diff --git a/openbb_terminal/dashboards/chains.ipynb b/openbb_terminal/dashboards/chains.ipynb new file mode 100644 index 00000000000..c17412479ee --- /dev/null +++ b/openbb_terminal/dashboards/chains.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e9ad60bc", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objs as go\n", + "import yfinance as yf\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beb36881", + "metadata": {}, + "outputs": [], + "source": [ + "opts = [\n", + " \"lastTradeDate\",\n", + " \"strike\",\n", + " \"lastPrice\",\n", + " \"bid\",\n", + " \"ask\",\n", + " \"change\",\n", + " \"percentChange\",\n", + " \"volume\",\n", + " \"openInterest\",\n", + " \"impliedVolatility\",\n", + "]\n", + "\n", + "\n", + "def clean_str(string):\n", + " new_str = \"\"\n", + " for letter in string:\n", + " if letter.isupper():\n", + " new_str += \" \"\n", + " new_str += letter\n", + " return new_str.title()\n", + "\n", + "\n", + "def format_plotly(fig, x, y, ticker, expiry, inst):\n", + " fig.update_yaxes(title=clean_str(y))\n", + " fig.update_xaxes(title=clean_str(x))\n", + " expires = \", \".join(expiry)\n", + " title = (\n", + " f\"{clean_str(y)} vs. {clean_str(x)} for {ticker.upper()} {inst}s on {expires}\"\n", + " )\n", + " fig.update_layout(\n", + " autosize=False,\n", + " width=1000,\n", + " height=500,\n", + " title={\n", + " \"text\": title,\n", + " \"y\": 0.95,\n", + " \"x\": 0.5,\n", + " \"xanchor\": \"center\",\n", + " \"yanchor\": \"top\",\n", + " },\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8c2f65c", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "class Chart:\n", + " def __init__(self):\n", + " self.last_ticker = \"\"\n", + " self.dfs = {}\n", + "\n", + " def update(self, ticker, expiry, inst, x, y):\n", + " if ticker:\n", + " stock = yf.Ticker(ticker)\n", + " if ticker != self.last_ticker:\n", + " exp_w.options = stock.options\n", + " self.last_ticker = ticker\n", + "\n", + " if expiry:\n", + " fig = go.Figure()\n", + " for expire in expiry:\n", + " if expire not in self.dfs:\n", + " self.dfs[expire] = stock.option_chain(expire)\n", + " group = self.dfs[expire]\n", + " df = group.calls if inst == \"Call\" else group.puts\n", + "\n", + " plot = go.Scatter(\n", + " x=df[x],\n", + " y=df[y],\n", + " mode=\"lines\",\n", + " connectgaps=True,\n", + " name=expire,\n", + " )\n", + " fig.add_trace(plot)\n", + "\n", + " format_plotly(fig, x, y, ticker, expiry, inst)\n", + "\n", + " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", + " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", + " else:\n", + " fig.show(config={\"showTips\": False})\n", + "\n", + "\n", + "x_w = widgets.Dropdown(options=opts, value=\"strike\", description=\"X\")\n", + "y_w = widgets.Dropdown(options=opts, value=\"lastPrice\", description=\"Y\")\n", + "tickers_w = widgets.Text(description=\"Ticker\")\n", + "exp_w = widgets.SelectMultiple(description=\"Expiry\")\n", + "inst_w = widgets.Dropdown(options=[\"Put\", \"Call\"], value=\"Call\", description=\"Type\")\n", + "\n", + "select_opt = widgets.VBox([tickers_w, exp_w])\n", + "select_plot = widgets.VBox([inst_w, x_w, y_w])\n", + "controls = widgets.HBox([select_opt, select_plot])\n", + "\n", + "chart = Chart()\n", + "stocks_view = widgets.interactive_output(\n", + " chart.update,\n", + " {\"ticker\": tickers_w, \"expiry\": exp_w, \"inst\": inst_w, \"x\": x_w, \"y\": y_w},\n", + ")\n", + "\n", + "title_html = \"

Option Chain Dashboard

\"\n", + "app_contents = [\n", + " widgets.HTML(title_html),\n", + " controls,\n", + " stocks_view,\n", + "]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a84c7390-6b43-4a25-8aa2-31aa84f52605", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/dashboards/correlation.ipynb b/openbb_terminal/dashboards/correlation.ipynb new file mode 100644 index 00000000000..7e2b67e59a0 --- /dev/null +++ b/openbb_terminal/dashboards/correlation.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "e9ad60bc", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from datetime import datetime, timedelta\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objs as go\n", + "import yfinance as yf\n", + "import pandas as pd\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6c554cd2-462c-4832-b73c-c214aae11ce4", + "metadata": {}, + "outputs": [], + "source": [ + "interval_opts = [\n", + " \"1m\",\n", + " \"2m\",\n", + " \"5m\",\n", + " \"15m\",\n", + " \"30m\",\n", + " \"60m\",\n", + " \"90m\",\n", + " \"1h\",\n", + " \"1d\",\n", + " \"5d\",\n", + " \"1wk\",\n", + " \"1mo\",\n", + " \"3mo\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f8c2f65c", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b462032b043b47a8899f8366f43ab28e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='

Correlation Analysis Dashboard

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

Correlation Analysis Dashboard

\"\n", + "warning_html = '

Use a comma after EVERY stock typed.

