TelegramTradeMsgBacktestML Table of Contents 1. Introduction 2. Test on HuggingFace 3. Repository Contents 4. Custom Order 5. Contact Information 6. Related GitHub Repositories 7. Other Products 8. YouTube Videos 9. Blog and Apps 10. Related GitLab Repositories --- Introduction Backtest telegram messages from a whole channel about trading (stocks or crypto) using Machine Learning (Named Entity Recognition or Token Classification). --- Test on HuggingFace Link to test this model --- Repository Contents 1. Label Data using LabelStudio Convert: to: 2. Data Conversion Script Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset: 3. Train NER Model Train the NER model using HuggingFace-autoTrain: 4. Predict Labels Use the model to predict labels on new data in LabelStudio: 5. Python Prediction Function Define a Python function to predict labels using the model: 6. Label New Data Only label new data from newly predicted-labels-dataset that has falsified labels: 7. Backtest Data Backtest truly labeled dataset against real historical data of the stock: 8. Evaluate Performance Eval

⚠ No verified equity curve — no track-record source connected.

No score history is stored yet — only the current score is shown.

⚠ No live verification account connected — ask for proof before buying.

Alerts on changes: coming soon

Prop-firm compatibility not provided.

Data-completeness & trust index (not a profitability rating)