diff --git a/openbb_terminal/__init__.py b/openbb_terminal/__init__.py index 9f27fdf85a3..c26bf10a4a3 100644 --- a/openbb_terminal/__init__.py +++ b/openbb_terminal/__init__.py @@ -6,6 +6,6 @@ from . import ( etf, forex, portfolio, - jupyter, + reports, stocks, ) diff --git a/openbb_terminal/api.py b/openbb_terminal/api.py index 2596ebcf1b7..d6a52f6ef15 100644 --- a/openbb_terminal/api.py +++ b/openbb_terminal/api.py @@ -10,6 +10,6 @@ from .etf import etf_api as etf from .forex import forex_api as forex from .mutual_funds import mutual_fund_api as funds from .portfolio import portfolio_api as portfolio -from .jupyter import widget_helpers as widgets +from .reports import widget_helpers as widgets from .config_terminal import theme diff --git a/openbb_terminal/jupyter/__init__.py b/openbb_terminal/dashboards/__init__.py similarity index 100% rename from openbb_terminal/jupyter/__init__.py rename to openbb_terminal/dashboards/__init__.py diff --git a/openbb_terminal/jupyter/dashboards/dashboards_controller.py b/openbb_terminal/dashboards/dashboards_controller.py similarity index 97% rename from openbb_terminal/jupyter/dashboards/dashboards_controller.py rename to openbb_terminal/dashboards/dashboards_controller.py index 67566a6fba5..1d470984521 100644 --- a/openbb_terminal/jupyter/dashboards/dashboards_controller.py +++ b/openbb_terminal/dashboards/dashboards_controller.py @@ -33,7 +33,7 @@ class DashboardsController(BaseController): "shortdata", "crypto", ] - PATH = "/jupyter/dashboards/" + PATH = "/dashboards/" def __init__(self, queue: List[str] = None): """Constructor""" @@ -56,7 +56,7 @@ class DashboardsController(BaseController): shortdata finra shortdata analysis crypto cryptocurrency exchange rates against USD[/cmds] """ - console.print(text=help_text, menu="Jupyter - Dashboards") + console.print(text=help_text, menu="Dashboards") @log_start_end(log=logger) def call_stocks(self, other_args: List[str]): diff --git a/openbb_terminal/jupyter/dashboards/widget_helpers.py b/openbb_terminal/dashboards/widget_helpers.py similarity index 100% rename from openbb_terminal/jupyter/dashboards/widget_helpers.py rename to openbb_terminal/dashboards/widget_helpers.py diff --git a/openbb_terminal/jupyter/dashboards/widgets/card.j2 b/openbb_terminal/dashboards/widgets/card.j2 similarity index 100% rename from openbb_terminal/jupyter/dashboards/widgets/card.j2 rename to openbb_terminal/dashboards/widgets/card.j2 diff --git a/openbb_terminal/jupyter/dashboards/widgets/currencies.json b/openbb_terminal/dashboards/widgets/currencies.json similarity index 100% rename from openbb_terminal/jupyter/dashboards/widgets/currencies.json rename to openbb_terminal/dashboards/widgets/currencies.json diff --git a/openbb_terminal/jupyter/dashboards/widgets/style.css b/openbb_terminal/dashboards/widgets/style.css similarity index 100% rename from openbb_terminal/jupyter/dashboards/widgets/style.css rename to openbb_terminal/dashboards/widgets/style.css diff --git a/openbb_terminal/jupyter/dashboards/chains.ipynb b/openbb_terminal/jupyter/dashboards/chains.ipynb deleted file mode 100644 index c17412479ee..00000000000 --- a/openbb_terminal/jupyter/dashboards/chains.ipynb +++ /dev/null @@ -1,177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "beb36881", - "metadata": {}, - "outputs": [], - "source": [ - "opts = [\n", - " \"lastTradeDate\",\n", - " \"strike\",\n", - " \"lastPrice\",\n", - " \"bid\",\n", - " \"ask\",\n", - " \"change\",\n", - " \"percentChange\",\n", - " \"volume\",\n", - " \"openInterest\",\n", - " \"impliedVolatility\",\n", - "]\n", - "\n", - "\n", - "def clean_str(string):\n", - " new_str = \"\"\n", - " for letter in string:\n", - " if letter.isupper():\n", - " new_str += \" \"\n", - " new_str += letter\n", - " return new_str.title()\n", - "\n", - "\n", - "def format_plotly(fig, x, y, ticker, expiry, inst):\n", - " fig.update_yaxes(title=clean_str(y))\n", - " fig.update_xaxes(title=clean_str(x))\n", - " expires = \", \".join(expiry)\n", - " title = (\n", - " f\"{clean_str(y)} vs. {clean_str(x)} for {ticker.upper()} {inst}s on {expires}\"\n", - " )\n", - " fig.update_layout(\n", - " autosize=False,\n", - " width=1000,\n", - " height=500,\n", - " title={\n", - " \"text\": title,\n", - " \"y\": 0.95,\n", - " \"x\": 0.5,\n", - " \"xanchor\": \"center\",\n", - " \"yanchor\": \"top\",\n", - " },\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_ticker = \"\"\n", - " self.dfs = {}\n", - "\n", - " def update(self, ticker, expiry, inst, x, y):\n", - " if ticker:\n", - " stock = yf.Ticker(ticker)\n", - " if ticker != self.last_ticker:\n", - " exp_w.options = stock.options\n", - " self.last_ticker = ticker\n", - "\n", - " if expiry:\n", - " fig = go.Figure()\n", - " for expire in expiry:\n", - " if expire not in self.dfs:\n", - " self.dfs[expire] = stock.option_chain(expire)\n", - " group = self.dfs[expire]\n", - " df = group.calls if inst == \"Call\" else group.puts\n", - "\n", - " plot = go.Scatter(\n", - " x=df[x],\n", - " y=df[y],\n", - " mode=\"lines\",\n", - " connectgaps=True,\n", - " name=expire,\n", - " )\n", - " fig.add_trace(plot)\n", - "\n", - " format_plotly(fig, x, y, ticker, expiry, inst)\n", - "\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config={\"showTips\": False})\n", - "\n", - "\n", - "x_w = widgets.Dropdown(options=opts, value=\"strike\", description=\"X\")\n", - "y_w = widgets.Dropdown(options=opts, value=\"lastPrice\", description=\"Y\")\n", - "tickers_w = widgets.Text(description=\"Ticker\")\n", - "exp_w = widgets.SelectMultiple(description=\"Expiry\")\n", - "inst_w = widgets.Dropdown(options=[\"Put\", \"Call\"], value=\"Call\", description=\"Type\")\n", - "\n", - "select_opt = widgets.VBox([tickers_w, exp_w])\n", - "select_plot = widgets.VBox([inst_w, x_w, y_w])\n", - "controls = widgets.HBox([select_opt, select_plot])\n", - "\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.update,\n", - " {\"ticker\": tickers_w, \"expiry\": exp_w, \"inst\": inst_w, \"x\": x_w, \"y\": y_w},\n", - ")\n", - "\n", - "title_html = \"

Option Chain Dashboard

\"\n", - "app_contents = [\n", - " widgets.HTML(title_html),\n", - " controls,\n", - " stocks_view,\n", - "]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a84c7390-6b43-4a25-8aa2-31aa84f52605", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/dashboards/correlation.ipynb b/openbb_terminal/jupyter/dashboards/correlation.ipynb deleted file mode 100644 index 7e2b67e59a0..00000000000 --- a/openbb_terminal/jupyter/dashboards/correlation.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 10, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from datetime import datetime, timedelta\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "import pandas as pd\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6c554cd2-462c-4832-b73c-c214aae11ce4", - "metadata": {}, - "outputs": [], - "source": [ - "interval_opts = [\n", - " \"1m\",\n", - " \"2m\",\n", - " \"5m\",\n", - " \"15m\",\n", - " \"30m\",\n", - " \"60m\",\n", - " \"90m\",\n", - " \"1h\",\n", - " \"1d\",\n", - " \"5d\",\n", - " \"1wk\",\n", - " \"1mo\",\n", - " \"3mo\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b462032b043b47a8899f8366f43ab28e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value='

Correlation Analysis Dashboard

'), HBox(children=(VBox(children=(Textarea(v…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_tickers = \"\"\n", - " self.last_interval = \"1d\"\n", - " self.df = pd.DataFrame()\n", - "\n", - " def create(self, data, start, end, interval, tickers):\n", - " global last_tickers, df, last_interval\n", - " if tickers and tickers[-1] == \",\":\n", - " if tickers != self.last_tickers or interval != self.last_interval:\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " self.df = yf.download(\n", - " tickers, period=\"max\", interval=interval, progress=False\n", - " )\n", - " else:\n", - " self.df = yf.download(\n", - " tickers, start=start, end=end, interval=interval, progress=False\n", - " )\n", - " self.last_tickers = tickers\n", - " self.last_interval = interval\n", - "\n", - " start_n = datetime(start.year, start.month, start.day)\n", - " end_n = datetime(end.year, end.month, end.day)\n", - "\n", - " df = self.df[data]\n", - "\n", - " if not isinstance(df, pd.Series):\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " result = df.loc[(df.index >= start_n) & (df.index <= end_n)].corr()\n", - " else:\n", - " result = df.corr()\n", - "\n", - " base = [\n", - " [\n", - " \"black\" if x == 1 else \"lightgreen\" if x > 0 else \"lightpink\"\n", - " for x in result[y].tolist()\n", - " ]\n", - " for y in result.columns\n", - " ]\n", - " base = [[\"lightgray\" for _ in range(result.shape[0])]] + base\n", - " result = result.reset_index()\n", - " result.rename(columns={\"index\": \"\"}, inplace=True)\n", - "\n", - " fig = go.Figure(\n", - " data=[\n", - " go.Table(\n", - " header=dict(\n", - " values=list(result.columns),\n", - " fill_color=\"lightgray\",\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " cells=dict(\n", - " values=[result[x] for x in result.columns],\n", - " fill_color=base,\n", - " format=[\"\"]\n", - " + [\".2f\" for _ in range(len(df.columns) - 1)],\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " )\n", - " ],\n", - " )\n", - " fig.update_layout(\n", - " autosize=True,\n", - " height=600,\n", - " showlegend=False,\n", - " )\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config={\"showTips\": False})\n", - "\n", - "\n", - "w_auto = widgets.Layout(width=\"auto\")\n", - "data_opts = [\"Open\", \"Close\", \"High\", \"Low\", \"Volume\"]\n", - "data_widget = widgets.Dropdown(\n", - " options=data_opts, value=\"Close\", layout=w_auto, description=\"Data\"\n", - ")\n", - "\n", - "base_date = (datetime.today() - timedelta(days=365)).date()\n", - "start_widget = widgets.DatePicker(value=base_date, layout=w_auto, description=\"Start\")\n", - "end_widget = widgets.DatePicker(\n", - " value=datetime.today().date(), layout=w_auto, description=\"End\"\n", - ")\n", - "interval_widget = widgets.Dropdown(\n", - " options=interval_opts, value=\"1d\", layout=w_auto, description=\"Interval\"\n", - ")\n", - "tickers_widget = widgets.Textarea(value=\"TSLA,AAPL,\", layout=w_auto)\n", - "\n", - "data_box = widgets.VBox([tickers_widget, data_widget])\n", - "date_box = widgets.VBox([start_widget, end_widget, interval_widget])\n", - "controls = widgets.HBox([data_box, date_box], layout=widgets.Layout(width=\"60%\"))\n", - "\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create,\n", - " {\n", - " \"data\": data_widget,\n", - " \"start\": start_widget,\n", - " \"end\": end_widget,\n", - " \"interval\": interval_widget,\n", - " \"tickers\": tickers_widget,\n", - " },\n", - ")\n", - "\n", - "title_html = \"

Correlation Analysis Dashboard

\"\n", - "warning_html = '

Use a comma after EVERY stock typed.