'\n", + "app_contents = [\n", + " widgets.HTML(title_html),\n", + " controls,\n", + " widgets.HTML(warning_html),\n", + " stocks_view,\n", + "]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37f46146-a206-4c25-b969-81c5abec4b1d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/dashboards/crypto.ipynb b/openbb_terminal/dashboards/crypto.ipynb new file mode 100644 index 00000000000..7972cda6037 --- /dev/null +++ b/openbb_terminal/dashboards/crypto.ipynb @@ -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\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcbbf867-af50-4d06-ae5c-7c09647aa55d", + "metadata": {}, + "outputs": [], + "source": [ + "COIN_LIST = (\n", + " \"BTC,ETH,USDT,BNB,USDC,XRP,LUNA,ADA,\"\n", + " + \"SOL,AVAX,DOT,BUSD,DOGE,UST,SHIB,WBTC,\"\n", + " + \"MATIC,CRO,DAI,LTC,ATOM,NEAR,LINK,BCH,\"\n", + " + \"UNI,TRX,FTT,ETC,LEO,ALGO,XLM,MANA,\"\n", + " + \"BTCB,HBAR,EGLD,ICP,SAND,XMR,WAVES,VET,\"\n", + " + \"APE,FIL,FTM,AXS,THETA,KLAY,XTZ,RUNE\"\n", + ").split(\",\")\n", + "exchange_rates = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deeeb80a-3675-44fb-9a1b-2325d8da4e7d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_exchange_rate(coin: str, days: int = 2):\n", + " \"\"\"Get exchange rate for a cryptocurrency agains USD.\"\"\"\n", + " (_, _, _, _, current_df, _,) = openbb.crypto.load(\n", + " coin=coin,\n", + " source=\"cp\",\n", + " days=days,\n", + " vs=\"usd\",\n", + " interval=\"1day\",\n", + " should_load_ta_data=True,\n", + " )\n", + " price_color = \"neutral_color\"\n", + " if current_df[\"Close\"].iloc[-1] > current_df[\"Close\"].iloc[-2]:\n", + " price_color = \"up_color\"\n", + " elif current_df[\"Close\"].iloc[-1] < current_df[\"Close\"].iloc[-2]:\n", + " price_color = \"down_color\"\n", + " price = str(current_df[\"Close\"].iloc[-1])[:7]\n", + "\n", + " return price, price_color" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18acf5cb-6cbf-484d-8042-107c030098de", + "metadata": {}, + "outputs": [], + "source": [ + "def show_rates(btn):\n", + " \"\"\"Show exchange rates in grid box widget.\"\"\"\n", + " grid.children = ()\n", + " for coin in COIN_LIST:\n", + " if coin not in exchange_rates.keys():\n", + " exchange_rates[coin] = {\"price\": None, \"color\": None}\n", + " try:\n", + " price, price_color = get_exchange_rate(coin=coin)\n", + " except:\n", + " price, price_color = \"-------\", \"neutral_color\"\n", + " widgets = list(grid.children)\n", + " widgets.append(\n", + " ipw.HTML(\n", + " openbb.widgets.price_card(\n", + " ticker=coin,\n", + " price=price,\n", + " price_color=price_color,\n", + " )\n", + " )\n", + " )\n", + " grid.children = tuple(widgets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf2021e9-de8a-466e-b341-fcd6abda64b5", + "metadata": {}, + "outputs": [], + "source": [ + "update_rates = ipw.Button(description=\"Update rates\")\n", + "update_rates.on_click(show_rates)\n", + "header = ipw.HBox([update_rates])\n", + "\n", + "layout = ipw.Layout(grid_template_columns=\"1fr \" * 8)\n", + "grid = ipw.GridBox(\n", + " [],\n", + " layout=layout,\n", + ")\n", + "\n", + "display(header, grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19dfcbfe-eaec-406e-bb3c-72d1b3538a6e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "voila": { + "theme": "dark" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/dashboards/currencies.ipynb b/openbb_terminal/dashboards/currencies.ipynb new file mode 100644 index 00000000000..ad612c6ce8e --- /dev/null +++ b/openbb_terminal/dashboards/currencies.ipynb @@ -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\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5905453-4de6-4f5f-8012-9acdfca50987", + "metadata": {}, + "outputs": [], + "source": [ + "def get_exchange_rate(currency_pair: str) -> str:\n", + " \"\"\"Get exchange rate for a currency pair.\"\"\"\n", + " from_symbol, to_symbol = currency_pair.split(\"/\")\n", + " exchange_data = openbb.forex.models.av.get_quote(\n", + " to_symbol=to_symbol, from_symbol=from_symbol\n", + " )\n", + " return exchange_data[\"Realtime Currency Exchange Rate\"][\"5. Exchange Rate\"][:6]\n", + "\n", + "\n", + "def get_candle_widget(\n", + " output: Optional[ipw.Output],\n", + " to_symbol: str,\n", + " from_symbol: str,\n", + ") -> ipw.Output:\n", + " \"\"\"Plot a candle chart for a currency pair.\"\"\"\n", + " start_date = datetime.now() - timedelta(days=1)\n", + " start_date = start_date.strftime(\"%Y-%m-%d\")\n", + "\n", + " exchange_rate_data = openbb.forex.models.av.get_historical(\n", + " to_symbol=to_symbol,\n", + " from_symbol=from_symbol,\n", + " start_date=start_date,\n", + " resolution=\"i\",\n", + " interval=\"15\",\n", + " )\n", + " fig, ax = plt.subplots(1, 1, figsize=(11, 4))\n", + " openbb.forex.candle(\n", + " data=exchange_rate_data,\n", + " to_symbol=to_symbol,\n", + " from_symbol=from_symbol,\n", + " external_axes=[ax],\n", + " )\n", + "\n", + " ax.set_xlim(0, len(exchange_rate_data.index))\n", + " fig.canvas.header_visible = False\n", + " fig.canvas.footer_visible = False\n", + " with output:\n", + " output.clear_output(True)\n", + " fig.canvas.show()\n", + " return output\n", + "\n", + "\n", + "def on_dropdown_change(change):\n", + " \"\"\"Update charts on change of dropdown selection.