,
- 4.6,
- 4.68,
4.64,
4.6,
- 4.63,
- 4.62,
- 4.63,
- 4.6,
- 4.53,
- 4.57,
- 4.51,
4.54,
- 4.56,
- 4.48,
- 4.51,
- 4.38,
4.34,
- 4.37,
- 4.41,
- 4.49,
- 4.47,
4.48,
- 4.5,
- 4.53,
- 4.69,
- 4.64,
4.63,
- 4.63,
- 4.63,
- 4.58,
- 4.59,
4.56,
- 4.54,
- 4.55,
- 4.54,
- 4.65,
- null,
- 4.67,
4.65,
- 4.66,
- 4.7,
4.69,
- 4.66,
- 4.57,
- 4.52,
- 4.41,
4.42,
- 4.41,
- 4.36,
- 4.37,
- 4.41,
4.39,
- 4.4,
- 4.48,
- 4.36,
- 4.31,
4.35,
- 4.38,
- 4.34,
- 4.28,
4.23,
- null,
- 4.26,
- 4.28,
- 4.17,
- 4.15,
4.07,
- 4.06,
- 4,
- 4.01,
3.83,
- 3.95,
- 4.03,
- 3.94,
- 3.97,
- 3.89,
3.89,
- 3.92,
- 4.02,
- 4.12,
4.15,
- 3.98,
- 4.05,
- 4.18,
- 4.24,
4.2,
- 4.14,
- 4.06,
- 4.04,
- 4.18,
4.23,
- 4.3,
- 4.21,
- 4.11,
4.04,
- 3.91,
- 3.91,
- 3.88,
3.86,
- 3.86,
- 3.82,
- 3.91,
- 3.82,
3.81,
- 3.72,
- 3.74,
3.66,
- 3.66,
- 3.52,
- 3.51,
- 3.68,
3.61,
- 3.61,
- 3.69,
- 3.78,
- 3.67,
- 3.62,
3.68,
- 3.61,
- 3.61,
- 3.74,
- 3.64,
3.62,
- 3.66,
- 3.7,
- 3.85,
3.76,
- 3.89,
- 3.93,
- 3.77,
- 3.79,
3.91,
- 3.88,
- 3.85,
- 3.71,
- 3.53,
3.54,
- 3.63,
- 3.7,
- 3.62,
- 3.56,
3.46,
- 3.6,
- 3.49,
- 3.56,
- 3.44,
- 3.34,
- 3.48,
- 3.38,
3.34,
3.56,
- 3.51,
- 3.51,
- 3.56,
- 3.47,
3.45,
3.57,
- 3.6,
- 3.61,
- 3.5,
- 3.57,
- 3.58,
- 3.49,
- 3.55,
- 3.49,
3.53,
- 3.6,
- 3.72,
- 3.75,
- 3.77,
3.75,
- 3.74,
- 3.77,
- 3.87,
- 3.91,
3.86,
- 3.85,
- 3.77,
- 3.78,
- 3.89,
3.88,
- 3.93,
- 3.87,
- 3.79,
- 3.77,
3.78,
- 3.9,
- 3.92,
3.83,
3.85,
- 3.83,
- 3.78,
- 3.81,
- 3.92,
- 3.85,
- 3.93,
- 4.03,
- 4.08,
- 4.06,
3.98,
- 3.92,
- 3.98,
- 4.06,
- 3.94,
4.02,
- 4.11,
- 4.1,
- 4.23,
- 4.27,
4.25,
- 4.23,
- 4.16,
- 4.22,
- 4.16,
4.19,
- 4.1,
- 4.12,
- 4.07,
- 3.99,
- 3.99,
- 4.01,
- 3.99,
3.99,
3.95,
- 3.91,
- 3.85,
- 3.83,
- 3.96,
3.9,
- 3.87,
- 3.97,
- 4.07,
- 4.11,
4.09,
- 4.14,
- 4.16,
- 4.03,
- 4.13,
4.06,
- 4.09,
- 4.07,
- 3.99,
- 3.97,
3.98,
- 4.04,
- 4.06,
- 3.92,
- 3.94,
3.99,
- 3.91,
- 3.94,
- 3.89,
- 3.84,
3.82,
- 3.83,
- 3.79,
- 3.84,
- 3.87,
- 3.79,
- 3.79,
- 3.77,
3.79,
3.83,
- 3.74,
- 3.71,
- 3.64,
- 3.66,
3.66,
- 3.62,
- 3.65,
- 3.64,
- 3.74,
3.47,
- 3.48,
- 3.41,
- 3.54,
- 3.78,
3.83,
- 3.85,
- 3.8,
- 3.88,
- 3.85,
3.61,
- 3.85,
- 3.77,
- 3.66,
- 3.63,
3.48,
- 3.5,
- 3.72,
- 3.84,
3.89,
- null,
- 4.08,
- 4.04,
- 3.99,
- 3.98,
3.91,
- 3.76,
- 3.65,
- 3.63,
- 3.76,
3.79,
- 3.89,
- 3.93,
- 4,
- 4.01,
3.96,
- 3.81,
- 3.73,
- 3.75,
- 3.83,
3.82,
- null,
- 3.75,
- 3.84,
- 3.72,
3.68,
- 3.53,
- 3.38,
- 3.1,
- 3.2,
3.35,
- 3.11,
- 2.99,
- 2.93,
2.72,
- 2.68,
- 2.67,
- 2.55,
- 2.67,
2.77,
- 2.67,
- 2.69,
- 2.64,
- 2.6,
2.53,
- 2.37,
- 2.2,
- 2.08,
- 2.13,
- 2.16,
- 2.18,
- 2.2,
2.16,
2.13,
- 2.11,
- 2.25,
- 2.46,
2.49,
- 2.51,
- 2.52,
- 2.47,
- 2.43,
2.34,
- 2.33,
- 2.24,
- 2.23,
2.36,
- 2.4,
- 2.56,
- 2.62,
- 2.65,
2.7,
- 2.59,
- 2.71,
- 2.87,
- 2.87,
2.76,
- 2.89,
- 2.95,
- 2.95,
- 3.05,
3.07,
- 2.9,
- 2.78,
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2.89,
- 2.64,
- 2.74,
- 2.85,
- 2.78,
2.78,
- 2.8,
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2.91,
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- 2.83,
- 2.83,
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- 2.89,
2.89,
2.97,
- 3.02,
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2.68,
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2.73,
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2.95,
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2.88,
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2.88,
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2.95,
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- 3.16,
- 3.21,
3.19,
- 3.2,
- 3.18,
- 3.29,
- 3.29,
- 3.17,
3.17,
- 3.11,
- 3.1,
- 3.14,
3.22,
- 3.25,
- 3.19,
- 3.35,
3.45,
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3.71,
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3.91,
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3.76,
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3.72,
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3.51,
3.52,
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3.38,
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3.61,
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3.75,
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3.66,
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3.8,
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3.48,
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3.48,
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3.4,
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3.45,
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3.42,
3.49,
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3.31,
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- 3.21,
3.24,
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3.4,
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3.41,
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3.45,
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3.33,
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3.37,
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3.21,
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3.44,
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- 3.45,
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3.56,
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3.69,
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- 3.82,
- 3.85,
- 3.82,
- 3.8,
3.85,
3.85,
- 3.77,
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- 3.85,
- 3.83,
3.85,
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- 3.8,
- 3.76,
3.7,
- 3.73,
- 3.68,
- 3.62,
- 3.62,
- 3.66,
- 3.65,
3.66,
3.68,
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- 3.68,
- 3.67,
- 3.73,
3.62,
- 3.59,
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- 3.67,
- 3.72,
- 3.73,
3.69,
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- 3.79,
- 3.78,
3.8,
- 3.69,
- 3.7,
- 3.64,
- 3.61,
- 3.61,
- 3.62,
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3.61,
- 3.69,
3.72,
3.71,
- 3.73,
- 3.73,
- 3.71,
- 3.71,
- 3.66,
- 3.65,
- 3.68,
- 3.7,
3.67,
- 3.69,
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- 3.91,
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- 3.88,
3.88,
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4.01,
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3.87,
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- 3.88,
- 3.86,
- 3.79,
3.83,
- 3.82,
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3.83,
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3.72,
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3.57,
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3.47,
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3.23,
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3.31,
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3.17,
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- 3.33,
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3.28,
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- 3.27,
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3.26,
- 3.18,
- 3.13,
- 3.14,
- 3.12,
3.05,
- 2.97,
- 2.97,
- 2.96,
- 3,
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3,
- 3.04,
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3.08,
- 3.15,
- 3.07,
- 3,
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2.99,
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- 2.9,
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3.03,
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2.99,
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2.86,
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- 2.79,
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2.58,
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2.6,
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2.54,
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2.72,
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2.72,
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2.54,
2.5,
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2.41,
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- null,
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2.52,
2.59,
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- 2.63,
2.66,
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- 2.67,
- 2.53,
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2.6,
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2.92,
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- 2.9,
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2.8,
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2.84,
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2.95,
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- 3.32,
3.29,
- 3.49,
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3.36,
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3.36,
3.36,
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3.32,
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3.35,
- 3.39,
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- 3.47,
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3.43,
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3.42,
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- 3.52,
- 3.58,
3.68,
- 3.68,
- 3.75,
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- 3.7,
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- 3.62,
- 3.61,
3.62,
- 3.58,
3.59,
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3.42,
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3.51,
- 3.56,
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- 3.37,
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3.36,
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- 3.22,
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3.34,
- 3.36,
- 3.42,
- 3.46,
- 3.47,
- 3.5,
- 3.47,
3.47,
- 3.46,
3.45,
- 3.5,
- 3.56,
- 3.58,
- 3.59,
3.59,
- 3.52,
- 3.49,
- 3.51,
- 3.43,
3.4,
3.39,
- 3.43,
- 3.42,
- 3.39,
- 3.34,
- 3.39,
- 3.34,
- 3.32,
3.31,
- 3.28,
- 3.25,
- 3.18,
- 3.19,
- 3.17,
- 3.23,
- 3.19,
- 3.22,
- 3.18,
- 3.15,
- 3.12,
- 3.18,
3.17,
3.15,
3.13,
- 3.12,
- 3.13,
- 3.07,
3.07,
- 3.05,
- 2.96,
- 3.04,
- 2.99,
3.01,
- 3.01,
- 2.98,
- 3.01,
- 2.99,
3,
- 3.11,
- 2.98,
- 2.93,
- 2.94,
2.97,
- 2.99,
- 3.01,
- 2.93,
- 2.88,
2.95,
- 3.05,
- 3.14,
- 3.18,
3.22,
- 3.16,
- 3.12,
- 3.17,
- 3.03,
- 2.94,
- 2.92,
- 2.92,
- 2.98,
2.94,
2.94,
- 2.91,
- 2.96,
- 3.03,
- 2.99,
3.03,
- 2.99,
- 3.01,
- 2.98,
- 2.82,
2.77,
- 2.66,
- 2.64,
- 2.47,
- 2.58,
2.4,
- 2.2,
- 2.17,
- 2.34,
- 2.24,
2.29,
- 2.23,
- 2.17,
- 2.08,
- 2.07,
2.1,
- 2.15,
- 2.29,
- 2.23,
- 2.19,
2.28,
- 2.19,
- 2.23,
- 2.15,
2.02,
- 1.98,
- 2.05,
- 2,
- 1.93,
1.94,
- 2,
- 2.03,
- 2.09,
- 2.08,
1.97,
- 1.95,
- 1.88,
- 1.72,
- 1.84,
1.91,
- 2,
- 2.03,
- 1.99,
- 1.92,
1.8,
- 1.81,
- 1.92,
- 2.01,
2.1,
- null,
- 2.18,
- 2.24,
- 2.19,
- 2.26,
- 2.18,
- 2.19,
2.18,
- 2.2,
- 2.23,
2.25,
- 2.14,
- 2.23,
- 2.42,
- 2.34,
2.17,
- 2.01,
- 2.03,
- 2.09,
- 2.06,
- 2.04,
- 2.1,
- 2,
2.04,
- null,
2.04,
- 2.06,
- 2.01,
- 1.96,
- 2.01,
- 1.97,
- 1.94,
- 1.89,
1.97,
1.97,
- 2,
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- 2.11,
- 2.05,
2.04,
- 2.08,
- 2.02,
- 1.99,
- 2.07,
2.03,
- 1.96,
- 1.92,
- 1.92,
- 1.86,
1.82,
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- 1.98,
- 1.97,
2.03,
- 2.02,
- 1.93,
- 1.91,
1.89,
- 1.97,
- 2,
- 2.02,
- 1.98,
1.98,
- 2,
- 1.93,
- 1.94,
1.89,
- 1.87,
- 1.92,
- 2.01,
- 2.05,
2.09,
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- 1.96,
- 1.93,
- 1.87,
- 1.83,
1.87,
- 1.86,
- 1.97,
- 1.93,
- 2,
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- 1.96,
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- 1.92,
1.93,
1.99,
2.01,
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- 2.01,
- 1.99,
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1.92,
- 1.94,
- 1.98,
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2,
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2.04,
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- 2.29,
- 2.29,
- 2.31,
2.39,
- 2.38,
- 2.31,
- 2.29,
- 2.25,
2.26,
- 2.2,
- 2.21,
- 2.18,
- 2.23,
2.22,
- 2.3,
- 2.25,
- 2.19,
2.06,
- 2.01,
- 2.05,
- 2.08,
- 2.02,
- 2,
- 2.03,
- 2,
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2,
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1.96,
1.95,
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1.92,
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1.78,
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1.75,
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1.53,
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1.6,
1.59,
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1.61,
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1.53,
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1.5,
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- 1.49,
1.47,
- 1.44,
- 1.43,
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1.53,
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1.59,
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1.65,
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1.82,
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- 1.71,
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1.65,
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1.57,
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1.68,
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1.85,
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1.74,
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- 1.66,
- 1.65,
- 1.64,
- 1.64,
1.64,
- 1.7,
1.75,
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- 1.74,
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1.7,
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- 1.79,
1.83,
- 1.79,
- 1.8,
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+ 1.74,
1.72,
- 1.78,
- 1.68,
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1.61,
- null,
- 1.59,
- 1.59,
- 1.58,
- 1.58,
1.61,
1.66,
- 1.69,
- 1.7,
- 1.66,
- 1.64,
1.63,
- 1.62,
- 1.62,
- 1.63,
- 1.62,
- 1.6,
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1.63,
- 1.66,
- 1.72,
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- 1.72,
1.78,
- 1.84,
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- 1.77,
1.79,
- 1.77,
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- 1.73,
1.78,
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- 1.92,
- 1.93,
1.92,
1.89,
- 1.88,
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- 1.89,
- 1.89,
- 1.86,
- 1.84,
- 1.89,
1.87,
- 1.86,
- 1.86,
- 1.88,
- 1.98,
2,
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- 2.03,
- 2.02,
- 2.04,
2,
- 2.04,
- 2,
- 1.99,
- 1.99,
1.99,
- 2.02,
- 2.05,
- 2,
2.01,
- 2.03,
- 2.02,
- 1.99,
- 1.97,
- 1.88,
1.88,
- 1.91,
- 1.89,
- 1.86,
1.88,
- 1.9,
- 1.95,
- 2,
- 2.06,
2.07,
- 2.03,
- 2.04,
- 2.04,
- 2.01,
- 1.96,
- 1.92,
1.96,
- 1.95,
1.93,
- 1.93,
- 1.92,
- 1.87,
- 1.87,
1.86,
- 1.88,
- 1.83,
- 1.78,
- 1.72,
1.76,
- 1.78,
- 1.84,
- 1.82,
- 1.75,
- 1.72,
- 1.75,
- 1.73,
1.72,
- 1.73,
1.72,
- 1.74,
- 1.73,
- 1.74,
- 1.7,
1.7,
- 1.7,
- 1.66,
- 1.66,
- 1.78,
1.8,
- 1.82,
- 1.81,
- 1.81,
- 1.9,
1.92,
- 1.96,
- 1.94,
- 1.87,
- 1.95,
1.97,
- 1.94,
- 2.03,
- 2.02,
2.01,
- 2.15,
- 2.13,
- 2.13,
- 2.16,
2.13,
- 2.14,
- 2.1,
- 2.08,
- 2.17,
2.22,
- 2.2,
- 2.25,
- 2.19,
- 2.14,
2.19,
- 2.2,
- 2.33,
- 2.41,
- 2.52,
2.57,
- 2.6,
- 2.55,
- 2.49,
- 2.52,
2.5,
- 2.48,
- 2.52,
- 2.73,
2.65,
- 2.65,
- 2.7,
- 2.6,
- 2.61,
2.57,
- 2.55,
- 2.52,
- 2.56,
2.5,
- 2.5,
- 2.53,
2.61,
- 2.61,
- 2.58,
- 2.61,
- 2.63,
- 2.6,
- 2.74,
- 2.63,
- 2.67,
2.67,
2.61,
- 2.58,
- 2.57,
- 2.61,
- 2.71,
- 2.71,
- 2.77,
- 2.84,
2.88,
- 2.82,
- 2.87,
- 2.9,
- 2.82,
2.79,
- 2.72,
2.78,
- 2.75,
- 2.78,
- 2.86,
- 2.9,
- 2.98,
- 2.94,
- 2.9,
- 2.96,
- 2.93,
- 2.92,
2.9,
2.88,
- 2.86,
- 2.69,
- 2.76,
- 2.75,
2.72,
- 2.67,
- 2.63,
- 2.66,
- 2.64,
2.64,
- 2.66,
- 2.63,
- 2.62,
- 2.66,
2.65,
- 2.66,
- 2.68,
- 2.71,
2.7,
- null,
- 2.75,
- 2.69,
- 2.61,
- 2.6,
2.63,
2.54,
- 2.51,
- 2.53,
- 2.53,
- 2.54,
- 2.53,
- 2.55,
- 2.57,
- 2.65,
- 2.63,
- 2.69,
- 2.67,
2.63,
2.77,
- null,
- 2.8,
- 2.75,
- 2.69,
- 2.71,
2.67,
- 2.71,
- 2.8,
- 2.79,
- 2.75,
2.74,
- 2.71,
- 2.74,
- 2.75,
- 2.81,
- 2.79,
- 2.84,
- 2.88,
- 2.88,
- 2.86,
2.81,
2.86,
2.89,
- 2.88,
- 2.89,
- 2.85,
- 2.89,
- 2.94,
- 2.89,
2.94,
2.99,
- 3,
- 3.02,
- 2.99,
- 3.04,
- 3,
- 3.01,
2.98,
- 2.96,
- 3.01,
- 2.97,
- 2.88,
2.84,
- 2.88,
- 2.9,
- 2.86,
2.84,
- 2.85,
- 2.87,
- 2.79,
- 2.75,
2.78,
- 2.77,
- 2.69,
- 2.72,
- 2.67,
2.61,
- 2.64,
2.7,
- 2.73,
- 2.71,
- 2.7,
- 2.75,
- 2.8,
- 2.73,
2.75,
- 2.71,
- 2.73,
- 2.76,
- 2.73,
2.75,
- 2.7,
- 2.67,
- 2.65,
- 2.66,
2.6,
- 2.7,
- 2.7,
- 2.74,
- 2.8,
2.79,
- 2.77,
- 2.73,
- 2.66,
- 2.65,
2.7,
- 2.68,
- 2.78,
- 2.79,
- 2.75,
2.74,
- 2.75,
- 2.71,
- 2.69,
- 2.73,
2.73,
- 2.77,
- 2.82,
- 2.8,
- 2.74,
- 2.71,
- 2.69,
2.71,
2.65,
- 2.63,
- 2.65,
- 2.64,
- 2.65,
2.73,
- 2.73,
- 2.73,
- 2.7,
- 2.7,
- 2.68,
2.7,
- 2.71,
- 2.67,
- 2.63,
- 2.6,
2.63,
- 2.61,
- 2.62,
- 2.61,
- 2.62,
2.66,
- 2.61,
- 2.54,
- 2.5,
- 2.52,
- 2.54,
- 2.52,
2.54,
- 2.56,
2.54,
- 2.52,
- 2.44,
- 2.45,
- 2.48,
2.54,
- 2.6,
- 2.61,
- 2.59,
- 2.6,
2.62,
- 2.64,
- 2.65,
- 2.58,
- 2.6,
- 2.61,
- 2.66,
2.61,
- 2.64,
2.63,
- 2.63,
- 2.59,
- 2.57,
- 2.53,
- 2.54,
2.53,
- 2.58,
- 2.64,
- 2.65,
2.63,
- 2.58,
- 2.57,
2.55,
- 2.53,
- 2.55,
- 2.56,
- 2.55,
- 2.47,
- 2.5,
2.49,
- 2.48,
- 2.48,
- 2.52,
- 2.48,
2.5,
- 2.47,
- 2.57,
- 2.58,
- 2.52,
2.51,
- 2.49,
- 2.49,
- 2.43,
2.44,
- 2.44,
- 2.46,
- 2.43,
- 2.4,
- 2.34,
- 2.39,
- 2.4,
- 2.43,
- 2.41,
- 2.4,
2.39,
2.39,
- 2.37,
- 2.34,
2.35,
- 2.42,
- 2.41,
- 2.45,
- 2.46,
2.48,
- 2.5,
- 2.54,
- 2.54,
- 2.62,
2.6,
- 2.6,
- 2.62,
- 2.63,
- 2.59,
2.57,
- 2.54,
- 2.57,
- 2.52,
- 2.54,
2.5,
- 2.52,
- 2.42,
- 2.44,
- 2.45,
2.43,
- 2.36,
- 2.35,
- 2.34,
2.31,
- null,
- 2.21,
- 2.15,
- 2.17,
- 2.22,
2.2,
- 2.23,
- 2.25,
- 2.29,
- 2.29,
2.27,
- 2.3,
- 2.34,
- 2.32,
- 2.35,
- 2.36,
- 2.35,
2.36,
- 2.39,
- 2.32,
2.38,
- null,
- 2.37,
- 2.35,
- 2.32,
- 2.34,
- 2.32,
- 2.36,
2.34,
- 2.31,
2.3,
- 2.27,
- 2.24,
- 2.18,
2.22,
- 2.28,
- 2.29,
- 2.25,
- 2.31,
2.26,
- 2.22,
- 2.18,
- 2.19,
- 2.1,
2.12,
- 2.07,
- 2.14,
- 2.22,
2.17,
- 2.17,
- 2.26,
- 2.27,
- 2.25,
2.22,
- 2.2,
- 2.17,
- 2.12,
2.04,
- 1.97,
- 1.96,
- 2.03,
- 1.98,
1.92,
- 1.91,
- 1.86,
- 1.77,
- 1.83,
- 1.82,
- 1.87,
- 1.9,
- 1.81,
1.83,
1.83,
- 1.73,
- 1.77,
- 1.68,
1.68,
- 1.79,
- 1.81,
- 1.83,
- 1.95,
1.96,
- 2.01,
- 2,
- 1.99,
2.02,
- 2.14,
- 2.07,
- 2.11,
- 2.13,
2.06,
- 1.99,
- 1.96,
- 2.03,
- 2,
2.08,
- 2.12,
- 2.12,
- 2.11,
- 2.24,
2.2,
- 2.14,
- 2.11,
2.1,
- 2.13,
- 2.1,
- 2.06,
- 1.93,
- 1.98,
- 1.93,
1.92,
- 1.88,
- 1.93,
- 2.01,
- 1.95,
1.96,
- 1.94,
- 1.87,
1.92,
- 1.92,
- 1.89,
- 1.92,
- 1.97,
- 1.96,
1.94,
1.9,
- 1.91,
- 1.9,
- 1.87,
- 1.9,
- 1.92,
- 1.99,
- 1.96,
- 1.93,
1.94,
- 2,
- 2.06,
- 2.05,
- 2.12,
- 2.16,
- 2.19,
- 2.25,
- 2.18,
2.16,
2.28,
- 2.28,
- 2.27,
- 2.23,
- 2.14,
2.23,
- 2.27,
- 2.26,
- 2.19,
2.21,
- 2.14,
- 2.14,
- 2.13,
- 2.12,
2.19,
- 2.27,
- 2.38,
- 2.31,
- 2.41,
- 2.39,
- 2.42,
- 2.5,
- 2.39,
2.39,
2.36,
- 2.32,
- 2.32,
- 2.35,
- 2.26,
2.37,
- 2.42,
- 2.38,
- 2.4,
- 2.49,
2.33,
- 2.35,
- 2.43,
- 2.4,
2.3,
- 2.27,
- 2.22,
- 2.32,
- 2.42,
2.44,
- 2.41,
- 2.36,
- 2.36,
- 2.34,
2.38,
- 2.35,
- 2.33,
- 2.28,
- 2.27,
2.23,
- 2.26,
- 2.29,
- 2.28,
- 2.2,
2.16,
- 2.23,
- 2.28,
- 2.23,
- 2.18,
2.24,
- 2.15,
- 2.14,
- 2.19,
- 2.2,
2.16,
- 2.2,
- 2.12,
- 2.09,
- 2.05,
2.01,
- 2.12,
- 2.18,
- 2.18,
- 2.19,
2.21,
- 2.17,
- 2.2,
- 2.18,
2.13,
- 2.2,
- 2.21,
- 2.23,
- 2.2,
2.18,
- 2.28,
- 2.3,
- 2.21,
- 2.13,
2.2,
- 2.14,
- 2.16,
- 2.13,
- 2.17,
2.1,
- 2.05,
- 2.06,
- 2.05,
- 1.99,
2.07,
- 2.05,
- 2.08,
2.12,
- 2.12,
- null,
- 2.06,
- 1.99,
- 2.04,
- 2.04,
- 2.04,
- 2.08,
- 2.04,
2.04,
- 2.09,
2.07,
- 2.05,
- 2.1,
- 2.19,
- 2.16,
2.2,
- 2.23,
- 2.25,
- 2.26,
- 2.34,
2.36,
- 2.32,
- null,
- 2.32,
- 2.28,
2.27,
2.25,
- 2.27,
- 2.24,
- 2.26,
- 2.25,
- 2.24,
- 2.23,
- 2.22,
2.21,
- 2.15,
- 2.18,
- 2.33,
- 2.28,
2.23,
- 2.24,
- 2.22,
- 2.24,
- 2.13,
2.23,
- 2.28,
- 2.3,
- 2.24,
- 2.19,
2.2,
2.24,
- 2.27,
- 2.25,
- 2.24,
- 2.32,
- 2.31,
- 2.27,
2.24,
- 2.25,
- 2.18,
- 2.16,
- 2.13,
2.17,
- 2.12,
- 2.08,
- 2.1,
2.03,
- 2.06,
- 2.01,
- 2.02,
- 2.07,
2.03,
- 2.01,
- 2.02,
- 2,
- 1.94,
1.97,
- 1.87,
- 1.88,
- 1.87,
- 1.86,
1.75,
1.74,
- 1.71,
- 1.63,
- 1.74,
- 1.78,
- 1.81,
- 1.75,
- 1.76,
1.77,
1.74,
- 1.75,
- 1.71,
- 1.76,
- 1.74,
- 1.83,
- 1.84,
- 1.83,
- 1.88,
1.91,
- 1.83,
- 1.9,
- 1.93,
- 1.98,
- 1.97,
1.97,
- 1.94,
- 1.91,
- 1.88,
1.92,
- 1.94,
- 1.88,
- 1.91,
1.89,
- 1.81,
- 1.83,
- 1.78,
- 1.79,
1.78,
1.73,
- 1.76,
- 1.7,
- 1.72,
- 1.73,
- 1.79,
- 1.77,
- 1.8,
- 1.76,
1.78,
- 1.79,
- 1.85,
- 1.88,
- 1.89,
1.91,
- 1.94,
- 1.87,
- 1.84,
- 1.83,
1.88,
- 1.81,
- 1.79,
- 1.76,
- 1.79,
1.77,
- 1.77,
- 1.73,
1.75,
- 1.71,
- 1.75,
- 1.76,
- 1.87,
- 1.85,
- 1.85,
- 1.84,
- 1.86,
- 1.87,
- 1.83,
- 1.85,
1.84,
1.85,
- 1.81,
- 1.71,
1.73,
- 1.72,
- 1.71,
- 1.68,
- 1.64,
- 1.62,
- 1.62,
- 1.6,
- 1.57,
1.62,
1.67,
- 1.71,
- 1.69,
- 1.74,
- 1.57,
- 1.46,
1.46,
- 1.5,
- 1.49,
1.46,
- 1.37,
- 1.38,
- 1.4,
- 1.37,
1.43,
- 1.53,
- 1.48,
- 1.53,
- 1.6,
- 1.59,
- 1.56,
1.59,
- 1.57,
- 1.57,
1.58,
- 1.57,
- 1.52,
- 1.52,
- 1.46,
- 1.51,
- 1.55,
- 1.55,
1.51,
1.59,
- 1.59,
- 1.55,
- 1.5,
- 1.57,
- 1.51,
- 1.55,
- 1.57,
- 1.56,
- 1.53,
- 1.58,
1.55,
1.55,
- 1.56,
- 1.58,
- 1.62,
- 1.57,
- 1.57,
- 1.58,
1.57,
1.6,
- 1.55,
- 1.54,
- 1.61,
- 1.67,
1.68,
- 1.73,
- 1.7,
- 1.71,
1.7,
- 1.7,
- 1.69,
- 1.66,
- 1.63,
- 1.62,
1.59,
- 1.56,
- 1.57,
- 1.56,
- 1.6,
1.63,
- 1.69,
- 1.72,
- 1.75,
1.73,
1.77,
- 1.79,
- 1.75,
- 1.8,
- 1.77,
- 1.75,
- 1.76,
- 1.76,
- 1.74,
- 1.77,
1.77,
- 1.79,
- 1.85,
- 1.86,
1.84,
1.83,
- 1.81,
- 1.82,
- 1.79,
- 1.83,
- 1.88,
- 2.07,
- 2.15,
- 2.23,
2.23,
- 2.22,
- 2.29,
- 2.34,
2.33,
- 2.31,
- 2.36,
- 2.36,
2.32,
- 2.3,
- 2.37,
- 2.45,
- 2.4,
2.39,
- 2.39,
- 2.34,
- 2.4,
- 2.47,
2.49,
- 2.48,
- 2.54,
- 2.6,
- 2.6,
2.54,
- 2.57,
- 2.55,
2.55,
- 2.55,
- 2.57,
- 2.51,
- 2.49,
2.45,
- 2.45,
- 2.46,
- 2.37,
- 2.42,
- 2.38,
- 2.38,
2.38,
- 2.36,
2.4,
- 2.33,
- 2.42,
- 2.47,
- 2.48,
2.41,
- 2.47,
- 2.53,
- 2.51,
2.49,
- 2.49,
- 2.45,
- 2.48,
- 2.48,
- 2.49,
- 2.42,
- 2.4,
- 2.34,
- 2.4,
- 2.41,
- 2.43,
- 2.47,
- 2.51,
- 2.45,
2.42,
2.43,
2.42,
- 2.38,
- 2.31,
2.36,
- 2.36,
- 2.46,
- 2.49,
- 2.49,
2.49,
- 2.52,
- 2.57,
- 2.6,
- 2.58,
2.62,
- 2.6,
- 2.51,
- 2.53,
- 2.5,
2.47,
- 2.43,
- 2.4,
- 2.41,
- 2.4,
2.38,
- 2.42,
- 2.39,
- 2.42,
- 2.4,
2.35,
- 2.36,
- 2.34,
- 2.34,
- 2.38,
2.37,
- 2.32,
- 2.28,
- 2.24,
2.26,
- 2.18,
- 2.21,
- 2.24,
- 2.24,
2.28,
- 2.35,
- 2.32,
- 2.3,
- 2.29,
- 2.33,
- 2.29,
2.33,
- 2.36,
- 2.36,
- 2.39,
- 2.42,
- 2.41,
2.39,
- 2.33,
2.34,
- 2.33,
- 2.22,
- 2.23,
- 2.23,
- 2.25,
- 2.29,
- 2.26,
2.25,
2.25,
- 2.21,
- 2.21,
- 2.21,
- 2.15,
- 2.18,
- 2.14,
2.18,
- 2.19,
- 2.21,
2.21,
- 2.21,
- 2.15,
- 2.16,
- 2.16,
2.19,
- 2.16,
- 2.16,
- 2.15,
- 2.15,
2.14,
- 2.21,
- 2.22,
- 2.27,
- 2.31,
2.35,
- 2.33,
- 2.37,
- 2.39,
2.38,
- 2.37,
- 2.33,
- 2.35,
- 2.33,
2.31,
- 2.27,
- 2.27,
- 2.27,
- 2.24,
2.26,
- 2.33,
- 2.29,
- 2.32,
2.3,
- 2.3,
- 2.26,
- 2.27,
- 2.24,
- 2.27,
2.26,
- 2.29,
- 2.24,
- 2.2,
- 2.19,
2.22,
- 2.27,
- 2.23,
- 2.19,
- 2.19,
2.18,
- 2.22,
- 2.17,
- 2.19,
- 2.17,
2.16,
- 2.13,
- 2.15,
- 2.12,
2.16,
- 2.07,
- 2.1,
- 2.05,
- 2.06,
2.14,
- 2.17,
- 2.2,
- 2.2,
- 2.2,
2.23,
- 2.24,
- 2.28,
- 2.27,
- 2.26,
2.22,
- 2.24,
- 2.31,
- 2.31,
- 2.33,
2.34,
- 2.33,
- 2.33,
- 2.35,
2.37,
- null,
- 2.35,
- 2.35,
- 2.33,
- 2.28,
- 2.3,
2.3,
- 2.34,
- 2.33,
- 2.39,
- 2.38,
- 2.42,
- 2.44,
- 2.46,
- 2.42,
- 2.37,
2.38,
2.37,
- 2.35,
- 2.34,
- 2.32,
2.32,
- 2.32,
- 2.33,
2.4,
- 2.4,
- 2.38,
- 2.33,
- 2.37,
- 2.35,
2.37,
- 2.36,
2.32,
- 2.34,
- 2.32,
- 2.34,
- 2.37,
- 2.42,
- 2.37,
- 2.37,
- 2.36,
- 2.33,
2.37,
- 2.38,
2.39,
- 2.4,
- 2.36,
- 2.35,
- 2.35,
2.39,
- 2.46,
- 2.49,
2.48,
- 2.48,
- 2.47,
- 2.42,
- 2.43,
2.4,
- 2.46,
- 2.44,
- 2.46,
- 2.47,
2.49,
2.55,
- 2.55,
- 2.54,
- 2.55,
- 2.54,
- 2.57,
- 2.62,
- 2.64,
- 2.66,
- 2.63,
- 2.65,
- 2.63,
2.66,
- 2.73,
- 2.72,
- 2.78,
- 2.84,
+ 2.7,
2.77,
- 2.79,
- 2.84,
- 2.85,
- 2.83,
2.86,
- 2.83,
- 2.91,
- 2.9,
- 2.87,
- 2.88,
- 2.94,
- 2.92,
- 2.88,
- 2.86,
- 2.9,
2.87,
- 2.81,
2.86,
2.88,
- 2.88,
- 2.89,
- 2.86,
- 2.9,
2.87,
- 2.84,
- 2.81,
- 2.82,
2.85,
2.85,
- 2.89,
- 2.89,
- 2.83,
- 2.82,
- 2.85,
- 2.78,
- 2.77,
- 2.74,
2.73,
- 2.79,
- 2.79,
- 2.83,
- 2.77,
2.78,
- 2.8,
- 2.79,
2.83,
- 2.82,
- 2.83,
- 2.82,
- 2.87,
- 2.92,
- 2.96,
2.98,
- 3,
- 3.03,
- 3,
- 2.96,
- 2.95,
- 2.97,
- 2.97,
- 2.94,
2.95,
2.95,
- 2.97,
- 3,
- 2.97,
- 2.97,
3,
- 3.08,
- 3.09,
- 3.11,
3.06,
- 3.06,
- 3.06,
- 3.01,
- 2.98,
2.93,
- 2.77,
- 2.84,
- 2.83,
- 2.89,
2.94,
- 2.92,
- 2.97,
- 2.93,
- 2.93,
2.96,
- 2.96,
- 2.98,
- 2.94,
- 2.93,
2.92,
- 2.89,
- 2.93,
- 2.9,
- 2.9,
2.87,
- 2.88,
- 2.83,
- 2.84,
- 2.85,
2.87,
- 2.83,
- 2.84,
- 2.82,
2.86,
- 2.87,
2.85,
- 2.85,
- 2.83,
- 2.85,
- 2.86,
- 2.88,
- 2.84,
- 2.89,
- 2.96,
- 2.95,
- 2.94,
- 2.98,
- 2.96,
- 2.98,
2.96,
- 3,
2.98,
- 2.95,
2.94,
- 2.98,
- 2.96,
- 2.93,
- 2.87,
2.88,
- 2.89,
- 2.86,
- 2.87,
- 2.87,
- 2.82,
- 2.85,
- 2.82,
- 2.82,
2.82,
2.85,
- 2.88,
- 2.89,
- 2.86,
2.86,
- 2.9,
- 2.9,
- 2.88,
- 2.94,
2.94,
- 2.98,
- 2.97,
- 2.97,
- 2.99,
2.99,
- 3.05,
- 3.08,
- 3.07,
- 3.07,
3.08,
- 3.1,
- 3.06,
- 3.06,
- 3.05,
3.09,
- 3.05,
- 3.15,
- 3.19,
3.23,
- null,
- 3.21,
- 3.22,
- 3.14,
- 3.15,
- 3.16,
3.16,
- 3.19,
- 3.17,
3.2,
- 3.2,
- 3.17,
- 3.1,
- 3.14,
3.08,
- 3.08,
- 3.12,
- 3.15,
- 3.14,
- 3.22,
3.2,
- 3.22,
- 3.22,
- 3.24,
3.19,
- null,
- 3.14,
- 3.12,
- 3.11,
- 3.08,
- 3.06,
3.06,
- 3.06,
- 3.05,
3.07,
- 3.06,
- 3.06,
- 3.03,
- 3.01,
2.98,
- 2.91,
- 2.87,
2.85,
- 2.85,
- 2.89,
- 2.91,
- 2.91,
- 2.89,
2.86,
- 2.82,
- 2.77,
- 2.79,
- 2.79,
2.74,
- 2.81,
- 2.77,
- 2.72,
2.69,
- 2.66,
- 2.56,
- 2.67,
2.7,
- 2.73,
- 2.74,
- 2.74,
- 2.71,
2.71,
- 2.72,
- 2.73,
- 2.75,
2.79,
- 2.74,
- 2.76,
- 2.72,
- 2.76,
2.75,
- 2.72,
- 2.7,
- 2.63,
- 2.7,
2.73,
- 2.71,
- 2.7,
- 2.65,
- 2.63,
2.65,
- 2.68,
- 2.71,
- 2.66,
2.66,
- 2.65,
- 2.65,
- 2.69,
- 2.65,
2.67,
- 2.64,
- 2.69,
- 2.73,
- 2.76,
2.72,
- 2.72,
- 2.69,
2.64,
- 2.62,
- 2.64,
- 2.61,
- 2.61,
- 2.63,
- 2.59,
2.6,
- 2.61,
- 2.54,
- 2.54,
- 2.44,
2.43,
- 2.41,
- 2.39,
- 2.39,
- 2.41,
2.49,
- 2.48,
- 2.52,
- 2.51,
- 2.5,
2.52,
- 2.51,
- 2.48,
- 2.51,
- 2.56,
2.55,
- 2.6,
- 2.59,
- 2.57,
2.59,
- 2.57,
- 2.53,
- 2.54,
- 2.51,
- 2.54,
- 2.51,
- 2.52,
- 2.55,
2.54,
2.51,
- 2.45,
- 2.49,
- 2.45,
- 2.47,
2.4,
- 2.42,
- 2.37,
- 2.4,
- 2.39,
2.41,
- 2.43,
- 2.39,
- 2.31,
2.32,
- 2.26,
- 2.25,
- 2.22,
- 2.14,
2.07,
- 2.12,