'\n", - "app_contents = [\n", - " widgets.HTML(title_html),\n", - " controls,\n", - " widgets.HTML(warning_html),\n", - " stocks_view,\n", - "]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37f46146-a206-4c25-b969-81c5abec4b1d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/dashboards/crypto.ipynb b/openbb_terminal/jupyter/dashboards/crypto.ipynb deleted file mode 100644 index 7972cda6037..00000000000 --- a/openbb_terminal/jupyter/dashboards/crypto.ipynb +++ /dev/null @@ -1,160 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6e198e40-0c33-45b3-a79b-2d9ee9eff7b9", - "metadata": { - "tags": [] - }, - "source": [ - "# Cryptocurrency exchange rates to USD ($)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "\n", - "import ipywidgets as ipw\n", - "from IPython.display import display\n", - "import pandas as pd\n", - "\n", - "from openbb_terminal import api as openbb\n", - "\n", - "ipw.HTML(f\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dcbbf867-af50-4d06-ae5c-7c09647aa55d", - "metadata": {}, - "outputs": [], - "source": [ - "COIN_LIST = (\n", - " \"BTC,ETH,USDT,BNB,USDC,XRP,LUNA,ADA,\"\n", - " + \"SOL,AVAX,DOT,BUSD,DOGE,UST,SHIB,WBTC,\"\n", - " + \"MATIC,CRO,DAI,LTC,ATOM,NEAR,LINK,BCH,\"\n", - " + \"UNI,TRX,FTT,ETC,LEO,ALGO,XLM,MANA,\"\n", - " + \"BTCB,HBAR,EGLD,ICP,SAND,XMR,WAVES,VET,\"\n", - " + \"APE,FIL,FTM,AXS,THETA,KLAY,XTZ,RUNE\"\n", - ").split(\",\")\n", - "exchange_rates = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "deeeb80a-3675-44fb-9a1b-2325d8da4e7d", - "metadata": {}, - "outputs": [], - "source": [ - "def get_exchange_rate(coin: str, days: int = 2):\n", - " \"\"\"Get exchange rate for a cryptocurrency agains USD.\"\"\"\n", - " (_, _, _, _, current_df, _,) = openbb.crypto.load(\n", - " coin=coin,\n", - " source=\"cp\",\n", - " days=days,\n", - " vs=\"usd\",\n", - " interval=\"1day\",\n", - " should_load_ta_data=True,\n", - " )\n", - " price_color = \"neutral_color\"\n", - " if current_df[\"Close\"].iloc[-1] > current_df[\"Close\"].iloc[-2]:\n", - " price_color = \"up_color\"\n", - " elif current_df[\"Close\"].iloc[-1] < current_df[\"Close\"].iloc[-2]:\n", - " price_color = \"down_color\"\n", - " price = str(current_df[\"Close\"].iloc[-1])[:7]\n", - "\n", - " return price, price_color" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18acf5cb-6cbf-484d-8042-107c030098de", - "metadata": {}, - "outputs": [], - "source": [ - "def show_rates(btn):\n", - " \"\"\"Show exchange rates in grid box widget.\"\"\"\n", - " grid.children = ()\n", - " for coin in COIN_LIST:\n", - " if coin not in exchange_rates.keys():\n", - " exchange_rates[coin] = {\"price\": None, \"color\": None}\n", - " try:\n", - " price, price_color = get_exchange_rate(coin=coin)\n", - " except:\n", - " price, price_color = \"-------\", \"neutral_color\"\n", - " widgets = list(grid.children)\n", - " widgets.append(\n", - " ipw.HTML(\n", - " openbb.widgets.price_card(\n", - " ticker=coin,\n", - " price=price,\n", - " price_color=price_color,\n", - " )\n", - " )\n", - " )\n", - " grid.children = tuple(widgets)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf2021e9-de8a-466e-b341-fcd6abda64b5", - "metadata": {}, - "outputs": [], - "source": [ - "update_rates = ipw.Button(description=\"Update rates\")\n", - "update_rates.on_click(show_rates)\n", - "header = ipw.HBox([update_rates])\n", - "\n", - "layout = ipw.Layout(grid_template_columns=\"1fr \" * 8)\n", - "grid = ipw.GridBox(\n", - " [],\n", - " layout=layout,\n", - ")\n", - "\n", - "display(header, grid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19dfcbfe-eaec-406e-bb3c-72d1b3538a6e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - }, - "voila": { - "theme": "dark" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/dashboards/currencies.ipynb b/openbb_terminal/jupyter/dashboards/currencies.ipynb deleted file mode 100644 index ad612c6ce8e..00000000000 --- a/openbb_terminal/jupyter/dashboards/currencies.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "da655985-c9b7-492e-aefa-fbe3bc1865d5", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import time\n", - "from datetime import datetime, timedelta\n", - "from typing import Optional\n", - "\n", - "import ipywidgets as ipw\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib_inline.backend_inline\n", - "from ipyflex import FlexLayout\n", - "from widget_helpers import price_card, stylesheet\n", - "\n", - "from openbb_terminal import api as openbb\n", - "\n", - "%matplotlib widget\n", - "openbb.theme.applyMPLstyle()\n", - "ipw.HTML(f\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5905453-4de6-4f5f-8012-9acdfca50987", - "metadata": {}, - "outputs": [], - "source": [ - "def get_exchange_rate(currency_pair: str) -> str:\n", - " \"\"\"Get exchange rate for a currency pair.\"\"\"\n", - " from_symbol, to_symbol = currency_pair.split(\"/\")\n", - " exchange_data = openbb.forex.models.av.get_quote(\n", - " to_symbol=to_symbol, from_symbol=from_symbol\n", - " )\n", - " return exchange_data[\"Realtime Currency Exchange Rate\"][\"5. Exchange Rate\"][:6]\n", - "\n", - "\n", - "def get_candle_widget(\n", - " output: Optional[ipw.Output],\n", - " to_symbol: str,\n", - " from_symbol: str,\n", - ") -> ipw.Output:\n", - " \"\"\"Plot a candle chart for a currency pair.\"\"\"\n", - " start_date = datetime.now() - timedelta(days=1)\n", - " start_date = start_date.strftime(\"%Y-%m-%d\")\n", - "\n", - " exchange_rate_data = openbb.forex.models.av.get_historical(\n", - " to_symbol=to_symbol,\n", - " from_symbol=from_symbol,\n", - " start_date=start_date,\n", - " resolution=\"i\",\n", - " interval=\"15\",\n", - " )\n", - " fig, ax = plt.subplots(1, 1, figsize=(11, 4))\n", - " openbb.forex.candle(\n", - " data=exchange_rate_data,\n", - " to_symbol=to_symbol,\n", - " from_symbol=from_symbol,\n", - " external_axes=[ax],\n", - " )\n", - "\n", - " ax.set_xlim(0, len(exchange_rate_data.index))\n", - " fig.canvas.header_visible = False\n", - " fig.canvas.footer_visible = False\n", - " with output:\n", - " output.clear_output(True)\n", - " fig.canvas.show()\n", - " return output\n", - "\n", - "\n", - "def on_dropdown_change(change):\n", - " \"\"\"Update charts on change of dropdown selection.\"\"\"\n", - " if change[\"type\"] == \"change\" and change[\"name\"] == \"value\":\n", - " output = ipw.Output()\n", - " get_candle_widget(\n", - " to_symbol=to_widget.value,\n", - " from_symbol=from_widget.value,\n", - " )\n", - " dashboard.children[\"Candle\"] = output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c768d15-37ad-441b-b069-506c4bdbb9a7", - "metadata": {}, - "outputs": [], - "source": [ - "exchange_rates = {\n", - " \"EUR/USD\": {\"latest\": None, \"previous\": None},\n", - " \"USD/JPY\": {\"latest\": None, \"previous\": None},\n", - " \"GBP/USD\": {\"latest\": None, \"previous\": None},\n", - " \"AUD/USD\": {\"latest\": None, \"previous\": None},\n", - " \"USD/CAD\": {\"latest\": None, \"previous\": None},\n", - " # \"USD/CNY\": {\"latest\": None, \"previous\": None},\n", - " # \"USD/CHF\": {\"latest\": None, \"previous\": None},\n", - " # \"USD/HKD\": {\"latest\": None, \"previous\": None},\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4968ab89-aafe-4234-933c-963bd672715d", - "metadata": {}, - "outputs": [], - "source": [ - "def compose_widgets():\n", - " widgets = {}\n", - " for currency_pair in exchange_rates:\n", - " price = exchange_rates[currency_pair][\"latest\"]\n", - " price_color = \"neutral_color\"\n", - " if exchange_rates[currency_pair][\"previous\"] is not None:\n", - " if (\n", - " exchange_rates[currency_pair][\"latest\"]\n", - " > exchange_rates[currency_pair][\"previous\"]\n", - " ):\n", - " price_color = \"up_color\"\n", - " elif (\n", - " exchange_rates[currency_pair][\"latest\"]\n", - " < exchange_rates[currency_pair][\"previous\"]\n", - " ):\n", - " price_color = \"down_color\"\n", - " widgets[currency_pair] = ipw.HTML(\n", - " price_card(ticker=currency_pair, price=price, price_color=price_color)\n", - " )\n", - " return widgets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f35d1500-a411-4cff-94ee-d65d4d1810f9", - "metadata": {}, - "outputs": [], - "source": [ - "# for currency_code in rates_to_usd:\n", - "# rates_to_usd[currency_code][\"previous\"] = rates_to_usd[currency_code][\"latest\"]\n", - "# rates_to_usd[currency_code][\"latest\"] = get_rate_against_usd(currency_code)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a72837b-4ddd-4c01-a493-ebb430202cbc", - "metadata": {}, - "outputs": [], - "source": [ - "widgets = compose_widgets()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c636543c-42fe-4dd6-a900-1552a3c28c7e", - "metadata": {}, - "outputs": [], - "source": [ - "currency_list = openbb.forex.models.av.get_currency_list()\n", - "from_widget = ipw.Dropdown(\n", - " options=currency_list,\n", - " value=\"EUR\",\n", - " description=\"From:\",\n", - " disabled=False,\n", - " layout=ipw.Layout(margin=\"130\"),\n", - ")\n", - "to_widget = ipw.Dropdown(\n", - " options=currency_list,\n", - " value=\"USD\",\n", - " description=\"To:\",\n", - " disabled=False,\n", - ")\n", - "exchange_selection = ipw.VBox([from_widget, to_widget])\n", - "\n", - "widgets[\"Select\"] = exchange_selection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d069845-d622-4878-8b18-b385c6cb7a7f", - "metadata": {}, - "outputs": [], - "source": [ - "output = ipw.Output()\n", - "output = get_candle_widget(\n", - " output=output,\n", - " to_symbol=to_widget.value,\n", - " from_symbol=from_widget.value,\n", - ")\n", - "widgets[\"Candle\"] = output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81c7e5c7-2e52-45ba-bf7d-aa240f95be2d", - "metadata": {}, - "outputs": [], - "source": [ - "from_widget.observe(on_dropdown_change)\n", - "to_widget.observe(on_dropdown_change)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0c7d921-e5dc-4ce8-97f1-a59b414a676a", - "metadata": {}, - "outputs": [], - "source": [ - "dashboard = FlexLayout(\n", - " layout_config={\"borderLeft\": False, \"borderRight\": False, \"enableSection\": False},\n", - " style={\n", - " \"height\": \"calc(100vh - 80px)\",\n", - " \"backgroundColor\": \"rgb(0 0 0)\",\n", - " \"fontFamily\": \"Consolas\",\n", - " \"fontWeight\": 800,\n", - " },\n", - " header={\n", - " \"title\": \"Currencies\",\n", - " \"style\": {\n", - " \"backgroundColor\": \"rgb(0 0 0)\",\n", - " \"fontWeight\": 400,\n", - " \"fontSize\": \"28px\",\n", - " },\n", - " \"buttons\": [],\n", - " },\n", - " widgets=widgets,\n", - " template=os.path.join(\"widgets\", \"currencies.json\"),\n", - " editable=False,\n", - ")\n", - "dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5fedac44-5e09-44f0-bbbe-fbad7c8716cd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c850ba2-408b-4061-947e-5a1b8a284d22", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - }, - "voila": { - "theme": "dark" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/dashboards/shortdata.ipynb b/openbb_terminal/jupyter/dashboards/shortdata.ipynb deleted file mode 100644 index 04e1791cfb2..00000000000 --- a/openbb_terminal/jupyter/dashboards/shortdata.ipynb +++ /dev/null @@ -1,255 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "import io\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import requests\n", - "from datetime import datetime, timedelta\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "\n", - "%matplotlib widget" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "class ShortDataDash:\n", - " def __init__(self):\n", - " self.df = pd.DataFrame()\n", - " self.days_slider = widgets.IntSlider(\n", - " value=5,\n", - " min=5,\n", - " max=252,\n", - " step=1,\n", - " description=\"Days Back\",\n", - " style={\"description_width\": \"initial\"},\n", - " )\n", - " self.count_slider = widgets.IntSlider(\n", - " value=10,\n", - " min=1,\n", - " max=25,\n", - " step=1,\n", - " description=\"Number to show.\",\n", - " style={\"description_width\": \"initial\"},\n", - " )\n", - " self.output1 = widgets.Output()\n", - " self.output2 = widgets.Output()\n", - " self.load_button = widgets.Button(\n", - " description=\"Load Data\", layout=widgets.Layout(width=\"200px\", height=\"40px\")\n", - " )\n", - " self.load_button.on_click(self.load_button_click)\n", - " self.show_button = widgets.Button(\n", - " description=\"Change Number Shown\", layout=self.load_button.layout\n", - " )\n", - " self.show_button.on_click(self.show_button_click)\n", - "\n", - " self.slider_box = widgets.HBox([self.days_slider, self.count_slider])\n", - " self.button_box = widgets.VBox([self.load_button, self.show_button])\n", - "\n", - " self.stock_input = widgets.Text(\n", - " value=\"GME\",\n", - " placeholder=\"GME\",\n", - " description=\"Ticker:\",\n", - " )\n", - " self.ticker_button = widgets.Button(description=\"Plot Ticker\")\n", - " self.ticker_button.on_click(self.ticker_button_click)\n", - "\n", - " def show_button_click(self, b):\n", - " self.output1.clear_output()\n", - " with self.output1:\n", - " self.update()\n", - "\n", - " def load_button_click(self, b):\n", - " self.output1.clear_output()\n", - " self.output2.clear_output()\n", - " with self.output1:\n", - " print(f\"Data Loading for {self.days_slider.value} days\")\n", - " self.fetch_new_data()\n", - " self.update()\n", - "\n", - " def ticker_button_click(self, b):\n", - " self.output2.clear_output()\n", - " with self.output2:\n", - " self.ticker_plot()\n", - "\n", - " def fetch_new_data(self):\n", - " self.df = pd.DataFrame()\n", - " today = datetime.now().date()\n", - " idx = 0\n", - " len_df = 0\n", - " while len_df < self.days_slider.value:\n", - " date = today - timedelta(days=idx)\n", - " r = requests.get(\n", - " f\"https://cdn.finra.org/equity/regsho/daily/CNMSshvol{date.strftime('%Y%m%d')}.txt\"\n", - " )\n", - " if r.status_code == 200:\n", - " self.df = pd.concat(\n", - " [self.df, pd.read_csv(io.StringIO(r.text), sep=\"|\")], axis=0\n", - " )\n", - " len_df += 1\n", - " idx += 1\n", - " self.df = self.df[self.df.Date > 20100101]\n", - " self.df.Date = self.df[\"Date\"].apply(\n", - " lambda x: datetime.strptime(str(x), \"%Y%m%d\")\n", - " )\n", - "\n", - " def update(self):\n", - " if not self.df.empty:\n", - " temp = (\n", - " self.df.groupby(\"Symbol\")[[\"ShortVolume\", \"TotalVolume\"]]\n", - " .agg(\"sum\")\n", - " .sort_values(by=\"ShortVolume\", ascending=False)\n", - " .head(self.count_slider.value)[::-1]\n", - " )\n", - " self.fig, self.ax = plt.subplots(figsize=(6, 6))\n", - " self.ax.barh(temp.index, temp.TotalVolume, alpha=0.4, label=\"Total Volume\")\n", - " self.ax.barh(temp.index, temp.ShortVolume, label=\"Short Volume\")\n", - " self.ax.set_title(\n", - " f\"Top {self.count_slider.value} Short Volume in Last {self.days_slider.value} Days\"\n", - " )\n", - " self.ax.legend()\n", - " self.fig.tight_layout()\n", - " plt.show()\n", - "\n", - " def ticker_plot(self):\n", - " stock_data = self.df.copy().loc[\n", - " self.df.Symbol == self.stock_input.value,\n", - " [\"Date\", \"ShortVolume\", \"TotalVolume\"],\n", - " ]\n", - " self.fig2, self.ax2 = plt.subplots(figsize=(6, 6))\n", - " self.ax2.plot(\n", - " stock_data.Date, stock_data.TotalVolume, alpha=0.4, label=\"Total Volume\"\n", - " )\n", - " self.ax2.plot(stock_data.Date, stock_data.ShortVolume, label=\"Short Volume\")\n", - " self.ax2.set_title(\n", - " f\"Stock Volume and Short Volume for {self.stock_input.value.upper()}\"\n", - " )\n", - " self.ax2.legend()\n", - " self.fig2.autofmt_xdate()\n", - " self.fig2.tight_layout()\n", - " plt.show()\n", - "\n", - " def build_app(self):\n", - " title_html = \"\"\"\n", - "

Finra Short Data

\n", - "

This widget downloads the consolidated NMS short data from FINRA and aggregates the data by summing over the entire time period.

\n", - "

Note that clicking the 'Load Data' button will reload all data. This can get time consuming, so if you pick a few hundred days, expect a few minutes for loading time.

