mirror of
https://github.com/OpenBB-finance/OpenBB.git
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Skip predictions in reports if prediction package is missing. (#1900)
* Add error handling if prediction package is missing. * More error handlers * Use try/except blocks * Another error handler * Convert try/except blocks to a variable with if statements * Add string in final report about no predictions. Make file encoding explicit. * Add a comment describing the flag * Update openbb_terminal/reports/equity.ipynb Co-authored-by: James Maslek <jmaslek11@gmail.com> Co-authored-by: James Maslek <jmaslek11@gmail.com>
This commit is contained in:
Sam Kerr
75
openbb_terminal/reports/equity.ipynb
vendored
75
openbb_terminal/reports/equity.ipynb
vendored
@@ -33,7 +33,15 @@
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"\n",
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"%matplotlib inline\n",
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"matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n",
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"warnings.filterwarnings(\"ignore\")"
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"# Detect if prediction capabilities are present. If they are not, disable prediction in the rest of the script\n",
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"# so that the report can still be generated without prediction results.\n",
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"predictions = True\n",
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"try:\n",
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" openbb.stocks.pred.models\n",
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"except Exception as e:\n",
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" predictions = False\n",
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]
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},
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{
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@@ -532,8 +540,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"regression_val = round(openbb.stocks.pred.models.regression.get_regression_model(ticker_data_all[\"Close\"], 1, 80, 20, 1)[0][-1], 2)\n",
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"regression_val"
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"if predictions:\n",
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" regression_val = round(openbb.stocks.pred.models.regression.get_regression_model(ticker_data_all[\"Close\"], 1, 80, 20, 1)[0][-1], 2)\n",
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" regression_val"
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]
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},
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{
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@@ -543,12 +552,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"regression = f.getvalue().decode(\"utf-8\")"
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"if predictions:\n",
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" fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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" openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n",
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" fig.tight_layout()\n",
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" f = io.BytesIO()\n",
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" fig.savefig(f, format=\"svg\")\n",
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" regression = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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@@ -558,12 +568,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.pred.mc(ticker_data[\"Close\"], 30, 100, external_axes=[ax1, ax2])\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"mc = f.getvalue().decode(\"utf-8\")"
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"if predictions:\n",
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" fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(11, 3), dpi=150)\n",
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" openbb.stocks.pred.mc(ticker_data[\"Close\"], 30, 100, external_axes=[ax1, ax2])\n",
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" fig.tight_layout()\n",
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" f = io.BytesIO()\n",
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" fig.savefig(f, format=\"svg\")\n",
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" mc = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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@@ -573,12 +584,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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"openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n",
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"fig.tight_layout()\n",
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"f = io.BytesIO()\n",
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"fig.savefig(f, format=\"svg\")\n",
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"regression = f.getvalue().decode(\"utf-8\")"
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"if predictions:\n",
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" fig, ax = plt.subplots(figsize=(11, 3), dpi=150)\n",
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" openbb.stocks.pred.regression(ticker, ticker_data_all[\"Close\"], 1, 80, 20, 1, external_axes=[ax])\n",
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" fig.tight_layout()\n",
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" f = io.BytesIO()\n",
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" fig.savefig(f, format=\"svg\")\n",
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" regression = f.getvalue().decode(\"utf-8\")"
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]
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},
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{
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@@ -605,11 +617,13 @@
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"\n",
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"htmlcode = openbb.widgets.h(3, \"KPIs\")\n",
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"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",
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"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",
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"if predictions:\n",
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" 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",
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"htmlcode += openbb.widgets.kpi([30, 70], [\"RSI level is oversold\", \"RSI level is normal\", \"RSI level is overbought\"], rsi_value) \n",
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"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",
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"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",
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"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",
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"if predictions:\n",
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" 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",
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"body += openbb.widgets.add_tab(\"SUMMARY\", htmlcode)\n",
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"\n",
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"htmlcode = openbb.widgets.row([price_chart])\n",
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@@ -647,8 +661,11 @@
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"htmlcode += openbb.widgets.row([bw_year])\n",
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"body += openbb.widgets.add_tab(\"Quantitative Analysis\", htmlcode)\n",
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"\n",
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"htmlcode = openbb.widgets.row([regression])\n",
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"htmlcode += openbb.widgets.row([mc])\n",
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"if predictions:\n",
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" htmlcode = openbb.widgets.row([regression])\n",
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" htmlcode += openbb.widgets.row([mc])\n",
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"else:\n",
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" htmlcode = openbb.widgets.row([\"Prediction features not enabled.\"])\n",
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"body += openbb.widgets.add_tab(\"Prediction Techniques\", htmlcode)\n",
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"\n",
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"body += openbb.widgets.tab_clickable_evt()\n",
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@@ -656,7 +673,7 @@
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"report = openbb.widgets.html_report(title=report_name, stylesheet=stylesheet, body=body)\n",
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"\n",
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"# to save the results\n",
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"with open(report_name + \".html\", \"w\") as fh:\n",
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"with open(report_name + \".html\", \"w\", encoding='utf-8') as fh:\n",
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" fh.write(report)"
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]
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},
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@@ -672,10 +689,10 @@
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"metadata": {
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"celltoolbar": "Tags",
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"interpreter": {
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"hash": "aa1c4d436101635411b2afa2ce061827695b6e3d0efa7f05338cbf11ad26165e"
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"hash": "81794d4967e6c3204c66dcd87b604927b115b27c00565d3d43f05ba2f3a2cb0d"
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Python 3.9.1 64-bit",
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"language": "python",
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"name": "python3"
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},
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@@ -689,7 +706,7 @@
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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": "3.8.6"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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