- 2.12,
- 2.12,
- 2.09,
- 2.15,
2.15,
- 2.13,
- 2.1,
2.09,
- 2.09,
- 2.06,
- 2.03,
- 2.01,
- 2.07,
2.02,
- 2,
- 2.05,
- 2.01,
- 2,
2.03,
- 1.98,
- 1.96,
- 2.04,
2.05,
- 2.07,
- 2.07,
- 2.13,
- 2.12,
2.09,
- 2.13,
- 2.06,
- 2.04,
- 2.05,
- 2.05,
- 2.08,
2.05,
- 2.08,
- 2.08,
2.06,
- 2.06,
- 2.02,
- 1.9,
- 1.86,
1.75,
- 1.73,
- 1.71,
- 1.72,
- 1.74,
1.65,
- 1.68,
- 1.59,
- 1.52,
- 1.55,
1.6,
- 1.55,
- 1.59,
- 1.62,
- 1.52,
1.54,
- 1.49,
- 1.47,
- 1.5,
1.5,
- 1.47,
- 1.47,
- 1.57,
- 1.55,
1.63,
- 1.72,
- 1.75,
- 1.79,
- 1.9,
1.84,
- 1.81,
- 1.8,
- 1.79,
- 1.74,
1.72,
- 1.64,
- 1.73,
- 1.7,
- 1.69,
1.68,
- 1.65,
- 1.6,
- 1.54,
- 1.52,
1.56,
- 1.54,
- 1.59,
- 1.67,
- 1.76,
- null,
- 1.77,
- 1.75,
1.76,
- 1.76,
- 1.8,
- 1.78,
- 1.77,
- 1.77,
1.8,
1.85,
- 1.84,
- 1.78,
- 1.69,
- 1.73,
1.79,
- 1.86,
- 1.81,
- 1.92,
1.94,
- null,
- 1.92,
- 1.88,
- 1.82,
- 1.84,
1.81,
- 1.79,
- 1.73,
- 1.77,
- 1.77,
1.76,
- 1.74,
- 1.77,
- 1.78,
1.83,
- 1.72,
- 1.77,
- 1.8,
- 1.84,
1.83,
- 1.85,
- 1.79,
- 1.9,
- 1.82,
- 1.89,
1.89,
- 1.92,
- 1.92,
- 1.92,
1.93,
1.9,
- 1.9,
- 1.88,
- 1.9,
- 1.92,
- 1.88,
- 1.8,
1.81,
- 1.83,
- 1.87,
- 1.85,
- 1.83,
1.85,
- 1.82,
- 1.79,
- 1.81,
1.84,
- 1.78,
- 1.77,
- 1.74,
- 1.7,
1.61,
- 1.65,
- 1.6,
- 1.57,
- 1.51,
1.54,
- 1.61,
- 1.66,
- 1.65,
- 1.59,
1.56,
1.59,
- 1.62,
- 1.61,
- 1.59,
- 1.55,
- 1.56,
- 1.52,
- 1.46,
1.38,
- 1.33,
- 1.33,
- 1.3,
- 1.13,
1.1,
- 1.02,
- 1.02,
- 0.92,
- 0.74,
0.54,
- 0.76,
- 0.82,
- 0.88,
- 0.94,
0.73,
- 1.02,
- 1.18,
- 1.12,
- 0.92,
0.76,
- 0.84,
- 0.88,
- 0.83,
- 0.72,
- 0.7,
0.7,
- 0.62,
- 0.63,
- 0.62,
0.67,
- 0.75,
- 0.77,
- 0.73,
0.76,
- 0.76,
- 0.63,
- 0.61,
- 0.65,
- 0.63,
- 0.58,
0.63,
- 0.61,
- 0.6,
0.67,
- 0.62,
- 0.63,
0.64,
- 0.64,
- 0.64,
- 0.66,
- 0.72,
- 0.63,
- 0.69,
0.73,
- 0.69,
- 0.64,
- 0.63,
- 0.64,
0.73,
- 0.7,
- 0.68,
- 0.68,
0.66,
- 0.69,
- 0.68,
- 0.7,
- 0.65,
0.66,
- 0.68,
- 0.77,
- 0.82,
- 0.91,
0.88,
- 0.84,
- 0.75,
- 0.66,
- 0.71,
0.71,
- 0.75,
- 0.74,
- 0.71,
- 0.7,
0.71,
- 0.72,
- 0.69,
- 0.68,
- 0.64,
0.64,
- 0.66,
- 0.69,
- 0.68,
0.69,
- 0.65,
- 0.67,
- 0.62,
- 0.65,
- 0.64,
- 0.63,
- 0.64,
- 0.62,
0.64,
0.62,
- 0.61,
- 0.6,
- 0.59,
- 0.59,
0.62,
- 0.59,
- 0.58,
- 0.55,
- 0.55,
0.56,
- 0.52,
- 0.55,
- 0.55,
- 0.57,
0.59,
- 0.64,
- 0.69,
- 0.71,
- 0.71,
0.69,
- 0.67,
- 0.68,
0.65,
- 0.64,
- 0.65,
- 0.69,
- 0.69,
- 0.74,
- 0.74,
0.72,
- 0.68,
- 0.66,
- 0.63,
0.72,
- 0.69,
- 0.71,
- 0.68,
- 0.67,
- 0.68,
- 0.68,
- 0.69,
- 0.69,
- 0.7,
0.68,
0.68,
- 0.68,
- 0.67,
- 0.66,
0.67,
- 0.66,
- 0.69,
- 0.68,
- 0.7,
- 0.78,
- 0.76,
- 0.81,
0.78,
0.79,
- null,
- 0.74,
- 0.73,
- 0.74,
- 0.76,
0.78,
0.81,
- 0.83,
- 0.87,
- 0.85,
- 0.81,
- 0.79,
- 0.79,
- 0.85,
- 0.88,
0.87,
- 0.9,
- 0.78,
- 0.79,
- 0.83,
0.96,
- 0.98,
- null,
- 0.88,
- 0.89,
0.91,
- 0.87,
- 0.88,
- 0.86,
- 0.83,
0.86,
- 0.88,
- 0.88,
0.84,
- 0.84,
- 0.92,
- 0.95,
- 0.92,
- 0.97,
0.94,
- 0.92,
- 0.95,
- 0.92,
- 0.9,
0.9,
- 0.92,
- 0.92,
- 0.94,
- 0.95,
0.95,
- 0.93,
- 0.96,
- 0.94,
- 0.94,
0.94,
0.93,
- 0.93,
- 0.93,
- 0.96,
- 1.04,
- 1.08,
- 1.13,
- 1.15,
- 1.15,
- 1.1,
1.15,
1.11,
- 1.1,
- 1.1,
- 1.12,
- 1.1,
- 1.05,
1.05,
- 1.04,
- 1.07,
- 1.11,
1.09,
- 1.12,
- 1.15,
- 1.15,
- 1.19,
1.19,
- 1.18,
- 1.15,
- 1.16,
1.2,
- 1.3,
- 1.29,
- 1.29,
- 1.34,
- 1.37,
1.37,
- 1.38,
- 1.54,
- 1.44,
1.45,
- 1.42,
- 1.47,
- 1.54,
- 1.56,
1.59,
- 1.55,
- 1.53,
- 1.54,
- 1.64,
- 1.62,
1.62,
- 1.63,
- 1.71,
- 1.74,
1.69,
- 1.63,
- 1.62,
- 1.63,
- 1.67,
1.73,
1.73,
- 1.74,
- 1.69,
- 1.73,
- 1.67,
- 1.68,
- 1.64,
- 1.67,
1.69,
- 1.64,
- 1.64,
- 1.56,
- 1.59,
- 1.61,
- 1.58,
- 1.57,
- 1.57,
- 1.58,
- 1.58,
- 1.63,
- 1.63,
- 1.65,
- 1.65,
- 1.63,
1.61,
- 1.59,
1.58,
- 1.6,
1.63,
- 1.64,
- 1.69,
- 1.66,
1.63,
1.64,
- 1.64,
- 1.68,
- 1.63,
- 1.63,
- 1.61,
- 1.56,
- 1.58,
1.61,
1.58,
- 1.62,
- 1.59,
- 1.63,
- 1.56,
1.57,
- 1.53,
- 1.5,
- 1.45,
- 1.47,
1.51,
- 1.51,
- 1.57,
- 1.52,
- 1.45,
- 1.5,
- 1.48,
1.5,
1.49,
- 1.54,
- 1.49,
- 1.49,
- 1.45,
- 1.48,
1.44,
- 1.37,
- 1.33,
- 1.3,
- 1.37,
1.38,
- 1.42,
- 1.37,
- 1.31,
- 1.31,
1.19,
- 1.23,
- 1.3,
- 1.27,
- 1.3,
1.29,
- 1.25,
- 1.26,
- 1.28,
- 1.24,
1.2,
- 1.19,
- 1.19,
- 1.23,
- 1.31,
1.33,
- 1.36,
- 1.35,
- 1.36,
- 1.29,
- 1.26,
- 1.26,
- 1.27,
- 1.24,
1.26,
1.25,
1.29,
- 1.35,
- 1.34,
- 1.31,
- 1.29,
- 1.3,
- 1.31,
- 1.29,
1.33,
- 1.38,
- 1.35,
- 1.3,
- 1.35,
1.33,
- 1.28,
- 1.31,
- 1.34,
- 1.37,
1.31,
- 1.33,
- 1.32,
- 1.41,
- 1.47,
- 1.48,
- 1.54,
- 1.55,
- 1.52,
1.48,
1.49,
- 1.54,
- 1.53,
- 1.58,
1.61,
- null,
- 1.59,
- 1.56,
- 1.52,
- 1.59,
1.59,
- 1.65,
- 1.65,
- 1.68,
- 1.66,
1.64,
- 1.63,
- 1.54,
- 1.57,
- 1.55,
1.58,
- 1.56,
- 1.6,
- 1.53,
- 1.45,
1.51,
- 1.46,
- 1.56,
- null,
- 1.58,
- 1.63,
1.63,
- 1.6,
- 1.59,
- 1.54,
1.63,
- 1.67,
- 1.64,
- null,
- 1.48,
1.52,
1.43,
- 1.43,
- 1.44,
- 1.35,
- 1.43,
- 1.48,
- 1.52,
- 1.49,
- 1.48,
1.42,
- 1.44,
- 1.47,
- 1.44,
- 1.41,
1.43,
1.48,
- 1.46,
- 1.5,
- 1.48,
- 1.49,
- 1.55,
- 1.52,
- 1.52,
1.63,
- 1.66,
- 1.71,
- 1.73,
- 1.76,
1.78,
- 1.75,
- 1.74,
- 1.7,
1.78,
- 1.87,
- 1.83,
- 1.83,
1.75,
- 1.75,
- 1.78,
- 1.85,
- 1.81,
- 1.78,
1.79,
- 1.81,
- 1.78,
- 1.82,
- 1.93,
- 1.92,
- 1.96,
- 1.94,
- 2.03,
1.92,
1.98,
- 2.05,
- 2.03,
- 1.97,
1.92,
- 1.94,
- 1.99,
- 1.96,
- 1.97,
1.83,
- 1.72,
- 1.86,
- 1.86,
- 1.74,
1.78,
- 1.86,
- 1.94,
- 1.98,
- 2,
- 2.14,
- 2.15,
- 2.19,
- 2.2,
2.14,
2.32,
- 2.38,
- 2.32,
- 2.34,
- 2.48,
2.46,
- 2.41,
- 2.35,
- 2.32,
- 2.39,
2.42,
- 2.54,
- 2.61,
- 2.66,
- 2.72,
2.79,
- 2.72,
- 2.7,
- 2.83,
- 2.85,
- 2.93,
2.85,
- 2.9,
- 2.9,
2.81,
- 2.77,
- 2.82,
- 2.85,
- 2.89,
2.99,
- 2.97,
- 2.93,
- 3.05,
- 3.12,
3.05,
- 2.99,
- 2.91,
- 2.84,
- 2.93,
2.88,
- 2.98,
- 2.89,
- 2.84,
- 2.78,
2.86,
- 2.76,
- 2.75,
- 2.75,
2.74,
- 2.85,
- 2.94,
- 2.92,
- 2.96,
- 3.04,
- 2.98,
- 3.03,
3.04,
- 3.15,
3.43,
- 3.49,
- 3.33,
- 3.28,
3.25,
- null,
- 3.31,
- 3.16,
- 3.09,
- 3.13,
- 3.2,
3.2,
- 3.1,
- 2.98,
2.88,
- 2.82,
- 2.93,
- 3.01,
- 3.09,
2.99,
2.96,
- 2.91,
- 2.96,
- 2.93,
- 2.96,
- 3.01,
- 3.04,
- 2.91,
- 2.77,
- 2.81,
2.81,
- 2.78,
- 2.68,
- 2.67,
2.6,
- 2.75,
- 2.73,
- 2.68,
- 2.83,
2.77,
- 2.8,
- 2.78,
- 2.87,
- 2.84,
2.79,
- 2.82,
- 2.89,
- 2.88,
- 2.98,
- 3.03,
- 3.05,
- 3.11,
3.03,
- 3.04,
3.12,
- 3.11,
- 3.15,
- 3.26,
3.2,
- 3.33,
- 3.27,
- 3.29,
- 3.33,
3.37,
- 3.42,
- 3.41,
- 3.45,
- 3.45,
3.49,
- 3.57,
- 3.51,
- 3.7,
- 3.69,
3.88,
- 3.97,
- 3.72,
- 3.76,
- 3.83,
3.67,
- 3.62,
- 3.76,
- 3.83,
3.89,
- null,
- 3.93,
- 3.91,
- 3.97,
- 4,
4.02,
- 4.01,
- 4.14,
- 4.24,
- 4.21,
4.25,
4.1,
- 4.04,
- 3.96,
- 4.02,
- 4.1,
- 4.07,
- 4.1,
- 4.14,
- 4.17,
4.22,
- 4.14,
- 4.12,
- 3.82,
- null,
3.88,
- 3.8,
- 3.67,
- 3.77,
- 3.82,
3.83,
- 3.76,
- 3.71,
- 3.68,
3.69,
- 3.75,
- 3.68,
- 3.53,
- 3.51,
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3.41,
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3.52,
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3.59,
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3.72,
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3.73,
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+ 3.72,
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+ 4.01,
+ 3.81,
+ 3.86,
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+ 4.34,
+ 4.2,
+ 4.18,
+ 4.29,
+ 4.32,
+ 4.55,
+ 4.69,
+ 4.78,
+ 4.71,
+ 4.86,
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+ 4.67,
+ 4.63,
+ 4.42,
null
]
}
@@ -24009,7 +6026,7 @@
},
"hovermode": "x",
"legend": {
- "bgcolor": "rgba(0, 0, 0, 0)",
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
"font": {
"size": 15
},
@@ -24018,6 +6035,30 @@
"y": 0.99,
"yanchor": "top"
},
+ "legend2": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
+ "legend3": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
+ "legend4": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
+ "legend5": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
"mapbox": {
"style": "dark"
},
@@ -24168,15 +6209,16 @@
}
],
"source": [
- "fig = OpenBBFigure()\n",
+ "\n",
+ "fig = go.Figure()\n",
"fig.add_scatter(x=data.index, y=data[\"Copper/Gold Ratio\"], name=\"Copper/Gold Ratio (x1000) %\")\n",
"fig.add_scatter(x=data.index, y=data[\"US 10-Year Constant Maturity\"], name=\"US 10-Year Constant Maturity %\")\n",
- "fig.update({'layout': \n",
- " {'xaxis': {'title': 'Date'},\n",
- " 'yaxis': {'title': '%'},\n",
- " 'title': 'Copper/Gold Ratio vs. US 10-Year Constant Maturity',\n",
- " 'title_y':0.90,\n",
- " 'title_x':0.5,\n",
+ "fig.update({\"layout\":\n",
+ " {\"xaxis\": {\"title\": \"Date\"},\n",
+ " \"yaxis\": {\"title\": \"%\"},\n",
+ " \"title\": \"Copper/Gold Ratio vs. US 10-Year Constant Maturity\",\n",
+ " \"title_y\":0.90,\n",
+ " \"title_x\":0.5,\n",
"}})\n",
"fig.update_layout(\n",
" legend=dict(\n",
@@ -24193,70737 +6235,5022 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "There is another way to create the figure object for the chart. Let's use the `plot` function from the `forecast` module to demonstrate."
+ "What we have currently is the price relationship between one Troy ounce of gold and one pound of copper. As we described the copper-to-gold ratio as the price-per-ounce of each, some adjustments are required to be true to the definition.\n",
+ "\n",
+ "- 1 ounce = 0.911458 Troy ounces\n",
+ "- 1 pound = 16 ounces\n",
+ " \n",
+ "To adjust the gold price as USD/ounce, multiply each row by 0.911458. To adjust the copper price, divide each row by 16."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Gold | \n",
+ " Copper | \n",
+ " Copper/Gold Ratio | \n",
+ " US 10-Year Constant Maturity | \n",
+ " Copper/Gold Ratio per Ounce (x1000) % | \n",
+ "
\n",
+ " \n",
+ " date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2023-11-20 | \n",
+ " 2002.199951 | \n",
+ " 3.7965 | \n",
+ " 1.896164 | \n",
+ " 4.42 | \n",
+ " 0.130023 | \n",
+ "
\n",
+ " \n",
+ " 2023-11-27 | \n",
+ " 2011.800049 | \n",
+ " 3.7625 | \n",
+ " 1.870216 | \n",
+ " NaN | \n",
+ " 0.128243 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Gold Copper Copper/Gold Ratio \\\n",
+ "date \n",
+ "2023-11-20 2002.199951 3.7965 1.896164 \n",
+ "2023-11-27 2011.800049 3.7625 1.870216 \n",
+ "\n",
+ " US 10-Year Constant Maturity \\\n",
+ "date \n",
+ "2023-11-20 4.42 \n",
+ "2023-11-27 NaN \n",
+ "\n",
+ " Copper/Gold Ratio per Ounce (x1000) % \n",
+ "date \n",
+ "2023-11-20 0.130023 \n",
+ "2023-11-27 0.128243 "
+ ]
+ },
+ "execution_count": 78,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[\"Copper/Gold Ratio per Ounce (x1000) %\"] = (\n",
+ " ((data[\"Copper\"]/16)/(data[\"Gold\"]*0.911458))*1000\n",
+ ")\n",
+ "\n",
+ "data.tail(2)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Each y-axis requires its own DataFrame, so let's make one for each next."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y1 = pd.DataFrame(data[\"Copper/Gold Ratio per Ounce (x1000) %\"])\n",
+ "y2 = pd.DataFrame(data[\"US 10-Year Constant Maturity\"])\n",
+ "fig = show_plot(y1, y2, external_axes = True)"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
- "displaylogo": false,
- "plotlyServerURL": "https://plot.ly",
- "scrollZoom": true
+ "plotlyServerURL": "https://plot.ly"
},
"data": [
{
- "hovertemplate": "%{y}
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+ "name": "Copper/Gold Ratio (x1000) %",
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5.26,
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- 5.08,
- 5.08,
- 5.12,
- 5.22,
- 5.13,
- 5.15,
- 5.07,
- 5.2,
- 5.16,
5.18,
+ 5.07,
5.09,
- 5.1,
- 5.1,
- 5,
- 4.92,
4.91,
- 4.88,
- 4.88,
- 4.98,
4.94,
- 4.96,
- 5.05,
- 5.07,
- 5.1,
5.12,
- 5.02,
- 5.02,
- 5.07,
- 5.02,
4.94,
- 4.92,
- 4.92,
- 4.93,
- 4.9,
4.91,
- 4.97,
- 5.01,
- 4.95,
4.86,
- 4.88,
- 4.88,
- 4.88,
- 4.84,
4.86,
- 4.93,
- 4.84,
- 4.88,
- 4.98,
- 5.02,
5.02,
- 5.06,
- 5.22,
- 5.33,
5.33,
5.32,
- 5.28,
- 5.4,
- 5.35,
- 5.32,
- 5.33,
- 5.4,
- 5.39,
- 5.4,
5.41,
- 5.35,
- 5.35,
- 5.42,
5.44,
- 5.36,
- 5.3,
- 5.28,
- 5.22,
5.25,
- 5.22,
- 5.24,
- 5.22,
- 5.18,
5.15,
- 5.2,
- 5.24,
- 5.23,
- 5.21,
5.19,
- 5.18,
- 5.11,
- 5.1,
- 5.08,
5.13,
- 5.11,
- 5.08,
- 5.13,
- 5.08,
5.1,
- 5.09,
- 5.24,
- 5.2,
- 5.15,
5.23,
- 5.32,
- 5.28,
- 5.2,
- 5.27,
5.21,
- 5.18,
- 5.13,
- 5.16,
5.16,
- 5.16,
- 5.11,
5.06,
- 5.08,
- 5.06,
- 5.04,
- 5.08,
- 5.04,
- 5.1,
5.07,
- 5.02,
- 4.98,
- 4.94,
- 4.83,
4.89,
- 4.88,
- 4.76,
- 4.85,
- 4.79,
4.87,
- 4.88,
- 4.75,
- 4.84,
- 4.86,
4.85,
- 4.77,
- 4.78,
4.84,
- 4.78,
- 4.66,
- 4.66,
- 4.63,
4.66,
- 4.75,
- 4.71,
- 4.66,
- 4.61,
4.51,
- 4.47,
- 4.49,
- 4.43,
- 4.43,
4.62,
- 4.65,
- 4.51,
- 4.47,
- 4.33,
- 4.29,
- 4.42,
- 4.35,
- 4.4,
- 4.27,
- 4.22,
- 4.12,
- 4.06,
- 4.17,
- 4.32,
4.29,
- 4.17,
- 4.2,
- 4.3,
- 4.25,
4.22,
4.29,
4.22,
- 4.16,
4.14,
- 3.98,
- 3.96,
- 3.91,
4.05,
- 4.05,
- 4,
- 4.07,
- 3.98,
- 3.92,
3.9,
- 3.87,
- 3.86,
- 3.79,
- 3.79,
3.7,
- 3.69,
- 3.77,
- 3.79,
- 3.69,
3.63,
- 3.72,
- 3.71,
- 3.7,
- 3.69,
3.64,
- 3.65,
- 3.61,
- 3.68,
3.83,
- null,
- 4.07,
- 4.06,
- 4.16,
- 4.14,
4.24,
- 4.27,
- 4.26,
- 4.16,
- 4.12,
4.1,
- 3.97,
- 3.99,
- 3.93,
- 4.01,
4.07,
- 4.1,
- 4.09,
- 3.88,
3.85,
- null,
- 3.84,
- 3.84,
- 4.03,
- 4.05,
4.02,
- 3.99,
- 4.08,
- 4.14,
- 4.18,
4.19,
- 4.08,
- 4.26,
4.22,
- 4.24,
- 4.18,
- 4.13,
- 4.09,
4.06,
- 4.06,
- 4.01,
- 4.01,
- 4.07,
4.15,
- 4.13,
- 4.06,
- 3.96,
- 3.97,
3.98,
- 3.95,
- 3.93,
- 3.83,
3.82,
- 3.83,
- 4.07,
- 4.05,
4.09,
- 4.04,
- 4,
- 4.19,
- 4.16,
4.15,
- 4.1,
- 4.1,
- 4.1,
4.05,
- 4.01,
- 3.95,
3.98,
- 3.94,
- 3.98,
- 4,
- 4.06,
- 4,
- 4,
4.01,
- 3.96,
- 4.02,
- 3.97,
- 3.96,
3.99,
- 3.98,
- 3.93,
- 3.89,
3.95,
- 3.94,
- 3.88,
- 3.85,
- 3.9,
3.86,
- 3.81,
- 3.78,
- 3.76,
- 3.71,
3.68,
- 3.65,
- 3.63,
- 3.67,
- 3.63,
3.59,
- 3.6,
- 3.6,
- 3.74,
- 3.72,
3.82,
- 3.91,
3.98,
- 4.01,
- 4.11,
- 3.98,
- 3.97,
- 3.96,
- 3.95,
- 3.92,
3.83,
- 3.84,
- 3.94,
- 3.93,
- 3.96,
4.03,
- 3.95,
- 3.93,
- 3.95,
- 4,
4.04,
- 3.98,
- 3.96,
- 3.98,
4,
- 4.01,
- 4.02,
- 3.93,
- 3.91,
3.92,
- 3.96,
- 3.89,
- 3.88,
- 3.94,
3.92,
- 3.84,
- 3.72,
- 3.7,
- 3.69,
3.64,
- 3.63,
- 3.53,
- 3.53,
3.46,
- 3.46,
- 3.38,
- 3.39,
- 3.34,
- 3.34,
- 3.41,
- 3.44,
3.34,
- 3.37,
3.43,
- 3.34,
- 3.3,
- 3.34,
- 3.37,
3.29,
- 3.2,
- 3.21,
- 3.18,
- 3.13,
3.18,
- 3.27,
- 3.37,
- 3.35,
- 3.4,
3.32,
- 3.29,
- 3.38,
- 3.55,
- 3.58,
3.54,
- 3.56,
- 3.56,
- 3.67,
3.74,
- 3.75,
- 3.73,
- 3.7,
- 3.66,
3.74,
- 3.94,
- 3.97,
- 3.98,
- 4,
4.19,
- 4.17,
- 4.12,
- 4.2,
- 4.22,
4.31,
- 4.42,
- 4.34,
- 4.49,
- 4.44,
4.35,
- 4.47,
- 4.32,
- 4.3,
- 4.27,
4.38,
- 4.37,
- 4.58,
- 4.55,
- 4.55,
4.49,
- 4.38,
- 4.45,
4.53,
- 4.48,
- 4.53,
- 4.5,
- 4.54,
- 4.42,
4.45,
- 4.61,
- 4.6,
- 4.52,
- 4.35,
4.41,
- 4.37,
- 4.28,
- 4.35,
- 4.27,
4.28,
- 4.29,
- 4.2,
- 4.19,
- 4.17,
4.26,
- 4.24,
- 4.16,
- 4.12,
- 4.04,
4.09,
- 3.96,
- 3.96,
- 4.03,
- 4.21,
4.17,
- 4.27,
- 4.27,
- 4.32,
4.29,
- null,
- 4.37,
- 4.43,
- 4.47,
4.41,
- 4.41,
- 4.38,
- 4.29,
- 4.34,
- 4.24,
4.3,
- 4.23,
- 4.31,
- 4.36,
- 4.33,
4.4,
- 4.33,
- 4.38,
- 4.45,
- 4.48,
4.49,
- null,
- 4.44,
- 4.3,
- 4.22,
4.18,
- 4.17,
- 4.24,
- 4.16,
- 4.15,
4.23,
- 4.19,
- 4.25,
4.4,
- 4.38,
- 4.41,
- 4.38,
- 4.23,
4.29,
- 4.32,
- 4.3,
- 4.27,
- 4.26,
4.28,
- 4.24,
- 4.19,
- 4.16,
- 4.15,
4.18,
- 4.28,
- 4.2,
4.24,
- 4.29,
- 4.27,
4.41,
- 4.29,
- 4.27,
- 4.27,
4.11,
- 4.11,
- 4.05,
- 4.01,
- 3.99,
4.04,
- 4.08,
- 4.05,
- 3.99,
- 4.09,
- 4.16,
- 4.11,
- 4.22,
- 4.22,
4.16,
4.18,
- 4.13,
- 4.15,
- 4.2,
- 4.12,
4.09,
- 4.13,
- 4.05,
- 4.1,
4.05,
4.05,
- 4.05,
- 4.05,
- 4.1,
- 4.05,
- 4.04,
- 4.02,
- 4.05,
- 3.99,
4,
- 4.05,
- 4.07,
- 4.04,
- 3.85,
- 3.78,
- 3.73,
- 3.74,
- 3.74,
3.78,
3.78,
- 3.7,
- 3.71,
- 3.76,
- 3.8,
3.74,
- 3.73,
- 3.73,
- 3.75,
- 3.85,
3.91,
- 3.91,
- 3.86,
- 3.91,
- 4.15,
4.24,
- 4.19,
- 4.19,
- 4.21,
4.25,
- 4.35,
- 4.4,
- 4.42,
- 4.37,
4.39,
- 4.43,
- 4.45,
- 4.4,
- 4.48,
4.46,
- 4.43,
- 4.5,
- 4.55,
- 4.53,
4.53,
- 4.56,
- 4.61,
- 4.63,
- 4.79,
4.81,
- 4.79,
- 4.83,
- 4.85,
- 4.79,
4.7,
- 4.74,
- 4.79,
- 4.72,
- 4.76,
4.75,
- 4.73,
- 4.67,
- 4.6,
4.66,
- 4.71,
- 4.74,
- 4.71,
4.78,
- 4.78,
- 4.78,
- 4.82,
- 4.81,
4.89,
- 4.69,
- 4.74,
- 4.71,
- 4.72,
4.7,
- 4.72,
- 4.71,
- 4.66,
- 4.66,
4.76,
- 4.7,
- 4.62,
- 4.57,
4.48,
- 4.49,
- 4.5,
- 4.49,
- 4.49,
4.46,
- 4.5,
- 4.5,
- 4.5,
4.38,
- 4.38,
- 4.47,
- 4.5,
- 4.48,
- 4.45,
4.49,
- 4.62,
- 4.61,
- 4.6,
- 4.5,
4.48,
- 4.45,
- 4.45,
- 4.43,
- 4.24,
4.28,
- 4.32,
- 4.3,
- 4.27,
- 4.22,
4.26,
- 4.21,
- 4.23,
- 4.22,
- 4.24,
- 4.28,
4.28,
- 4.26,
- 4.22,
- 4.23,
4.19,
- 4.13,
- 4.13,
- 4.2,
4.3,
- 4.26,
- 4.18,
- 4.22,
- 4.19,
4.16,
- 4.15,
- 4.18,
- 4.08,
- 4.14,
4.07,
- 4.05,
- 4,
- 4.02,
- 4.04,
4.01,
- 4.02,
- 4.1,
- 4.14,
- 4.21,
4.19,
- 4.18,
- 4.23,
- 4.26,
4.15,
- null,
- 4.12,
- 4.09,
- 4.03,
- 4.07,
4.07,
- 4.07,
- 4.01,
- 4.01,
- 4,
3.99,
- 4.01,
- 4.11,
- 4.09,
- 4.05,
4.11,
- 4.1,
- 4.09,
- 4.1,
- 4.21,
4.22,
- 4.22,
- 4.25,
- null,
- 4.2,
- 4.2,
- 4.21,
- 4.14,
- 4.12,
4.2,
4.18,
- 4.19,
- 4.2,
4.34,
- 4.36,
- 4.38,
- 4.4,
- 4.27,
4.24,
- 4.23,
- 4.14,
- 4.19,
- 4.16,
4.16,
- 4.14,
- 4.09,
- 4.19,
- 4.21,
4.21,
- 4.18,
- 4.21,
- 4.23,
4.3,
- 4.31,
- 4.33,
- 4.27,
4.23,
4.29,
- 4.29,
- 4.29,
- 4.29,
- 4.29,
- 4.26,
- 4.25,
- 4.2,
4.23,
- 4.21,
- 4.2,
- 4.17,
- 4.16,
4.14,
- 4.2,
- 4.21,
- 4.22,
- 4.16,
4.14,
- 4.15,
- 4.15,
- 4.18,
- 4.09,
4.07,
- 4.05,
- 4,
- 4.07,
- 4.1,
4.08,
- 4.1,
- 4.16,
- 4.19,
- 4.27,
- 4.29,
- 4.27,
- 4.29,
4.27,
4.36,
- 4.38,
- 4.38,
- 4.39,
- 4.32,
4.31,
- 4.38,
- 4.52,
- 4.48,
- 4.56,
- 4.52,
- 4.54,
4.52,
- 4.47,
- 4.51,
4.53,
- 4.63,
- 4.61,
- 4.6,
4.64,
- 4.6,
- 4.5,
- 4.46,
4.47,
- 4.48,
- 4.44,
- 4.49,
- 4.5,
4.45,
- 4.38,
- 4.38,
- 4.37,
4.27,
- 4.27,
- 4.21,
- 4.22,
- 4.32,
4.26,
- 4.26,
- 4.28,
- 4.25,
- 4.21,
4.21,
- 4.21,
- 4.2,
- 4.19,
- 4.28,
4.29,
- 4.23,
- 4.21,
- 4.18,
- 4.12,
- 4.13,
- 4.12,
- 4.07,
- 4.11,
4.13,
4.07,
- 4.04,
- 4.08,
4.08,
- 4.08,
- 4,
- 3.91,
- 3.89,
- 3.98,
3.96,
- 3.92,
- 3.95,
- 3.98,
- 4.05,
- 4.09,
- 4.13,
- 4.12,
- 4.09,
4.09,
4.11,
- 4.06,
- 3.95,
- 3.96,
- 3.92,
3.9,
- 3.97,
- 3.99,
- 3.94,
4.06,
4.11,
- 4.08,
- 4.05,
- 4.11,
- 4.11,
- 4.15,
- 4.17,
- 4.19,
- 4.18,
4.22,
- 4.2,
- 4.17,
- 4.28,
- 4.23,
4.25,
- 4.24,
- 4.27,
- 4.2,
- 4.28,
4.32,
- 4.34,
- 4.3,
- 4.32,
- 4.4,
4.42,
- 4.41,
- 4.4,
- 4.32,
- 4.24,
4.27,
- 4.23,
- 4.28,
- 4.21,
- 4.21,
4.22,
4.2,
- 4.19,
- 4.18,
- 4.2,
- 4.2,
- 4.16,
- 4.02,
- 4.02,
4.03,
- 4.09,
- 4.15,
- 4.15,
- 4.14,
4.18,
- 4.14,
- 4.17,
- 4.22,
- 4.26,
- 4.25,
- 4.26,
- 4.19,
- 4.19,
4.25,
4.3,
- 4.3,
- 4.26,
- 4.29,
- 4.34,
4.39,
- 4.38,
- 4.36,
- 4.37,
4.35,
- null,
- 4.39,
- 4.45,
- 4.48,
- 4.48,
4.5,
- 4.49,
- 4.47,
- 4.46,
- 4.39,
4.45,
- 4.54,
- 4.6,
- 4.57,
- 4.58,
4.57,
- 4.58,
- 4.61,
- 4.65,
- 4.66,
4.65,
- 4.57,
- 4.64,
- 4.55,
- null,
4.61,
- 4.56,
- 4.49,
- 4.46,
- 4.5,
4.46,
- 4.43,
- 4.47,
4.41,
- 4.48,
- 4.49,
- 4.52,
- 4.52,
4.57,
- 4.49,
- 4.52,
- 4.47,
- 4.54,
4.56,
- 4.54,
- 4.45,
- 4.47,
- 4.45,
4.45,
- 4.47,
- 4.49,
- 4.44,
4.38,
- 4.34,
- 4.38,
- 4.37,
4.39,
- 4.37,
- 4.36,
- 4.36,
4.38,
- 4.38,
- 4.43,
- 4.46,
- 4.42,
4.36,
- 4.34,
- 4.34,
- 4.38,
- 4.37,
4.36,
- 4.4,
- 4.49,
- 4.53,
- 4.52,
- 4.54,
- 4.53,
- 4.57,
- 4.57,
4.54,
4.55,
- 4.57,
- 4.56,
- 4.54,
- 4.59,
4.58,
- 4.62,
- 4.61,
- 4.59,
4.54,
- 4.57,
- 4.53,
- 4.56,
- 4.58,
- 4.59,
- 4.55,
4.59,
- 4.64,
- 4.68,
- 4.74,
- 4.74,
- 4.73,
4.74,
- 4.76,
4.77,
- 4.71,
- 4.73,
- 4.65,
- 4.68,
4.66,
- 4.71,
- 4.7,
- 4.73,
- 4.67,
4.7,
- 4.79,
- 4.81,
- 4.86,
- 4.86,
4.88,
- 4.87,
- 4.84,
- 4.9,
- 4.97,
4.97,
- 4.93,
- 4.98,
- 5.05,
- 5.01,
- 4.99,
- 5.04,
- 5.04,
5.01,
4.99,
- 5.07,
- 5.12,
- 5.09,
- 5.07,
5.14,
5.12,
5.15,
- 5.16,
- 5.12,
- 5.12,
- 5.13,
- 5.13,
- 5.14,
- 5.19,
- 5.15,
- 5.1,
- 5.16,
- 5.08,
- 5.05,
5.04,
- 5.07,
- 5.03,
- 5.07,
5.06,
- 5.09,
- 5.12,
- 5.11,
- 5,
- 5.02,
- 5.01,
5.02,
- 5,
- 4.98,
4.99,
- 4.97,
- 5.05,
- 5.1,
- 5.13,
5.14,
- 5.15,
- 5.16,
- 5.2,
- 5.23,
- 5.25,
- 5.21,
5.25,
- 5.22,
5.15,
- 5.23,
- 5.19,
- 5.14,
5.13,
- 5.1,
- 5.1,
- 5.08,
- 5.07,
5.07,
- 5.13,
- 5.06,
- 5.03,
5.05,
- 5.05,
- 5.07,
- 5.04,
- 5.07,
- 5,
- 4.99,
4.99,
- 4.96,
- 4.96,
- 4.91,
- 4.93,
4.93,
- 4.92,
- 4.93,
- 4.97,
5,
- 4.93,
- 4.87,
- 4.87,
- 4.84,
- 4.82,
4.82,
- 4.82,
- 4.81,
- 4.79,
4.8,
- 4.79,
- 4.76,
- 4.74,
4.73,
- null,
- 4.78,
- 4.8,
- 4.8,
- 4.78,
- 4.8,
- 4.78,
- 4.77,
- 4.79,
4.8,
4.81,
- 4.74,
- 4.73,
- 4.65,
- 4.6,
4.56,
- 4.59,
- 4.6,
- 4.63,
- 4.64,
4.62,
- 4.62,
- 4.57,
- 4.61,
4.7,
- null,
- 4.75,
- 4.78,
- 4.79,
- 4.81,
- 4.79,
- 4.78,
- 4.77,
4.79,
- 4.79,
- 4.83,
4.83,
- 4.78,
- 4.73,
4.68,
- 4.68,
- 4.61,
- 4.57,
- 4.6,
- 4.72,
4.71,
- 4.66,
- 4.64,
- 4.62,
- 4.59,
- 4.61,
- 4.57,
- 4.61,
- 4.66,
4.61,
4.6,
- 4.58,
- 4.57,
- 4.55,
4.54,
- 4.51,
- 4.52,
- 4.46,
- 4.43,
4.43,
- 4.45,
- 4.48,
- 4.49,
- 4.56,
4.52,
- 4.49,
- 4.58,
- 4.6,
- 4.6,
4.6,
- 4.6,
- 4.6,
- 4.55,
4.63,
- null,
- 4.61,
- 4.66,
- 4.7,
4.71,
- null,
- 4.68,
- 4.67,
- 4.62,
- 4.65,
4.66,
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- 4.69,
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4.77,
- 4.75,
- 4.79,
- 4.75,
- 4.78,
4.76,
- 4.81,
- 4.81,
- 4.87,
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4.9,
- 4.88,
- 4.83,
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- 4.83,
4.81,
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- 4.74,
- 4.73,
- 4.79,
4.8,
- 4.82,
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- 4.7,
4.69,
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4.63,
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- 4.56,
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4.51,
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- 4.5,
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4.56,
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- 4.53,
- 4.54,
- 4.55,
4.58,
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- 4.53,
- 4.6,
- 4.62,
4.6,
- 4.62,
- 4.62,
- 4.64,
- 4.65,
4.65,
- 4.67,
- 4.66,
- 4.68,
4.75,
- 4.73,
4.74,
- 4.74,
- 4.76,
- 4.74,
- 4.69,
4.66,
- 4.68,
- 4.68,
- 4.66,
- 4.63,
- 4.66,
- 4.69,
- 4.71,
4.63,
4.64,
- 4.65,
- 4.68,
- 4.65,
- 4.64,
- 4.63,
- 4.67,
- 4.65,
- 4.67,
4.69,
- 4.71,
- 4.71,
- 4.76,
- 4.81,
4.79,
- 4.83,
4.86,
- 4.86,
- 4.86,
- 4.88,
- 4.88,
- 4.9,
- 4.95,
4.93,
- 4.98,
- 4.97,
- 5.11,
- 5.12,
5.14,
- 5.26,
- 5.2,
- 5.23,
- 5.16,
5.15,
5.09,
- 5.14,
- 5.16,
- 5.14,
- 5.09,
- 5.1,
- 5.09,
- 5.12,
- 5.03,
5,
- 5.05,
- 5.16,
- 5.19,
5.16,
- 5.03,
- 5.09,
- 5.13,
- 5.11,
5.05,
- 5.08,
- 5.02,
- 5.04,
- 4.96,
4.97,
- 4.94,
- 4.92,
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- 4.8,
4.82,
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4.72,
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- 4.84,
- 4.79,
- 4.81,
4.78,
- 4.73,
- 4.69,
- 4.6,
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4.64,
4.6,
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- 4.6,
- 4.53,
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4.54,
- 4.56,
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- 4.38,
4.34,
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- 4.47,
4.48,
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4.63,
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4.56,
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- 4.54,
- 4.65,
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4.65,
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- 4.7,
4.69,
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- 4.57,
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4.42,
- 4.41,
- 4.36,
- 4.37,
- 4.41,
4.39,
- 4.4,
- 4.48,
- 4.36,
- 4.31,
4.35,
- 4.38,
- 4.34,
- 4.28,
4.23,
- null,
- 4.26,
- 4.28,
- 4.17,
- 4.15,
4.07,
- 4.06,
- 4,
- 4.01,
3.83,
- 3.95,
- 4.03,
- 3.94,
- 3.97,
- 3.89,
3.89,
- 3.92,
- 4.02,
- 4.12,
4.15,
- 3.98,
- 4.05,
- 4.18,
- 4.24,
4.2,
- 4.14,
- 4.06,
- 4.04,
- 4.18,
4.23,
- 4.3,
- 4.21,
- 4.11,
4.04,
- 3.91,
- 3.91,
- 3.88,
3.86,
- 3.86,
- 3.82,
- 3.91,
- 3.82,
3.81,
- 3.72,
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3.66,
- 3.66,
- 3.52,
- 3.51,
- 3.68,
3.61,
- 3.61,
- 3.69,
- 3.78,
- 3.67,
- 3.62,
3.68,
- 3.61,
- 3.61,
- 3.74,
- 3.64,
3.62,
- 3.66,
- 3.7,
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3.76,
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3.91,
- 3.88,
- 3.85,
- 3.71,
- 3.53,
3.54,
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- 3.7,
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3.46,
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- 3.38,
3.34,
3.56,
- 3.51,
- 3.51,
- 3.56,
- 3.47,
3.45,
3.57,
- 3.6,
- 3.61,
- 3.5,
- 3.57,
- 3.58,
- 3.49,
- 3.55,
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3.53,
- 3.6,
- 3.72,
- 3.75,
- 3.77,
3.75,
- 3.74,
- 3.77,
- 3.87,
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3.86,
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- 3.77,
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3.88,
- 3.93,
- 3.87,
- 3.79,
- 3.77,
3.78,
- 3.9,
- 3.92,
- 3.83,
- 3.85,
3.83,
- 3.78,
- 3.81,
- 3.92,
3.85,
- 3.93,
- 4.03,
- 4.08,
- 4.06,
- 3.98,
- 3.92,
3.98,
- 4.06,
- 3.94,
4.02,
- 4.11,
- 4.1,
- 4.23,
- 4.27,
4.25,
- 4.23,
- 4.16,
- 4.22,
- 4.16,
4.19,
- 4.1,
- 4.12,
- 4.07,
- 3.99,
- 3.99,
- 4.01,
- 3.99,
3.99,
3.95,
- 3.91,
- 3.85,
- 3.83,
- 3.96,
3.9,
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- 3.97,
- 4.07,
- 4.11,
4.09,
- 4.14,
- 4.16,
- 4.03,
- 4.13,
4.06,
- 4.09,
- 4.07,
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3.98,
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3.99,
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3.82,
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3.79,
3.83,
- 3.74,
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3.66,
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3.47,
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3.83,
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3.61,
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3.48,
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3.89,
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- 3.98,
3.91,
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3.79,
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- 3.93,
- 4,
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3.96,
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- 3.83,
3.82,
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- 3.75,
- 3.84,
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3.68,
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3.35,
- 3.11,
- 2.99,
- 2.93,
2.72,
- 2.68,
- 2.67,
- 2.55,
- 2.67,
2.77,
- 2.67,
- 2.69,
- 2.64,
- 2.6,
2.53,
- 2.37,
- 2.2,
- 2.08,
- 2.13,
- 2.16,
- 2.18,
- 2.2,
2.16,
2.13,
- 2.11,
- 2.25,
- 2.46,
2.49,
- 2.51,
- 2.52,
- 2.47,
- 2.43,
