[REVIEW] hot fix for 0.4 release

.gitignore

@@ -7,3 +7,5 @@ dist/
 build/
 gquant.egg-info/
 *.swp
+docker/Dockerfile.Rapids
+docker/gQuant

docker/build.sh

@@ -28,9 +28,10 @@ esac
 echo -e "\nPlease, select your cuda version:\n" \
      " - '1' for cuda 9.2\n" \
      " - '2' for cuda 10.0\n" \
-     " - '3' for cuda 10.1.2"
+     " - '3' for cuda 10.1.2\n" \
+     " - '4' for cuda 10.2"
 
-read -p "Enter your option and hit return [1]-3: " CUDA_VERSION
+read -p "Enter your option and hit return [1]-4: " CUDA_VERSION
 
 RAPIDS_VERSION="0.13"
 
@@ -44,6 +45,10 @@ case $CUDA_VERSION in
 	echo "cuda 10.1.2 selected."
 	CONTAINER_VER='10.1'
 	;;
+    4)
+	echo "cuda 10.2 selected."
+	CONTAINER_VER='10.2'
+	;;
     *)
 	echo "cuda 9.2 selected."
 	CONTAINER_VER='9.2'
@@ -60,6 +65,7 @@ cp -r ../task_example ./gQuant
 cp ../setup.cfg ./gQuant
 cp ../setup.py ./gQuant
 cp ../LICENSE ./gQuant
+cp ../download_data.sh ./gQuant
 rsync -av --progress ../notebooks ./gQuant --exclude data --exclude .cache --exclude many-small --exclude storage --exclude dask-worker-space --exclude __pycache__
 
 gquant_ver=$(grep version gQuant/setup.py | sed "s/^.*version='\([^;]*\)'.*/\1/")
@@ -84,15 +90,13 @@ RUN source activate rapids \
     && cd /rapids/gQuant \
     && pip install .
 RUN source activate rapids \ 
-    && conda install -y -c conda-forge dask-labextension recommonmark numpydoc sphinx_rtd_theme pudb \
-    python-graphviz bqplot=0.11.5 nodejs=11.11.0 jupyterlab=0.35.4 ipywidgets=7.4.2 pytables mkl numexpr \
-    pydot
+    && conda install -y -c conda-forge python-graphviz bqplot nodejs ipywidgets pytables mkl numexpr pydot
 #
 # required set up
 #
 RUN source activate rapids \ 
-    && jupyter labextension install @jupyter-widgets/jupyterlab-manager@0.38.1 --no-build \
-    && jupyter labextension install bqplot@0.4.5 --no-build \
+    && jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build \
+    && jupyter labextension install bqplot --no-build \
     && mkdir /.local /.jupyter /.config /.cupy  \
     && chmod 777 /.local /.jupyter /.config /.cupy
 RUN source activate rapids \ 

notebooks/02_single_stock_trade.ipynb

@@ -179,7 +179,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82c116c664634180af16d211f382ba01",
+       "model_id": "bef20587c5a94adbae804f78392f53b7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -220,7 +220,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5841ae2258049d3aa706ba3622d98b5",
+       "model_id": "80e2670f612249d3ae3bb8957f34f9fd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -252,7 +252,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "19571440a1564ccd9e167e534fd9704d",
+       "model_id": "ddef9aedb65e4bf4af3380b1e0209f27",
        "version_major": 2,
        "version_minor": 0
       },
@@ -342,5 +342,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

notebooks/04_portfolio_trade.ipynb

@@ -266,27 +266,27 @@
      "output_type": "stream",
      "text": [
       "id:sort process time:0.144s\n",
-      "id:add_return process time:1.137s\n",
-      "id:add_indicator process time:0.041s\n",
-      "id:volume_mean process time:0.112s\n",
-      "id:rename_mean_volume process time:0.001s\n",
-      "id:left_merge_mean_volume process time:2.703s\n",
-      "id:max_returns process time:0.025s\n",
+      "id:add_return process time:0.401s\n",
+      "id:add_indicator process time:0.046s\n",
+      "id:volume_mean process time:0.106s\n",
+      "id:rename_mean_volume process time:0.002s\n",
+      "id:left_merge_mean_volume process time:0.026s\n",
+      "id:max_returns process time:0.022s\n",
       "id:rename_max_return process time:0.001s\n",
-      "id:left_merge_max_return process time:0.027s\n",
+      "id:left_merge_max_return process time:0.038s\n",
       "id:min_returns process time:0.022s\n",
       "id:rename_min_return process time:0.001s\n",
-      "id:left_merge_min_return process time:0.041s\n",
-      "id:filter_value process time:0.344s\n",
-      "id:drop_columns process time:0.012s\n",
-      "id:sort_2 process time:0.060s\n",
-      "id:exp_strategy process time:0.940s\n",
-      "id:backtest process time:0.043s\n",
-      "id:portfolio_opt process time:0.040s\n",
+      "id:left_merge_min_return process time:0.037s\n",
+      "id:filter_value process time:0.323s\n",
+      "id:drop_columns process time:0.009s\n",
+      "id:sort_2 process time:0.049s\n",
+      "id:exp_strategy process time:0.936s\n",
+      "id:backtest process time:0.038s\n",
+      "id:portfolio_opt process time:0.039s\n",
       "id:sharpe_ratio process time:0.001s\n",
-      "id:cumlative_return process time:2.090s\n",
-      "CPU times: user 7.89 s, sys: 2.1 s, total: 9.99 s\n",
-      "Wall time: 10.2 s\n"
+      "id:cumlative_return process time:2.063s\n",
+      "CPU times: user 5.36 s, sys: 1.09 s, total: 6.45 s\n",
+      "Wall time: 6.6 s\n"
      ]
     }
    ],
@@ -351,7 +351,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1889a24e21e4f3e9ef987deba9dee7a",
+       "model_id": "b7479ffb4ae24389b92e15aa48847969",
        "version_major": 2,
        "version_minor": 0
       },