\"\"\"\n", + " if change[\"type\"] == \"change\" and change[\"name\"] == \"value\":\n", + " output = ipw.Output()\n", + " get_candle_widget(\n", + " to_symbol=to_widget.value,\n", + " from_symbol=from_widget.value,\n", + " )\n", + " dashboard.children[\"Candle\"] = output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c768d15-37ad-441b-b069-506c4bdbb9a7", + "metadata": {}, + "outputs": [], + "source": [ + "exchange_rates = {\n", + " \"EUR/USD\": {\"latest\": None, \"previous\": None},\n", + " \"USD/JPY\": {\"latest\": None, \"previous\": None},\n", + " \"GBP/USD\": {\"latest\": None, \"previous\": None},\n", + " \"AUD/USD\": {\"latest\": None, \"previous\": None},\n", + " \"USD/CAD\": {\"latest\": None, \"previous\": None},\n", + " # \"USD/CNY\": {\"latest\": None, \"previous\": None},\n", + " # \"USD/CHF\": {\"latest\": None, \"previous\": None},\n", + " # \"USD/HKD\": {\"latest\": None, \"previous\": None},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4968ab89-aafe-4234-933c-963bd672715d", + "metadata": {}, + "outputs": [], + "source": [ + "def compose_widgets():\n", + " widgets = {}\n", + " for currency_pair in exchange_rates:\n", + " price = exchange_rates[currency_pair][\"latest\"]\n", + " price_color = \"neutral_color\"\n", + " if exchange_rates[currency_pair][\"previous\"] is not None:\n", + " if (\n", + " exchange_rates[currency_pair][\"latest\"]\n", + " > exchange_rates[currency_pair][\"previous\"]\n", + " ):\n", + " price_color = \"up_color\"\n", + " elif (\n", + " exchange_rates[currency_pair][\"latest\"]\n", + " < exchange_rates[currency_pair][\"previous\"]\n", + " ):\n", + " price_color = \"down_color\"\n", + " widgets[currency_pair] = ipw.HTML(\n", + " price_card(ticker=currency_pair, price=price, price_color=price_color)\n", + " )\n", + " return widgets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f35d1500-a411-4cff-94ee-d65d4d1810f9", + "metadata": {}, + "outputs": [], + "source": [ + "# for currency_code in rates_to_usd:\n", + "# rates_to_usd[currency_code][\"previous\"] = rates_to_usd[currency_code][\"latest\"]\n", + "# rates_to_usd[currency_code][\"latest\"] = get_rate_against_usd(currency_code)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a72837b-4ddd-4c01-a493-ebb430202cbc", + "metadata": {}, + "outputs": [], + "source": [ + "widgets = compose_widgets()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c636543c-42fe-4dd6-a900-1552a3c28c7e", + "metadata": {}, + "outputs": [], + "source": [ + "currency_list = openbb.forex.models.av.get_currency_list()\n", + "from_widget = ipw.Dropdown(\n", + " options=currency_list,\n", + " value=\"EUR\",\n", + " description=\"From:\",\n", + " disabled=False,\n", + " layout=ipw.Layout(margin=\"130\"),\n", + ")\n", + "to_widget = ipw.Dropdown(\n", + " options=currency_list,\n", + " value=\"USD\",\n", + " description=\"To:\",\n", + " disabled=False,\n", + ")\n", + "exchange_selection = ipw.VBox([from_widget, to_widget])\n", + "\n", + "widgets[\"Select\"] = exchange_selection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d069845-d622-4878-8b18-b385c6cb7a7f", + "metadata": {}, + "outputs": [], + "source": [ + "output = ipw.Output()\n", + "output = get_candle_widget(\n", + " output=output,\n", + " to_symbol=to_widget.value,\n", + " from_symbol=from_widget.value,\n", + ")\n", + "widgets[\"Candle\"] = output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81c7e5c7-2e52-45ba-bf7d-aa240f95be2d", + "metadata": {}, + "outputs": [], + "source": [ + "from_widget.observe(on_dropdown_change)\n", + "to_widget.observe(on_dropdown_change)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0c7d921-e5dc-4ce8-97f1-a59b414a676a", + "metadata": {}, + "outputs": [], + "source": [ + "dashboard = FlexLayout(\n", + " layout_config={\"borderLeft\": False, \"borderRight\": False, \"enableSection\": False},\n", + " style={\n", + " \"height\": \"calc(100vh - 80px)\",\n", + " \"backgroundColor\": \"rgb(0 0 0)\",\n", + " \"fontFamily\": \"Consolas\",\n", + " \"fontWeight\": 800,\n", + " },\n", + " header={\n", + " \"title\": \"Currencies\",\n", + " \"style\": {\n", + " \"backgroundColor\": \"rgb(0 0 0)\",\n", + " \"fontWeight\": 400,\n", + " \"fontSize\": \"28px\",\n", + " },\n", + " \"buttons\": [],\n", + " },\n", + " widgets=widgets,\n", + " template=os.path.join(\"widgets\", \"currencies.json\"),\n", + " editable=False,\n", + ")\n", + "dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fedac44-5e09-44f0-bbbe-fbad7c8716cd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c850ba2-408b-4061-947e-5a1b8a284d22", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "voila": { + "theme": "dark" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/dashboards/shortdata.ipynb b/openbb_terminal/dashboards/shortdata.ipynb new file mode 100644 index 00000000000..04e1791cfb2 --- /dev/null +++ b/openbb_terminal/dashboards/shortdata.ipynb @@ -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", + "