2.34,
- 2.33,
- 2.24,
- 2.23,
2.36,
- 2.4,
- 2.56,
- 2.62,
- 2.65,
2.7,
- 2.59,
- 2.71,
- 2.87,
- 2.87,
2.76,
- 2.89,
- 2.95,
- 2.95,
- 3.05,
3.07,
- 2.9,
- 2.78,
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2.89,
- 2.64,
- 2.74,
- 2.85,
- 2.78,
2.78,
- 2.8,
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- 2.98,
- 3.02,
2.91,
- 2.93,
- 3.01,
- 2.83,
- 2.83,
- 2.89,
- 2.99,
- 2.95,
- 2.89,
2.89,
2.97,
- 3.02,
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2.68,
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2.73,
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2.95,
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2.88,
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2.88,
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- 3.03,
2.95,
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- 3.12,
- 3.16,
- 3.21,
3.19,
- 3.2,
- 3.18,
- 3.29,
- 3.29,
- 3.17,
3.17,
- 3.11,
- 3.1,
- 3.14,
3.22,
- 3.25,
- 3.19,
- 3.35,
3.45,
- 3.5,
3.71,
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3.91,
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3.76,
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3.72,
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3.51,
3.52,
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3.38,
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3.61,
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- 3.7,
3.75,
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3.66,
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3.8,
- 3.71,
- 3.72,
- 3.59,
- 3.55,
3.48,
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3.48,
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3.4,
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3.45,
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- 3.42,
- 3.47,
- 3.48,
3.42,
3.49,
- 3.49,
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- 3.44,
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- 3.34,
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3.31,
- 3.31,
- 3.21,
3.24,
- 3.24,
- 3.27,
- 3.21,
- 3.27,
3.4,
- null,
- 3.34,
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3.41,
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3.59,
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3.45,
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3.52,
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- 3.33,
3.33,
- 3.36,
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- 3.36,
3.37,
- 3.32,
- 3.28,
- 3.21,
3.21,
- 3.28,
- 3.32,
- 3.39,
- 3.48,
3.44,
- 3.4,
- 3.45,
- 3.49,
- 3.55,
3.56,
- 3.6,
- 3.61,
- 3.5,
- 3.55,
3.69,
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- 3.77,
- 3.82,
- 3.85,
- 3.82,
- 3.8,
- 3.85,
- 3.85,
- 3.77,
3.85,
3.85,
- 3.83,
3.85,
- 3.74,
- 3.8,
- 3.76,
3.7,
- 3.73,
- 3.68,
- 3.62,
- 3.62,
- 3.66,
- 3.65,
3.66,
3.68,
- 3.63,
- 3.68,
- 3.67,
- 3.73,
- 3.62,
- 3.59,
3.62,
- 3.67,
- 3.72,
- 3.73,
3.69,
- 3.66,
- 3.74,
- 3.79,
- 3.78,
3.8,
- 3.69,
- 3.7,
- 3.64,
- 3.61,
- 3.61,
- 3.62,
- 3.63,
3.61,
- 3.69,
3.72,
3.71,
- 3.73,
- 3.73,
- 3.71,
- 3.71,
- 3.66,
- 3.65,
- 3.68,
- 3.7,
3.67,
- 3.69,
- 3.84,
- 3.91,
- 3.86,
- 3.88,
3.88,
- 3.84,
- 3.89,
4.01,
- 3.98,
- 3.89,
- 3.91,
- 3.9,
3.87,
- 3.84,
- 3.88,
- 3.86,
- 3.79,
3.83,
- 3.82,
- 3.77,
- 3.8,
- 3.84,
3.83,
- 3.71,
- 3.8,
- 3.76,
- 3.69,
3.72,
- 3.63,
- 3.58,
- 3.41,
- 3.45,
3.57,
- 3.56,
- 3.56,
- 3.55,
- 3.44,
3.47,
- 3.38,
- 3.36,
- 3.25,
- 3.2,
3.23,
- 3.18,
- 3.21,
- 3.34,
3.31,
- 3.29,
- 3.35,
- 3.39,
- 3.2,
3.17,
- 3.18,
- 3.2,
- 3.33,
- 3.24,
3.28,
- 3.32,
- 3.27,
- 3.21,
- 3.24,
3.26,
- 3.18,
- 3.13,
- 3.14,
- 3.12,
3.05,
- 2.97,
- 2.97,
- 2.96,
3,
- 2.95,
- 3,
- 3.04,
- 3.07,
3.08,
- 3.15,
- 3.07,
- 3,
- 2.96,
2.99,
- 2.98,
- 2.9,
- 2.96,
- 3.02,
- 3.03,
- 3.08,
- 3.03,
3.03,
- 2.94,
2.99,
- 2.94,
- 2.98,
- 2.94,
2.86,
- 2.86,
- 2.79,
- 2.72,
- 2.74,
- 2.68,
- 2.58,
- 2.64,
- 2.64,
2.58,
- 2.62,
2.6,
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2.54,
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- 2.66,
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- 2.47,
- 2.58,
- 2.63,
2.72,
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2.74,
- 2.77,
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2.72,
- 2.61,
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- 2.62,
- 2.54,
- 2.48,
- 2.52,
- 2.53,
2.54,
2.5,
- 2.5,
- 2.41,
2.41,
- 2.41,
- null,
- 2.44,
- 2.46,
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- 2.59,
2.52,
- 2.5,
- 2.51,
- 2.57,
2.59,
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- 2.75,
- 2.69,
- 2.63,
2.66,
- 2.63,
- 2.67,
- 2.53,
- 2.58,
2.6,
- 2.72,
- 2.65,
- null,
- 2.76,
2.92,
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- 2.89,
- 2.9,
- 2.88,
2.8,
- 2.77,
- 2.93,
- 2.87,
2.84,
- 2.81,
- 2.97,
- 3.01,
- 3.03,
2.95,
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- 3.23,
- 3.32,
3.29,
- 3.49,
- 3.53,
- 3.47,
- 3.33,
- 3.36,
- 3.35,
- 3.36,
- 3.41,
3.36,
- 3.5,
- 3.35,
- 3.38,
- 3.3,
3.36,
3.36,
- 3.5,
- 3.44,
- 3.34,
3.32,
- 3.37,
- 3.4,
- 3.34,
3.35,
- 3.39,
- 3.37,
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3.43,
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3.42,
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- 3.48,
- 3.52,
- 3.58,
3.68,
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- 3.75,
- 3.65,
- 3.7,
- 3.64,
3.62,
- 3.61,
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- 3.58,
3.59,
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- 3.46,
- 3.42,
3.42,
- 3.41,
- 3.46,
- 3.58,
- 3.49,
3.51,
- 3.56,
- 3.48,
- 3.37,
- 3.4,
3.36,
- 3.33,
- 3.22,
- 3.25,
- 3.28,
- 3.34,
3.34,
- 3.36,
- 3.42,
- 3.46,
- 3.47,
- 3.5,
- 3.47,
3.47,
- 3.46,
3.45,
- 3.5,
- 3.56,
- 3.58,
- 3.59,
3.59,
- 3.52,
- 3.49,
- 3.51,
- 3.43,
3.4,
3.39,
- 3.43,
- 3.42,
- 3.39,
- 3.34,
- 3.39,
- 3.34,
- 3.32,
3.31,
- 3.28,
- 3.25,
- 3.18,
- 3.19,
- 3.17,
- 3.23,
- 3.19,
- 3.22,
- 3.18,
- 3.15,
- 3.12,
- 3.18,
3.17,
3.15,
3.13,
- 3.12,
- 3.13,
- 3.07,
3.07,
- 3.05,
- 2.96,
- 3.04,
- 2.99,
- 3.01,
- 3.01,
- 2.98,
3.01,
- 2.99,
3,
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- 2.98,
- 2.93,
- 2.94,
2.97,
- 2.99,
- 3.01,
- 2.93,
- 2.88,
2.95,
- 3.05,
- 3.14,
- 3.18,
3.22,
- 3.16,
- 3.12,
- 3.17,
- 3.03,
- 2.94,
- 2.92,
- 2.92,
- 2.98,
2.94,
2.94,
- 2.91,
- 2.96,
- 3.03,
- 2.99,
3.03,
- 2.99,
- 3.01,
- 2.98,
- 2.82,
2.77,
- 2.66,
- 2.64,
- 2.47,
- 2.58,
2.4,
- 2.2,
- 2.17,
- 2.34,
- 2.24,
2.29,
- 2.23,
- 2.17,
- 2.08,
- 2.07,
2.1,
- 2.15,
- 2.29,
- 2.23,
- 2.19,
2.28,
- 2.19,
- 2.23,
- 2.15,
2.02,
- 1.98,
- 2.05,
- 2,
- 1.93,
1.94,
- 2,
- 2.03,
- 2.09,
- 2.08,
1.97,
- 1.95,
- 1.88,
- 1.72,
- 1.84,
1.91,
- 2,
- 2.03,
- 1.99,
- 1.92,
1.8,
- 1.81,
- 1.92,
- 2.01,
2.1,
- null,
- 2.18,
- 2.24,
- 2.19,
- 2.26,
- 2.18,
- 2.19,
2.18,
- 2.2,
- 2.23,
2.25,
- 2.14,
- 2.23,
- 2.42,
- 2.34,
2.17,
- 2.01,
- 2.03,
- 2.09,
- 2.06,
- 2.04,
- 2.1,
- 2,
2.04,
- null,
2.04,
- 2.06,
- 2.01,
- 1.96,
- 2.01,
1.97,
- 1.94,
- 1.89,
1.97,
- 1.97,
- 2,
- 2.08,
- 2.11,
- 2.05,
2.04,
- 2.08,
- 2.02,
- 1.99,
- 2.07,
2.03,
- 1.96,
- 1.92,
- 1.92,
- 1.86,
1.82,
- 1.94,
- 1.98,
- 1.97,
2.03,
- 2.02,
- 1.93,
- 1.91,
1.89,
- 1.97,
- 2,
- 2.02,
- 1.98,
1.98,
- 2,
- 1.93,
- 1.94,
1.89,
- 1.87,
- 1.92,
- 2.01,
- 2.05,
2.09,
- 2.08,
- 2.01,
- 1.96,
- 1.93,
- 1.87,
- 1.83,
1.87,
- 1.86,
- 1.97,
- 1.93,
- 2,
- 2.01,
- 2.04,
- 1.96,
- 1.99,
- 1.92,
1.93,
1.99,
2.01,
- 2.05,
- 2.01,
- 1.99,
- 1.98,
1.92,
- 1.94,
- 1.98,
- 2.03,
- 1.99,
2,
- 1.96,
- 1.98,
- 2.03,
- 2.04,
2.04,
- 2.14,
- 2.29,
- 2.29,
- 2.31,
2.39,
- 2.38,
- 2.31,
- 2.29,
- 2.25,
2.26,
- 2.2,
- 2.21,
- 2.18,
- 2.23,
2.22,
- 2.3,
- 2.25,
- 2.19,
2.06,
- 2.01,
- 2.05,
- 2.08,
- 2.02,
- 2,
- 2.03,
- 2,
- 1.98,
- 1.99,
- 1.96,
2,
- 2.01,
- 1.98,
1.96,
1.95,
- 1.98,
- 1.96,
- 1.96,
- 1.91,
1.92,
- 1.88,
- 1.87,
- 1.89,
- 1.84,
1.78,
- 1.76,
- 1.76,
- 1.7,
- 1.71,
1.75,
- 1.79,
- 1.73,
- 1.77,
1.75,
- 1.74,
- 1.63,
- 1.59,
- 1.47,
1.53,
- 1.57,
- 1.66,
- 1.66,
- 1.65,
- 1.6,
- 1.67,
- 1.61,
- 1.64,
1.6,
1.59,
- 1.64,
- 1.65,
- 1.63,
- 1.69,
1.63,
- 1.66,
- 1.65,
- 1.6,
- 1.67,
1.61,
- 1.65,
- 1.62,
- 1.57,
1.53,
- 1.53,
- 1.54,
1.5,
- 1.52,
- 1.5,
- 1.53,
- 1.52,
- 1.54,
- 1.49,
1.47,
- 1.44,
- 1.43,
- 1.45,
- 1.58,
1.53,
- 1.51,
- 1.56,
- 1.51,
- 1.6,
1.59,
- 1.66,
- 1.68,
- 1.69,
- 1.65,
1.65,
- 1.73,
- 1.8,
- 1.83,
- 1.81,
1.82,
- 1.8,
- 1.71,
- 1.68,
- 1.68,
1.65,
- 1.64,
- 1.66,
- 1.63,
1.57,
- 1.59,
- 1.6,
- 1.68,
- 1.67,
1.68,
- 1.7,
- 1.77,
- 1.75,
- 1.88,
1.85,
- 1.82,
- 1.79,
- 1.8,
- 1.77,
1.74,
- 1.7,
- 1.64,
- 1.66,
- 1.65,
1.64,
- 1.64,
- 1.64,
- 1.7,
1.75,
- null,
- 1.74,
- 1.72,
- 1.7,
- 1.69,
1.7,
- 1.75,
1.83,
- 1.86,
- 1.79,
- 1.83,
- 1.79,
- 1.8,
- 1.86,
- 1.78,
- 1.72,
- 1.75,
- 1.75,
+ 1.74,
1.72,
- 1.78,
- 1.68,
- 1.62,
1.61,
- null,
- 1.59,
- 1.59,
- 1.58,
- 1.58,
1.61,
1.66,
- 1.69,
- 1.7,
- 1.66,
- 1.64,
- 1.63,
- 1.62,
- 1.62,
1.63,
- 1.62,
- 1.6,
- 1.59,
- 1.64,
1.63,
- 1.66,
- 1.72,
- 1.74,
- 1.72,
1.78,
- 1.84,
- 1.82,
- 1.81,
- 1.77,
1.79,
- 1.77,
- 1.74,
- 1.73,
1.78,
- 1.86,
- 1.92,
- 1.93,
1.92,
1.89,
- 1.88,
- 1.91,
- 1.89,
- 1.89,
- 1.86,
- 1.84,
- 1.89,
1.87,
- 1.86,
- 1.86,
- 1.88,
- 1.98,
- 2,
- 2.03,
- 2.03,
- 2.02,
- 2.04,
2,
- 2.04,
2,
1.99,
- 1.99,
- 1.99,
- 2.02,
- 2.05,
- 2,
2.01,
- 2.03,
- 2.02,
- 1.99,
- 1.97,
- 1.88,
1.88,
- 1.91,
- 1.89,
- 1.86,
1.88,
- 1.9,
- 1.95,
- 2,
- 2.06,
2.07,
- 2.03,
- 2.04,
- 2.04,
- 2.01,
- 1.96,
- 1.92,
1.96,
- 1.95,
1.93,
- 1.93,
- 1.92,
- 1.87,
- 1.87,
1.86,
- 1.88,
- 1.83,
- 1.78,
- 1.72,
1.76,
- 1.78,
- 1.84,
- 1.82,
- 1.75,
1.72,
- 1.75,
- 1.73,
- 1.72,
- 1.73,
1.72,
- 1.74,
- 1.73,
- 1.74,
- 1.7,
1.7,
- 1.7,
- 1.66,
- 1.66,
- 1.78,
1.8,
- 1.82,
- 1.81,
- 1.81,
- 1.9,
1.92,
- 1.96,
- 1.94,
- 1.87,
- 1.95,
1.97,
- 1.94,
- 2.03,
- 2.02,
2.01,
- 2.15,
2.13,
- 2.13,
- 2.16,
- 2.13,
- 2.14,
- 2.1,
- 2.08,
- 2.17,
2.22,
- 2.2,
- 2.25,
- 2.19,
- 2.14,
2.19,
- 2.2,
- 2.33,
- 2.41,
- 2.52,
2.57,
- 2.6,
- 2.55,
- 2.49,
- 2.52,
2.5,
- 2.48,
- 2.52,
- 2.73,
2.65,
- 2.65,
- 2.7,
- 2.6,
- 2.61,
2.57,
- 2.55,
- 2.52,
- 2.56,
2.5,
- 2.5,
- 2.53,
- 2.61,
- 2.61,
- 2.58,
2.61,
- 2.63,
- 2.6,
- 2.74,
- 2.63,
- 2.67,
2.67,
2.61,
- 2.58,
- 2.57,
- 2.61,
- 2.71,
- 2.71,
- 2.77,
- 2.84,
2.88,
- 2.82,
- 2.87,
- 2.9,
- 2.82,
2.79,
- 2.72,
- 2.78,
- 2.75,
2.78,
- 2.86,
- 2.9,
- 2.98,
- 2.94,
- 2.9,
- 2.96,
- 2.93,
- 2.92,
2.9,
2.88,
- 2.86,
- 2.69,
- 2.76,
- 2.75,
2.72,
- 2.67,
- 2.63,
- 2.66,
- 2.64,
2.64,
- 2.66,
- 2.63,
- 2.62,
- 2.66,
2.65,
- 2.66,
- 2.68,
- 2.71,
2.7,
- null,
- 2.75,
- 2.69,
- 2.61,
- 2.6,
2.63,
2.54,
- 2.51,
- 2.53,
- 2.53,
- 2.54,
- 2.53,
- 2.55,
- 2.57,
- 2.65,
- 2.63,
- 2.69,
- 2.67,
2.63,
2.77,
- null,
- 2.8,
- 2.75,
- 2.69,
- 2.71,
2.67,
- 2.71,
- 2.8,
- 2.79,
- 2.75,
- 2.74,
- 2.71,
2.74,
- 2.75,
- 2.81,
- 2.79,
- 2.84,
- 2.88,
- 2.88,
- 2.86,
2.81,
2.86,
2.89,
- 2.88,
- 2.89,
- 2.85,
- 2.89,
- 2.94,
- 2.89,
2.94,
2.99,
- 3,
- 3.02,
- 2.99,
- 3.04,
- 3,
- 3.01,
2.98,
- 2.96,
- 3.01,
- 2.97,
- 2.88,
2.84,
- 2.88,
- 2.9,
- 2.86,
2.84,
- 2.85,
- 2.87,
- 2.79,
- 2.75,
2.78,
- 2.77,
- 2.69,
- 2.72,
- 2.67,
2.61,
- 2.64,
- 2.7,
- 2.73,
- 2.71,
2.7,
2.75,
- 2.8,
- 2.73,
- 2.75,
- 2.71,
- 2.73,
- 2.76,
- 2.73,
2.75,
- 2.7,
- 2.67,
- 2.65,
- 2.66,
2.6,
- 2.7,
- 2.7,
- 2.74,
- 2.8,
2.79,
- 2.77,
- 2.73,
- 2.66,
- 2.65,
2.7,
- 2.68,
- 2.78,
- 2.79,
- 2.75,
2.74,
- 2.75,
- 2.71,
- 2.69,
- 2.73,
2.73,
- 2.77,
- 2.82,
- 2.8,
- 2.74,
2.71,
- 2.69,
- 2.71,
- 2.65,
- 2.63,
2.65,
- 2.64,
- 2.65,
- 2.73,
- 2.73,
2.73,
2.7,
- 2.7,
- 2.68,
- 2.7,
- 2.71,
- 2.67,
- 2.63,
- 2.6,
2.63,
- 2.61,
- 2.62,
- 2.61,
- 2.62,
2.66,
- 2.61,
2.54,
- 2.5,
- 2.52,
- 2.54,
- 2.52,
2.54,
- 2.56,
- 2.54,
- 2.52,
- 2.44,
- 2.45,
- 2.48,
2.54,
- 2.6,
- 2.61,
- 2.59,
- 2.6,
2.62,
- 2.64,
- 2.65,
- 2.58,
- 2.6,
- 2.61,
- 2.66,
2.61,
- 2.64,
2.63,
- 2.63,
- 2.59,
- 2.57,
- 2.53,
- 2.54,
2.53,
- 2.58,
- 2.64,
- 2.65,
2.63,
- 2.58,
- 2.57,
- 2.55,
- 2.53,
2.55,
- 2.56,
- 2.55,
- 2.47,
- 2.5,
2.49,
- 2.48,
- 2.48,
- 2.52,
- 2.48,
2.5,
- 2.47,
- 2.57,
- 2.58,
- 2.52,
2.51,
- 2.49,
- 2.49,
- 2.43,
2.44,
- 2.44,
- 2.46,
- 2.43,
- 2.4,
- 2.34,
- 2.39,
- 2.4,
- 2.43,
- 2.41,
- 2.4,
2.39,
2.39,
- 2.37,
- 2.34,
2.35,
- 2.42,
- 2.41,
- 2.45,
- 2.46,
2.48,
- 2.5,
- 2.54,
- 2.54,
- 2.62,
2.6,
- 2.6,
- 2.62,
- 2.63,
- 2.59,
- 2.57,
- 2.54,
2.57,
- 2.52,
- 2.54,
2.5,
- 2.52,
- 2.42,
- 2.44,
- 2.45,
2.43,
- 2.36,
- 2.35,
- 2.34,
2.31,
- null,
- 2.21,
- 2.15,
- 2.17,
- 2.22,
2.2,
- 2.23,
- 2.25,
- 2.29,
- 2.29,
2.27,
- 2.3,
- 2.34,
- 2.32,
- 2.35,
- 2.36,
- 2.35,
2.36,
- 2.39,
- 2.32,
2.38,
- null,
- 2.37,
- 2.35,
- 2.32,
2.34,
- 2.32,
- 2.36,
- 2.34,
- 2.31,
2.3,
- 2.27,
- 2.24,
- 2.18,
2.22,
- 2.28,
- 2.29,
- 2.25,
- 2.31,
2.26,
- 2.22,
- 2.18,
- 2.19,
- 2.1,
2.12,
- 2.07,
- 2.14,
- 2.22,
2.17,
- 2.17,
- 2.26,
- 2.27,
- 2.25,
2.22,
- 2.2,
- 2.17,
- 2.12,
2.04,
- 1.97,
- 1.96,
- 2.03,
- 1.98,
1.92,
- 1.91,
- 1.86,
- 1.77,
- 1.83,
- 1.82,
- 1.87,
- 1.9,
- 1.81,
1.83,
1.83,
- 1.73,
- 1.77,
- 1.68,
1.68,
- 1.79,
- 1.81,
- 1.83,
- 1.95,
1.96,
- 2.01,
- 2,
- 1.99,
2.02,
- 2.14,
- 2.07,
- 2.11,
- 2.13,
2.06,
- 1.99,
- 1.96,
- 2.03,
- 2,
2.08,
- 2.12,
- 2.12,
- 2.11,
- 2.24,
2.2,
- 2.14,
- 2.11,
- 2.1,
- 2.13,
2.1,
- 2.06,
- 1.93,
- 1.98,
- 1.93,
1.92,
- 1.88,
- 1.93,
- 2.01,
- 1.95,
1.96,
- 1.94,
- 1.87,
- 1.92,
- 1.92,
- 1.89,
1.92,
- 1.97,
- 1.96,
1.94,
1.9,
- 1.91,
- 1.9,
- 1.87,
- 1.9,
- 1.92,
- 1.99,
- 1.96,
- 1.93,
1.94,
- 2,
- 2.06,
- 2.05,
- 2.12,
- 2.16,
- 2.19,
- 2.25,
- 2.18,
2.16,
2.28,
- 2.28,
- 2.27,
- 2.23,
- 2.14,
2.23,
- 2.27,
- 2.26,
- 2.19,
2.21,
- 2.14,
- 2.14,
- 2.13,
- 2.12,
2.19,
- 2.27,
- 2.38,
- 2.31,
- 2.41,
- 2.39,
- 2.42,
- 2.5,
- 2.39,
2.39,
2.36,
- 2.32,
- 2.32,
- 2.35,
- 2.26,
2.37,
- 2.42,
- 2.38,
- 2.4,
- 2.49,
2.33,
- 2.35,
- 2.43,
- 2.4,
2.3,
- 2.27,
- 2.22,
- 2.32,
- 2.42,
2.44,
- 2.41,
- 2.36,
- 2.36,
- 2.34,
2.38,
- 2.35,
- 2.33,
- 2.28,
- 2.27,
2.23,
- 2.26,
- 2.29,
- 2.28,
- 2.2,
2.16,
- 2.23,
- 2.28,
- 2.23,
- 2.18,
2.24,
- 2.15,
- 2.14,
- 2.19,
- 2.2,
2.16,
- 2.2,
- 2.12,
- 2.09,
- 2.05,
2.01,
- 2.12,
- 2.18,
- 2.18,
- 2.19,
2.21,
- 2.17,
- 2.2,
- 2.18,
2.13,
- 2.2,
- 2.21,
- 2.23,
- 2.2,
2.18,
- 2.28,
- 2.3,
- 2.21,
- 2.13,
2.2,
- 2.14,
- 2.16,
- 2.13,
- 2.17,
2.1,
- 2.05,
- 2.06,
- 2.05,
- 1.99,
2.07,
- 2.05,
- 2.08,
2.12,
- 2.12,
- null,
- 2.06,
- 1.99,
- 2.04,
- 2.04,
- 2.04,
- 2.08,
- 2.04,
2.04,
- 2.09,
2.07,
- 2.05,
- 2.1,
- 2.19,
- 2.16,
2.2,
- 2.23,
- 2.25,
- 2.26,
- 2.34,
2.36,
- 2.32,
- null,
- 2.32,
- 2.28,
- 2.27,
- 2.25,
2.27,
- 2.24,
- 2.26,
2.25,
- 2.24,
- 2.23,
- 2.22,
2.21,
- 2.15,
- 2.18,
- 2.33,
- 2.28,
2.23,
- 2.24,
- 2.22,
- 2.24,
- 2.13,
2.23,
- 2.28,
- 2.3,
- 2.24,
- 2.19,
2.2,
2.24,
- 2.27,
- 2.25,
- 2.24,
- 2.32,
- 2.31,
- 2.27,
2.24,
- 2.25,
- 2.18,
- 2.16,
- 2.13,
2.17,
- 2.12,
- 2.08,
- 2.1,
2.03,
- 2.06,
- 2.01,
- 2.02,
- 2.07,
2.03,
- 2.01,
- 2.02,
- 2,
- 1.94,
1.97,
- 1.87,
- 1.88,
- 1.87,
- 1.86,
1.75,
1.74,
- 1.71,
- 1.63,
- 1.74,
- 1.78,
- 1.81,
- 1.75,
- 1.76,
1.77,
1.74,
- 1.75,
- 1.71,
- 1.76,
- 1.74,
- 1.83,
- 1.84,
- 1.83,
- 1.88,
1.91,
- 1.83,
- 1.9,
- 1.93,
- 1.98,
- 1.97,
1.97,
- 1.94,
- 1.91,
- 1.88,
1.92,
- 1.94,
- 1.88,
- 1.91,
1.89,
- 1.81,
- 1.83,
1.78,
- 1.79,
- 1.78,
- 1.73,
- 1.76,
- 1.7,
- 1.72,
1.73,
- 1.79,
- 1.77,
- 1.8,
- 1.76,
1.78,
- 1.79,
- 1.85,
- 1.88,
- 1.89,
1.91,
- 1.94,
- 1.87,
- 1.84,
- 1.83,
1.88,
- 1.81,
- 1.79,
- 1.76,
- 1.79,
1.77,
- 1.77,
- 1.73,
1.75,
- 1.71,
- 1.75,
- 1.76,
- 1.87,
- 1.85,
- 1.85,
- 1.84,
- 1.86,
- 1.87,
- 1.83,
- 1.85,
1.84,
1.85,
- 1.81,
- 1.71,
1.73,
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1.62,
1.67,
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1.46,
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1.46,
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1.43,
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1.59,
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1.58,
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1.51,
1.59,
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1.55,
1.55,
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1.57,
1.6,
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1.68,
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1.7,
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1.59,
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1.63,
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1.73,
1.77,
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1.77,
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1.84,
1.83,
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- 1.82,
- 1.79,
- 1.83,
- 1.88,
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2.23,
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2.33,
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- 2.36,
2.32,
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2.39,
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2.49,
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2.54,
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2.55,
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2.45,
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2.4,
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2.43,
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2.49,
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2.35,
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2.37,
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2.26,
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2.28,
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2.33,
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2.34,
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2.25,
2.25,
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2.18,
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2.21,
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2.19,
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2.26,
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2.26,
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2.22,
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2.18,
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2.16,
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2.16,
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2.14,
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2.23,
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2.22,
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- 3,
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- 3.06,
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- 3.09,
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- 3.23,
- null,
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- 3.15,
- 3.16,
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- 3.19,
- 3.17,
- 3.2,
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- 3.17,
- 3.1,
- 3.14,
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- 3.12,
- 3.15,
- 3.14,
- 3.22,
- 3.2,
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- 2.12,
- 2.09,
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- 2.13,
- 2.1,
- 2.09,
- 2.09,
- 2.06,
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- 2.01,
- 2.07,
- 2.02,
- 2,
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- 2.01,
- 2,
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- 2.08,
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- 0.88,
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- 0.83,
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- 0.84,
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- 0.92,
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- 0.92,
- 0.94,
- 0.95,
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- 0.96,
- 0.94,
- 0.94,
- 0.94,
- 0.93,
- 0.93,
- 0.93,
- 0.96,
- 1.04,
- 1.08,
- 1.13,
- 1.15,
- 1.15,
- 1.1,
- 1.15,
- 1.11,
- 1.1,
- 1.1,
- 1.12,
- 1.1,
- 1.05,
- 1.05,
- 1.04,
- 1.07,
- 1.11,
- 1.09,
- 1.12,
- 1.15,
- 1.15,
- 1.19,
- 1.19,
- 1.18,
- 1.15,
- 1.16,
- 1.2,
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- 5,
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- 5.08,
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- 3.29,
- 3.49,
- 3.53,
- 3.47,
- 3.33,
- 3.36,
- 3.35,
- 3.36,
- 3.41,
- 3.36,
- 3.5,
- 3.35,
- 3.38,
- 3.3,
- 3.36,
- 3.36,
- 3.5,
- 3.44,
- 3.34,
- 3.32,
- 3.37,
- 3.4,
- 3.34,
- 3.35,
- 3.39,
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- 3.47,
- 3.44,
- 3.43,
- 3.35,
- 3.45,
- 3.42,
- 3.36,
- 3.42,
- 3.48,
- 3.52,
- 3.58,
- 3.68,
- 3.68,
- 3.75,
- 3.65,
- 3.7,
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- 3.62,
- 3.61,
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- 3.58,
- 3.59,
- 3.46,
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- 3.42,
- 3.42,
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- 3.51,
- 3.56,
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- 3.37,
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- 3.36,
- 3.33,
- 3.22,
- 3.25,
- 3.28,
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- 3.34,
- 3.36,
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- 3.46,
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- 3.47,
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- 3.52,
- 3.49,
- 3.51,
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- 3.4,
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- 3.42,
- 3.39,
- 3.34,
- 3.39,
- 3.34,
- 3.32,
- 3.31,
- 3.28,
- 3.25,
- 3.18,
- 3.19,
- 3.17,
- 3.23,
- 3.19,
- 3.22,
- 3.18,
- 3.15,
- 3.12,
- 3.18,
- 3.17,
- 3.15,
- 3.13,
- 3.12,
- 3.13,
- 3.07,
- 3.07,
- 3.05,
- 2.96,
- 3.04,
- 2.99,
- 3.01,
- 3.01,
- 2.98,
- 3.01,
- 2.99,
- 3,
- 3.11,
- 2.98,
- 2.93,
- 2.94,
- 2.97,
- 2.99,
- 3.01,
- 2.93,
- 2.88,
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- 3.18,
- 3.22,
- 3.16,
- 3.12,
- 3.17,
- 3.03,
- 2.94,
- 2.92,
- 2.92,
- 2.98,
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- 2.91,
- 2.96,
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- 2.17,
- 2.34,
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- 2.29,
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- 2.08,
- 2.07,
- 2.1,
- 2.15,
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- 2.23,
- 2.19,
- 2.28,
- 2.19,
- 2.23,
- 2.15,
- 2.02,
- 1.98,
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- 2,
- 1.93,
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- 2.09,
- 2.08,
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- 1.88,
- 1.72,
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- 2,
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- 1.99,
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- 1.8,
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- null,
- 2.18,
- 2.24,
- 2.19,
- 2.26,
- 2.18,
- 2.19,
- 2.18,
- 2.2,
- 2.23,
- 2.25,
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- 2.23,
- 2.42,
- 2.34,
- 2.17,
- 2.01,
- 2.03,
- 2.09,
- 2.06,
- 2.04,
- 2.1,
- 2,
- 2.04,
- null,
- 2.04,
- 2.06,
- 2.01,
- 1.96,
- 2.01,
- 1.97,
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- 1.89,
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- 2,
- 2.08,
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- 2.05,
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- 2,
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- 2.14,
- 2.29,
- 2.29,
- 2.31,
- 2.39,
- 2.38,
- 2.31,
- 2.29,
- 2.25,
- 2.26,
- 2.2,
- 2.21,
- 2.18,
- 2.23,
- 2.22,
- 2.3,
- 2.25,
- 2.19,
- 2.06,
- 2.01,
- 2.05,
- 2.08,
- 2.02,
- 2,
- 2.03,
- 2,
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- 1.7,
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- 1.64,
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- 1.7,
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- 1.74,
- 1.72,
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- 2.02,
- 2.04,
- 2,
- 2.04,
- 2,
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- 1.99,
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- 1.9,
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- 2.04,
- 2.01,
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- 2.16,
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- 2.14,
- 2.1,
- 2.08,
- 2.17,
- 2.22,
- 2.2,
- 2.25,
- 2.19,
- 2.14,
- 2.19,
- 2.2,
- 2.33,
- 2.41,
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- 2.9,
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- 2.9,
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- 2.9,
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- 2.92,
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- 2.75,
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- 2.55,
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- 2.88,
- 2.89,
- 2.85,
- 2.89,
- 2.94,
- 2.89,
- 2.94,
- 2.99,
- 3,
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- 3.04,
- 3,
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- 2.98,
- 2.96,
- 3.01,
- 2.97,
- 2.88,
- 2.84,
- 2.88,
- 2.9,
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- 2.84,
- 2.85,
- 2.87,
- 2.79,
- 2.75,
- 2.78,
- 2.77,
- 2.69,
- 2.72,
- 2.67,
- 2.61,
- 2.64,
- 2.7,
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- 2.7,
- 2.75,
- 2.8,
- 2.73,
- 2.75,
- 2.71,
- 2.73,
- 2.76,
- 2.73,
- 2.75,
- 2.7,
- 2.67,
- 2.65,
- 2.66,
- 2.6,
- 2.7,
- 2.7,
- 2.74,
- 2.8,
- 2.79,
- 2.77,
- 2.73,
- 2.66,
- 2.65,
- 2.7,
- 2.68,
- 2.78,
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- 2.75,
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- 2.75,
- 2.71,
- 2.69,
- 2.73,
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- 2.8,
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- 2.65,
- 2.63,
- 2.65,
- 2.64,
- 2.65,
- 2.73,
- 2.73,
- 2.73,
- 2.7,
- 2.7,
- 2.68,
- 2.7,
- 2.71,
- 2.67,
- 2.63,
- 2.6,
- 2.63,
- 2.61,
- 2.62,
- 2.61,
- 2.62,
- 2.66,
- 2.61,
- 2.54,
- 2.5,
- 2.52,
- 2.54,
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- 2.58,
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- 2.49,
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- 2.44,
- 2.44,
- 2.46,
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- 2.4,
- 2.34,
- 2.39,
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- 2.43,
- 2.41,
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- 2.39,
- 2.39,
- 2.37,
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- 2.35,
- 2.42,
- 2.41,
- 2.45,
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- 2.48,
- 2.5,
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- 2.62,
- 2.6,
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- 2.63,
- 2.59,
- 2.57,
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- 2.42,
- 2.44,
- 2.45,
- 2.43,
- 2.36,
- 2.35,
- 2.34,
- 2.31,
