I constantly share the price forecasts of a good bot on X. You can reach my X account on my profile. The difference between the estimated and actual price for 4H is expected to be less than 0.5% and the 1D model less than 1%. If you want to support me, you can follow me and like the posts. My X: @Daemon_inStocks #ETH #BTC #MicroStrategyAcquiresBTC
I constantly share the price forecasts of a good bot on X. You can reach my X account on my profile. The difference between the estimated and actual price for 4H is expected to be less than 0.5% and the 1D model less than 1%. If you want to support me, you can follow me and like the posts.
I constantly share the price forecasts of a good bot on X. You can reach my X account on my profile. The difference between the estimated and actual price for 4H is expected to be less than 0.5% and the 1D model less than 1%. If you want to support me, you can follow me and like the posts.
I constantly share the price forecasts of a good bot on X. You can reach my X account on my profile.
The difference between the estimated and actual price for 4H is expected to be less than 0.5% and the 1D model less than 1%. If you want to support me, you can follow me and like the posts.
Here is the Fast AI Trend Predictor for the ETH 1H chart: Don't forget to check the description for the evaluation/understand method.
AI models try to predict movements of 2.5% and above in a short period of time and their direction. The first chart shows the ETH price on a 1-hour timeframe. The second chart shows potential up (green/blue) and down (red/purple) trends. The shaded areas indicate possible trend zones based on the model's predictions. Light blue/red represents a slight trend, while dark blue/red indicates a stronger trend based on forecast differences. This is an experimental study and is expected to improve over time as the model learns.
Here is the Fast AI Trend Predictor for the ETH 1H chart: Don't forget to check the description for the evaluation/understand method.
AI models try to predict movements of 2.5% and above in a short period of time and their direction. The first chart shows the ETH price on a 1-hour timeframe. The second chart shows potential up (green/blue) and down (red/purple) trends. The shaded areas indicate possible trend zones based on the model's predictions. Light blue/red represents a slight trend, while dark blue/red indicates a stronger trend based on forecast differences. This is an experimental study and is expected to improve over time as the model learns.
Hi I am an AI developer. I have produced the following models. I am waiting for your comments and looking for opportunities.
Chart Analysis Explanations: The first chart shows the ETH price on a 1-hour timeframe with predictions marked by green and red stars: Green Stars indicate expected short-term uptrends: Minimum 2.5% rise required.Uptrend must occur within 24 hours.Stop loss should not exceed 2.5%. Red Stars indicate expected short-term downtrends: Minimum 2.5% drop required.Decline must occur within 24 hours.Stop loss should not exceed 2.5%. Phase I and Phase II Models: Phase I Models: Machine learning models trained on data from 2015-2024 to predict Green and Red stars. Higher scores suggest a higher likelihood of the predicted trend. Relevant graphs include Phase I Long Trend Forecasting V1 and V2 for uptrends, and Phase I Short Trend Forecasting V1 and V2 for downtrends. Phase II Models: Real-time models that improve over time: General Model: Trained on 75% of the data, optimized on 25%, for general market trends.Special Model: Trained on the first 75% of data and optimized on the last 25%, useful for specific market structures but less reliable in uncertain conditions. Anomaly Models: Anomaly Models (Anom_Model_1 to Anom_Model_4): Real-time models detecting market anomalies:Greater distance from 0 indicates more abnormal pricing.Increasing anomaly with falling prices suggests undervaluation; increasing anomaly with rising prices suggests overvaluation. Anomalies may result from news, manipulation, or uncertainty. Trading Tactics: Trade if at least 2 out of 4 Phase I and Phase II models show a strong score. Use anomaly levels to determine risk.