TelegramTradeMsgBacktestML

Backtest telegram mesaage from whole channel about trading(as in stocks or crypto) using Mahcine Learnig (Named Entity Recognition or Token Classification).

Test now on HuggingFace :

:: Link to test this model ::

What this repository contains? :

1. Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool. convert to

2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script

3. Train NER model on Hugginface-autoTrain.

4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.

5. Define python function to predict labels using Hugginface-autoTrain model.

6. Only label new data from newly predicted-labels-dataset that has falsified labels.

7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad_trader.

8. Evaluate total gained percentage since inception summation-wise and compounded and plot.

9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.

10. Serve the app as flask web API for web request and respond to it as labelled tokens.

11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.

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Contact us

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Email : hemangjoshi37a@gmail.com

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Our HuggingFace Datasets :

• hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.

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