#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
#ALPHA/USDT analysis :
#ALPHA is currently in an uptrend, forming higher highs (HHs) and higher lows (HLs). The price has retraced to a support zone, and a bounce back is anticipated, with expectations to test previous highs.
TF : 1D
Entry : $0.0870
Target : $0.1340
SL : $0.0674
#ALPHA/USDT analysis :
#ALPHA is presently in a downtrend, trading below the 200 EMA. The price is establishing a pattern of lower lows and lower highs. Currently, it encounters resistance near the 200 EMA, indicating a possible continuation of its bearish trend and a retest of previous lows.
TF : 4h
Entry : $0.0558
Target : $0.0520
SL : $0.0584
Custom Signal: Combination of
👉 Double Bottom
👉 Bollinger Breakout
👉 Trend Line Breakout
We expect the price goes up after double bottom but we can’t trade by using only double bottom pattern. We look for another bullish moves like bollinger or trend line breakout.
What if double bottom, Trend Line Breakout and Bollinger Breakout occur for any asset and it’s price hasn’t risen yet
Bingo !!
#ALPHA did 15% in just 3 hours 👌
Just check the chart, let’s create custom signal