mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-31 14:31:20 +00:00
Update api from gst
This commit is contained in:
james
177
openbb_terminal/jupyter/dashboards/chains.ipynb
vendored
Normal file
177
openbb_terminal/jupyter/dashboards/chains.ipynb
vendored
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@@ -0,0 +1,177 @@
|
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{
|
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"cells": [
|
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{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "e9ad60bc",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
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"import os\n",
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"import ipywidgets as widgets\n",
|
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"import plotly.graph_objs as go\n",
|
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"import yfinance as yf\n",
|
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"from IPython.display import display"
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]
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},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"id": "beb36881",
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"metadata": {},
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"outputs": [],
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"source": [
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"opts = [\n",
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" \"lastTradeDate\",\n",
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" \"strike\",\n",
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" \"lastPrice\",\n",
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" \"bid\",\n",
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" \"ask\",\n",
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" \"change\",\n",
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" \"percentChange\",\n",
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" \"volume\",\n",
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" \"openInterest\",\n",
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" \"impliedVolatility\",\n",
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"]\n",
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"\n",
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"\n",
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"def clean_str(string):\n",
|
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" new_str = \"\"\n",
|
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" for letter in string:\n",
|
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" if letter.isupper():\n",
|
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" new_str += \" \"\n",
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" new_str += letter\n",
|
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" return new_str.title()\n",
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"\n",
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"\n",
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"def format_plotly(fig, x, y, ticker, expiry, inst):\n",
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" fig.update_yaxes(title=clean_str(y))\n",
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" fig.update_xaxes(title=clean_str(x))\n",
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" expires = \", \".join(expiry)\n",
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" title = (\n",
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" f\"{clean_str(y)} vs. {clean_str(x)} for {ticker.upper()} {inst}s on {expires}\"\n",
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" )\n",
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" fig.update_layout(\n",
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" autosize=False,\n",
|
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" width=1000,\n",
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" height=500,\n",
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" title={\n",
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" \"text\": title,\n",
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" \"y\": 0.95,\n",
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" \"x\": 0.5,\n",
|
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" \"xanchor\": \"center\",\n",
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" \"yanchor\": \"top\",\n",
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" },\n",
|
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" )"
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]
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},
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{
|
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"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "f8c2f65c",
|
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"metadata": {
|
||||
"tags": [
|
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"parameters"
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]
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},
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"outputs": [],
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"source": [
|
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"class Chart:\n",
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" def __init__(self):\n",
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" self.last_ticker = \"\"\n",
|
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" self.dfs = {}\n",
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"\n",
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" def update(self, ticker, expiry, inst, x, y):\n",
|
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" if ticker:\n",
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" stock = yf.Ticker(ticker)\n",
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" if ticker != self.last_ticker:\n",
|
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" exp_w.options = stock.options\n",
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" self.last_ticker = ticker\n",
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"\n",
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" if expiry:\n",
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" fig = go.Figure()\n",
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" for expire in expiry:\n",
|
