region:us
# Stocks NER 2000 Sample Test Dataset for Named Entity Recognition

This dataset has been automatically processed by AutoTrain for the project stocks-ner-2000-sample-test, and is perfect for training models for Named Entity Recognition (NER) in the stock market domain.

## Dataset Description

The dataset includes 2000 samples of stock market related text, with each sample consisting of a sequence of tokens and their corresponding named entity tags. The language of the dataset is English (BCP-47 code: 'en').

## Dataset Structure

The dataset is structured as a list of data instances, where each instance includes the following fields:

- **tokens**: a sequence of strings representing the text in the sample.
- **tags**: a sequence of integers representing the named entity tags for each token in the sample. There are a total of 12 named entities in the dataset, including 'NANA', 'btst', 'delivery', 'enter', 'entry_momentum', 'exit', 'exit2', 'exit3', 'intraday', 'sl', 'symbol', and 'touched'.

Each sample in the dataset looks like this:

```
[
  {
    "tokens": [
      "MAXVIL",
      " : CONVERGENCE OF AVERAGES HAPPENING,  VOLUMES ABOVE AVERAGE  RSI FULLY BREAK OUT "
    ],
    "tags": [
      10,
      0
    ]
  },
  {
    "tokens": [
      "INTRADAY",
      " : BUY ",
      "CAMS",
      " ABOVE ",
      "2625",
      " SL ",
      "2595",
      " TARGET ",
      "2650",
      " - ",
      "2675",
      " - ",
      "2700",
      " "
    ],
    "tags": [
      8,
      0,
      10,
      0,
      3,
      0,
      9,
      0,
      5,
      0,
      6,
      0,
      7,
      0
    ]
  }
]
```
## Dataset Splits
The dataset is split into a train and validation split, with 1261 samples in the train split and 480 samples in the validation split.

This dataset is designed to train models for Named Entity Recognition in the stock market domain and can be used for natural language processing (NLP) research and development. Download this dataset now and take the first step towards building your own state-of-the-art NER model for stock market text.

# GitHub Link to this project : [Telegram Trade Msg Backtest ML](https://github.com/hemangjoshi37a/TelegramTradeMsgBacktestML)

# Need custom model for your application? : Place a order on hjLabs.in : [Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning](https://hjlabs.in/product/custom-token-classification-or-named-entity-recognition-ner-model-as-in-natural-language-processing-nlp-machine-learning/)

## What this repository contains? :

1. Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool.
 ![Screenshot from 2022-09-30 12-28-50](https://user-images.githubusercontent.com/12392345/193394190-3ad215d1-3205-4af3-949e-6d95cf866c6c.png) convert to  ![Screenshot from 2022-09-30 18-59-14](https://user-images.githubusercontent.com/12392345/193394213-9bb936e7-34ea-4cbc-9132-80c7e5a006d7.png)

2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script
![Screenshot from 2022-10-01 10-36-03](https://user-images.githubusercontent.com/12392345/193394227-32e293d4-6736-4e71-b687-b0c2fcad732c.png)

3. Train NER model on Hugginface-autoTrain.
 ![Screenshot from 2022-10-01 10-38-24](https://user-images.githubusercontent.com/12392345/193394247-bf51da86-45bb-41b4-b4da-3de86014e6a5.png)

4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.
 ![Screenshot from 2022-10-01 10-41-07](https://user-images.githubusercontent.com/12392345/193394251-bfba07d4-c56b-4fe8-ba7f-08a1c69f0e2c.png)
 ![Screenshot from 2022-10-01 10-42-36](https://user-images.githubusercontent.com/12392345/193394261-df4bc8f8-9ffd-4819-ba26-04fddbba8e7b.png)
 ![Screenshot from 2022-10-01 10-44-56](https://user-images.githubusercontent.com/12392345/193394267-c5a111c3-8d00-4d6f-b3c6-0ea82e4ac474.png)

5. Define python function to predict labels using Hugginface-autoTrain model.
 ![Screenshot from 2022-10-01 10-47-08](https://user-images.githubusercontent.com/12392345/193394278-81389606-f690-454a-bb2b-ef3f1db39571.png)
![Screenshot from 2022-10-01 10-47-25](https://user-images.githubusercontent.com/12392345/193394288-27a0c250-41af-48b1-9c57-c146dc51da1d.png)

6. Only label new data from newly predicted-labels-dataset that has falsified labels.
 ![Screenshot from 2022-09-30 22-47-23](https://user-images.githubusercontent.com/12392345/193394294-fdfaf40a-c9cd-4c2d-836e-1878b503a668.png)

7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad_trader.
 ![Screenshot from 2022-10-01 00-05-55](https://user-images.githubusercontent.com/12392345/193394303-137c2a2a-3341-4be3-8ece-5191669ec53a.png)

8. Evaluate total gained percentage since inception summation-wise and compounded and plot.
 ![Screenshot from 2022-10-01 00-06-59](https://user-images.githubusercontent.com/12392345/193394308-446eddd9-c5d1-47e3-a231-9edc620284bb.png)

9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.
 ![Screenshot from 2022-10-01 00-09-29](https://user-images.githubusercontent.com/12392345/193394319-8cc915b7-216e-4e05-a7bf-28360b17de99.png)

10. Serve the app as flask web API for web request and respond to it as labelled tokens.
 ![Screenshot from 2022-10-01 00-12-12](https://user-images.githubusercontent.com/12392345/193394323-822c2a59-ca72-45b1-abca-a6e5df3364b0.png)

11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.
 ![Screenshot from 2022-10-01 11-16-27](https://user-images.githubusercontent.com/12392345/193394685-53235198-04f8-4d3c-a341-535dd9093252.png)



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