- null,
- 2.21,
- 2.15,
- 2.17,
- 2.22,
- 2.2,
- 2.23,
- 2.25,
- 2.29,
- 2.29,
- 2.27,
- 2.3,
- 2.34,
- 2.32,
- 2.35,
- 2.36,
- 2.35,
- 2.36,
- 2.39,
- 2.32,
- 2.38,
- null,
- 2.37,
- 2.35,
- 2.32,
- 2.34,
- 2.32,
- 2.36,
- 2.34,
- 2.31,
- 2.3,
- 2.27,
- 2.24,
- 2.18,
- 2.22,
- 2.28,
- 2.29,
- 2.25,
- 2.31,
- 2.26,
- 2.22,
- 2.18,
- 2.19,
- 2.1,
- 2.12,
- 2.07,
- 2.14,
- 2.22,
- 2.17,
- 2.17,
- 2.26,
- 2.27,
- 2.25,
- 2.22,
- 2.2,
- 2.17,
- 2.12,
- 2.04,
- 1.97,
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- 2.03,
- 1.98,
- 1.92,
- 1.91,
- 1.86,
- 1.77,
- 1.83,
- 1.82,
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- 1.9,
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- 1.83,
- 1.73,
- 1.77,
- 1.68,
- 1.68,
- 1.79,
- 1.81,
- 1.83,
- 1.95,
- 1.96,
- 2.01,
- 2,
- 1.99,
- 2.02,
- 2.14,
- 2.07,
- 2.11,
- 2.13,
- 2.06,
- 1.99,
- 1.96,
- 2.03,
- 2,
- 2.08,
- 2.12,
- 2.12,
- 2.11,
- 2.24,
- 2.2,
- 2.14,
- 2.11,
- 2.1,
- 2.13,
- 2.1,
- 2.06,
- 1.93,
- 1.98,
- 1.93,
- 1.92,
- 1.88,
- 1.93,
- 2.01,
- 1.95,
- 1.96,
- 1.94,
- 1.87,
- 1.92,
- 1.92,
- 1.89,
- 1.92,
- 1.97,
- 1.96,
- 1.94,
- 1.9,
- 1.91,
- 1.9,
- 1.87,
- 1.9,
- 1.92,
- 1.99,
- 1.96,
- 1.93,
- 1.94,
- 2,
- 2.06,
- 2.05,
- 2.12,
- 2.16,
- 2.19,
- 2.25,
- 2.18,
- 2.16,
- 2.28,
- 2.28,
- 2.27,
- 2.23,
- 2.14,
- 2.23,
- 2.27,
- 2.26,
- 2.19,
- 2.21,
- 2.14,
- 2.14,
- 2.13,
- 2.12,
- 2.19,
- 2.27,
- 2.38,
- 2.31,
- 2.41,
- 2.39,
- 2.42,
- 2.5,
- 2.39,
- 2.39,
- 2.36,
- 2.32,
- 2.32,
- 2.35,
- 2.26,
- 2.37,
- 2.42,
- 2.38,
- 2.4,
- 2.49,
- 2.33,
- 2.35,
- 2.43,
- 2.4,
- 2.3,
- 2.27,
- 2.22,
- 2.32,
- 2.42,
- 2.44,
- 2.41,
- 2.36,
- 2.36,
- 2.34,
- 2.38,
- 2.35,
- 2.33,
- 2.28,
- 2.27,
- 2.23,
- 2.26,
- 2.29,
- 2.28,
- 2.2,
- 2.16,
- 2.23,
- 2.28,
- 2.23,
- 2.18,
- 2.24,
- 2.15,
- 2.14,
- 2.19,
- 2.2,
- 2.16,
- 2.2,
- 2.12,
- 2.09,
- 2.05,
- 2.01,
- 2.12,
- 2.18,
- 2.18,
- 2.19,
- 2.21,
- 2.17,
- 2.2,
- 2.18,
- 2.13,
- 2.2,
- 2.21,
- 2.23,
- 2.2,
- 2.18,
- 2.28,
- 2.3,
- 2.21,
- 2.13,
- 2.2,
- 2.14,
- 2.16,
- 2.13,
- 2.17,
- 2.1,
- 2.05,
- 2.06,
- 2.05,
- 1.99,
- 2.07,
- 2.05,
- 2.08,
- 2.12,
- 2.12,
- null,
- 2.06,
- 1.99,
- 2.04,
- 2.04,
- 2.04,
- 2.08,
- 2.04,
- 2.04,
- 2.09,
- 2.07,
- 2.05,
- 2.1,
- 2.19,
- 2.16,
- 2.2,
- 2.23,
- 2.25,
- 2.26,
- 2.34,
- 2.36,
- 2.32,
- null,
- 2.32,
- 2.28,
- 2.27,
- 2.25,
- 2.27,
- 2.24,
- 2.26,
- 2.25,
- 2.24,
- 2.23,
- 2.22,
- 2.21,
- 2.15,
- 2.18,
- 2.33,
- 2.28,
- 2.23,
- 2.24,
- 2.22,
- 2.24,
- 2.13,
- 2.23,
- 2.28,
- 2.3,
- 2.24,
- 2.19,
- 2.2,
- 2.24,
- 2.27,
- 2.25,
- 2.24,
- 2.32,
- 2.31,
- 2.27,
- 2.24,
- 2.25,
- 2.18,
- 2.16,
- 2.13,
- 2.17,
- 2.12,
- 2.08,
- 2.1,
- 2.03,
- 2.06,
- 2.01,
- 2.02,
- 2.07,
- 2.03,
- 2.01,
- 2.02,
- 2,
- 1.94,
- 1.97,
- 1.87,
- 1.88,
- 1.87,
- 1.86,
- 1.75,
- 1.74,
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- 1.77,
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- 1.75,
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- 1.74,
- 1.83,
- 1.84,
- 1.83,
- 1.88,
- 1.91,
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- 1.88,
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- 2.23,
- 2.23,
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- 2.36,
- 2.36,
- 2.32,
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- 2.35,
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- 2.38,
- 2.37,
- 2.32,
- 2.28,
- 2.24,
- 2.26,
- 2.18,
- 2.21,
- 2.24,
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- 2.3,
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- 3,
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- 3,
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- 3.1,
- 3.06,
- 3.06,
- 3.05,
- 3.09,
- 3.05,
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- 3.23,
- null,
- 3.21,
- 3.22,
- 3.14,
- 3.15,
- 3.16,
- 3.16,
- 3.19,
- 3.17,
- 3.2,
- 3.2,
- 3.17,
- 3.1,
- 3.14,
- 3.08,
- 3.08,
- 3.12,
- 3.15,
- 3.14,
- 3.22,
- 3.2,
- 3.22,
- 3.22,
- 3.24,
- 3.19,
- null,
- 3.14,
- 3.12,
- 3.11,
- 3.08,
- 3.06,
- 3.06,
- 3.06,
- 3.05,
- 3.07,
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- 2.71,
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- 2.7,
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- 2.65,
- 2.68,
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- 2.69,
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- 2.72,
- 2.72,
- 2.69,
- 2.64,
- 2.62,
- 2.64,
- 2.61,
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- 2.39,
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- 2.49,
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- 2.43,
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- 2.22,
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- 2.12,
- 2.09,
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- 2.13,
- 2.1,
- 2.09,
- 2.09,
- 2.06,
- 2.03,
- 2.01,
- 2.07,
- 2.02,
- 2,
- 2.05,
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- 2,
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- 2.07,
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- 2.05,
- 2.08,
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- 2.06,
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- 0.76,
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- "fig.update({'layout': \n",
- " {'xaxis': {'title': 'Date'},\n",
- " 'yaxis': {'title': '%'},\n",
- " 'title': 'Copper/Gold Ratio vs. US 10-Year Constant Maturity',\n",
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- "source": [
- "What we have currently is the price relationship between one Troy ounce of gold and one pound of copper. As we described the copper-to-gold ratio as the price-per-ounce of each, some adjustments are required to be true to the definition.\n",
- "\n",
- "- 1 ounce = 0.911458 Troy ounces\n",
- "- 1 pound = 16 ounces\n",
- " \n",
- "To adjust the gold price as USD/ounce, multiply each row by 0.911458. To adjust the copper price, divide each row by 16."
- ]
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\n",
- "\n",
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\n",
- " \n",
- " \n",
- " | \n",
- " Gold | \n",
- " Copper | \n",
- " Copper/Gold Ratio | \n",
- " US 10-Year Constant Maturity | \n",
- " Copper/Gold Ratio per Ounce (x1000) % | \n",
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\n",
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- " Gold Copper Copper/Gold Ratio \\\n",
- "Date \n",
- "2023-05-24 1962.800049 3.5535 1.810424 \n",
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- " US 10-Year Constant Maturity \\\n",
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- "2023-05-25 NaN \n",
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- " Copper/Gold Ratio per Ounce (x1000) % \n",
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- "2023-05-24 0.124143 \n",
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- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data['Copper/Gold Ratio per Ounce (x1000) %'] = (\n",
- " ((data['Copper']/16)/(data['Gold']*0.911458))*1000\n",
- ")\n",
- "\n",
- "data.tail(2)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The conversion to a per-ounce relationship has decreased the number where it wouldn't compare well on the same y-axis. Let's import the plotting function from the `/economy` menu in the OpenBB Terminal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "from openbb_terminal.economy.plot_view import show_plot "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
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- "\u001b[0;31mSignature:\u001b[0m\n",
- "\u001b[0mshow_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdataset_yaxis_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mdataset_yaxis_2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mexport\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m \u001b[0mexternal_axes\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
- "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mDocstring:\u001b[0m\n",
- "The ability to plot any data coming from EconDB, FRED or Yahoo Finance.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "dataset_yaxis_1: pd.DataFrame\n",
- " Data you wish to plot on the first y-axis.\n",
- "dataset_yaxis_2 : pd.DataFrame\n",
- " Data you wish to plot on the second y-axis.\n",
- "external_axes : bool, optional\n",
- " Whether to return the figure object or not, by default False\n",
- "\n",
- "Returns\n",
- "-------\n",
- "Plots the data.\n",
- "\u001b[0;31mFile:\u001b[0m ~/GitHub/OpenBBTerminal/openbb_terminal/economy/plot_view.py\n",
- "\u001b[0;31mType:\u001b[0m function"
- ]
- }
- ],
- "source": [
- "show_plot?"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Each y-axis requires its own DataFrame, so let's make one for each next."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [],
- "source": [
- "y1 = pd.DataFrame(data[\"Copper/Gold Ratio per Ounce (x1000) %\"])\n",
- "y2 = pd.DataFrame(data[\"US 10-Year Constant Maturity\"])\n",
- "fig = show_plot(y1, y2, external_axes = True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
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- "application/vnd.plotly.v1+json": {
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- 4.01,
- 4.07,
- 4.1,
- 4.09,
- 3.88,
- 3.85,
- 3.84,
- 3.84,
- 4.03,
- 4.05,
- 4.02,
- 3.99,
- 4.08,
- 4.14,
- 4.18,
- 4.19,
- 4.08,
- 4.26,
- 4.22,
- 4.24,
- 4.18,
- 4.13,
- 4.09,
- 4.06,
- 4.06,
- 4.01,
- 4.01,
- 4.07,
- 4.15,
- 4.13,
- 4.06,
- 3.96,
- 3.97,
- 3.98,
- 3.95,
- 3.93,
- 3.83,
- 3.82,
- 3.83,
- 4.07,
- 4.05,
- 4.09,
- 4.04,
- 4,
- 4.19,
- 4.16,
- 4.15,
- 4.1,
- 4.1,
- 4.1,
- 4.05,
- 4.01,
- 3.95,
- 3.98,
- 3.94,
- 3.98,
- 4,
- 4.06,
- 4,
- 4,
- 4.01,
- 3.96,
- 4.02,
- 3.97,
- 3.96,
- 3.99,
- 3.98,
- 3.93,
- 3.89,
- 3.95,
- 3.94,
- 3.88,
- 3.85,
- 3.9,
- 3.86,
- 3.81,
- 3.78,
- 3.76,
- 3.71,
- 3.68,
- 3.65,
- 3.63,
- 3.67,
- 3.63,
- 3.59,
- 3.6,
- 3.6,
- 3.74,
- 3.72,
- 3.82,
- 3.91,
- 3.98,
- 4.01,
- 4.11,
- 3.98,
- 3.97,
- 3.96,
- 3.95,
- 3.92,
- 3.83,
- 3.84,
- 3.94,
- 3.93,
- 3.96,
- 4.03,
- 3.95,
- 3.93,
- 3.95,
- 4,
- 4.04,
- 3.98,
- 3.96,
- 3.98,
- 4,
- 4.01,
- 4.02,
- 3.93,
- 3.91,
- 3.92,
- 3.96,
- 3.89,
- 3.88,
- 3.94,
- 3.92,
- 3.84,
- 3.72,
- 3.7,
- 3.69,
- 3.64,
- 3.63,
- 3.53,
- 3.53,
- 3.46,
- 3.46,
- 3.38,
- 3.39,
- 3.34,
- 3.34,
- 3.41,
- 3.44,
- 3.34,
- 3.37,
- 3.43,
- 3.34,
- 3.3,
- 3.34,
- 3.37,
- 3.29,
- 3.2,
- 3.21,
- 3.18,
- 3.13,
- 3.18,
- 3.27,
- 3.37,
- 3.35,
- 3.4,
- 3.32,
- 3.29,
- 3.38,
- 3.55,
- 3.58,
- 3.54,
- 3.56,
- 3.56,
- 3.67,
- 3.74,
- 3.75,
- 3.73,
- 3.7,
- 3.66,
- 3.74,
- 3.94,
- 3.97,
- 3.98,
- 4,
- 4.19,
- 4.17,
- 4.12,
- 4.2,
- 4.22,
- 4.31,
- 4.42,
- 4.34,
- 4.49,
- 4.44,
- 4.35,
- 4.47,
- 4.32,
- 4.3,
- 4.27,
- 4.38,
- 4.37,
- 4.58,
- 4.55,
- 4.55,
- 4.49,
- 4.38,
- 4.45,
- 4.53,
- 4.48,
- 4.53,
- 4.5,
- 4.54,
- 4.42,
- 4.45,
- 4.61,
- 4.6,
- 4.52,
- 4.35,
- 4.41,
- 4.37,
- 4.28,
- 4.35,
- 4.27,
- 4.28,
- 4.29,
- 4.2,
- 4.19,
- 4.17,
- 4.26,
- 4.24,
- 4.16,
- 4.12,
- 4.04,
- 4.09,
- 3.96,
- 3.96,
- 4.03,
- 4.21,
- 4.17,
- 4.27,
- 4.27,
- 4.32,
- 4.29,
- 4.37,
- 4.43,
- 4.47,
- 4.41,
- 4.41,
- 4.38,
- 4.29,
- 4.34,
- 4.24,
- 4.3,
- 4.23,
- 4.31,
- 4.36,
- 4.33,
- 4.4,
- 4.33,
- 4.38,
- 4.45,
- 4.48,
- 4.49,
- 4.44,
- 4.3,
- 4.22,
- 4.18,
- 4.17,
- 4.24,
- 4.16,
- 4.15,
- 4.23,
- 4.19,
- 4.25,
- 4.4,
- 4.38,
- 4.41,
- 4.38,
- 4.23,
- 4.29,
- 4.32,
- 4.3,
- 4.27,
- 4.26,
- 4.28,
- 4.24,
- 4.19,
- 4.16,
- 4.15,
- 4.18,
- 4.28,
- 4.2,
- 4.24,
- 4.29,
- 4.27,
- 4.41,
- 4.29,
- 4.27,
- 4.27,
- 4.11,
- 4.11,
- 4.05,
- 4.01,
- 3.99,
- 4.04,
- 4.08,
- 4.05,
- 3.99,
- 4.09,
- 4.16,
- 4.11,
- 4.22,
- 4.22,
- 4.16,
- 4.18,
- 4.13,
- 4.15,
- 4.2,
- 4.12,
- 4.09,
- 4.13,
- 4.05,
- 4.1,
- 4.05,
- 4.05,
- 4.05,
- 4.05,
- 4.1,
- 4.05,
- 4.04,
- 4.02,
- 4.05,
- 3.99,
- 4,
- 4.05,
- 4.07,
- 4.04,
- 3.85,
- 3.78,
- 3.73,
- 3.74,
- 3.74,
- 3.78,
- 3.78,
- 3.7,
- 3.71,
- 3.76,
- 3.8,
- 3.74,
- 3.73,
- 3.73,
- 3.75,
- 3.85,
- 3.91,
- 3.91,
- 3.86,
- 3.91,
- 4.15,
- 4.24,
- 4.19,
- 4.19,
- 4.21,
- 4.25,
- 4.35,
- 4.4,
- 4.42,
- 4.37,
- 4.39,
- 4.43,
- 4.45,
- 4.4,
- 4.48,
- 4.46,
- 4.43,
- 4.5,
- 4.55,
- 4.53,
- 4.53,
- 4.56,
- 4.61,
- 4.63,
- 4.79,
- 4.81,
- 4.79,
- 4.83,
- 4.85,
- 4.79,
- 4.7,
- 4.74,
- 4.79,
- 4.72,
- 4.76,
- 4.75,
- 4.73,
- 4.67,
- 4.6,
- 4.66,
- 4.71,
- 4.74,
- 4.71,
- 4.78,
- 4.78,
- 4.78,
- 4.82,
- 4.81,
- 4.89,
- 4.69,
- 4.74,
- 4.71,
- 4.72,
- 4.7,
- 4.72,
- 4.71,
- 4.66,
- 4.66,
- 4.76,
- 4.7,
- 4.62,
- 4.57,
- 4.48,
- 4.49,
- 4.5,
- 4.49,
- 4.49,
- 4.46,
- 4.5,
- 4.5,
- 4.5,
- 4.38,
- 4.38,
- 4.47,
- 4.5,
- 4.48,
- 4.45,
- 4.49,
- 4.62,
- 4.61,
- 4.6,
- 4.5,
- 4.48,
- 4.45,
- 4.45,
- 4.43,
- 4.24,
- 4.28,
- 4.32,
- 4.3,
- 4.27,
- 4.22,
- 4.26,
- 4.21,
- 4.23,
- 4.22,
- 4.24,
- 4.28,
- 4.28,
- 4.26,
- 4.22,
- 4.23,
- 4.19,
- 4.13,
- 4.13,
- 4.2,
- 4.3,
- 4.26,
- 4.18,
- 4.22,
- 4.19,
- 4.16,
- 4.15,
- 4.18,
- 4.08,
- 4.14,
- 4.07,
- 4.05,
- 4,
- 4.02,
- 4.04,
- 4.01,
- 4.02,
- 4.1,
- 4.14,
- 4.21,
- 4.19,
- 4.18,
- 4.23,
- 4.26,
- 4.15,
- 4.12,
- 4.09,
- 4.03,
- 4.07,
- 4.07,
- 4.07,
- 4.01,
- 4.01,
- 4,
- 3.99,
- 4.01,
- 4.11,
- 4.09,
- 4.05,
- 4.11,
- 4.1,
- 4.09,
- 4.1,
- 4.21,
- 4.22,
- 4.22,
- 4.25,
- 4.2,
- 4.2,
- 4.21,
- 4.14,
- 4.12,
- 4.2,
- 4.18,
- 4.19,
- 4.2,
- 4.34,
- 4.36,
- 4.38,
- 4.4,
- 4.27,
- 4.24,
- 4.23,
- 4.14,
- 4.19,
- 4.16,
- 4.16,
- 4.14,
- 4.09,
- 4.19,
- 4.21,
- 4.21,
- 4.18,
- 4.21,
- 4.23,
- 4.3,
- 4.31,
- 4.33,
- 4.27,
- 4.23,
- 4.29,
- 4.29,
- 4.29,
- 4.29,
- 4.29,
- 4.26,
- 4.25,
- 4.2,
- 4.23,
- 4.21,
- 4.2,
- 4.17,
- 4.16,
- 4.14,
- 4.2,
- 4.21,
- 4.22,
- 4.16,
- 4.14,
- 4.15,
- 4.15,
- 4.18,
- 4.09,
- 4.07,
- 4.05,
- 4,
- 4.07,
- 4.1,
- 4.08,
- 4.1,
- 4.16,
- 4.19,
- 4.27,
- 4.29,
- 4.27,
- 4.29,
- 4.27,
- 4.36,
- 4.38,
- 4.38,
- 4.39,
- 4.32,
- 4.31,
- 4.38,
- 4.52,
- 4.48,
- 4.56,
- 4.52,
- 4.54,
- 4.52,
- 4.47,
- 4.51,
- 4.53,
- 4.63,
- 4.61,
- 4.6,
- 4.64,
- 4.6,
- 4.5,
- 4.46,
- 4.47,
- 4.48,
- 4.44,
- 4.49,
- 4.5,
- 4.45,
- 4.38,
- 4.38,
- 4.37,
- 4.27,
- 4.27,
- 4.21,
- 4.22,
- 4.32,
- 4.26,
- 4.26,
- 4.28,
- 4.25,
- 4.21,
- 4.21,
- 4.21,
- 4.2,
- 4.19,
- 4.28,
- 4.29,
- 4.23,
- 4.21,
- 4.18,
- 4.12,
- 4.13,
- 4.12,
- 4.07,
- 4.11,
- 4.13,
- 4.07,
- 4.04,
- 4.08,
- 4.08,
- 4.08,
- 4,
- 3.91,
- 3.89,
- 3.98,
- 3.96,
- 3.92,
- 3.95,
- 3.98,
- 4.05,
- 4.09,
- 4.13,
- 4.12,
- 4.09,
- 4.09,
- 4.11,
- 4.06,
- 3.95,
- 3.96,
- 3.92,
- 3.9,
- 3.97,
- 3.99,
- 3.94,
- 4.06,
- 4.11,
- 4.08,
- 4.05,
- 4.11,
- 4.11,
- 4.15,
- 4.17,
- 4.19,
- 4.18,
- 4.22,
- 4.2,
- 4.17,
- 4.28,
- 4.23,
- 4.25,
- 4.24,
- 4.27,
- 4.2,
- 4.28,
- 4.32,
- 4.34,
- 4.3,
- 4.32,
- 4.4,
- 4.42,
- 4.41,
- 4.4,
- 4.32,
- 4.24,
- 4.27,
- 4.23,
- 4.28,
- 4.21,
- 4.21,
- 4.22,
- 4.2,
- 4.19,
- 4.18,
- 4.2,
- 4.2,
- 4.16,
- 4.02,
- 4.02,
- 4.03,
- 4.09,
- 4.15,
- 4.15,
- 4.14,
- 4.18,
- 4.14,
- 4.17,
- 4.22,
- 4.26,
- 4.25,
- 4.26,
- 4.19,
- 4.19,
- 4.25,
- 4.3,
- 4.3,
- 4.26,
- 4.29,
- 4.34,
- 4.39,
- 4.38,
- 4.36,
- 4.37,
- 4.35,
- 4.39,
- 4.45,
- 4.48,
- 4.48,
- 4.5,
- 4.49,
- 4.47,
- 4.46,
- 4.39,
- 4.45,
- 4.54,
- 4.6,
- 4.57,
- 4.58,
- 4.57,
- 4.58,
- 4.61,
- 4.65,
- 4.66,
- 4.65,
- 4.57,
- 4.64,
- 4.55,
- 4.61,
- 4.56,
- 4.49,
- 4.46,
- 4.5,
- 4.46,
- 4.43,
- 4.47,
- 4.41,
- 4.48,
- 4.49,
- 4.52,
- 4.52,
- 4.57,
- 4.49,
- 4.52,
- 4.47,
- 4.54,
- 4.56,
- 4.54,
- 4.45,
- 4.47,
- 4.45,
- 4.45,
- 4.47,
- 4.49,
- 4.44,
- 4.38,
- 4.34,
- 4.38,
- 4.37,
- 4.39,
- 4.37,
- 4.36,
- 4.36,
- 4.38,
- 4.38,
- 4.43,
- 4.46,
- 4.42,
- 4.36,
- 4.34,
- 4.34,
- 4.38,
- 4.37,
- 4.36,
- 4.4,
- 4.49,
- 4.53,
- 4.52,
- 4.54,
- 4.53,
- 4.57,
- 4.57,
- 4.54,
- 4.55,
- 4.57,
- 4.56,
- 4.54,
- 4.59,
- 4.58,
- 4.62,
- 4.61,
- 4.59,
- 4.54,
- 4.57,
- 4.53,
- 4.56,
- 4.58,
- 4.59,
- 4.55,
- 4.59,
- 4.64,
- 4.68,
- 4.74,
- 4.74,
- 4.73,
- 4.74,
- 4.76,
- 4.77,
- 4.71,
- 4.73,
- 4.65,
- 4.68,
- 4.66,
- 4.71,
- 4.7,
- 4.73,
- 4.67,
- 4.7,
- 4.79,
- 4.81,
- 4.86,
- 4.86,
- 4.88,
- 4.87,
- 4.84,
- 4.9,
- 4.97,
- 4.97,
- 4.93,
- 4.98,
- 5.05,
- 5.01,
- 4.99,
- 5.04,
- 5.04,
- 5.01,
- 4.99,
- 5.07,
- 5.12,
- 5.09,
- 5.07,
- 5.14,
- 5.12,
- 5.15,
- 5.16,
- 5.12,
- 5.12,
- 5.13,
- 5.13,
- 5.14,
- 5.19,
- 5.15,
- 5.1,
- 5.16,
- 5.08,
- 5.05,
- 5.04,
- 5.07,
- 5.03,
- 5.07,
- 5.06,
- 5.09,
- 5.12,
- 5.11,
- 5,
- 5.02,
- 5.01,
- 5.02,
- 5,
- 4.98,
- 4.99,
- 4.97,
- 5.05,
- 5.1,
- 5.13,
- 5.14,
- 5.15,
- 5.16,
- 5.2,
- 5.23,
- 5.25,
- 5.21,
- 5.25,
- 5.22,
- 5.15,
- 5.23,
- 5.19,
- 5.14,
- 5.13,
- 5.1,
- 5.1,
- 5.08,
- 5.07,
- 5.07,
- 5.13,
- 5.06,
- 5.03,
- 5.05,
- 5.05,
- 5.07,
- 5.04,
- 5.07,
- 5,
- 4.99,
- 4.99,
- 4.96,
- 4.96,
- 4.91,
- 4.93,
- 4.93,
- 4.92,
- 4.93,
- 4.97,
- 5,
- 4.93,
- 4.87,
- 4.87,
- 4.84,
- 4.82,
- 4.82,
- 4.82,
- 4.81,
- 4.79,
- 4.8,
- 4.79,
- 4.76,
- 4.74,
- 4.73,
- 4.78,
- 4.8,
- 4.8,
- 4.78,
- 4.8,
- 4.78,
- 4.77,
- 4.79,
- 4.8,
- 4.81,
- 4.74,
- 4.73,
- 4.65,
- 4.6,
- 4.56,
- 4.59,
- 4.6,
- 4.63,
- 4.64,
- 4.62,
- 4.62,
- 4.57,
- 4.61,
- 4.7,
- 4.75,
- 4.78,
- 4.79,
- 4.81,
- 4.79,
- 4.78,
- 4.77,
- 4.79,
- 4.79,
- 4.83,
- 4.83,
- 4.78,
- 4.73,
- 4.68,
- 4.68,
- 4.61,
- 4.57,
- 4.6,
- 4.72,
- 4.71,
- 4.66,
- 4.64,
- 4.62,
- 4.59,
- 4.61,
- 4.57,
- 4.61,
- 4.66,
- 4.61,
- 4.6,
- 4.58,
- 4.57,
- 4.55,
- 4.54,
- 4.51,
- 4.52,
- 4.46,
- 4.43,
- 4.43,
- 4.45,
- 4.48,
- 4.49,
- 4.56,
- 4.52,
- 4.49,
- 4.58,
- 4.6,
- 4.6,
- 4.6,
- 4.6,
- 4.6,
- 4.55,
- 4.63,
- 4.61,
- 4.66,
- 4.7,
- 4.71,
- 4.68,
- 4.67,
- 4.62,
- 4.65,
- 4.66,
- 4.66,
- 4.69,
- 4.74,
- 4.77,
- 4.75,
- 4.79,
- 4.75,
- 4.78,
- 4.76,
- 4.81,
- 4.81,
- 4.87,
- 4.88,
- 4.9,
- 4.88,
- 4.83,
- 4.84,
- 4.83,
- 4.81,
- 4.77,
- 4.74,
- 4.73,
- 4.79,
- 4.8,
- 4.82,
- 4.74,
- 4.7,
- 4.69,
- 4.68,
- 4.69,
- 4.73,
- 4.68,
- 4.63,
- 4.5,
- 4.56,
- 4.56,
- 4.52,
- 4.51,
- 4.53,
- 4.5,
- 4.51,
- 4.59,
- 4.56,
- 4.5,
- 4.53,
- 4.54,
- 4.55,
- 4.58,
- 4.56,
- 4.53,
- 4.6,
- 4.62,
- 4.6,
- 4.62,
- 4.62,
- 4.64,
- 4.65,
- 4.65,
- 4.67,
- 4.66,
- 4.68,
- 4.75,
- 4.73,
- 4.74,
- 4.74,
- 4.76,
- 4.74,
- 4.69,
- 4.66,
- 4.68,
- 4.68,
- 4.66,
- 4.63,
- 4.66,
- 4.69,
- 4.71,
- 4.63,
- 4.64,
- 4.65,
- 4.68,
- 4.65,
- 4.64,
- 4.63,
- 4.67,
- 4.65,
- 4.67,
- 4.69,
- 4.71,
- 4.71,
- 4.76,
- 4.81,
- 4.79,
- 4.83,
- 4.86,
- 4.86,
- 4.86,
- 4.88,
- 4.88,
- 4.9,
- 4.95,
- 4.93,
- 4.98,
- 4.97,
- 5.11,
- 5.12,
- 5.14,
- 5.26,
- 5.2,
- 5.23,
- 5.16,
- 5.15,
- 5.09,
- 5.14,
- 5.16,
- 5.14,
- 5.09,
- 5.1,
- 5.09,
- 5.12,
- 5.03,
- 5,
- 5.05,
- 5.16,
- 5.19,
- 5.16,
- 5.03,
- 5.09,
- 5.13,
- 5.11,
- 5.05,
- 5.08,
- 5.02,
- 5.04,
- 4.96,
- 4.97,
- 4.94,
- 4.92,
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- 4.77,
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- 4.79,
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- 4.28,
- 4.23,
- 4.26,
- 4.28,
- 4.17,
- 4.15,
- 4.07,
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- 4,
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- 4.23,
- 4.27,
- 4.25,
- 4.23,
- 4.16,
- 4.22,
- 4.16,
- 4.19,
- 4.1,
- 4.12,
- 4.07,
- 3.99,
- 3.99,
- 4.01,
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- 3.99,
- 3.95,
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- 3.79,
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- 4,
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- 3.83,
- 3.82,
- 3.75,
- 3.84,
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- 2.72,
- 2.68,
- 2.67,
- 2.55,
- 2.67,
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- 2.37,
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- 2.08,
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- 2.16,
- 2.18,
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- 2.16,
- 2.13,
- 2.11,
- 2.25,
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- 2.47,
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- 2.71,
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- 3.21,
- 3.19,
- 3.2,
- 3.18,
- 3.29,
- 3.29,
- 3.17,
- 3.17,
- 3.11,
- 3.1,
- 3.14,
- 3.22,
- 3.25,
- 3.19,
- 3.35,
- 3.45,
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- 3.71,
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- 3.32,
- 3.27,
- 3.21,
- 3.24,
- 3.26,
- 3.18,
- 3.13,
- 3.14,
- 3.12,
- 3.05,
- 2.97,
- 2.97,
- 2.96,
- 3,
- 2.95,
- 3,
- 3.04,
- 3.07,
- 3.08,
- 3.15,
- 3.07,
- 3,
- 2.96,
- 2.99,
- 2.98,
- 2.9,
- 2.96,
- 3.02,
- 3.03,
- 3.08,
- 3.03,
- 3.03,
- 2.94,
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- 2.94,
- 2.98,
- 2.94,
- 2.86,
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- 2.79,
- 2.72,
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- 2.81,
- 2.97,
- 3.01,
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- 2.95,
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- 3.26,
- 3.23,
- 3.32,
- 3.29,
- 3.49,
- 3.53,
- 3.47,
- 3.33,
- 3.36,
- 3.35,
- 3.36,
- 3.41,
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- 3.5,
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- 3.38,
- 3.3,
- 3.36,
- 3.36,
- 3.5,
- 3.44,
- 3.34,
- 3.32,
- 3.37,
- 3.4,
- 3.34,
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- 3.39,
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- 3.47,
- 3.44,
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- 3.45,
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- 3.75,
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- 3.37,
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- 3.36,
- 3.33,
- 3.22,
- 3.25,
- 3.28,
- 3.34,
- 3.34,
- 3.36,
- 3.42,
- 3.46,
- 3.47,
- 3.5,
- 3.47,
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- 3.4,
- 3.39,
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- 3.42,
- 3.39,
- 3.34,
- 3.39,
- 3.34,
- 3.32,
- 3.31,
- 3.28,
- 3.25,
- 3.18,
- 3.19,
- 3.17,
- 3.23,
- 3.19,
- 3.22,
- 3.18,
- 3.15,
- 3.12,
- 3.18,
- 3.17,
- 3.15,
- 3.13,
- 3.12,
- 3.13,
- 3.07,
- 3.07,
- 3.05,
- 2.96,
- 3.04,
- 2.99,
- 3.01,
- 3.01,
- 2.98,
- 3.01,
- 2.99,
- 3,
- 3.11,
- 2.98,
- 2.93,
- 2.94,
- 2.97,
- 2.99,
- 3.01,
- 2.93,
- 2.88,
- 2.95,
- 3.05,
- 3.14,
- 3.18,
- 3.22,
- 3.16,
- 3.12,
- 3.17,
- 3.03,
- 2.94,
- 2.92,
- 2.92,
- 2.98,
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- 2.94,
- 2.91,
- 2.96,
- 3.03,
- 2.99,
- 3.03,
- 2.99,
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- 2.98,
- 2.82,
- 2.77,
- 2.66,
- 2.64,
- 2.47,
- 2.58,
- 2.4,
- 2.2,
- 2.17,
- 2.34,
- 2.24,
- 2.29,
- 2.23,
- 2.17,
- 2.08,
- 2.07,
- 2.1,
- 2.15,
- 2.29,
- 2.23,
- 2.19,
- 2.28,
- 2.19,
- 2.23,
- 2.15,
- 2.02,
- 1.98,
- 2.05,
- 2,
- 1.93,
- 1.94,
- 2,
- 2.03,
- 2.09,
- 2.08,
- 1.97,
- 1.95,
- 1.88,
- 1.72,
- 1.84,
- 1.91,
- 2,
- 2.03,
- 1.99,
- 1.92,
- 1.8,
- 1.81,
- 1.92,
- 2.01,
- 2.1,
- 2.18,
- 2.24,
- 2.19,
- 2.26,
- 2.18,
- 2.19,
- 2.18,
- 2.2,
- 2.23,
- 2.25,
- 2.14,
- 2.23,
- 2.42,
- 2.34,
- 2.17,
- 2.01,
- 2.03,
- 2.09,
- 2.06,
- 2.04,
- 2.1,
- 2,
- 2.04,
- 2.04,
- 2.06,
- 2.01,
- 1.96,
- 2.01,
- 1.97,
- 1.94,
- 1.89,
- 1.97,
- 1.97,
- 2,
- 2.08,
- 2.11,
- 2.05,
- 2.04,
- 2.08,
- 2.02,
- 1.99,
- 2.07,
- 2.03,
- 1.96,
- 1.92,
- 1.92,
- 1.86,
- 1.82,
- 1.94,
- 1.98,
- 1.97,
- 2.03,
- 2.02,
- 1.93,
- 1.91,
- 1.89,
- 1.97,
- 2,
- 2.02,
- 1.98,
- 1.98,
- 2,
- 1.93,
- 1.94,
- 1.89,
- 1.87,
- 1.92,
- 2.01,
- 2.05,
- 2.09,
- 2.08,
- 2.01,
- 1.96,
- 1.93,
- 1.87,
- 1.83,
- 1.87,
- 1.86,
- 1.97,
- 1.93,
- 2,
- 2.01,
- 2.04,
- 1.96,
- 1.99,
- 1.92,
- 1.93,
- 1.99,
- 2.01,
- 2.05,
- 2.01,
- 1.99,
- 1.98,
- 1.92,
- 1.94,
- 1.98,
- 2.03,
- 1.99,
- 2,
- 1.96,
- 1.98,
- 2.03,
- 2.04,
- 2.04,
- 2.14,
- 2.29,
- 2.29,
- 2.31,
- 2.39,
- 2.38,
- 2.31,
- 2.29,
- 2.25,
- 2.26,
- 2.2,
- 2.21,
- 2.18,
- 2.23,
- 2.22,
- 2.3,
- 2.25,
- 2.19,
- 2.06,
- 2.01,
- 2.05,
- 2.08,
- 2.02,
- 2,
- 2.03,
- 2,
- 1.98,
- 1.99,
- 1.96,
- 2,
- 2.01,
- 1.98,
- 1.96,
- 1.95,
- 1.98,
- 1.96,
- 1.96,
- 1.91,
- 1.92,
- 1.88,
- 1.87,
- 1.89,
- 1.84,
- 1.78,
- 1.76,
- 1.76,
- 1.7,
- 1.71,
- 1.75,
- 1.79,
- 1.73,
- 1.77,
- 1.75,
- 1.74,
- 1.63,
- 1.59,
- 1.47,
- 1.53,
- 1.57,
- 1.66,
- 1.66,
- 1.65,
- 1.6,
- 1.67,
- 1.61,
- 1.64,
- 1.6,
- 1.59,
- 1.64,
- 1.65,
- 1.63,
- 1.69,
- 1.63,
- 1.66,
- 1.65,
- 1.6,
- 1.67,
- 1.61,
- 1.65,
- 1.62,
- 1.57,
- 1.53,
- 1.53,
- 1.54,
- 1.5,
- 1.52,
- 1.5,
- 1.53,
- 1.52,
- 1.54,
- 1.49,
- 1.47,
- 1.44,
- 1.43,
- 1.45,
- 1.58,
- 1.53,
- 1.51,
- 1.56,
- 1.51,
- 1.6,
- 1.59,
- 1.66,
- 1.68,
- 1.69,
- 1.65,
- 1.65,
- 1.73,
- 1.8,
- 1.83,
- 1.81,
- 1.82,
- 1.8,
- 1.71,
- 1.68,
- 1.68,
- 1.65,
- 1.64,
- 1.66,
- 1.63,
- 1.57,
- 1.59,
- 1.6,
- 1.68,
- 1.67,
- 1.68,
- 1.7,
- 1.77,
- 1.75,
- 1.88,
- 1.85,
- 1.82,
- 1.79,
- 1.8,
- 1.77,
- 1.74,
- 1.7,
- 1.64,
- 1.66,
- 1.65,
- 1.64,
- 1.64,
- 1.64,
- 1.7,
- 1.75,
- 1.74,
- 1.72,
- 1.7,
- 1.69,
- 1.7,
- 1.75,
- 1.83,
- 1.86,
- 1.79,
- 1.83,
- 1.79,
- 1.8,
- 1.86,
- 1.78,
- 1.72,
- 1.75,
- 1.75,
- 1.72,
- 1.78,
- 1.68,
- 1.62,
- 1.61,
- 1.59,
- 1.59,
- 1.58,
- 1.58,
- 1.61,
- 1.66,
- 1.69,
- 1.7,
- 1.66,
- 1.64,
- 1.63,
- 1.62,
- 1.62,
- 1.63,
- 1.62,
- 1.6,
- 1.59,
- 1.64,
- 1.63,
- 1.66,
- 1.72,
- 1.74,
- 1.72,
- 1.78,
- 1.84,
- 1.82,
- 1.81,
- 1.77,
- 1.79,
- 1.77,
- 1.74,
- 1.73,
- 1.78,
- 1.86,
- 1.92,
- 1.93,
- 1.92,
- 1.89,
- 1.88,
- 1.91,
- 1.89,
- 1.89,
- 1.86,
- 1.84,
- 1.89,
- 1.87,
- 1.86,
- 1.86,
- 1.88,
- 1.98,
- 2,
- 2.03,
- 2.03,
- 2.02,
- 2.04,
- 2,
- 2.04,
- 2,
- 1.99,
- 1.99,
- 1.99,
- 2.02,
- 2.05,
- 2,
- 2.01,
- 2.03,
- 2.02,
- 1.99,
- 1.97,
- 1.88,
- 1.88,
- 1.91,
- 1.89,
- 1.86,
- 1.88,
- 1.9,
- 1.95,
- 2,
- 2.06,
- 2.07,
- 2.03,
- 2.04,
- 2.04,
- 2.01,
- 1.96,
- 1.92,
- 1.96,
- 1.95,
- 1.93,
- 1.93,
- 1.92,
- 1.87,
- 1.87,
- 1.86,
- 1.88,
- 1.83,
- 1.78,
- 1.72,
- 1.76,
- 1.78,
- 1.84,
- 1.82,
- 1.75,
- 1.72,
- 1.75,
- 1.73,
- 1.72,
- 1.73,
- 1.72,
- 1.74,
- 1.73,
- 1.74,
- 1.7,
- 1.7,
- 1.7,
- 1.66,
- 1.66,
- 1.78,
- 1.8,
- 1.82,
- 1.81,
- 1.81,
- 1.9,
- 1.92,
- 1.96,
- 1.94,
- 1.87,
- 1.95,
- 1.97,
- 1.94,
- 2.03,
- 2.02,
- 2.01,
- 2.15,
- 2.13,
- 2.13,
- 2.16,
- 2.13,
- 2.14,
- 2.1,
- 2.08,
- 2.17,
- 2.22,
- 2.2,
- 2.25,
- 2.19,
- 2.14,
- 2.19,
- 2.2,
- 2.33,
- 2.41,
- 2.52,
- 2.57,
- 2.6,
- 2.55,
- 2.49,
- 2.52,
- 2.5,
- 2.48,
- 2.52,
- 2.73,
- 2.65,
- 2.65,
- 2.7,
- 2.6,
- 2.61,
- 2.57,
- 2.55,
- 2.52,
- 2.56,
- 2.5,
- 2.5,
- 2.53,
- 2.61,
- 2.61,
- 2.58,
- 2.61,
- 2.63,
- 2.6,
- 2.74,
- 2.63,
- 2.67,
- 2.67,
- 2.61,
- 2.58,
- 2.57,
- 2.61,
- 2.71,
- 2.71,
- 2.77,
- 2.84,
- 2.88,
- 2.82,
- 2.87,
- 2.9,
- 2.82,
- 2.79,
- 2.72,
- 2.78,
- 2.75,
- 2.78,
- 2.86,
- 2.9,
- 2.98,
- 2.94,
- 2.9,
- 2.96,
- 2.93,
- 2.92,
- 2.9,
- 2.88,
- 2.86,
- 2.69,
- 2.76,
- 2.75,
- 2.72,
- 2.67,
- 2.63,
- 2.66,
- 2.64,
- 2.64,
- 2.66,
- 2.63,
- 2.62,
- 2.66,
- 2.65,
- 2.66,
- 2.68,
- 2.71,
- 2.7,
- 2.75,
- 2.69,
- 2.61,
- 2.6,
- 2.63,
- 2.54,
- 2.51,
- 2.53,
- 2.53,
- 2.54,
- 2.53,
- 2.55,
- 2.57,
- 2.65,
- 2.63,
- 2.69,
- 2.67,
- 2.63,
- 2.77,
- 2.8,
- 2.75,
- 2.69,
- 2.71,
- 2.67,
