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Dobromir Popov
2025-02-04 20:53:00 +02:00
parent c8043a9dcd
commit 20d6542d2c
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@ -4,6 +4,15 @@ pip install ccxt torch numpy
run: >conda activate gpt-gpu
python .\index.py
Usage:
Run the script with a command-line argument — for example:
• python index-deep-new.py --mode train
• python index-deep-new.py --mode live
• python index-deep-new.py --mode inference
prompts:
1.
create a 8b neural network (ai) that will consume live and historical HLOCv (candle sticks) data with a specific time window and in different time periods (1s, 1m 15m, 1h, 1d) and perform buy/sell operations. It will be based on the latest RL unsupervised training techniques, will continiously and retrospectively improve itself (without entering separate modes for training/inference) and the info it can digest will be able to be extendable and dynamic. for example, we should be able to feed sentiment analysis on current X feeds or news. We will also prepare/ calculte various indicators on top of the incomming HLOCV data (stocastic, rsi, etc - all most popular). we should be able to support up to 100 indicators (additional data) channels. The signals of the NN will be used by a bot first to trade on Solana using jupiter api.