\n", - "\"\"\"\n", - " middle_html = \"\"\"\n", - "Here we allow the user to query for a single stock. This will work with the loaded data. Note that if you want to reload the data, this will once again take some time.\n", - " \"\"\"\n", - " return [\n", - " widgets.HTML(\n", - " title_html, layout=widgets.Layout(margin=\"0 0 3em 0\", max_width=\"800px\")\n", - " ),\n", - " self.slider_box,\n", - " self.button_box,\n", - " self.output1,\n", - " widgets.HTML(\n", - " middle_html,\n", - " layout=widgets.Layout(margin=\"0 0 3em 0\", max_width=\"800px\"),\n", - " ),\n", - " self.stock_input,\n", - " self.ticker_button,\n", - " self.output2,\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "dash = ShortDataDash()\n", - "app = widgets.VBox(\n", - " dash.build_app(), layout=widgets.Layout(max_width=\"1024px\", margin=\"0 auto 0 auto\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dcda08c8d643465fb862d01341d71370", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value=\"\\n

Finra Short Data

\\n

This widget downloads the consolidated NMS short da…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(app)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/openbb_terminal/jupyter/dashboards/stocks.ipynb b/openbb_terminal/jupyter/dashboards/stocks.ipynb deleted file mode 100644 index dcdafd3df9c..00000000000 --- a/openbb_terminal/jupyter/dashboards/stocks.ipynb +++ /dev/null @@ -1,404 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "e9ad60bc", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from datetime import datetime, timedelta\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objs as go\n", - "import yfinance as yf\n", - "import pandas as pd\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "beb36881", - "metadata": {}, - "outputs": [], - "source": [ - "interval_opts = [\n", - " \"1m\",\n", - " \"2m\",\n", - " \"5m\",\n", - " \"15m\",\n", - " \"30m\",\n", - " \"60m\",\n", - " \"90m\",\n", - " \"1h\",\n", - " \"1d\",\n", - " \"5d\",\n", - " \"1wk\",\n", - " \"1mo\",\n", - " \"3mo\",\n", - "]\n", - "\n", - "rows = [\n", - " \"sector\",\n", - " \"marketCap\",\n", - " \"beta\",\n", - " \"fiftyTwoWeekHigh\",\n", - " \"fiftyTwoWeekLow\",\n", - " \"floatShares\",\n", - " \"sharesShort\",\n", - " \"exDividendDate\",\n", - "]\n", - "\n", - "views = {\n", - " \"Raw Data\": lambda x, y: x,\n", - " \"Percent Change\": lambda x, y: x.pct_change(),\n", - " \"Rolling Average\": lambda x, y: x.rolling(y).mean(),\n", - " \"Rolling Variance\": lambda x, y: x.rolling(y).var(),\n", - " \"Rolling Standard Deviation\": lambda x, y: x.rolling(y).var() ** 0.5,\n", - " \"Rolling Coefficient of Variation\": lambda x, y: (x.rolling(y).var() ** 0.5)\n", - " / (x.rolling(y).mean()),\n", - "}\n", - "\n", - "clean_row = {\n", - " \"sector\": \"Sector\",\n", - " \"marketCap\": \"M Cap\",\n", - " \"beta\": \"Beta\",\n", - " \"fiftyTwoWeekHigh\": \"52W High\",\n", - " \"fiftyTwoWeekLow\": \"52W Low\",\n", - " \"floatShares\": \"Floats\",\n", - " \"sharesShort\": \"Shorts\",\n", - " \"exDividendDate\": \"Ex-Div\",\n", - "}\n", - "\n", - "clean_data = {\n", - " \"sector\": lambda x: \"N/A\" if x is None else x,\n", - " \"marketCap\": lambda x: \"N/A\" if x is None else big_num(x),\n", - " \"beta\": lambda x: \"N/A\" if x is None else f\"{round(x,2)}\",\n", - " \"fiftyTwoWeekHigh\": lambda x: \"N/A\" if x is None else f\"${round(x,2)}\",\n", - " \"fiftyTwoWeekLow\": lambda x: \"N/A\" if x is None else f\"${round(x,2)}\",\n", - " \"floatShares\": lambda x: \"N/A\" if x is None else big_num(x),\n", - " \"sharesShort\": lambda x: \"N/A\" if x is None else big_num(x),\n", - " \"exDividendDate\": lambda x: \"N/A\"\n", - " if x is None\n", - " else datetime.fromtimestamp(x).strftime(\"%Y/%m/%d\"),\n", - "}\n", - "\n", - "\n", - "def big_num(num):\n", - " if num > 1_000_000_000_000:\n", - " return f\"{round(num/1_000_000_000_000,2)}T\"\n", - " if num > 1_000_000_000:\n", - " return f\"{round(num/1_000_000_000,2)}B\"\n", - " if num > 1_000_000:\n", - " return f\"{round(num/1_000_000,2)}M\"\n", - " if num > 1_000:\n", - " return f\"{num/round(1_000,2)}K\"\n", - " return f\"{round(num,2)}\"\n", - "\n", - "\n", - "def clean_str(string):\n", - " new_str = \"\"\n", - " for letter in string:\n", - " if letter.isupper():\n", - " new_str += \" \"\n", - " new_str += letter\n", - " return new_str.title()\n", - "\n", - "\n", - "def format_plotly(fig, data, start, end, chart, calc=None):\n", - " fig.update_yaxes(title=None)\n", - " fig.update_xaxes(title=None)\n", - " start_t = start.strftime(\"%Y/%m/%d\")\n", - " end_t = end.strftime(\"%Y/%m/%d\")\n", - " if calc:\n", - " if len(calc) == 1:\n", - " fig_title = f\"{calc[0]} of {data} from {start_t} to {end_t}\"\n", - " else:\n", - " fig_title = f\"{', '.join(calc)} of {data} from {start_t} to {end_t}\"\n", - " else:\n", - " fig_title = \"Volume\"\n", - " height = 500 if chart == \"main\" else 300\n", - " fig.update_layout(\n", - " margin=dict(l=0, r=10, t=10, b=10),\n", - " autosize=False,\n", - " width=900,\n", - " height=height,\n", - " legend=dict(orientation=\"h\"),\n", - " title={\n", - " \"text\": fig_title,\n", - " \"y\": 0.95,\n", - " \"x\": 0.5,\n", - " \"xanchor\": \"center\",\n", - " \"yanchor\": \"top\",\n", - " },\n", - " )\n", - "\n", - "\n", - "def create_line(visual, x, y, name, data, fig):\n", - " if visual == \"line\":\n", - " plot = go.Scatter(x=x, y=y[data], mode=\"lines\", name=name, connectgaps=True)\n", - " if visual == \"scatter\":\n", - " plot = go.Scatter(x=x, y=y[data], mode=\"markers\", name=name)\n", - " if visual == \"candle\":\n", - " plot = go.Candlestick(\n", - " x=x,\n", - " open=y[\"Open\"],\n", - " close=y[\"Close\"],\n", - " high=y[\"High\"],\n", - " low=y[\"Low\"],\n", - " name=name,\n", - " )\n", - " fig.add_trace(plot)\n", - "\n", - "\n", - "def show_fig(fig):\n", - " config = {\"showTips\": False, \"scrollZoom\": True}\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config=config, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config=config)\n", - "\n", - "\n", - "def table_data(infos):\n", - " cols = [\"Ticker\"] + list(infos)\n", - " data = pd.DataFrame(columns=cols)\n", - " data[\"Ticker\"] = [clean_row[x] for x in rows]\n", - " for ticker in list(infos):\n", - " data[ticker] = [clean_data[x](infos[ticker].get(x, None)) for x in rows]\n", - " new_cols = {k: clean_str(k) for k in rows}\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8c2f65c", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_tickers = \"\"\n", - " self.last_interval = \"1d\"\n", - " self.df = pd.DataFrame()\n", - " self.infos = {}\n", - "\n", - " def create_stock(\n", - " self, calculation, data, rolling, start, end, interval, tickers, chart\n", - " ):\n", - " if tickers and tickers[-1] == \",\":\n", - " if tickers != self.last_tickers or interval != self.last_interval:\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " self.df = yf.download(\n", - " tickers, period=\"max\", interval=interval, progress=False\n", - " )\n", - " else:\n", - " end_date = end + timedelta(days=1)\n", - " self.df = yf.download(\n", - " tickers,\n", - " start=start,\n", - " end=end_date,\n", - " interval=interval,\n", - " progress=False,\n", - " )\n", - " self.last_tickers = tickers\n", - " self.last_interval = interval\n", - "\n", - " start_n = datetime(start.year, start.month, start.day)\n", - " end_n = datetime(end.year, end.month, end.day)\n", - " fig = go.Figure()\n", - " for item in calculation:\n", - " calcs = views[item](self.df, rolling)\n", - " if interval in [\"1d\", \"5d\", \"1wk\", \"1mo\", \"3mo\"]:\n", - " result = calcs.loc[\n", - " (calcs.index >= start_n) & (calcs.index <= end_n)\n", - " ]\n", - " else:\n", - " result = calcs\n", - "\n", - " if len(result.columns) == 6:\n", - " name = f\"{tickers.split(',')[0]} {item}\"\n", - " create_line(chart, result.index, result, name, data, fig)\n", - "\n", - " else:\n", - " for val in result.columns.levels[1]:\n", - " vals = result.xs(val, axis=1, level=1, drop_level=True)\n", - " name = f\"{val.upper()} {item}\"\n", - " create_line(chart, result.index, vals, name, data, fig)\n", - "\n", - " format_plotly(fig, data, start, end, \"main\", calculation)\n", - " show_fig(fig)\n", - "\n", - " def create_volume(self, start, end, interval, tickers):\n", - " start_n = datetime(start.year, start.month, start.day)\n", - " end_n = datetime(end.year, end.month, end.day)\n", - " result = self.df.loc[(self.df.index >= start_n) & (self.df.index <= end_n)]\n", - " fig = go.Figure()\n", - " if len(result.columns) == 6:\n", - " name = f\"{tickers.split(',')[0]}\"\n", - " create_line(\"line\", result.index, result, name, \"Volume\", fig)\n", - " else:\n", - " for val in result.columns.levels[1]:\n", - " vals = result.xs(val, axis=1, level=1, drop_level=True)\n", - " name = f\"{val.upper()}\"\n", - " create_line(\"line\", result.index, vals, name, \"Volume\", fig)\n", - " format_plotly(fig, \"Volume\", start, end, \"volume\")\n", - " show_fig(fig)\n", - "\n", - " def create_table(self, tickers):\n", - " if tickers and tickers[-1] == \",\":\n", - " clean_tickers = [x for x in tickers.split(\",\") if x]\n", - " for ticker in clean_tickers:\n", - " if ticker not in self.infos:\n", - " self.infos[ticker] = yf.Ticker(ticker).info\n", - " for ticker in self.infos:\n", - " if ticker not in tickers:\n", - " self.infos.pop(ticker)\n", - " result = table_data(self.infos)\n", - " fig = go.Figure(\n", - " data=[\n", - " go.Table(\n", - " header=dict(\n", - " values=result.columns,\n", - " fill_color=\"lightgray\",\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " cells=dict(\n", - " values=[result[x] for x in result.columns],\n", - " font=dict(color=\"black\"),\n", - " align=\"left\",\n", - " ),\n", - " )\n", - " ],\n", - " )\n", - " fig.update_layout(margin=dict(l=0, r=20, t=0, b=0), width=350)\n", - " show_fig(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0d4de31-1b4f-4c1f-8467-ca314a578d56", - "metadata": {}, - "outputs": [], - "source": [ - "w_auto = widgets.Layout(width=\"auto\")\n", - "calc_widget = widgets.SelectMultiple(\n", - " options=list(views.keys()), value=[\"Raw Data\"], layout=w_auto\n", - ")\n", - "\n", - "data_opts = [\"Open\", \"Close\", \"High\", \"Low\"]\n", - "data_widget = widgets.Dropdown(\n", - " options=data_opts, value=\"Close\", layout=w_auto, description=\"Data\"\n", - ")\n", - "rolling_widget = widgets.Dropdown(\n", - " options=list(range(2, 101)), value=60, layout=w_auto, description=\"Rolling\"\n", - ")\n", - "\n", - "base_date = (datetime.today() - timedelta(days=365)).date()\n", - "start_widget = widgets.DatePicker(value=base_date, layout=w_auto, description=\"Start\")\n", - "end_widget = widgets.DatePicker(\n", - " value=datetime.today().date(), layout=w_auto, description=\"End\"\n", - ")\n", - "\n", - "interval_widget = widgets.Dropdown(\n", - " options=interval_opts, value=\"1d\", layout=w_auto, description=\"Interval\"\n", - ")\n", - "tickers_widget = widgets.Textarea(\n", - " value=\"TSLA,\", layout=widgets.Layout(width=\"auto\", height=\"100%\")\n", - ")\n", - "chart_opts = [\"line\", \"scatter\", \"candle\"]\n", - "chart_widget = widgets.Dropdown(\n", - " options=chart_opts, value=\"line\", layout=w_auto, description=\"Chart\"\n", - ")\n", - "data_box = widgets.VBox([data_widget, rolling_widget, chart_widget])\n", - "date_box = widgets.VBox([start_widget, end_widget, interval_widget])\n", - "controls = widgets.HBox(\n", - " [tickers_widget, calc_widget, date_box, data_box],\n", - " layout=widgets.Layout(width=\"90%\"),\n", - ")\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create_stock,\n", - " {\n", - " \"calculation\": calc_widget,\n", - " \"data\": data_widget,\n", - " \"rolling\": rolling_widget,\n", - " \"start\": start_widget,\n", - " \"end\": end_widget,\n", - " \"interval\": interval_widget,\n", - " \"tickers\": tickers_widget,\n", - " \"chart\": chart_widget,\n", - " },\n", - ")\n", - "\n", - "volume_view = widgets.interactive_output(\n", - " chart.create_volume,\n", - " {\n", - " \"start\": start_widget,\n", - " \"end\": end_widget,\n", - " \"interval\": interval_widget,\n", - " \"tickers\": tickers_widget,\n", - " },\n", - ")\n", - "\n", - "table_view = widgets.interactive_output(chart.create_table, {\"tickers\": tickers_widget})\n", - "\n", - "charts = widgets.VBox(\n", - " [stocks_view, volume_view],\n", - " layout=widgets.Layout(width=\"100%\", padding=\"0\", margin=\"0\"),\n", - ")\n", - "figures = widgets.HBox(\n", - " [charts, table_view], layout=widgets.Layout(padding=\"0\", margin=\"0\")\n", - ")\n", - "\n", - "title_html = \"

Stock Analysis Dashboard

\"\n", - "warning_html = '

Use a comma after EVERY stock typed.