- 2.71,
- 2.8,
- 2.79,
- 2.75,
- 2.74,
- 2.71,
- 2.74,
- 2.75,
- 2.81,
- 2.79,
- 2.84,
- 2.88,
- 2.88,
- 2.86,
- 2.81,
- 2.86,
- 2.89,
- 2.88,
- 2.89,
- 2.85,
- 2.89,
- 2.94,
- 2.89,
- 2.94,
- 2.99,
- 3,
- 3.02,
- 2.99,
- 3.04,
- 3,
- 3.01,
- 2.98,
- 2.96,
- 3.01,
- 2.97,
- 2.88,
- 2.84,
- 2.88,
- 2.9,
- 2.86,
- 2.84,
- 2.85,
- 2.87,
- 2.79,
- 2.75,
- 2.78,
- 2.77,
- 2.69,
- 2.72,
- 2.67,
- 2.61,
- 2.64,
- 2.7,
- 2.73,
- 2.71,
- 2.7,
- 2.75,
- 2.8,
- 2.73,
- 2.75,
- 2.71,
- 2.73,
- 2.76,
- 2.73,
- 2.75,
- 2.7,
- 2.67,
- 2.65,
- 2.66,
- 2.6,
- 2.7,
- 2.7,
- 2.74,
- 2.8,
- 2.79,
- 2.77,
- 2.73,
- 2.66,
- 2.65,
- 2.7,
- 2.68,
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- 2.79,
- 2.75,
- 2.74,
- 2.75,
- 2.71,
- 2.69,
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- 2.69,
- 2.71,
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- 2.65,
- 2.64,
- 2.65,
- 2.73,
- 2.73,
- 2.73,
- 2.7,
- 2.7,
- 2.68,
- 2.7,
- 2.71,
- 2.67,
- 2.63,
- 2.6,
- 2.63,
- 2.61,
- 2.62,
- 2.61,
- 2.62,
- 2.66,
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- 2.54,
- 2.5,
- 2.52,
- 2.54,
- 2.52,
- 2.54,
- 2.56,
- 2.54,
- 2.52,
- 2.44,
- 2.45,
- 2.48,
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- 2.6,
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- 2.59,
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- 2.64,
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- 2.59,
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- 2.53,
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- 2.57,
- 2.55,
- 2.53,
- 2.55,
- 2.56,
- 2.55,
- 2.47,
- 2.5,
- 2.49,
- 2.48,
- 2.48,
- 2.52,
- 2.48,
- 2.5,
- 2.47,
- 2.57,
- 2.58,
- 2.52,
- 2.51,
- 2.49,
- 2.49,
- 2.43,
- 2.44,
- 2.44,
- 2.46,
- 2.43,
- 2.4,
- 2.34,
- 2.39,
- 2.4,
- 2.43,
- 2.41,
- 2.4,
- 2.39,
- 2.39,
- 2.37,
- 2.34,
- 2.35,
- 2.42,
- 2.41,
- 2.45,
- 2.46,
- 2.48,
- 2.5,
- 2.54,
- 2.54,
- 2.62,
- 2.6,
- 2.6,
- 2.62,
- 2.63,
- 2.59,
- 2.57,
- 2.54,
- 2.57,
- 2.52,
- 2.54,
- 2.5,
- 2.52,
- 2.42,
- 2.44,
- 2.45,
- 2.43,
- 2.36,
- 2.35,
- 2.34,
- 2.31,
- 2.21,
- 2.15,
- 2.17,
- 2.22,
- 2.2,
- 2.23,
- 2.25,
- 2.29,
- 2.29,
- 2.27,
- 2.3,
- 2.34,
- 2.32,
- 2.35,
- 2.36,
- 2.35,
- 2.36,
- 2.39,
- 2.32,
- 2.38,
- 2.37,
- 2.35,
- 2.32,
- 2.34,
- 2.32,
- 2.36,
- 2.34,
- 2.31,
- 2.3,
- 2.27,
- 2.24,
- 2.18,
- 2.22,
- 2.28,
- 2.29,
- 2.25,
- 2.31,
- 2.26,
- 2.22,
- 2.18,
- 2.19,
- 2.1,
- 2.12,
- 2.07,
- 2.14,
- 2.22,
- 2.17,
- 2.17,
- 2.26,
- 2.27,
- 2.25,
- 2.22,
- 2.2,
- 2.17,
- 2.12,
- 2.04,
- 1.97,
- 1.96,
- 2.03,
- 1.98,
- 1.92,
- 1.91,
- 1.86,
- 1.77,
- 1.83,
- 1.82,
- 1.87,
- 1.9,
- 1.81,
- 1.83,
- 1.83,
- 1.73,
- 1.77,
- 1.68,
- 1.68,
- 1.79,
- 1.81,
- 1.83,
- 1.95,
- 1.96,
- 2.01,
- 2,
- 1.99,
- 2.02,
- 2.14,
- 2.07,
- 2.11,
- 2.13,
- 2.06,
- 1.99,
- 1.96,
- 2.03,
- 2,
- 2.08,
- 2.12,
- 2.12,
- 2.11,
- 2.24,
- 2.2,
- 2.14,
- 2.11,
- 2.1,
- 2.13,
- 2.1,
- 2.06,
- 1.93,
- 1.98,
- 1.93,
- 1.92,
- 1.88,
- 1.93,
- 2.01,
- 1.95,
- 1.96,
- 1.94,
- 1.87,
- 1.92,
- 1.92,
- 1.89,
- 1.92,
- 1.97,
- 1.96,
- 1.94,
- 1.9,
- 1.91,
- 1.9,
- 1.87,
- 1.9,
- 1.92,
- 1.99,
- 1.96,
- 1.93,
- 1.94,
- 2,
- 2.06,
- 2.05,
- 2.12,
- 2.16,
- 2.19,
- 2.25,
- 2.18,
- 2.16,
- 2.28,
- 2.28,
- 2.27,
- 2.23,
- 2.14,
- 2.23,
- 2.27,
- 2.26,
- 2.19,
- 2.21,
- 2.14,
- 2.14,
- 2.13,
- 2.12,
- 2.19,
- 2.27,
- 2.38,
- 2.31,
- 2.41,
- 2.39,
- 2.42,
- 2.5,
- 2.39,
- 2.39,
- 2.36,
- 2.32,
- 2.32,
- 2.35,
- 2.26,
- 2.37,
- 2.42,
- 2.38,
- 2.4,
- 2.49,
- 2.33,
- 2.35,
- 2.43,
- 2.4,
- 2.3,
- 2.27,
- 2.22,
- 2.32,
- 2.42,
- 2.44,
- 2.41,
- 2.36,
- 2.36,
- 2.34,
- 2.38,
- 2.35,
- 2.33,
- 2.28,
- 2.27,
- 2.23,
- 2.26,
- 2.29,
- 2.28,
- 2.2,
- 2.16,
- 2.23,
- 2.28,
- 2.23,
- 2.18,
- 2.24,
- 2.15,
- 2.14,
- 2.19,
- 2.2,
- 2.16,
- 2.2,
- 2.12,
- 2.09,
- 2.05,
- 2.01,
- 2.12,
- 2.18,
- 2.18,
- 2.19,
- 2.21,
- 2.17,
- 2.2,
- 2.18,
- 2.13,
- 2.2,
- 2.21,
- 2.23,
- 2.2,
- 2.18,
- 2.28,
- 2.3,
- 2.21,
- 2.13,
- 2.2,
- 2.14,
- 2.16,
- 2.13,
- 2.17,
- 2.1,
- 2.05,
- 2.06,
- 2.05,
- 1.99,
- 2.07,
- 2.05,
- 2.08,
- 2.12,
- 2.12,
- 2.06,
- 1.99,
- 2.04,
- 2.04,
- 2.04,
- 2.08,
- 2.04,
- 2.04,
- 2.09,
- 2.07,
- 2.05,
- 2.1,
- 2.19,
- 2.16,
- 2.2,
- 2.23,
- 2.25,
- 2.26,
- 2.34,
- 2.36,
- 2.32,
- 2.32,
- 2.28,
- 2.27,
- 2.25,
- 2.27,
- 2.24,
- 2.26,
- 2.25,
- 2.24,
- 2.23,
- 2.22,
- 2.21,
- 2.15,
- 2.18,
- 2.33,
- 2.28,
- 2.23,
- 2.24,
- 2.22,
- 2.24,
- 2.13,
- 2.23,
- 2.28,
- 2.3,
- 2.24,
- 2.19,
- 2.2,
- 2.24,
- 2.27,
- 2.25,
- 2.24,
- 2.32,
- 2.31,
- 2.27,
- 2.24,
- 2.25,
- 2.18,
- 2.16,
- 2.13,
- 2.17,
- 2.12,
- 2.08,
- 2.1,
- 2.03,
- 2.06,
- 2.01,
- 2.02,
- 2.07,
- 2.03,
- 2.01,
- 2.02,
- 2,
- 1.94,
- 1.97,
- 1.87,
- 1.88,
- 1.87,
- 1.86,
- 1.75,
- 1.74,
- 1.71,
- 1.63,
- 1.74,
- 1.78,
- 1.81,
- 1.75,
- 1.76,
- 1.77,
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- 1.75,
- 1.71,
- 1.76,
- 1.74,
- 1.83,
- 1.84,
- 1.83,
- 1.88,
- 1.91,
- 1.83,
- 1.9,
- 1.93,
- 1.98,
- 1.97,
- 1.97,
- 1.94,
- 1.91,
- 1.88,
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- 1.78,
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- 1.7,
- 1.72,
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- 1.79,
- 1.77,
- 1.8,
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- 1.78,
- 1.79,
- 1.85,
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- 1.79,
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- 1.85,
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- 1.84,
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- 1.81,
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- 1.72,
- 1.71,
- 1.68,
- 1.64,
- 1.62,
- 1.62,
- 1.6,
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- 1.62,
- 1.67,
- 1.71,
- 1.69,
- 1.74,
- 1.57,
- 1.46,
- 1.46,
- 1.5,
- 1.49,
- 1.46,
- 1.37,
- 1.38,
- 1.4,
- 1.37,
- 1.43,
- 1.53,
- 1.48,
- 1.53,
- 1.6,
- 1.59,
- 1.56,
- 1.59,
- 1.57,
- 1.57,
- 1.58,
- 1.57,
- 1.52,
- 1.52,
- 1.46,
- 1.51,
- 1.55,
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- 1.51,
- 1.59,
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- 1.7,
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- 1.7,
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- 1.63,
- 1.62,
- 1.59,
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- 1.56,
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- 1.8,
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- 1.86,
- 1.84,
- 1.83,
- 1.81,
- 1.82,
- 1.79,
- 1.83,
- 1.88,
- 2.07,
- 2.15,
- 2.23,
- 2.23,
- 2.22,
- 2.29,
- 2.34,
- 2.33,
- 2.31,
- 2.36,
- 2.36,
- 2.32,
- 2.3,
- 2.37,
- 2.45,
- 2.4,
- 2.39,
- 2.39,
- 2.34,
- 2.4,
- 2.47,
- 2.49,
- 2.48,
- 2.54,
- 2.6,
- 2.6,
- 2.54,
- 2.57,
- 2.55,
- 2.55,
- 2.55,
- 2.57,
- 2.51,
- 2.49,
- 2.45,
- 2.45,
- 2.46,
- 2.37,
- 2.42,
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- 2.38,
- 2.38,
- 2.36,
- 2.4,
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- 2.4,
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- 2.43,
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- 2.42,
- 2.43,
- 2.42,
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- 2.52,
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- 2.39,
- 2.42,
- 2.4,
- 2.35,
- 2.36,
- 2.34,
- 2.34,
- 2.38,
- 2.37,
- 2.32,
- 2.28,
- 2.24,
- 2.26,
- 2.18,
- 2.21,
- 2.24,
- 2.24,
- 2.28,
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- 2.32,
- 2.3,
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- 2.33,
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2.34,
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2.37,
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2.3,
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2.38,
2.37,
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2.32,
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2.4,
- 2.4,
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2.37,
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2.32,
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2.37,
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2.39,
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2.39,
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2.48,
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2.66,
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2.77,
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2.86,
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2.87,
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2.86,
2.88,
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2.87,
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2.85,
2.85,
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2.73,
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2.78,
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2.83,
- 2.82,
- 2.83,
- 2.82,
- 2.87,
- 2.92,
- 2.96,
2.98,
- 3,
- 3.03,
- 3,
- 2.96,
- 2.95,
- 2.97,
- 2.97,
- 2.94,
2.95,
2.95,
- 2.97,
- 3,
- 2.97,
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3,
- 3.08,
- 3.09,
- 3.11,
3.06,
- 3.06,
- 3.06,
- 3.01,
- 2.98,
2.93,
- 2.77,
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2.94,
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2.96,
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2.92,
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2.87,
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2.87,
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2.86,
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2.85,
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2.96,
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2.98,
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2.94,
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2.88,
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2.82,
2.85,
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2.86,
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2.94,
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- 2.97,
- 2.99,
2.99,
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- 3.08,
- 3.07,
- 3.07,
3.08,
- 3.1,
- 3.06,
- 3.06,
- 3.05,
3.09,
- 3.05,
- 3.15,
- 3.19,
3.23,
- 3.21,
- 3.22,
- 3.14,
- 3.15,
- 3.16,
3.16,
- 3.19,
- 3.17,
3.2,
- 3.2,
- 3.17,
- 3.1,
- 3.14,
3.08,
- 3.08,
- 3.12,
- 3.15,
- 3.14,
- 3.22,
3.2,
- 3.22,
- 3.22,
- 3.24,
3.19,
- 3.14,
- 3.12,
- 3.11,
- 3.08,
- 3.06,
3.06,
- 3.06,
- 3.05,
3.07,
- 3.06,
- 3.06,
- 3.03,
- 3.01,
2.98,
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2.85,
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- 2.89,
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- 2.91,
- 2.89,
2.86,
- 2.82,
- 2.77,
- 2.79,
- 2.79,
2.74,
- 2.81,
- 2.77,
- 2.72,
2.69,
- 2.66,
- 2.56,
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2.7,
- 2.73,
- 2.74,
- 2.74,
- 2.71,
2.71,
- 2.72,
- 2.73,
- 2.75,
2.79,
- 2.74,
- 2.76,
- 2.72,
- 2.76,
2.75,
- 2.72,
- 2.7,
- 2.63,
- 2.7,
2.73,
- 2.71,
- 2.7,
- 2.65,
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2.65,
- 2.68,
- 2.71,
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2.66,
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- 2.65,
- 2.69,
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2.67,
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2.72,
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- 2.69,
2.64,
- 2.62,
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- 2.63,
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2.6,
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- 2.54,
- 2.44,
2.43,
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- 2.39,
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- 2.41,
2.49,
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- 2.5,
2.52,
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- 2.48,
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2.55,
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2.59,
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2.54,
2.51,
- 2.52,
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2.4,
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- 2.37,
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- 2.39,
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+ 2.41,
2.32,
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- 2.22,
- 2.14,
2.07,
- 2.12,
- 2.12,
- 2.12,
- 2.09,
- 2.15,
2.15,
- 2.13,
- 2.1,
2.09,
- 2.09,
- 2.06,
- 2.03,
- 2.01,
- 2.07,
2.02,
- 2,
- 2.05,
- 2.01,
- 2,
2.03,
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- 1.96,
- 2.04,
2.05,
- 2.07,
- 2.07,
- 2.13,
- 2.12,
2.09,
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- 2.06,
- 2.04,
- 2.05,
2.05,
- 2.08,
- 2.05,
- 2.08,
- 2.08,
2.06,
- 2.06,
- 2.02,
- 1.9,
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1.75,
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- 1.71,
- 1.72,
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1.65,
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1.6,
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1.54,
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1.5,
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1.63,
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1.84,
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1.72,
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- 1.7,
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1.68,
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1.56,
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1.76,
- 1.76,
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1.8,
1.85,
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1.79,
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1.94,
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1.81,
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1.76,
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1.83,
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1.83,
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- 1.82,
- 1.89,
1.89,
- 1.92,
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- 1.92,
1.93,
1.9,
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1.81,
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1.85,
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1.84,
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- 1.7,
1.61,
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1.54,
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- 1.66,
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1.56,
1.59,
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- 1.56,
- 1.52,
- 1.46,
1.38,
- 1.33,
- 1.33,
- 1.3,
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1.1,
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- 1.02,
- 0.92,
- 0.74,
0.54,
- 0.76,
- 0.82,
- 0.88,
- 0.94,
0.73,
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0.76,
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- 0.72,
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0.7,
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0.67,
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0.76,
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0.63,
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0.67,
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0.64,
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0.73,
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0.73,
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0.66,
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- 0.7,
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0.66,
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0.88,
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0.71,
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0.71,
- 0.7,
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0.64,
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0.69,
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- 0.64,
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- 0.64,
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0.64,
0.62,
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0.62,
- 0.59,
- 0.58,
- 0.55,
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0.56,
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- 0.57,
0.59,
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0.69,
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0.65,
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0.72,
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0.72,
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0.68,
0.68,
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0.67,
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0.78,
0.79,
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0.78,
0.81,
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0.87,
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0.86,
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0.84,
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0.94,
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0.9,
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0.95,
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0.94,
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0.93,
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- 1.15,
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1.15,
1.11,
- 1.1,
- 1.1,
- 1.12,
- 1.1,
- 1.05,
1.05,
- 1.04,
- 1.07,
- 1.11,
1.09,
- 1.12,
- 1.15,
- 1.15,
- 1.19,
1.19,
- 1.18,
- 1.15,
- 1.16,
1.2,
- 1.3,
- 1.29,
- 1.29,
- 1.34,
- 1.37,
1.37,
- 1.38,
- 1.54,
- 1.44,
1.45,
- 1.42,
- 1.47,
- 1.54,
- 1.56,
1.59,
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- 1.53,
- 1.54,
- 1.64,
- 1.62,
1.62,
- 1.63,
- 1.71,
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1.69,
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- 1.62,
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- 1.67,
1.73,
1.73,
- 1.74,
- 1.69,
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- 1.64,
- 1.67,
1.69,
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- 1.56,
- 1.59,
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- 1.58,
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- 1.58,
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1.61,
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1.58,
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1.63,
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1.63,
1.64,
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1.61,
1.58,
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1.57,
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- 1.5,
- 1.45,
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1.51,
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1.5,
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- 1.54,
1.49,
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1.44,
- 1.37,
- 1.33,
- 1.3,
- 1.37,
1.38,
- 1.42,
- 1.37,
- 1.31,
- 1.31,
1.19,
- 1.23,
- 1.3,
- 1.27,
- 1.3,
1.29,
- 1.25,
- 1.26,
- 1.28,
- 1.24,
1.2,
- 1.19,
- 1.19,
- 1.23,
- 1.31,
1.33,
- 1.36,
- 1.35,
- 1.36,
- 1.29,
- 1.26,
- 1.26,
- 1.27,
- 1.24,
1.26,
1.25,
1.29,
- 1.35,
- 1.34,
- 1.31,
- 1.29,
- 1.3,
- 1.31,
- 1.29,
1.33,
- 1.38,
- 1.35,
- 1.3,
- 1.35,
1.33,
- 1.28,
- 1.31,
- 1.34,
- 1.37,
1.31,
- 1.33,
- 1.32,
- 1.41,
- 1.47,
- 1.48,
- 1.54,
- 1.55,
- 1.52,
1.48,
1.49,
- 1.54,
- 1.53,
- 1.58,
1.61,
1.59,
- 1.56,
- 1.52,
- 1.59,
- 1.59,
- 1.65,
- 1.65,
- 1.68,
- 1.66,
1.64,
- 1.63,
- 1.54,
- 1.57,
- 1.55,
1.58,
- 1.56,
- 1.6,
- 1.53,
- 1.45,
1.51,
- 1.46,
- 1.56,
- 1.58,
- 1.63,
1.63,
- 1.6,
- 1.59,
- 1.54,
1.63,
- 1.67,
- 1.64,
- 1.48,
1.52,
1.43,
- 1.43,
- 1.44,
- 1.35,
- 1.43,
- 1.48,
- 1.52,
- 1.49,
- 1.48,
1.42,
- 1.44,
- 1.47,
- 1.44,
- 1.41,
1.43,
1.48,
- 1.46,
- 1.5,
- 1.48,
- 1.49,
- 1.55,
- 1.52,
- 1.52,
1.63,
- 1.66,
- 1.71,
- 1.73,
- 1.76,
1.78,
- 1.75,
- 1.74,
- 1.7,
1.78,
- 1.87,
- 1.83,
- 1.83,
1.75,
- 1.75,
- 1.78,
- 1.85,
- 1.81,
- 1.78,
1.79,
- 1.81,
- 1.78,
- 1.82,
- 1.93,
- 1.92,
- 1.96,
- 1.94,
- 2.03,
1.92,
1.98,
- 2.05,
- 2.03,
- 1.97,
1.92,
- 1.94,
- 1.99,
- 1.96,
- 1.97,
1.83,
- 1.72,
- 1.86,
- 1.86,
- 1.74,
1.78,
- 1.86,
- 1.94,
- 1.98,
- 2,
- 2.14,
- 2.15,
- 2.19,
- 2.2,
2.14,
2.32,
- 2.38,
- 2.32,
- 2.34,
- 2.48,
2.46,
- 2.41,
- 2.35,
- 2.32,
- 2.39,
2.42,
- 2.54,
- 2.61,
- 2.66,
- 2.72,
2.79,
- 2.72,
- 2.7,
- 2.83,
- 2.85,
- 2.93,
2.85,
- 2.9,
- 2.9,
2.81,
- 2.77,
- 2.82,
- 2.85,
- 2.89,
2.99,
- 2.97,
- 2.93,
- 3.05,
- 3.12,
3.05,
- 2.99,
- 2.91,
- 2.84,
- 2.93,
2.88,
- 2.98,
- 2.89,
- 2.84,
- 2.78,
2.86,
- 2.76,
- 2.75,
- 2.75,
2.74,
- 2.85,
- 2.94,
- 2.92,
- 2.96,
3.04,
- 2.98,
- 3.03,
- 3.04,
- 3.15,
3.43,
- 3.49,
- 3.33,
- 3.28,
3.25,
- 3.31,
- 3.16,
- 3.09,
- 3.13,
- 3.2,
3.2,
- 3.1,
- 2.98,
2.88,
- 2.82,
- 2.93,
- 3.01,
- 3.09,
2.99,
2.96,
- 2.91,
- 2.96,
- 2.93,
- 2.96,
- 3.01,
- 3.04,
- 2.91,
- 2.77,
- 2.81,
2.81,
- 2.78,
- 2.68,
- 2.67,
2.6,
- 2.75,
- 2.73,
- 2.68,
- 2.83,
2.77,
- 2.8,
- 2.78,
- 2.87,
- 2.84,
2.79,
- 2.82,
- 2.89,
- 2.88,
- 2.98,
- 3.03,
- 3.05,
- 3.11,
3.03,
- 3.04,
3.12,
- 3.11,
- 3.15,
- 3.26,
3.2,
- 3.33,
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- 3.29,
- 3.33,
3.37,
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- 3.41,
- 3.45,
- 3.45,
3.49,
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3.88,
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- 3.72,
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- 3.83,
3.67,
- 3.62,
- 3.76,
- 3.83,
3.89,
- 3.93,
- 3.91,
- 3.97,
- 4,
4.02,
- 4.01,
- 4.14,
- 4.24,
- 4.21,
4.25,
4.1,
- 4.04,
- 3.96,
- 4.02,
- 4.1,
- 4.07,
- 4.1,
- 4.14,
- 4.17,
4.22,
- 4.14,
- 4.12,
- 3.82,
3.88,
- 3.8,
- 3.67,
- 3.77,
- 3.82,
3.83,
- 3.76,
- 3.71,
- 3.68,
3.69,
- 3.75,
- 3.68,
- 3.53,
- 3.51,
3.6,
- 3.51,
- 3.42,
- 3.48,
- 3.57,
3.61,
- 3.51,
- 3.49,
- 3.44,
- 3.48,
3.57,
- 3.69,
- 3.68,
- 3.67,
3.75,
- 3.84,
- 3.88,
- 3.83,
3.88,
- 3.79,
- 3.69,
- 3.71,
- 3.55,
- 3.53,
- 3.61,
- 3.54,
- 3.43,
- 3.49,
3.53,
- 3.37,
- 3.39,
- 3.48,
- 3.52,
- 3.46,
- 3.46,
3.49,
3.52,
3.55,
- 3.52,
- 3.39,
- 3.4,
- 3.53,
3.63,
- 3.67,
- 3.63,
- 3.67,
- 3.74,
3.72,
- 3.77,
- 3.81,
- 3.86,
3.82,
- 3.95,
- 3.93,
- 3.88,
- 3.95,
3.92,
- 3.92,
- 4.01,
- 4.08,
- 3.97,
- 3.98,
- 3.97,
3.98,
- 3.93,
- 3.7,
3.55,
- 3.64,
- 3.51,
- 3.56,
- 3.39,
3.47,
- 3.59,
- 3.48,
- 3.38,
- 3.38,
3.53,
- 3.55,
- 3.57,
- 3.55,
- 3.48,
- 3.43,
- 3.35,
- 3.3,
- 3.3,
- 3.41,
3.43,
3.41,
- 3.45,
- 3.52,
3.6,
- 3.58,
- 3.6,
- 3.54,
- 3.57,
3.52,
- 3.4,
- 3.43,
- 3.53,
- 3.44,
3.59,
- 3.44,
- 3.38,
- 3.37,
- 3.44,
3.52,
- 3.53,
- 3.43,
- 3.39,
- 3.46,
3.5,
- 3.54,
- 3.57,
- 3.65,
- 3.7,
3.72,
- 3.7,
- 3.73
+ 3.8,
+ 3.69,
+ 3.73,
+ 3.77,
+ 3.72,
+ 3.86,
+ 4.01,
+ 3.81,
+ 3.86,
+ 3.97,
+ 4.09,
+ 4.19,
+ 4.34,
+ 4.2,
+ 4.18,
+ 4.29,
+ 4.32,
+ 4.55,
+ 4.69,
+ 4.78,
+ 4.71,
+ 4.86,
+ 4.88,
+ 4.67,
+ 4.63,
+ 4.42,
+ null
],
"yaxis": "y2"
}
],
"layout": {
- "annotations": [
- {
- "font": {
- "color": "gray",
- "size": 17
- },
- "opacity": 0.5,
- "showarrow": false,
- "text": "OpenBB Terminal",
- "x": 1,
- "xanchor": "right",
- "xref": "paper",
- "xshift": 40,
- "y": 0,
- "yanchor": "bottom",
- "yref": "paper",
- "yshift": -80
- }
- ],
- "hoverdistance": 2,
"legend": {
- "x": 0.9,
+ "font": {
+ "size": 10
+ },
+ "x": 1,
"xanchor": "right",
"y": 1,
"yanchor": "top"
},
- "margin": {
- "b": 80,
- "l": 40,
- "r": 50,
- "t": 50
- },
- "modebar": {
- "activecolor": "#d1030d",
- "bgcolor": "#2A2A2A",
- "color": "#FFFFFF",
- "orientation": "v"
- },
- "newshape": {
- "line": {
- "color": "gold"
- }
- },
- "spikedistance": 2,
"template": {
"data": {
"bar": [
@@ -95666,7 +11993,7 @@
},
"hovermode": "x",
"legend": {
- "bgcolor": "rgba(0, 0, 0, 0)",
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
"font": {
"size": 15
},
@@ -95675,6 +12002,30 @@
"y": 0.99,
"yanchor": "top"
},
+ "legend2": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
+ "legend3": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
+ "legend4": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
+ "legend5": {
+ "bgcolor": "rgba(0, 0, 0, 0.5)",
+ "font": {
+ "size": 15
+ }
+ },
"mapbox": {
"style": "dark"
},
@@ -95805,35 +12156,33 @@
"title": {
"text": "Copper/Gold Ratio vs. US 10-Year Constant Maturity",
"x": 0.5,
- "y": 0.98
+ "y": 0.9
},
"xaxis": {
- "anchor": "y",
- "domain": [
- 0,
- 0.94
- ],
"title": {
"text": "Date"
}
},
"yaxis": {
- "anchor": "x",
- "domain": [
- 0,
- 1
- ],
+ "position": 0,
+ "showgrid": false,
"side": "left",
"title": {
- "text": "Copper/Gold Ratio"
+ "font": {
+ "size": 12
+ },
+ "text": "Copper/Gold Ratio (x1000) %"
}
},
"yaxis2": {
- "anchor": "x",
"overlaying": "y",
+ "position": 1,
"side": "right",
"title": {
- "text": "10Y Yield"
+ "font": {
+ "size": 12
+ },
+ "text": "US 10-Year Constant Maturity %"
}
}
}
@@ -95844,22 +12193,31 @@
}
],
"source": [
- "fig.update({'layout': \n",
- " {'xaxis': {'title': 'Date'},\n",
- " 'yaxis': {'title': 'Copper/Gold Ratio'},\n",
- " 'yaxis2': {'title': '10Y Yield'},\n",
- " 'title': 'Copper/Gold Ratio vs. US 10-Year Constant Maturity',\n",
- " 'title_y':0.98,\n",
- " 'title_x':0.5,\n",
- "}})\n",
+ "fig = go.Figure()\n",
+ "\n",
+ "# Add the first scatter trace with its own y-axis\n",
+ "fig.add_scatter(x=data.index, y=data[\"Copper/Gold Ratio\"], name=\"Copper/Gold Ratio (x1000) %\", yaxis=\"y1\")\n",
+ "\n",
+ "# Add the second scatter trace with its own y-axis\n",
+ "fig.add_scatter(x=data.index, y=data[\"US 10-Year Constant Maturity\"], name=\"US 10-Year Constant Maturity %\", yaxis=\"y2\")\n",
+ "\n",
+ "# Update the layout to include the y-axes and their titles\n",
"fig.update_layout(\n",
- " legend=dict(\n",
- " yanchor=\"top\",\n",
- " y=1,\n",
- " xanchor=\"right\",\n",
- " x=0.9\n",
- " )\n",
- ")"
+ " yaxis=dict(title=\"Copper/Gold Ratio (x1000) %\", side=\"left\", position=0, titlefont=dict(size=12), showgrid=False),\n",
+ " yaxis2=dict(title=\"US 10-Year Constant Maturity %\", side=\"right\", overlaying=\"y\", position=1, titlefont=dict(size=12)),\n",
+ " xaxis=dict(title=\"Date\"),\n",
+ " title=\"Copper/Gold Ratio vs. US 10-Year Constant Maturity\",\n",
+ " title_y=0.90,\n",
+ " title_x=0.5,\n",
+ ")\n",
+ "\n",
+ "# Set the legend position\n",
+ "fig.update_layout(\n",
+ " legend=dict(yanchor=\"top\", y=1, xanchor=\"right\", x=1.0, font=dict(size=10))\n",
+ ")\n",
+ "\n",
+ "# Show the plot\n",
+ "fig.show()"
]
},
{
@@ -95867,7 +12225,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "There you have it, folks! The OpenBB SDK provides endless possibilities for creating unique indicators and analysis with the wide variety of data available at your fingertips. We love seeing the creations of users, so be sure to tag us on social media and show off your work."
