Need sentiment analysis bot with this requirement Step 1 — Data Collection Pull historical OHLCV data (Open, High, Low, Close, Volume) for target assets. Fetch news/sentiment data using APIs like: Twitter/X API/ yfinance etc NewsAPI, Benzinga, Finnhub, or Alpha Vantage Align timestamps: match price moves >2% with news published within minutes. Step 2 — Labeling & Preprocessing Label each event: Percent as per our requirements +1: positive move (> +2%) -1: negative move (< -2%) Clean and embed text from news using transformer models: FinBERT, Llama-3, or GPT-4-turbo for finance-specific tone. Create features: Sentiment score Price momentum Volume spikes Time of day, volatility index (VIX if stocks) Step 3 — Model Training Use ensemble models: Short-term signals: RandomForest / XGBoost Pattern recognition: LSTM / Temporal Convolutional Network Sentiment fusion: BERT or FinBERT embeddings + numerical data Train, validate, and test on different market phases (bull, bear, sideways). Step 4 — Backtesting Simulate trades using your signal logic: Entry after >2% move with confirmation Dynamic stop-loss (ATR or volatility-based) Measure: Win rate, Sharpe ratio, max drawdown Step 5 — Automation Build the bot in Python: Libraries: ccxt (for exchanges), backtrader, pandas, TA-Lib Schedule checks every few minutes Integrate laddering (scale-in/out positions) Step 6 — Live Testing Track performance vs benchmarks. Forword testing needed Artificial intelligence, data scientist, LLM,