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" if expire not in self.dfs:\n",
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" self.dfs[expire] = stock.option_chain(expire)\n",
|
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" group = self.dfs[expire]\n",
|
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" df = group.calls if inst == \"Call\" else group.puts\n",
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"\n",
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" plot = go.Scatter(\n",
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" x=df[x],\n",
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" y=df[y],\n",
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" mode=\"lines\",\n",
|
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" connectgaps=True,\n",
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" name=expire,\n",
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" )\n",
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" fig.add_trace(plot)\n",
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"\n",
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" format_plotly(fig, x, y, ticker, expiry, inst)\n",
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"\n",
|
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" if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n",
|
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" fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n",
|
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" else:\n",
|
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" fig.show(config={\"showTips\": False})\n",
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"\n",
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"\n",
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"x_w = widgets.Dropdown(options=opts, value=\"strike\", description=\"X\")\n",
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"y_w = widgets.Dropdown(options=opts, value=\"lastPrice\", description=\"Y\")\n",
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"tickers_w = widgets.Text(description=\"Ticker\")\n",
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"exp_w = widgets.SelectMultiple(description=\"Expiry\")\n",
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"inst_w = widgets.Dropdown(options=[\"Put\", \"Call\"], value=\"Call\", description=\"Type\")\n",
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"\n",
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"select_opt = widgets.VBox([tickers_w, exp_w])\n",
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"select_plot = widgets.VBox([inst_w, x_w, y_w])\n",
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"controls = widgets.HBox([select_opt, select_plot])\n",
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"\n",
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"chart = Chart()\n",
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"stocks_view = widgets.interactive_output(\n",
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" chart.update,\n",
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" {\"ticker\": tickers_w, \"expiry\": exp_w, \"inst\": inst_w, \"x\": x_w, \"y\": y_w},\n",
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")\n",
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"\n",
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"title_html = \"<h1>Option Chain Dashboard</h1>\"\n",
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"app_contents = [\n",
|
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" widgets.HTML(title_html),\n",
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" controls,\n",
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" stocks_view,\n",
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"]\n",
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"app = widgets.VBox(app_contents)\n",
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"display(app)"
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]
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},
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{
|
||||
"cell_type": "code",
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"execution_count": null,
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"id": "a84c7390-6b43-4a25-8aa2-31aa84f52605",
|
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"metadata": {},
|
||||
"outputs": [],
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"source": []
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}
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],
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"metadata": {
|
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"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
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"language": "python",
|
||||
"name": "python3"
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},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
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"version": 3
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},
|
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
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"name": "python",
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
|
||||
},
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc"
|
||||
}
|
||||
}
|
||||
},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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222
openbb_terminal/jupyter/dashboards/correlation.ipynb
vendored
Normal file
222
openbb_terminal/jupyter/dashboards/correlation.ipynb
vendored
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@@ -0,0 +1,222 @@
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||||
{
|
||||
"cells": [
|
||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 10,
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||||
"id": "e9ad60bc",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
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"import os\n",
|
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"from datetime import datetime, timedelta\n",
|
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"import ipywidgets as widgets\n",
|
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"import plotly.graph_objs as go\n",
|
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"import yfinance as yf\n",
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"import pandas as pd\n",
