Fix #26 - Add new issues template

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,28 @@
+---
+name: Bug report
+about: Create a bug report to help us improve gQuant
+title: "[BUG]"
+labels: "? - Needs Triage, bug"
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Steps/Code to reproduce bug**
+Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Environment overview (please complete the following information)**
+ - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)]
+ - Method of gQuant install: [Docker build, or from source]
+
+
+**Environment details**
+Please run and paste the output of the `/print_env.sh` script here, to gather any other relevant environment details
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/documentation-request.md

@@ -0,0 +1,35 @@
+---
+name: Documentation request
+about: Report incorrect or needed documentation
+title: "[DOC]"
+labels: "? - Needs Triage, doc"
+assignees: ''
+
+---
+
+## Report incorrect documentation
+
+**Location of incorrect documentation**
+Provide links and line numbers if applicable.
+
+**Describe the problems or issues found in the documentation**
+A clear and concise description of what you found to be incorrect.
+
+**Steps taken to verify documentation is incorrect**
+List any steps you have taken:
+
+**Suggested fix for documentation**
+Detail proposed changes to fix the documentation if you have any.
+
+---
+
+## Report needed documentation
+
+**Report needed documentation**
+A clear and concise description of what documentation you believe it is needed and why.
+
+**Describe the documentation you'd like**
+A clear and concise description of what you want to happen.
+
+**Steps taken to search for needed documentation**
+List any steps you have taken:

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for gQuant
+title: "[FEA]"
+labels: "? - Needs Triage, feature request"
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I wish I could use gQuant to do [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context, code examples, or references to existing implementations about the feature request here.

.github/ISSUE_TEMPLATE/submit-question.md

@@ -0,0 +1,10 @@
+---
+name: Submit question
+about: Ask a general question about gQuant
+title: "[QST]"
+labels: "? - Needs Triage, question"
+assignees: ''
+
+---
+
+**What is your question?**

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,49 @@
+<!--
+
+Thank you for contributing to gQuant :)
+
+Here are some guidelines to help the review process go smoothly.
+
+1. Please write a description in this text box of the changes that are being
+   made.
+
+2. Please ensure that you have written units tests for the changes made/features
+   added.
+
+3. There are CI checks in place to enforce that committed code follows our style
+   and syntax standards. Please see our contribution guide in `CONTRIBUTING.MD`
+   in the project root for more information about the checks we perform and how
+   you can run them locally.
+
+4. If you are closing an issue please use one of the automatic closing words as
+   noted here: https://help.github.com/articles/closing-issues-using-keywords/
+
+5. If your pull request is not ready for review but you want to make use of the
+   continuous integration testing facilities please label it with `[WIP]`.
+
+6. If your pull request is ready to be reviewed without requiring additional
+   work on top of it, then remove the `[WIP]` label (if present) and replace
+   it with `[REVIEW]`. If assistance is required to complete the functionality,
+   for example when the C/C++ code of a feature is complete but Python bindings
+   are still required, then add the label `[HELP-REQ]` so that others can triage
+   and assist. The additional changes then can be implemented on top of the
+   same PR. If the assistance is done by members of the rapidsAI team, then no
+   additional actions are required by the creator of the original PR for this,
+   otherwise the original author of the PR needs to give permission to the
+   person(s) assisting to commit to their personal fork of the project. If that
+   doesn't happen then a new PR based on the code of the original PR can be
+   opened by the person assisting, which then will be the PR that will be
+   merged.
+
+7. Once all work has been done and review has taken place please do not add
+   features or make changes out of the scope of those requested by the reviewer
+   (doing this just add delays as already reviewed code ends up having to be
+   re-reviewed/it is hard to tell what is new etc!). Further, please do not
+   rebase your branch on master/force push/rewrite history, doing any of these
+   causes the context of any comments made by reviewers to be lost. If
+   conflicts occur against master they should be resolved by merging master
+   into the branch used for making the pull request.
+
+Many thanks in advance for your cooperation!
+
+-->

notebook/cuIndicator/viz/__init__.py

@@ -0,0 +1,14 @@
+import ipywidgets as widgets
+
+def get_para_widgets():
+    para_selector = widgets.IntSlider(min=2, max=60, description="TRIX")
+    para_selector_widgets = [para_selector]                    
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return  (stock_df["close"],) + tuple([w.value for w in para_selector_widgets])
+
+def process_outputs(output, stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = stock_df['out'].fillna(0)
+    return stock_df