Finra Short Data

\n", + "

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

\n", + "

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

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

Finra Short Data

\\n

This widget downloads the consolidated NMS short da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(app)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/openbb_terminal/dashboards/stocks.ipynb b/openbb_terminal/dashboards/stocks.ipynb new file mode 100644 index 00000000000..dcdafd3df9c --- /dev/null +++ b/openbb_terminal/dashboards/stocks.ipynb @@ -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 = \"

Stock Analysis Dashboard

\"\n", + "warning_html = '

Use a comma after EVERY stock typed.

'\n", + "\n", + "app_contents = [widgets.HTML(title_html), controls, widgets.HTML(warning_html), figures]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f33793c-adc9-4bc1-8fd4-aeff5a00ccba", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "metadata": { + "interpreter": { + "hash": "e896a00ead8b521528d79ac9ef24990696f2b751eb283ab8e0d078c9c4971ffc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/dashboards/vsurf.ipynb b/openbb_terminal/dashboards/vsurf.ipynb new file mode 100644 index 00000000000..816c8b52128 --- /dev/null +++ b/openbb_terminal/dashboards/vsurf.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "68b664c7-c52d-42c1-b170-575370323d22", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "import sys\n", + "import ipywidgets as widgets\n", + "import plotly.graph_objects as go\n", + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "module_path = os.path.abspath(os.path.join(\"../../..\"))\n", + "\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)\n", + "\n", + "from openbb_terminal import api as openbb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c98dc75-6325-43da-93a2-1b8ec96bd368", + "metadata": {}, + "outputs": [], + "source": [ + "z_dict = {\n", + " \"IV\": \"Implied Volatility\",\n", + " \"OI\": \"Open Interest\",\n", + " \"LP\": \"Last Price\",\n", + "}\n", + "strings = {\n", + " \"IV\": \"DTE: %{x}
Strike: %{y}
IV: %{z}\",\n", + " \"OI\": \"DTE: %{x}
Strike: %{y}
OI: %{z}\",\n", + " \"LP\": \"DTE: %{x}
Strike: %{y}
LP: %{z}\",\n", + "}\n", + "\n", + "\n", + "def create_layout(fig, width, height, z, ticker):\n", + " fig.update_layout(\n", + " title=f\"{z.upper()} Surface for {ticker.upper()}\",\n", + " autosize=False,\n", + " width=width,\n", + " height=height,\n", + " scene=dict(\n", + " xaxis_title=\"Days to Expiration\",\n", + " yaxis_title=\"Strike\",\n", + " zaxis_title=z_dict[z],\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1bc5c6a-ccb2-4ef5-9c79-1f619fbf95a6", + "metadata": {}, + "outputs": [], + "source": [ + "class Chart:\n", + " def __init__(self):\n", + " self.last_ticker = \"\"\n", + " self.df = pd.DataFrame()\n", + "\n", + " def create(self, z, ticker, height, width):\n", + " if ticker:\n", + " if ticker != self.last_ticker:\n", + " self.df = openbb.stocks.options.models.yfinance.get_iv_surface(ticker)\n", + " self.last_ticker = ticker\n", + "\n", + " if not self.df.empty:\n", + " z_dict = {\n", + " \"IV\": self.df.impliedVolatility,\n", + " \"OI\": self.df.openInterest,\n", + " \"LP\": self.df.lastPrice,\n", + " }\n", + " fig = go.Figure(\n", + " data=[\n", + " go.Mesh3d(\n", + " z=z_dict[z],\n", + " x=self.df.dte,\n", + " y=self.df.strike,\n", + " intensity=np.sqrt(z_dict[z]),\n", + " colorscale=\"Jet\",\n", + " hovertemplate=strings[z],\n", + " showscale=False,\n", + " )\n", + " ]\n", + " )\n", + "\n", + " create_layout(fig, height, width, z, ticker)\n", + " if os.environ.get(\"SERVER_SOFTWARE\", \"jupyter\").startswith(\"voila\"):\n", + " fig.show(config={\"showTips\": False}, renderer=\"notebook\")\n", + " else:\n", + " fig.show(config={\"showTips\": False})\n", + "\n", + "\n", + "opts = [\"IV\", \"OI\", \"LP\"]\n", + "z_widget = widgets.Dropdown(options=opts, value=\"IV\", description=\"Data\")\n", + "tickers_widget = widgets.Text(description=\"Ticker\", value=\"TSLA\")\n", + "h_widget = widgets.IntText(value=1000, description=\"Height\")\n", + "w_widget = widgets.IntText(value=1000, description=\"Width\")\n", + "\n", + "controls = widgets.VBox([tickers_widget, z_widget])\n", + "size = widgets.VBox([h_widget, w_widget])\n", + "controls = widgets.HBox([controls, size])\n", + "\n", + "chart = Chart()\n", + "stocks_view = widgets.interactive_output(\n", + " chart.create,\n", + " {\"z\": z_widget, \"ticker\": tickers_widget, \"height\": h_widget, \"width\": w_widget},\n", + ")\n", + "\n", + "title_html = \"