'\n", - "\n", - "app_contents = [widgets.HTML(title_html), controls, widgets.HTML(warning_html), figures]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f33793c-adc9-4bc1-8fd4-aeff5a00ccba", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - }, - "metadata": { - "interpreter": { - "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/dashboards/vsurf.ipynb b/openbb_terminal/jupyter/dashboards/vsurf.ipynb deleted file mode 100644 index 816c8b52128..00000000000 --- a/openbb_terminal/jupyter/dashboards/vsurf.ipynb +++ /dev/null @@ -1,162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "68b664c7-c52d-42c1-b170-575370323d22", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "import sys\n", - "import ipywidgets as widgets\n", - "import plotly.graph_objects as go\n", - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.display import display\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "module_path = os.path.abspath(os.path.join(\"../../..\"))\n", - "\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)\n", - "\n", - "from openbb_terminal import api as openbb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c98dc75-6325-43da-93a2-1b8ec96bd368", - "metadata": {}, - "outputs": [], - "source": [ - "z_dict = {\n", - " \"IV\": \"Implied Volatility\",\n", - " \"OI\": \"Open Interest\",\n", - " \"LP\": \"Last Price\",\n", - "}\n", - "strings = {\n", - " \"IV\": \"DTE: %{x}
Strike: %{y}
IV: %{z}\",\n", - " \"OI\": \"DTE: %{x}
Strike: %{y}
OI: %{z}\",\n", - " \"LP\": \"DTE: %{x}
Strike: %{y}
LP: %{z}\",\n", - "}\n", - "\n", - "\n", - "def create_layout(fig, width, height, z, ticker):\n", - " fig.update_layout(\n", - " title=f\"{z.upper()} Surface for {ticker.upper()}\",\n", - " autosize=False,\n", - " width=width,\n", - " height=height,\n", - " scene=dict(\n", - " xaxis_title=\"Days to Expiration\",\n", - " yaxis_title=\"Strike\",\n", - " zaxis_title=z_dict[z],\n", - " ),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1bc5c6a-ccb2-4ef5-9c79-1f619fbf95a6", - "metadata": {}, - "outputs": [], - "source": [ - "class Chart:\n", - " def __init__(self):\n", - " self.last_ticker = \"\"\n", - " self.df = pd.DataFrame()\n", - "\n", - " def create(self, z, ticker, height, width):\n", - " if ticker:\n", - " if ticker != self.last_ticker:\n", - " self.df = openbb.stocks.options.models.yfinance.get_iv_surface(ticker)\n", - " self.last_ticker = ticker\n", - "\n", - " if not self.df.empty:\n", - " z_dict = {\n", - " \"IV\": self.df.impliedVolatility,\n", - " \"OI\": self.df.openInterest,\n", - " \"LP\": self.df.lastPrice,\n", - " }\n", - " fig = go.Figure(\n", - " data=[\n", - " go.Mesh3d(\n", - " z=z_dict[z],\n", - " x=self.df.dte,\n", - " y=self.df.strike,\n", - " intensity=np.sqrt(z_dict[z]),\n", - " colorscale=\"Jet\",\n", - " hovertemplate=strings[z],\n", - " showscale=False,\n", - " )\n", - " ]\n", - " )\n", - "\n", - " create_layout(fig, height, width, z, ticker)\n", - " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", - " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", - " else:\n", - " fig.show(config={\"showTips\": False})\n", - "\n", - "\n", - "opts = [\"IV\", \"OI\", \"LP\"]\n", - "z_widget = widgets.Dropdown(options=opts, value=\"IV\", description=\"Data\")\n", - "tickers_widget = widgets.Text(description=\"Ticker\", value=\"TSLA\")\n", - "h_widget = widgets.IntText(value=1000, description=\"Height\")\n", - "w_widget = widgets.IntText(value=1000, description=\"Width\")\n", - "\n", - "controls = widgets.VBox([tickers_widget, z_widget])\n", - "size = widgets.VBox([h_widget, w_widget])\n", - "controls = widgets.HBox([controls, size])\n", - "\n", - "chart = Chart()\n", - "stocks_view = widgets.interactive_output(\n", - " chart.create,\n", - " {\"z\": z_widget, \"ticker\": tickers_widget, \"height\": h_widget, \"width\": w_widget},\n", - ")\n", - "\n", - "title_html = \"