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"from IPython.display import display"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": 11,
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||||
"id": "6c554cd2-462c-4832-b73c-c214aae11ce4",
|
||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"interval_opts = [\n",
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" \"1m\",\n",
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" \"2m\",\n",
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" \"5m\",\n",
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" \"15m\",\n",
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" \"30m\",\n",
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" \"60m\",\n",
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" \"90m\",\n",
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" \"1h\",\n",
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" \"1d\",\n",
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" \"5d\",\n",
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" \"1wk\",\n",
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" \"1mo\",\n",
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" \"3mo\",\n",
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"]"
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]
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},
|
||||
{
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||||
"cell_type": "code",
|
||||
"execution_count": 13,
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||||
"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='<h1>Correlation Analysis Dashboard</h1>'), 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",
|
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"\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",
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"\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 = \"<h1>Correlation Analysis Dashboard</h1>\"\n",
|
||||
"warning_html = '<p style=\"color:red\">Use a comma after EVERY stock typed.</p>'\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
|
||||
}
|
||||
160
openbb_terminal/jupyter/dashboards/crypto.ipynb
vendored
Normal file
160
openbb_terminal/jupyter/dashboards/crypto.ipynb
vendored
Normal file
@@ -0,0 +1,160 @@
|
||||
{
|
||||
"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\"<style>{openbb.widgets.price_card_stylesheet()}</style>\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
284
openbb_terminal/jupyter/dashboards/currencies.ipynb
vendored
Normal file
284
openbb_terminal/jupyter/dashboards/currencies.ipynb
vendored
Normal file
@@ -0,0 +1,284 @@
|
||||
{
|
||||
"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\"<style>{stylesheet()}</style>\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
255
openbb_terminal/jupyter/dashboards/shortdata.ipynb
vendored
Normal file
255
openbb_terminal/jupyter/dashboards/shortdata.ipynb
vendored
Normal file
@@ -0,0 +1,255 @@
|
||||
{
|
||||
"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",
|
||||
"<h2>Finra Short Data</h2>\n",
|
||||
"<p>This widget downloads the consolidated NMS short data from FINRA and aggregates the data by summing over the entire time period.</p>\n",
|
||||
"<p>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.</p>\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<h2>Finra Short Data</h2>\\n<p>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
|
||||
}
|
||||
404
openbb_terminal/jupyter/dashboards/stocks.ipynb
vendored
Normal file
404
openbb_terminal/jupyter/dashboards/stocks.ipynb
vendored
Normal file
@@ -0,0 +1,404 @@
|
||||
{
|
||||
"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 = \"<h1>Stock Analysis Dashboard</h1>\"\n",
|
||||
"warning_html = '<p style=\"color:red\"=>Use a comma after EVERY stock typed.</p>'\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
|
||||
}
|
||||
161
openbb_terminal/jupyter/dashboards/vsurf.ipynb
vendored
Normal file
161
openbb_terminal/jupyter/dashboards/vsurf.ipynb
vendored
Normal file
@@ -0,0 +1,161 @@
|
||||
{
|
||||
"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",
|
||||
"from openbb_terminal import api as openbb\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": "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} <br>Strike: %{y} <br>IV: %{z}<extra></extra>\",\n",
|
||||
" \"OI\": \"DTE: %{x} <br>Strike: %{y} <br>OI: %{z}<extra></extra>\",\n",
|
||||
" \"LP\": \"DTE: %{x} <br>Strike: %{y} <br>LP: %{z}<extra></extra>\",\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 = \"<h1>Option Surface Dashboard</h1>\"\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": "3.8.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
273
openbb_terminal/jupyter/reports/crypto_market.ipynb
vendored
Normal file
273
openbb_terminal/jupyter/reports/crypto_market.ipynb
vendored
Normal file
@@ -0,0 +1,273 @@
|
||||
{
|
||||
"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",
|
||||
"\n",
|
||||
"coin_map_df = (\n",
|
||||
" cryptocurrency_helpers.prepare_all_coins_df().set_index(\"Symbol\").loc[\"btc\"]\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 3.8.12 64-bit ('gst': conda)",
|
||||
"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": "3.9.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
253
openbb_terminal/jupyter/reports/dark_pool.ipynb
vendored
Normal file
253
openbb_terminal/jupyter/reports/dark_pool.ipynb
vendored
Normal file
@@ -0,0 +1,253 @@
|
||||
{
|
||||
"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": "",
|
||||
"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
|
||||
}
|
||||
445
openbb_terminal/jupyter/reports/due_dilligence.ipynb
vendored
Normal file
445
openbb_terminal/jupyter/reports/due_dilligence.ipynb
vendored
Normal file
@@ -0,0 +1,445 @@
|
||||
{
|
||||
"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": "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
|
||||
}
|
||||
648
openbb_terminal/jupyter/reports/econ_data.ipynb
vendored
Normal file
648
openbb_terminal/jupyter/reports/econ_data.ipynb
vendored
Normal file
@@ -0,0 +1,648 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
397
openbb_terminal/jupyter/reports/similar_analysis.ipynb
vendored
Normal file
397
openbb_terminal/jupyter/reports/similar_analysis.ipynb
vendored
Normal file
@@ -0,0 +1,397 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "phantom-facility",
|
||||
"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": "compact-count",
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"parameters"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Parameters that will be replaced when calling this notebook\n",