notebook/cuIndicator/viz/_accumulation_distribution.py

@@ -0,0 +1,30 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import accumulation_distribution as indicator_fun
+
+def get_para_widgets():
+    para_selector = widgets.IntSlider(min=2, max=60, description="Accumulation Distribution")
+    para_selector_widgets = [para_selector]
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return (stock_df["high"], stock_df["low"], stock_df["close"], stock_df["volume"]) + tuple([w.value for w in para_selector_widgets])
+
+def process_outputs(output,stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = output.fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='Accumulation Distribution', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)
+    return figs

notebook/cuIndicator/viz/_ease_of_movement.py

@@ -0,0 +1,30 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import ease_of_movement as indicator_fun
+
+def get_para_widgets():
+    para_selector = widgets.IntSlider(min=2, max=60, description="Ease of Movement")
+    para_selector_widgets = [para_selector]
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return (stock_df["high"], stock_df["low"], stock_df["volume"]) + tuple([w.value for w in para_selector_widgets])
+
+def process_outputs(output,stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = output.fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='Ease of Movement', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)
+    return figs

notebook/cuIndicator/viz/_keltner_channel.py

@@ -0,0 +1,30 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import keltner_channel as indicator_fun
+
+def get_para_widgets():
+    para_selector = widgets.IntSlider(min=2, max=60, description="Keltner Channel")
+    para_selector_widgets = [para_selector]
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return (stock_df["high"], stock_df["low"], stock_df["close"]) + tuple([w.value for w in para_selector_widgets])
+
+def process_outputs(output,stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = output.fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='Keltner Channel', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)
+    return figs

notebook/cuIndicator/viz/_on_balance_volume.py

@@ -0,0 +1,30 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import on_balance_volume as indicator_fun
+
+def get_para_widgets():
+    para_selector = widgets.IntSlider(min=2, max=60, description="On Balance volume")
+    para_selector_widgets = [para_selector]
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return (stock_df["close"], stock_df["volume"]) + tuple([w.value for w in para_selector_widgets])
+
+def process_outputs(output,stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = output.fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='On Balance volume', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)
+    return figs

notebook/cuIndicator/viz/_stochastic_oscillator_k.py

@@ -0,0 +1,30 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import stochastic_oscillator_k as indicator_fun
+
+def get_para_widgets():
+    #para_selector = widgets.IntSlider(min=2, max=60, description="Ultimate Oscillator")
+    para_selector_widgets = []
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return (stock_df["high"], stock_df["low"], stock_df["close"]) 
+
+def process_outputs(output,stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = output.fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='Stochastic Oscillator K', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)
+    return figs

notebook/cuIndicator/viz/admi.py

@@ -0,0 +1,39 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import average_directional_movement_index as indicator_fun
+
+def get_para_widgets():
+    para_selector = widgets.IntRangeSlider(value=[10, 30],
+                                            min=3,
+                                            max=60,
+                                            step=1,
+                                            description="ADMI:",
+                                            disabled=False,
+                                            continuous_update=False,
+                                            orientation='horizontal',
+                                            readout=True)
+    para_selector_widgets = [para_selector]                    
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    widget = para_selector_widgets[0]
+    return  (stock_df["high"], stock_df["low"], stock_df["close"], widget.value[0], widget.value[1])
+
+def process_outputs(output, stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = stock_df['out'].fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='ADMI', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)     
+    return figs

notebook/cuIndicator/viz/average_true_range.py

@@ -0,0 +1,30 @@
+import ipywidgets as widgets
+from bqplot.colorschemes import CATEGORY20
+from bqplot import Axis, Figure, LinearScale, Lines
+
+from gquant.cuindicator import average_true_range as indicator_fun
+
+def get_para_widgets():
+    para_selector = widgets.IntSlider(min=2, max=60, description="Average True Range")
+    para_selector_widgets = [para_selector]
+    return para_selector_widgets
+
+def get_parameters(stock_df, para_selector_widgets):
+    return (stock_df["high"],stock_df["low"], stock_df["close"]) + tuple([w.value for w in para_selector_widgets])
+
+def process_outputs(output,stock_df):
+    stock_df['out'] = output
+    stock_df['out'] = output.fillna(0)
+    return stock_df
+
+def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, figure_width, add_new_indicator):
+    sc_co = LinearScale()
+    ax_y = Axis(label='Average True Range', scale=sc_co, orientation='vertical')
+    new_line = Lines(x=stock.datetime, y=stock['out'], scales={'x': dt_scale, 'y': sc_co}, colors=[CATEGORY20[color_id[0]]])
+    new_fig = Figure(marks=[new_line], axes=[ax_y])
+    new_fig.layout.height = indicator_figure_height
+    new_fig.layout.width = figure_width                    
+    figs = [new_line]
+    # add new figure
+    add_new_indicator(new_fig)
+    return figs