+ "There you have it, folks! The OpenBB Platform provides endless possibilities for creating unique indicators and analysis with the wide variety of data available at your fingertips. We love seeing the creations of users, so be sure to tag us on social media and show off your work."
]
}
],
@@ -95887,7 +12245,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.8"
+ "version": "3.11.4"
},
"orig_nbformat": 4
},
diff --git a/examples/financialStatements.ipynb b/examples/financialStatements.ipynb
new file mode 100644
index 000000000000..9869ea122bfa
--- /dev/null
+++ b/examples/financialStatements.ipynb
@@ -0,0 +1,1059 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Financial Statements in the OpenBB Platform\n",
+ "\n",
+ "OpenBB Platform data extensions provide access to financial statements as quarterly or annual. There are also endpoints for ratios and other common non-GAAP metrics. Most data providers require a subscription to access all data. Refer to the website of a specific provider for details on entitlements and coverage.\n",
+ "\n",
+ "Financial statement functions are grouped under the `obb.equity.fundamental` module.\n",
+ "\n",
+ "## Endpoints\n",
+ "\n",
+ "The typical financial statements consist of three endpoints:\n",
+ "\n",
+ "- Balance Sheet: `obb.equity.fundamental.balance()`\n",
+ "- Income Statement: `obb.equity.fundamental.income()`\n",
+ "- Cash Flow Statement: `obb.equity.fundamental.cash()`\n",
+ "\n",
+ "The main parameters are:\n",
+ "\n",
+ "- `symbol`: The company's symbol.\n",
+ "- `period`: 'annual' or 'quarter'. Default is 'annual'.\n",
+ "- `limit`: Limit the number of results returned, from the latest. Default is 5. For perspective, 150 will go back to 1985. The amount of historical records varies by provider.\n",
+ "\n",
+ "### Field Names\n",
+ "\n",
+ "Some considerations to keep in mind when working with financial statements data are:\n",
+ "\n",
+ "- Every data provider has their own way of parsing and organizing the three financial statements.\n",
+ "- Items within each statement will vary by source and by the type of company reporting.\n",
+ "- Names of line items will vary by source.\n",
+ "- \"Date\" values may differ because they are from the period starting/ending or date of reporting.\n",
+ "\n",
+ "This example highlights how different providers will have different labels for compnay facts."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " yfinance | \n",
+ " fmp | \n",
+ " intrinio | \n",
+ " polygon | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 42779000000 | \n",
+ " 42779000000 | \n",
+ " 42779000000 | \n",
+ " 42779000000 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 51248000000 | \n",
+ " 51248000000 | \n",
+ " 51248000000 | \n",
+ " 51248000000 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 53811000000 | \n",
+ " 53811000000 | \n",
+ " 53811000000 | \n",
+ " 53811000000 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 53335000000 | \n",
+ " 53335000000 | \n",
+ " 53335000000 | \n",
+ " 53335000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " yfinance fmp intrinio polygon\n",
+ "0 42779000000 42779000000 42779000000 42779000000\n",
+ "1 51248000000 51248000000 51248000000 51248000000\n",
+ "2 53811000000 53811000000 53811000000 53811000000\n",
+ "3 53335000000 53335000000 53335000000 53335000000"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from openbb import obb\n",
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.DataFrame()\n",
+ "\n",
+ "df[\"yfinance\"] = (\n",
+ " obb.equity.fundamental.balance(\"TGT\", provider=\"yfinance\", limit=4)\n",
+ " .to_df()[\"TotalAssets\"].reset_index(drop=True)\n",
+ ")\n",
+ "\n",
+ "df[\"fmp\"] = (\n",
+ " obb.equity.fundamental.balance(\"TGT\", provider=\"fmp\", limit=4)\n",
+ " .to_df()[\"assets\"].convert_dtypes().reset_index(drop=True)\n",
+ ")\n",
+ "\n",
+ "df[\"intrinio\"] = (\n",
+ " obb.equity.fundamental.balance(\"TGT\", provider=\"intrinio\", limit=4)\n",
+ " .to_df()[\"total assets\"].convert_dtypes().reset_index(drop=True)\n",
+ ")\n",
+ "\n",
+ "df[\"polygon\"] = (\n",
+ " obb.equity.fundamental.balance(\"TGT\", provider=\"polygon\", limit=4)\n",
+ " .to_df()[\"total_assets\"].convert_dtypes().reset_index(drop=True)\n",
+ ")\n",
+ "\n",
+ "df\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Weighted Average Shares Outstanding\n",
+ "\n",
+ "This key metric will be found under the income statement. It might also be called, 'basic', and the numbers do not include authorized but unissued shares. A declining count over time is a sign that the company is returning capital to shareholders in the form of buy backs. Under ideal circumstances, it is more capital-efficient, for both company and shareholders, because distributions are double-taxed. The company pays income tax on paid dividends, and the beneficiary pays income tax again on receipt.\n",
+ "\n",
+ "A company will disclose how many shares are outstanding at the end of the period as a weighted average over the reporting period - three months.\n",
+ "\n",
+ "Let's take a look at Target. To make the numbers easier to read, we'll divide the entire column by one million."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "date\n",
+ "1986-07-31 1168.816\n",
+ "Name: weighted_average_shares_outstanding, dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "date\n",
+ "2023-10-31 461.6\n",
+ "Name: weighted_average_shares_outstanding, dtype: float64"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data = (\n",
+ " obb.equity.fundamental.income(\"TGT\", provider=\"fmp\", limit=150, period=\"quarter\")\n",
+ " .to_df()\n",
+ ")\n",
+ "\n",
+ "shares = data[\"weighted_average_shares_outstanding\"]/1000000\n",
+ "\n",
+ "display(shares.head(1))\n",
+ "\n",
+ "display(shares.tail(1))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Thirty-seven years later, the share count is approaching a two-thirds reduction. 12.2% over the past five years. In four reporting periods, 1.3 million shares have been taken out of the float."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-0.12226659060657907"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "-1.3000000000000114"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(shares.pct_change(20).iloc[-1])\n",
+ "\n",
+ "display(shares.iloc[-4] - shares.iloc[-1])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With an average closing price of $143.37, that represents approximately $190M in buy backs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "187.55"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "price = (\n",
+ " obb.equity.price.historical(\"TGT\", start_date=\"2022-10-01\", provider=\"fmp\")\n",
+ " .to_df()\n",
+ ")\n",
+ "\n",
+ "round((price[\"close\"].mean()*1300000)/1000000, 2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Dividends Paid\n",
+ "\n",
+ "Dividends paid is in the cash flow statement. We can calculate the amount-per-share with the reported data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "date\n",
+ "2023-01-28 -1.079731\n",
+ "2023-04-29 -1.078325\n",
+ "2023-07-29 -1.081023\n",
+ "2023-10-31 -1.098354\n",
+ "Name: div_per_share, dtype: float64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dividends = (\n",
+ " obb.equity.fundamental.cash(\"TGT\", provider=\"fmp\", limit=150, period=\"quarter\")\n",
+ " .to_df()[[\"dividends_paid\"]]\n",
+ ")\n",
+ "\n",
+ "dividends[\"shares\"] = data[\"weighted_average_shares_outstanding\"]\n",
+ "dividends[\"div_per_share\"] = dividends[\"dividends_paid\"]/dividends[\"shares\"]\n",
+ "\n",
+ "dividends[\"div_per_share\"].tail(4)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This can be compared against the real amounts paid to common share holders, as announced. Note that the dates above represent the report date, and that dividends paid are attributed to the quarter they were paid in. The value from \"2023-01-28\" equates to the fourth quarter of 2022."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "date\n",
+ "2022-11-15 1.08\n",
+ "2023-02-14 1.08\n",
+ "2023-05-16 1.08\n",
+ "2023-08-15 1.10\n",
+ "Name: dividend, dtype: float64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(\n",
+ " obb.equity.fundamental.dividends(\"TGT\", provider=\"fmp\")\n",
+ " .to_df()[\"dividend\"]\n",
+ " .loc[\"2022-11-15\":\"2023-08-15\"]\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The numbers check out, and the $2B paid to investors over four quarters is more than ten times the $190M returned through share buy backs.\n",
+ "\n",
+ "### Financial Attributes\n",
+ "\n",
+ "The `openbb-intrinio` data extension has an endpoint for extracting a single fact from financial statements. There is a helper function for looking up the correct `tag`.\n",
+ "\n",
+ "#### Search Financial Attributes\n",
+ "\n",
+ "Search attributes by keyword."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " id | \n",
+ " name | \n",
+ " tag | \n",
+ " statement_code | \n",
+ " statement_type | \n",
+ " parent_name | \n",
+ " sequence | \n",
+ " factor | \n",
+ " transaction | \n",
+ " type | \n",
+ " unit | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " tag_BgkbWy | \n",
+ " Market Capitalization | \n",
+ " marketcap | \n",
+ " calculations | \n",
+ " industrial | \n",
+ " None | \n",
+ " NaN | \n",
+ " None | \n",
+ " None | \n",
+ " valuation | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " tag_kylOqz | \n",
+ " Market Capitalization | \n",
+ " marketcap | \n",
+ " calculations | \n",
+ " financial | \n",
+ " None | \n",
+ " NaN | \n",
+ " None | \n",
+ " None | \n",
+ " valuation | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " tag_XLRlqy | \n",
+ " Market Sector | \n",
+ " market_sector | \n",
+ " current | \n",
+ " None | \n",
+ " None | \n",
+ " NaN | \n",
+ " None | \n",
+ " None | \n",
+ " security | \n",
+ " string | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " tag_2gBA8y | \n",
+ " Market Category | \n",
+ " market_category | \n",
+ " current | \n",
+ " None | \n",
+ " None | \n",
+ " NaN | \n",
+ " None | \n",
+ " None | \n",
+ " security | \n",
+ " string | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " tag_DzonXe | \n",
+ " Marketing Expense | \n",
+ " marketingexpense | \n",
+ " income_statement | \n",
+ " industrial | \n",
+ " totaloperatingexpenses | \n",
+ " 9.0 | \n",
+ " + | \n",
+ " debit | \n",
+ " income_statement_metric | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 95 | \n",
+ " tag_nzJAmX | \n",
+ " Total Long-Term Debt | \n",
+ " ltdebtandcapleases | \n",
+ " calculations | \n",
+ " financial | \n",
+ " None | \n",
+ " NaN | \n",
+ " None | \n",
+ " None | \n",
+ " metric | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " 96 | \n",
+ " tag_9XaL5g | \n",
+ " Other Net Changes in Cash | \n",
+ " othernetchangesincash | \n",
+ " cash_flow_statement | \n",
+ " industrial | \n",
+ " netchangeincash | \n",
+ " 33.0 | \n",
+ " + | \n",
+ " debit | \n",
+ " cash_flow_statement_metric | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " 97 | \n",
+ " tag_5X7p6z | \n",
+ " Other Net Changes in Cash | \n",
+ " othernetchangesincash | \n",
+ " cash_flow_statement | \n",
+ " financial | \n",
+ " netchangeincash | \n",
+ " 37.0 | \n",
+ " + | \n",
+ " debit | \n",
+ " cash_flow_statement_metric | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " 98 | \n",
+ " tag_qzEwng | \n",
+ " Changes in Operating Assets and Liabilities, net | \n",
+ " increasedecreaseinoperatingcapital | \n",
+ " cash_flow_statement | \n",
+ " financial | \n",
+ " netcashfromcontinuingoperatingactivities | \n",
+ " 8.0 | \n",
+ " + | \n",
+ " debit | \n",
+ " cash_flow_statement_metric | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ " 99 | \n",
+ " tag_pgVB2g | \n",
+ " Changes in Operating Assets and Liabilities, net | \n",
+ " increasedecreaseinoperatingcapital | \n",
+ " cash_flow_statement | \n",
+ " industrial | \n",
+ " netcashfromcontinuingoperatingactivities | \n",
+ " 7.0 | \n",
+ " + | \n",
+ " debit | \n",
+ " cash_flow_statement_metric | \n",
+ " usd | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
100 rows × 11 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id name \\\n",
+ "0 tag_BgkbWy Market Capitalization \n",
+ "1 tag_kylOqz Market Capitalization \n",
+ "2 tag_XLRlqy Market Sector \n",
+ "3 tag_2gBA8y Market Category \n",
+ "4 tag_DzonXe Marketing Expense \n",
+ ".. ... ... \n",
+ "95 tag_nzJAmX Total Long-Term Debt \n",
+ "96 tag_9XaL5g Other Net Changes in Cash \n",
+ "97 tag_5X7p6z Other Net Changes in Cash \n",
+ "98 tag_qzEwng Changes in Operating Assets and Liabilities, net \n",
+ "99 tag_pgVB2g Changes in Operating Assets and Liabilities, net \n",
+ "\n",
+ " tag statement_code statement_type \\\n",
+ "0 marketcap calculations industrial \n",
+ "1 marketcap calculations financial \n",
+ "2 market_sector current None \n",
+ "3 market_category current None \n",
+ "4 marketingexpense income_statement industrial \n",
+ ".. ... ... ... \n",
+ "95 ltdebtandcapleases calculations financial \n",
+ "96 othernetchangesincash cash_flow_statement industrial \n",
+ "97 othernetchangesincash cash_flow_statement financial \n",
+ "98 increasedecreaseinoperatingcapital cash_flow_statement financial \n",
+ "99 increasedecreaseinoperatingcapital cash_flow_statement industrial \n",
+ "\n",
+ " parent_name sequence factor transaction \\\n",
+ "0 None NaN None None \n",
+ "1 None NaN None None \n",
+ "2 None NaN None None \n",
+ "3 None NaN None None \n",
+ "4 totaloperatingexpenses 9.0 + debit \n",
+ ".. ... ... ... ... \n",
+ "95 None NaN None None \n",
+ "96 netchangeincash 33.0 + debit \n",
+ "97 netchangeincash 37.0 + debit \n",
+ "98 netcashfromcontinuingoperatingactivities 8.0 + debit \n",
+ "99 netcashfromcontinuingoperatingactivities 7.0 + debit \n",
+ "\n",
+ " type unit \n",
+ "0 valuation usd \n",
+ "1 valuation usd \n",
+ "2 security string \n",
+ "3 security string \n",
+ "4 income_statement_metric usd \n",
+ ".. ... ... \n",
+ "95 metric usd \n",
+ "96 cash_flow_statement_metric usd \n",
+ "97 cash_flow_statement_metric usd \n",
+ "98 cash_flow_statement_metric usd \n",
+ "99 cash_flow_statement_metric usd \n",
+ "\n",
+ "[100 rows x 11 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(\n",
+ " obb.equity.fundamental.search_attributes(\"marketcap\")\n",
+ " .to_df()\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `tag` is what we need, in this case it is what we searched for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " value | \n",
+ "
\n",
+ " \n",
+ " date | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2022-12-31 | \n",
+ " 6.692963e+10 | \n",
+ "
\n",
+ " \n",
+ " 2023-03-31 | \n",
+ " 7.502370e+10 | \n",
+ "
\n",
+ " \n",
+ " 2023-06-30 | \n",
+ " 5.991695e+10 | \n",
+ "
\n",
+ " \n",
+ " 2023-09-30 | \n",
+ " 5.061437e+10 | \n",
+ "
\n",
+ " \n",
+ " 2023-11-22 | \n",
+ " 6.049500e+10 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " value\n",
+ "date \n",
+ "2022-12-31 6.692963e+10\n",
+ "2023-03-31 7.502370e+10\n",
+ "2023-06-30 5.991695e+10\n",
+ "2023-09-30 5.061437e+10\n",
+ "2023-11-22 6.049500e+10"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "marketcap = (\n",
+ " obb.equity.fundamental.historical_attributes(symbol=\"TGT\", tag = \"marketcap\", period=\"quarter\")\n",
+ " .to_df()\n",
+ ")\n",
+ "\n",
+ "marketcap.tail(5)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Doing some quick math, and ignoring the most recent value, we can see that the market cap of Target was down nearly a quarter over the last four reporting periods."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-0.24376733082703084"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(\n",
+ " (marketcap.loc[\"2023-09-30\"] - marketcap.loc[\"2022-12-31\"])/marketcap.loc[\"2022-12-31\"]\n",
+ ").value\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ratios and Other Metrics\n",
+ "\n",
+ "Other valuation functions are derivatives of the financial statements, but the data provider does the math. Values are typically ratios between line items, on a per-share basis, or as a percent growth.\n",
+ "\n",
+ "This data set is where you can find EPS, FCF, P/B, EBIT, quick ratio, etc.\n",
+ "\n",
+ "### Quick Ratio\n",
+ "\n",
+ "Target's quick ratio could be one reason why its share price is losing traction against the market. Its ability to pay current obligations is not optimistically reflected in a 0.27 score, approximately 50% below the historical median."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Current Quick Ratio: 0.2701'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Median Quick Ratio: 0.5844'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ratios = (\n",
+ " obb.equity.fundamental.ratios(\"TGT\", limit=50, provider=\"fmp\")\n",
+ " .to_df()\n",
+ ")\n",
+ "\n",
+ "display(f\"Current Quick Ratio: {round(ratios['quick_ratio'].iloc[-1], 4)}\")\n",
+ "display(f\"Median Quick Ratio: {round(ratios['quick_ratio'].median(), 4)}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Free Cash Flow Yield\n",
+ "\n",
+ "The `metrics` endpoint, with the `openbb-fmp` data extension, has a field for free cash flow yield. It is calculated by taking the free cash flow per share divided by the current share price. We could arrive at this answer by writing some code, but these types of endpoints do the work so we don't have to. This is part of the value-add that API data distributors provide, they allow you to get straight to work with data.\n",
+ "\n",
+ "We'll use this endpoint to extract the data, and compare with some of Target's competition over the last ten years."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " calendar_year | \n",
+ " 2023 | \n",
+ " 2022 | \n",
+ " 2021 | \n",
+ " 2020 | \n",
+ " 2019 | \n",
+ " 2018 | \n",
+ " 2017 | \n",
+ " 2016 | \n",
+ " 2015 | \n",
+ " 2014 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Costco Wholesale Corporation | \n",
+ " 0.027922 | \n",
+ " 0.014860 | \n",
+ " 0.026582 | \n",
+ " 0.039351 | \n",
+ " 0.025906 | \n",
+ " 0.027438 | \n",
+ " 0.060884 | \n",
+ " 0.008941 | \n",
+ " 0.030741 | \n",
+ " 0.037483 | \n",
+ "
\n",
+ " \n",
+ " BJ's Wholesale Club Holdings, Inc. | \n",
+ " NaN | \n",
+ " 0.044709 | \n",
+ " 0.067213 | \n",
+ " 0.113551 | \n",
+ " 0.056630 | \n",
+ " 0.091107 | \n",
+ " 0.026186 | \n",
+ " 0.065871 | \n",
+ " 0.016947 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " Dollar Tree, Inc. | \n",
+ " NaN | \n",
+ " 0.010756 | \n",
+ " 0.013957 | \n",
+ " 0.075627 | \n",
+ " 0.040338 | \n",
+ " 0.041252 | \n",
+ " 0.034069 | \n",
+ " 0.063465 | \n",
+ " 0.016602 | \n",
+ " 0.041047 | \n",
+ "
\n",
+ " \n",
+ " Dollar General Corporation | \n",
+ " NaN | \n",
+ " 0.008256 | \n",
+ " 0.037507 | \n",
+ " 0.058973 | \n",
+ " 0.036922 | \n",
+ " 0.046197 | \n",
+ " 0.042609 | \n",
+ " 0.050776 | \n",
+ " 0.039524 | \n",
+ " 0.046052 | \n",
+ "
\n",
+ " \n",
+ " Walmart Inc. | \n",
+ " 0.031243 | \n",
+ " 0.028372 | \n",
+ " 0.065462 | \n",
+ " 0.044591 | \n",
+ " 0.062023 | \n",
+ " 0.057275 | \n",
+ " 0.101038 | \n",
+ " 0.073506 | \n",
+ " 0.059712 | \n",
+ " 0.041544 | \n",
+ "
\n",
+ " \n",
+ " Big Lots, Inc. | \n",
+ " NaN | \n",
+ " -0.550469 | \n",
+ " 0.025262 | \n",
+ " 0.115757 | \n",
+ " 0.069464 | \n",
+ " -0.111853 | \n",
+ " 0.037219 | \n",
+ " 0.100721 | \n",
+ " 0.110443 | \n",
+ " 0.089253 | \n",
+ "
\n",
+ " \n",
+ " Macy's, Inc. | \n",
+ " NaN | \n",
+ " 0.050473 | \n",
+ " 0.270980 | \n",
+ " 0.039111 | \n",
+ " 0.091301 | \n",
+ " 0.101426 | \n",
+ " 0.155761 | \n",
+ " 0.098993 | \n",
+ " 0.065634 | \n",
+ " 0.072322 | \n",
+ "
\n",
+ " \n",
+ " Kohl's Corporation | \n",
+ " NaN | \n",
+ " -0.143961 | \n",
+ " 0.189677 | \n",
+ " 0.147968 | \n",
+ " 0.119492 | \n",
+ " 0.139799 | \n",
+ " 0.096137 | \n",
+ " 0.198790 | \n",
+ " 0.081652 | \n",
+ " 0.110697 | \n",
+ "
\n",
+ " \n",
+ " The TJX Companies, Inc. | \n",
+ " 0.027159 | \n",
+ " 0.023497 | \n",
+ " 0.051769 | \n",
+ " 0.040167 | \n",
+ " 0.048827 | \n",
+ " 0.039935 | \n",
+ " 0.053697 | \n",
+ " 0.043328 | \n",
+ " 0.046442 | \n",
+ " 0.040643 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "calendar_year 2023 2022 2021 2020 \\\n",
+ "Costco Wholesale Corporation 0.027922 0.014860 0.026582 0.039351 \n",
+ "BJ's Wholesale Club Holdings, Inc. NaN 0.044709 0.067213 0.113551 \n",
+ "Dollar Tree, Inc. NaN 0.010756 0.013957 0.075627 \n",
+ "Dollar General Corporation NaN 0.008256 0.037507 0.058973 \n",
+ "Walmart Inc. 0.031243 0.028372 0.065462 0.044591 \n",
+ "Big Lots, Inc. NaN -0.550469 0.025262 0.115757 \n",
+ "Macy's, Inc. NaN 0.050473 0.270980 0.039111 \n",
+ "Kohl's Corporation NaN -0.143961 0.189677 0.147968 \n",
+ "The TJX Companies, Inc. 0.027159 0.023497 0.051769 0.040167 \n",
+ "\n",
+ "calendar_year 2019 2018 2017 2016 \\\n",
+ "Costco Wholesale Corporation 0.025906 0.027438 0.060884 0.008941 \n",
+ "BJ's Wholesale Club Holdings, Inc. 0.056630 0.091107 0.026186 0.065871 \n",
+ "Dollar Tree, Inc. 0.040338 0.041252 0.034069 0.063465 \n",
+ "Dollar General Corporation 0.036922 0.046197 0.042609 0.050776 \n",
+ "Walmart Inc. 0.062023 0.057275 0.101038 0.073506 \n",
+ "Big Lots, Inc. 0.069464 -0.111853 0.037219 0.100721 \n",
+ "Macy's, Inc. 0.091301 0.101426 0.155761 0.098993 \n",
+ "Kohl's Corporation 0.119492 0.139799 0.096137 0.198790 \n",
+ "The TJX Companies, Inc. 0.048827 0.039935 0.053697 0.043328 \n",
+ "\n",
+ "calendar_year 2015 2014 \n",
+ "Costco Wholesale Corporation 0.030741 0.037483 \n",
+ "BJ's Wholesale Club Holdings, Inc. 0.016947 NaN \n",
+ "Dollar Tree, Inc. 0.016602 0.041047 \n",
+ "Dollar General Corporation 0.039524 0.046052 \n",
+ "Walmart Inc. 0.059712 0.041544 \n",
+ "Big Lots, Inc. 0.110443 0.089253 \n",
+ "Macy's, Inc. 0.065634 0.072322 \n",
+ "Kohl's Corporation 0.081652 0.110697 \n",
+ "The TJX Companies, Inc. 0.046442 0.040643 "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# List of other retail chains\n",
+ "tickers = [\"COST\", \"BJ\", \"DLTR\", \"DG\", \"WMT\", \"BIG\", \"M\", \"KSS\", \"TJX\"]\n",
+ "\n",
+ "# Create a dictionary of tickers and company names.\n",
+ "names = {\n",
+ " ticker: obb.equity.fundamental.overview(ticker, provider=\"fmp\").results.company_name\n",
+ " for ticker in tickers\n",
+ "}\n",
+ "# Create a column for each.\n",
+ "fcf_yield = pd.DataFrame()\n",
+ "for ticker in tickers:\n",
+ " fcf_yield[names[ticker]] = (\n",
+ " obb.equity.fundamental.metrics(ticker, provider=\"fmp\", period=\"annual\", limit=10)\n",
+ " .to_df()\n",
+ " .reset_index()\n",
+ " .set_index(\"calendar_year\")\n",
+ " .sort_index(ascending=False)\n",
+ " [\"free_cash_flow_yield\"]\n",
+ " )\n",
+ "fcf_yield.transpose()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are more usage examples on our [website](https://docs.openbb.co/platform/usage)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "obb-sdk4",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/findSymbols.ipynb b/examples/findSymbols.ipynb
new file mode 100644
index 000000000000..1a4eeb196fd3
--- /dev/null
+++ b/examples/findSymbols.ipynb
@@ -0,0 +1,1409 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Finding Symbols\n",
+ "\n",
+ "\n",
+ "Finding the ticker symbol, security identifier, the sector, and other metadata is easy if you know where to look. This guide is intended to introduce some methods for searching, screening, and discovery.\n",
+ "\n",
+ "For maximum coverage and functionality, install OpenBB with `[all]` packages.\n",
+ "\n",
+ "The examples here will assume that the OpenBB Platform has been installed, the environment is active, and it has been imported into a Python session. If the installation is fresh, or an extension was just installed, the Python interface will need to be rebuilt. It will only take a few moments to complete."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from openbb import obb\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The simplest way to find tickers for a company is with a simple fuzzy query.\n",
+ "\n",
+ "## Search the SEC\n",
+ "\n",
+ "Perform a quick search using the `openbb-sec` provider.\n",
+ "\n",
+ "### Find a Company\n",
+ "\n",
+ "Use an empty string, `\"\"`, to return the complete list - over 10,000."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10840"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "all_companies = obb.equity.search(\"\", provider=\"sec\")\n",
+ "\n",
+ "len(all_companies.results)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The SEC sorts this list by market cap. Applying the `to_df()` method to `all_companies` will show Apple on top"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symbol | \n",
+ " name | \n",
+ " cik | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " AAPL | \n",
+ " Apple Inc. | \n",
+ " 320193 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " MSFT | \n",
+ " MICROSOFT CORP | \n",
+ " 789019 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " GOOGL | \n",
+ " Alphabet Inc. | \n",
+ " 1652044 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " AMZN | \n",
+ " AMAZON COM INC | \n",
+ " 1018724 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " NVDA | \n",
+ " NVIDIA CORP | \n",
+ " 1045810 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " META | \n",
+ " Meta Platforms, Inc. | \n",
+ " 1326801 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " BRK-B | \n",
+ " BERKSHIRE HATHAWAY INC | \n",
+ " 1067983 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " TSLA | \n",
+ " Tesla, Inc. | \n",
+ " 1318605 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " LLY | \n",
+ " ELI LILLY & Co | \n",
+ " 59478 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " V | \n",
+ " VISA INC. | \n",
+ " 1403161 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " symbol name cik\n",
+ "0 AAPL Apple Inc. 320193\n",
+ "1 MSFT MICROSOFT CORP 789019\n",
+ "2 GOOGL Alphabet Inc. 1652044\n",
+ "3 AMZN AMAZON COM INC 1018724\n",
+ "4 NVDA NVIDIA CORP 1045810\n",
+ "5 META Meta Platforms, Inc. 1326801\n",
+ "6 BRK-B BERKSHIRE HATHAWAY INC 1067983\n",
+ "7 TSLA Tesla, Inc. 1318605\n",
+ "8 LLY ELI LILLY & Co 59478\n",
+ "9 V VISA INC. 1403161"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "all_companies.to_df().head(10)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Find an Institution\n",
+ "\n",
+ "Some reporting companies, like invesment trusts and insurance companies, do not have a ticker symbol directly associated with them. Filers in the US will have a CIK number, used to retrieve documents from the SEC."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " name | \n",
+ " cik | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " BERKSHIRE HATHAWAY ENERGY CO | \n",
+ " 0001081316 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " BERKSHIRE HATHAWAY FINANCE CORP | \n",
+ " 0001274791 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " BERKSHIRE HATHAWAY HOMESTATE INSURANCE CO. | \n",
+ " 0000829771 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " BERKSHIRE HATHAWAY INC /DE/ | \n",
+ " 0000109694 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " BERKSHIRE HATHAWAY INC/DE | \n",
+ " 0000109694 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " BERKSHIRE HATHAWAY INC | \n",
+ " 0001067983 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " BERKSHIRE HATHAWAY LIFE INSURANCE CO OF NEBRASKA | \n",
+ " 0001015867 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " LMZ & BERKSHIRE HATHAWAY CO | \n",
+ " 0001652795 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " name cik\n",
+ "0 BERKSHIRE HATHAWAY ENERGY CO 0001081316\n",
+ "1 BERKSHIRE HATHAWAY FINANCE CORP 0001274791\n",
+ "2 BERKSHIRE HATHAWAY HOMESTATE INSURANCE CO. 0000829771\n",
+ "3 BERKSHIRE HATHAWAY INC /DE/ 0000109694\n",
+ "4 BERKSHIRE HATHAWAY INC/DE 0000109694\n",
+ "5 BERKSHIRE HATHAWAY INC 0001067983\n",
+ "6 BERKSHIRE HATHAWAY LIFE INSURANCE CO OF NEBRASKA 0001015867\n",
+ "7 LMZ & BERKSHIRE HATHAWAY CO 0001652795"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "instututions = obb.regulators.sec.institutions_search(\"Berkshire Hathaway\").to_df()\n",
+ "instututions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Find a Filing\n",
+ "\n",
+ "Search for filings by CIK or ticker symbol."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "type 13F-NT\n",
+ "link https://www.sec.gov/Archives/edgar/data/000082...\n",
+ "report_date 2023-09-30\n",
+ "accepted_date 2023-11-14 16:15:06+00:00\n",
+ "act 34\n",
+ "items \n",
+ "primary_doc_description \n",
+ "primary_doc xslForm13F_X02/primary_doc.xml\n",
+ "accession_number 0000950123-23-010929\n",
+ "file_number 028-02226\n",
+ "film_number 231406391\n",
+ "is_inline_xbrl 0\n",
+ "is_xbrl 0\n",
+ "size 2960\n",
+ "complete_submission_url https://www.sec.gov/Archives/edgar/data/000082...\n",
+ "filing_detail_url https://www.sec.gov/Archives/edgar/data/000082...\n",
+ "Name: 2023-11-14 00:00:00, dtype: object"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "homestate_filings = obb.equity.fundamental.filings(cik=\"0000829771\", provider=\"sec\")\n",
+ "\n",
+ "homestate_filings.to_df().iloc[-1]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or, search by form type."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "type 4\n",