|
||||
"ticker = \"TSLA\"\n",
|
||||
"\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": [
|
||||
"display(\n",
|
||||
" Markdown(\n",
|
||||
" f\"# {ticker.upper()} - Comparison - {datetime.now().strftime('%Y/%m/%d %H:%M:%S')}\"\n",
|
||||
" )\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b9eaca4a-a46d-45a3-9306-e80e74234f12",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import (\n",
|
||||
" finviz_compare_model,\n",
|
||||
" yahoo_finance_model,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"ticker = ticker.upper()\n",
|
||||
"finviz_similar = finviz_compare_model.get_similar_companies(\n",
|
||||
" ticker, [\"Sector\", \"Industry\", \"Country\"]\n",
|
||||
")[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"from openbb_terminal.stocks.fundamental_analysis import yahoo_finance_model\n",
|
||||
"\n",
|
||||
"df_info = yahoo_finance_model.get_info(ticker)\n",
|
||||
"if \"Long business summary\" in df_info.index:\n",
|
||||
" summary = df_info.loc[\"Long business summary\"].values[0]\n",
|
||||
"if \"Website\" in df_info.index:\n",
|
||||
" website = df_info.loc[\"Website\"].values[0]\n",
|
||||
"\n",
|
||||
"if finviz_similar:\n",
|
||||
" print(f\"{ticker}: {website}\")\n",
|
||||
" print(summary)\n",
|
||||
"\n",
|
||||
" for symbol in finviz_similar:\n",
|
||||
" df_info = yahoo_finance_model.get_info(symbol)\n",
|
||||
" if \"Long business summary\" in df_info.index:\n",
|
||||
" summary = df_info.loc[\"Long business summary\"].values[0]\n",
|
||||
" if \"Website\" in df_info.index:\n",
|
||||
" website = df_info.loc[\"Website\"].values[0]\n",
|
||||
"\n",
|
||||
" print(\"\")\n",
|
||||
" print(f\"{symbol}: {website}\")\n",
|
||||
" print(summary)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "temporal-clerk",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"### Historical prices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1fdfac4c-62cd-4bd3-b7df-41ba67263798",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"from openbb_terminal.stocks.comparison_analysis import yahoo_finance_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" for i in range(math.ceil(len(finviz_similar) / 4)):\n",
|
||||
" yahoo_finance_view.display_historical(\n",
|
||||
" similar_tickers=finviz_similar[4 * (i) : 4 * (i + 1)],\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2a07f866-7939-4fb7-8b14-c0701087a8ae",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Historical correlation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "eb1850dc-1d62-4ad4-971f-1bbce6209726",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" plt.figure(figsize=(25, 10))\n",
|
||||
" yahoo_finance_view.display_correlation(\n",
|
||||
" similar_tickers=finviz_similar,\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "21005668-7f21-4884-a231-846130695602",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"### Historical volumes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a7bf528c-427c-4d84-8cb9-ec5656f920e1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" for i in range(math.ceil(len(finviz_similar) / 4)):\n",
|
||||
" yahoo_finance_view.display_volume(\n",
|
||||
" similar_tickers=finviz_similar[4 * (i) : 4 * (i + 1)],\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6e370470-8014-4e97-95f2-13b4f66645c5",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"### Overview"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f82b7162-f7e5-4d5c-8ca9-50466f789f48",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" finviz_compare_view.screener(\n",
|
||||
" similar=finviz_similar,\n",
|
||||
" data_type=\"overview\",\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5277ae24-b7d7-48e6-b46c-fcffdf84631f",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"### Valuation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "caed21ad-a31d-4063-a4cb-f5b6fd1c63ed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" finviz_compare_view.screener(\n",
|
||||
" similar=finviz_similar,\n",
|
||||
" data_type=\"valuation\",\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "98030d12-9a77-4f08-b4ad-2c558d9ad225",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"### Financial"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e98bf90d-bf2b-4eea-b533-08211e8eec47",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" finviz_compare_view.screener(\n",
|
||||
" similar=finviz_similar,\n",
|
||||
" data_type=\"financial\",\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" finviz_compare_view.screener(\n",
|
||||
" similar=finviz_similar,\n",
|
||||
" data_type=\"ownership\",\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" finviz_compare_view.screener(\n",
|
||||
" similar=finviz_similar,\n",
|
||||
" data_type=\"performance\",\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"from openbb_terminal.stocks.comparison_analysis import finviz_compare_view\n",
|
||||
"\n",
|
||||
"if finviz_similar and finviz_similar != [\"\"]:\n",
|
||||
" finviz_compare_view.screener(\n",
|
||||
" similar=finviz_similar,\n",
|
||||
" data_type=\"technical\",\n",
|
||||
" )\n",
|
||||
"else:\n",
|
||||
" print(\"Ticker not found in CoinGeckoAPI\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "sought-phase",
|
||||
"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": "3.8.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
28
website/content/api/_index.md
vendored
28
website/content/api/_index.md
vendored
@@ -20,7 +20,7 @@ scripts and IPython Notebooks (Jupyter).
|
||||
The API wraps the functionality of the terminal in a way that the python commands follow
|
||||
the same convention as the terminal.
|
||||
|
||||
For example `stocks/load aapl` becomes `gst.stocks.load("aapl")`.
|
||||
For example `stocks/load aapl` becomes `openbb.stocks.load("aapl")`.
|
||||
|
||||
---
|
||||
|
||||
@@ -29,7 +29,7 @@ For example `stocks/load aapl` becomes `gst.stocks.load("aapl")`.
|
||||
Import the GST API into your python script or notebook with
|
||||
|
||||
```python
|
||||
from openbb_terminal import api as gst
|
||||
from openbb_terminal import api as openbb
|
||||
```
|
||||
|
||||
## Understanding the API functions
|
||||
@@ -39,7 +39,7 @@ docstring. Docstrings can be viewed using the code introspection tools of your
|
||||
text/notebook editor or the built-in python `help` command. Docstrings contain both the
|
||||
description of the functionality and the overview of the parameters.
