--- language: - en --- # AutoTrain Dataset for project: ratnakar_1000_sample_curated ## Dataset Description This dataset has been automatically processed by AutoTrain for project ratnakar_1000_sample_curated. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "tokens": [ "INTRADAY", "NAHARINDUS", " ABOVE ", "128", " - 129 SL ", "126", " TARGET ", "140", " " ], "tags": [ 8, 10, 0, 3, 0, 9, 0, 5, 0 ] }, { "tokens": [ "INTRADAY", "ASTRON", " ABV ", "39", " SL ", "37.50", " TARGET ", "45", " " ], "tags": [ 8, 10, 0, 3, 0, 9, 0, 5, 0 ] } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)", "tags": "Sequence(feature=ClassLabel(num_classes=12, names=['NANA', 'btst', 'delivery', 'enter', 'entry_momentum', 'exit', 'exit2', 'exit3', 'intraday', 'sl', 'symbol', 'touched'], id=None), length=-1, id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 726 | | valid | 259 | # 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) Place a custom order on hjLabs.in : [https://hjLabs.in](https://hjlabs.in/?product=custom-algotrading-software-for-zerodha-and-angel-w-source-code) ---------------------------------------------------------------------- ### Contact us Mobile : [+917016525813](tel:+917016525813) Whatsapp & Telegram : [+919409077371](tel:+919409077371) Email : [hemangjoshi37a@gmail.com](mailto:hemangjoshi37a@gmail.com) Place a custom order on hjLabs.in : [https://hjLabs.in](https://hjlabs.in/) Please contribute your suggestions and corections to support our efforts. Thank you. Buy us a coffee for $5 on PayPal ? 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