+ "link https://www.sec.gov/Archives/edgar/data/000032...\n",
+ "report_date 2023-11-10\n",
+ "accepted_date 2023-11-14 18:31:09+00:00\n",
+ "act \n",
+ "items \n",
+ "primary_doc_description FORM 4\n",
+ "primary_doc xslF345X05/wk-form4_1700004649.xml\n",
+ "accession_number 0000320193-23-000109\n",
+ "file_number \n",
+ "film_number \n",
+ "is_inline_xbrl 0\n",
+ "is_xbrl 0\n",
+ "size 5066\n",
+ "complete_submission_url https://www.sec.gov/Archives/edgar/data/000032...\n",
+ "filing_detail_url https://www.sec.gov/Archives/edgar/data/000032...\n",
+ "Name: 2023-11-14 00:00:00, dtype: object"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "aapl_filings = obb.equity.fundamental.filings(\"AAPL\", type=\"4\", provider=\"sec\")\n",
+ "\n",
+ "aapl_filings.to_df().iloc[-1]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Screen Markets\n",
+ "\n",
+ "Screeners provide a targeted search, a tool for comparison and discovery. Find stocks from around the world with the screener endpoint, and the `openbb-fmp` provider.\n",
+ "\n",
+ "### Find Stocks From India"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1822"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symbol | \n",
+ " name | \n",
+ " market_cap | \n",
+ " sector | \n",
+ " industry | \n",
+ " beta | \n",
+ " price | \n",
+ " last_annual_dividend | \n",
+ " volume | \n",
+ " exchange | \n",
+ " exchange_name | \n",
+ " country | \n",
+ " is_etf | \n",
+ " actively_trading | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " RELIANCE.NS | \n",
+ " Reliance Industries Limited | \n",
+ " 15895961017165 | \n",
+ " Energy | \n",
+ " Oil & Gas Refining & Marketing | \n",
+ " 0.721409 | \n",
+ " 2349.35 | \n",
+ " 9 | \n",
+ " 2244671 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " TCS.NS | \n",
+ " Tata Consultancy Services Limited | \n",
+ " 12878392323686 | \n",
+ " Technology | \n",
+ " Information Technology Services | \n",
+ " 0.528 | \n",
+ " 3519.6 | \n",
+ " 36 | \n",
+ " 1473232 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " HDFCBANK.NS | \n",
+ " HDFC Bank Limited | \n",
+ " 11345550050918 | \n",
+ " Financial Services | \n",
+ " Banks—Regional | \n",
+ " 0.714285 | \n",
+ " 1505.2 | \n",
+ " 19 | \n",
+ " 9665132 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " ICICIBANK.NS | \n",
+ " ICICI Bank Limited | \n",
+ " 6456176076595 | \n",
+ " Financial Services | \n",
+ " Banks—Regional | \n",
+ " 0.808 | \n",
+ " 921.45 | \n",
+ " 8 | \n",
+ " 10561389 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " INFY.NS | \n",
+ " Infosys Limited | \n",
+ " 5945857635942 | \n",
+ " Technology | \n",
+ " Information Technology Services | \n",
+ " 0.596 | \n",
+ " 1436.6 | \n",
+ " 36 | \n",
+ " 3584088 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " symbol name market_cap \\\n",
+ "0 RELIANCE.NS Reliance Industries Limited 15895961017165 \n",
+ "1 TCS.NS Tata Consultancy Services Limited 12878392323686 \n",
+ "2 HDFCBANK.NS HDFC Bank Limited 11345550050918 \n",
+ "3 ICICIBANK.NS ICICI Bank Limited 6456176076595 \n",
+ "4 INFY.NS Infosys Limited 5945857635942 \n",
+ "\n",
+ " sector industry beta price \\\n",
+ "0 Energy Oil & Gas Refining & Marketing 0.721409 2349.35 \n",
+ "1 Technology Information Technology Services 0.528 3519.6 \n",
+ "2 Financial Services Banks—Regional 0.714285 1505.2 \n",
+ "3 Financial Services Banks—Regional 0.808 921.45 \n",
+ "4 Technology Information Technology Services 0.596 1436.6 \n",
+ "\n",
+ " last_annual_dividend volume exchange exchange_name \\\n",
+ "0 9 2244671 NSE National Stock Exchange of India \n",
+ "1 36 1473232 NSE National Stock Exchange of India \n",
+ "2 19 9665132 NSE National Stock Exchange of India \n",
+ "3 8 10561389 NSE National Stock Exchange of India \n",
+ "4 36 3584088 NSE National Stock Exchange of India \n",
+ "\n",
+ " country is_etf actively_trading \n",
+ "0 IN False True \n",
+ "1 IN False True \n",
+ "2 IN False True \n",
+ "3 IN False True \n",
+ "4 IN False True "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results = obb.equity.screener(country=\"IN\", provider=\"fmp\").to_df()\n",
+ "display(len(results))\n",
+ "results.head(5).convert_dtypes()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Search by Sector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "190"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symbol | \n",
+ " name | \n",
+ " market_cap | \n",
+ " sector | \n",
+ " industry | \n",
+ " beta | \n",
+ " price | \n",
+ " last_annual_dividend | \n",
+ " volume | \n",
+ " exchange | \n",
+ " exchange_name | \n",
+ " country | \n",
+ " is_etf | \n",
+ " actively_trading | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " HDFCBANK.NS | \n",
+ " HDFC Bank Limited | \n",
+ " 11345550050918 | \n",
+ " Financial Services | \n",
+ " Banks—Regional | \n",
+ " 0.714285 | \n",
+ " 1505.2 | \n",
+ " 19.0 | \n",
+ " 9665132 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " ICICIBANK.NS | \n",
+ " ICICI Bank Limited | \n",
+ " 6456176076595 | \n",
+ " Financial Services | \n",
+ " Banks—Regional | \n",
+ " 0.808 | \n",
+ " 921.45 | \n",
+ " 8.0 | \n",
+ " 10561389 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " SBIN.NS | \n",
+ " State Bank of India | \n",
+ " 5031248625920 | \n",
+ " Financial Services | \n",
+ " Banks—Regional | \n",
+ " 0.74 | \n",
+ " 563.75 | \n",
+ " 11.3 | \n",
+ " 12710950 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " BAJFINANCE.NS | \n",
+ " Bajaj Finance Limited | \n",
+ " 4282998873754 | \n",
+ " Financial Services | \n",
+ " Credit Services | \n",
+ " 0.947 | \n",
+ " 7068.6 | \n",
+ " 30.0 | \n",
+ " 1901753 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " LICI.NS | \n",
+ " Life Insurance Corporation of India | \n",
+ " 3870900117504 | \n",
+ " Financial Services | \n",
+ " Insurance—Life | \n",
+ " <NA> | \n",
+ " 612.0 | \n",
+ " 3.0 | \n",
+ " 500820 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " symbol name market_cap \\\n",
+ "0 HDFCBANK.NS HDFC Bank Limited 11345550050918 \n",
+ "1 ICICIBANK.NS ICICI Bank Limited 6456176076595 \n",
+ "2 SBIN.NS State Bank of India 5031248625920 \n",
+ "3 BAJFINANCE.NS Bajaj Finance Limited 4282998873754 \n",
+ "4 LICI.NS Life Insurance Corporation of India 3870900117504 \n",
+ "\n",
+ " sector industry beta price \\\n",
+ "0 Financial Services Banks—Regional 0.714285 1505.2 \n",
+ "1 Financial Services Banks—Regional 0.808 921.45 \n",
+ "2 Financial Services Banks—Regional 0.74 563.75 \n",
+ "3 Financial Services Credit Services 0.947 7068.6 \n",
+ "4 Financial Services Insurance—Life
612.0 \n",
+ "\n",
+ " last_annual_dividend volume exchange exchange_name \\\n",
+ "0 19.0 9665132 NSE National Stock Exchange of India \n",
+ "1 8.0 10561389 NSE National Stock Exchange of India \n",
+ "2 11.3 12710950 NSE National Stock Exchange of India \n",
+ "3 30.0 1901753 NSE National Stock Exchange of India \n",
+ "4 3.0 500820 NSE National Stock Exchange of India \n",
+ "\n",
+ " country is_etf actively_trading \n",
+ "0 IN False True \n",
+ "1 IN False True \n",
+ "2 IN False True \n",
+ "3 IN False True \n",
+ "4 IN False True "
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sector_results = obb.equity.screener(country=\"IN\", sector=\"Financial Services\", provider=\"fmp\").to_df()\n",
+ "display(len(sector_results))\n",
+ "sector_results.head(5).convert_dtypes()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Search by Industry"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "119"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symbol | \n",
+ " name | \n",
+ " market_cap | \n",
+ " sector | \n",
+ " industry | \n",
+ " beta | \n",
+ " price | \n",
+ " last_annual_dividend | \n",
+ " volume | \n",
+ " exchange | \n",
+ " exchange_name | \n",
+ " country | \n",
+ " is_etf | \n",
+ " actively_trading | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " PAGEIND.NS | \n",
+ " Page Industries Limited | \n",
+ " 418555116755 | \n",
+ " Consumer Cyclical | \n",
+ " Apparel Manufacturing | \n",
+ " 0.462 | \n",
+ " 37525.45 | \n",
+ " 300.0 | \n",
+ " 11199 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " KPRMILL.NS | \n",
+ " K.P.R. Mill Limited | \n",
+ " 284132900800 | \n",
+ " Consumer Cyclical | \n",
+ " Apparel Manufacturing | \n",
+ " 0.689 | \n",
+ " 831.25 | \n",
+ " 4.15 | \n",
+ " 342210 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " ABFRL.NS | \n",
+ " Aditya Birla Fashion and Retail Limited | \n",
+ " 203500974285 | \n",
+ " Consumer Cyclical | \n",
+ " Apparel Manufacturing | \n",
+ " 0.152 | \n",
+ " 214.4 | \n",
+ " <NA> | \n",
+ " 1363689 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " TRIDENT.NS | \n",
+ " Trident Limited | \n",
+ " 189060118374 | \n",
+ " Consumer Cyclical | \n",
+ " Textile Manufacturing | \n",
+ " 0.532 | \n",
+ " 37.1 | \n",
+ " 0.36 | \n",
+ " 7863777 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " WELSPUNIND.NS | \n",
+ " Welspun India Limited | \n",
+ " 157481652339 | \n",
+ " Consumer Cyclical | \n",
+ " Textile Manufacturing | \n",
+ " 0.423 | \n",
+ " 162.05 | \n",
+ " 0.1 | \n",
+ " 3348644 | \n",
+ " NSE | \n",
+ " National Stock Exchange of India | \n",
+ " IN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " symbol name market_cap \\\n",
+ "0 PAGEIND.NS Page Industries Limited 418555116755 \n",
+ "1 KPRMILL.NS K.P.R. Mill Limited 284132900800 \n",
+ "2 ABFRL.NS Aditya Birla Fashion and Retail Limited 203500974285 \n",
+ "3 TRIDENT.NS Trident Limited 189060118374 \n",
+ "4 WELSPUNIND.NS Welspun India Limited 157481652339 \n",
+ "\n",
+ " sector industry beta price \\\n",
+ "0 Consumer Cyclical Apparel Manufacturing 0.462 37525.45 \n",
+ "1 Consumer Cyclical Apparel Manufacturing 0.689 831.25 \n",
+ "2 Consumer Cyclical Apparel Manufacturing 0.152 214.4 \n",
+ "3 Consumer Cyclical Textile Manufacturing 0.532 37.1 \n",
+ "4 Consumer Cyclical Textile Manufacturing 0.423 162.05 \n",
+ "\n",
+ " last_annual_dividend volume exchange exchange_name \\\n",
+ "0 300.0 11199 NSE National Stock Exchange of India \n",
+ "1 4.15 342210 NSE National Stock Exchange of India \n",
+ "2 1363689 NSE National Stock Exchange of India \n",
+ "3 0.36 7863777 NSE National Stock Exchange of India \n",
+ "4 0.1 3348644 NSE National Stock Exchange of India \n",
+ "\n",
+ " country is_etf actively_trading \n",
+ "0 IN False True \n",
+ "1 IN False True \n",
+ "2 IN False True \n",
+ "3 IN False True \n",
+ "4 IN False True "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "industry_results = obb.equity.screener(country=\"IN\", industry=\"manufacturing\").to_df()\n",
+ "display(len(industry_results))\n",
+ "industry_results.head(5).convert_dtypes()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Search by Exchange\n",
+ "\n",
+ "Some countries, like America, have multiple exchanges. Narrow the search by combining two or more parameters. The example below finds the companies listed on the American Stock Exchange (AMEX) that are domiciled in China."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symbol | \n",
+ " name | \n",
+ " market_cap | \n",
+ " sector | \n",
+ " industry | \n",
+ " beta | \n",
+ " price | \n",
+ " volume | \n",
+ " exchange | \n",
+ " exchange_name | \n",
+ " country | \n",
+ " is_etf | \n",
+ " actively_trading | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " AMBO | \n",
+ " Ambow Education Holding Ltd. | \n",
+ " 4314134 | \n",
+ " Consumer Defensive | \n",
+ " Education & Training Services | \n",
+ " 0.448 | \n",
+ " 0.1521 | \n",
+ " 54634 | \n",
+ " AMEX | \n",
+ " American Stock Exchange | \n",
+ " CN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " ITP | \n",
+ " IT Tech Packaging, Inc. | \n",
+ " 2922131 | \n",
+ " Basic Materials | \n",
+ " Paper & Paper Products | \n",
+ " -0.1 | \n",
+ " 0.2903 | \n",
+ " 17827 | \n",
+ " AMEX | \n",
+ " American Stock Exchange | \n",
+ " CN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " DXF | \n",
+ " Dunxin Financial Holdings Limited | \n",
+ " 1232011 | \n",
+ " Financial Services | \n",
+ " Credit Services | \n",
+ " 1.632 | \n",
+ " 0.31 | \n",
+ " 259614 | \n",
+ " AMEX | \n",
+ " American Stock Exchange | \n",
+ " CN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " CPHI | \n",
+ " China Pharma Holdings, Inc. | \n",
+ " 1084973 | \n",
+ " Healthcare | \n",
+ " Drug Manufacturers—Specialty & Generic | \n",
+ " 0.875 | \n",
+ " 0.0949 | \n",
+ " 483393 | \n",
+ " AMEX | \n",
+ " American Stock Exchange | \n",
+ " CN | \n",
+ " False | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " symbol name market_cap sector \\\n",
+ "0 AMBO Ambow Education Holding Ltd. 4314134 Consumer Defensive \n",
+ "1 ITP IT Tech Packaging, Inc. 2922131 Basic Materials \n",
+ "2 DXF Dunxin Financial Holdings Limited 1232011 Financial Services \n",
+ "3 CPHI China Pharma Holdings, Inc. 1084973 Healthcare \n",
+ "\n",
+ " industry beta price volume exchange \\\n",
+ "0 Education & Training Services 0.448 0.1521 54634 AMEX \n",
+ "1 Paper & Paper Products -0.1 0.2903 17827 AMEX \n",
+ "2 Credit Services 1.632 0.31 259614 AMEX \n",
+ "3 Drug Manufacturers—Specialty & Generic 0.875 0.0949 483393 AMEX \n",
+ "\n",
+ " exchange_name country is_etf actively_trading \n",
+ "0 American Stock Exchange CN False True \n",
+ "1 American Stock Exchange CN False True \n",
+ "2 American Stock Exchange CN False True \n",
+ "3 American Stock Exchange CN False True "
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "exchange_results = obb.equity.screener(exchange=\"amex\", country=\"CN\").to_df()\n",
+ "display(len(exchange_results))\n",
+ "exchange_results.convert_dtypes()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Find an Index\n",
+ "\n",
+ "List all indices from a source with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "274"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "indices = obb.index.available(provider=\"yfinance\").to_df()\n",
+ "display(len(indices))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Filter the list down by querying the DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " name | \n",
+ " code | \n",
+ " symbol | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 88 | \n",
+ " S&P/ASX 200 Index (AUD) | \n",
+ " au_asx200 | \n",
+ " ^AXJO | \n",
+ "
\n",
+ " \n",
+ " 90 | \n",
+ " S&P/ASX 200 Energy Sector Index (AUD) | \n",
+ " au_energy | \n",
+ " ^AXEJ | \n",
+ "
\n",
+ " \n",
+ " 91 | \n",
+ " S&P/ASX 200 Resources Sector Index (AUD) | \n",
+ " au_resources | \n",
+ " ^AXJR | \n",
+ "
\n",
+ " \n",
+ " 92 | \n",
+ " S&P/ASX 200 Materials Sector Index (AUD) | \n",
+ " au_materials | \n",
+ " ^AXMJ | \n",
+ "
\n",
+ " \n",
+ " 94 | \n",
+ " S&P/ASX 200 Industrials Sector Index (AUD) | \n",
+ " au_industrials | \n",
+ " ^AXNJ | \n",
+ "
\n",
+ " \n",
+ " 95 | \n",
+ " S&P/ASX 200 Consumer Discretionary Sector Inde... | \n",
+ " au_discretionary | \n",
+ " ^AXDJ | \n",
+ "
\n",
+ " \n",
+ " 96 | \n",
+ " S&P/ASX 200 Consumer Staples Sector Index (AUD) | \n",
+ " au_staples | \n",
+ " ^AXSJ | \n",
+ "
\n",
+ " \n",
+ " 97 | \n",
+ " S&P/ASX 200 Health Care Sector Index (AUD) | \n",
+ " au_health | \n",
+ " ^AXHJ | \n",
+ "
\n",
+ " \n",
+ " 98 | \n",
+ " S&P/ASX 200 Financials Sector Index (AUD) | \n",
+ " au_financials | \n",
+ " ^AXFJ | \n",
+ "
\n",
+ " \n",
+ " 99 | \n",
+ " S&P/ASX 200 A-REIT Industry Index (AUD) | \n",
+ " au_reit | \n",
+ " ^AXPJ | \n",
+ "
\n",
+ " \n",
+ " 100 | \n",
+ " S&P/ASX 200 Info Tech Sector Index (AUD) | \n",
+ " au_tech | \n",
+ " ^AXIJ | \n",
+ "
\n",
+ " \n",
+ " 101 | \n",
+ " S&P/ASX 200 Communications Sector Index (AUD) | \n",
+ " au_communications | \n",
+ " ^AXTJ | \n",
+ "
\n",
+ " \n",
+ " 102 | \n",
+ " S&P/ASX 200 Utilities Sector Index (AUD) | \n",
+ " au_utilities | \n",
+ " ^AXUJ | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " name code \\\n",
+ "88 S&P/ASX 200 Index (AUD) au_asx200 \n",
+ "90 S&P/ASX 200 Energy Sector Index (AUD) au_energy \n",
+ "91 S&P/ASX 200 Resources Sector Index (AUD) au_resources \n",
+ "92 S&P/ASX 200 Materials Sector Index (AUD) au_materials \n",
+ "94 S&P/ASX 200 Industrials Sector Index (AUD) au_industrials \n",
+ "95 S&P/ASX 200 Consumer Discretionary Sector Inde... au_discretionary \n",
+ "96 S&P/ASX 200 Consumer Staples Sector Index (AUD) au_staples \n",
+ "97 S&P/ASX 200 Health Care Sector Index (AUD) au_health \n",
+ "98 S&P/ASX 200 Financials Sector Index (AUD) au_financials \n",
+ "99 S&P/ASX 200 A-REIT Industry Index (AUD) au_reit \n",
+ "100 S&P/ASX 200 Info Tech Sector Index (AUD) au_tech \n",
+ "101 S&P/ASX 200 Communications Sector Index (AUD) au_communications \n",
+ "102 S&P/ASX 200 Utilities Sector Index (AUD) au_utilities \n",
+ "\n",
+ " symbol \n",
+ "88 ^AXJO \n",
+ "90 ^AXEJ \n",
+ "91 ^AXJR \n",
+ "92 ^AXMJ \n",
+ "94 ^AXNJ \n",
+ "95 ^AXDJ \n",
+ "96 ^AXSJ \n",
+ "97 ^AXHJ \n",
+ "98 ^AXFJ \n",
+ "99 ^AXPJ \n",
+ "100 ^AXIJ \n",
+ "101 ^AXTJ \n",
+ "102 ^AXUJ "
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "indices[indices[\"name\"].str.contains(\"ASX 200\")]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With the `openbb-yfinance` extension, index time series can be loaded using the ticker symbol or short code. Non-American indices have a code beginning with the two-letter country code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " open | \n",
+ " high | \n",
+ " low | \n",
+ " close | \n",
+ " volume | \n",
+ "
\n",
+ " \n",
+ " date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2023-11-17 | \n",
+ " True | \n",
+ " True | \n",
+ " True | \n",
+ " True | \n",
+ " True | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " open high low close volume\n",
+ "date \n",
+ "2023-11-17 True True True True True"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(\n",
+ " obb.index.market(\"au_utilities\", provider=\"yfinance\").to_df().tail(1)\n",
+ " == obb.index.market(\"^AXUJ\", provider=\"yfinance\").to_df().tail(1)\n",
+ ")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "obb-sdk4",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/googleColab.ipynb b/examples/googleColab.ipynb
new file mode 100644
index 000000000000..8bc0c17f6d40
--- /dev/null
+++ b/examples/googleColab.ipynb
@@ -0,0 +1,2125 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "fc6a4747cea243f4bac6a19c43264fec": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DropdownModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DropdownModel",
+ "_options_labels": [
+ "Total Open Interest",
+ "Call Open Interest",
+ "Put Open Interest",
+ "Total Volume",
+ "Call Volume",
+ "Put Volume"
+ ],
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "DropdownView",
+ "description": "",
+ "description_tooltip": null,
+ "disabled": false,
+ "index": 0,
+ "layout": "IPY_MODEL_bf10b4a3831f4e6595398f2d62a0f7b2",
+ "style": "IPY_MODEL_6095af63918f4075a891fc950c9790d3"
+ }
+ },
+ "bf10b4a3831f4e6595398f2d62a0f7b2": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
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+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
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+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
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+ "overflow_x": null,
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+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ }
+ }
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Installing the OpenBB Platform in Google Colab\n",
+ "\n",
+ "This notebook will install the OpenBB Platform, fetch some data and prepare it for display as a bar chart.\n",
+ "\n",
+ "Sign up for a free account here: https://my.openbb.co"
+ ],
+ "metadata": {
+ "id": "xIOXTKkqBReO"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xvU65bhqKNns"
+ },
+ "outputs": [],
+ "source": [
+ "# Install the OpenBB Platform with all available extensions.\n",
+ "# Messages indicating package version conflicts at the end of installation can be safely ignored.\n",
+ "\n",
+ "!pip install openbb[all]\n",
+ "\n",
+ "# There is also a nightly distribution available, openbb-nightly"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Before running this cell, restart the runtime by selecting, \"Restart runtime\", from the \"Runtime\" menu.\n",
+ "\n",
+ "# Import statements - for many scenarios, the only import needed will be `from openbb import obb`\n",
+ "from typing import Literal\n",
+ "from IPython.display import display\n",
+ "from IPython.display import clear_output\n",
+ "import ipywidgets as widgets\n",
+ "import pandas as pd\n",
+ "import pandas_ta as ta\n",
+ "from datetime import datetime\n",
+ "from plotly import graph_objects as go\n",
+ "\n",
+ "from openbb import obb"
+ ],
+ "metadata": {
+ "id": "_69FIu9YKRhI"
+ },
+ "execution_count": 98,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Login to OpenBB Hub to retrieve stored API keys.\n",
+ "# https://my.openbb.co/app/platform/pat\n",
+ "# https://my.openbb.co/app/platform/api-keys\n",
+ "\n",
+ "obb.account.login(pat=\"replace with your PAT\")\n",
+ "\n",
+ "# This is not required"
+ ],
+ "metadata": {
+ "id": "1OLsZHDYMBSS"
+ },
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Verify that the credentials from Hub were loaded successfully.\n",
+ "\n",
+ "obb.user.credentials"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gJ7FwTC6MTzv",
+ "outputId": "b9d4e888-7f3f-4756-b1a4-ae438e61c2b5"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Credentials\n",
+ "\n",
+ "alpha_vantage_api_key: **********\n",
+ "benzinga_api_key: None\n",
+ "biztoc_api_key: None\n",
+ "fmp_api_key: **********\n",
+ "fred_api_key: **********\n",
+ "intrinio_api_key: **********\n",
+ "nasdaq_api_key: **********\n",
+ "polygon_api_key: **********\n",
+ "tiingo_token: None\n",
+ "tradingeconomics_api_key: None"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set the output preference, if desired. The examples below use Pandas DataFrames.\n",
+ "\n",
+ "obb.user.preferences.output_type = \"dataframe\""
+ ],
+ "metadata": {
+ "id": "27JtqRAQ2HTb"
+ },
+ "execution_count": 67,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Get Some Data\n",
+ "symbol = \"SPY\"\n",
+ "\n",
+ "options = obb.derivatives.options.chains(symbol, provider=\"cboe\")\n",
+ "\n",
+ "options"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 444
+ },
+ "id": "GWI_60zD3M3l",
+ "outputId": "47338fab-8ab5-467f-c26e-44148c848c63"
+ },
+ "execution_count": 88,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " contract_symbol expiration strike option_type volume open \\\n",
+ "0 SPY231130C00387000 2023-11-30 387.0 call 7.0 67.60 \n",
+ "1 SPY231130P00387000 2023-11-30 387.0 put 2.0 0.01 \n",
+ "2 SPY231130C00388000 2023-11-30 388.0 call 1.0 66.47 \n",
+ "3 SPY231130P00388000 2023-11-30 388.0 put 1.0 0.01 \n",
+ "4 SPY231130C00389000 2023-11-30 389.0 call 0.0 0.00 \n",
+ "... ... ... ... ... ... ... \n",
+ "8215 SPY260116P00670000 2026-01-16 670.0 put 0.0 0.00 \n",
+ "8216 SPY260116C00675000 2026-01-16 675.0 call 0.0 0.00 \n",
+ "8217 SPY260116P00675000 2026-01-16 675.0 put 0.0 0.00 \n",
+ "8218 SPY260116C00680000 2026-01-16 680.0 call 0.0 0.00 \n",
+ "8219 SPY260116P00680000 2026-01-16 680.0 put 0.0 0.00 \n",
+ "\n",
+ " open_interest high low implied_volatility ... last_trade_price \\\n",
+ "0 5.0 67.60 66.55 0.0000 ... 66.55 \n",
+ "1 290.0 0.01 0.01 0.0000 ... 0.01 \n",
+ "2 0.0 66.47 66.47 0.0000 ... 66.47 \n",
+ "3 1.0 0.01 0.01 0.0000 ... 0.01 \n",
+ "4 0.0 0.00 0.00 0.0000 ... 0.00 \n",
+ "... ... ... ... ... ... ... \n",
+ "8215 0.0 0.00 0.00 0.2367 ... 216.04 \n",
+ "8216 0.0 0.00 0.00 0.1387 ... 0.00 \n",
+ "8217 0.0 0.00 0.00 0.2385 ... 0.00 \n",
+ "8218 0.0 0.00 0.00 0.1411 ... 0.00 \n",
+ "8219 0.0 0.00 0.00 0.2432 ... 223.91 \n",
+ "\n",
+ " tick prev_close change change_percent rho \\\n",
+ "0 down 67.69 -1.135 -1.68 0.0003 \n",
+ "1 no_change 0.00 0.005 100.00 0.0000 \n",
+ "2 down 66.68 -0.210 -0.31 0.0003 \n",
+ "3 no_change 0.00 0.005 100.00 0.0000 \n",
+ "4 no_change 65.69 0.000 0.00 0.0003 \n",
+ "... ... ... ... ... ... \n",
+ "8215 down 215.44 0.000 0.00 -0.3427 \n",
+ "8216 no_change 2.50 0.000 0.00 0.4385 \n",
+ "8217 no_change 220.50 0.000 0.00 -0.3453 \n",
+ "8218 no_change 2.50 0.000 0.00 0.4078 \n",
+ "8219 down 225.50 0.000 0.00 -0.3478 \n",
+ "\n",
+ " last_trade_timestamp dte bid ask \n",
+ "0 2023-11-30 12:27:59 -1 67.77 70.67 \n",
+ "1 2023-11-30 09:30:12 -1 0.00 0.01 \n",
+ "2 2023-11-30 09:42:00 -1 67.56 68.77 \n",
+ "3 2023-11-30 10:16:04 -1 0.00 0.01 \n",
+ "4 NaT -1 65.77 68.67 \n",
+ "... ... ... ... ... \n",
+ "8215 2023-11-20 12:31:47 777 211.56 216.50 \n",
+ "8216 NaT 777 0.00 5.00 \n",
+ "8217 NaT 777 216.50 221.50 \n",
+ "8218 NaT 777 0.00 5.00 \n",
+ "8219 2023-11-27 14:07:53 777 221.50 226.50 \n",
+ "\n",
+ "[8220 rows x 27 columns]"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " contract_symbol | \n",
+ " expiration | \n",
+ " strike | \n",
+ " option_type | \n",
+ " volume | \n",
+ " open | \n",
+ " open_interest | \n",
+ " high | \n",
+ " low | \n",
+ " implied_volatility | \n",
+ " ... | \n",
+ " last_trade_price | \n",
+ " tick | \n",
+ " prev_close | \n",
+ " change | \n",
+ " change_percent | \n",
+ " rho | \n",
+ " last_trade_timestamp | \n",
+ " dte | \n",
+ " bid | \n",
+ " ask | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " SPY231130C00387000 | \n",
+ " 2023-11-30 | \n",
+ " 387.0 | \n",
+ " call | \n",
+ " 7.0 | \n",
+ " 67.60 | \n",
+ " 5.0 | \n",
+ " 67.60 | \n",
+ " 66.55 | \n",
+ " 0.0000 | \n",
+ " ... | \n",
+ " 66.55 | \n",
+ " down | \n",
+ " 67.69 | \n",
+ " -1.135 | \n",
+ " -1.68 | \n",
+ " 0.0003 | \n",
+ " 2023-11-30 12:27:59 | \n",
+ " -1 | \n",
+ " 67.77 | \n",
+ " 70.67 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " SPY231130P00387000 | \n",
+ " 2023-11-30 | \n",
+ " 387.0 | \n",
+ " put | \n",
+ " 2.0 | \n",
+ " 0.01 | \n",
+ " 290.0 | \n",
+ " 0.01 | \n",
+ " 0.01 | \n",
+ " 0.0000 | \n",
+ " ... | \n",
+ " 0.01 | \n",
+ " no_change | \n",
+ " 0.00 | \n",
+ " 0.005 | \n",
+ " 100.00 | \n",
+ " 0.0000 | \n",
+ " 2023-11-30 09:30:12 | \n",
+ " -1 | \n",
+ " 0.00 | \n",
+ " 0.01 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " SPY231130C00388000 | \n",
+ " 2023-11-30 | \n",
+ " 388.0 | \n",
+ " call | \n",
+ " 1.0 | \n",
+ " 66.47 | \n",
+ " 0.0 | \n",
+ " 66.47 | \n",
+ " 66.47 | \n",
+ " 0.0000 | \n",
+ " ... | \n",
+ " 66.47 | \n",
+ " down | \n",
+ " 66.68 | \n",
+ " -0.210 | \n",
+ " -0.31 | \n",
+ " 0.0003 | \n",
+ " 2023-11-30 09:42:00 | \n",
+ " -1 | \n",
+ " 67.56 | \n",
+ " 68.77 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " SPY231130P00388000 | \n",
+ " 2023-11-30 | \n",
+ " 388.0 | \n",
+ " put | \n",
+ " 1.0 | \n",
+ " 0.01 | \n",
+ " 1.0 | \n",
+ " 0.01 | \n",
+ " 0.01 | \n",
+ " 0.0000 | \n",
+ " ... | \n",
+ " 0.01 | \n",
+ " no_change | \n",
+ " 0.00 | \n",
+ " 0.005 | \n",
+ " 100.00 | \n",
+ " 0.0000 | \n",
+ " 2023-11-30 10:16:04 | \n",
+ " -1 | \n",
+ " 0.00 | \n",
+ " 0.01 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " SPY231130C00389000 | \n",
+ " 2023-11-30 | \n",
+ " 389.0 | \n",
+ " call | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.00 | \n",
+ " 0.0000 | \n",
+ " ... | \n",
+ " 0.00 | \n",
+ " no_change | \n",
+ " 65.69 | \n",
+ " 0.000 | \n",
+ " 0.00 | \n",
+ " 0.0003 | \n",
+ " NaT | \n",
+ " -1 | \n",
+ " 65.77 | \n",
+ " 68.67 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 8215 | \n",
+ " SPY260116P00670000 | \n",
+ " 2026-01-16 | \n",
+ " 670.0 | \n",
+ " put | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.00 | \n",
+ " 0.2367 | \n",
+ " ... | \n",
+ " 216.04 | \n",
+ " down | \n",
+ " 215.44 | \n",
+ " 0.000 | \n",
+ " 0.00 | \n",
+ " -0.3427 | \n",
+ " 2023-11-20 12:31:47 | \n",
+ " 777 | \n",
+ " 211.56 | \n",
+ " 216.50 | \n",
+ "
\n",
+ " \n",
+ " 8216 | \n",
+ " SPY260116C00675000 | \n",
+ " 2026-01-16 | \n",
+ " 675.0 | \n",
+ " call | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.00 | \n",
+ " 0.1387 | \n",
+ " ... | \n",
+ " 0.00 | \n",
+ " no_change | \n",
+ " 2.50 | \n",
+ " 0.000 | \n",
+ " 0.00 | \n",
+ " 0.4385 | \n",
+ " NaT | \n",
+ " 777 | \n",
+ " 0.00 | \n",
+ " 5.00 | \n",
+ "
\n",
+ " \n",
+ " 8217 | \n",
+ " SPY260116P00675000 | \n",
+ " 2026-01-16 | \n",
+ " 675.0 | \n",
+ " put | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.00 | \n",
+ " 0.2385 | \n",
+ " ... | \n",
+ " 0.00 | \n",
+ " no_change | \n",
+ " 220.50 | \n",
+ " 0.000 | \n",
+ " 0.00 | \n",
+ " -0.3453 | \n",
+ " NaT | \n",
+ " 777 | \n",
+ " 216.50 | \n",
+ " 221.50 | \n",
+ "
\n",
+ " \n",
+ " 8218 | \n",
+ " SPY260116C00680000 | \n",
+ " 2026-01-16 | \n",
+ " 680.0 | \n",
+ " call | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.00 | \n",
+ " 0.1411 | \n",
+ " ... | \n",
+ " 0.00 | \n",
+ " no_change | \n",
+ " 2.50 | \n",
+ " 0.000 | \n",
+ " 0.00 | \n",
+ " 0.4078 | \n",
+ " NaT | \n",
+ " 777 | \n",
+ " 0.00 | \n",
+ " 5.00 | \n",
+ "
\n",
+ " \n",
+ " 8219 | \n",
+ " SPY260116P00680000 | \n",
+ " 2026-01-16 | \n",
+ " 680.0 | \n",
+ " put | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.0 | \n",
+ " 0.00 | \n",
+ " 0.00 | \n",
+ " 0.2432 | \n",
+ " ... | \n",
+ " 223.91 | \n",
+ " down | \n",
+ " 225.50 | \n",
+ " 0.000 | \n",
+ " 0.00 | \n",