Option Surface Dashboard

\"\n", - "app_contents = [\n", - " widgets.HTML(title_html),\n", - " controls,\n", - " stocks_view,\n", - "]\n", - "app = widgets.VBox(app_contents)\n", - "display(app)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "014f267a-92ba-4f23-afb1-9720d8a68cbb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/jupyter_controller.py b/openbb_terminal/jupyter/jupyter_controller.py deleted file mode 100644 index bcc5bfb8e2e..00000000000 --- a/openbb_terminal/jupyter/jupyter_controller.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Jupyter Controller Module""" -__docformat__ = "numpy" - -import logging -from typing import List - -from prompt_toolkit.completion import NestedCompleter - -from openbb_terminal import feature_flags as obbff -from openbb_terminal.decorators import log_start_end -from openbb_terminal.menu import session -from openbb_terminal.parent_classes import BaseController -from openbb_terminal.rich_config import console - -# pylint: disable=import-outside-toplevel - - -logger = logging.getLogger(__name__) - - -class JupyterController(BaseController): - """Resources Controller class""" - - CHOICES_COMMANDS = [ - "reports", - "dashboards", - ] - PATH = "/jupyter/" - - def __init__(self, queue: List[str] = None): - """Constructor""" - super().__init__(queue) - - if session and obbff.USE_PROMPT_TOOLKIT: - choices: dict = {c: {} for c in self.controller_choices} - self.completer = NestedCompleter.from_nested_dict(choices) - - def print_help(self): - """Print help""" - help_text = """[menu] -> reports creates jupyter reports -> dashboards shows interactive jupyter dashboards[/menu] - """ - console.print(text=help_text, menu="Jupyter") - - @log_start_end(log=logger) - def call_reports(self, _): - """Process reports command""" - from openbb_terminal.jupyter.reports.reports_controller import ( - ReportController, - ) - - self.queue = self.load_class(ReportController, self.queue) - - @log_start_end(log=logger) - def call_dashboards(self, _): - """Process dashboards command""" - from openbb_terminal.jupyter.dashboards.dashboards_controller import ( - DashboardsController, - ) - - self.queue = self.load_class(DashboardsController, self.queue) diff --git a/openbb_terminal/jupyter/reports/crypto_market.ipynb b/openbb_terminal/jupyter/reports/crypto_market.ipynb deleted file mode 100644 index f91f8c618c1..00000000000 --- a/openbb_terminal/jupyter/reports/crypto_market.ipynb +++ /dev/null @@ -1,281 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "elect-highway", - "metadata": {}, - "outputs": [], - "source": [ - "# Import most generic modules\n", - "import os\n", - "import sys\n", - "from datetime import datetime\n", - "from IPython.display import display, Markdown\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "module_path = os.path.abspath(os.path.join(\"../../..\"))\n", - "\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fatty-colony", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_crypto_market\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "medieval-majority", - "metadata": {}, - "outputs": [], - "source": [ - "display(Markdown(f\"# Crypto Market - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"))" - ] - }, - { - "cell_type": "markdown", - "id": "basic-sucking", - "metadata": {}, - "source": [ - "## Bitcoin Chart" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "318022a0", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency import cryptocurrency_helpers\n", - "import pandas as pd\n", - "\n", - "coin_map_df = (\n", - " cryptocurrency_helpers.prepare_all_coins_df().set_index(\"Symbol\").loc[\"btc\"]\n", - ")\n", - "\n", - "coin_map_df = (\n", - " coin_map_df.dropna().iloc[0]\n", - " if isinstance(coin_map_df, pd.DataFrame)\n", - " else coin_map_df\n", - ")\n", - "\n", - "cryptocurrency_helpers.plot_chart(\n", - " coin=\"btc-bitcoin\", source=\"cp\", currency=\"USD\", days=60, coin_map_df=coin_map_df\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "animal-titanium", - "metadata": {}, - "source": [ - "## Global Market Information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "artistic-stewart", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.overview import coinpaprika_view\n", - "\n", - "coinpaprika_view.display_global_market(export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "twelve-visiting", - "metadata": {}, - "source": [ - "## Top coins market information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "photographic-payday", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.overview import coinpaprika_view\n", - "\n", - "coinpaprika_view.display_all_coins_market_info(\n", - " currency=\"USD\", sortby=\"volume_24h\", descend=True, top=10, export=\"\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "median-spirituality", - "metadata": {}, - "source": [ - "## Top gainers in last 24h" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "threaded-archives", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", - "\n", - "pycoingecko_view.display_gainers(period=\"24h\", top=10, export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "recognized-intent", - "metadata": {}, - "source": [ - "## Top losers in last 24h" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "exceptional-objective", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", - "\n", - "pycoingecko_view.display_losers(period=\"24h\", top=10, export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "rough-routine", - "metadata": {}, - "source": [ - "## Coins trending on coingecko" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "matched-syria", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", - "\n", - "pycoingecko_view.display_trending(\n", - " export=\"\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "established-order", - "metadata": {}, - "source": [ - "## Decentralized Finance Global Market Info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "artistic-grocery", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.overview import pycoingecko_view\n", - "\n", - "pycoingecko_view.display_global_defi_info(export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "elect-universe", - "metadata": {}, - "source": [ - "## Top DeFi Coins" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "configured-macedonia", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.discovery import pycoingecko_view\n", - "\n", - "pycoingecko_view.display_coins(\"decentralized-finance-defi\", top=10, export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "comparative-congo", - "metadata": {}, - "source": [ - "## Top Decentralized Exchanges" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "scientific-healthcare", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.cryptocurrency.discovery import dappradar_view\n", - "\n", - "dappradar_view.display_top_dexes(top=10, export=\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "knowing-imagination", - "metadata": {}, - "outputs": [], - "source": [ - "!jupyter nbconvert {report_name + '.ipynb'} --to html --no-input" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "interpreter": { - "hash": "158b8ab50e95ded652d8f2b468df64cac8eaa223c5e60f12cdaaeaccc6039b6d" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/reports/dark_pool.ipynb b/openbb_terminal/jupyter/reports/dark_pool.ipynb deleted file mode 100644 index 0855884a3e0..00000000000 --- a/openbb_terminal/jupyter/reports/dark_pool.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "driven-billion", - "metadata": {}, - "outputs": [], - "source": [ - "# Import most generic modules\n", - "import importlib\n", - "import pathlib\n", - "import os\n", - "import sys\n", - "from datetime import datetime, timedelta\n", - "import pandas as pd\n", - "from IPython.display import display, Markdown\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "module_path = os.path.abspath(os.path.join(\"../..\"))\n", - "\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "intended-sherman", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# Parameters that will be replaced when calling this notebook\n", - "ticker = \"AMC\"\n", - "\n", - "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_dark_pool_{ticker.upper()}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "mounted-phase", - "metadata": {}, - "outputs": [], - "source": [ - "display(\n", - " Markdown(\n", - " f\"# {ticker.upper()} - Dark Pool - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "capable-allergy", - "metadata": {}, - "source": [ - "## Top 10 Negative Dark Pool Positions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "statewide-debate", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks.dark_pool_shorts import stockgrid_view\n", - "\n", - "stockgrid_view.dark_pool_short_positions(\n", - " num=10, sort_field=\"dpp_dollar\", ascending=True, export=\"\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "artificial-tampa", - "metadata": {}, - "source": [ - "## Top 10 Most Highly Shorted Stocks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "smaller-association", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks.dark_pool_shorts import shortinterest_view\n", - "\n", - "shortinterest_view.high_short_interest(num=10, export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "cleared-contemporary", - "metadata": {}, - "source": [ - "## Dark Pool Activity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "medical-tomato", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks.dark_pool_shorts import stockgrid_view\n", - "\n", - "stockgrid_view.net_short_position(ticker=ticker.upper(), num=60, raw=False, export=\"\")\n", - "\n", - "stockgrid_view.net_short_position(ticker=ticker.upper(), num=10, raw=True, export=\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "comprehensive-nashville", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks.dark_pool_shorts import stockgrid_view\n", - "\n", - "stockgrid_view.short_interest_volume(\n", - " ticker=ticker.upper(), num=60, raw=False, export=\"\"\n", - ")\n", - "\n", - "stockgrid_view.short_interest_volume(ticker=ticker.upper(), num=10, raw=True, export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "decreased-mills", - "metadata": {}, - "source": [ - "## Options info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "medical-shaft", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks.options import barchart_view\n", - "\n", - "barchart_view.print_options_data(ticker=ticker.upper(), export=\"\")" - ] - }, - { - "cell_type": "markdown", - "id": "understood-houston", - "metadata": {}, - "source": [ - "## Put and Call Open Interest Expiring next" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acute-burning", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks.options import yfinance_model, yfinance_view\n", - "\n", - "selected_date = yfinance_model.option_expirations(ticker)[0]\n", - "options = yfinance_model.get_option_chain(ticker, selected_date)\n", - "\n", - "yfinance_view.plot_oi(\n", - " ticker=ticker,\n", - " expiry=selected_date,\n", - " min_sp=-1,\n", - " max_sp=-1,\n", - " calls_only=False,\n", - " puts_only=False,\n", - " export=\"\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "convinced-orleans", - "metadata": {}, - "source": [ - "## Failure-to-deliver (30 days lag)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "capital-narrow", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.stocks import stocks_helper\n", - "from openbb_terminal.stocks.dark_pool_shorts import sec_view\n", - "\n", - "stock = stocks_helper.load(ticker)\n", - "\n", - "sec_view.fails_to_deliver(\n", - " ticker=ticker.upper(),\n", - " stock=stock,\n", - " start=datetime.now() - timedelta(days=60),\n", - " end=datetime.now(),\n", - " num=0,\n", - " raw=False,\n", - " export=\"\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "above-prior", - "metadata": {}, - "outputs": [], - "source": [ - "!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/reports/due_dilligence.ipynb b/openbb_terminal/jupyter/reports/due_dilligence.ipynb deleted file mode 100644 index 2ef664cb209..00000000000 --- a/openbb_terminal/jupyter/reports/due_dilligence.ipynb +++ /dev/null @@ -1,453 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "70ba2c8a", - "metadata": {}, - "source": [ - "## Notebook setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "driven-billion", - "metadata": {}, - "outputs": [], - "source": [ - "import io\n", - "import warnings\n", - "from datetime import datetime, timedelta\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib_inline.backend_inline\n", - "\n", - "from openbb_terminal import api as openbb\n", - "from openbb_terminal.helper_classes import TerminalStyle\n", - "\n", - "%matplotlib inline\n", - "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", - "except:\n", - " pass\n", - "stylesheet = openbb.widgets.html_report_stylesheet()" - ] - }, - { - "cell_type": "markdown", - "id": "807fda4e", - "metadata": {}, - "source": [ - "## Select Ticker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "intended-sherman", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# Parameters that will be replaced when calling this notebook\n", - "ticker = \"AMC\"\n", - "report_name = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", - "metadata": {}, - "outputs": [], - "source": [ - "ticker_data = openbb.stocks.load(ticker, start=datetime.now() - timedelta(days=365))\n", - "ticker_data = openbb.stocks.process_candle(ticker_data)\n", - "\n", - "report_title = f\"{ticker.upper()} Due Diligence report ({datetime.now().strftime('%Y-%m-%d %H:%M:%S')})\"\n", - "report_title" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb", - "metadata": {}, - "outputs": [], - "source": [ - "overview = openbb.stocks.fa.models.yahoo_finance.get_info(ticker=ticker).transpose()[\n", - " \"Long business summary\"\n", - "][0]\n", - "overview" - ] - }, - { - "cell_type": "markdown", - "id": "f8a0d747", - "metadata": {}, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aca4b6ef", - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " df_year_estimates,\n", - " df_quarter_earnings,\n", - " df_quarter_revenues,\n", - ") = openbb.stocks.dd.models.business_insider.get_estimates(ticker)" - ] - }, - { - "cell_type": "markdown", - "id": "b9c8feff", - "metadata": { - "tags": [] - }, - "source": [ - "### 1. Yearly Estimates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0f0a1ca", - "metadata": {}, - "outputs": [], - "source": [ - "display_year = sorted(df_year_estimates.columns.tolist())[:3]\n", - "df_year_estimates = df_year_estimates[display_year].head(5)\n", - "df_year_estimates" - ] - }, - { - "cell_type": "markdown", - "id": "64d3d490", - "metadata": { - "tags": [] - }, - "source": [ - "### 2. Quarterly Earnings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fde46353", - "metadata": {}, - "outputs": [], - "source": [ - "df_quarter_earnings" - ] - }, - { - "cell_type": "markdown", - "id": "d13a1e5e", - "metadata": { - "tags": [] - }, - "source": [ - "### 3. Quarterly Revenues" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51dfd9d2", - "metadata": {}, - "outputs": [], - "source": [ - "df_quarter_revenues" - ] - }, - { - "cell_type": "markdown", - "id": "6303e520", - "metadata": { - "tags": [] - }, - "source": [ - "### 4. SEC Filings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3022130e", - "metadata": {}, - "outputs": [], - "source": [ - "df_sec_filings = openbb.stocks.dd.models.marketwatch.get_sec_filings(ticker)[\n", - " [\"Type\", \"Category\", \"Link\"]\n", - "].head(5)\n", - "df_sec_filings" - ] - }, - { - "cell_type": "markdown", - "id": "1e201003", - "metadata": { - "tags": [] - }, - "source": [ - "### 5. Analyst Ratings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4662b1ac", - "metadata": {}, - "outputs": [], - "source": [ - "df_analyst = openbb.stocks.dd.models.finviz.get_analyst_data(ticker)\n", - "df_analyst[\"target_to\"] = df_analyst[\"target_to\"].combine_first(df_analyst[\"target\"])\n", - "df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target_to\"]].rename(\n", - " columns={\n", - " \"category\": \"Category\",\n", - " \"analyst\": \"Analyst\",\n", - " \"rating\": \"Rating\",\n", - " \"target_to\": \"Price Target\",\n", - " }\n", - ")\n", - "df_analyst" - ] - }, - { - "cell_type": "markdown", - "id": "680b2b57", - "metadata": {}, - "source": [ - "# Testing - Invesment Thesis" - ] - }, - { - "cell_type": "markdown", - "id": "ade589d6-d80f-4651-acb7-929943114ef0", - "metadata": {}, - "source": [ - "## Plots" - ] - }, - { - "cell_type": "markdown", - "id": "621ed931-57e9-4ca6-afe8-8ef278122c77", - "metadata": {}, - "source": [ - "### 1. Price history" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "394668e1-1158-4a2b-8a38-04c72a6abdd6", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (candles, volume) = plt.subplots(nrows=2, ncols=1, figsize=(5, 3), dpi=150)\n", - "openbb.stocks.candle(\n", - " s_ticker=ticker,\n", - " df_stock=ticker_data,\n", - " use_matplotlib=True,\n", - " external_axes=[candles, volume],\n", - ")\n", - "candles.set_xticklabels(\"\")\n", - "fig.tight_layout()\n", - "\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "price_chart = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "markdown", - "id": "bf07a909", - "metadata": {}, - "source": [ - "### 2. Price Target" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "900686a3", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n", - "openbb.stocks.dd.pt(\n", - " ticker=ticker,\n", - " start=\"2021-10-25\",\n", - " interval=\"1440min\",\n", - " stock=ticker_data,\n", - " num=10,\n", - " raw=False,\n", - " external_axes=[ax],\n", - ")\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "price_target_chart = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "markdown", - "id": "63c0538c", - "metadata": {}, - "source": [ - "### 3. Ratings over time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3733ff2", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n", - "openbb.stocks.dd.rot(\n", - " ticker=ticker,\n", - " num=10,\n", - " raw=False,\n", - " export=\"\",\n", - " external_axes=[ax],\n", - ")\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "ratings_over_time_chart = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "markdown", - "id": "ac501a32-124d-4d21-aae1-a8d147bb1071", - "metadata": {}, - "source": [ - "## Render the report template to a file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "mounted-phase", - "metadata": {}, - "outputs": [], - "source": [ - "body = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12", - "metadata": {}, - "outputs": [], - "source": [ - "# Title\n", - "body += openbb.widgets.h(1, report_title)\n", - "\n", - "body += openbb.widgets.h(2, \"Overview\")\n", - "body += openbb.widgets.row([openbb.widgets.p(overview)])\n", - "\n", - "\n", - "# Analysts ratings\n", - "body += openbb.widgets.h(2, \"Analyst assessments\")\n", - "body += openbb.widgets.row([price_target_chart])\n", - "body += openbb.widgets.row([df_analyst.to_html()])\n", - "body += openbb.widgets.row([ratings_over_time_chart])\n", - "\n", - "# Price history and yearly estimates\n", - "body += openbb.widgets.row(\n", - " [\n", - " openbb.widgets.h(3, \"Price history\") + price_chart,\n", - " openbb.widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html(),\n", - " ]\n", - ")\n", - "\n", - "# Earnings and revenues\n", - "body += openbb.widgets.h(2, \"Earnings and revenues\")\n", - "body += openbb.widgets.row([df_quarter_earnings.head().to_html()])\n", - "body += openbb.widgets.row([df_quarter_revenues.head().to_html()])\n", - "\n", - "# Sec filings and insider trading\n", - "body += openbb.widgets.h(2, \"SEC filings\")\n", - "body += openbb.widgets.row([df_sec_filings.to_html()])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb26492f-d13f-4302-b36d-ea8ee0020f82", - "metadata": {}, - "outputs": [], - "source": [ - "report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee07c30c-9b38-4035-b128-20b7c9989951", - "metadata": {}, - "outputs": [], - "source": [ - "# to save the results\n", - "with open(report_name + \".html\", \"w\") as fh:\n", - " fh.write(report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34991096-bc3a-4134-b519-aa3a490abe98", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "interpreter": { - "hash": "31090e8428bc3cbe850895ffb75aca3a30f914a21038b56ab61f04d2ddd7301b" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/reports/econ_data.ipynb b/openbb_terminal/jupyter/reports/econ_data.ipynb deleted file mode 100644 index c4e9799de42..00000000000 --- a/openbb_terminal/jupyter/reports/econ_data.ipynb +++ /dev/null @@ -1,648 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "another-behalf", - "metadata": {}, - "outputs": [], - "source": [ - "# Import most generic modules\n", - "import importlib\n", - "import pathlib\n", - "import os\n", - "import sys\n", - "from datetime import datetime\n", - "from IPython.display import display, Markdown\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "module_path = os.path.abspath(os.path.join(\"../..\"))\n", - "\n", - "if module_path not in sys.path:\n", - " sys.path.append(module_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "racial-graduation", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_econ_data\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "commercial-cargo", - "metadata": {}, - "outputs": [], - "source": [ - "display(Markdown(f\"# Economy Data - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"))" - ] - }, - { - "cell_type": "markdown", - "id": "informative-behavior", - "metadata": {}, - "source": [ - "## Dow Jones - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "stylish-elite", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^DJI\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "fifty-happiness", - "metadata": {}, - "source": [ - "## Dow Jones - 1 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "identical-classification", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^DJI\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=360,\n", - " low_trend=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "boxed-turning", - "metadata": {}, - "source": [ - "## S&P 500 - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "threaded-language", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^GSPC\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "fleet-transparency", - "metadata": {}, - "source": [ - "## Market Volatility (VIX) - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "disturbed-workstation", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^VIX\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=False,\n", - " time_delta=720,\n", - " high_trend=False,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "civilian-november", - "metadata": {}, - "source": [ - "## Market Volatility (VIX) - 1 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "several-inventory", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^VIX\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=False,\n", - " time_delta=360,\n", - " high_trend=False,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bizarre-record", - "metadata": {}, - "source": [ - "## Yield on 5-Year US Treasury Notes - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "military-temperature", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^FVX\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=False,\n", - " time_delta=720,\n", - " low_trend=False,\n", - " high_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "greater-party", - "metadata": {}, - "source": [ - "## Yield on 10-Year US Treasury Notes - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aboriginal-violin", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^TNX\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=False,\n", - " time_delta=720,\n", - " low_trend=False,\n", - " high_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "continent-sally", - "metadata": {}, - "source": [ - "## Yield on 30-Year US Treasury Notes - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "formed-heating", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"^TYX\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=False,\n", - " time_delta=720,\n", - " low_trend=False,\n", - " high_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "handled-insurance", - "metadata": {}, - "source": [ - "## Gold - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "advisory-johnston", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"GLD\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "played-writer", - "metadata": {}, - "source": [ - "## Silver - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "light-harvest", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"SLV\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "floral-giant", - "metadata": {}, - "source": [ - "## Euro vs US Dollar - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "corrected-estimate", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"FXE\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "neither-montana", - "metadata": {}, - "source": [ - "## Yen vs US Dollar - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "middle-geneva", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"FXY\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "theoretical-grammar", - "metadata": {}, - "source": [ - "## Energy - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "swedish-thickness", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"XLE\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "gentle-humor", - "metadata": {}, - "source": [ - "## Crude Oil - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "instructional-serve", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"CL=F\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "classical-listening", - "metadata": {}, - "source": [ - "## Real Estate - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "enclosed-administration", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"XLRE\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "turned-percentage", - "metadata": {}, - "source": [ - "## Real Estate - 1 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sharing-removal", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"VNQ\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=360,\n", - " low_trend=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "swedish-happiness", - "metadata": {}, - "source": [ - "## Financials - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "beginning-aspect", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"XLF\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "restricted-glucose", - "metadata": {}, - "source": [ - "## Financials - 1 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "personal-whole", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"XLF\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=360,\n", - " low_trend=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "detailed-strike", - "metadata": {}, - "source": [ - "## Materials - 2 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "negative-mitchell", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"XLB\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=720,\n", - " low_trend=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "boolean-rotation", - "metadata": {}, - "source": [ - "## Materials - 1 year trend with 20, 50 and 200 day SMA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "settled-orange", - "metadata": {}, - "outputs": [], - "source": [ - "from openbb_terminal.economy import econ_data_helper\n", - "\n", - "econ_data_helper.draw_graph(\n", - " \"XLB\",\n", - " \"\",\n", - " line_type=\"line\",\n", - " draw_mas=(20, 50, 200),\n", - " draw_volume=True,\n", - " time_delta=360,\n", - " low_trend=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "premium-transportation", - "metadata": {}, - "outputs": [], - "source": [ - "!jupyter nbconvert {report_name + \".ipynb\"} --to html --no-input" - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "", - "language": "python", - "name": "" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/reports/equity.ipynb b/openbb_terminal/jupyter/reports/equity.ipynb deleted file mode 100644 index df6fdc86f45..00000000000 --- a/openbb_terminal/jupyter/reports/equity.ipynb +++ /dev/null @@ -1,682 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "70ba2c8a", - "metadata": {}, - "source": [ - "## Notebook setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "driven-billion", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import