major_upgrade_training_working

.gitignore

@@ -127,3 +127,4 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+kite_session.pkl

Inference/inference_notebook-Copy1.ipynb

@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-03T14:26:58.213760Z",
@@ -98,14 +98,14 @@
        "'Hemang Atulkumar Joshi'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from jugaad_trader import Zerodha\n",
-    "kite = Zerodha(user_id='ZERODHA_CLIENT_ID',password='ZERODHA_PASSWORD',twofa='ZERODHA_PIN')\n",
+    "kite = Zerodha(user_id='user_id',password='password',twofa='twofa')\n",
     "kite.login()\n",
     "kite.profile()['user_name']"
    ]

Training/notebook62326ade97_1X.ipynb

@@ -62,6 +62,17 @@
     "device"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import joblib\n",
+    "# this_inst_df = joblib.load(f'./dt11112222/1793.p')\n",
+    "# this_inst_df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -233,7 +244,6 @@
     "    pyplot.grid(True, which='both')\n",
     "    pyplot.axhline(y=0, color='k')\n",
     "    pyplot.savefig(f'./plots2/transformer-epoch_{plot_counter}_{epoch}_{tknip}.png')\n",
-    "   \n",
     "    pyplot.close()\n",
     "    \n",
     "    return total_loss / i\n",
@@ -1045,7 +1055,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.10"
   },
   "varInspector": {
    "cols": {

Training/notebook62326ade97_5X.ipynb

@@ -1200,7 +1200,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.10"
   },
   "varInspector": {
    "cols": {

agenda.md

@@ -1,7 +1,7 @@
-# this is a github python repo in which user can install this using pip. to use this repo you have to load the data using `data_loader.py` and train the model using `trainer.py` file and infer the trained model using `inferencer.py` file. all the files should contain appropriate comments and docstring for better readability of the code.
+# this is a github python repo in which user can install this using pip. to use this repo you have to load the data using `data_loader.py` and train the model using `trainer.py` file and infer the trained model using `inferencer.py` file. all the files should contain appropriate comments and docstring for better readability of the code
 
-# the `data_loader.py` file should have three functionalites in it's code , first is to autheniutacte user by prompitng for entering user auth details of zerodha account, then ask for the stock symbol for which a user is using this repo. then download the data using zerodha kite class. then saving it approriately in a local file for furhter model training usecases. here the file `notebook62326ade97_1X.ipynb` and `inference_notebook-Copy1.ipynb` can be referenced for this .
+# the `data_loader.py` file should have three functionalites in it's code , first is to autheniutacte user by prompitng for entering user auth details of zerodha account, then ask for the stock symbol for which a user is using this repo. then download the data using zerodha kite class. then saving it approriately in a local file for furhter model training usecases. here the file `notebook62326ade97_1X.ipynb` and `inference_notebook-Copy1.ipynb` can be referenced for this 
 
-# the `trainer.py` file should have code for this . this file should contain code for loading local file that is saved by the `data_loader.py` file's code execution , if not found then give assering error for this . then asking user for transofrmer parameters for training the model. then one method for starting the training process with live plot display and saving the plots . after completion of training of this model it should saved locally with well defined name.
+# the `trainer.py` file should have code for this . this file should contain code for loading local file that is saved by the `data_loader.py` file's code execution , if not found then give assering error for this . then asking user for transofrmer parameters for training the model. then one method for starting the training process with live plot display and saving the plots . after completion of training of this model it should saved locally with well defined name
 
-# here in the file `inferencer.py` it should have code for loading the model that is saved by the file `trainer.py` and then for inferring this model it should ask for authenticating using zerodha using preovious files if not already logged in. and then ask in a prompt for inferring the stock symbol for this . then  after getting name of the symbol that the user want to infer it should get data and load the model and run inference code and how the results in a plot and analytical table.
+# here in the file `inferencer.py` it should have code for loading the model that is saved by the file `trainer.py` and then for inferring this model it should ask for authenticating using zerodha using preovious files if not already logged in. and then ask in a prompt for inferring the stock symbol for this . then  after getting name of the symbol that the user want to infer it should get data and load the model and run inference code and how the results in a plot and analytical table