+ " -0.3478 | \n",
+ " 2023-11-27 14:07:53 | \n",
+ " 777 | \n",
+ " 221.50 | \n",
+ " 226.50 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
8220 rows × 27 columns
\n",
+ "
\n",
+ "
\n",
+ "
\n"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 88
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Prepare A View - Volume and Open Interest by Expiration or Strike\n",
+ "\n",
+ "def filter_options_data(options, by: Literal[\"expiration\", \"strike\"] = \"expiration\"):\n",
+ " data = pd.DataFrame()\n",
+ " data[\"Total Open Interest\"] = options.groupby(by)[\"open_interest\"].sum()\n",
+ " data[\"Call Open Interest\"] = options[options[\"option_type\"] == \"call\"].groupby(by)[\"open_interest\"].sum()\n",
+ " data[\"Put Open Interest\"] = options[options[\"option_type\"] == \"put\"].groupby(by)[\"open_interest\"].sum()\n",
+ " data[\"Total Volume\"] = options.groupby(by)[\"volume\"].sum()\n",
+ " data[\"Call Volume\"] = options[options[\"option_type\"] == \"call\"].groupby(by)[\"volume\"].sum()\n",
+ " data[\"Put Volume\"] = options[options[\"option_type\"] == \"put\"].groupby(by)[\"volume\"].sum()\n",
+ "\n",
+ " return data\n",
+ "\n",
+ "data = filter_options_data(options, \"strike\")\n",
+ "\n",
+ "data"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 455
+ },
+ "id": "F-SpeJUi3l1k",
+ "outputId": "ee703998-297f-4831-8aad-422e16a5b3eb"
+ },
+ "execution_count": 101,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Total Open Interest Call Open Interest Put Open Interest \\\n",
+ "strike \n",
+ "120.0 22821.0 74.0 22747.0 \n",
+ "130.0 1506.0 19.0 1487.0 \n",
+ "140.0 488.0 2.0 486.0 \n",
+ "150.0 72684.0 2072.0 70612.0 \n",
+ "155.0 12298.0 18.0 12280.0 \n",
+ "... ... ... ... \n",
+ "700.0 24945.0 24835.0 110.0 \n",
+ "705.0 478.0 478.0 0.0 \n",
+ "710.0 1511.0 1511.0 0.0 \n",
+ "715.0 981.0 981.0 0.0 \n",
+ "720.0 102823.0 102821.0 2.0 \n",
+ "\n",
+ " Total Volume Call Volume Put Volume \n",
+ "strike \n",
+ "120.0 78.0 0.0 78.0 \n",
+ "130.0 61.0 0.0 61.0 \n",
+ "140.0 1.0 0.0 1.0 \n",
+ "150.0 65.0 4.0 61.0 \n",
+ "155.0 21.0 1.0 20.0 \n",
+ "... ... ... ... \n",
+ "700.0 2.0 2.0 0.0 \n",
+ "705.0 0.0 0.0 0.0 \n",
+ "710.0 3.0 1.0 2.0 \n",
+ "715.0 0.0 0.0 0.0 \n",
+ "720.0 36.0 36.0 0.0 \n",
+ "\n",
+ "[272 rows x 6 columns]"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Total Open Interest | \n",
+ " Call Open Interest | \n",
+ " Put Open Interest | \n",
+ " Total Volume | \n",
+ " Call Volume | \n",
+ " Put Volume | \n",
+ "
\n",
+ " \n",
+ " strike | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 120.0 | \n",
+ " 22821.0 | \n",
+ " 74.0 | \n",
+ " 22747.0 | \n",
+ " 78.0 | \n",
+ " 0.0 | \n",
+ " 78.0 | \n",
+ "
\n",
+ " \n",
+ " 130.0 | \n",
+ " 1506.0 | \n",
+ " 19.0 | \n",
+ " 1487.0 | \n",
+ " 61.0 | \n",
+ " 0.0 | \n",
+ " 61.0 | \n",
+ "
\n",
+ " \n",
+ " 140.0 | \n",
+ " 488.0 | \n",
+ " 2.0 | \n",
+ " 486.0 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ " 150.0 | \n",
+ " 72684.0 | \n",
+ " 2072.0 | \n",
+ " 70612.0 | \n",
+ " 65.0 | \n",
+ " 4.0 | \n",
+ " 61.0 | \n",
+ "
\n",
+ " \n",
+ " 155.0 | \n",
+ " 12298.0 | \n",
+ " 18.0 | \n",
+ " 12280.0 | \n",
+ " 21.0 | \n",
+ " 1.0 | \n",
+ " 20.0 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 700.0 | \n",
+ " 24945.0 | \n",
+ " 24835.0 | \n",
+ " 110.0 | \n",
+ " 2.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " 705.0 | \n",
+ " 478.0 | \n",
+ " 478.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " 710.0 | \n",
+ " 1511.0 | \n",
+ " 1511.0 | \n",
+ " 0.0 | \n",
+ " 3.0 | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ "
\n",
+ " \n",
+ " 715.0 | \n",
+ " 981.0 | \n",
+ " 981.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " 720.0 | \n",
+ " 102823.0 | \n",
+ " 102821.0 | \n",
+ " 2.0 | \n",
+ " 36.0 | \n",
+ " 36.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
272 rows × 6 columns
\n",
+ "
\n",
+ "
\n",
+ "
\n"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 101
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Do not run this cell if you are following the example above.\n",
+ "\n",
+ "# In this scenario, \"data\" could be anything that would be displayed as a bar chart. Alternatively, it could be company fundamentals data.\n",
+ "\n",
+ "# Note: This requires a valid FMP API key\n",
+ "\n",
+ "\n",
+ "symbol=\"AAPL\"\n",
+ "\n",
+ "data = obb.equity.fundamental.ratios(symbol, limit = 100, period=\"quarter\", provider=\"fmp\")\n",
+ "\n",
+ "data.index = data.index.strftime(\"%Y-%m-%d\")\n",
+ "\n",
+ "data"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 649
+ },
+ "id": "2fAclYaEMVz5",
+ "outputId": "10413c8a-253d-4aae-c837-644f8880923b"
+ },
+ "execution_count": 93,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " symbol period current_ratio quick_ratio cash_ratio \\\n",
+ "date \n",
+ "1998-12-26 AAPL Q1 2.605795 2.352426 0.822776 \n",
+ "1999-03-27 AAPL Q2 2.650259 2.413212 0.879534 \n",
+ "1999-06-26 AAPL Q3 2.792723 2.600390 1.152047 \n",
+ "1999-09-25 AAPL Q4 2.766301 2.522272 0.856036 \n",
+ "2000-01-01 AAPL Q1 2.498219 2.316539 0.807125 \n",
+ "... ... ... ... ... ... \n",
+ "2022-09-24 AAPL Q4 0.879356 0.709408 0.153563 \n",
+ "2022-12-31 AAPL Q1 0.938020 0.768724 0.149578 \n",
+ "2023-04-01 AAPL Q2 0.940354 0.764281 0.205597 \n",
+ "2023-07-01 AAPL Q3 0.981563 0.813585 0.227331 \n",
+ "2023-09-30 AAPL Q4 0.988012 0.843312 0.206217 \n",
+ "\n",
+ " days_of_sales_outstanding days_of_inventory_outstanding \\\n",
+ "date \n",
+ "1998-12-26 48.052632 1.832248 \n",
+ "1999-03-27 47.294118 1.437445 \n",
+ "1999-06-26 51.758665 0.557029 \n",
+ "1999-09-25 45.875749 1.890756 \n",
+ "2000-01-01 34.263764 0.777650 \n",
+ "... ... ... \n",
+ "2022-09-24 60.833315 8.551997 \n",
+ "2022-12-31 41.622138 9.185598 \n",
+ "2023-04-01 34.068392 12.738933 \n",
+ "2023-07-01 43.115762 14.577604 \n",
+ "2023-09-30 61.327069 11.611542 \n",
+ "\n",
+ " operating_cycle days_of_payables_outstanding \\\n",
+ "date \n",
+ "1998-12-26 49.884879 48.004886 \n",
+ "1999-03-27 48.731562 63.167702 \n",
+ "1999-06-26 52.315694 63.023873 \n",
+ "1999-09-25 47.766505 76.764706 \n",
+ "2000-01-01 35.041414 60.864055 \n",
+ "... ... ... \n",
+ "2022-09-24 69.385312 110.859542 \n",
+ "2022-12-31 50.807736 78.007542 \n",
+ "2023-04-01 46.807325 73.118615 \n",
+ "2023-07-01 57.693367 92.607747 \n",
+ "2023-09-30 72.938611 114.833405 \n",
+ "\n",
+ " cash_conversion_cycle ... price_earnings_ratio \\\n",
+ "date ... \n",
+ "1998-12-26 1.879993 ... 8.732568 \n",
+ "1999-03-27 -14.436140 ... 8.397059 \n",
+ "1999-06-26 -10.708179 ... 7.484225 \n",
+ "1999-09-25 -28.998201 ... 23.552897 \n",
+ "2000-01-01 -25.822641 ... 22.618570 \n",
+ "... ... ... ... \n",
+ "2022-09-24 -41.474230 ... 29.094281 \n",
+ "2022-12-31 -27.199806 ... 17.208993 \n",
+ "2023-04-01 -26.311290 ... 26.938138 \n",
+ "2023-07-01 -34.914381 ... 38.288645 \n",
+ "2023-09-30 -41.894793 ... 29.085850 \n",
+ "\n",
+ " price_to_free_cash_flows_ratio \\\n",
+ "date \n",
+ "1998-12-26 24.355053 \n",
+ "1999-03-27 18.137648 \n",
+ "1999-06-26 75.027047 \n",
+ "1999-09-25 51.769733 \n",
+ "2000-01-01 49.423264 \n",
+ "... ... \n",
+ "2022-09-24 115.723695 \n",
+ "2022-12-31 68.334817 \n",
+ "2023-04-01 101.516990 \n",
+ "2023-07-01 125.370206 \n",
+ "2023-09-30 137.421101 \n",
+ "\n",
+ " price_to_operating_cash_flows_ratio price_cash_flow_ratio \\\n",
+ "date \n",
+ "1998-12-26 23.808976 23.808976 \n",
+ "1999-03-27 16.856551 16.856551 \n",
+ "1999-06-26 69.058986 69.058986 \n",
+ "1999-09-25 47.970120 47.970120 \n",
+ "2000-01-01 44.388186 44.388186 \n",
+ "... ... ... \n",
+ "2022-09-24 99.948206 99.948206 \n",
+ "2022-12-31 60.724643 60.724643 \n",
+ "2023-04-01 91.152020 91.152020 \n",
+ "2023-07-01 115.423282 115.423282 \n",
+ "2023-09-30 123.658630 123.658630 \n",
+ "\n",
+ " price_earnings_to_growth_ratio price_sales_ratio dividend_yield \\\n",
+ "date \n",
+ "1998-12-26 0.203760 3.104913 0.000000 \n",
+ "1999-03-27 -0.699755 2.963668 0.000000 \n",
+ "1999-06-26 0.178003 3.900636 0.000000 \n",
+ "1999-09-25 -0.467319 7.827460 0.000000 \n",
+ "2000-01-01 0.350588 7.066493 0.000000 \n",
+ "... ... ... ... \n",
+ "2022-09-24 3.879237 26.750498 0.001536 \n",
+ "2022-12-31 0.369993 17.625873 0.001825 \n",
+ "2023-04-01 -1.414252 27.450564 0.001402 \n",
+ "2023-07-01 -2.253139 37.224668 0.001264 \n",
+ "2023-09-30 1.846951 29.841774 0.001407 \n",
+ "\n",
+ " enterprise_value_multiple price_fair_value calendarYear \n",
+ "date \n",
+ "1998-12-26 28.650009 2.760999 1999 \n",
+ "1999-03-27 62.597153 2.084787 1999 \n",
+ "1999-06-26 209.281398 2.046192 1999 \n",
+ "1999-09-25 70.913430 3.369035 1999 \n",
+ "2000-01-01 58.063853 3.750973 2000 \n",
+ "... ... ... ... \n",
+ "2022-09-24 90.344514 47.589406 2022 \n",
+ "2022-12-31 55.366190 36.401387 2023 \n",
+ "2023-04-01 86.117046 41.882005 2023 \n",
+ "2023-07-01 135.913479 50.517075 2023 \n",
+ "2023-09-30 92.900618 42.975881 2023 \n",
+ "\n",
+ "[100 rows x 57 columns]"
+ ],
+ "text/html": [
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+ " Q2 | \n",
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+ " ... | \n",
+ " 8.397059 | \n",
+ " 18.137648 | \n",
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+ "
\n",
+ " \n",
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+ " AAPL | \n",
+ " Q3 | \n",
+ " 2.792723 | \n",
+ " 2.600390 | \n",
+ " 1.152047 | \n",
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+ " 63.023873 | \n",
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+ " ... | \n",
+ " 7.484225 | \n",
+ " 75.027047 | \n",
+ " 69.058986 | \n",
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+ " 0.178003 | \n",
+ " 3.900636 | \n",
+ " 0.000000 | \n",
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+ " Q4 | \n",
+ " 2.766301 | \n",
+ " 2.522272 | \n",
+ " 0.856036 | \n",
+ " 45.875749 | \n",
+ " 1.890756 | \n",
+ " 47.766505 | \n",
+ " 76.764706 | \n",
+ " -28.998201 | \n",
+ " ... | \n",
+ " 23.552897 | \n",
+ " 51.769733 | \n",
+ " 47.970120 | \n",
+ " 47.970120 | \n",
+ " -0.467319 | \n",
+ " 7.827460 | \n",
+ " 0.000000 | \n",
+ " 70.913430 | \n",
+ " 3.369035 | \n",
+ " 1999 | \n",
+ "
\n",
+ " \n",
+ " 2000-01-01 | \n",
+ " AAPL | \n",
+ " Q1 | \n",
+ " 2.498219 | \n",
+ " 2.316539 | \n",
+ " 0.807125 | \n",
+ " 34.263764 | \n",
+ " 0.777650 | \n",
+ " 35.041414 | \n",
+ " 60.864055 | \n",
+ " -25.822641 | \n",
+ " ... | \n",
+ " 22.618570 | \n",
+ " 49.423264 | \n",
+ " 44.388186 | \n",
+ " 44.388186 | \n",
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+ " 7.066493 | \n",
+ " 0.000000 | \n",
+ " 58.063853 | \n",
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\n",
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+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
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+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 2022-09-24 | \n",
+ " AAPL | \n",
+ " Q4 | \n",
+ " 0.879356 | \n",
+ " 0.709408 | \n",
+ " 0.153563 | \n",
+ " 60.833315 | \n",
+ " 8.551997 | \n",
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+ " 110.859542 | \n",
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+ " ... | \n",
+ " 29.094281 | \n",
+ " 115.723695 | \n",
+ " 99.948206 | \n",
+ " 99.948206 | \n",
+ " 3.879237 | \n",
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+ " 0.001536 | \n",
+ " 90.344514 | \n",
+ " 47.589406 | \n",
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+ "
\n",
+ " \n",
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+ " AAPL | \n",
+ " Q1 | \n",
+ " 0.938020 | \n",
+ " 0.768724 | \n",
+ " 0.149578 | \n",
+ " 41.622138 | \n",
+ " 9.185598 | \n",
+ " 50.807736 | \n",
+ " 78.007542 | \n",
+ " -27.199806 | \n",
+ " ... | \n",
+ " 17.208993 | \n",
+ " 68.334817 | \n",
+ " 60.724643 | \n",
+ " 60.724643 | \n",
+ " 0.369993 | \n",
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+ " 55.366190 | \n",
+ " 36.401387 | \n",
+ " 2023 | \n",
+ "
\n",
+ " \n",
+ " 2023-04-01 | \n",
+ " AAPL | \n",
+ " Q2 | \n",
+ " 0.940354 | \n",
+ " 0.764281 | \n",
+ " 0.205597 | \n",
+ " 34.068392 | \n",
+ " 12.738933 | \n",
+ " 46.807325 | \n",
+ " 73.118615 | \n",
+ " -26.311290 | \n",
+ " ... | \n",
+ " 26.938138 | \n",
+ " 101.516990 | \n",
+ " 91.152020 | \n",
+ " 91.152020 | \n",
+ " -1.414252 | \n",
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+ " 0.001402 | \n",
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+ " 41.882005 | \n",
+ " 2023 | \n",
+ "
\n",
+ " \n",
+ " 2023-07-01 | \n",
+ " AAPL | \n",
+ " Q3 | \n",
+ " 0.981563 | \n",
+ " 0.813585 | \n",
+ " 0.227331 | \n",
+ " 43.115762 | \n",
+ " 14.577604 | \n",
+ " 57.693367 | \n",
+ " 92.607747 | \n",
+ " -34.914381 | \n",
+ " ... | \n",
+ " 38.288645 | \n",
+ " 125.370206 | \n",
+ " 115.423282 | \n",
+ " 115.423282 | \n",
+ " -2.253139 | \n",
+ " 37.224668 | \n",
+ " 0.001264 | \n",
+ " 135.913479 | \n",
+ " 50.517075 | \n",
+ " 2023 | \n",
+ "
\n",
+ " \n",
+ " 2023-09-30 | \n",
+ " AAPL | \n",
+ " Q4 | \n",
+ " 0.988012 | \n",
+ " 0.843312 | \n",
+ " 0.206217 | \n",
+ " 61.327069 | \n",
+ " 11.611542 | \n",
+ " 72.938611 | \n",
+ " 114.833405 | \n",
+ " -41.894793 | \n",
+ " ... | \n",
+ " 29.085850 | \n",
+ " 137.421101 | \n",
+ " 123.658630 | \n",
+ " 123.658630 | \n",
+ " 1.846951 | \n",
+ " 29.841774 | \n",
+ " 0.001407 | \n",
+ " 92.900618 | \n",
+ " 42.975881 | \n",
+ " 2023 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
100 rows × 57 columns
\n",
+ "
\n",
+ "
\n",
+ "
\n"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 93
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Create a widget for selecting the data to display.\n",
+ "\n",
+ "clear_output(wait = False)\n",
+ "\n",
+ "data_choices = data.columns.tolist()\n",
+ "data_selection = widgets.Dropdown(\n",
+ " options = data_choices,\n",
+ " value = None,\n",
+ ")\n",
+ "output = widgets.Output()\n",
+ "\n",
+ "\n",
+ "def generate_figure(data, data_choice):\n",
+ " data = data[data[data_choice].notnull()]\n",
+ " fig = go.Figure()\n",
+ " fig.add_bar(\n",
+ " y = data[data_choice][data[data_choice] > 0].values,\n",
+ " x = data[data_choice][data[data_choice] > 0].index,\n",
+ " name = data_choice,\n",
+ " marker = dict(color = \"blue\"),\n",
+ " )\n",
+ " fig.add_bar(\n",
+ " y = data[data_choice][data[data_choice] < 0].values,\n",
+ " x = data[data_choice][data[data_choice] < 0].index,\n",
+ " name = data_choice,\n",
+ " marker = dict(color = \"red\")\n",
+ " )\n",
+ " fig.update_xaxes(type=\"category\")\n",
+ " fig.update_traces(width=0.98, selector=dict(type=\"bar\"))\n",
+ " fig.update_layout(\n",
+ " showlegend=False,\n",
+ " width=1400,\n",
+ " height=600,\n",
+ " title = dict(\n",
+ " text=f\"{symbol} {data_choice.replace('_', ' ').title()}\",\n",
+ " xanchor = \"center\",\n",
+ " x = 0.5,\n",
+ " font = dict(size = 20)\n",
+ " ),\n",
+ " barmode=\"overlay\",\n",
+ " bargap=0,\n",
+ " bargroupgap=0,\n",
+ " yaxis=dict(\n",
+ " ticklen=0,\n",
+ " showgrid=True,\n",
+ " tickfont=dict(size=14),\n",
+ " ),\n",
+ " xaxis=dict(\n",
+ " showgrid=False,\n",
+ " autorange=True,\n",
+ " tickangle=90,\n",
+ " tickfont=dict(size=11),\n",
+ " ),\n",
+ " )\n",
+ " return fig\n",
+ "\n",
+ "def on_value_change(change):\n",
+ " clear_output(wait = True)\n",
+ " display(data_selection)\n",
+ " with output:\n",
+ " data_selection.value\n",
+ "\n",
+ "data_selection.observe(on_value_change, names=\"value\")\n",
+ "display(data_selection)\n",
+ "\n",
+ "# Select from the drop-down menu below."
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "fc6a4747cea243f4bac6a19c43264fec",
+ "bf10b4a3831f4e6595398f2d62a0f7b2",
+ "6095af63918f4075a891fc950c9790d3"
+ ]
+ },
+ "id": "jvvAtHfvMkXB",
+ "outputId": "6155ecd8-5bec-4fb7-ef77-52b3bb325af7"
+ },
+ "execution_count": 102,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Dropdown(options=('Total Open Interest', 'Call Open Interest', 'Put Open Interest', 'Total Volume', 'Call Volu…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "fc6a4747cea243f4bac6a19c43264fec"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Play this cell to display the choice\n",
+ "\n",
+ "if data_selection.value is not None:\n",
+ "\n",
+ " generate_figure(data, data_selection.value).show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 617
+ },
+ "id": "J9D5Qq0sQOwH",
+ "outputId": "91092fb7-c313-4305-eed2-00d6b5750a8b"
+ },
+ "execution_count": 106,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ ""
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/examples/loadHistoricalPriceData.ipynb b/examples/loadHistoricalPriceData.ipynb
index 10e0db2e7111..08f361e8dcd5 100644
--- a/examples/loadHistoricalPriceData.ipynb
+++ b/examples/loadHistoricalPriceData.ipynb
@@ -5,21 +5,23 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Downloading Historical Price Data Using the OpenBB SDK\n",
+ "# Historical Prices With the OpenBB Platform\n",
"\n",
- "This notebook demonstrates the different ways to approach loading historical price data using the OpenBB SDK. There are obvious methods, and some less conventional ones. To begin, let's look at the Stocks module, but first, we need to initialize the notebook with the import statements block.\n",
+ "This notebook demonstrates some of the ways to approach loading historical price data using the OpenBB Platform. The action is in the Equity module; but first, we need to initialize the notebook with the import statements block.\n",
"\n",
"## Import Statements"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
+ "from datetime import datetime, timedelta\n",
+ "\n",
"import pandas as pd\n",
- "from openbb_terminal.sdk import openbb"
+ "from openbb import obb\n"
]
},
{
@@ -27,39 +29,23 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## The Stocks Module\n",
+ "## The Equity Module\n",
"\n",
- "The `load` function is likely the first place to reach when requesting daily, or intraday, historical price data. `openbb.stocks.load()` is a versatile data aggregator, and not only for stocks.\n",
+ "Historical market prices typically come in the form of OHLC+V - open, high, low, close, volume. There may be additional fields returned by a provider, but those are the expected columns. Granularity and amount of historical data will vary by provider and subscription status. Visit their websites to understand what your entitlements are.\n",
"\n",
- "### openbb.stocks.load()\n",
+ "### openbb.equity.price.historical()\n",
"\n",
- "The input parameters for the function:\n",
+ "- This endpoint has the most number of providers out of any function. At the time of writing, choices are:\n",
"\n",
- "```python\n",
- "Parameters\n",
- "----------\n",
- "symbol: str\n",
- " Ticker to get data\n",
- "start_date: str or datetime, optional\n",
- " Start date to get data from with. - datetime or string format (YYYY-MM-DD)\n",
- "interval: int\n",
- " Interval (in minutes) to get data 1, 5, 15, 30, 60 or 1440\n",
- "end_date: str or datetime, optional\n",
- " End date to get data from with. - datetime or string format (YYYY-MM-DD)\n",
- "prepost: bool\n",
- " Pre and After hours data\n",
- "source: str\n",
- " Source of data extracted\n",
- "weekly: bool\n",
- " Flag to get weekly data\n",
- "monthly: bool\n",
- " Flag to get monthly data\n",
- "verbose: bool\n",
- " Display verbose information on what was the symbol that was loaded\n",
- "```\n",
+ "['alpha_vantage', 'cboe', 'fmp', 'intrinio', 'polygon', 'tiingo', 'yfinance']\n",
"\n",
+ "- Common parameters have been standardized across all souces, `start_date`, `end_date`, `interval`.\n",
"\n",
- "Set `verbose = False` to silence the console print. Execute the two cells below to demonstrate."
+ "- The default interval will be `1d`.\n",
+ "\n",
+ "- The depth of historical data and choices for granularity will vary by provider and subscription status. Refer to the website and documentation of each source understand your specific entitlements.\n",
+ "\n",
+ "- For demonstration purposes, we will use the `openbb-yfinance` data extension."
]
},
{
@@ -70,28 +56,79 @@
{
"data": {
"text/html": [
- "Loading Daily data for SPY with starting period 2020-05-18.\n",
- "
\n"
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " open | \n",
+ " high | \n",
+ " low | \n",
+ " close | \n",
+ " volume | \n",
+ " dividends | \n",
+ " stock splits | \n",
+ " capital gains | \n",
+ "
\n",
+ " \n",
+ " date | \n",
+ " | \n",
+ " | \n",
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+ " | \n",
+ " | \n",
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+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2022-11-22 | \n",
+ " 396.63 | \n",
+ " 400.07 | \n",
+ " 395.15 | \n",
+ " 399.9 | \n",
+ " 60429000 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
],
"text/plain": [
- "Loading Daily data for SPY with starting period 2020-05-18.\n"
+ " open high low close volume dividends stock splits \\\n",
+ "date \n",
+ "2022-11-22 396.63 400.07 395.15 399.9 60429000 0.0 0.0 \n",
+ "\n",
+ " capital gains \n",
+ "date \n",
+ "2022-11-22 0.0 "
]
},
+ "execution_count": 2,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
- "df_daily = openbb.stocks.load(symbol = 'spy')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_daily = openbb.stocks.load(symbol = 'spy', verbose = False)"
+ "df_daily = obb.equity.price.historical(symbol = \"spy\", provider=\"yfinance\")\n",
+ "df_daily.to_df().head(1)\n"
]
},
{
@@ -104,6966 +141,107 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
- "Loading Daily data for SPY with starting period 1993-01-29.\n",
- "
\n"
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " open | \n",
+ " high | \n",
+ " low | \n",
+ " close | \n",
+ " volume | \n",
+ " dividends | \n",
+ " stock splits | \n",
+ " capital gains | \n",
+ "
\n",
+ " \n",
+ " date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1993-01-29 | \n",
+ " 43.97 | \n",
+ " 43.97 | \n",
+ " 43.75 | \n",
+ " 43.94 | \n",
+ " 1003200 | \n",
+ " 0.0 | \n",
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+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
],
"text/plain": [
- "Loading Daily data for SPY with starting period 1993-01-29.\n"
+ " open high low close volume dividends stock splits \\\n",
+ "date \n",
+ "1993-01-29 43.97 43.97 43.75 43.94 1003200 0.0 0.0 \n",
+ "\n",
+ " capital gains \n",
+ "date \n",
+ "1993-01-29 0.0 "
]
},
+ "execution_count": 3,
"metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_daily = openbb.stocks.load(symbol = 'spy', start_date = '1990-01-01')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Weekly and monthly intervals are selected with either argument as `True`. The `load` function can be used as an input parameter to functions, like `candle`. This draws a chart without needing to load anything into memory."
- ]
- },
- {
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"source": [
- "openbb.stocks.candle(\n",
- " data = openbb.stocks.load(symbol = 'spy', start_date = '2013-06-01', monthly = True, verbose = False),\n",
- " symbol = \"SPY Monthly\",\n",
- " ha = True,\n",
- ")"
+ "df_daily = obb.equity.price.historical(symbol = \"spy\", start_date = \"1990-01-01\", provider=\"yfinance\").to_df()\n",
+ "df_daily.head(1)\n"
]
},
{
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- "The default settings retrieve daily, intraday data is selected using the `interval` argument, with a choice of: `[1, 5, 15, 30, 60]`. Include pre/post market data by setting the argument, `prepost = True`. The amount of history for each interval, and level of market coverage, will vary by the source, which is selectable via the `source` argument. The block below packs an object with all intervals."
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- "cell_type": "code",
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- "from datetime import datetime,timedelta\n",
- "\n",
- "def load_historical(symbol: str = \"\", start_date: str = \"\", end_date: str = \"\", source: str = \"\", prepost: bool = False) -> object:\n",
+ "#### Intervals\n",
"\n",
- " if symbol == \"\":\n",
- " print(\"Please enter a ticker symbol\")\n",
- " if start_date == \"\":\n",
- " start_date = (datetime.now() - timedelta(weeks = 104)).date()\n",
- " if end_date == \"\":\n",
- " end_date = datetime.now().date()\n",
- " if source == \"\":\n",
- " source = \"YahooFinance\"\n",
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- " class HistoricalPrices:\n",
- " def __init__(self) -> None:\n",
- " self.one: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, interval = 1, source=source, verbose = False, prepost = prepost\n",
- " ).convert_dtypes()\n",
- " self.five: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, interval = 5, source = source, verbose = False, prepost = prepost\n",
- " ).convert_dtypes()\n",
- " self.fifteen: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, interval = 15, verbose = False, prepost = prepost\n",
- " ).convert_dtypes()\n",
- " self.thirty: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, interval = 30, verbose = False, prepost = prepost\n",
- " ).convert_dtypes()\n",
- " self.sixty: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, interval = 60, verbose = False, prepost = prepost\n",
- " ).convert_dtypes()\n",
- " self.daily: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, source = source, verbose = False\n",
- " ).convert_dtypes()\n",
- " self.weekly: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, source = source, weekly = True, verbose = False\n",
- " ).convert_dtypes()\n",
- " self.monthly: pd.DataFrame = openbb.stocks.load(\n",
- " symbol=symbol, start_date=start_date, end_date=end_date, source=source, monthly = True, verbose = False\n",
- " ).convert_dtypes()\n",
- " prices = HistoricalPrices()\n",
+ "The intervals are entered according to this pattern:\n",
"\n",
- " return prices"
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- "source": [
- "prices = load_historical(\"SPY\")\n",
- "openbb.stocks.candle(data = prices.fifteen[\"2023-05-15\":], symbol = \"SPY - Fifteen Minute Candles\")"
- ]
- },
- {
- "attachments": {},
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- "source": [
- "To demonstrate the difference between sources, let's compare values for daily volume from several sources."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 113,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Yahoo was the source for the previously loaded data.\n",
- "yahoo = prices\n",
+ "- `1m` = One Minute\n",
+ "- `1h` = One Hour\n",
+ "- `1d` = One Day\n",
+ "- `1W` = One Week\n",
+ "- `1M` = One Month\n",
"\n",
- "polygon = load_historical(\"SPY\", source = \"Polygon\")\n",
- "alphavantage = load_historical(\"SPY\", source = \"AlphaVantage\")"
+ "The date for monthly value is the first or last, depending on the provider. This can be easily resampled from daily data."
]
},
{
"cell_type": "code",
- "execution_count": 114,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -7087,67 +265,88 @@
" \n",
" \n",
" | \n",
- " AlphaVantage Volume | \n",
- " Polygon Volume | \n",
- " Yahoo Volume | \n",
+ " open | \n",
+ " high | \n",
+ " low | \n",
+ " close | \n",
+ " volume | \n",
+ " dividends | \n",
+ " stock splits | \n",
+ " capital gains | \n",
"
\n",
" \n",
" date | \n",
" | \n",
" | \n",
" | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
"
\n",
" \n",
" \n",
" \n",
- " 2023-05-22 | \n",
- " 60745399 | \n",
- " 60754935 | \n",
- " 60745400 | \n",
+ " 2023-10-01 | \n",
+ " 426.62 | \n",
+ " 438.14 | \n",
+ " 409.21 | \n",
+ " 418.20 | \n",
+ " 1999149700 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
- " 2023-05-23 | \n",
- " 85145553 | \n",
- " 86383531 | \n",
- " 86301900 | \n",
+ " 2023-11-01 | \n",
+ " 419.20 | \n",
+ " 456.38 | \n",
+ " 418.65 | \n",
+ " 455.02 | \n",
+ " 1161239576 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
"\n",
" "
],
"text/plain": [
- " AlphaVantage Volume Polygon Volume Yahoo Volume\n",
- "date \n",
- "2023-05-22 60745399 60754935 60745400\n",
- "2023-05-23 85145553 86383531 86301900"
+ " open high low close volume dividends \\\n",
+ "date \n",
+ "2023-10-01 426.62 438.14 409.21 418.20 1999149700 0.0 \n",
+ "2023-11-01 419.20 456.38 418.65 455.02 1161239576 0.0 \n",
+ "\n",
+ " stock splits capital gains \n",
+ "date \n",
+ "2023-10-01 0.0 0.0 \n",
+ "2023-11-01 0.0 0.0 "
]
},
- "execution_count": 114,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# Make a new DataFrame with just the volume columns\n",
- "compare = pd.DataFrame()\n",
- "compare[\"AlphaVantage Volume\"] = alphavantage.daily[\"Volume\"]\n",
- "compare[\"Polygon Volume\"] = polygon.daily[\"Volume\"]\n",
- "compare[\"Yahoo Volume\"] = yahoo.daily[\"Volume\"]\n",
- "\n",
- "compare.tail(2)"
+ "df_monthly = obb.equity.price.historical(\"spy\", start_date=\"1990-01-01\", interval=\"1M\", provider=\"yfinance\").to_df()\n",
+ "df_monthly.tail(2)\n"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "Then at the one-minute interval."
+ "#### Resample a Time Series\n",
+ "\n",
+ "`yfinance` returns the monthly data for the first day of each month. Let's resample it to take from the last, using the daily information captured in the previous cells."
]
},
{
"cell_type": "code",
- "execution_count": 115,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -7171,169 +370,146 @@
"