io\n", - "import warnings\n", - "import datetime\n", - "import numpy as np\n", - "import base64\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib_inline.backend_inline\n", - "\n", - "# import sys\n", - "# sys.path.append('../../../')\n", - "\n", - "from openbb_terminal import api as openbb\n", - "from openbb_terminal.helper_classes import TerminalStyle\n", - "\n", - "%matplotlib inline\n", - "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", - "except:\n", - " pass\n", - "stylesheet = openbb.widgets.html_report_stylesheet()\n", - "with open(\"./openbb_terminal/jupyter/reports/OpenBB_reports_logo.png\", \"rb\") as image_file:\n", - " openbb_image_encoded = base64.b64encode(image_file.read())" - ] - }, - { - "cell_type": "markdown", - "id": "807fda4e", - "metadata": {}, - "source": [ - "## Select Ticker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "intended-sherman", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# Parameters that will be replaced when calling this notebook\n", - "ticker = \"TSLA\"\n", - "report_name = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", - "metadata": {}, - "outputs": [], - "source": [ - "ticker_data = openbb.stocks.load(ticker, start=datetime.datetime.now() - datetime.timedelta(days=4*30))\n", - "ticker_data = openbb.stocks.process_candle(ticker_data)\n", - "\n", - "author = \"Didier Rodrigues Lopes\"\n", - "report_title = f\"INVESTMENT RESEARCH REPORT ON {ticker.upper()}\"\n", - "report_date = datetime.datetime.now().strftime('%d %B, %Y')\n", - "report_time = datetime.datetime.now().strftime('%H:%M') \n", - "report_timezone = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo\n", - "report_title, report_date, report_time, report_timezone" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb", - "metadata": {}, - "outputs": [], - "source": [ - "overview = openbb.stocks.fa.models.yahoo_finance.get_info(ticker=ticker).transpose()[\n", - " \"Long business summary\"\n", - "][0]\n", - "overview" - ] - }, - { - "cell_type": "markdown", - "id": "f8a0d747", - "metadata": {}, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aca4b6ef", - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " df_year_estimates,\n", - " df_quarter_earnings,\n", - " df_quarter_revenues,\n", - ") = openbb.stocks.dd.models.business_insider.get_estimates(ticker)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0f0a1ca", - "metadata": {}, - "outputs": [], - "source": [ - "display_year = sorted(df_year_estimates.columns.tolist())[:3]\n", - "df_year_estimates = df_year_estimates[display_year].head(5)\n", - "df_year_estimates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fde46353", - "metadata": {}, - "outputs": [], - "source": [ - "df_quarter_earnings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51dfd9d2", - "metadata": {}, - "outputs": [], - "source": [ - "df_quarter_revenues" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3022130e", - "metadata": {}, - "outputs": [], - "source": [ - "df_sec_filings = openbb.stocks.dd.models.marketwatch.get_sec_filings(ticker)[\n", - " [\"Type\", \"Category\", \"Link\"]\n", - "].head(5)\n", - "df_sec_filings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4662b1ac", - "metadata": {}, - "outputs": [], - "source": [ - "df_analyst = openbb.stocks.dd.models.finviz.get_analyst_data(ticker)\n", - "df_analyst[\"target_to\"] = df_analyst[\"target_to\"].combine_first(df_analyst[\"target\"])\n", - "df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target_to\"]].rename(\n", - " columns={\n", - " \"category\": \"Category\",\n", - " \"analyst\": \"Analyst\",\n", - " \"rating\": \"Rating\",\n", - " \"target_to\": \"Price Target\",\n", - " }\n", - ")\n", - "df_analyst" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "324e2d4b-b2f6-4da3-bac4-ef785ad4e436", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax1 = plt.subplots(figsize=(11, 5), dpi=150)\n", - "ax2 = ax1.twinx()\n", - "openbb.stocks.dps.spos(\n", - " ticker,\n", - " num=84,\n", - " raw=False,\n", - " export=\"\",\n", - " external_axes=[ax1, ax2],\n", - ")\n", - "fig.tight_layout()\n", - "\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "net_short_position = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac0bfbda-e4b8-4b3b-b8e7-c02639fdb971", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", - "openbb.stocks.dps.dpotc(\n", - " ticker,\n", - " external_axes=[ax1, ax2],\n", - ")\n", - "fig.tight_layout()\n", - "\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "dark_pools = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e40a2b8f-8076-4220-89ad-388def38971c", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 5), dpi=150)\n", - "openbb.stocks.dps.volexch(\n", - " ticker,\n", - " external_axes=[ax],\n", - ")\n", - "fig.tight_layout()\n", - "\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "volexch = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8bb53c1-147e-4342-af61-0d0a9cc56e59", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", - "ax3 = ax1.twinx()\n", - "openbb.stocks.dps.psi_sg(\n", - " ticker,\n", - " external_axes=[ax1, ax2, ax3],\n", - ")\n", - "fig.tight_layout()\n", - "\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "price_vs_short_interest = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "394668e1-1158-4a2b-8a38-04c72a6abdd6", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (candles, volume) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", - "openbb.stocks.candle(\n", - " s_ticker=ticker,\n", - " df_stock=ticker_data,\n", - " use_matplotlib=True,\n", - " external_axes=[candles, volume],\n", - ")\n", - "candles.set_xticklabels(\"\")\n", - "fig.tight_layout()\n", - "\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "price_chart = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "900686a3", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.dd.pt(\n", - " ticker=ticker,\n", - " start=\"2022-01-01\",\n", - " interval=\"1440min\",\n", - " stock=ticker_data,\n", - " num=10,\n", - " raw=False,\n", - " external_axes=[ax],\n", - ")\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "price_target_chart = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61d62e1a-199e-481d-b57d-38ab486951be", - "metadata": {}, - "outputs": [], - "source": [ - "df = openbb.stocks.dd.models.business_insider.get_price_target_from_analysts(ticker)\n", - "avg_ratings_last_30_days = round(np.mean(df[datetime.datetime.now() - datetime.timedelta(days=30):][\"Price Target\"].values), 2)\n", - "last_price = round(ticker_data[\"Close\"][-1], 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3733ff2", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.dd.rot(\n", - " ticker=ticker,\n", - " num=10,\n", - " raw=False,\n", - " export=\"\",\n", - " external_axes=[ax],\n", - ")\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "ratings_over_time_chart = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "924f8042-e58a-449c-91a3-7923158ff714", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 3), dpi=150)\n", - "openbb.stocks.ta.rsi(ticker_data[\"Close\"], external_axes=[ax1, ax2])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "ta_rsi = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "953a340e-7ea1-4aa3-ae1d-872683dced41", - "metadata": {}, - "outputs": [], - "source": [ - "df = openbb.stocks.ta.models.momentum.rsi(ticker_data[\"Close\"])\n", - "rsi_value = round(df.values[-1][0], 2)\n", - "rsi_value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dce71711-20b3-4c69-af52-e0f9a69991f8", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "model = LinearRegression().fit(np.array(range(len(ticker_data[\"Close\"][-30:].index))).reshape(-1,1), ticker_data[\"Close\"][-30:].values)\n", - "regression_slope = round(model.coef_[0], 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6272e34b-9511-4b48-9eea-ea882ce9846a", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "df_insider = pd.DataFrame.from_dict(openbb.stocks.ins.models.finviz.get_last_insider_activity(ticker)).head(10)\n", - "df_insider[\"Val ($)\"] = df_insider[\"Value ($)\"].replace({',': ''}, regex=True)\n", - "df_insider[\"Trade\"] = df_insider.apply(\n", - " lambda row: (-1*float(row[\"Val ($)\"])) if row[\"Transaction\"] == \"Sale\" else (float(row[\"Val ($)\"]) if row[\"Transaction\"] == \"Buy\" else 0),\n", - " axis=1,\n", - " )\n", - "last_10_insider_trading = round(sum(df_insider[\"Trade\"])/1_000_000, 2)\n", - "df_insider = df_insider.drop(columns=[\"Val ($)\", \"Trade\"])\n", - "df_insider" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29254666-58c6-4686-87e6-2635ad8387e2", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.ba.headlines(ticker, external_axes=[ax])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "finbrain_sentiment = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8251b47-8ecc-47be-83f3-d9f108596898", - "metadata": {}, - "outputs": [], - "source": [ - "df_sentiment_finbrain = openbb.stocks.ba.models.finbrain.get_sentiment(ticker)\n", - "finbrain_sentiment_val = float(df_sentiment_finbrain.values[-1][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fe6e513-d568-41b4-8aa0-37cc578c9dc3", - "metadata": {}, - "outputs": [], - "source": [ - "watchlist_count, n_cases, n_bull, n_bear = openbb.stocks.ba.models.stocktwits.get_bullbear(ticker)\n", - "stocktwits_sentiment = f\"Watchlist count: {watchlist_count}
\"\n", - "if n_cases > 0:\n", - " stocktwits_sentiment += f\"\\nLast {n_cases} sentiment messages:
\"\n", - " stocktwits_sentiment += f\"Bullish {round(100*n_bull/n_cases, 2)}%
\"\n", - " stocktwits_sentiment += f\"Bearish {round(100*n_bear/n_cases, 2)}%\"\n", - "else:\n", - " stocktwits_sentiment += \"No messages found\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfb75909-b877-4b3a-82a7-21ce174a7bb4", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", - "openbb.stocks.ba.snews(ticker, external_axes=[ax1, ax2])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "snews = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "329c9e1b-e1de-4836-91fd-656cc87f7111", - "metadata": {}, - "outputs": [], - "source": [ - "ticker_data_all = openbb.stocks.load(ticker, start=datetime.datetime.now() - datetime.timedelta(days=5*12*21))\n", - "ticker_data_all[\"Returns\"] = ticker_data_all[\"Adj Close\"].pct_change()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37743ef3-2f16-4166-bfcc-a0fe3eb959bc", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.qa.bw(ticker, ticker_data_all, \"Returns\", False, external_axes=[ax])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "bw_month = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16b33868-23dc-4c77-ac00-acb452da386c", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.qa.bw(ticker, ticker_data_all, \"Returns\", True, external_axes=[ax])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "bw_year = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c688fa89-3773-41fe-8ab8-bb6a610f2287", - "metadata": {}, - "outputs": [], - "source": [ - "regression_val = round(openbb.stocks.pred.models.regression.get_regression_model(ticker_data_all[\"Close\"], 1, 80, 20, 1)[0][-1], 2)\n", - "regression_val" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66e51d40-3dca-46c0-abd6-4c1f04f59732", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "regression = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90e77854-0652-4f62-9c0b-89d3fda456c6", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(11, 3), dpi=150)\n", - "openbb.stocks.pred.mc(ticker_data[\"Close\"], 30, 100, external_axes=[ax1, ax2])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "mc = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb97c78c-4c4e-4e24-a2a1-29cdf47839c5", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", - "openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "regression = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "markdown", - "id": "ac501a32-124d-4d21-aae1-a8d147bb1071", - "metadata": {}, - "source": [ - "## Render the report template to a file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12", - "metadata": {}, - "outputs": [], - "source": [ - "body = \"\"\n", - "\n", - "img = f'\"OpenBB\"'\n", - "body += openbb.widgets.header(img, author, report_date, report_time, report_timezone, f'INVESTMENT RESEARCH REPORT: {ticker}')\n", - "\n", - "body += openbb.widgets.tablinks([\"SUMMARY\", \"Overview\", \"Analyst Opinions\", \"Fundamental Analysis\", \"Dark Pool and Shorts\", \"Technical Analysis\", \"Insider Trading\", \"Behavioural Analysis\", \"Quantitative Analysis\", \"Prediction Techniques\"])\n", - "\n", - "htmlcode = openbb.widgets.h(3, \"KPIs\")\n", - "htmlcode += openbb.widgets.kpi([last_price], [\"Last closing price is above the average price ratings of last 30 days\", \"Average price ratings of last 30 day is above last closing price\"], avg_ratings_last_30_days)\n", - "htmlcode += openbb.widgets.kpi([0], [\"Regression (dollars per market day) on last 30 market days is negative\", \"Regression (dollars per market day) on last 30 market days is positive\"], regression_slope)\n", - "htmlcode += openbb.widgets.kpi([30, 70], [\"RSI level is oversold\", \"RSI level is normal\", \"RSI level is overbought\"], rsi_value) \n", - "htmlcode += openbb.widgets.kpi([0], [\"The sum of last 10 insider trading (in millions) was negative\", \"The sum of last 10 insider trading (in millions) was positive\"], last_10_insider_trading)\n", - "htmlcode += openbb.widgets.kpi([-.1, .1], [\"Last FinBrain sentiment is bearish\", \" Last FinBrain sentiment is neutral\", \"Last FinBrain sentiment is bullish\"], finbrain_sentiment_val) \n", - "htmlcode += openbb.widgets.kpi([0], [\"The regression for the next 20th market price is below closing price\", \"The regression for the next 20th market price is above closing price\"], round(regression_val - last_price, 2)) \n", - "body += openbb.widgets.add_tab(\"SUMMARY\", htmlcode)\n", - "\n", - "htmlcode = openbb.widgets.row([price_chart])\n", - "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Description\") + openbb.widgets.p(overview)])\n", - "body += openbb.widgets.add_tab(\"Overview\", htmlcode)\n", - "\n", - "htmlcode = openbb.widgets.row([price_target_chart])\n", - "htmlcode += openbb.widgets.row([ratings_over_time_chart])\n", - "htmlcode += openbb.widgets.row([df_analyst.to_html()])\n", - "body += openbb.widgets.add_tab(\"Analyst Opinions\", htmlcode)\n", - "\n", - "htmlcode = openbb.widgets.row([openbb.widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html()])\n", - "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Earnings\") + df_quarter_earnings.head().to_html()])\n", - "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Revenues\") + df_quarter_revenues.head().to_html()])\n", - "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"SEC filings\") + df_sec_filings.head().to_html()])\n", - "body += openbb.widgets.add_tab(\"Fundamental Analysis\", htmlcode)\n", - " \n", - "htmlcode = openbb.widgets.row([net_short_position])\n", - "htmlcode += openbb.widgets.row([price_vs_short_interest])\n", - "htmlcode += openbb.widgets.row([dark_pools])\n", - "htmlcode += openbb.widgets.row([volexch])\n", - "body += openbb.widgets.add_tab(\"Dark Pool and Shorts\", htmlcode)\n", - "\n", - "body += openbb.widgets.add_tab(\"Technical Analysis\", openbb.widgets.row([ta_rsi]))\n", - "\n", - "htmlcode = openbb.widgets.row([openbb.widgets.h(3, \"Last Activity\") + df_insider.head(10).to_html(col_space='75px')])\n", - "body += openbb.widgets.add_tab(\"Insider Trading\", htmlcode)\n", - "\n", - "htmlcode = openbb.widgets.row([finbrain_sentiment])\n", - "htmlcode += openbb.widgets.row([snews])\n", - "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Stocktwits\") + stocktwits_sentiment])\n", - "body += openbb.widgets.add_tab(\"Behavioural Analysis\", htmlcode)\n", - "\n", - "htmlcode = openbb.widgets.row([bw_month])\n", - "htmlcode += openbb.widgets.row([bw_year])\n", - "body += openbb.widgets.add_tab(\"Quantitative Analysis\", htmlcode)\n", - "\n", - "htmlcode = openbb.widgets.row([regression])\n", - "htmlcode += openbb.widgets.row([mc])\n", - "body += openbb.widgets.add_tab(\"Prediction Techniques\", htmlcode)\n", - "\n", - "body += openbb.widgets.tab_clickable_evt()\n", - "\n", - "report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)\n", - "\n", - "# to save the results\n", - "with open(report_name + \".html\", \"w\") as fh:\n", - " fh.write(report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e15cef06-0d10-4153-b6d3-9762be0ed1dd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "interpreter": { - "hash": "aa1c4d436101635411b2afa2ce061827695b6e3d0efa7f05338cbf11ad26165e" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/jupyter/reports/similar_analysis.ipynb b/openbb_terminal/jupyter/reports/similar_analysis.ipynb deleted file mode 100644 index 243ea6f2268..00000000000 --- a/openbb_terminal/jupyter/reports/similar_analysis.ipynb +++ /dev/null @@ -1,528 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7e02d340-956a-496d-b4e5-fd74cc85f13a", - "metadata": {}, - "source": [ - "## Notebook setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "phantom-facility", - "metadata": {}, - "outputs": [], - "source": [ - "import io\n", - "import math\n", - "import warnings\n", - "from datetime import datetime, timedelta\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib_inline.backend_inline\n", - "import pandas as pd\n", - "from tqdm.notebook import tqdm\n", - "\n", - "from openbb_terminal import api as openbb\n", - "from openbb_terminal.helper_classes import TerminalStyle\n", - "\n", - "%matplotlib inline\n", - "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d318a555-d1db-444e-ba8c-8427850dec59", - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", - "except:\n", - " pass\n", - "stylesheet = openbb.widgets.html_report_stylesheet()" - ] - }, - { - "cell_type": "markdown", - "id": "fa5bd393-2f66-4ba3-98f8-66230636ce0e", - "metadata": {}, - "source": [ - "## Select Ticker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "compact-count", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# Parameters that will be replaced when calling this notebook\n", - "ticker = \"clx\"\n", - "report_name = f\"{datetime.now().strftime('%Y%m%d_%H%M%S')}_comparison_{ticker.upper()}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "possible-constraint", - "metadata": {}, - "outputs": [], - "source": [ - "ticker = ticker.upper()\n", - "report_title = (\n", - " f\"{ticker.upper()} Comparison ({datetime.now().strftime('%Y-%m-%d %H:%M:%S')})\"\n", - ")\n", - "report_title" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9eaca4a-a46d-45a3-9306-e80e74234f12", - "metadata": {}, - "outputs": [], - "source": [ - "finviz_similar = openbb.stocks.ca.models.finviz_compare.get_similar_companies(\n", - " ticker, [\"Sector\", \"Industry\", \"Country\"]\n", - ")[0]\n", - "finviz_similar.pop(finviz_similar.index(ticker))\n", - "print(f\"Similar companies:\\n{' '.join([i for i in finviz_similar])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "135b09f6-c22b-4cc5-b68e-d77f9e3b3a60", - "metadata": { - "tags": [] - }, - "source": [ - "### Overview" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9907d7ea-3ad0-4123-ab2e-f03defa4eea2", - "metadata": {}, - "outputs": [], - "source": [ - "overview = openbb.stocks.ca.models.finviz_compare.get_comparison_data(\n", - " similar=finviz_similar,\n", - " data_type=\"overview\",\n", - ")\n", - "overview = overview.sort_values(\"Volume\", ascending=False)\n", - "overview.head(4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc7d52a4-fbe6-4b39-a3d5-bfae841dacac", - "metadata": {}, - "outputs": [], - "source": [ - "# Update the list of similar companies to take up only up to 12 with top volume\n", - "finviz_similar = overview[\"Ticker\"].to_list()[:12]\n", - "print(f\"Similar companies:\\n{' '.join([i for i in finviz_similar])}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ffea4266-e801-4a4e-8cac-3d60e735aa4e", - "metadata": {}, - "source": [ - "### Similar companies and descriptions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21122245-457b-4347-b89f-d4aabb5bdc9a", - "metadata": {}, - "outputs": [], - "source": [ - "def get_info_as_dict(ticker):\n", - " df_info = openbb.stocks.fa.models.yahoo_finance.get_info(ticker)\n", - " if \"Long business summary\" in df_info.index:\n", - " summary = df_info.loc[\"Long business summary\"].values[0]\n", - " elif \"Long name\" in df_info.index:\n", - " summary = df_info.loc[\"Long name\"].values[0]\n", - " else:\n", - " summary = \"\"\n", - " if \"Website\" in df_info.index:\n", - " website = df_info.loc[\"Website\"].values[0]\n", - " else:\n", - " website = \"\"\n", - " return {\"summary\": summary, \"website\": website}\n", - "\n", - "\n", - "similar_companies = dict()\n", - "similar_companies[ticker] = get_info_as_dict(ticker)\n", - "\n", - "if finviz_similar:\n", - " for symbol in tqdm(finviz_similar):\n", - " similar_companies[symbol] = get_info_as_dict(symbol)" - ] - }, - { - "cell_type": "markdown", - "id": "temporal-clerk", - "metadata": { - "tags": [] - }, - "source": [ - "### Historical prices" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e7509a7-961a-4fb8-bd87-0df4b8c98d04", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for c in tqdm(finviz_similar):\n", - " fig, ax = plt.subplots(figsize=(4.2, 3), dpi=150)\n", - " openbb.stocks.ca.hist(\n", - " normalize=True,\n", - " similar_tickers=[ticker, c],\n", - " external_axes=[ax],\n", - " )\n", - " ax.set_title(\"\")\n", - " fig.tight_layout()\n", - " f = io.BytesIO()\n", - " fig.savefig(f, format=\"svg\")\n", - " similar_companies[c][\"historical_price\"] = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "markdown", - "id": "2a07f866-7939-4fb7-8b14-c0701087a8ae", - "metadata": {}, - "source": [ - "### Historical correlation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2df60c41-4f87-483c-abe7-7063fa7648cf", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(9.5, 6), dpi=150)\n", - "\n", - "openbb.stocks.ca.hcorr(\n", - " similar_tickers=finviz_similar,\n", - " external_axes=[ax],\n", - ")\n", - "\n", - "fig.tight_layout()\n", - "f = io.BytesIO()\n", - "fig.savefig(f, format=\"svg\")\n", - "historical_correlation = f.getvalue().decode(\"utf-8\")" - ] - }, - { - "cell_type": "markdown", - "id": "21005668-7f21-4884-a231-846130695602", - "metadata": { - "tags": [] - }, - "source": [ - "### Historical volumes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f84b055f-ec6d-4b61-89f3-17f06a1ca12d", - "metadata": {}, - "outputs": [], - "source": [ - "volume_groups = []\n", - "for i in range(math.ceil(len(finviz_similar) / 3)):\n", - " volume_groups.append(finviz_similar[3 * (i) : 3 * (i + 1)])\n", - "volume_groups" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8812a2f6-f912-4161-a938-5e2a343d782a", - "metadata": {}, - "outputs": [], - "source": [ - "historical_volumes = []\n", - "for vgroup in volume_groups:\n", - " fig, ax = plt.subplots(figsize=(4, 3), dpi=150)\n", - " openbb.stocks.ca.volume(\n", - " similar_tickers=vgroup,\n", - " external_axes=[ax],\n", - " )\n", - " ax.set_title(\" \".join([i for i in vgroup]))\n", - " f = io.BytesIO()\n", - " fig.savefig(f, format=\"svg\")\n", - " historical_volumes.append(f.getvalue().decode(\"utf-8\"))" - ] - }, - { - "cell_type": "markdown", - "id": "5277ae24-b7d7-48e6-b46c-fcffdf84631f", - "metadata": { - "tags": [] - }, - "source": [ - "### Valuation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ef50e7ba-70bf-4ffc-b01c-8e28266c13b4", - "metadata": {}, - "outputs": [], - "source": [ - "valuation = openbb.stocks.ca.models.finviz_compare.get_comparison_data(\n", - " similar=finviz_similar,\n", - " data_type=\"valuation\",\n", - ")\n", - "valuation.head(4)" - ] - }, - { - "cell_type": "markdown", - "id": "98030d12-9a77-4f08-b4ad-2c558d9ad225", - "metadata": { - "tags": [] - }, - "source": [ - "### Financial" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fdfac4c-62cd-4bd3-b7df-41ba67263798", - "metadata": {}, - "outputs": [], - "source": [ - "financial = openbb.stocks.ca.models.finviz_compare.get_comparison_data(\n", - " similar=finviz_similar,\n", - " data_type=\"financial\",\n", - ")\n", - "financial.head(4)" - ] - }, - { - "cell_type": "markdown", - "id": "f7cf8774-5162-47ea-8bfe-5005ed2fb9d9", - "metadata": { - "tags": [] - }, - "source": [ - "### Ownership" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3f90d1f-7a40-403f-bf3e-17703e151f4b", - "metadata": {}, - "outputs": [], - "source": [ - "ownership = openbb.stocks.ca.models.finviz_compare.get_comparison_data(\n", - " similar=finviz_similar,\n", - " data_type=\"ownership\",\n", - ")\n", - "ownership.head(4)" - ] - }, - { - "cell_type": "markdown", - "id": "c1833238-5ea8-486b-a6e7-6043e7c0aa0e", - "metadata": { - "tags": [] - }, - "source": [ - "### Performance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62057b47-2364-49ed-80e7-7d6938f2819d", - "metadata": {}, - "outputs": [], - "source": [ - "performance = openbb.stocks.ca.models.finviz_compare.get_comparison_data(\n", - " similar=finviz_similar,\n", - " data_type=\"performance\",\n", - ")\n", - "performance.head(4)" - ] - }, - { - "cell_type": "markdown", - "id": "6d0494c0-e66a-414a-ba2d-89e4072e1a8f", - "metadata": { - "tags": [] - }, - "source": [ - "### Technical" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1178a080-83ce-4170-a355-9faa8cf9abe6", - "metadata": {}, - "outputs": [], - "source": [ - "technical = openbb.stocks.ca.models.finviz_compare.get_comparison_data(\n", - " similar=finviz_similar,\n", - " data_type=\"technical\",\n", - ")\n", - "technical.head(4)" - ] - }, - { - "cell_type": "markdown", - "id": "16a5fc9a-0d9b-4589-b680-f954e4e6f78a", - "metadata": {}, - "source": [ - "## Render the report template to a file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eebac826-906b-482d-a387-55cb78eb5e18", - "metadata": {}, - "outputs": [], - "source": [ - "body = \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26727f90-0950-4434-97f9-6b52180e4473", - "metadata": {}, - "outputs": [], - "source": [ - "body += openbb.widgets.h(1, report_title)\n", - "\n", - "body += openbb.widgets.h(2, \"Overview\")\n", - "\n", - "for company in similar_companies:\n", - " if company == ticker:\n", - " continue\n", - " # widgets.append()\n", - " body += openbb.widgets.row(\n", - " [\n", - " openbb.widgets.h(3, company)\n", - " + \"
\"\n", - " + similar_companies[company][\"website\"]\n", - " + \"
\"\n", - " + \"
\"\n", - " + similar_companies[company][\"summary\"],\n", - " openbb.widgets.h(3, f\"{ticker} vs {company}\")\n", - " + similar_companies[company][\"historical_price\"],\n", - " ]\n", - " )\n", - "\n", - "body += openbb.widgets.h(2, \"Historical correlation\")\n", - "body += openbb.widgets.row([historical_correlation])\n", - "\n", - "body += openbb.widgets.h(2, \"Historical volumes\")\n", - "for chart in range(0, len(historical_volumes), 2):\n", - " body += openbb.widgets.row(\n", - " [historical_volumes[chart], historical_volumes[chart + 1]]\n", - " )\n", - "\n", - "\n", - "body += openbb.widgets.h(2, \"Screeners\")\n", - "\n", - "body += openbb.widgets.h(3, \"overview\")\n", - "body += openbb.widgets.row([overview.to_html()])\n", - "\n", - "body += openbb.widgets.h(3, \"valuation\")\n", - "body += openbb.widgets.row([valuation.to_html()])\n", - "\n", - "body += openbb.widgets.h(3, \"financial\")\n", - "body += openbb.widgets.row([financial.to_html()])\n", - "\n", - "body += openbb.widgets.h(3, \"ownership\")\n", - "body += openbb.widgets.row([ownership.to_html()])\n", - "\n", - "body += openbb.widgets.h(3, \"performance\")\n", - "body += openbb.widgets.row([performance.to_html()])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7697fa8-cc27-4bf8-b5e6-ea6ab4ec6a21", - "metadata": {}, - "outputs": [], - "source": [ - "report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42e6a584-b7c9-435f-98cb-ea93ac6b8812", - "metadata": {}, - "outputs": [], - "source": [ - "# to save the results\n", - "with open(report_name + \".html\", \"w\") as fh:\n", - " fh.write(report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6de79d28-fc26-4bf0-bece-f823a424b4fc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/openbb_terminal/reports/.ipynb_checkpoints/test-checkpoint.html b/openbb_terminal/reports/.ipynb_checkpoints/test-checkpoint.html new file mode 100644 index 00000000000..4098468caa3 --- /dev/null +++ b/openbb_terminal/reports/.ipynb_checkpoints/test-checkpoint.html @@ -0,0 +1,36444 @@ + + + + + + test + + + + + + + +
+ OpenBB +
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Analyst: Didier Rodrigues Lopes

