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@ -34,6 +34,10 @@ modify the chart so we can see if the buy/sell is in or out. also, draw a dotted
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we're trying to 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.
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we're trying to 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.
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we're stuck, and the code needs fixing
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we're stuck, and the code needs fixing
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The Nn should have one task - to predict next low/high on the short term charts ( 1m, 5m or other - configurable) based on the all past info in parallel from all the different timeframes candles and all the passed indicators candles. It should also have a dedicated NN module todiscover and pay attention to spefic parts in the charts - building and training it's own indicator in a sense. We later use the predicted high/low 5m/1h in the future to buy now and sell later or to short now and close later in the bot. We will have a threshhold of certainty to act, and also do it only if multiple timeframes predictions align. So we may use a transformer module to predict future candles and train that with RL while the candles are rolling until the NN can predict with small loss.
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The Nn should have one task - to predict next low/high on the short term charts ( 1m, 5m or other - configurable) based on the all past info in parallel from all the different timeframes candles and all the passed indicators candles. It should also have a dedicated NN module todiscover and pay attention to spefic parts in the charts - building and training it's own indicator in a sense. We later use the predicted high/low 5m/1h in the future to buy now and sell later or to short now and close later in the bot. We will have a threshhold of certainty to act, and also do it only if multiple timeframes predictions align. So we may use a transformer module to predict future candles and train that with RL while the candles are rolling until the NN can predict with small loss.
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make sure we feed the NN different timeframes in parallel in different "channels" and the indicators as well. we should somehow synchronize them so the NN can make sense of the data and know how 1m 5m and 1h data syncs. incidators will be calculated for each timeframe channel separately. additionaly there should be room for meta learned parameters and indicators. NN will produce predictions for the next couple of candles. that will be used to generate signals and also for backtesting with RL. we buy at the bottom of anticipated valley and sell on the top
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existing (running but unfinished ) code:
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existing (running but unfinished ) code:
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implement these suggestions into our code and add arguments for easy switching of modes:
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implement these suggestions into our code and add arguments for easy switching of modes:
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