|
||||
|
||||
As an example this is the docstring of `gst.economy.bigmac`:
|
||||
As an example this is the docstring of `openbb.economy.bigmac`:
|
||||
|
||||
```txt
|
||||
Display Big Mac Index for given countries
|
||||
@@ -61,47 +61,47 @@ external_axes : Optional[List[plt.Axes]], optional
|
||||
Load stock data into a dataframe:
|
||||
|
||||
```python
|
||||
aapl_data = gst.stocks.load(
|
||||
aapl_data = openbb.stocks.load(
|
||||
ticker="aapl",
|
||||
start="2021-06-10",
|
||||
)
|
||||
aapl_data = gst.stocks.process_candle(aapl_data)
|
||||
aapl_data = openbb.stocks.process_candle(aapl_data)
|
||||
```
|
||||
|
||||
Plot candle chart
|
||||
|
||||
```python
|
||||
gst.stocks.candle("AAPL", aapl_data, True)
|
||||
openbb.stocks.candle("AAPL", aapl_data, True)
|
||||
```
|
||||
|
||||
Call the "What if" command of the backtesting menu
|
||||
|
||||
```python
|
||||
gst.stocks.bt.whatif("aapl", 10, datetime.strptime("2012-03-03", "%Y-%m-%d"))
|
||||
openbb.stocks.bt.whatif("aapl", 10, datetime.strptime("2012-03-03", "%Y-%m-%d"))
|
||||
```
|
||||
|
||||
Get sentiment for a list of tickers
|
||||
|
||||
```python
|
||||
gst.stocks.ca.models.finbrain.get_sentiments(tickers=["aapl"])
|
||||
openbb.stocks.ca.models.finbrain.get_sentiments(tickers=["aapl"])
|
||||
```
|
||||
|
||||
Print key fundamental metrics for past 2 years
|
||||
|
||||
```python
|
||||
gst.stocks.fa.fmp.metrics(ticker="AAPL", number=2)
|
||||
openbb.stocks.fa.fmp.metrics(ticker="AAPL", number=2)
|
||||
```
|
||||
|
||||
Get fundamental metrics for past 3 years as a pandas dataframe
|
||||
|
||||
```python
|
||||
gst.stocks.fa.fmp.models.fmp.get_key_metrics(ticker="AAPL", number=3)
|
||||
openbb.stocks.fa.fmp.models.fmp.get_key_metrics(ticker="AAPL", number=3)
|
||||
```
|
||||
|
||||
Perform an OLS regression on multiple custom datasets
|
||||
|
||||
```python
|
||||
gst.econometrics.ols(
|
||||
openbb.econometrics.ols(
|
||||
regression_variables=[
|
||||
'close-tsla', 'volume-aapl'
|
||||
],
|
||||
@@ -124,14 +124,14 @@ Example:
|
||||
```python
|
||||
# Load stock data
|
||||
ticker = "AAPL"
|
||||
stock_data = gst.stocks.load(ticker="aapl")
|
||||
stock_data = openbb.stocks.load(ticker="aapl")
|
||||
|
||||
# Create a matplotlib figure
|
||||
fig, axes = plt.subplots(3, 1, figsize=(10, 7))
|
||||
|
||||
# Use GST to add charts to the axes of the created figure
|
||||
gst.stocks.ta.macd(s_ticker=ticker, series=aapl["Close"], external_axes=axes[0:2])
|
||||
gst.stocks.options.voi_yf(
|
||||
openbb.stocks.ta.macd(s_ticker=ticker, series=aapl["Close"], external_axes=axes[0:2])
|
||||
openbb.stocks.options.voi_yf(
|
||||
ticker=ticker,
|
||||
expiry="2022-03-11",
|
||||
min_sp=110,
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
usage: load [-n NAME] [-h]
|
||||
```
|
||||
|
||||
Load a file from gamestonk_terminal/portfolio/portfolios
|
||||
Load a file from openbb_terminal/portfolio/portfolios
|
||||
|
||||
```
|
||||
optional arguments:
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
usage: save [-n NAME] [-h]
|
||||
```
|
||||
|
||||
Save a file from gamestonk_terminal/portfolio/portfolios
|
||||
Save a file from openbb_terminal/portfolio/portfolios
|
||||
|
||||
```
|
||||
optional arguments:
|
||||
|
||||
Reference in New Issue
Block a user