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Date : 09 May, 2022

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Time : 00:55 BST

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INVESTMENT RESEARCH REPORT: OSTK

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KPIs:

✅ Average price ratings last 30 days (56.5) > Last closing price (34.95)

❌ Regression on the last 30 market days (-0.49 dollars/market day) is negative

🟡 RSI level is NORMAL. 30 < 41.47 < 70

❌ Last 10 insider trading. -0.75 M was sold in value

✅ Last FinBrain sentiment is BULLISH: 10

✅ Regression for next 20th market price (37.46) > Last closing price (34.95)

Opinion:



No comment.

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+ + + + + + + 2022-05-09T00:56:41.851370 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Description

Overstock.com, Inc. operates as an online retailer in the United States. The company offers furniture, décor, area rug, bedding and bath, home improvement, outdoor, and kitchen and dining items. It provides its products and services through its internet websites comprising overstock.com, o.co, overstock.ca, and overstockgovernment.com. The company also offers businesses advertising products or services on its website; Market Partner, a service that allows its partners to sell their products through third party sites; products to international customers using third party logistics providers; and Supplier Oasis, a single integration point through its partners can manage their products, inventory, and sales channels, as well as obtain multi-channel fulfillment services through its distribution network. The company was formerly known as D2-Discounts Direct and changed its name to Overstock.com, Inc. in October 1999. Overstock.com, Inc. was founded in 1997 and is headquartered in Midvale, Utah.

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No comment.

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+ + + + + + + 2022-05-09T00:56:43.469257 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + 2022-05-09T00:56:45.030963 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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CategoryAnalystRatingPrice Target
date
2022-04-25DowngradeBofA SecuritiesBuy -> Neutral38.0
2021-11-23InitiatedGuggenheimNeutral111.0
2021-09-07ResumedBofA SecuritiesBuy89.0
2021-07-08InitiatedNeedhamBuy150.0
2021-05-19ResumedCredit SuisseOutperform105.0
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No comment.

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Estimates

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202220232024
YEARLY ESTIMATES
Revenue2,4732,6702,978
Dividend0.080.02-
Dividend Yield (in %)0.23 %0.06 %-
EPS1.471.972.26
P/E Ratio23.7517.7315.50
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Earnings

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Previous QuarterCurrent QuarterNext QuarterCurrent YearNext Year
QUARTER EARNINGS ESTIMATES
Dateending 03/31/22ending 06/30/22ending 09/30/22ending 12/31/22ending 12/31/23
No. of Analysts66666
Average Estimate0.233 USD0.370 USD0.398 USD1.472 USD1.972 USD
Year Ago0.560 USD0.730 USD0.540 USD2.190 USD1.472 USD
Publish Date4/28/20228/4/202211/10/2022--
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Revenues

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Previous QuarterCurrent QuarterNext QuarterCurrent YearNext Year
QUARTER REVENUES ESTIMATES
Dateending 03/31/22ending 06/30/22ending 09/30/22ending 12/31/22ending 12/31/23
No. of Analysts66666
Average Estimate577 USD645 USD647 USD2,473 USD2,670 USD
Year Ago660 USD795 USD689 USD2,800 USD2,473 USD
Publish Date4/28/20228/4/202211/10/2022--
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SEC filings

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TypeCategoryLink
Filing Date
05/04/202210-QQuarterly Reportshttps://www.marketwatch.com/investing/stock/ostk/financials/secfilings?docid=15785307
04/28/20228-KSpecial Eventshttps://www.marketwatch.com/investing/stock/ostk/financials/secfilings?docid=15760186
03/23/2022DEF 14AProxy Statementhttps://www.marketwatch.com/investing/stock/ostk/financials/secfilings?docid=15679466
03/09/20228-KSpecial Eventshttps://www.marketwatch.com/investing/stock/ostk/financials/secfilings?docid=15645760
03/04/2022PRE 14AProxy Statementhttps://www.marketwatch.com/investing/stock/ostk/financials/secfilings?docid=15635805
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No comment.

+ +
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+ + + + + + + 2022-05-09T00:56:48.256480 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+ + + + + + + 2022-05-09T00:56:48.520908 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +

No comment.

+ +
+ + + + + + + 2022-05-09T00:56:49.603604 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+ + + + + + + 2022-05-09T00:56:49.233337 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + \ No newline at end of file diff --git a/openbb_terminal/jupyter/reports/OpenBB_reports_logo.png b/openbb_terminal/reports/OpenBB_reports_logo.png similarity index 100% rename from openbb_terminal/jupyter/reports/OpenBB_reports_logo.png rename to openbb_terminal/reports/OpenBB_reports_logo.png diff --git a/openbb_terminal/jupyter/README.md b/openbb_terminal/reports/README.md similarity index 100% rename from openbb_terminal/jupyter/README.md rename to openbb_terminal/reports/README.md diff --git a/tests/openbb_terminal/jupyter/__init__.py b/openbb_terminal/reports/__init__.py similarity index 100% rename from tests/openbb_terminal/jupyter/__init__.py rename to openbb_terminal/reports/__init__.py diff --git a/openbb_terminal/jupyter/reports/reports_controller.py b/openbb_terminal/reports/reports_controller.py similarity index 97% rename from openbb_terminal/jupyter/reports/reports_controller.py rename to openbb_terminal/reports/reports_controller.py index 4ac40899e58..2d2b0742911 100644 --- a/openbb_terminal/jupyter/reports/reports_controller.py +++ b/openbb_terminal/reports/reports_controller.py @@ -79,7 +79,7 @@ class ReportController(BaseController): + f"{args if args != '<>' else ''}\n" ) CHOICES_MENUS = report_names + ids_reports - PATH = "/jupyter/reports/" + PATH = "/reports/" def __init__(self, queue: List[str] = None): """Constructor""" @@ -97,7 +97,7 @@ class ReportController(BaseController): help_text = f"""[info] Select one of the following reports:[/info][cmds] {self.reports_opts}[/cmds]""" - console.print(text=help_text, menu="Jupyter - Reports") + console.print(text=help_text, menu="Reports - WORK IN PROGRESS") @log_start_end(log=logger) def switch(self, an_input: str): @@ -182,7 +182,7 @@ Select one of the following reports:[/info][cmds] console.print("") notebook_template = os.path.join( - "openbb_terminal", "jupyter", "reports", report_to_run + "openbb_terminal", "reports", report_to_run ) args_to_output = f"_{'_'.join(other_args)}" if "_".join(other_args) else "" report_output_name = ( @@ -192,7 +192,6 @@ Select one of the following reports:[/info][cmds] ) notebook_output = os.path.join( "openbb_terminal", - "jupyter", "reports", "stored", report_output_name, @@ -215,6 +214,7 @@ Select one of the following reports:[/info][cmds] report_output_path = os.path.join( os.path.abspath(os.path.join(".")), notebook_output + ".html" ) + print(report_output_path) webbrowser.open(f"file://{report_output_path}") console.print("") diff --git a/openbb_terminal/jupyter/reports/stored/.gitkeep b/openbb_terminal/reports/stored/.gitkeep similarity index 100% rename from openbb_terminal/jupyter/reports/stored/.gitkeep rename to openbb_terminal/reports/stored/.gitkeep diff --git a/openbb_terminal/reports/stored/20220512_040312_equity_NVDA.html b/openbb_terminal/reports/stored/20220512_040312_equity_NVDA.html new file mode 100644 index 00000000000..33722e4bff4 --- /dev/null +++ b/openbb_terminal/reports/stored/20220512_040312_equity_NVDA.html @@ -0,0 +1,38448 @@ + + + + + + openbb_terminal/reports/stored/20220512_040312_equity_NVDA + + + + + + + +
+ OpenBB +
+

Analyst: Didier Rodrigues Lopes

+

Date : 12 May, 2022

+

Time : 04:03 BST

+
+

INVESTMENT RESEARCH REPORT: NVDA

+
+



+

+ No comment. +

KPIs

✅ Average price ratings of last 30 day is above last closing price. 261.75 > 166.3

❌ Regression (dollars per market day) on last 30 market days is negative. -3.38 < 0

🟡 RSI level is normal. 30 < 33.13 < 70

❌ The sum of last 10 insider trading (in millions) was negative. -247.17 < 0

✅ Last FinBrain sentiment is bullish. 0.178 > 0.1

❌ The regression for the next 20th market price is below closing price. -50.33 < 0

+

+

+ No comment. +

+ +
+ + + + + + + 2022-05-12T04:04:06.660540 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +

Description

NVIDIA Corporation provides graphics, and compute and networking solutions in the United States, Taiwan, China, and internationally. The company's Graphics segment offers GeForce GPUs for gaming and PCs, the GeForce NOW game streaming service and related infrastructure, and solutions for gaming platforms; Quadro/NVIDIA RTX GPUs for enterprise workstation graphics; vGPU software for cloud-based visual and virtual computing; automotive platforms for infotainment systems; and Omniverse software for building 3D designs and virtual worlds. Its Compute & Networking segment provides Data Center platforms and systems for AI, HPC, and accelerated computing; Mellanox networking and interconnect solutions; automotive AI Cockpit, autonomous driving development agreements, and autonomous vehicle solutions; cryptocurrency mining processors; Jetson for robotics and other embedded platforms; and NVIDIA AI Enterprise and other software. The company's products are used in gaming, professional visualization, datacenter, and automotive markets. NVIDIA Corporation sells its products to original equipment manufacturers, original device manufacturers, system builders, add-in board manufacturers, retailers/distributors, independent software vendors, Internet and cloud service providers, automotive manufacturers and tier-1 automotive suppliers, mapping companies, start-ups, and other ecosystem participants. It has a strategic collaboration with Kroger Co. NVIDIA Corporation was incorporated in 1993 and is headquartered in Santa Clara, California.