notebooks/cuIndicator/indicator_demo.ipynb

@@ -120,15 +120,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "771fb5e210dd4dd190e9bba55600e5ce",
+       "model_id": "f3a3621fc2104f31b96493bbe902121c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -406,13 +404,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "03d106aeb75a404db845c85ae4fadee3",
+       "model_id": "dd37ad79dadd4362a1266fda125ee788",
        "version_major": 2,
        "version_minor": 0
       },
@@ -452,9 +450,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

notebooks/cuIndicator/rsi_perf.ipynb

@@ -165,6 +165,7 @@
    "source": [
     "all_stock = gendata(2, 1) \n",
     "df = cudf.DataFrame.from_pandas(all_stock)\n",
+    "df['Dte'] = df['Dte'].astype('datetime64[ms]')\n",
     "ids = all_stock.Sm_id.unique()\n",
     "list_stocks = { 'stock%d' % (i) : i for i in ids}\n",
     "\n",
@@ -206,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,11 +240,11 @@
     "    UpI = np.zeros(len(df))\n",
     "    DoI = np.zeros(len(df))\n",
     "    updown_movement(df['High'].values, df['Low'].values, UpI, DoI)\n",
-    "    UpI = pd.Series(UpI, nan_as_null=False)\n",
-    "    DoI = pd.Series(DoI, nan_as_null=False)\n",
-    "    PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean(), nan_as_null=False)\n",
-    "    NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean(), nan_as_null=False)\n",
-    "    RSI = pd.Series(PosDI / (PosDI + NegDI), name='RSI', nan_as_null=False)\n",
+    "    UpI = pd.Series(UpI)\n",
+    "    DoI = pd.Series(DoI)\n",
+    "    PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean())\n",
+    "    NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean())\n",
+    "    RSI = pd.Series(PosDI / (PosDI + NegDI), name='RSI')\n",
     "    df = df.join(RSI)\n",
     "    return df\n",
     "\n",
@@ -264,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +315,7 @@
     "    :param n: time steps to do EWM average\n",
     "    :return: Relative Strength Index in cudf.Series\n",
     "    \"\"\"\n",
-    "    UpI, DoI = upDownMove(high_ar.to_gpu_array(), low_ar.to_gpu_array())\n",
+    "    UpI, DoI = upDownMove(high_arr.to_gpu_array(), low_arr.to_gpu_array())\n",
     "    UpI_s = shift(UpI, 1)\n",
     "    UpI_s[0] = 0\n",
     "    DoI_s = shift(DoI, 1)\n",
@@ -336,13 +337,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "436b8844c8d34ed99713c596661240d3",
+       "model_id": "ebc595e4f7854d299f2b6fbd43f0ad94",
        "version_major": 2,
        "version_minor": 0
       },
@@ -577,10 +578,14 @@
     "\n",
     "def stock_selection(*stock):\n",
     "    if add_stock_selector.value is None:\n",
+    "        with out:\n",
+    "            print('here')\n",
     "        return\n",
     "    f, year_selector, stock_selector, indicator_selector, para_selectors = get_figure(add_stock_selector.value, df)\n",
     "    vbox = w.children[1]\n",
     "    vbox.children += (widgets.HBox([f, widgets.VBox([year_selector, stock_selector, indicator_selector, para_selectors])]),) \n",
+    "    with out:\n",
+    "        print(widgets)\n",
     "    year_selectors.append(year_selector)\n",
     "    update_range()\n",
     "    add_stock_selector.value = None\n",
@@ -623,9 +628,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef7aeb5f807c4854a8b553a2b0db8dac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output(layout=Layout(border='1px solid black'), outputs=({'name': 'stdout', 'text': \"<module 'ipywidgets' from…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "out\n"
    ]
@@ -654,9 +674,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

notebooks/cuIndicator/viz/accumulation_distribution.py

@@ -12,7 +12,9 @@ def get_para_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):
+def process_outputs(output, stock_df):
+    output.index = stock_df.index
+
     stock_df['out'] = output
     stock_df['out'] = output.fillna(0)
     return stock_df
@@ -23,8 +25,8 @@ def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, fig
     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                    
+    new_fig.layout.width = figure_width
     figs = [new_line]
     # add new figure
     add_new_indicator(new_fig)
-    return figs
+    return figs

notebooks/cuIndicator/viz/admi.py

@@ -14,14 +14,16 @@ def get_para_widgets():
                                             continuous_update=False,
                                             orientation='horizontal',
                                             readout=True)
-    para_selector_widgets = [para_selector]                    
+    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):
+    output.index = stock_df.index
+
     stock_df['out'] = output
     stock_df['out'] = stock_df['out'].fillna(0)
     return stock_df
@@ -32,8 +34,8 @@ def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, fig
     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                    
+    new_fig.layout.width = figure_width
     figs = [new_line]
     # add new figure
-    add_new_indicator(new_fig)     
+    add_new_indicator(new_fig)
     return figs

notebooks/cuIndicator/viz/average_true_range.py

@@ -12,7 +12,9 @@ def get_para_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):
+def process_outputs(output, stock_df):
+    output.index = stock_df.index
+
     stock_df['out'] = output
     stock_df['out'] = output.fillna(0)
     return stock_df
@@ -23,8 +25,8 @@ def create_figure(stock, dt_scale, sc, color_id, f, indicator_figure_height, fig
     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                    
+    new_fig.layout.width = figure_width
     figs = [new_line]
     # add new figure
     add_new_indicator(new_fig)
-    return figs
+    return figs