Option Surface Dashboard

\"\n", + "app_contents = [\n", + " widgets.HTML(title_html),\n", + " controls,\n", + " stocks_view,\n", + "]\n", + "app = widgets.VBox(app_contents)\n", + "display(app)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "014f267a-92ba-4f23-afb1-9720d8a68cbb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/openbb_terminal/reports/equity.ipynb b/openbb_terminal/reports/equity.ipynb new file mode 100644 index 00000000000..fd9270bb184 --- /dev/null +++ b/openbb_terminal/reports/equity.ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70ba2c8a", + "metadata": {}, + "source": [ + "## Notebook setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "driven-billion", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import io\n", + "import warnings\n", + "import datetime\n", + "import numpy as np\n", + "import base64\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib_inline.backend_inline\n", + "\n", + "# import sys\n", + "# sys.path.append('../../../')\n", + "\n", + "from openbb_terminal import api as openbb\n", + "from openbb_terminal.helper_classes import TerminalStyle\n", + "\n", + "%matplotlib inline\n", + "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3fe7db5-ec6a-42cf-9e66-52dc1de22370", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " theme = TerminalStyle(\"light\", \"light\", \"light\")\n", + "except:\n", + " pass\n", + "stylesheet = openbb.widgets.html_report_stylesheet()\n", + "with open(\"./openbb_terminal/reports/OpenBB_reports_logo.png\", \"rb\") as image_file:\n", + " openbb_image_encoded = base64.b64encode(image_file.read())" + ] + }, + { + "cell_type": "markdown", + "id": "807fda4e", + "metadata": {}, + "source": [ + "## Select Ticker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "intended-sherman", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# Parameters that will be replaced when calling this notebook\n", + "ticker = \"TSLA\"\n", + "report_name = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1be26dae-cafe-4a22-80aa-eff296fc1a9b", + "metadata": {}, + "outputs": [], + "source": [ + "ticker_data = openbb.stocks.load(ticker, start=datetime.datetime.now() - datetime.timedelta(days=4*30))\n", + "ticker_data = openbb.stocks.process_candle(ticker_data)\n", + "\n", + "author = \"Didier Rodrigues Lopes\"\n", + "report_title = f\"INVESTMENT RESEARCH REPORT ON {ticker.upper()}\"\n", + "report_date = datetime.datetime.now().strftime('%d %B, %Y')\n", + "report_time = datetime.datetime.now().strftime('%H:%M') \n", + "report_timezone = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo\n", + "report_title, report_date, report_time, report_timezone" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3b15d6f-dc9e-4f97-aa07-37d949a4e4bb", + "metadata": {}, + "outputs": [], + "source": [ + "overview = openbb.stocks.fa.models.yahoo_finance.get_info(ticker=ticker).transpose()[\n", + " \"Long business summary\"\n", + "][0]\n", + "overview" + ] + }, + { + "cell_type": "markdown", + "id": "f8a0d747", + "metadata": {}, + "source": [ + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aca4b6ef", + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " df_year_estimates,\n", + " df_quarter_earnings,\n", + " df_quarter_revenues,\n", + ") = openbb.stocks.dd.models.business_insider.get_estimates(ticker)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0f0a1ca", + "metadata": {}, + "outputs": [], + "source": [ + "display_year = sorted(df_year_estimates.columns.tolist())[:3]\n", + "df_year_estimates = df_year_estimates[display_year].head(5)\n", + "df_year_estimates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fde46353", + "metadata": {}, + "outputs": [], + "source": [ + "df_quarter_earnings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51dfd9d2", + "metadata": {}, + "outputs": [], + "source": [ + "df_quarter_revenues" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3022130e", + "metadata": {}, + "outputs": [], + "source": [ + "df_sec_filings = openbb.stocks.dd.models.marketwatch.get_sec_filings(ticker)[\n", + " [\"Type\", \"Category\", \"Link\"]\n", + "].head(5)\n", + "df_sec_filings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4662b1ac", + "metadata": {}, + "outputs": [], + "source": [ + "df_analyst = openbb.stocks.dd.models.finviz.get_analyst_data(ticker)\n", + "df_analyst[\"target_to\"] = df_analyst[\"target_to\"].combine_first(df_analyst[\"target\"])\n", + "df_analyst = df_analyst[[\"category\", \"analyst\", \"rating\", \"target_to\"]].rename(\n", + " columns={\n", + " \"category\": \"Category\",\n", + " \"analyst\": \"Analyst\",\n", + " \"rating\": \"Rating\",\n", + " \"target_to\": \"Price Target\",\n", + " }\n", + ")\n", + "df_analyst" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "324e2d4b-b2f6-4da3-bac4-ef785ad4e436", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax1 = plt.subplots(figsize=(11, 5), dpi=150)\n", + "ax2 = ax1.twinx()\n", + "openbb.stocks.dps.spos(\n", + " ticker,\n", + " num=84,\n", + " raw=False,\n", + " export=\"\",\n", + " external_axes=[ax1, ax2],\n", + ")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "net_short_position = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac0bfbda-e4b8-4b3b-b8e7-c02639fdb971", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", + "openbb.stocks.dps.dpotc(\n", + " ticker,\n", + " external_axes=[ax1, ax2],\n", + ")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "dark_pools = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e40a2b8f-8076-4220-89ad-388def38971c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 5), dpi=150)\n", + "openbb.stocks.dps.volexch(\n", + " ticker,\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "volexch = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8bb53c1-147e-4342-af61-0d0a9cc56e59", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", + "ax3 = ax1.twinx()\n", + "openbb.stocks.dps.psi_sg(\n", + " ticker,\n", + " external_axes=[ax1, ax2, ax3],\n", + ")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_vs_short_interest = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "394668e1-1158-4a2b-8a38-04c72a6abdd6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (candles, volume) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", + "openbb.stocks.candle(\n", + " s_ticker=ticker,\n", + " df_stock=ticker_data,\n", + " use_matplotlib=True,\n", + " external_axes=[candles, volume],\n", + ")\n", + "candles.set_xticklabels(\"\")\n", + "fig.tight_layout()\n", + "\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "900686a3", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.dd.pt(\n", + " ticker=ticker,\n", + " start=\"2022-01-01\",\n", + " interval=\"1440min\",\n", + " stock=ticker_data,\n", + " num=10,\n", + " raw=False,\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "price_target_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61d62e1a-199e-481d-b57d-38ab486951be", + "metadata": {}, + "outputs": [], + "source": [ + "df = openbb.stocks.dd.models.business_insider.get_price_target_from_analysts(ticker)\n", + "avg_ratings_last_30_days = round(np.mean(df[datetime.datetime.now() - datetime.timedelta(days=30):][\"Price Target\"].values), 2)\n", + "last_price = round(ticker_data[\"Close\"][-1], 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3733ff2", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.dd.rot(\n", + " ticker=ticker,\n", + " num=10,\n", + " raw=False,\n", + " export=\"\",\n", + " external_axes=[ax],\n", + ")\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "ratings_over_time_chart = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "924f8042-e58a-449c-91a3-7923158ff714", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 3), dpi=150)\n", + "openbb.stocks.ta.rsi(ticker_data[\"Close\"], external_axes=[ax1, ax2])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "ta_rsi = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "953a340e-7ea1-4aa3-ae1d-872683dced41", + "metadata": {}, + "outputs": [], + "source": [ + "df = openbb.stocks.ta.models.momentum.rsi(ticker_data[\"Close\"])\n", + "rsi_value = round(df.values[-1][0], 2)\n", + "rsi_value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce71711-20b3-4c69-af52-e0f9a69991f8", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "model = LinearRegression().fit(np.array(range(len(ticker_data[\"Close\"][-30:].index))).reshape(-1,1), ticker_data[\"Close\"][-30:].values)\n", + "regression_slope = round(model.coef_[0], 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6272e34b-9511-4b48-9eea-ea882ce9846a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df_insider = pd.DataFrame.from_dict(openbb.stocks.ins.models.finviz.get_last_insider_activity(ticker)).head(10)\n", + "df_insider[\"Val ($)\"] = df_insider[\"Value ($)\"].replace({',': ''}, regex=True)\n", + "df_insider[\"Trade\"] = df_insider.apply(\n", + " lambda row: (-1*float(row[\"Val ($)\"])) if row[\"Transaction\"] == \"Sale\" else (float(row[\"Val ($)\"]) if row[\"Transaction\"] == \"Buy\" else 0),\n", + " axis=1,\n", + " )\n", + "last_10_insider_trading = round(sum(df_insider[\"Trade\"])/1_000_000, 2)\n", + "df_insider = df_insider.drop(columns=[\"Val ($)\", \"Trade\"])\n", + "df_insider" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29254666-58c6-4686-87e6-2635ad8387e2", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.ba.headlines(ticker, external_axes=[ax])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "finbrain_sentiment = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8251b47-8ecc-47be-83f3-d9f108596898", + "metadata": {}, + "outputs": [], + "source": [ + "df_sentiment_finbrain = openbb.stocks.ba.models.finbrain.get_sentiment(ticker)\n", + "finbrain_sentiment_val = float(df_sentiment_finbrain.values[-1][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fe6e513-d568-41b4-8aa0-37cc578c9dc3", + "metadata": {}, + "outputs": [], + "source": [ + "watchlist_count, n_cases, n_bull, n_bear = openbb.stocks.ba.models.stocktwits.get_bullbear(ticker)\n", + "stocktwits_sentiment = f\"Watchlist count: {watchlist_count}
\"\n", + "if n_cases > 0:\n", + " stocktwits_sentiment += f\"\\nLast {n_cases} sentiment messages:
\"\n", + " stocktwits_sentiment += f\"Bullish {round(100*n_bull/n_cases, 2)}%
\"\n", + " stocktwits_sentiment += f\"Bearish {round(100*n_bear/n_cases, 2)}%\"\n", + "else:\n", + " stocktwits_sentiment += \"No messages found\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfb75909-b877-4b3a-82a7-21ce174a7bb4", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(11, 5), dpi=150)\n", + "openbb.stocks.ba.snews(ticker, external_axes=[ax1, ax2])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "snews = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "329c9e1b-e1de-4836-91fd-656cc87f7111", + "metadata": {}, + "outputs": [], + "source": [ + "ticker_data_all = openbb.stocks.load(ticker, start=datetime.datetime.now() - datetime.timedelta(days=5*12*21))\n", + "ticker_data_all[\"Returns\"] = ticker_data_all[\"Adj Close\"].pct_change()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37743ef3-2f16-4166-bfcc-a0fe3eb959bc", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.qa.bw(ticker, ticker_data_all, \"Returns\", False, external_axes=[ax])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "bw_month = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16b33868-23dc-4c77-ac00-acb452da386c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.qa.bw(ticker, ticker_data_all, \"Returns\", True, external_axes=[ax])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "bw_year = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c688fa89-3773-41fe-8ab8-bb6a610f2287", + "metadata": {}, + "outputs": [], + "source": [ + "regression_val = round(openbb.stocks.pred.models.regression.get_regression_model(ticker_data_all[\"Close\"], 1, 80, 20, 1)[0][-1], 2)\n", + "regression_val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66e51d40-3dca-46c0-abd6-4c1f04f59732", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "regression = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90e77854-0652-4f62-9c0b-89d3fda456c6", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(11, 3), dpi=150)\n", + "openbb.stocks.pred.mc(ticker_data[\"Close\"], 30, 100, external_axes=[ax1, ax2])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "mc = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb97c78c-4c4e-4e24-a2a1-29cdf47839c5", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n", + "openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n", + "fig.tight_layout()\n", + "f = io.BytesIO()\n", + "fig.savefig(f, format=\"svg\")\n", + "regression = f.getvalue().decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac501a32-124d-4d21-aae1-a8d147bb1071", + "metadata": {}, + "source": [ + "## Render the report template to a file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abfe37e4-ea38-42c3-8fb5-7cceb233fb12", + "metadata": {}, + "outputs": [], + "source": [ + "body = \"\"\n", + "\n", + "img = f'\"OpenBB\"'\n", + "body += openbb.widgets.header(img, author, report_date, report_time, report_timezone, f'INVESTMENT RESEARCH REPORT: {ticker}')\n", + "\n", + "body += openbb.widgets.tablinks([\"SUMMARY\", \"Overview\", \"Analyst Opinions\", \"Fundamental Analysis\", \"Dark Pool and Shorts\", \"Technical Analysis\", \"Insider Trading\", \"Behavioural Analysis\", \"Quantitative Analysis\", \"Prediction Techniques\"])\n", + "\n", + "htmlcode = openbb.widgets.h(3, \"KPIs\")\n", + "htmlcode += openbb.widgets.kpi([last_price], [\"Last closing price is above the average price ratings of last 30 days\", \"Average price ratings of last 30 day is above last closing price\"], avg_ratings_last_30_days)\n", + "htmlcode += openbb.widgets.kpi([0], [\"Regression (dollars per market day) on last 30 market days is negative\", \"Regression (dollars per market day) on last 30 market days is positive\"], regression_slope)\n", + "htmlcode += openbb.widgets.kpi([30, 70], [\"RSI level is oversold\", \"RSI level is normal\", \"RSI level is overbought\"], rsi_value) \n", + "htmlcode += openbb.widgets.kpi([0], [\"The sum of last 10 insider trading (in millions) was negative\", \"The sum of last 10 insider trading (in millions) was positive\"], last_10_insider_trading)\n", + "htmlcode += openbb.widgets.kpi([-.1, .1], [\"Last FinBrain sentiment is bearish\", \" Last FinBrain sentiment is neutral\", \"Last FinBrain sentiment is bullish\"], finbrain_sentiment_val) \n", + "htmlcode += openbb.widgets.kpi([0], [\"The regression for the next 20th market price is below closing price\", \"The regression for the next 20th market price is above closing price\"], round(regression_val - last_price, 2)) \n", + "body += openbb.widgets.add_tab(\"SUMMARY\", htmlcode)\n", + "\n", + "htmlcode = openbb.widgets.row([price_chart])\n", + "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Description\") + openbb.widgets.p(overview)])\n", + "body += openbb.widgets.add_tab(\"Overview\", htmlcode)\n", + "\n", + "htmlcode = openbb.widgets.row([price_target_chart])\n", + "htmlcode += openbb.widgets.row([ratings_over_time_chart])\n", + "htmlcode += openbb.widgets.row([df_analyst.to_html()])\n", + "body += openbb.widgets.add_tab(\"Analyst Opinions\", htmlcode)\n", + "\n", + "htmlcode = openbb.widgets.row([openbb.widgets.h(3, \"Estimates\") + df_year_estimates.head().to_html()])\n", + "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Earnings\") + df_quarter_earnings.head().to_html()])\n", + "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Revenues\") + df_quarter_revenues.head().to_html()])\n", + "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"SEC filings\") + df_sec_filings.head().to_html()])\n", + "body += openbb.widgets.add_tab(\"Fundamental Analysis\", htmlcode)\n", + " \n", + "htmlcode = openbb.widgets.row([net_short_position])\n", + "htmlcode += openbb.widgets.row([price_vs_short_interest])\n", + "htmlcode += openbb.widgets.row([dark_pools])\n", + "htmlcode += openbb.widgets.row([volexch])\n", + "body += openbb.widgets.add_tab(\"Dark Pool and Shorts\", htmlcode)\n", + "\n", + "body += openbb.widgets.add_tab(\"Technical Analysis\", openbb.widgets.row([ta_rsi]))\n", + "\n", + "htmlcode = openbb.widgets.row([openbb.widgets.h(3, \"Last Activity\") + df_insider.head(10).to_html(col_space='75px')])\n", + "body += openbb.widgets.add_tab(\"Insider Trading\", htmlcode)\n", + "\n", + "htmlcode = openbb.widgets.row([finbrain_sentiment])\n", + "htmlcode += openbb.widgets.row([snews])\n", + "htmlcode += openbb.widgets.row([openbb.widgets.h(3, \"Stocktwits\") + stocktwits_sentiment])\n", + "body += openbb.widgets.add_tab(\"Behavioural Analysis\", htmlcode)\n", + "\n", + "htmlcode = openbb.widgets.row([bw_month])\n", + "htmlcode += openbb.widgets.row([bw_year])\n", + "body += openbb.widgets.add_tab(\"Quantitative Analysis\", htmlcode)\n", + "\n", + "htmlcode = openbb.widgets.row([regression])\n", + "htmlcode += openbb.widgets.row([mc])\n", + "body += openbb.widgets.add_tab(\"Prediction Techniques\", htmlcode)\n", + "\n", + "body += openbb.widgets.tab_clickable_evt()\n", + "\n", + "report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)\n", + "\n", + "# to save the results\n", + "with open(report_name + \".html\", \"w\") as fh:\n", + " fh.write(report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e15cef06-0d10-4153-b6d3-9762be0ed1dd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Tags", + "interpreter": { + "hash": "aa1c4d436101635411b2afa2ce061827695b6e3d0efa7f05338cbf11ad26165e" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}