+ +
+

+

+ No comment. +

+ +
+ + + + + + + 2022-05-12T04:04:08.583265 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+ + + + + + + 2022-05-12T04:04:09.466130 + image/svg+xml + + + Matplotlib v3.5.1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CategoryAnalystRatingPrice Target
date
2022-05-03ResumedMorgan StanleyEqual-Weight217.0
2022-04-13UpgradeNew StreetNeutral -> Buy280.0
2022-04-11DowngradeRobert W. BairdOutperform -> Neutral225.0
2022-03-15ReiteratedCowenOutperform350.0
2022-03-07ResumedGoldmanNeutral245.0
+ +
+

+

+ No comment. +

+ +

Estimates

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
202220232024
YEARLY ESTIMATES
Revenue26,66334,88340,596
Dividend0.340.280.29
Dividend Yield (in %)0.19 %0.16 %0.16 %
EPS4.345.666.67
P/E Ratio40.5231.0826.36
+ +
+ +

Earnings

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Previous QuarterCurrent QuarterNext QuarterCurrent YearNext Year
QUARTER EARNINGS ESTIMATES
Date---ending 01/31/22ending 01/31/23
No. of Analysts---3740
Average Estimate---4.342 USD5.661 USD
Year Ago---2.500 USD4.342 USD
Publish Date---6/2/2022-
+ +
+ +

Revenues

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Previous QuarterCurrent QuarterNext QuarterCurrent YearNext Year
QUARTER REVENUES ESTIMATES
Date---ending 01/31/22ending 01/31/23
No. of Analysts---3942
Average Estimate---26,663 USD34,883 USD
Year Ago---16,675 USD26,663 USD
Publish Date---6/2/2022-
+ +
+ +

SEC filings

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeCategoryLink
Filing Date
04/19/2022DEF 14AProxy Statementhttps://www.marketwatch.com/investing/stock/nvda/financials/secfilings?docid=15739033
04/19/2022DEF 14AProxy Statementhttps://www.marketwatch.com/investing/stock/nvda/financials/secfilings?docid=15739000
04/08/2022PRE 14AProxy Statementhttps://www.marketwatch.com/investing/stock/nvda/financials/secfilings?docid=15722884
03/18/202210-KAnnual Reportshttps://www.marketwatch.com/investing/stock/nvda/financials/secfilings?docid=15668939
03/09/20228-KSpecial Eventshttps://www.marketwatch.com/investing/stock/nvda/financials/secfilings?docid=15645553
+ +
+

+

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+ +
+
+ + + \ No newline at end of file diff --git a/openbb_terminal/jupyter/widget_helpers.py b/openbb_terminal/reports/widget_helpers.py similarity index 100% rename from openbb_terminal/jupyter/widget_helpers.py rename to openbb_terminal/reports/widget_helpers.py diff --git a/openbb_terminal/jupyter/widgets/card.j2 b/openbb_terminal/reports/widgets/card.j2 similarity index 100% rename from openbb_terminal/jupyter/widgets/card.j2 rename to openbb_terminal/reports/widgets/card.j2 diff --git a/openbb_terminal/jupyter/widgets/report.css b/openbb_terminal/reports/widgets/report.css similarity index 100% rename from openbb_terminal/jupyter/widgets/report.css rename to openbb_terminal/reports/widgets/report.css diff --git a/openbb_terminal/jupyter/widgets/report.j2 b/openbb_terminal/reports/widgets/report.j2 similarity index 100% rename from openbb_terminal/jupyter/widgets/report.j2 rename to openbb_terminal/reports/widgets/report.j2 diff --git a/openbb_terminal/jupyter/widgets/row.j2 b/openbb_terminal/reports/widgets/row.j2 similarity index 100% rename from openbb_terminal/jupyter/widgets/row.j2 rename to openbb_terminal/reports/widgets/row.j2 diff --git a/openbb_terminal/jupyter/widgets/style.css b/openbb_terminal/reports/widgets/style.css similarity index 100% rename from openbb_terminal/jupyter/widgets/style.css rename to openbb_terminal/reports/widgets/style.css diff --git a/terminal.py b/terminal.py index a0b5e6322e9..53ed880c1cb 100644 --- a/terminal.py +++ b/terminal.py @@ -66,7 +66,8 @@ class TerminalController(BaseController): "portfolio", "forex", "etf", - "jupyter", + "reports", + "dashboards", "funds", "alternative", "econometrics", @@ -145,8 +146,10 @@ class TerminalController(BaseController): > funds > alternative > econometrics + > portfolio -> jupyter[/menu] +> dashboards +> reports[/menu] """, menu="Home", ) @@ -209,12 +212,28 @@ class TerminalController(BaseController): self.queue = self.load_class(ForexController, self.queue) - def call_jupyter(self, _): - """Process jupyter command""" + def call_reports(self, _): + """Process reports command""" if not obbff.PACKAGED_APPLICATION: - from openbb_terminal.jupyter.jupyter_controller import JupyterController + from openbb_terminal.reports.reports_controller import ( + ReportController, + ) - self.queue = self.load_class(JupyterController, self.queue) + self.queue = self.load_class(ReportController, self.queue) + else: + console.print("This feature is coming soon.") + console.print( + "Use the source code and an Anaconda environment if you are familiar with Python." + ) + + def call_dashboards(self, _): + """Process dashboards command""" + if not obbff.PACKAGED_APPLICATION: + from openbb_terminal.dashboards.dashboards_controller import ( + DashboardsController, + ) + + self.queue = self.load_class(DashboardsController, self.queue) else: console.print("This feature is coming soon.") console.print( diff --git a/tests/openbb_terminal/jupyter/dashboards/__init__.py b/tests/openbb_terminal/jupyter/dashboards/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/openbb_terminal/jupyter/dashboards/test_dashboards_controller.py b/tests/openbb_terminal/jupyter/dashboards/test_dashboards_controller.py deleted file mode 100644 index dc6814a407f..00000000000 --- a/tests/openbb_terminal/jupyter/dashboards/test_dashboards_controller.py +++ /dev/null @@ -1,143 +0,0 @@ -import os - -import pytest - -from openbb_terminal.jupyter.dashboards import dashboards_controller - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "queue, expected", - [ - (["load", "help"], []), - (["quit", "help"], ["help"]), - ], -) -def test_menu_with_queue(expected, mocker, queue): - path_controller = "openbb_terminal.jupyter.dashboards.dashboards_controller" - - # MOCK SWITCH - mocker.patch( - target=f"{path_controller}.DashboardsController.switch", - return_value=["quit"], - ) - result_menu = dashboards_controller.DashboardsController(queue=queue).menu() - - assert result_menu == expected - - -@pytest.mark.vcr(record_mode="none") -def test_menu_without_queue_completion(mocker): - path_controller = "openbb_terminal.jupyter.dashboards.dashboards_controller" - - # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU - mocker.patch( - target="openbb_terminal.feature_flags.USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="openbb_terminal.parent_classes.session", - ) - mocker.patch( - target="openbb_terminal.parent_classes.session.prompt", - return_value="quit", - ) - - # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER - mocker.patch.object( - target=dashboards_controller.obbff, - attribute="USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target=f"{path_controller}.session", - ) - mocker.patch( - target=f"{path_controller}.session.prompt", - return_value="quit", - ) - - result_menu = dashboards_controller.DashboardsController(queue=None).menu() - - assert result_menu == [] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.record_stdout -def test_print_help(): - controller = dashboards_controller.DashboardsController(queue=None) - controller.print_help() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "an_input, expected_queue", - [ - ("", []), - ("/help", ["home", "help"]), - ("help/help", ["help", "help"]), - ("q", ["quit"]), - ("h", []), - ( - "r", - ["quit", "quit", "reset", "jupyter", "dashboards"], - ), - ], -) -def test_switch(an_input, expected_queue): - controller = dashboards_controller.DashboardsController(queue=None) - queue = controller.switch(an_input=an_input) - - assert queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -def test_call_cls(mocker): - mocker.patch("os.system") - - controller = dashboards_controller.DashboardsController(queue=None) - controller.call_cls([]) - - assert controller.queue == [] - os.system.assert_called_once_with("cls||clear") - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func, queue, expected_queue", - [ - ( - "call_exit", - [], - ["quit", "quit", "quit"], - ), - ("call_exit", ["help"], ["quit", "quit", "quit", "help"]), - ("call_home", [], ["quit", "quit"]), - ("call_help", [], []), - ("call_quit", [], ["quit"]), - ("call_quit", ["help"], ["quit", "help"]), - ( - "call_reset", - [], - ["quit", "quit", "reset", "jupyter", "dashboards"], - ), - ( - "call_reset", - ["help"], - [ - "quit", - "quit", - "reset", - "jupyter", - "dashboards", - "help", - ], - ), - ], -) -def test_call_func_expect_queue(expected_queue, func, queue): - controller = dashboards_controller.DashboardsController(queue=queue) - result = getattr(controller, func)([]) - - assert result is None - assert controller.queue == expected_queue diff --git a/tests/openbb_terminal/jupyter/dashboards/txt/test_dashboards_controller/test_print_help.txt b/tests/openbb_terminal/jupyter/dashboards/txt/test_dashboards_controller/test_print_help.txt deleted file mode 100644 index 462da2bae0e..00000000000 --- a/tests/openbb_terminal/jupyter/dashboards/txt/test_dashboards_controller/test_print_help.txt +++ /dev/null @@ -1,8 +0,0 @@ - - stocks historic stock information - correlation stock correlations - vsurf options volatility surface - chains options chain analysis - shortdata finra shortdata analysis - crypto cryptocurrency exchange rates against USD - diff --git a/tests/openbb_terminal/jupyter/test_jupyter_controller.py b/tests/openbb_terminal/jupyter/test_jupyter_controller.py deleted file mode 100644 index ea90ad3e018..00000000000 --- a/tests/openbb_terminal/jupyter/test_jupyter_controller.py +++ /dev/null @@ -1,149 +0,0 @@ -import os - -import pytest - -from openbb_terminal.jupyter import jupyter_controller - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "queue, expected", - [ - (["load", "help"], []), - (["quit", "help"], ["help"]), - ], -) -def test_menu_with_queue(expected, mocker, queue): - path_controller = "openbb_terminal.jupyter.jupyter_controller" - - # MOCK SWITCH - mocker.patch( - target=f"{path_controller}.JupyterController.switch", - return_value=["quit"], - ) - result_menu = jupyter_controller.JupyterController(queue=queue).menu() - - assert result_menu == expected - - -@pytest.mark.vcr(record_mode="none") -def test_menu_without_queue_completion(mocker): - path_controller = "openbb_terminal.jupyter.jupyter_controller" - - # ENABLE AUTO-COMPLETION : HELPER_FUNCS.MENU - mocker.patch( - target="openbb_terminal.feature_flags.USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target="openbb_terminal.parent_classes.session", - ) - mocker.patch( - target="openbb_terminal.parent_classes.session.prompt", - return_value="quit", - ) - - # DISABLE AUTO-COMPLETION : CONTROLLER.COMPLETER - mocker.patch.object( - target=jupyter_controller.obbff, - attribute="USE_PROMPT_TOOLKIT", - new=True, - ) - mocker.patch( - target=f"{path_controller}.session", - ) - mocker.patch( - target=f"{path_controller}.session.prompt", - return_value="quit", - ) - - result_menu = jupyter_controller.JupyterController(queue=None).menu() - - assert result_menu == [] - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.record_stdout -def test_print_help(): - controller = jupyter_controller.JupyterController(queue=None) - controller.print_help() - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "an_input, expected_queue", - [ - ("", []), - ("/help", ["home", "help"]), - ("help/help", ["help", "help"]), - ("q", ["quit"]), - ("h", []), - ( - "r", - [ - "quit", - "reset", - "jupyter", - ], - ), - ], -) -def test_switch(an_input, expected_queue): - controller = jupyter_controller.JupyterController(queue=None) - queue = controller.switch(an_input=an_input) - - assert queue == expected_queue - - -@pytest.mark.vcr(record_mode="none") -def test_call_cls(mocker): - mocker.patch("os.system") - - controller = jupyter_controller.JupyterController(queue=None) - controller.call_cls([]) - - assert controller.queue == [] - os.system.assert_called_once_with("cls||clear") - - -@pytest.mark.vcr(record_mode="none") -@pytest.mark.parametrize( - "func, queue, expected_queue", - [ - ( - "call_exit", - [], - ["quit", "quit"], - ), - ("call_exit", ["help"], ["quit", "quit", "help"]), - ("call_home", [], ["quit"]), - ("call_help", [], []), - ("call_quit", [], ["quit"]), - ("call_quit", ["help"], ["quit", "help"]), - ( - "call_reset", - [], - [ - "quit", - "reset", - "jupyter", - ], - ), - ( - "call_reset", - ["help"], - [ - "quit", - "reset", - "jupyter", - "help", - ], - ), - ], -) -def test_call_func_expect_queue(expected_queue, func, queue): - controller = jupyter_controller.JupyterController(queue=queue) - result = getattr(controller, func)([]) - - assert result is None - assert controller.queue == expected_queue diff --git a/tests/openbb_terminal/jupyter/txt/test_jupyter_controller/test_print_help.txt b/tests/openbb_terminal/jupyter/txt/test_jupyter_controller/test_print_help.txt deleted file mode 100644 index 8690ae78b11..00000000000 --- a/tests/openbb_terminal/jupyter/txt/test_jupyter_controller/test_print_help.txt +++ /dev/null @@ -1,4 +0,0 @@ - -> reports creates jupyter reports -> dashboards shows interactive jupyter dashboards -